Elektronische neuzen in de (intensieve) veehouderij

Elektronische neuzen in de (intensieve) veehouderij
Ervaringen en een literatuur onderzoek.
Auteur: Drs Ries MJM Kock voorzitter Stichting Leefbaar Buitengebied Gelderland
Inhoud:
1.
2.
3.
4.
5.
Inleiding
De geurwet van 2007
De evaluatie
E-nose systeem
E-nose systeem voor gebruik in de (intensieve) veehouderij
a. Onderzoek uit Canada
b. EMS
6. Conclusies en aanbevelingen
1
Inleiding
De leden van de burgergroeperingen zijn ervaringsdeskundigen als het gaat om stank uit (intensieve)
veehouderijen.
Al jaren ageren zij tegen de aanwezigheid, de uitbreiding of de komst van (intensieve) veehouderijen.
Daarbij is vrijwel altijd de stank de directe aanleiding om zich te organiseren en te verdiepen in de
oorzaken. Wij zien dat bij een buurtgroepering het standpunt “not in my backyard” al snel verandert
in “not in anybodies backyard”. Waarom zou een bewoner op het platteland in de stank moeten
wonen? Nog verdere verdieping leidt er toe dat de groeperingen tot de conclusie komen dat de
(intensieve) veehouderij in Nederland uit de hand gelopen is. De overtuiging is groeiende dat niet
schaalvergroting de (intensieve) veehouderij duurzaam zal maken, maar de kringloop van productie
sluiten een voorwaarde is. Of, als voorbeeld, geen mestvergisters en mestproducten naar de
wijngebieden, maar in stand houden van bossen in Zuid Amerika, minder vlees eten en produceren in
Europa en afbouw van niet-gesloten-kringloop-landbouw in Europa.
Onlangs kwam in het nieuws dat 20% van de intensieve varkenshouderijen in Nederland technisch
failliet is. De bedrijven zijn de facto door de banken overgenomen maar lijden voortdurend verlies.
Volgens De Telegraaf formuleerde de staatssecretaris haar standpunt nadrukkelijker dan de NVV
en LTO het weergeven. De krant citeert haar als volgt:
„Er wordt gewoon te veel varkensvlees geproduceerd, niet alleen in Nederland", zegt ze. Dat
probleem speelde volgens haar al voor de Russische boycot. „Het wegvallen van de Russische
markt heeft het probleem alleen maar versneld", stelt de PvdA-bewindsvrouw. Zij wijst er
bovendien op dat er ook problemen zijn in landen die varkenshouders geen extra kosten opleggen.
„We produceren gewoon te veel", herhaalt ze.
De sector wil hetzelfde aantal dieren of zelfs meer produceren, ook al verdient ze er niet aan. De
staatssecretaris wil productie toestaan, zolang we eraan verdienen. Maar waar blijft het besef bij de
sector en de staatssecretaris dat we duurzaam moeten produceren? Dat de voedselproductie
zodanig dient te zijn dat de gehele mensheid nu en in de toekomst gezond voedsel ter beschikking
heeft? Zoals onlangs is geformuleerd door het VN High Level Panel:
1. Uitbanning van armoede
2. Verbetering van de positie van vrouwen en meisjes en het bereiken van gelijkheid van
sekse
3. Kwalitatief onderwijs
4. Gezonde levens
5. Toegang tot voedzaam eten en voedselzekerheid
6. Universele toegang tot water en sanitaire voorzieningen
7. Verzekerde en duurzame energie
8. Groei van banen en welzijn
9. Duurzaam gebruik van natuurlijke hulpbronnen
10. Goed bestuur en effectieve instituties
11. Creëren van stabiele en vreedzame samenlevingen
12. Een eerlijk mondiaal systeem met lange termijn financieringsmogelijkheden
De bestrijding van stank uit (intensieve) veehouderij zal in verband gebracht worden met
omvattende problematiek van “toegang tot voedzaam eten en voedselzekerheid”.
2
De geurwet van 2007
Aan de orde is de evaluatie van de geurwet van 2007. Deze is onder meer bedoeld om omwonenden
van (intensieve) veehouderijen te beschermen tegen geurhinder.
Deze wet faalt in die opzet om een aantal redenen:
-
-
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De zogenaamde grens van 14 odeur garandeert stankoverlast. Buren van intensieve
veehouderijen klagen al jaren over stankoverlast en worden geconfronteerd met goedgekeurde
vergunningen. De praktijk leert dat de wet niet werkt. De grens van 14 odeur beschermt
weliswaar het ondernemerschap van de ondernemer, maar niet zijn welzijn en zeker niet die van
zijn buren. Bij de buren gaat de vlag uit wanneer een (intensieve) veehouderij stopt of failliet
gaat.
De wet schrijft een berekening voor (V-Stacks) die toegepast wordt bij het al dan niet toekennen
van een vergunning van een uitbreiding of nieuwbouw. Slechts een gedeelte van de stank die een
bedrijf voortbrengt wordt aan de berekening onderworpen. Zo wordt de stank bij het
overpompen van mest, het roeren in mest, het vullen van silo’s, het maken van brijvoer, het in
werking hebben van mestvijvers, het indikken van mest niet meegenomen in de berekening. De
maximale 14 odeur uit de berekening blijkt in de praktijk vaak veel hoger.
De cumulatie van stank van meerdere bedrijven op een bewoner wordt niet meegenomen in de
geurwet.
De 50% regeling.
De wet blijkt niet te handhaven zo is de ervaring van buren. Wanneer een bedrijf onder gelijke
omstandigheden op het ene moment (bijvoorbeeld in het weekend) buitengewoon meer stinkt
dan op het andere, dan komt de controlerende instantie niet onmiddellijk vaststellen wat er aan
de hand is, maar vele dagen later, wanneer de stank zich niet meer voordoet.
De geurwet 2007 heeft tot gevolg dat een ondernemer kan zeggen dat hij/zij aan de wet voldoet,
terwijl buren klagen over overmatige stank. Beiden hebben gelijk.
De gevolgen ervan zijn:
-
Een ondernemer die voldoet aan de wet wordt desondanks gezien als een buur die het
woongenot van zijn buren ernstig benadeelt.
Dat naast de geuroverlast en de gevolgen voor de gezondheid ook andere emissies van
(intensieve) veehouderijen nadelig zijn voor de gezondheid (voorbeeld: Q-koorts)
Daling van waarde van onroerend goed van buren. Er zijn voorbeelden bekend van een daling
van de WOZ waarde van 35% bij een afstand van 70 meter; en 20% bij 250 meter. Taxaties
bevestigen dat.
Doordat buren gaan klagen en de ondernemer daarvoor geen reden ziet gaat de cohesie in buurten
achteruit. Verjaardagen worden niet samen meer gevierd, kinderen spelen niet meer samen en een
enkele keer leidt het tot woordenwisselingen en zelfs bedreigingen. De geurwet van 2007 heeft een
haat creërende werking.
3
De evaluatie
In de evaluatie spelen twee onderzoeken een belangrijke rol. Dat van PRA Odournet uit 2006 en het
recente onderzoek van GGD/IRAS. Beide onderzoeken hebben verschillende uitkomsten. Er is een
groot verschil in de gerapporteerde geurbelasting. Die valt bij GGD/IRAS veel hoger uit dan bij PRA
Odournet.
Het RIVM stelt op grond van een vergelijking van de onderzoeken vast: De rekenmodellen voor
geurbelasting zijn verouderd, waardoor de berekende geurbelasting kan afwijken van de praktijk.
Huidige stallen hebben meerdere uitlaatpunten met warmte inhoud; de emissies variëren
(afhankelijk van groei van de dieren, verschil tussen dag en nacht); er zijn veranderingen in
stalontwerp, huisvesting, ventilatie en voeding. Al deze factoren kunnen niet genegeerd worden. Het
verdient aanbeveling rekening te houden met verschillen in perceptie (plaats, tijd en persoon) die
kunnen leiden tot andere hinderpercentages bij gelijkblijvende blootstelling. En daarmee ook tot een
grotere of minder grote mate van geurhinder. Het hanteren van een algemene modellen is daarom
niet in alle situaties verstandig.
De burgergroeperingen, vertegenwoordigd in de werkgroep max5odeur, hebben er een hard hoofd
in dat er ooit nog eens modellen komen die vrij nauwkeurig de werkelijke geurbelasting kunnen
voorspellen. Op meerdere terreinen is in de agrarische sector sprake van een kloof tussen de
gemodelleerde werkelijkheid en de feitelijke, gemeten werkelijkheid. Daarbij komt dat modellen
voor de leek ondoorgrondelijk zijn en fraude bij toepassing niet kan worden uitgesloten.
Om die reden zijn burgergroeperingen groot voorstander van geurmetingen. Bij de
vergunningverlening zou meer waarde moeten worden toegekend aan metingen dan aan
berekeningen. Dat kan door vergunningen af te geven onder voorwaarde dat met behulp van
sensoren objectief is vastgesteld dat omwonenden geen stankoverlast ondervinden. Wanneer
daarna toch stankoverlast wordt gerapporteerd, kunnen er via handhaving aanvullende maatregelen
afgedwongen worden.
Er is internationaal onderzoek gaande naar de mogelijkheden om geur uit veehouderijen elektronisch
te meten. We behandelen hier Canadees onderzoek uit 2011 en de ontwikkeling van een geursensor
op basis van Multi Gas Sensor Array (MGA) voor het gebruik van geurmetingen bij varkensstallen van
het Nederlandse bedrijf EMS.
4
E-nose systeem
Een oplossing van de problemen van de gebreken van de geurwet van 2007 boven genoemd is een
systeem dat
-
24 uur per dag 7 dagen in de week de stank objectief kan meten
24 uur per dag 7 dagen in de week, de ondernemer, zijn / haar buren en de controlerende
instantie actief informeert
informatie geeft over de aard van de stank
stimuleert dat (intensieve) veehouderijen verder bij buren vandaan komen te staan
De kern van zo’n systeem is een elektronische neus.
Deze bestaat uit een rij van elektronische sensoren, die (delen van de) geur meten en die via
elektronica en software wordt omgezet in kennis van de geur.
An electronic nose (e-nose) is a device that identifies the specific components of an odor
and analyzes its chemical makeup to identify it. An electronic nose consists of a mechanism
for chemical detection, such as an array of electronic sensors, and a mechanism for pattern
recognition, such as a neural network . Electronic noses have been around for several years
but have typically been large and expensive. Current research is focused on making the
devices smaller, less expensive, and more sensitive. The smallest version, a nose-on-a-chip
is a single computer chip containing both the sensors and the processing components.
An odor is composed of molecules, each of which has a specific size and shape. Each of
these molecules has a correspondingly sized and shaped receptor in the human nose. When
a specific receptor receives a molecule, it sends a signal to the brain and the brain identifies
the smell associated with that particular molecule. Electronic noses based on the biological
model work in a similar manner, albeit substituting sensors for the receptors, and
transmitting the signal to a program for processing, rather than to the brain. Electronic
noses are one example of a growing research area called biomimetics , or biomimicry,
which involves human-made applications patterned on natural phenomena.
Electronic noses were originally used for quality control applications in the food, beverage
and cosmetics industries. Current applications include detection of odors specific to
diseases for medical diagnosis, and detection of pollutants and gas leaks for environmental
protection.
Bron: http://whatis.techtarget.com/definition/electronic-nose-e-nose
Samengevat:
-
een elektronische neus is een apparaat dat een aantal componenten van een geur identificeert
en de sterkte ervan vaststelt.
dat gebeurt via een reeks sensoren, die de aanwezigheid van een chemische geurstof meetbaar
maakt
een computer bewerkt de gegevens en geeft als resultaat een gemeten geurwaarde af
5
E-nose systeem voor gebruik in de intensieve veehouderij
Twee publicaties zijn in dit verband relevant.
1. Remote Monitoring and Analyzing Livestock Farm Odour Using Wireless Electronic Noses
door Leilei Pan The University of Guelph , Ontario, Canada © Leilei Pan, November, 2011
2. Rapportage ontwikkeling geur sensor voor het gebruik van geurmetingen bij varkensstallen.
Electronic determination of odour by olfactory with the Multi Gas Analyser (MGA)
Interpretation by an Artificial Neural Network
Opmerking: Er zijn veel meer publicaties die facetten van e-noses bespreken. Deze twee springen
eruit omdat ze betrekking hebben op e-noses in (intensieve) veehouderijen.
Beide publicaties zijn als bijlage toegevoegd.
Remote Monitoring and Analyzing Livestock Farm Odour Using Wireless Electronic Noses
Array met sensoren
Array met sensoren met luchtpomp
6
E-nose met: Processor (computer), data verwerving en preproces bord, voeding, carbon filter,
sensoren kamer en luchtpomp.
Conclusies in dit rapport:
A portable electronic nose with 14 gas sensors, a humidity sensor and a temperature sensor
was successfully developed especially for objective measurement of livestock farm odours.
Experiments were conducted in field downwind from 14 livestock and poultry farms. Experimental
results demonstrate that “Odour Expert” is a useful tool in supporting livestock and poultry farm
odour management, which greatly improves the efficiency of livestock farm odour management
and thus benefit livestock industry. The electronic nose is capable of producing accurate output with
greater consistency in odour measurement. It is easier and cheaper to operate than using
olfactometry or human panel. Thus it has good commercial potential.
However, an electronic nose can only evaluate odour events on one point; it cannot provide an
overall odour mapping around livestock facilities that is necessary for an overall odour management
strategy. It is suggested that an electronic nose network combined with an air dispersion model is
possible to predict the downwind odour strengths on the basis of odour emission rates, topography
and meteorological data. Therefore, it can locate odour strength around livestock facilities in real
time, and help the development of an overall odour management strategy.
De conclusies bevestigen dat een elektronische neus met 14 gas sensoren en een temperatuur
sensor in staat is om objectief de geur van een kippenstal te meten. Daarmee kan met succes
geurmanagement worden bedreven. De elektronische neus is in staat om voldoende precies te
meten. Het is goedkoper dan een menselijk panel. Het kan de sterkte van geur rondom een stal
meten in real time.
7
Het bedrijf EMS B.V.,
Raiffeisenstraat 24, 4697 CG SINT-ANNALAND zegt op zijn website:
Geur sensor op basis van Multi Gas Sensor Array (MGA) voor het gebruik van
geurmetingen bij varkensstallen en ten behoeve van geuroverlast bepalingen.
De Elektronische neus
Geur is een samenstelling van moleculaire stofdeeltjes in lucht. Om geuren te ruiken heeft
een mens ongeveer 30 miljoen reukcellen. Deze cellen reageren op de stofdeeltjes in lucht
waardoor de hersenen de geur kunnen analyseren. Dit heet het reukvermogen.
Het reukvermogen is het meest gevoelige zintuig van de mens. Het is het enige zintuig dat
direct met het emotionele brein in werking staat. Hierdoor wordt het handelen van de mens
direct gemanipuleerd door de omgevingslucht. Omdat dit niet in alle situaties gewenst is
kan er ook worden gesproken over geuroverlast.
Geuroverlast wordt veroorzaakt door een te hoge concentratie stofdeeltjes in lucht waardoor
de fysieke of geestelijke gezondheid van de mens in gevaar wordt gebracht. Het vakgebied
wat de geurbeleving bestudeert heet olfactometrie. Olfactometrie is een zeer complex
gebied omdat zowel met objectieve als subjectieve waarnemingen wordt gewerkt.
In de intensieve veehouderij is geuroverlast een groot probleem omdat een grote
concentratie geur bronnen (varkens) aanwezig is in een dicht bevolkt gebied. Om
geuroverlast te bepalen worden geurmetingen gedaan waarbij een groep mensen de
geurende lucht gaan beoordelen. Deze methode is tijdrovend, duur en niet reproduceerbaar.
Omdat er geen goede alternatieven op de markt zijn om eenvoudig geurmetingen uit te
voeren bij varkensstallen heeft EMS gekeken naar de haalbaarheid van een elektronische
geurmeting. EMS heeft een geurmeetsysteem ontwikkeld dat gebruikt kan worden voor
metingen van geuroverlast bij een varkensstal.
Het elektronisch bepalen van geuroverlast.
De geuroverlast wordt in 3 stappen bepaald:
1. De sensor (Multi Gas Analyzer) bepaald een “vingerafdruk” van de geurende lucht.
Het bepalen van hiervan is zo geoptimaliseerd dat het relatief weinig tijd inneemt.
Deze vingerafdruk wordt vervolgens ingevoerd in een kunstmatig neuraal netwerk.
2. Het kunstmatig neuraal netwerk is een computer algoritme wat aan de hand van de
vingerafdruk de concentratie van de verschillende geurcomponenten kan bepalen.
3. De bepaalde concentraties worden omgezet naar European Odour Units (OUe). Dit
is een eenheid voor geurconcentratie vastgelegd in de NEN-13725 en VDI 3882.
Vervolgens wordt hieruit de geurbeleving bepaald. Aan de hand van deze
geurbeleving is het mogelijk om te bepalen of er sprake is van geuroverlast.
8
Bij navraag ontvingen wij de volgende aanvullende informatie:
EMS: ‘’Bijgevoegd een application note met de beschrijving van een project dat we hebben
uitgevoerd onder SBIR. Het systeem is momenteel niet commercieel te verkrijgen. (Dat zegt niet dat
we dit niet willen en kunnen, maar we hebben op dit moment geen "standaard artikelnr.") Het hier
beschreven systeem is bedoeld als prototype. Inmiddels hebben we 2 van deze systemen
gebouwd.’’
EMS is bereid om een proefopstelling te laten zien. ‘’Evt. kan een bezoek aan ons lab erg
waardevol zijn. We kunnen in ons lab elke geur maken die men wenst. Inmiddels zijn we aan een
systeem bezig waarmee we een geur uit 24 gascomponenten kunnen laten bestaan. Dit vormt een
extreem dichte benadering van de daadwerkelijke geur. Met deze opstelling leren we de geursensor
ook in.’’
EMS is tevens bereid om een presentatie te geven over dit onderwerp voor de werkgroep evaluatie
Wet geurhinder veehouderij. Doel is dan om over "geurmeten" meer algemene en basisinformatie
te verstrekken.
Bijgevoegd nog 2 foto's, 1 foto is de MGA gemonteerd bij een gaswasser van een varkensstal. De
2e foto is een foto van een geurmeetsysteem voor de bepaling van geur van vis. (Project dit jaar
uitgevoerd).
Het gaat bij EMS dus om echte kwalitatieve en kwantitatieve bepaling van de meer complexe
geurprofielen. Daarnaast ontwerpen, bouwen en leveren wij voor de agrarische markt
meetsystemen met lage detectiegrenzen voor allerhande toepassingen. Denk aan bewaring van
producten, zoals fruit groente, bloembollen, maar ook permanente meting van (schadelijke)
sprorengassen tijdens reguliere teelt in de glastuinbouw.
Opmerkelijk is dat de hedonische waarde (de mate waarin een geur vies gevonden wordt) een rol
speelt bij uitkomst van de verwerking van de meting.
In de bijlage meer informatie over het project van EMS.
De ‘’Rapportage ontwikkeling geur sensor voor het gebruik van geurmetingen bij varkensstallen’’
sluit af met: ‘’De praktische uitvoering en het gebruik van deze sensor is onder handbereik, maar
hangt af van de vraag van de overheden en de handhavende instanties.’’
9
Conclusies:
-
E-nose systemen zijn in ontwikkeling
Eén Nederlandse ondernemer geeft aan dat e-nose-systeem gereed is om getest te worden. Hij
wil de werkgroep evaluatie WGV en de Burgergroeperingen nader informeren.
Onderzoeksvragen
Omdat de huidige methodiek die gebruikt wordt in de geurwet van2007 zoveel te wensen overlaat
heeft met spoed ontwikkelen van een e-nose-systeem een hoge prioriteit.
Wij formuleren de volgende onderzoeksvragen.
Is er een e-nose systeem dat voldoet aan de volgende criteria:
-
-
Het systeem is in staat om real time de stank van een (intensieve) veehouderij te meten.
Het systeem kan zo ingesteld worden dat het consistent onderscheid kan maken tussen de
volgende vijf situaties
o Er is een geur maar die is niet waarneembaar voor mensen
o Er is een geur maar die is maar door de helft van de mensen waarneembaar (zeer
mild)
o Er is een geur maar die wordt niet als stank ervaren door 4 van de 5 mensen
o Er is een geur die wordt door 4 van de 5 panel mensen als stank ervaren
o Er is een geur die wordt door 4 van de 5 panel mensen als ernstige stank ervaren
Het systeem is in staat om in 4 van de 5 gevallen een oorzaak van de stank aan te wijzen
Het systeem is in staat om bij de aanwezigheid van meer dan één stal, van elke stal vast te
stellen:
o Er is een geur maar die is niet waarneembaar voor mensen
o Er is een geur maar die is maar door de helft van de mensen waarneembaar (zeer
mild)
o Er is een geur maar die wordt niet als stank ervaren door 4 van de 5 mensen
o Er is een geur en die wordt door 4 van de 5 mensen als stank ervaren
o Er is een geur en die wordt door 4 van de 5 mensen als ernstige stank ervaren
Wanneer de vraag bevestigend wordt beantwoord is de volgende vraag?
o
o
Is het systeem te bouwen voor een prijs lager dan € 10.000 en onderhoudskosten lager dan €
1000, per jaar, per (intensieve) veehouderij.
Op welke termijn zijn de systemen bij een afname van 1000 stuks te leveren?
Bijlagen 2.
10
Remote Monitoring and Analyzing Livestock Farm Odour
Using Wireless Electronic Noses
by
Leilei Pan
A Thesis
presented to
The University of Guelph
In partial fulfilment of requirements
for the degree of
Doctor of Philosophy
in
Engineering
Guelph, Ontario, Canada
© Leilei Pan, November, 2011
ABSTRACT
REMOTE MONITORING AND ANALYZING LIVESTOCK FARM ODOUR
USING WIRELESS ELECTRONIC NOSES
Leilei Pan
Advisor:
University of Guelph, 2011
Professor Simon X. Yang
A wireless electronic nose network system has been developed for monitoring and
analyzing livestock farm odour. The system utilizes electronic noses (e-noses) that
can measure odour compounds and environment factors such as temperature and humidity. The e-noses are deployed at various locations on the farm, and sensor signals
are transmitted via a wireless communication to a central station, where the data processing and sensor fusion algorithms analyze the collected odour data, compute the
odour concentration, and display the odour dispersion plume. This system would provide users with convenient odour monitoring capabilities and help the development of
an effective overall odour management strategy. In addition, an adaptive neuro-fuzzy
inference approach is proposed to calibrate the e-nose responses to human panelists’
perception. The proposed method can handle non-numeric information and human
expert knowledge in livestock farm odour models, and can adjust the parameters in a
systematic manner for optimal system performance. The proposed approach has been
tested against a livestock farm odour database. Several livestock farm odour models
have been developed for comparative studies. The results show that the proposed
approach provides a more accurate odour prediction than a typical multi-layer feedforward neural network. Furthermore, to model odour dispersion around livestock
facilities, a biologically inspired odour dispersion model is proposed, and is tested
using computer simulations and a livestock farm odour database. Results show that
the proposed approach is effective in providing accurate modelling of odour dispersion from multiple and various types of odour sources in both static and non-static
environments.
Dedication
To my loving and understanding parents and to my dear husband.
iii
Acknowledgements
Fist of all, I would like to acknowledge the contributions of the many people who
provided invaluable help and support for the completion of this thesis.
I would like to thank my advisor, Dr. Simon X. Yang, for his invaluable guidance,
assistance, encouragement, and support during the course of this research and through
my graduate studies at the University of Guelph. Special thanks as well to my
advisory committee member Dr. Gauri Mittal, Dr. Staphano Gregori, and Dr. Fangju
Wang, who has provided me precious advice and suggestions.
The School of Engineering has provided a supportive environment in which to
study. I would like to take this opportunity to express my sincere appreciation to all
the faculty, staff, fellow students, and my friends.
On a personal front, I am forever indebted to my family for my continued study.
Special thanks to my parents and my husband, who have provided both moral and
financial support over the period of my education. Without all of their love and
support, I wouldn’t have been able to finish this thesis.
There have been many people who have actively contributed to this project. All
of their help and support is greatly appreciated.
iv
Contents
List of Tables
viii
List of Figures
ix
List of Symbols
xi
List of Abbreviations
xiii
1 Introduction
1
1.1
Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
1.2
Objectives of This Thesis . . . . . . . . . . . . . . . . . . . . . . . . .
4
1.3
Contributions of This Thesis . . . . . . . . . . . . . . . . . . . . . . .
5
1.4
Organization of This Thesis . . . . . . . . . . . . . . . . . . . . . . .
7
2 Background and Literature Survey
2.1
2.2
2.3
9
Odour Measurement Technologies . . . . . . . . . . . . . . . . . . . .
9
2.1.1
Human Olfactory Based Methods . . . . . . . . . . . . . . . .
9
2.1.2
Non-Olfactory Odour Measurement Methods . . . . . . . . . .
11
Livestock Farm Odour Calibration Modelling
. . . . . . . . . . . . .
14
2.2.1
Single Component Calibration Models . . . . . . . . . . . . .
14
2.2.2
Multi-Component Calibration Models . . . . . . . . . . . . . .
15
2.2.3
Multi-Component Multi-Factor Calibration Models . . . . . .
15
Odour Calibration Algorithms . . . . . . . . . . . . . . . . . . . . . .
16
2.3.1
16
Statistical Methods . . . . . . . . . . . . . . . . . . . . . . . .
v
2.4
2.3.2
Neural Network Based Methods . . . . . . . . . . . . . . . . .
18
2.3.3
Fuzzy Logic Based Methods . . . . . . . . . . . . . . . . . . .
20
2.3.4
Other Methods . . . . . . . . . . . . . . . . . . . . . . . . . .
21
Air Dispersion Modelling
. . . . . . . . . . . . . . . . . . . . . . . .
22
2.4.1
Fluid Dynamics Models . . . . . . . . . . . . . . . . . . . . .
22
2.4.2
Gaussian Dispersion Models . . . . . . . . . . . . . . . . . . .
24
2.4.3
Lagrangian Particle Models . . . . . . . . . . . . . . . . . . .
29
3 A Wireless Electronic Nose Network for Monitoring Livestock Farm
Odour
30
3.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
30
3.2
The Developed Wireless Electronic Nose Network System . . . . . . .
32
3.2.1
Development of the Electronic Noses . . . . . . . . . . . . . .
33
3.2.2
The Wireless Electronic Nose Network . . . . . . . . . . . . .
36
3.2.3
Software Integration . . . . . . . . . . . . . . . . . . . . . . .
39
3.3
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
47
3.4
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
49
4 A Neuron-fuzzy Model for Odour Calibration
50
4.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
50
4.2
The Proposed Neuro-fuzzy Approach . . . . . . . . . . . . . . . . . .
52
4.3
Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . .
55
4.3.1
Data Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
56
4.3.2
Livestock Odour Calibration Models . . . . . . . . . . . . . .
56
4.3.3
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
59
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
60
4.4
5 A Neuron-dynamics Model for Odour Dispersion
64
5.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
64
5.2
The Proposed Neuro-dynamics Approach . . . . . . . . . . . . . . . .
66
5.3
Stability Analysis of the Proposed Dispersion Model . . . . . . . . . .
68
vi
5.4
Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . .
69
5.4.1
Odour Dispersion in Static Environments . . . . . . . . . . . .
69
5.4.2
Odour Dispersion in Non-static Environments . . . . . . . . .
71
5.5
Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . .
75
5.6
Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
77
5.6.1
Comparison to Gaussian Models . . . . . . . . . . . . . . . . .
77
5.6.2
Comparison to Fluid Dynamics Models . . . . . . . . . . . . .
81
5.6.3
Comparison to Lagrangian Particle Models . . . . . . . . . . .
82
Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
83
5.7
6 Conclusions and Future Work
84
6.1
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
84
6.2
Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
85
References
87
Appendix: Selected Publications
101
vii
List of Tables
2.1
Pasquill-Gifford air stability classes . . . . . . . . . . . . . . . . . . .
26
3.1
Sensors used in the development of the electronic nose
34
viii
. . . . . . . .
List of Figures
2.1
A multi-component multi-factor model for livestock farm odour. . . .
17
3.1
The architecture of the proposed wireless electronic nose network. . .
33
3.2
The developed sensor array. . . . . . . . . . . . . . . . . . . . . . . .
35
3.3
The developed chamber. . . . . . . . . . . . . . . . . . . . . . . . . .
36
3.4
An electronic nose consists of the sensor array inside a chamber, a data
acquisition board, a mote processor, and a power supply. . . . . . . .
37
3.5
The block diagram of the wireless network. . . . . . . . . . . . . . . .
37
3.6
The mote processor MPR2400 and the gateway MIB600. . . . . . . .
38
3.7
The gateway with connection to a laptop computer. . . . . . . . . . .
39
3.8
Topology of the wireless network. . . . . . . . . . . . . . . . . . . . .
42
3.9
The interface of data acquisition system. . . . . . . . . . . . . . . . .
42
3.10 The interface of database. . . . . . . . . . . . . . . . . . . . . . . . .
43
3.11 The interface of data records management console.
. . . . . . . . . .
44
3.12 The main interface of the software. . . . . . . . . . . . . . . . . . . .
45
3.13 The dynamic display console. . . . . . . . . . . . . . . . . . . . . . .
46
3.14 The odour analysis system. . . . . . . . . . . . . . . . . . . . . . . . .
47
4.1
The structure of an ANFIS consists of a five-layer feedforward network. 53
4.2
An ANFIS model with three inputs and 27 IF-THEN rules for calibrating the livestock farm odour. . . . . . . . . . . . . . . . . . . . .
58
4.3
Structure of NN model for calibrating the livestock farm odour. . . .
58
4.4
Comparison of the initial membership functions before the learning (A)
with the final membership functions after learning (B) using Model 1.
ix
61
4.5
Comparison of the initial membership functions before the learning (A)
with the final membership functions after learning (B) using Model 2.
62
5.1
Odour dispersion from a point source simulated by the proposed model. 70
5.2
Gaussian distribution test on the crosswind distributions generated by
the proposed shunting model. . . . . . . . . . . . . . . . . . . . . . .
72
5.3
Odour dispersion from a line source simulated by the proposed model.
73
5.4
Odour dispersion from a regular area source simulated by the proposed
model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.5
Odour dispersion from an irregular area source simulated by the proposed model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.6
75
Odour dispersion in changing wind speed simulated by the proposed
model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.8
74
Odour dispersion from an instantaneous emission simulated by the proposed model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.7
73
76
Comparison of odour dispersion from a point source using the proposed
model and a typical Gaussian dispersion model. . . . . . . . . . . . .
x
78
List of Symbols
api
The Sugeno parameter associated with the i-th input
and the p-th output in a fuzzy rule
A
The passive decay rate of neural activity
Aij
The j-th linguistic label associated with the i-th input
B
The upper bound of the neural activity
c̄ (x, y, z)
Steady-state concentration of pollutant at location (x, y, z)
at a specific unit (g/m3 )
C
Concentration of the pollutant
C(Xi , t)
The odour concentration at location Xi and time t
dij
The Euclidean distance between positions Xi and Xj
D
The lower bounds of the neural activity
fp
The p-th consequent output of the a rule
H
Effective source height
i, j, k
Indexes of a neuron in the neural network
Ii+
The excitatory inputs to the i-th neuron
Ii−
The inhibitory inputs to the i-th neuron
Kx
Diffusion coefficient at horizontal downwind direction
Ky
Diffusion coefficient at horizontal crosswind direction
Kz
Diffusion coefficient at vertical direction
m
Number of input membership functions of a fuzzy rule
MDP
The mass consumption term that represents the deposition
and biochemical interaction effects
MSC
The source term that represents the mass flow-in
xi
n
Number of inputs of a fuzzy rule
Opk
The p-th output of the k-th layer of a ANFIS model
Q
The source strength or emission rate of the pollutant (g/s)
r
Correlation of the predicted value and perceived value
u(θ)
The angle function of wind direction (θ)
Vw (t)
The wind speed at time t
Vx
Horizontal downwind wind velocity (m/s)
Vy
Horizontal crosswind wind velocity (m/s)
Vz
Vertical wind velocity (m/s)
wij
The connection weight from the j-th neuron to the i-th neuron
wp
The firing strength of the p-th rule
x
Downwind distance to the odour source (m)
xi
The i-th input linguistic variable in the antecedent part of
a fuzzy rule
Xi
The spatial location of the i-th neuron
y
Crosswind distance to the central line of the odour source (m)
z
Vertical distance to the central line of the odour source (m)
μAij
Membership function of the linguistic label Aij
θ
The angle between the wind direction and the position of the
neighbour neuron
ξi
The neural activity of the i-th neuron
σy
Lateral Gaussian dispersion coefficient at crosswind direction
σz
Vertical Gaussian dispersion coefficient
xii
List of Abbreviations
A/D
Analog/Digital
ANFIS
Adaptive network-based fuzzy inference system
ANN
Artificial neural network
BP
Back propagation
CA
Cluster analysis
CNN
Competitive neural network
DC
Direct current
DT
Dilution to threshold
DFA
Discriminant function analysis
FCNN
Fuzzy clustering neural network
FD
Fluid dynamics
FE
Finite element
FV
Finite volume
GA
Genetic Algorithm
GC/MS
Gas chromatography/Mass Spectrometry
GRNN
Generalized regression neural network
MCE-nose
Multi-chamber electronic nose
MLP
Multi-layer perceptron
MOS
Metal-oxide semiconductor
MSE
Mean squared error
NN
neural network
NN1
neural network with all factors and components
NN2
neural network without considering ammonia
xiii
NN3
neural network without considering hydrogen sulfide
PCA
Principal components analysis
PLS
Partial least squares
RBF
Radial basis function
RMSE
Root mean squared error
SLF
Structural learning with forgetting
VOC
Volatile organic compound
xiv
Chapter 1
Introduction
Despite its significant contributions to the Ontario economy, the livestock industry is
now facing an increasing number of public complaints and concerns from non-farming
rural residents about environmental and health problems caused by odour emissions
from livestock operations. It is recognized that long-term exposure to nuisance odour
usually results in undesirable reactions in people, including emotional stresses, respiratory problems, headaches, nausea, and vomiting (Sucker et al., 2009; Kartavtsev
et al., 2011; Sakawi et al., 2011). The Environmental Protection Act has addressed
the issue of odours that create health problems. In order to reduce the impact of
odour emissions from livestock facilities, governments have devoted significant effort
to limiting the expansion and development of livestock operations; as a result, the
growth of the livestock industry has been impeded by its odour problems.
The development of odour control technologies is progressing. However, evaluating
the effectiveness of existing odour control technologies and methods is a difficult task
because there is no convenient and reliable method for quantifying livestock farm
odour strength. The lack of livestock farm odour measurement tools stems from the
complexity of livestock farm odour and a limited understanding of the odour itself.
In addition, the lack of convenient odour measurement tools prevents governments
and environmental agencies from establishing regulations or standards to mitigate
livestock farm odour problems.
1
1.1
Problem Statement
Odour is a sensation caused by odorous chemicals in the air. Odour strength perceived by the human nose is a result of combined processes of physiological detection
of stimuli by the olfactory receptors in the nose and psychological processing of the
stimuli by the human brain (Kammermeier et al., 2001; Masaoka et al., 2007). People’s sensitivity to odour is very personal, and varies with gender, age and state of
health. Therefore, it is difficult to achieve an objective and analytical measure of
odour.
Over 100 odorants have been identified in livestock farm odours. In the human
nose, each odorant is detected by a combination of different olfactory receptors, and
responses from all receptors are combined in neurons in the olfactory cortex in the
brain. It is reported that the responses of cortical neurons are not just a combination
of responses to the individual odorants of an odour, but actually generate novel odour
perceptions in humans (Zou and Buck, 2006). Therefore, the strength of an odorant
mixture is not a simple sum of the strength of all odorous compounds within it. This
character of the human olfactory perception adds difficulty to odour measurement.
The following problems affect research on livestock farm odours:
1. The human nose is still the “instrument” for odour measurement most commonly used today. Odour measurement technologies such as olfactometry and
scentometer use trained odour panelists to smell air samples to determine the
strength of an odour. Those human olfactory-based methods are susceptible to
the analyst’s personal bias in odour measurement. It is difficult to overcome
an individual’s bias regarding the influence of various factors - such as age and
health, and personal background and experience - on the human sense of smell
(Schiffman et al., 1995; Sucker et al., 2009; Sakawi et al., 2011). In addition,
use of a human panel is expensive. Therefore, the need exists for unbiased
automatic odour evaluating technologies.
Electronic noses are objective, automatic, and low-cost odour evaluating devices that can detect and recognize complex odours. However, odour sources
2
in a modern livestock facilities usually are arrays of points (exhaust fans), lines
(side curtain opening) and areas (manure spreadings). The odour profile is
affected by farmstead factors such as building layout schemes, separation distances between buildings, and location of exhaust fans. Odour measurements
at a single location, therefore, is not sufficient to provide a comprehensive odour
impact evaluation around the farm; measurements taken at multiple locations
simultaneously are required for an overall odour profile. A single electronic nose
(enose) can assess odour levels at only one location at a time; it cannot provide a comprehensive odour impact evaluation based on various topography and
farmstead factors, a fact that has led to an unfocused and inefficient research
approach on reducing livestock farm odour. Therefore, an odour measurement
technology that can provide a comprehensive odour profile around livestock facilities is required in the development of an overall odour management strategy.
2. Efforts to reduce odours from livestock production facilities have been impeded
by the lack of knowledge on odour generation mechanisms. Livestock farm
odours result from complex mixtures of hundreds of compounds that interact
with each other; and the strength of an odour is not a simply the sum of the
strengths of all odorous compounds within it (Zhang et al., 2003). Besides,
many factors such as air temperature, air velocity, and relative humidity influence people’s perception of odour. However, how and to what extent these
factors and components influence odour concentration is still unclear (Powers
and Bastyr, 2004; Schauberger et al., 2005; Lin et al., 2007).
3. Detecting and locating odour around livestock farms has long been an elusive
goal of researchers and environmental agencies. Air dispersion models are used
as mathematical tools to calculate the downwind odour strength based on odour
emission rates, topography and meteorological information. Researchers have
identified important aspects in which agricultural odour sources are different
from industrial pollutant sources (Zhu, 1998; Xing, 2006). For instance, odour
sources in livestock farms are low-lying - usually at or near ground level; and the
3
rise of a livestock odour plume is not obvious because the vertical momentum
or lower density of a mass flow of warm gas is not significant. Researchers have
also indicated that some industrial-oriented air dispersion models are not suitable for agricultural odour dispersion (Xing, 2006). These differences have to be
taken into account when calculating air dispersion. In addition, most existing
air dispersion models require prior knowledge about the environment such as
emission rate of gas at the source, incoming solar radiation, and mixing height
- for example, the distance between the Earth’s surface and the bottom of the
inversion atmospheric layer. These parameters are difficult to obtain without
access to special professional tools and expertise, thus preventing a convenient
estimation of the impact of livestock farm odour on the environment. Because
odour emissions from livestock farms have unique characteristics, an odour dispersion model designed specifically for livestock farms can greatly improve the
efficiency and accuracy of odour dispersion prediction. However, none of the
existing dispersion models are specifically developed for livestock farm odour.
1.2
Objectives of This Thesis
The objectives of this research are: (1) to develop an electronic nose network system
for monitoring and analyzing livestock farm odours; (2) to develop an artificial intelligent model for analyzing and exploring odour generation; and (3) to propose a
neuro-dynamics model for dynamic simulation of odour dispersion around livestock
farms. The wireless network based system can provide objective, comprehensive and
remote measurement for odours around livestock farms. Together with the artificial
intelligence models, this electronic nose system can provide farmers and researchers
with more precise odour management capabilities for more efficient operation of odour
control systems. When combined with odour calibration and dispersion analysis approaches, the system can help in the development of an overall odour management
strategy by providing dynamic, comprehensive information about the livestock farm
environment and odour dispersion.
4
1.3
Contributions of This Thesis
This work is different from other odour-related work in the following ways:
1. In this study, a wireless network system is developed for monitoring and analyzing livestock farm odour. The system utilizes a wireless network built from
electronic noses that can detect odour compounds and environment factors such
as temperature, wind speed, and humidity. The electronic noses are located at
various points on the farm, and sensor signals are delivered via wireless link
to an analysis station, where sensor fusion algorithms and odour dispersion algorithms calculate the odour concentration and dispersion; display the odour
data in real time; and, should any environment factor change, predict the odour
concentration.
The proposed electronic nose network approach can automatically monitor livestock farm odours, but also has potential industry applications. It differs from
existing electronic noses - which can measure at only a single location - in that
it provides multi-location odour monitoring. The proposed wireless electronic
nose network, therefore, can provide a more comprehensive odour profile than
a single electronic nose. Second, by using this wireless electronic nose network
system, users can monitor the odours from a remote location such as inside a
house or office, as opposed to working in the field, making odour measurement
easier in unfavourable weather. In addition, the developed electronic nose network system with multiple electronic noses is quite robust to sensor failure and
noise. One or more failed or incorrect sensor readings can be corrected through
the redundant information from the other electronic noses.
2. A multi-component, multi-factor odour calibration model is proposed to adequately model livestock farm odour. Both numeric factors and linguistic factors
are considered in this model. The proposed multi-component multi-factor odour
calibration model and neuro-fuzzy based approach can incorporate more potential odour generation factors into the model. Many non-numeric factors and
5
expert knowledge that cannot be incorporated into previous statistical models
or neural network models can be considered by this approach. Thus it enables
a more adequate and comprehensive odour calibration modelling
3. A neuro-fuzzy based method is proposed for analysis of the livestock farm odour.
The proposed approach incorporates neuro-adaptive learning techniques into a
fuzzy logic method. In addition, instead of choosing by trial and error the
parameters associated with a given membership function, the parameters are
tuned automatically in a systematic manner so as to adjust the membership
functions of the input/output variables for optimal system performance. This
feature is important as it can avoid inappropriate predetermination of membership functions, thus minimizing errors resulting from the limited knowledge on
the livestock farm odour.
4. Air dispersion models have been used to predict the concentration of pollutants
from industrial sources. Dispersion models use source emission rates and meteorological data to calculate the downwind concentration of chemical compounds
as a function of the distance to the source. However, odour concentration is
different from the concentration of chemical compounds, as it is a result of the
combined processes of physical dilution of chemical compounds and biological
olfactory sensing. Therefore, existing dispersion models are not very suitable
for simulating odour dispersion.
A novel neuro-dynamics approach is proposed to model dynamic odour dispersion around livestock farms. The dynamics of each neuron is characterized
by a shunting equation derived from Hodgkin and Huxleys’s membrane equation (Grossberg, 1982). The proposed dispersion model can dynamically represent complex or non-steady-state environmental features around livestock farms.
The model can also model odour dispersions from multiple odour sources, and
from various types of sources like point sources, line sources, and area sources.
The proposed neuro-dynamics odour dispersion model is developed specifically
for odours around livestock farms. In this model, odour plume is represented by
6
the landscape of the neural activity for all neurons in the topologically organized
neural network, and the dynamics of each neuron is characterized by a shunting
neural equation. The dispersion parameters are therefore tuned according to
input/output data to represent the nonlinear relationship among inputs and
outputs. The proposed model is capable of simulating air dispersion both in
static and non-static environments, and is able to model emissions from various
types of odour sources, such as ventilation hoses, manure pits, and manure
spreadings in fields. It is therefore more suitable than existing dispersion models
for representing odour dispersion around livestock farms.
1.4
Organization of This Thesis
This thesis consists of six chapters. Chapter 1 provides an introduction to livestock
farm odour problems and outlines the objectives and contributions of the research.
Chapter 2 summarizes the background and reviews the state-of-the-art research on
odour measurement technologies, odour calibration algorithms, and odour dispersion
modelling methods.
Chapter 3 briefly presents the current status of odour measurement research, our
previous work on development of an electronic nose for measuring livestock farm
odour, and problems existing in current odour measurement technologies. Based
on this review, a wireless electronic nose network is proposed for providing remote,
automatic and objective odour measurements at multiple locations around livestock
facilities. The architecture of the wireless network system and the design and development of compact electronic nose nodes and wireless communication links are
discussed in detail. The development of a data management software system is described for supporting users in the processing and analysis of livestock farm odour
data collected by the wireless electronic nose network. The structure and design of
the software system is also presented.
Chapter 4 reviews the limitations of current odour calibration models and algorithms. A multi-component, multi-factor model is proposed for better representing
7
livestock farm odour, and an adaptive network-based fuzzy inference system (ANFIS)
is proposed to overcome limitations of existing odour calibration algorithms. The proposed approach is tested with a livestock farm odour database. Various comparative
studies have been performed and the proposed method demonstrates the effectiveness
of the proposed multi-component, multi-factor model and ANFIS method.
Chapter 5 reviews existing air dispersion models and their limitations in modelling
odour dispersion around livestock facilities. A novel neural network model is proposed
for modelling dynamic odour dispersion around livestock farms. The development of
the proposed model is explained in detail, and the stability and sensitivity analysis of
the model are discussed. Odour dispersions in various farmstead (point, line, and area
odour source) situations, and in static and non-static environments, are investigated
by computer simulation. Preliminary testing with field livestock farm odour data is
described, showing the effectiveness of the proposed neural network-based method in
modelling odour dispersion around livestock farms.
Chapter 6 summarizes, and draws conclusions from, the study on monitoring and
analyzing livestock farm odour using the developed wireless electronic nose network,
and the proposed odour calibration method and odour dispersion model. Recommendations for future work based on the findings of this research are presented.
8
Chapter 2
Background and Literature Survey
Developing a system for monitoring and analyzing livestock farm odours requires
technical knowledge in various areas. These areas can be categorized into odour measurement technologies, livestock farm odour calibration modelling, odour calibration
algorithms, and air dispersion modelling. This chapter provides background knowledge and reviews existing research in these areas.
2.1
Odour Measurement Technologies
A number of technologies are available for measuring odour strength around livestock facilities, such as dynamic dilution olfactometry and scentometer, which takes
advantage of the human sensory response, and automatic electronic methods.
2.1.1
Human Olfactory Based Methods
Currently trained human panelists are most commonly engaged in odour measurement technologies. Human olfactory based technologies include dynamic-dilution
olfactometry for laboratory environment and scentometer for field measurement.
9
Dynamic-Dilution Olfactometry
Dynamic-dilution olfactometry is widely used as a standard method of odour measurement (ASTM, 1986; Brose et al., 2001; McGinley and McGinley, 2004; Schmidt
and Cain, 2009; Monse et al., 2010; Liu et al., 2010). When using dynamic-dilution
olfactometry, air samples are presented to trained odour panelists over a series of
dilution ratios through a sniffing port. Panelists smell the continuously diluted air
samples, and evaluate the odour strength based on odour detection threshold. Odour
concentration is then established as the dilution factor at the detection threshold.
Several types of dynamic-dilution olfactometry are currently available. They vary
from single port olfactometry to three ports olfactometry, and range from single
panelist unit to multi-panelist units. When using single port olfactometry, panelists
only evaluates the diluted air from the single sniffing port, while when using three
ports olfactometry, three air samples are presented through the ports, two of which are
odour-free air and one is diluted odour. The panelist must identify the diluted odour
from then three samples. Only one panelist at a time can measure odour when using
single panelist dynamic-dilution olfactometry, but multi-panelist can simultaneously
evaluate odour through multi-panelist olfactometry.
However, dynamic-dilution olfactometry has to be conducted in strictly controlled
laboratory environment, where gas and dust concentrations are significantly different than in natural, open space. Thus, this method is not suitable for sampling
odour downwind from odour sources in the field. Moreover, odour measurement using dynamic-dilution olfactometry requires a sufficient number of panelists. Panelist
selecting and training procedures are time-consuming and of high cost.
Scentometers
Similar to dynamic-dilution olfactometry, scentometers is a hand-held dilution device
that are also used to measures odour strength based on odour threshold. But in
comparison to dynamic-dilution olfactometry, a scentometer is specifically intended
for field studies. It dilutes odorous ambient air with carbon-filtered odourless air,
10
and odour concentration is established in “Dilutions-to-Threshold” (D/T) values.
The Dilution-to-threshold is the dilution ratio of volume of carbon-filtered air to
the volume of odorous air at the odour threshold (Kosmider and Krajewska, 2007;
Bereznicki et al., 2010). A scentometer is portable, simple to use, relatively of low
cost, and can be used to provide real-time odour measurement in the field. However,
it has limited dilution levels; therefore it is less accurate than dynamic olfactometry.
Because human sensory is involved, both dynamic-dilution olfactometry and scentometers are susceptible to the panelists’ personal bias in odour measurement. Many
factors such as age and health, and individual’s background and experience, would
influence the human sense of smell (Schiffman et al., 1995; Sucker et al., 2009; Sakawi
et al., 2011). It is difficult to overcome this personal bias in odour measurement. In
addition, human panelists are expensive to use. Therefore, unbiased automatic odour
evaluating technologies are desirable.
2.1.2
Non-Olfactory Odour Measurement Methods
Non-olfactory odour measurement technologies in are summarized in this section.
The advantages and weaknesses of each technology are discussed.
Gas Chromatography/Mass Spectroscopy Analysis
Gas Chromatography/Mass Spectrometry analysis (GC/MS) is an analytical technology to identify and quantify of volatile organic chemical compounds within a sample
gas. Its applications include drug detection (Hans et al., 2007), food and beverage
quality control (Petrova et al., 2007), environment analysis (Eckenrode, 2001), and
identifying chemical components in livestock odours (Schiffman et al., 2001a; Koziel
et al., 2010). GC/MS analysis is conducted in strict laboratory environment, gas
compounds should be maintained at certain vapour pressures during analysis. Before
the gas sample is introduced into the GC/MS instrument, sample preparation and
cleanup is required, e.g. the sample gases need to be dissolved in a suitable solvent.
Formal training and expertise in mass spectrometry and chemistry is required for the
11
operation of the instrument. In addition, the analysis procedure typically takes 20
to 100 minutes, and additional data analysis can take more time depending on the
amount of information required (McMaster, 2008).
Generally, GC/MS can be used for identification and quantization of chemical
compounds in livestock farm odours, but it doesn’t directly measure the strength
of odour. Additional data analysis procedure is in need to transform the quantity
of chemical compounds into odour strength. Moreover, it should be performed in
laboratory, it is not suitable for field analysis; the analysis procedure is relatively
slow, a GC/MS instrument is very expensive compared to other analytical systems
and operational cost is high.
Electronic Noses
An electronic nose is a sensor-based electrical odour analysis system that is used for
measurement of both quality and quantity of complex odours. Electronic nose is built
to mimic human olfactory system, and usually consists of an array of electronic gas
sensors (as counterpart of olfactory receptors in human nose) and a signal-processing
system (mimics the part of human brain that processes odour stimuli) (Brattoli et
al., 2011). During a measurement, odorous gases are introduced into the electronic
nose, flow through the gas sensors. Gas sensors respond to the odour, and produce
electrical signal outputs. The signal-processing system analyzes sensor signals, and
transforms digital signals into meaningful odour quality/quantity results. The output
of an electronic nose can be the identity and amount of chemical components in the
odour, a prediction of odour strength, or any other characteristic properties of the
odour which might be perceived by a human.
Many applications of electronic noses have been reported. The most common
application is to classify the smell of beverages or foods, in order to measure the
freshness for quality control (Zhang et al., 2008). Electronic nose technology also
has proved to be a useful tool in medicine for diagnosing (Kodogiannis et al., 2008),
and in environmental monitoring for analyzing fuel mixtures (Sobaski et al., 2006),
testing food and water quality (Devarajan et al., 2011), and identifying household
12
odours (Al-Bastaki, 2009). Gonzalez-Jimenez et al. (2011) proposed a multi-chamber
electronic nose (MCE-nose) to overcome a major disadvantage of using metal oxide
semiconductor (MOS) sensors for electronic noses, e.g. the long recovery period
needed after each gas exposure. The proposed MCE-nose comprises several chambers,
and in each chamber there is an identical sets of MOS sensors. Chambers and sensors
alternate between sensing and recovering states, thus provide rapid sensing ability for
the electronic nose.
However, the number of studies dedicated to using an electronic nose in agricultural environment management is still very limited (Osarenren, 2002; Powers and
Bastyr, 2004). No commercially available electronic noses were developed in particular for detecting livestock farm odour. Moreover, most electronic noses developed
previously are bulky, desk-top instruments that can only be used in laboratories. The
environments of laboratories are apparently different from those of the fields. Thus,
the accuracy and reliability of the measurements are limited. It is highly desirable
to develop new electronic nose for the measurement livestock farm odour which has
great commercial potential.
In my previous study, an intelligent portable electronic nose was developed particularly for measurement and analysis of livestock farm odour, and can be used in
both laboratory and field studies (Pan and Yang, 2007). Experiments in 14 Ontario
livestock farms showed that the electronic nose is capable of producing an objective
output, has a greater consistency in odour measurement, and is easier to operate than
using olfactometry or human panel.
Electronic noses are unbiased, automatic, and low-cost odour evaluating devices
which can detect and recognize complex odours. However, a modern livestock facility
is usually not a point source but rather an array of points (exhaust fans), lines (side
curtain opening) and areas (outdoor storage surfaces). The odour profile is affected
by building layout schemes, separation distances between buildings, locations of exhaust fans, and landscape around livestock facilities. An electronic nose can only
evaluate odour events at one location each time; it cannot provide a comprehensive
odour impact evaluation according to topography, meteorological factors, and farm13
stead factors such as building layout schemes, which has led to an unfocused and
inefficient research approach on reducing livestock farm odour. Therefore, an odour
measurement technology that can provide an overall odour profile around livestock
facilities is desirable for developing an overall odour management strategy.
2.2
Livestock Farm Odour Calibration Modelling
The outputs from automated odour measurement instruments such as GC/MS and
electronic nose are in terms of concentration of chemical compounds or electrical voltages or currents. They need to be correlated with human olfactory perception, and
transformed into odour strength units based on odour calibration models. Odour
strength is relative personal and subjective matter. It is subject to human olfactory
sensitivity and human brain’s response to stimuli. Limited understanding of odour
system and human olfactory and brain system has impeded the progress of odour
calibration modelling. Researchers have indicated livestock farm odours are complex
mixtures of hundreds of compounds that interact with each other; and the concentration of an odour is not a simply sum of concentrations of all odorous compounds
within it (Zhang et al., 2003). Besides, many factors such as air temperature, air flow
speed, and relative humidity (O’Neill and Philips, 1992; Schiffman et al., 2001b; Zahn
et al., 2001) influence people’s perception of odour.
2.2.1
Single Component Calibration Models
Researchers initially attempted to identify a single chemical compound as an odour
indicator. Hydrogen sulfide and ammonia have been used as such indicators (Liu et
al., 1993; Guo et al., 2000). Later research found that this single-component analysis
method is incomplete, and a more thorough multi-component analysis is required
(DiSpirito et al., 1999; Janes et al., 2004).
14
2.2.2
Multi-Component Calibration Models
Some researchers have investigated multi-component calibration approach. Schiffman et al. (2001a) demonstrated there are hundreds of chemical components in livestock farm odour, and odour strengths are resulted from aggregate effect of hundreds
odorous chemicals. Many chemicals at lower concentration actually produce higher
odour strengths than other chemicals at higher concentration do. Therefore multicomponent analysis must be performed when correlate odorous chemicals with odour
strengths. Zahn et al. (2001) conducted direct multiple-component analysis of malodorous volatile organic compounds (VOCs) present in ambient air from 29 livestock
production facilities using a 19-component statistical model. This study provided evidence that a direct multi-component analysis of VOCs may be useful in monitoring
livestock odours, however, it did not show that multi-component analysis of odours
is more accurate than single-component analysis.
2.2.3
Multi-Component Multi-Factor Calibration Models
Many researchers have indicated that the multi-component calibration approach should
be extended to include odour generation factors, such as temperature, wind speed,
relative humidity, solar radiation, etc. (Powers and Bastyr, 2004; Janes et al., 2005).
Factors that probably influence livestock odour also include type of livestock facilities,
number of animals, location of odour source, and many other relevant factors (Janes
et al., 2005; Miller et al., 2004; Lin et al., 2009). Zahn et al. (2002) investigated the
influence of seasonal and environmental factors on odour emissions from a livestock
lagoon using statistical methods. The factors being analyzed included effluent temperature, air temperature, relative humidity, wind speed, and effluent concentration
of hydrogen sulfide and ammonia. The results showed that these factors are highly
related to gaseous odour emissions from livestock farms. Powers and Bastyr (2004)
conducted research on climatic effects to the perception of odour downwind from
poultry and livestock facilities. It was found out that wind speed would affect concentrations of phenol, decane, and undecane; and solar cover would influence odour
15
in the way that odour dilution threshold are greater on sunny days while concentrations of hydrogen sulfide and acetic acid are lower. Janes et al. (2005) proposed a
multi-component, multi-factor model for the analysis of livestock farm odours using
neural-networks. The location of measurement (livestock barn source or livestock
manure storage source) is considered as a contributing factor to downwind odour
concentrations. The results of the study showed improved accuracy on prediction of
odour concentrations. These multi-component, multi-factor studies demonstrate that
the accuracy and precision of odour strength prediction can be improved by taking
into consideration contributing factors such as environmental conditions, type of facilities, and odour prevention operations. A possible multi-component, multi-factor
neural network model for livestock farm odour is shown in Figure 1.1.
2.3
Odour Calibration Algorithms
The chemical components and contributing factors need to be analyzed to determine
their underlying relationships with odour strength. The analysis methods used can
be conveniently subdivided into two basic types: statistical approaches which seek to
probabilistically formulate the underlying relationships of the input and the output
data, and non-statistical approaches which try to simulate to the human cognitive
process to solve the problems (Gardner and Bartlett, 1999). Researchers have obtained considerable experience in the use of various statistical and non-statistical
pattern recognition technologies. These include the use of conventional statistical
techniques, artificial neural networks, fuzzy logics, and genetic algorithms.
2.3.1
Statistical Methods
Statistical methods, such as linear or nonlinear multiple regression models, have been
the primary tools used to analyze and calibrate the relationship between chemical
components, contributing factors and odour strengths (Janes et al., 2004; Bereznicki
et al., 2010). They are relatively easy to use and fast in computing. Principal components analysis (PCA) (Hanumantharaya et al., 1999; Brambilla and Navarotto, 2010),
16
Odor components
Amonia
Hydrogen Sulfide
Acetic Acid
Indole
Environmental Conditions
Temperature
Wind speed
Odour
Concentration
Humidity
Location
Type of Facility
Farrowing
Gestation
Nursery
Finishing
Figure 2.1: A multi-component multi-factor model for livestock farm odour.
17
discriminant function analysis (DFA) (Chung et al., 2003; Brambilla and Navarotto,
2010) and cluster analysis (CA) (Pearce et al., 2003) are commonly used statistical
methods for characterizing and identifying chemical compounds in livestock odours.
Also, when large amounts of data are available, PCA and DFA are often used to
pre-process data and identify useful information. However, statistical methods are
sensitive to noises in the data. In addition, they are not good at incorporating nonnumerical or subjective human expert knowledge, such as influence of environmental
factors and type of facilities, to help calibrate the perception of odour. Thus they are
not well suited for dealing with vague and imprecise information available in odour
measurement.
2.3.2
Neural Network Based Methods
Artificial neural networks (ANNs) have been used to calibrate odour compounds
and strength levels. The most popular type of ANN employed is the feed forward,
multi-layer perceptron (MLP) training using the error-correction, back-propagation
algorithm (BP) (Sohn et al., 2003; Janes et al., 2004). Hobbs et al. (2001) used a
radial basis function (RBF) neural network approach to develop a relationship between odour concentration and major odorants from livestock wastes. This method
could achieve good prediction result, and showed higher accuracy when compared
with prediction results from a linear regression model. Carmel et al. (2003) employed
a Hopfield neural network to identify chemicals and determine their concentration
for an electronic nose. The algorithm was tested against real field data, and showed
high quality performance, and could identify chemicals which were not previously
presented to the enose system. Janes et al. (2004) used a fast back-propagation (fast
BP) method to train a MLP neural network. The inputs of the neural network are
concentrations of hydrogen-sulfide and ammonia, and measurement locations. The
calibration outcomes were compared with those from a statistical model, the result
showed that the ANN approach significantly improved odour prediction accuracy. Pan
et al. (2006) developed a multi-component, multi-factor neural network model for pig
farm odours, and used a structural learning with forgetting (SLF) approach to ana18
lyze potential contributing components and factors of pig farm odour. By using this
method, during the training, unnecessary neural connections would fade away, and
skeletal neural network would emerge, revealing relative contributions of each input
(components and factors) to the outputs (odour strength). This approach would allow identification of significant chemical components and major contributing factors,
and potentially help improve efficiency of odour control methods. Sohn et al. (2009)
evaluated the capability of a gas sensor array system to monitor the odour abatement performance of a biofiltration system for piggery buildings. Three calibration
algorithms: PCA regression, ANN and partial least squares (PLS) were employed in
quantification of the sensor array responses. It showed that ANN and PLS yields good
quantification accuracy while PCA regression approach was least accurate. Sun et al.
(2008) applied two ANN techniques: BP neural networks and generalized regression
neural network (GRNN) to quantify the diurnal and seasonal gas concentrations and
emission rates from livestock facilities. Both approached demonstrated high accuracy
and good systemic performance. GRNN showed fast training speed and relatively
easy network parameter settings in comparison to the BP neural network method,
thus was more preferred in modelling livestock farm odour. Men et al. (2011) presented a optimized competitive neural networks (CNNs) to classify odour information
gathered electronic noses. The proposed CNN could automatically adjust (increase,
divide, or delete) the number of neurons involved the learning, and the learning rate
changes according to the variety of training times of each sample. In comparison to
the traditional CNN, the new method demonstrated its ability to optimize network
structures and achieve good classification results.
It is widely accepted that there are many advantages in using ANNs algorithms
over statistical techniques. These advantages include ANNs’ generalization abilities, their ability to model highly non-linear and complex systems, and their ability
to automatically learn the relationship that exits between the inputs and outputs
(Haykin, 1999). However, neural networks have some disadvantages as well. For example, they can not explicitly model human expert knowledge and non-numeric data
which arise from several different sources such as environmental conditions, manure
19
systems, and type of livestock facilities. As a result, ANN algorithms sometimes fail
to produce a good prediction.
2.3.3
Fuzzy Logic Based Methods
Fuzzy logic utilizes fuzzy sets and fuzzy reasoning to mimic human reasoning in
the computer programming. Fuzzy sets provide a way to represent fuzzy knowledge, which allow intermediate values to be defined between conventional values such
as true/false and high/low. allows intermediate values to be defined between conventional evaluations such as true/false and high/low (Zadeh, 1965; Zadeh and Bellman, 1977). Fuzzy logic based methods can incorporate human experience and expert
knowledge, and handle ambiguous and uncertain data. Furthermore, they are capable
of dealing with linguistic information. Limited research has been reported on applying
the fuzzy logic based approach to odour evaluations (Francesco et al., 2001; Huang
and Miller, 2003; Jatmiko et al., 2009). Francesco et al. (2001) developed a fuzzy logicbased pattern recognition system for an conducting polymer sensors based electronic
nose. In this method, the shapes of sensor responses to odorants were transformed
into fuzzy linguistic expressions. Time space and signal space are fuzzified, e.g. the
time interval during which odour samples were taken and the amplitude values of
the signal were represented by triangular fuzzy sets. The output of the system was
classified odorant. Experimental results showed that it could achieve 87% recognition rate on unknown odorants. Huang and Miller (2003) applied a multiple criteria
decision making approach based on fuzzy scales to evaluate swine odour management
strategies. The strategies been evaluated included manual oil sprinkling, auto oil
sprinkling, web scrubber, draining shallow pit weekly, and so on. The objectives of
the odour management practice are to maximize reduction in odour emissions, and
to be constrained with limited financial resources. The experimental results showed
that as the fuzziness scale of each strategy’s membership function varies, the preference of each strategy to the objectives of odour management practice is changed.
Havlerson et al. (2007) proposed a method to assess odour annoyance using fuzzy set
theory to handle ambiguity in the data. Personal reaction and past experience are
20
considered as inputs, and are fuzzified by various membership functions. The output
is odour annoyance level. The predicted annoyance from this method achieved good
agreement with outcomes of human expert assessments.
Despite their abilities to handle vagueness, uncertainty, and linguistic information,
fuzzy logics alone do not have learning ability, thus it is difficult to analyze complex
systems without prior knowledge on the system being analyzed.
2.3.4
Other Methods
Other methods have also been employed in calibrating odour strengths. These methods include fuzzy clustering neural network (FCNN) (Karlik and Yksek, 2007) and
genetic algorithm optimized fuzzy neural (GA-FNN) system (Kusumoputro and Arsyad, 2005).
Karlik and Yksek (2007) developed a novel fuzzy clustering neural network (FCNN)
algorithm for recognizing odours in real-time. In this method, an unsupervised classifier called fuzzy c-means clustering is used on top of a multi-layer perceptron (MLP)
network to derive the activations of input neurons. The number of input data points
is reduced by fuzzy c-means clustering before they are presented to MLP, therefore the
training time of the MLP is reduced. In addition, the data points are fuzzified by the
fuzzy clustering, divided into fuzzy partitions, and are optimized by fuzzy c-means
algorithm. Experimental results showed that FCNN can provide high recognition
probability in determining odour categories, and can recognize more odours than a
typical MLP network. Kusumoputro and Arsyad (2005) used a genetic algorithm
(GA) to optimize the network size of a fuzzy neural system. The system was applied
to classify mixture of odours. The objective of GA is to minimize activated neuron connections, error rate, and training epochs. The performance of the optimized
system was compared with that of a conventional MLP neural system trained with
back-propagation (BP) algorithm and a un-optimized fuzzy neural system. It demonstrated the GA-optimized fuzzy neural system has better performance in recognition
capability compared to the other two systems.
21
2.4
Air Dispersion Modelling
Detecting and accurately locating odour around livestock farms has been an elusive
goal of researchers and environmental agencies. Air dispersion models are mathematical tools to estimate the impact of downwind odour concentrations on the basis of
source factors such as odour emission rates, topography and meteorological data.
Various types of dispersion models have been developed to simulate how chemical
compounds dispersed in ambient air. Air dispersion models usually can be categorized into two types: steady state models such as Gaussian models, and non steady
state models such as Fluid dynamics (FD) models and Lagrangian particle trajectory
theory.
2.4.1
Fluid Dynamics Models
Fluid dynamics models apply fluid mechanics to solve and analyze problems that
involves fluid flows.
The basis of fluid dynamics models are advection-diffusion
equations that describes the temporal-spatial dynamics of a conservative or nonconservative pollutant (Lin et al., 2009; Hong et al., 2011). They are based on
mass conservation equation, and the theory that chemical contaminants are transported in air via two processes: advection by the air flow, and turbulent diffusion.
A general basic advection-diffusion equation for modelling air dispersion is given as
(Brown, 1990; Zannetti, 1990)
∂C
∂t
=−
∂(Vx C)
∂x
+
∂
∂x
+
∂(Vy C)
∂y
Kx ∂C
∂x +
+
∂
∂y
∂(Vz C)
∂z
Ky ∂C
∂y +
∂
∂z
Kz ∂C
∂z
+ MSC − MDP ,
(2.1)
where C is the concentration of the pollutan; Vx , Vy and Vz are the velocity components; Kx , Ky and Kz are the diffusion coefficients; MSC is the source term that
represents the mass flow-in; and MDP is mass consumption term that represents the
deposition and biochemical interaction effects. The first term on the right side of
Eq.(2.1) is the advection contribution and the second term represents the diffusion
22
effect. This basic advection-diffusion equation provides a generally valid description
of air flow. Many air dispersion models which describe the actual distribution of
pollutants in the wind field are derived from this basic advection-diffusion equation.
These advection-diffusion models can be solved either by analytical methods if some
further assumptions are made to simplify the models and parameters. Gaussian plume
model is an example of this approach (which is discussed in next section); or by using
numerical methods (Zannetti, 1990; Holmes and Morawska, 2006).
Various numerical approaches have been employed to solve the advection-diffusion
equations. These numerical methods include finite volume (FV) methods (Gao and
Niu, 2007; Tsai and Chen, 2004), finite difference (FD) methods (Meerschaert et
al., 2006; Fatehifar et al., 2008), and finite element (FE) methods (Ei-Awad, 2002;
Kamiski et al., 2007). Fatehifar et al. (2008) developed a finite difference approach
to model the dispersion of pollutants from a network of industrial stacks. A three
dimensional advection-diffusion equation was developed to describe the activity of
pollutants. Assumptions made in this approach include: diffusion in wind direction
is negligible in comparison to advection in that direction, and there is no wind velocity at vertical and crosswind direction. Diffusion coefficients were selected based on
Pasquill’s stability class (Pasquill, 1974). Environmental parameters needed for deciding diffusion coefficients include: ambient temperature, air pressure, air viscosity,
air density, wind velocity, day-time solar radiation, cloud cover, earth surface type,
boundary layer height, etc. Computer simulation results showed that dispersion of
pollutants is proportional to air temperature and inversely proportional to wind velocity. Tsai and Chen (2004) used a finite volume approach to solve a three-dimensional
dispersion model for simulating the dispersion of air pollutant in an urban street
canyon. Parameters needed were turbulent kinetic energy, turbulent energy dissipation rate, and Stefan-Boltzmann constant. Values of dispersion parameters were
obtained according to (Launder and Spalding, 1974). Simulation results were compared with measurement results, and predicted pollutant concentration were of high
accuracy. Kamiski et al. (2007) used a finite element method to model the threedimensional dispersion of carbon monoxide along roads. Environment parameters
23
considered were wind speed and direction, temperature, pressure, air humidity, cloud
cover, and traffic velocity. A software was developed based on this method, and field
data was collected for validating the model. Calculated results based on the approach
agreed well with measured levels of carbon monoxide.
However, numerical approaches are complicated, a number of initial conditions
and boundary conditions which are difficult to obtain in the field are in need to solve
advection-diffusion equations. And usually many assumptions are made to simplify
the solution. Numerical approaches require high computational and memory capacity.
Even with simplified models and high-speed computers, only approximate solutions
can be achieved in many cases.
2.4.2
Gaussian Dispersion Models
Gaussian dispersion models are used for air dispersion in a steady-state environment.
They are a closed solution of the advection-diffusion equation. They are relatively
easy to use, and require small number of inputs. The basic Gaussian plume model
is the corner stone in most dispersion models. It is based on following assumptions
(Beychok, 2005; Schnelle and Dey, 1999):
1. Vertical and crosswind diffusion follow Gaussian distributions;
2. The source emissions are continuous and the mass-flow rate (i.e. g/s)is constant;
3. The horizontal wind velocity (i.e. m/s) is constant and the mean wind direction
is constant;
4. Diffusion of emissions in the downwind direction is negligible compared to their
transport by the wind; and
5. There is no deposition or washout of emissions, no absorption of emissions by
the ground or other physical bodies, and no chemical conversion of the emission.
24
The basic Gaussian plume model was initially developed for a continuous point
source in a uniform flow with homogeneous turbulence, which is expressed as
y2
Q
exp − 2
c̄ (x, y, z, H) =
2πūσy σz
2σy
(z − H)2
(z + H)2
exp −
+
exp
−
2σz2
2σz2
,
(2.2)
where c̄ (x, y, z) is the steady-state concentration of pollutant at a specific point
(g/m3 ); Q is the source’s strength or emission rate of the pollutant (g/s); H is the
effective source height; ū is the mean transport velocity across (m/s); x is the downwind distance from the source to the receptor (m); y is crosswind distance from the
receptor to the plume centreline (m); z is vertical distance from the receptor to the
centreline (m); σy is the horizontal dispersion coefficient; and σz is the vertical dispersion coefficient. For the centreline concentration of ground-level odour source, the
value of z = H = y = 0, therefore the model becomes
c̄ (x, 0, 0) =
Q
.
2πūσy σz
(2.3)
The dispersion coefficients σy and σz are empirical parameters depending on air
stability classes, and commonly they are functions of the downwind distance from the
source to the receptor.
The air stability classes defines the atmospheric turbulence that occurs in the ambient air (Barratt, 2001). The most commonly used air stability classes categories are
the Pasquill stability classes, which are defined by wind speed, daytime incoming solar radiation, and night-time cloud cover. The Pasquill stability classes are presented
in Table 1.1 (Pasquill, 1961).
Air stability is considered to have a major effect on the dispersion of air pollution plumes by many researchers, because it enhances the plume dispersion (Barratt,
2001; Beychok, 2005; Hoff and Bundy, 2003). By using Pasquill air stability classes,
verticalσz and lateral σy dispersion coefficients are expressed as functions of distance
from the source and Pasquill-Gifford curves, and can be computed as follows
σy = 465.11628(x) tan(θ),
(2.4)
σz = axb .
(2.5)
25
Table 2.1: Pasquill-Gifford air stability classes (from Pasquill, 1961)
Surface
Daytime
Night-time
wind speed
Incoming solar radiation
Cloud cover
(m/s)
strong
moderate
slight
> 50%
< 50%
<2
A
A-B
B
E
F
2-3
A-B
B
C
E
F
3-5
B
B-C
C
D
E
5-6
C
C-D
D
D
D
>6
C
D
D
D
D
Pasqui-Gifford stability class
Definition
A
very unstable
B
unstable
C
slightly unstable
D
neural
E
slightly stable
F
stable
26
However, Pasquill dispersion coefficients are based on field experiments and are only
appropriate when used for modelling air dispersion in open, rural areas (Pasquill,
1976).
Gaussian dispersion models have been widely used in evaluation odour impacts
around livestock facilities and in determining set-back distance between livestock
farms and local residents. Chastain and Wolak (2000) adopted a Gaussian plume
model to predict odour concentration downwind from the odour source, and used
it to assist selection of sites of livestock facilities. The dispersion coefficients were
selected based on air stability classes defined by (Turner, 1994). Surface roughness
was considered. Urban empirical dispersion parameters were used for forested areas,
and open terrain dispersion coefficients were used for cut grass or crop area. Sensitivity analysis was performed on primary input variables to provide objective basis for
selecting livestock facilities locations. Analysis results also showed that forest barriers and odour source control can help reduce odour dispersion. Hayes et al. (2006)
used a commercial Gaussian atmospheric dispersion model to evaluate to evaluate
odour impact of existing intensive poultry farms. The model was also used to determine setback distance of new poultry production units. Pasquill stability classes
were used. It was assumed that the surrounding terrain was uniformly flat, therefore,
topological data was not used in this study. A new odour annoyance criterion was
developed in the study, and it suggested the new odour annoyance criterion could
largely reduce the maximum setback distance of new poultry facilities. Latos et al.
(2010) developed a Gaussian dispersion model for odorous chemical compounds hydrogen sulfide and ammonia emitted from pig farms. In this study, in order to predict
the maximum concentration with a single breath’s time-scale (few seconds) instead
of a long-term average concentration, dispersion coefficients were modified based the
“peak-to-mean”. Complex terrain around the farm and the height of the emission
stack were considered. The analysis results indicated that a standard Gaussian dispersion model that predicts long-term average concentration underestimates the impact
of odorous pollutant, and short-term “peak-to-mean” modification should be adopted
in odour dispersion model. Yu (2010) developed a livestock farm odour dispersion
27
model based on Gaussian plume theory. Pasquill-Gifford dispersion coefficients were
used for long-time odour emission, and Hogstrom dispersion coefficients were applied
for modelling short-time odour fluctuation. Atmospheric parameters such as friction
velocity, sensible heat flux, and mixing height were considered. Also, predicted odour
concentration was modified by “peak-to-mean” ratio in order to reflect short-term
peak odour concentration during a single breath’s time. The model showed its capability to predict mean odour concentration, instantaneous odour concentration, peak
odour concentration and frequency of odour concentration. The model was tested
against field odour plume measurement data, and achieved good odour concentration
and odour frequency predictions. Li (2009) evaluated impact of odorous emissions
from livestock farms using two Gaussian model based commercial air dispersion models: AERMOD and CALPUFF. Sensitivity analysis of the two models showed that
out of five major climatic parameters, under steady-state condition, air stability class
and wind speed had greatest impact on odour dispersion, while ambient temperature
and wind direction had little impact, and mixing height had no impact on odour dispersion. The two models were validated using field odour data, and it was found that
both models had poor performance in modelling dispersion of low strength livestock
farm odours.
Since Gaussian models are build upon assumptions that the dispersion field is in
steady-state conditions, the accuracy of Gaussian models depends upon the validity
of the those assumptions against the environment being analyzed. Therefore, their
applications to real world problems are greatly limited. For instance, by assuming
the dispersion filed is non-variable, Gaussian models exclude the effect of pollutant
emissions and environment factors from previous hours. They are more suitable
for modelling air dispersion in relatively simple meteorological and topographical
conditions, such as flat field without complex ground surface conditions. However,
in rural areas where most livestock facilities are locate, topographical conditions are
usually complicated with farm facilities, hills, rivers, forests, and various vegetation
heights. Gaussian dispersion models are not suitable for modelling the dispersion of
odour emissions around livestock farms.
28
When applying existing air dispersion models to model livestock odour dispersion,
the livestock odour is treated as a chemical pollutant which follows mass conservation
law. However, downwind odour strength is not only resulted from physical dilution
process of chemical compounds, but is also a result of biological olfactory sensing process. Livestock farm odour consists of over 100 odorous compounds. Each compounds
has different odour perception threshold. During dispersion, the concentrations of
some compounds fall below their perception thresholds, e.g. the quantity of odour is
not conservable even if there is no chemical reaction among the chemical compounds.
Therefore, the odour perceived by human panel at a downwind location is different
than the odour at source given the distance to the source is far enough, because there
are less perceivable odorous compounds in the downwind odour. This would influence odour dispersion modelling at longer distance range where many compounds
of livestock farm odour are at their perception thresholds. Therefore, most existing
air dispersion model which are based on mass conservation law are not suitable for
modelling livestock odour dispersion.
2.4.3
Lagrangian Particle Models
Lagrangian particle models have been applied to simulate livestock farm odour dispersion (Duyzer et al., 2004). In a Lagrangian model, the odorous emissions released
from a source are assumed to be a cloud of particles, where a particle represents a
pollutant mixture. Lagrangian models compute the random spatial trajectories of the
particles following a statistical approach. Then the odour concentration is calculated
as the total number of particles falling in a unit volume (Lin et al., 2011). However,
Lagrangian particle models require extensive computing time and memory resource.
To adequately modelling odour concentration around a livestock farm, the trajectories of several tens of thousands of particles have to be simulated simultaneously in
small consecutive time steps. Therefore, it leads to very time-consuming computing,
which are not practical for real-time odour monitoring application.
29
Chapter 3
A Wireless Electronic Nose
Network for Monitoring Livestock
Farm Odour
Odour control technologies are greatly impeded by the lack of science-based approaches to assess the effects of odour. With the current state of technology, the
most commonly used methods to measure odours from livestock facilities is human
sniffing methods, but they are very expensive and difficult to overcome the analyst’s
personal bias. Therefore, unbiased automatic odour evaluating technologies are desirable.
3.1
Introduction
An electronic nose is an alternative tool to human nose for objectively measuring
livestock farm odour. Electronic noses have been developed for monitoring agricultural environment (Osarenren, 2002; Qu et al., 2001; Hudon et al., 2000; Pan and
Yang, 2007). In our previous study, an electronic nose consisting of a sensor array
and an analysis system was developed, and proved its capability of producing a qualitative output, lower operation cost, and greater consistency in odour measurement
(Pan and Yang, 2007). The impact area of odorous gas emissions is difficult to de30
termine because it continuously changes with wind speed and direction. In addition,
a modern livestock facility is usually not a point odour source but rather an array of
points (ventilation hose), lines (side curtain opening) and areas (manure pits).
However, an electronic nose can only evaluate odour events at one location; it cannot provide a comprehensive odour impact evaluation according to farmstead factors
such as building layout schemes, landscape and topography around the farm, and meteorological factors, which has led to an unfocused and inefficient research approach
on reducing livestock farm odour. Therefore, an odour measurement technology that
can provide an overall odour profile around livestock facilities is desirable for developing an overall odour management strategy. it cannot provide an comprehensive odour
map around livestock facilities which is necessary for an effective odour management
strategy.
Wireless communication technology has been widely applied in environmental observations (Wang et al., 2006; Werner-Allen et al., 2006), medical diagnostic (Koizumi
et al., 2008; Greer et al., 2008), habitat monitoring (Mainwaring et al., 2004), military
applications (Zhao et al., 2006; Liang, 2006; Song and Hatzinakos, 2007) and various
industries (Basset et al., 2007; Yi, 2008). Disseminating data via wireless network
enables long-term, remote data collection, and can provide localized measurements
and detailed information that is hard to obtain through traditional instrumentation
(Tubaishat and Madria, 2003; Wang et al., 2006).
In this research, a novel wireless electronic nose network system is proposed for
monitoring livestock farm odours. The system utilizes an electronic nose network
built from compact electronic noses which can detect odour compounds and environment conditions such as temperature and humidity. The electronic noses are placed at
various applicable locations on the farm, and odour data collected by them are delivered via a wireless network to the analysis station, typically a laptop computer, where
several data analysis algorithms process and analyze the data, display sensor signals
in real-time, and model the odour dispersion. A software package is also developed
for performing all the data analysis, and for providing a platform for collaborations
among the users.
31
The development of the proposed wireless electronic nose network system includes
following parts: design of several simplified electronic noses for detecting odour components and environmental conditions, network architecture design, design of a software package to process and analyze odour data, development of data processing and
analysis algorithms, and integration of the whole network. In this chapter, the design
of the entire electronic nose network is presented. Section 3.2 firstly introduces the
architecture of the proposed wireless electronic nose network system; the development
of each single electronic nose is given in details in Subsection 3.2.1; Subsection 3.2.2
presents the set-up of the wireless network; and the software developed for the electronic nose network are covered in Subsection 3.2.3. At the end, several advantages
of the proposed electronic nose network are addressed in Subsection 3.3.3.
3.2
The Developed Wireless Electronic Nose Network System
In this study, an electronic nose network-based system is developed for monitoring livestock farm odour remotely. The system utilizes an electronic nose network
built from compact electronic noses which can detect odour compounds and environment conditions such as temperature and humidity. The architecture of the proposed
wireless electronic nose network is illustrated in Fig. 3.1. Essentially, the wireless
electronic nose network is a simple star network, with all the sensor nodes communicating with a central control station. It consists of 3 electronic noses at this time,
and can be extended to accommodate more electronic noses when necessary. The
electronic noses are placed at various location of interest on the farm, and odour data
collected by them are delivered via wireless link to the analysis station, where the
sensor fusion algorithms process and analyze the data. Usually if an electronic nose
fails, other electronic noses cover that area can also provide the missing information.
Therefore, the more electronic noses in a certain area, the more robust the wireless
network system is to the sensor failure. However, a larger density of electronic noses
32
User Interface Tire
Sensor
Control
Gateway
Data
Analysis
Data
Display
Data
Manage
User
Database
Tier
Data
Acquisition
Wireless Connection
Electronic nose
Electronic nose
Electronic nose
Electronic nose
Figure 3.1: The architecture of the proposed wireless electronic nose network.
costs more, and large redundant information makes wireless transmission slow.
The proposed wireless electronic nose network consists of various hardware components. In this section, the architecture of the wireless electronic nose network and
the development of essential hardware components are presented.
3.2.1
Development of the Electronic Noses
To be utilized in the wireless network, the electronic noses should be compact in size,
energy efficient, and of low cost. The design toward the final electronic nose of the
wireless sensor network comprised several steps such as the selection of sensors to
detect compounds most prevalent in livestock farm odour, the incorporation of these
sensors into a sensor array, the design of a chamber to regulate gas toward the sensors
and the acquisition of the sensor data for wireless transfer.
33
Sensor Selection and Sensor Array
Each electronic nose in the network has a sensor array with 4 metal-oxide semiconductor (MOS) gas sensors to detect odorous components in ambient air, and 2 sensors
to detect environmental conditions, i.e., a humidity sensor and a temperature sensor.
The gas sensors are selected based on previous extensive investigation and experiments, as well as various sensors available for odour compounds measurement. These
4 sensors can measure most of the major gas compounds in livestock farm odours,
with high sensitivity to target gases, quick response to low concentrations of gases,
high stability and reliability over a long period, relatively low cost and long life in
comparison to other sensing technologies. A section of this sensor list is shown below
in Table 3.1. The selected gas sensors are purchased from Figaro Engineering Inc.
The gas sensors are fixed in an array. A developed sensor array is shown below in
Fig. 3.2.
Table 3.1: Sensors used in the development of the electronic nose
Sensors
Compounds
Material
TGS826
ammonia, amine compounds, toluene
tungsten
dioxide
TGS825
hydrogen sulfide
tin dioxide
TGS822
broad spectrum including skatole, indole, methane, tin oxide
benzene, ethanol, acetone, butane, propane, alcohol,
etc.
..
.
..
.
..
.
34
Figure 3.2: The developed sensor array.
Design of the Chamber
A chamber is designed to accommodate the sensor array and to regulate the flow of
odorous air. The sensor array is placed within this chamber, and the odorous air is
directed toward the array. Since the sensors have specific requirements on the flow
rate for proper operation, a pump is used to control the flow speed. The sensor array
faces directly into the coming air so that the surface area of the array which is exposed
to odorous air is maximized. This ensures best detecting results and minimizes the
possibility of attaining improper readings.
The chamber also has an input valve which is controlled by a solenoid. The valve
is switched between odorous and carbon filtered air ports through the application
a 12 V signal to the valve, so that the filtered odorous air can refresh the chamber
and sensor when necessary. The pump used is a standard high-volume, low-pressure
pump which ideally suited our requirement for low-pressure, continuous sampling,
and allows the air to become steady in the chamber. The chamber also contains a
simple diffuser to focus the incoming odorous and filtered air onto the sensor array.
A developed chamber is shown in Fig. 3.3.
35
Figure 3.3: The developed chamber.
Integration of the Electronic Nose
The analog sensor signals collected by the electronic nose are converted to 12 bit
digital signals by a data acquisition board (MDA300) right at the electronic nose
side. The multi-function data acquisition board MDA300 can convert up to 11 channels analog input. The digital signals are delivered by a 2.4 GHz mote processor
(MPR2400) to a gateway (the sink). Both the data acquisition board MDA300 and
the mote MPR2400 are powered by two AA batteries. In addition, a 12 V DC power
supply is used to power the gas sensors and the air pump. A developed electronic
nose is shown in Fig. 3.4. These electronic noses are placed in areas of interest around
livestock facilities to collect odour data.
3.2.2
The Wireless Electronic Nose Network
The odour data collected by the electronic noses located around the farm is delivered
by 2.4 GHz wireless link to a gateway. The gateway will gather the sensor signals and
input them into the control centre via Ethernet. The block diagram of the wireless
network is shown in Fig. 3.5.
For the purposes of wireless transmission of sensor signals, in this work, the MicaZ wireless network platform from Crossbow (Crossbow Technology Inc., USA) is
36
Figure 3.4: An electronic nose consists of the sensor array inside a chamber, a data
acquisition board, a mote processor, and a power supply.
Mote MPR 2400
Mote MPR 2400
Wireless
Communication
DAQ MDA 300
Gateway MIB 600
Ethernet
Sensor Node
Internet
Global Connection
Odorous Air
Figure 3.5: The block diagram of the wireless network.
37
adopted. This wireless network platform has advantages of the small physical size,
low cost and low power consumption, making it ideal for this odour monitoring application. The mote processor (MPR2400), and the gateway (MIB600) are utilized
as shown in Fig. 3.6.
Figure 3.6: The mote processor MPR2400 and the gateway MIB600.
The mote processor MPR2400 includes an ATmega128L low-power micro-controller,
a high data rate radio, and batteries. The micro-controller runs network communication applications from its internal flash memory. A single MPR2400 processor can
support sensor control, data processing and radio communication applications simultaneously. Power is provided by any 3 V power source, typically two AA batteries.
The radio on the MPR2400 module consists of an antenna and a single-chip 2.4 GHz
radio frequency transceiver which is compliant to IEEE 802.15.4/ZigBee protocol.
The transceiver providing a spreading gain of 9 dB and an effective data rate of 250
kbps.
The MIB600 is an Ethernet interface board. It provides Ethernet connectivity for
data communication ability via TCP/IP. A MIB600 board attached with a MPR2400
38
is defined as a getaway (sink). It allows the aggregation of in-coming network data
into a computer. The gateway with connection to a laptop computer is shown in
Fig. 3.7. The wireless link provides communication ability to transfer sensor data
and status information across the network, it also support remote control of network
nodes.
Figure 3.7: The gateway with connection to a laptop computer.
3.2.3
Software Integration
In order to utilize the previously mentioned hardware, certain software packages are
needed. An application provided by Crossbow called MoteWorks is used to set up
a wireless network for data transfer. Then MoteWorks integrates and synchronizes
the wireless network, configures and acquires the IP address of the network, and pro39
grams the mote to interface with the electronic noses. An application software called
MoteView from Crossbow is used to establish the network and wireless communication, monitor the network status, and manage the network topology. A screen of the
wireless network topology interface is shown in Fig. 3.8.
The odour data collected by the electronic nose network need to be processed and
analyzed for the purpose of supporting odour control decisions. Hence, a software
suite is designed for data acquisition, management, display and analysis. The software
is running on a central analysis station, and it includes four main components: (1)
a data acquisition system to acquire the sensor signals that are transferred to the
computer via the wireless network; (2) a database which stores the odour data for
future applications; (3) an odour analysis system, which identifies risk factors of
odour, calculates odour strength and odour dispersion plume; and (4) a user interface
tier interacts with users and displays odour data and reports analysis results.
Data Acquisition System
After analog sensor signals are converted into digital signals, the digital signals are
sent through wireless link to the gateway, which receives and collect the digital signals.
Then the data acquisition system in the computer interfaces with the getaway and
captures the digital signals into the database. The data acquisition interface is shown
in Fig. 3.9. Communication to the gateway can be established by specify the IP
address of the gateway. The status of the connection is shown on the interface. In
this data acquisition system, currently there are 9 channels, a channel shows electronic
nose node ID, a temperature sensor channel, a humidity sensor channel, four channels
adc0 to adc3 are used for the 4 gas sensors in each electronic nose, and channels adc5
and adc6 are reserved for future development.
Sensor signals generated by the electronic nose usually are contaminated by white
noise. An exponentially weighted moving average filter is used to minimize the noise
(Smith, 1997). The filter is given as
x̄k =
n
n
x̄k−1 + (1 −
)xk ,
n+1
n+1
40
(3.1)
where n is number of data points in the filter; k is the k-th data point; and x̄k is a
simple moving average filter given by
x̄k =
k
1 xi .
n i=k−n+1
(3.2)
Database and Data Management System
All collected data are organized and stored in a central database. Database plays an
important role as a data storage, query, and exchange centre for various applications.
In this system data structures (records and files) are optimized to deal with large
amounts of data. A data storage system based on Microsoft SQL Server 2005 database
server is developed for storing sensor signals and odour information. The database is
organized and managed by a database management system. The stored odour data in
the database can be retrieved, managed, and processed through user interface. The
interface of the database management system is shown in Fig. 3.10.
The data management system has link to the data acquisition system. Therefore,
the data acquisition system can be conveniently launched from the data management
system to capture odour data into database. Collected data records are then structured, categorized, and tagged in the database for future use. Because data records
are well-organized, historical data can easily be queried and retrieved by name, date
and time of collection, description, and characters. A picture of data records management console is shown in Fig. 3.11
User Interface Tier
Users interact with the control station directly through a series of user interfaces.
These interfaces are within the user interface tier. The goals of user interfaces are to
provide a user-friendly environment, and to adequately support the operation undertaking. User interfaces provide graphical front-end pages to the data base records,
analysis result reports, task management, and the other components. Microsoft C#
environment is used for user interface development. The main interface is the first
displayed page when users start the application. Users log onto the system from this
41
Figure 3.8: Topology of the wireless network.
Figure 3.9: The interface of data acquisition system.
42
Figure 3.10: The interface of database.
43
Figure 3.11: The interface of data records management console.
page to get the access to other functional pages such as data display interface and
analysis system interface. Figure 3.12 shows the main interface of the application.
To satisfy various requirements, the user interface tire can provide a widely used
standard for analyzing record data and also accommodates applications developed in
other programming language environment.
The interface of data display system is shown in Fig. 3.13. To provide better
odour strength observation, a data dynamic display system is developed. This system
provides basic sensor signal on-line and off-line display. Several display attributes can
be selected by users, such as different update interval, number of visible points and
data range, persistent display mode enable/disable, average number display, etc. This
would be very helpful for routine operation and various studies.
Odour Analysis System
Odour analysis system is the key component of the software. The system utilizes
the data analysis applications to identify rules and patterns in the data, to tell the
44
Figure 3.12: The main interface of the software.
45
Figure 3.13: The dynamic display console.
users where is the odour, how strong the odour is, to give suggestion on odour control
practice, and to predict the resulted odour strength if any odour control means are
applied. The data analysis system has many sub-systems: odour strength analysis
system calculates odour concentration/intensity using artificial intelligent techniques;
odour dispersion analysis system calculates odour dispersion plume using odour dispersion models based on odour emission rates, topological and meteorological information; risk factor analysis module identifies significant risk factors which cause
odour problems; and decision making module assists users to make odour control
decisions based on analysis results and human expert knowledge. The interface of
odour analysis system is shown in Fig. 3.14
This analysis system also provides decision support for farmer’s odour control
practice. In the analysis system, the sensor fusion algorithm calculates the odour
strength based on collected sensor signals, odour dispersion algorithm calculates and
draws the odour dispersion. The odour dispersion analysis system provides an overall
odour mapping around live facilities which is necessary for an overall odour manage-
46
Figure 3.14: The odour analysis system.
ment strategy, and it also gives suggestions on possible odour control practice based
on farmstead, topography and meteorological data, such as when to spread manure,
change vent direction, and where to plant trees to block odour. This will help the
livestock producers to make strategic funding decisions by investing only in odour
reduction research and implementation that deals with relevant factors of the odour
problem.
3.3
Discussion
The proposed electronic nose-based wireless network approach to automatically monitoring of livestock farm odours differs from any existing electronic noses in the following ways
1. The developed wireless electronic nose network system can provide multi-location
odour monitoring while existing electronic noses can only provide odour monitoring at a single location. Detecting and accurately locating odour around
livestock farms has been an elusive goal of researchers and livestock producers
for a long time. Odour concentration changes with meteorology, distance to
the source, emission height above ground, and source configuration (exhaust
pipe diameter, building geometry etc). The proposed wireless electronic nose
47
network can provide a more comprehensive odour plume profile than a single
electronic nose.
2. The developed electronic nose network can automatically monitor livestock farm
odours, and have many potential industry applications. It integrates sensor system, data acquisition system, data analysis system, data transmission systems,
and user interface system. The developed wireless network can perform remote
multi-point odour monitoring, and the integrated software package provides
data analysis capabilities which would assist users’ decision making on odour
control practice. Based on meteorological information provided by weather forecast, the software can estimate future impacts of odours around the farm, and
help farmers plan odour control strategies. In addition, if the odour strength
reaches a pre-set threshold, the network system can alerts farmers, and provide
suggestions on reducing odour levels. The electronic network system can provide farmers and researchers with more efficient odour control capabilities by
providing automated, robust, and comprehensive data about the environment
and odour profile around livestock farms.
3. By using this wireless electronic nose network system, users can monitor the
odour from a remote location such as inside a house or office, so that they do
not have to stay in field, which makes odour measurement easier in unfavourable
weather.
4. The developed electronic nose network system with multiple electronic noses is
more robust to sensor failure and noise. One or more failed or incorrect sensor
reading could be corrected through the redundant information from the other
electronic noses.
The developed wireless electronic nose network system is a useful tool in remote
monitoring livestock farm odour, which can greatly improve the efficiency of livestock
farm odour reduction and thus benefit livestock industry.
48
3.4
Summary
The design of an electronic nose network-based livestock farm odour monitoring system is presented. The system is composed of electronic noses that can detect odour
compounds and environment factors. The electronic noses are deployed around the
farm and odour data are delivered via a wireless network to the central analysis station. The sensor fusion algorithm and odour dispersion algorithm in the analysis
system calculate the odour dispersion, display the odour plume dispersion dynamically, and predict odour plume.
By using the proposed wireless electronic nose network based remote odour monitoring system, real-time downwind odour measurement becomes possible. The developed real-time odour monitoring system can enable the accurate detection and
location of a livestock farm odour plume. It should be effective in quantifying the
instantaneous and long term odour strength. The odour monitoring and analysis
system can provide farmers and researchers with more accurate odour management
capabilities for more efficient operation of odour control systems such as ventilation
fans, and can help the development of an overall odour management strategy by
providing dynamic, comprehensive information about the livestock farm environment
and odour dispersion.
49
Chapter 4
A Neuron-fuzzy Model for Odour
Calibration
Calibration of odour concentration against human panelists’ perception is difficult
due to the complexity of the livestock farm odour system and limited knowledge of
the odour itself (Zhang et al., 2002c). Many factors influence odour strength. Research has indicated that environmental conditions such as temperature and humidity
(Powers and Bastyr, 2004), type of facilities (Janes et al., 2004), and farm management practises affect odour generation (Zhang et al., 2002). However, how and by how
much a factor would influence livestock farm odour still remains unclear (Schiffman
et al., 2001a; Powers and Bastyr, 2004; Pan et al., 2006). Resulting from a poor
understanding of the odour problem, existing odour calibration technologies are not
efficient.
4.1
Introduction
Most previous studies on modelling livestock farm odour have concentrated on the
modelling of the odour components and have not considered other potential contributing factors, such as outdoor temperature, air flow speed, or type of facilities, due to
the limitation of statistical modelling techniques. In statistical methods, odour system models being analyzed are not capable of incorporating non-numeric or subjective
50
human expert knowledge, such as environmental conditions, type of livestock facilities. Thus they are not well suited for dealing with vague and imprecise information
available in odour measurement and control.
The rapid development of soft computing techniques, such as neural networks and
fuzzy logic technologies, allows the incorporation of those non-numeric contributing
factors and expert knowledge into livestock farm odour models. Artificial neural networks (ANNs) have been used to calculate odour strength (Pan et al., 2006; Kodogiannis et al., 2008; Al-Bastaki, 2009). It is widely accepted that there are several advantages in using ANNs algorithms over the conventional techniques, such as its generalization ability to automatically learn the relationship that exits between the inputs
and outputs. However, ANNs are not capable of properly incorporating non-numeric
or subjective human expert knowledge (Haykin, 1999).
Fuzzy logic utilizes fuzzy sets and fuzzy reasoning to provide a more human-like
inference process. It can handle imprecise, ambiguous, and vague information, and
can incorporate human experience and expert knowledge (Zadeh and Bellman, 1977).
Fuzzy logic has been used in a wide range of applications such as control systems,
project management, non-linear system models, expert systems, forecasting, system
identification and analysis (Zimmermann, 2001). However, fuzzy logic does not have
learning ability, thus it is difficult to analyze complex systems without prior and
accurate knowledge on the system being analyzed.
To overcome the limitations of neural networks and fuzzy systems, an adaptive
network-based fuzzy inference system (ANFIS) approach is proposed to predict livestock farm odour concentration. Two ANFIS livestock farm odour models are developed, both numeric data and non-numeric data are incorporated in the odour models.
Various comparative studies are also presented to demonstrate the effectiveness of the
proposed method.
51
4.2
The Proposed Neuro-fuzzy Approach
In common fuzzy logic systems, membership functions are chosen by trial and error
and are fixed once selected. Rule structure of a fuzzy logic system is also predetermined based on user’s interpretation of the variables in the model. However, in
practise, due to limited knowledge about the system being analyzed, the selection
of membership functions and rule structure may not be appropriate, thus results in
poor performance of the model (Jang et al., 1997).
An adaptive neuro-fuzzy inference system (ANFIS) approach is proposed for analyzing livestock farm odour. The proposed ANFIS approach incorporates an adaptive
neural learning technique into the fuzzy logic model. The parameters are tuned automatically during the learning so the membership functions can well represent the
nonlinear system being studied with an optimal performance.
The proposed ANFIS uses a Sugeno-type fuzzy relation (Jang et al., 1997; Karray
and De Silva, 2005), where the fuzzy rules take the following form:
Rule 1: IF x1 is A11 and x2 is A21 . . . and xn is An1 , THEN f1 = a10 +a11 x1 +...+a1n xn ,
Rule 2: IF x1 is A12 and x2 is A22 . . . and xn is An2 , THEN f2 = a20 +a21 x1 +...+a2n xn ,
..
.
Rule p: IF x1 is A1m and x2 is A2m . . . and xn is Anm , THEN fp = ap0 + ap1 x1 +
... + apn xn ,
where xi , i = 1, ..., n is i-th input linguistic variable in the antecedent part of the rule;
variable Aij is the j-th linguistic label (for instance, large, small, etc.) associated with
xi . Function fp is the consequent output of the rule and api is the Sugeno parameter.
An general ANFIS model is shown in Fig. 4.1, which has n inputs and each input
variable has m membership functions, resulting in a total of mn rules.
There are 5 layers in the general ANFIS model shown in Fig. 4.1. The first layer
is a fuzzification layer, in which the crisp inputs of the network are fuzzified through
mapping into corresponding membership functions. Layer 2 is a rule operation layer.
In this layer, the incoming signals of each rule node are multiplied and the product
is sent out. The output of each node (e.g. the product of the incoming signals of the
52
Layer 1
μA (x 1 )
11
x
1
μA (x 1 )
1m
μA (x 2 )
21
x
A111
A
Layer 2
A122
A
Π
A
A1m2
Π
B211
A
w1
Layer 3
N
N
Π
N
Layer 4
w1
Σ
w2
Σ
Layer 5
w1 f 1
Odour
Concentration
w3
Σ
A222
B
2
μA (x 2 )
2m
μA n1(x n )
x
n
μAnm(x n )
AB2m
Bn11
A
An22
B
Π
N
Π
N
wm n
wmn-1
wmn
Σ
wmn f mn
ABnm2
Figure 4.1: The structure of an ANFIS consists of a five-layer feedforward network.
53
node) in layer 2 is referred as the firing strength of a rule. Layer 3 is a normalization
layer, the firing strengths of the fuzzy rules are normalized in the way that, the output
of each node in this layer is the ratio of the i-th rule’s firing strength to the sum of
all rules’ firing strength. Layer 4 is a consequent layer, in which the values of the
consequent of the rules are multiplied by normalized firing strengths. The last layer
is aggregation layer. The overall output of this layer is computed as a summation of
all incoming signals. During the training, the membership parameters can be tuned
automatically in a systematic manner so as to adjust the membership functions of
the input/output variables for optimal system performance.
Layer 1: Fuzzification Layer
In this layer, the crisp inputs xi , i = 1,...,n, is fuzzified through mapping into membership function μAij of the linguistic labels Aij , i = 1,...,n, j = 1, ..., m. The membership
functions can be any appropriate parameterized functions. In this study, all the membership functions are bell-shaped functions as
μAij (xi ) =
1+
1
xi −cij
aij
2bij ,
i = 1, 2, ..., n; j = 1, 2, ..., m,
(4.1)
where aij , bij and cij are the parameters; n is the number of inputs; and m is the
number of membership functions of each input. The bell-shaped functions vary accordingly when these parameters are changed, and thus exhibit various forms of membership functions on Aij . The output Op1 of each node i in layer 1 is the membership
functions of Aij .
Op1 = μAij (xi ),
i = 1, 2, ..., n; j = 1, 2, ..., m; p = 1, 2, ..., m × n,
(4.2)
where variable Op1 represents the degree to which the given xi associates with Aij .
Layer 2: Rule Operation Layer
In this layer, each rule node multiplies the incoming signals and sends the product
out. The output of each node in layer 2 represents the firing strength of a rule, which
54
is given as
Op2 = wp =
n
i=1
ji = 1, 2, ..., m; p = 1, 2, ..., mn ,
μAiji (xi ),
(4.3)
where j1 , j2 ,..., jn is a combination of m membership functions for the n input variables, thus there are a total of mn combinations, resulting in mn nodes in this layer.
Each node in fact performs and AND operation within the rule antecedent. Other
t-norm operators can also be used as the node function in this layer.
Layer 3: Normalization Layer
In this layer, the firing strengths of the fuzzy rules are normalized. The output of
each node in this layer is the ratio of the i-th rule’s firing strength to the sum of all
rules’ firing strength, and is calculated based on
wp
Op3 = wp = mn
p=1 wp
,
p = 1, 2, ..., mn .
(4.4)
Layer 4: Consequent Layer
In this layer, the values of the consequent are multiplied by normalized firing strengths
according to
Op4 = wp fp ,
p = 1, 2, ..., mn .
(4.5)
Layer 5: Aggregation Layer
The overall output of this layer is computed as a summation of all incoming signals:
n
5
O =
m
p=1
4.3
wp fp = p
wp fp
.
p wp
(4.6)
Experimental Results
The proposed ANFIS approach is tested by a livestock database collected in the fields
downwind from poultry, dairy and swine facilities. This section provides detailed
information about the data samples. Three odour calibration models are developed
for comparative studies. The structure of each model is also presented in this section.
55
4.3.1
Data Sets
Outdoor temperature, wind speed, distances to the odour source, and odour dilutionto-threshold (D/T) concentration levels perceived by human assessors were recorded.
At the same time, an electronic nose took measurement at each site. The sensor signals from the electronic nose represent the concentrations of gas compounds in the air.
The detailed measuring procedure is described in the paper by (Pan et al., 2006). The
data of temperature, type of facility and concentration of hydrogen sulfide are used in
odour generation modelling in this study. Factors such like wind speed and distance
to odour source are not considered as they mainly influence downwind transportation of odour emissions. By using statistical multi-variant linear regression method,
temperature and concentration of hydrogen sulfide being independent variables and
odour concentration being dependent variable, a low accuracy (r = 0.40, p < 0.01) is
obtained on prediction of odour concentration. It is not surprising that low accuracy
is achieved using multi-variant linear regression method, as livestock farm odour systems yield highly non-linear character. There are a total of 64 samples in the data
available for the development and testing of models. Approximately 80% of the data
points are randomly selected training the neural network, and the remaining 20% are
used for testing.
4.3.2
Livestock Odour Calibration Models
Three models are developed based on the database mentioned above: an ANFIS
model with only 2 numerical inputs, a second ANFIS model with a linguistic input
and 2 numerical inputs, and a neural network model with 2 numerical inputs.
Model 1: an ANFIS Model with Two Inputs
To make comparative study, an ANFIS model (Model 1) which contains only numeric
inputs is developed. These inputs include concentration of chemical components that
is represented by the output signals of sensors and environmental conditions such as
temperature. Researchers have indicated that the concentration of hydrogen sulfide
56
can act as an indicator for livestock farm odour strength (Lunn and Van De Vyver,
1977; Williams, 1984). The concentration of hydrogen sulfide is represented by the
magnitude of the output signal of a sensor that is selected for detecting hydrogen
sulfide. All the inputs are numeric data that have to be fuzzified in the fuzzification
layer. The output of Model 1 is the odour concentration. Three membership functions
are associated with each input, thus resulting in 9 IF-THEN rules in the model. Model
1 is trained by a back-propagation (BP) algorithm (Haykin, 1999).
Model 2: an ANFIS Model with Three Inputs
In order to test the ability of the proposed approach of incorporating linguistic information, another ANFIS model (Model 2) is developed. The Model 2 has identical
inputs as those of model 1, but with 1 more non-numeric input: type of facilities.
The type of facilities can generally represent such measures as type of animals, farm
activity, odour emission rates, and other physical measures that may influence the
dour concentration. The output is the odour concentration. Model 2 also has 3 membership functions for each input, resulting in 27 IF-THEN rules in total. Model 2 is
illustrated in Fig. 4.2. The model is trained by BP algorithm.
Model 3: a Typical Neural Network Model
In order to analyze the effectiveness of the proposed approach, a feedforward neural
network (NN) model (Model 3) is designed with an input layer, one hidden layer,
and an output layer. A three-layer neural network is theoretically able to learn
any nonlinear relationship at a desired level of precision with sufficient number of
hidden neurons in the network architecture and through sufficient learning. Figure
4.3 illustrates the proposed network structure for livestock farm odour. Model 3 has
identical inputs as those of Model 1. The output is the odour concentration. The
neural network is trained by a typical BP algorithm, and the results are compared
with the results from Model 1 and Model 2.
57
Layer 1
Layer 2
Layer 3
Layer 4
Π
N
Σ
Π
N
Σ
Π
N
Π
N
Π
N
Layer 5
Dairy
Type of
facility
A
Swine
2
Poultry
2
Cold1
Temperature
Odour
Concentration
Σ
B
Medium
2
Hot
B
Low1
B
H2S
2
Medium
High2
B
Σ
Figure 4.2: An ANFIS model with three inputs and 27 IF-THEN rules for calibrating
the livestock farm odour.
Input layer
Hidden layer
Output layer
Temperature
Odour
Concentration
H2S
Figure 4.3: Structure of NN model for calibrating the livestock farm odour.
58
4.3.3
Results
In this section, the results of proposed ANFIS approach are analyzed. In order
to demonstrate the effectiveness of the proposed approach, the odour concentration
prediction results from both ANFIS models are compared with that from NN model.
In neuro-fuzzy technology, parameters such as learning rate and training epochs,
and the number of membership functions for each input are usually determined by
trial and error. In this study, the sensitivity of these parameters are investigated and
showed following results. (1) With each increment of the training epoch, the accuracy
of odour concentration prediction improves. After 100 epochs, the curve of RMSE
tends to stabilize. (2) The number of membership functions for each input reflects
the complexity of ANFIS for tuning parameters. If the number is too small, it is not
enough to approximate the complex relationship of data; if the number is too large, it
is produces the redundancy for the nonlinear relationship of data. Three membership
functions for each input variable results in highest accuracy of odour concentration
prediction in this study. (3) The higher the learning rate, the faster the network
converges. However, if the learning rate is too large, the network oscillates. In this
study, a learning rate of 0.1 is selected by trial.
Performance of Model 1
The performance of models are evaluated based on the accuracy of odour concentration prediction and root mean squared error (RMSE) of the predicted odour intensities. By using the proposed ANFIS approach, the odour concentration prediction
accuracy is significantly improved (correlation coefficient r = 0.946; RMSE = 1.11)
compared to that from Model 3. The result demonstrated that the proposed ANFIS
model can incorporate human experience and knowledge, which allows prior knowledge to be embedded in the model, and this improve the results of learning. The
comparison of initial membership functions before the learning with final membership functions after learning using Model 1 is shown in Fig. 4.4. The membership
functions are all changed during the learning. The result showed that the proposed
59
ANFIS approach is capable of tuning the membership functions to improve the performance index.
Performance of Model 2
By incorporating type of facility, the prediction accuracy is improved (r = 0.976;
RMSE = 1.03) compared to those of Model 1 and Model 3. The comparison between the initial membership functions before the learning and the final membership
functions after learning using Model 2 is shown in Fig. 4.5. The parameters of type
of facilities are changed during the learning. This result shows that type of facility
does affect odour concentration. The proposed ANFIS approach is able to interpret
linguistic variables such as type of facility and cloud cover conditions, and therefore
increase the accuracy of odour concentration prediction.
Factors such as temperature and type of facility are actually measured or known
exactly before the analysis. Their data are not fuzzy. However, by fuzzifying these
data and applying ANFIS, the predicted odour concentrations are of higher accuracy
than those by using classical or crisp methods (multi-variant linear regression or
neural network). By using fuzzy sets and fuzzy reasoning, the proposed method is
able to break stiffness and inflexibility that lie in accurate and precise classical or
crisp logic, and make fuzzy logic more “soft” and dexterous.
Performance of Model 3
The correlation coefficient of the predicted odour concentration and perceived odour
concentration is 0.522 by using neural network technique alone. The RMSE is 7.18.
The predicted odour intensities are not as accurate as those of Model 1 and Model 2.
4.4
Summary
The calibration of odour strength around livestock facilities has been investigated
by many researchers over past decades. Calibration of the odour concentration is
difficult due to limited knowledge on livestock farm odour itself. It is known that
60
Membership
1
0.8
0.6
0.4
0.2
0
0
Cold
1
0.8
0.6
0.4
0.2
0
0
Low
Hot
Medium
5
10
15
20
25
Temperature, C
30
35
High
Medium
25
50
40
75 100 125 150 175 200 225 250
Concentration of H2S, ppm
Membership
(A)
1
0.8
0.6
0.4
0.2
0
0
Cold
1
0.8
0.6
0.4
0.2
0
0
Low
Medium
5
25
10
15
20
25
30
Temperature, C
Medium
50
Hot
35
40
High
75 100 125 150 175 200 225 250
Concentration of H2S, ppm
(B)
Figure 4.4: Comparison of the initial membership functions before the learning (A)
with the final membership functions after learning (B) using Model 1.
61
Dairy
1
0.5
0
Swine
Membership
Dairy
1
0.5
0
0
Cold
1
0.5
0
0
Low
Swine
Facility Type
Poultry
Poultry
Medium
5
10
15
20
25
30
Temperature, C
Medium
25
50
Hot
35
40
High
75 100 125 150 175 200 225 250
Concentration of H 2S, ppm
Membership
(A)
1
0.5
0
Dairy
1
0.5
0
0
Cold
1
0.5
0
Low
Swine
Dairy
0
Swine
Facility Type
Poultry
Poultry
Hot
Medium
5
25
10
15
20
25
30
Temperature, C
Medium
50
75
35
40
High
100 125 150 175 200 225 250
Concentration of H 2S, ppm
(B)
Figure 4.5: Comparison of the initial membership functions before the learning (A)
with the final membership functions after learning (B) using Model 2.
62
a livestock farm odour system is a highly non-linear system that is influenced by
many odorous components and various factors such as environmental conditions and
type of facilities. The rapid development of soft computing techniques provides more
opportunities for improving accuracy of livestock farm odour modelling.
An adaptive neuro-fuzzy based approach which incorporates neuro-adaptive learning techniques into fuzzy logic method is developed to analyze livestock farm odour.
This approach can incorporate non-numeric data and subjective human expert knowledge, which allows prior knowledge to be included in the model. In addition, the
membership functions are tuned during the learning according to input/output data,
therefore to optimize the performance. This feature is important as it can avoid inappropriate predetermination of membership functions, thus minimize errors resulted
from the limited knowledge about the livestock farm odour system. A livestock farm
odour database is used in this study. Several livestock farm odour models are developed, both numeric data and non-numeric data are incorporated in the odour
models. Various comparative studies are presented to demonstrate the effectiveness
of the proposed method. The results show that the proposed approach is effective
and provides a much more accurate odour prediction in comparison with a typical
multi-layer feedforward neural network.
63
Chapter 5
A Neuron-dynamics Model for
Odour Dispersion
The concentration of livestock farm odours in the field is influenced by meteorology
conditions, distance to the source, and farmstead layout (e.g., source configuration).
As livestock farms have special odour emissions, farmsteads, and odour management
systems, an ad-hoc dispersion model for livestock farm odours can greatly improve
the efficiency and accuracy of odour dispersion modelling.
5.1
Introduction
No air dispersion models have been developed specially for modelling livestock farm
odour. Most used air dispersion models like US EPA regulatory models ISCST3 and
Australian model AUSPLUME are initially designed to predict the concentration of
pollutants from industrial sources. The empirical dispersion parameters they adopt
are developed for modelling dilution process of industrial pollutants in ambient air,
not for odour emissions. However, downwind odour strength is not only resulted
from physical dilution process of chemical compounds, but is also a result of biological olfactory sensing process. These dispersion models may represent the dispersion
of gas compounds, but are not very suitable to accurately model perceived odour
concentration as a whole.
64
In this study, a novel neural network model is proposed for modelling the dynamic
odour dispersion around livestock farms. The proposed neural network model for
livestock farm odour dispersion is motivated by Hodgkin and Huxley (1952) biological
membrane model and the later developed Grossberg’s shunting model (Grossberg,
1973; Grossberg, 1982; Grossberg, 1983; Grossberg, 1988).
Hodgkin and Huxley (1952) initially proposed a shunting model for a patch of
membrane in a biological neural system using electrical circuit elements (Hodgkin and
Huxley, 1952). The dynamics of voltage across the membrane Vm can be described
using state equation technique as
Cm
dVm
= −(Ep + Vm )gp + (EN a − Vm )gN a − (EK + Vm )gK ,
dt
(5.1)
where Cm is the membrane capacitance; EK , EN a and Ep are the Nernst potentials
for potassium ions, sodium ions and the passive leak current in the membrane, respectively; and gK , gN a and gp represent the conductances of potassium, sodium and
passive channels, respectively. This model together with Hodgkin and Huxley’s other
experiments led them to a Nobel prize in 1963.
Based on Hodgkin and Huxley (1952)’s biological membrane model (Eq. (5.1)),
Grossberg proposed a shunting model to describe the stable adaptive behaviour in
real time to complex and varying environmental events (Grossberg, 1973; Grossberg,
1988). It has a lot of applications in biological and machine vision, sensory motor
control and some other area (Li and Ogmen, 1994; Ogmen and Gagne, 1990a; Ogmen
and Gagne, 1990b).
Substituting Cm = 1, ξi = Ep + Vm , A = gp , B = EN a + Ep , D = Ek − Ep ,
Si+ = gN a and Si− = gK in Eq. (5.1), Grossberg obtained a typical shunting model
characterized as
dξi
= −Aξi + (B − ξi )Ii+ (t) − (D + ξi )Ii− (t),
dt
(5.2)
where ξi is the neural activity (membrane potential) of the i-th neuron; A is a positive
constants representing the passive decay rate; B is the upper bound of the neural
activity; and D is the lower bound of the neural activity. Variables Ii+ and Ii− are
the excitatory and inhibitory inputs to the neuron, respectively.
65
The shunting model in (5.2) shows that the excitatory input Ii+ and inhibition
input Ii− are multiplied by two auto gain control terms, (B − xi ) and (D + xi ),
respectively, which depend on the activity ξi (t). The interaction of excitatory and
inhibitory inputs are nonlinear via multiplication. In addition, (5.2) shows that the
activity of the cell, ξi , increases at a rate of (Bi − ξi )Ii+ . Hence, the cell activity ξi
is forced to stay below the upper bound Bi . Similar to the inhibitory term, it forces
the cell activity stay above its lower bound −Di . Therefore, once the activity goes
into the [−Di , Bi ] region, it is guaranteed that the cell activity stays in this region
for any excitatory and inhibitory inputs (Li and Ogmen, 1994).
5.2
The Proposed Neuro-dynamics Approach
Based Grossberg’s shunting model in Eq. (5.2), a dynamic odour dispersion model
in two dimensions is proposed as
∂C(Xi ,t)
∂t
= −AC(Xi , t) + (B − C(Xi , t))[I(Xi , t)]+ +
N
j=1
wij [C(Xj , t)]+
(5.3)
−
− (D + C(Xi , t))[I(Xi , t)] ,
where Xi = (x, y) is the spatial location of the i-th neuron; x is the downwind distance
of the i-th neuron to the odour source, while y is the crosswind distance of the i-th
neuron to the odour source; C(Xi , t) is the odour concentration at location Xi and
time t; [I(Xi , t)]+ and [I(Xi , t)]− are excitatory and inhibitory inputs, respectively,
where [e]+ and [e]− are linear-above threshold functions defined as
⎧
⎪
⎨ [e]+ = max {e, 0}
⎪
⎩ [e]− = max {−e, 0}
.
(5.4)
Parameters Arepresent the passive decay rate; B is the upper bound of the neural
activity; and D is the lower bound of the neural activity. Parameters B and D are
constants, and A can be a function of wind speed, i.e.
A = qVW (t),
66
(5.5)
where q is a positive constants; and VW (t) represents the wind speed at time t.
Therefore, parameter A represents the transportation effect of air flow, the higher the
air flow speed, the faster the odorous compounds being dispersed.
The variable I(Xi , t) is external input to the i-th neuron defined as
⎧
⎪
⎪
⎪
S(t),
⎪
⎪
⎨
I(Xi , t) =
if there is a odour source at positionXi
−R(t),
⎪
⎪
⎪
⎪
⎪
⎩ 0,
if there is odour reduction means at position Xi ,
(5.6)
otherwise
where S(t) and R(t) are positive functions of time. The odour reduction means
includes odour control practise operated by livestock producers, dry deposition to the
earth’s surface, and wet deposition by precipitation (rain, fog, snow, etc.).
In Eq. (5.15), item
N
j=1
wij [C(Xj , t)]+ represents the interaction among the neigh-
bouring neurons; N is the total number of neighbouring neurons of the i-th neuron;
wij is the connection weight from j-th neuron to the i-th neuron, which is a function
of distance defined as
⎧
⎪
⎨ 0,
wij = ⎪
⎩
if dij > r0
u/dij ,
if dij ≤ r0
,
(5.7)
where r0 is a positive constant; and dij = |Xi − Xj | represents the Euclidean distance
between positions Xi and Xj in the state space. Therefore, the neuron has only local
lateral connections in a small region (0, r0 ). Function u(θ) is a function of wind
direction (θ), which is defined as
θ
u(θ) = −E cos( ),
2
(5.8)
where θ is the angle between the wind direction and the position of the neighbour
neuron. When 0 ≤ θ ≤ 90, the neighbour position Xj is downwind of Xi , the position being inspected, therefore, odour emissions are transported from position Xi to
Xj , which causes a depletion of odour concentration/intensity at position Xi ; when
90 < θ ≤ 180, the neighbour position Xj is upwind of Xi , therefore, odour emissions
are transported from position Xj to Xi , which results in a replenishment of odour concentration/intensity at position Xj ; When θ = 180 or θ = 0, the position Xj is right
67
on the centreline of the wind direction, therefore, the odour concentration/intensity
at Xi yields a maximum replenishment/depletion effect.
The emission rate from the odour source, the environment (wind direction and
speed, etc.), and farming activities (spreading manure, applying odour reduction
means, etc.) may vary with time. The activity landscape of the neural network
dynamically changes due to the varying external inputs and the internal neural connections, and therefore dynamically representing the changes of odour dispersion.
5.3
Stability Analysis of the Proposed Dispersion
Model
The proposed neural network is a stable system, where the neural activity is bounded
in a finite interval [-D, B] (Grossberg, 1982; Grossberg, 1983; Grossberg, 1988). In
addition, the stability and convergence of the proposed neural network system can be
rigorously proved using a Lyapunov stability theory. From the definition of Ii− , Ii+ ,
and wij , Eq. (5.15) can be rewritten into Grossberg’s general form (Grossberg, 1988),
⎛
⎞
n
dxi
= ai (xi ) ⎝bi (xi ) −
cij dj (xj )⎠ ,
dt
j=1
(5.9)
by the following substitutions
xi = Ci (Xi , t),
ai (xi ) = B − xi ,
1 bi (xi ) = −
AB + (B − xi )(A + [Ii ]+ ) + (D + xi )[Ii ]− ,
B − xi
cij = −wij ,
dj (xj ) = [xi ]+ .
Since B and D are positive constants, and xi = Ci (Xi , t) ∈ [−D, B], where D = 0
by definition, then ai (xi ) ≥ 0 (positivity); since wij = wji , then cij = cji (symmetric);
from the definition of function [e]+ , the function dj (xj ) = [xj ]+ has a nonnegative
derivation, i.e. dj (xj ) ≥ 0 (monotonicity). Therefore, the proposed neural network in
68
Eq. (5.15) satisfies all the three stability conditions required by Grossberg’s general
form (Grossberg, 1988). The rigorous proof of the stability and convergence of the
general form in Eq. (5.9) can be found in Grossberg (1983). Therefore, the proposed
odour dispersion system is stable. The dynamics of the network is guaranteed to
converge to an equilibrium state of the system.
5.4
Simulation Results
The proposed neural network model is capable of dynamically modelling odour dispersion in an arbitrary environment. In the simulation study, the proposed model
is applied to odour dispersion of a livestock farm. Several cases are studied: odour
dispersion in a static environment with various kinds of odour sources (point, line,
area, and irregular area sources), odour dispersion in a dynamic environment, and
odour dispersion in sudden environmental changes. In the following, these simulations results are outlined. The simulation results show that the proposed model is
capable of describing odour dispersion in various environments.
5.4.1
Odour Dispersion in Static Environments
In this section, the proposed model is applied to solve the odour dispersion problem
in static environment in various cases. In all of the cases studied here, the wind speed
is assumed to be 2.5 m/s, odour source height and receptor height are assumed to
be the same, the ground roughness is assumed to be 0.1 m(typically low grass on the
ground), and there is no rain drop.
Odour Dispersion from a Point Source
The proposed model is applied to simulate the odour dispersion from a point source,
typically a ventilation hose. The simulated odour dispersion is shown in Fig. 5.1A.
The odour strength along the centreline of the odour plume simulated by using the
proposed model is shown in Fig. 5.1B, and the odour dispersion along crosswind
direction is shown in Fig. 5.1C.
69
Odour concentration (DT)
60
48
36
24
12
20
ind
sw
os
Cr
0
0
)
(m
40
200
150
100
50
0
Downwind distance (m)
(A)
3.6
Odour concentration (DT)
60
3.0
48
2.4
36
1.8
24
1.2
12
0
0.6
0
50
100
150
200
0
0
5
10
15
20
25
30
35
Downwind distance (m)
Crosswind distance (m)
(B)
(C)
40
45
Figure 5.1: Odour dispersion from a point source simulated by the proposed model.
(A) Odour dispersion from a point source; (B) Odour dispersion along the centreline
of the odour plume; (C) Odour dispersion along crosswind direction.
70
It is believed by many researchers that without obstacles crosswind diffusion
should occur according to Gaussian distributions. Many atmospheric dispersions
models such as Gaussian air dispersion models are developed under this assumption
(Beychok, 2005; Schnelle and Dey, 1999). In this study, the Shapiro-Wilk test is applied to test whether the crosswind dispersion generated by the proposed shunting
model is in accordance with Gaussian distribution. The test results are shown in
Fig. 5.2. It is clear that the crosswind distributions simulated by the proposed model
follow Gaussian distribution.
Odour Dispersion from a Line Source
The proposed model is applied to simulate the odour dispersion from a line source,
typically side curtain opening of a barn. The simulated odour dispersion is shown in
Fig. 5.3.
Odour dispersion from a Regular Area Source
The proposed model is applied to simulate the odour dispersion from an area source,
typically a manure pit. The simulated odour dispersion is shown in Fig. 5.4.
Odour Dispersion from an Irregular Area Source
The proposed model is applied to simulate the odour dispersion from an irregular
area source, typically manure spread. The simulated odour dispersion is shown in
Fig. 5.5.
5.4.2
Odour Dispersion in Non-static Environments
In this section, the proposed model is applied to solve the odour dispersion problem
in non-statistic environment in various cases.
71
2.0
Expected Normal Value
Expected Normal Value
2.5
1.5
1.0
0.5
0.0
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-1.5
-2.0
-2.0
-2.5
-2.5
-0.0050.000 0.005 0.010 0.015 0.020 0.025 0.030 0.0350.040 -0.002 0.002 0.006 0.010 0.014 0.018 0.022 0.026
Observed Value
Observed Value
-0.5
-1.0
(B)
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0
-2.5
0
1E-8 1.2E-8 1.4E-8
Expected Normal Value
Expected Normal Value
(A)
2.5
2.0
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0
-2.5
0
2E-9
4E-9
6E-9 8E-9
Observed Value
2E-28
6E-28
1E-27 1.4E-27
Observed Value
1.8E-27
(D)
(C)
Figure 5.2: Gaussian distribution test on the crosswind distributions generated by the
proposed shunting model. (A) 50 m away from source (SW-W = 0.819, p ¡ 0.001);
(B) 100 m away from source (SW-W = 0.897, p = 0.0014); (C) 150 m away from
source (SW-W = 0.920, p = 0.0067); (D) 200 m away from source (SW-W = 0.909,
p = 0.0031).
72
Odour concentration (DT)
60
48
36
24
12
0
0
50
30
100
150
Downwind distance (m)
200
0
40
20
10
Crosswind distance (m)
Figure 5.3: Odour dispersion from a line source simulated by the proposed model.
Odour concentration (DT)
60
48
36
24
12
0
0
50
30
100
150
Downwind distance (m)
200 0
40
20
10
Crosswind distance (m)
Figure 5.4: Odour dispersion from a regular area source simulated by the proposed
model.
73
Odour concentration (DT)
60
48
36
24
12
0
0
50
30
100
150
Downwind distance (m)
200
0
40
20
10
Crosswind distance (m)
Figure 5.5: Odour dispersion from an irregular area source simulated by the proposed
model.
Odour Dispersion from an Instantaneous Source
So far the odour sources studied above are continuous sources that are emitting
continuously or for time periods equal to or greater than the travel times from the
source to the point of interest. Cases of instantaneous release, as from short-term
releases on the order of seconds, are also of practical concern.
The proposed model is applied to simulate the odour dispersion from an instantaneous emission. The simulated odour dispersion is shown in Fig. 5.6.
Odour Dispersion in Changing Wind Speed
The proposed model is applied to simulate the odour dispersion in changing wind.
The wind speed is increasing linearly as a function of time.
The comparison of odour plume centrelines when wind speed are at 1.74 m/s, 1,77
m/s and 1.8 m/s is shown in Fig. 5.7. From the simulation result, it is obvious that
the odour level is lower at higher wind speed, while at low air flow rate, the odorous
74
Odour concentration (DT)
24
18
12
6
0
0
50
Downwind distance (m)
100 0
10
20
30
40
Crosswind distance (m)
Figure 5.6: Odour dispersion from an instantaneous emission simulated by the proposed model.
particles are dissipated slower and not as far. This result is agree with that observed
in downwind odour measurement on livestock farms (Pan et al., 2007). Wind helps
transportation of odours; odours are dissipated faster as wind speed increases.
5.5
Experimental Results
The proposed odour dispersion model is tested using a livestock database. Odour data
samples were collected in the fields downwind from poultry, dairy and swine facilities
in 2004 by Ontario Ministry of Agriculture and Food. At each farm, human assessors
walked around the farm, identifying wind direction and odour plume. Measurements
were taken at various locations within the odour plume. Outdoor temperature, wind
speed, distances to the odour source, and odour dilution-to-threshold (DT) concentration perceived by human assessors were recorded. Types of odour sources include
ventilation fans, vents, manure pits, and combinations of above sources. Maximum
odour concentration is 60 DT, and minimum concentration is 0 DT. Totally 120 data
75
Odour concentration (DT)
60
Wind speed at 1.74m/sec
Wind speed at 1.77m/sec
Wind speed at 1.8m/sec
50
40
30
20
10
0
0
20
40
60
80
100
Downwind distance (m)
Figure 5.7: Odour dispersion in changing wind speed simulated by the proposed
model.
sets were collected, where 43 were from dairy farms and 77 were from swine farms.
In the proposed approach, parameters B and D are the upper and lower bounds of
the neural activity, respectively. In this study, the upper limit of odour concentration
is 60 DT and the lower limit of odour concentration is 0 DT (e.g., no odour is detected
by human panel). In computer simulation, odour values are normalized between 0
and 1, and B=1 and D=0 are chosen for all cases. The value of other parameters such
as a, u, and d are adjusted by trial and error to minimize the differences of odour
concentrations calculated by the neural network model and observed by human panel.
As the result, a is selected between 0.8 and 3, the value of u is between -0.5 and 2
√
and the value of d is between 0 and 2. The performances of models are evaluated
based on the accuracy and root mean squared error (RMSE) of the calculated odour
concentration in comparison to the experimental odour concentration perceived by
human panels. The neuron grid is scaled to the real odour field, and the calculated
values from the neuron network are also scaled up to 0 DT and 60 DT accordingly
using linear regression. By using the proposed odour dispersion model, the accuracy
76
of the calculated odour concentration can reach correlation coefficient r = 0.606 and
RMSE = 12.04 DT in comparison to human perceived odour levels. The calculated
odour concentrations using the proposed approach are in close accord with the experimental data. The results show that the proposed approach is effective in calculating
odour dispersion around livestock farms.
5.6
Discussion
Above simulation results demonstrated that the proposed odour dispersion model has
several advantages over commonly used air dispersion models, such as computational
fluid dynamics models, Gaussian models and Lagrangian models.
5.6.1
Comparison to Gaussian Models
Gaussian dispersion models have been proved to be useful tools in predicting odour
concentration downwind of an odour source in a stationary environment (Beychok,
2005; Schnelle and Dey, 1999).
The basic Gaussian plume model is initially developed for modelling emissions
from point sources. The formula of the basic Gaussian plume model is given below:
Q
y2
C (x, y, z, H) =
exp − 2
2πūσy σz
2σy
(z − H)2
(z + H)2
exp −
+
exp
−
2σz2
2σz2
,
(5.10)
where C is the odour concentration; Q is the emission rate at the point source; ū
is the mean transport velocity across the plume; σy and σz are the Gaussian plume
dispersion parameters; H is the height of the odour source; x is the downwind distance
to the odour source; y is the crosswind distance; and z is the vertical distance.
Performance in Static Environments
To make comparative studies, the proposed odour dispersion model and a typical
Gaussian dispersion model is applied to simulate odour dispersion from a point odour
source in static environment. The odour dispersion alone the centreline of the odour
77
Odour concentration (DT)
60
Calculated by Shunting model
Calculated by Gaussian model
50
40
30
20
10
0
0
20
40
60
80 100 120 140 160 180 200
Downwind distance (m)
(A)
1.8
Calculated by Shunting model
Calculated by Gaussian model
Odour concentration (DT)
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-20
-15
-10
-5
0
5
10
Crosswind distance (m)
15
20
(B)
Figure 5.8: Comparison of odour dispersion from a point source downwind along
plume centreline (A) and along crosswind direction (B), using the proposed model
and a typical Gaussian dispersion model.
plume simulated by both the models is shown in Fig. 5.8A, and the odour dispersion
along crosswind direction is shown in Fig. 5.8B.
In Fig. 5.8, the calculated odour concentrations along both downwind direction
and crosswind direction by the two methods yield very close results. This demonstrates that the proposed odour dispersion model, in static regime, can achieve similar
results and accuracy as traditional Gaussian dispersion models. However, difference
is observed in Fig. 5.8A: the odour concentration calculated by the proposed model
decreases to 0 quickly at a distance around 120 m while the odour concentration
78
predicted by the Gaussian model still decreases slowly. This is because Gaussian
dispersion models are originally designed to simulate the dispersion of industry pollutants (such as ammonia and hydrogen sulfide) in air. However, those industry
pollutants are different from livestock farm odours, in the way that pollutants are
physical substances while an odour actually is a result from human olfactory perception. Perception of an odour is subject to a threshold which is the concentration of
odorous compounds at which it is noticeable by the human nose. Therefore, once
released from the source, a pollutant always exists in the air no matter how low the
concentration is, but an odour may not be perceived when the concentration of odorous compounds are below the threshold. Thus, the behaviour of the proposed odour
dispersion model is more consistent with our knowledge and expectations of odour
diffusion processed in the environment.
Capability of Modelling Odour Dispersion from Complex Odour Sources
The basic Gaussian plume model is initially developed for modelling emissions from
point sources (Beychok, 2005; Schnelle and Dey, 1999). When modelling emissions
from line sources and area source, integration and double integration of the Gaussian
plume kernel shown in Eq. (5.10) is performed respectively. To model a finite line
source, the odour concentration is calculated as
C (x, y, z, H) =
L
y2
Ql
exp − 2
2πūσy σz
2σy
(z − H)2
(z + H)2
exp −
+
exp
−
2σz2
2σz2
dl,
(5.11)
where Ql is the line source emission rate; L is the length of line source; and dl is
incremental length.
To model an area source, the odour concentration is calculated as
C (x, y, z, H) =
S
Qs
y2
exp − 2
2πūσy σz
2σy
(z − H)2
(z + H)2
exp −
+
exp
−
2σz2
2σz2
ds,
(5.12)
where Qs is the area source emission rate; S is area of the source; and ds is incremental
length.
However, these integrations are complicated and require great computing effort,
79
especially when modelling an irregular odour source. Besides, when modelling odour
dispersion from multiple sources, Gaussian models calculate odour dispersion from
each individual source, and then the total odour dispersion is computed as the arithmetic sum of dispersions from each of the sources. This method deserves further
Discussions as the odour system is a highly nonlinear system that the odour concentration at one location is not a simple sum of concentrations of individual odours at
that location.
The proposed odour dispersion model can easily model odour emissions from complex sources, without integrating Gaussian plume equations. The odour dispersion is
represented by the landscape of the neural activity for all neurons, and the dynamics
of each neuron in the topologically organized neural network is characterized by a
shunting neural equation. Therefore, the proposed model is much easier and simpler than traditional Gaussian dispersion models in modelling odour dispersion from
various sources.
Capability of Modelling Odour Dispersion in Non-Static Environments
The most significant advantage of the proposed model over traditional Gaussian dispersion models is that the proposed model can simulate odour dispersion in non-static
environment. Gaussian dispersion models take assumptions that the source emissions
are at a continuous and constant mass-flow rate, and the horizontal wind velocity the
mean wind direction are both constant (Beychok, 2005; Schnelle and Dey, 1999).
Therefore, Gaussian models are only suitable for modelling air dispersion in static
environment, which greatly limits the applications of Gaussian dispersion models to
solve real world air dispersion problems.
Livestock farm odour emissions are mostly released by near ground sources (less
than 1000 m high). Due to the surface friction near the ground, the wind speed
varies with height such that Gaussian models are not appropriate (Turner, 1994).
Demonstrated by the study above, the proposed model is capable of simulating the
air dispersion in a dynamic environment, therefore, it is more suitable than Gaussian
models to simulate livestock farm odour dispersion.
80
5.6.2
Comparison to Fluid Dynamics Models
To compare the proposed method with a general 2D fluid dynamics model (FD), Eq.
(2.1) can be rewritten as
∂C
∂t
=−
∂(Vx C)
∂x
+
∂(Vy C)
∂y
+
∂
∂x
Kx ∂C
∂x +
∂
∂y
Ky ∂C
∂y
+ MSC − MDP ,
(5.13)
where C is the concentration of the pollutant; Vx and Vy are the velocity components;
Kx and Ky are the diffusion coefficients; MSC is the source term that represents
the mass flow-in; and MDP represents mass consumption term that represents the
deposition and biochemical interaction effects. The first term on the right side of Eq.
(2.1) is the advection contribution and the second term represents the diffusion effect.
The 2D dispersion model in Eq. (5.13) uses a fixed coordinate system that is fixed
to earth, so the wind direction is variable. Therefore, Vx and Vy are both variables in
this fluid dynamics model. If a variable coordinate system which is adaptive to the
wind direction, is adopted by this model, such that the x-axis always points to the
downwind direction and y-axis is on crosswind direction, therefore the wind direction
is no longer variable, and Vy and
∂(Vy C)
∂y
all become zero. In this way, Eq. (5.13)
becomes
∂C
∂t
=
− ∂(V∂xx C)
+
∂
∂x
Kx ∂C
∂x
+
∂
∂y
Ky ∂C
∂y
+ MSC − MDP .
(5.14)
The above Eq. (5.14) shares some similarity with the proposed neuro-dynamics
based odour dispersion model. The proposed odour dispersion model is given as
∂C(Xi ,t)
∂t
= −AC(Xi , t) + (B − C(Xi , t))[I(Xi , t)]+ +
N
j=1
wij [C(Xj , t)]+
(5.15)
−
− (D + C(Xi , t))[I(Xi , t)] ,
where the excitatory input [I(Xi , t)]+ represents mass generation term, as the source
term MSC in Eq. (5.14); the inhibitory input [I(Xi , t)]− is mass consumption term
which represents the wet and dry depositions, as MDP in the FD model; the influence
from neighbouring neurons
N
j=1
wij [Cj (Xj , t)]+ represents the diffusion effect, as the
81
second term on the right in Eq. (5.14). And the first right term −ACi (Xi , t) in Eq.
(5.15) is the advection term as − ∂(V∂xx C) in Eq. (5.14).
Since the proposed shunting dispersion model is based on a neuron network, it
is discrete in spatial domain. If discretize the spatial domain of the FD model, the
advection term − ∂(V∂xx C) in Eq. (5.14), it becomes
x C+Vx C
x C
− V(x+1)−x
= − Vx C+V
= −Vx C − Vx C.
1
(5.16)
Assume that the mass concentration within each discrete unit is uniform, that is
C = 0 in Eq. (5.16). So the advection term in FD finally becomes −Vx C, which
is very similar to the first right term AC in Eq. (5.15).
As shown above, although originate from different areas, the proposed shunting
approach is similar to FD models. It includes advection, diffusion, source and sink
effects in the model, which describes the time and space evolution of pollutant as
FD models do. However, the proposed shunting approach adopts a spatial discrete
neuron network, thus by discretizing the spatial domain, it replaces first order (advection) and second order (diffusion) partial differential terms in FD models with simple
multiplications. Therefore, the computing resource and time can be greatly reduced.
5.6.3
Comparison to Lagrangian Particle Models
The proposed model is computationally efficient. To adequately modelling odour
concentration around a livestock farm, a Lagrangian particle model would require
hundreds of thousands of virtual particles be simulated simultaneously (Hurley, 1994),
and the trajectories of these particles have to be calculated in small consecutive time
steps. Therefore, it leads to very time-consuming simulations. Using the proposed
odour dispersion model, a small neuron grid is enough to simulate odours downwind
of a livestock facility. Therefore, it greatly reduced the computing efforts required in
comparison with Lagrangian particle models.
82
5.7
Summary
The locating and identifying odour dispersion around livestock facilities has been investigated by many researchers over past decades. Prediction of the odour dispersion
is difficult due to the complex characters of odour itself, and complicated farmstead
scheme, landscape and topology, and meteorology conditions around livestock facilities.
In this study, an odour dispersion model is proposed to dynamically simulate
odour dispersion around livestock facilities. The proposed dispersion model dynamically represents steady-state or non-steady-state environment features around livestock farms. The model can simulate odour dispersions from various types of sources
such like point source, line source, and area source, and from multiple odour sources.
In addition, the proposed model is relatively computationally efficient. The stability
of the proposed neural network system is guaranteed by qualitative analysis and the
Lyapunov stability theory.
Computer simulation is performed over the proposed model. Complex and nonsteady-state environmental features around livestock farms are studied, including
simulation of odour dispersions from multiple odour sources, and from various types
of sources such like point sources, line sources, and area sources. Field odour data
gathered from Ontario livestock farms is also used to test the proposed method.
The results show that the proposed approach is effective in providing accurate odour
dispersion prediction.
83
Chapter 6
Conclusions and Future Work
The measurement and analysis of livestock farm odour is an important research topic
in both agricultural and engineering areas, the findings from this study expand the
body of knowledge in this area. This chapter gives a brief summary of the research.
Future work is also discussed in this chapter.
6.1
Conclusions
A wireless network based electronic system has been developed for measuring and
analyzing odours around livestock facilities. The system utilizes a wireless sensor
network built from electronic noses that can detect odour compounds and environment factors such as temperature, wind speed, and humidity. The electronic noses
are located at various points on the farm and sensor signals are delivered via wireless
links to an analyzing station, typically a laptop computer, where a sensor fusion algorithm and odour dispersion model calculate the odour strength; display the odour
dispersion plume; and, if any environment factors have changed or if different control
strategies have been applied, analyze the resulting odour plume. The odour monitoring and analyzing system can provide the farmer and researcher with more precise
odour management capabilities for more efficient operation of odour control systems,
such as ventilation fans and biofiltrations; and can help the development of an overall
odour management strategy by providing dynamic, comprehensive information about
84
the livestock farm environment and odour dispersion.
A multi-component, multi-factor odour calibration model is developed for adequately modelling livestock farm odour. An adaptive neuro-fuzzy based approach
which incorporates neuro-adaptive learning techniques into the fuzzy logic method
has been developed to analyze livestock farm odour. This approach can incorporate
non-numeric data and subjective human expert knowledge, allowing prior knowledge
to be included in the model. In addition, to optimize the performance, the membership functions can be tuned during the learning according to input/output data. This
feature is important as it can avoid inappropriate predetermination of membership
functions, thus minimizing errors resulting from limited knowledge about the livestock farm odour system. A livestock farm database collected by our research team
has been used in the study. The results show that the proposed approach is effective and provides a much more accurate odour prediction than a typical multi-layer
feedforward neural network.
An odour dispersion model is proposed for modelling odour dispersion around
livestock facilities. The proposed dispersion model dynamically represents complex
or non-steady-state meteorological and topographical features in livestock farm areas.
The model can also model odour dispersions in both static environments and nonstatic environments, and from various types of sources like point source, line source,
and area source. This model can enable a more realistic prediction of odour dispersion,
thus improving the predictions of odour impact and helping to better define the parameters for odour control options. Odour reduction research for livestock farms can
then be directed toward the most significant odour impact factors, thereby increasing
the efficiency of developing odour reduction technologies and methods for livestock
producers, and greatly benefiting the livestock industry in Canada and elsewhere.
6.2
Future Work
There remain many important and critical issues that should be explored in future
research, including the following issues.
85
1. The cost efficiency of the wireless electronic nose network is an important factor.
A study should be performed on reducing the price of each electronic nose as well
as the whole network system. First, gas sensors and other network components
that have higher accuracy, are less expensive, and have a longer life should
be selected to achieve maximum measurement accuracy while keeping cost to
a minimum. The number of electronic nose nodes should also be examined.
If an electronic nose fails, other electronic noses usually cover that area and
can provide the information lost by the nose failure. Therefore, the greater
the number of electronic noses in a certain area, the more robust the wireless
network system is to the sensor failure. However, a larger density of electronic
noses is expensive and is more difficult to maintain, and a large redundancy in
information makes wireless transmission slow. The objective of the optimization
of the wireless electronic nose network is to minimize both measurement error
and total cost of the network, while maximizing data transmission quality and
keeping the network functional, stable and robust.
2. Continue the research on odour calibration and dispersion procedures; improve
the accuracy of odour calibration and dispersion models. More field odour
dispersion data need to be gathered from real livestock facilities. More farm
landscape, topological and meteorological factors should be studied for validation of the dispersion model proposed in this study. Livestock farm operation
methods, feeding materials and methods, and manure processing means should
be considered in odour calibration models, as well as more advanced computing
algorithms are for the calibration of odour levels.
86
References
Al-Bastaki, Y. (2009). An artificial neural networks-based on-line monitoring odor
sensing system. Journal of Computer Science 5(11), 878–882.
ASTM (1986). Standard practice for determination of odour and thresholds by a
forced-choice ascending concentration series method of limits.. Technical standard e679. American Society for Testing and Materials.
Barratt, B. (2001). Atmospheric Dispersion Model: an Introduction to Practical Applications. Earthscan Publication Limited. London, V.A., USA.
Basset, P., A. Kaiser, D. Collard and L. Buchailot (2007). Complete system for wireless powring and remote control of electrostatic actuators by inductive coupling.
IEEE/ASME Transactions on Mechatronics 12(1), 23–31.
Bereznicki, S.D., A.J. Heber and R.B. Jacko (2010). Downwind odour analysis using
a field olfactometer and a handheld weather station. In: Proceedings of International Symposium on Air Quality and Manure Management for Agriculture
Conference. Dallas, USA. pp. 1–3.
Beychok, M.R. (2005). Fundamentals of Stack Gas Dispersion, 4th Edition. AuthorPublished. Newport Beach, USA. ISBN 0-9644588-0-2.
Brambilla, M. and P. Navarotto (2010). Sensorial analysis of pig barms odour emissions. Chemical Engineering Transactions 23, 243–249.
Brattoli, M., G. de Gennaro and V. de Pinto (2011). Odour detection methods: olfactometry and chemical sensors. Sensors 11, 5290–5322.
Brose, G., E. Gallmann, E. Hartung, T. Jungbluth and K. Jiang (2001). Detection
87
of the dynamics of odour emissions from pig farms using dynamic olfactometery
and an electronic odour sense. Water Science and Technology 44(9), 59–64.
Brown, B. (1990). Fluid Mechanics of the Atmosphere. Academic Press. London, UK.
Carmel, L., N. Sever, D. Lancet and D. Harel (2003). An enose algorithm for identifying chemicals and determinig their concentration. Sensors and Actuators B
93, 77–83.
Chastain, J.P. and F.J. Wolak (2000). Application of a gaussian plume model of
odour dispersion to select a site for livestock facilities. In: Proceedings of the
Water Environment Federation, Odours and VOC Emissions. Cincinnati, USA.
pp. 745–758.
Chung, H.Y., J.A. Partridge and B.R. Harte (2003). Evaluation of light deterioration in cheddar cheese using the olfactory sensing technique. In: Proceedings of
Institute of Food Technologists Annual Meeting. Chicago, USA. pp. 180–181.
Devarajan, Y.S., B.W. Zoecklein, K. Mallikarjuna and D.M. Gardner (2011). Electronic nose evaluation of the effects of canopy side on cabernet franc grape and
wine volatiles. American Journal of Enology and Viticulture 62(1), 73–80.
DiSpirito, A.A., J.A. Zahn and Y.S. Do (1999). Development of a standardized
method for odour quantification from livestock production facilities. Final report
to the national pork producers council, project no. 99-056. Iowa State University,
USA.
Duyzer, J., B. Nijenhuis and H. Weststrate (2004). Monitoring and modelling of
ammonia concentrations and deposition in agricultural areas of the netherland.
Water, Air, and Soil Pollution: Focus 1(5-6), 131–144.
Eckenrode, B.A. (2001). Environmental and forensic applications of field-portable
gc-ms: an overview. Journal of the American Society for Mass Spectrometry
12(6), 683–693.
Ei-Awad, M.M. (2002). Cost-effective formulation of a finite-element model for atmospheric dispersion of dense gases. In: Recent Advances In Computational Science
88
And Engineering (H.P. Lee and K. Kumar, Eds.). pp. 147–150. Institute of High
Performance Computing. Singapore.
Fatehifar, E., A. Elkamel, A.A Osalu and A. Charchi (2008). Developing a new model
for simulation of pollution dispersion from a networks of stacks. Applied Mathematics and Computation 206, 662–668.
Francesco, D.F., B. Lazzerini, F. Marcelloni and G. Pioggia (2001). An electronic nose
for odour annoyance assessment. Atmospheric Environment 35(7), 1225–1234.
Gao, N.P. and J.L. Niu (2007). Modeling particle dispersion and deposition in indoor
environments. Atmospheric Environment 41(18), 3862–3876.
Gardner, J.W. and P.N. Bartlett (1999). Electronic Noses: Principals and Applications. Oxford University Press. Oxford, U.K.
Gonzalez-Jimenez, J., J. Monroy and J.L. Blanco (2011). The multi-chamber electronic nose - an improved olfaction sensor for mobile robotics. Sensors 11, 6145–
6164.
Greer, A.D., P.M. Newhook and G.R. Sutherland (2008). Human-machine interface
for robotic surgery and stereotaxy. IEEE/ASME Transactions on Mechatronics
13(3), 355–361.
Grossberg, S. (1973). Contour enhancement, short term memory, and constancies in
reverberating neural networks. Studies in Applied Mathematics 52, 217–257.
Grossberg, S. (1982). Studies of Mind and Brain: Neural Principles of Learning,
Perception, Development, Cognition, and Motor Control. Reidel Press. Boston,
USA.
Grossberg, S. (1983). Absolute stability of global pattern formation and parallel memory storage by competitive neural networks. IEEE Transactions on Systems,
Man, and Cybernetics 13(5), 815–926.
Grossberg, S. (1988). Nonlinear neural networks: principals, mechanisms, and architecture. Neural Networks 1, 17–61.
89
Guo, H., L. Jacobson, D. Schmidt and R. Nicolai (2000). Correlation of odour threshold and hydrogen sulphide and ammonia concentrations for animal feedlots. In:
Proceedings of 2000 Amercian Society of Agricultural Engineers Annual International Meeting. Milwaukee, USA. Paper No.004043.
Hans, H., K. Pfleger and A.A. Weber (2007). Mass Spectral and GC Data of Drugs,
Poisons, Pesticides, Pollutants and Their Metabolites. Wiley. Weinheim, Germany.
Hanumantharaya, U., J. Leis and N. Hancock (1999). Quantitative odour modeling
using electronic nose information. In: Proceedings of the 5th International Symposium on Signal Processing and its Applications. Brisbane, Australia. pp. 729–
732.
Havlerson, M., D.D. Jones and D. Schulte (2007). A knowledge-based model to assess
odor annoyance. In: Proceedings of International Symposium on Air Quality and
Waste Management for Agriculture. Colorado, USA. pp. 1–3.
Hayes, E.T., T.P. Curran and V.A. Dodd (2006). A dispersion modelling approcan
to determine the odour impact of intensive poultry production units in ireland.
Bioresource Technology 97(15), 1733–1779.
Haykin, S. (1999). Neural networks: A comprehensive foundation. Prentice Hall. Upper Saddle River, USA.
Hobbs, P.J., T.H. Misselbrook, M.S. Dhanoa and K.C. Persaud (2001). Development
of a relationship between olfactory response and major odorants from organic
wastes. Journal of the Science of Food and Agriculture 81(2), 188–193.
Hodgkin, A.L. and A.F. Huxley (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology 117, 50–544.
Hoff, S.J. and D.S. Bundy (2003). Modelling odour dispersion from multiple sources to
multiple receptors. In: Proceedings of the International Symposium on Gaseous
90
and Odour Emissions from Animal Production Facilites. Horsens, Denmark.
pp. 331–339.
Holmes, N.S. and L. Morawska (2006). A review of dispersion modelling and its
application to the dispersion of particles: an overview of different dispersion
models available. Atmospheric Environment 40, 5902–5928.
Hong, S., I. Lee, H. Hwang, I. Seo, J. Bitog, K. Kwon, J. Song, O. Moon, K. Kimd,
H. Ko and S. Chung (2011). Cfd modelling of livestock odour dispersion over complex terrain, part ii: Dispersion modelling. Biosystems Engineering 108(3), 265–
279.
Huang, H.X. and G.Y. Miller (2003). Evaluation of swine odour management
strageties in a fuzzy multi-criteria decision environment. In: Proceedings of
2003 Annual Meeting of American Agricultural Economics Association. Montreal, Canada. Paper No. 21966.
Hudon, G., C. Guy and J. Hermia (2000). Measurement of odour intensity by an
electronic nose. Air and Wastes Management Association 50, 1750–1758.
Hurley, P. (1994). Partpuff-a lagrangian particle-puff approach for plume dispersion
modelling applications. Journal of Applied Meteorology 33(2), 285–294.
Janes, K., S.X. Yang and R.R. Hacker (2004). Single component modelling of pig
farm odour with statistical methods and neural networks. Biosystem Engineering
88(3), 217–279.
Janes, K., S.X. Yang and R.R. Hacker (2005). Pork farm odour modelling using
multiple-component multiple-factor analysis and neural networks. Applied Soft
Computing 6(1), 53–61.
Jang, J.S.R., C. Sun and E. Mizutani (1997). Neuro-Fuzzy and Soft Computing: A
Computational Approach to Learning and Machine Intelligence. Prentice Hall.
New Jersey, USA.
Jatmiko, W., R. Rochmatullah, B. Kusumoputro, K. Sekiyama and T. Fukuda (2009).
Fuzzy learning vector quantization based on particle swarm optimization for ar91
tificial odour discrimination system. World Scientific and Engineering Academy
and Society (WSEAS) Transactions on Systems 8(12), 1239–1252.
Kamiski, W., M. Kucharski, Tomczak E. and Petera J. (2007). Modelling of carbon monoxide dispersion along roads with the use of the finite element method.
In: Wessex Institute of Technology Transaction on Ecology and the Environment (C.A. Brebbia, Ed.). Vol. 101. pp. 63–70. Wessex Institute of Technology.
Ashurst,UK.
Kammermeier, P., M. Buss and G. Schmidt (2001). A systems theorietical model
for human perception in multimodal presence systems. IEEE Transactions on
Mechatronics 6(3), 234–244.
Karlik, B. and K. Yksek (2007). Fuzzy clustering neural networks for real-time odor
recognition system. Journal of Automated Methods & Management in Chemistry
2007, 6. Article ID 38405.
Karray, F. and C. De Silva (2005). Soft Computing and Tools of Intelligent Systems
Design: Theory and Applications. Addusion Wesley. New Jersey, USA.
Kartavtsev, O., Voznyuk O., L. Petruk, L. Mikhina and A. Sheliuk (2011). Evaluation of the odor threshold concentration levels of the pollutants emitted by
stationary sources of one of the ukrannian industrial facilities. Water Science
and Technology 22(1), S225.
Kodogiannis, V., J.N. Lygouras, A. Tarczynski and H.S. Chowdrey (2008). Artificial odor discrimination system using electronic nose and neural networks for
the identification of urinary tract infection. IEEE Transactions on Information
Technology in Biomedicine 12(6), 707–713.
Koizumi, N., S. Warisawa, H. Hashizume and M. Mitsuishi (2008). Continuous path
controller for the remote ultrasound diagnostic system. IEEE/ASME Transactions on Mechatronics 13(2), 206–216.
Kosmider, J. and B. Krajewska (2007). Determining temporary odour concentration
92
under field conditions - comparison of methods. Polish Journal of Environment
Study 16(2), 215–225.
Koziel, J.A., Y.M. Lo, L. Cai and D.W. Wright (2010). Multidimensional gas chromatography - mass spectrometry-olfactometery. Chemical Engineering Transactions 23, 73–79.
Kusumoputro, B. and T.P. Arsyad (2005). Recognizing odor mixtures using optimized fuzzy neural network through genetic algorithms. Journal of Advanced
Computational Intelligence and Intelligent Informatics 9(3), 290–296.
Latos, M., P. Karageorgos, C.H. Mpasiakos and M. Lazaridis (2010). Dispersion modeling of odours emitted from pig farms: winder-spring measurements. Glocal
NEST Journal 12(1), 46–53.
Launder, B.E. and D.B. Spalding (1974). Numerical computation of turbulent flows.
Computers and Mathematics in Applied Mechanics and Engineering 3, 269–289.
Li, L. and G. Ogmen (1994). Visually guided motor control: adaptive sensorimotor mappting with on-line visual-error correction. In: Proceedings of the World
Congress on Neural Networks. pp. 127–134.
Li, Y. (2009). Evaluation of aermod and calpuff air dispersion models for livestock
odour dispersion simulation. Master thesis. Agricultural and Bioresource Engineering, University of Saskatchewan. Saskatoon,Canada.
Liang, Q. (2006). Ad hoc wireless network traffic-self-similarity and forecasting. IEEE
Communication Letters 6(7), 297–299.
Lin, J.C., Brunner D. and Christoph Gerbig (2011). Studying atmospheric transport
through lagrangian models. EOS, Transactions, American Geophysical Union
92(21), 177–184.
Lin, X.J., S. Barrington, D. Choinire and Prasher S. (2009). Effect of weather conditions on windbreak odour dispersion. Journal of Wind Engineering and Industrial
Aerodynamics 97(11-12), 487–496.
93
Lin, X.J., S. Barrington, J. Nicell, D. Choinire and S. King (2007). Livestock odour
dispersion as affected by natural windbreaks. Water, Air, and Soil Pollution
182(1-4), 263–273.
Liu, Q., D. Bundy and S. Hoff (1993). Utilizing ammonia concentrations as an odour
threshold indicator for livestock facilities. In: Proceedings on the Fourth International Livestock Environment Symposium. Coventry, England. pp. 678–685.
Liu, W., Y. Xu, C. Li, K. Zou and J. Bao (2010). Dynamic dilution olfactometr based
on triangle odour bag method. In: Proceedings of 2010 4th International Conference on Bioinformatics and Biomedical Engineering. Chengdu, China. pp. 1–3.
Lunn, F. and J. Van De Vyver (1977). Sampling and analysis of air in pig houses.
Agriculture and Environment 3(2), 159–169.
Mainwaring, A., J. Polastre, R. Szewczyk, D. Culler and J. Anderson (2004). Wireless
sensor networks. Kluwer Academic Publishers. Norwell, MA, USA.
Masaoka, Y., N. Yoshimura, M. Inoue, M. Kawamura and I. Homma (2007). Impairment of odor recognition in parkinson’s disease caused by weak activations of
the orbitofrontal cortex. Neuroscience Letters 412(1), 45–50.
McGinley, M.A. and C.M. McGinley (2004). Comparison of field olfactometers in
a controled chamber using hydrogen sulfide ans the test odorant. In: International Water Association 2nd International Conference on Odour and VOCs:
Measurement, Regulation, and Control Techniques. Singapore.
McMaster, M.C. (2008). GC/MS A Practical User’s Guide. Weiley. New Jersey, USA.
Meerschaert, M.M., H. Scheffler and C. Tadjeran (2006). Finite difference methods
for two-dimensional fractional dispersion equation. Journal of Computational
Physics 211, 249–261.
Men, H., H. Liu, Y. Pan, L. Wang and H. Zhang (2011). Electronic nose based on an
optimized competition neural network. Sensors 11, 5005–5009.
Miller, G.Y., R.G. Maghirang, G.L. Riskowski, A.J. Heber, M.J. Robert and M.E.T.
Muyot (2004). Influence on air quality and odour from mechanically ventilated
94
swine finishing buildings in illinois. Food, Agriculture and Environment 2(2), 353–
360.
Monse, C., H.C. Broding, F. Hoffmeyer, B. Jettkant, H. Berresheim, T. Bruning,
J. Bunger and K. Sucker (2010). Use of a calibration gas generator for irritation threshold assessment and as supplement of dynamic dilution olfactometry.
Chemical Sense 35(6), 523–530.
Ogmen, H. and S. Gagne (1990a). Neural models for sustained and on-off units of
insect lamina. Biological Cybernetics 63, 51–60.
Ogmen, H. and S. Gagne (1990b). Neural network architecture for motion perception and elementary motion detection in the fly visual system. Neural Networks
3, 487–505.
O’Neill, D.H. and V.R. Philips (1992). A review of the control of odour nuisance from
livestock buildings. Joural of Agricultural Engineering Research 53(1), 23–50.
Osarenren, J.O. (2002). Computer aided electronic nose for sensing odours in the dairy
farms. In: 2002 ASAE Annual International Meeting. Chicago, USA. Paper No.
024169.
Pan, L. and S.Y Yang (2007). An new intelligent electronic nose for measuring and
analyzing livestock and poultry farm odours. Environmental Monitoring and
Assessment 135(1-3), 399–408.
Pan, L., S.X. Yang and J. DeBruyn (2007). Factor analysis of downwind odour from
livestock farms. Biosystems Engineering 96, 387–397.
Pan, L., S.X. Yang, L. Otten and R.R. Hacker (2006). Component and factor analysis
of pork farm odour using structural learning with forgetting method. Biosystems
Engineering 94(1), 85–93.
Pasquill, F. (1961). The estimation of the dispersion of windborne material. The
Meteorological Magazine 90(1063), 33–49.
Pasquill, F. (1974). Atmospheric Diffusion, 2nd Edition. Halsted Press. New York,
USA.
95
Pasquill, F. (1976). Atmospheric dispersion parameters in gaussian plume modeling,
part ii: possible requirements for change in the turner workbook values. Epa600/4-76-030b. US Environmental Protection Agency.
Pearce, T.C., S.S. Schiffman, H.T. Nagle and J.W. Gardner (2003). Handbook of
Machine Olfaction: Electronic Nose Technology. Wiley. Wiley-VCH, Weinheim.
ISBN: 3-527-30358-8.
Petrova, A., K. Alipieva, E. Kostadinova, D. Antonova, M. Lacheva, M. Gjosheva,
S. Popov and V. Bankova (2007). Gc-ms studies of the chemical composition of
two inedible mushrooms of the genus agaricus. Chemistry Central Journal 1, 33.
Powers, W. and S. Bastyr (2004). Downwind air quality measurements from poultry
and livestock facilities. Animal industry report. Iowa State University.
Qu, G., J.J.R. Feddes, W.W. Armstrong, R.N. Coleman and J.J. Leonard (2001).
Measuring odour concentration with an electronic nose. Transactions of the
American Society of Angriculture Engineers 44(6), 1807–1812.
Sakawi, Z., S. Mastura, O. Jaafar and M. Mahumd (2011). Sensitive receivers’ responses on odour annoyance of a neighbourhood open landfill site. Journal of
Applied Sciences in Environmental Sanitation 6(2), 191–199.
Schauberger, G., M. Piringer and E. Petz (2005). Sensitivity analyses of dispersion
models to assess odour nuisance: a qualitative comparison with complaint statistics. In: Proceedings of 17th International Congress of Biometeorology. GarmischPartenkirchen, Australia. pp. 1–5.
Schiffman, S.S., E.A. Sattely-Miller, M.S. Suggs and B.G. Graham (1995). The effect
of environment odours emanating from commercial swine operations on the mood
of nearby residents. Brain Research Bulletin 37(4), 367–375.
Schiffman, S.S., J.L. Bennett and J.H. Raymer (2001a). Quantification of odours
and odorants from swine operation in north caralina. Agricultural and Forest
Meteorology 108, 213–240.
Schiffman, S.S., R. Gutierrez-Osuna and H.T. Nagle (2001b). Use of an electronic nose
96
to evaluate odours from swine operations. In: Proceedings of 8th International
Symposium on Olfaction and Electronic Nose. Washionton D.C., USA. pp. 200–
205.
Schmidt, R. and W.S. Cain (2009). Making scents: Dynamic olfactometry for threshold measurement. Chemical Senses 35(2), 109–120.
Schnelle, K.B. and P.R. Dey (1999). Atmospheric Dispersion Modelling: Compliance
Guide. McGraw-Hill. New York, USA.
Smith, S. (1997). The Scientist and Engineer’s Guide to Digital Signal Processing.
California Technical Publishing. San Diego, C.A., USA.
Sobaski, T., A. Szczurek, K. Nitsch, B.W. Licznerski and Radwan W. (2006). Electronic nose applied to automotive fuel qualification. Sensors and Actuators B:
Chemical 116(1-2), 207–212.
Sohn, J.H., M. Dunlog, M. Hudson, T.I. Kim and Y.H. Yoo (2009). Non-specific
conducting polymer-based array capable of monitoring odour emissions from a
bilfiltration system in a piggery building. Sensors and Actuators B: Chemical
135(2), 455–464.
Sohn, J.H., R. Smith, E. Yoong, J. Leis and G. Galvin (2003). Qualification of odours
from piggery effluent ponds using an electronic nose and an artificial neural
netwrok. Biosystems Engineering 86(4), 339–410.
Song, L. and F. Hatzinakos (2007). A cross-layer architecture of wireless sensor networks for target tracking. IEEE/ACM Transactions on Networking 15(1), 145–
158.
Sucker, K., R. Both and G. Winneke (2009). Review of adverse health effects of odours
in field studies. Water Science and Technology 59(7), 1281–1289.
Sun, G., S.J. Hoff, B.C. Zelle and M.A. Nelson (2008). Development and comparison of backpropagation and generalized regression neural network models to
predict diurnal and seasonal gas and pm10 concentrations and emissions from
97
swine buildings. Transactions of American Society of Agricultural and Biological
Engineers 51(2), 685–694.
Tsai, M.Y. and K.S. Chen (2004). Measurements and three-dimensional modeling of
air pollutant dispersion in an urban street canyon. Atmospheric Environment
38(35), 5911–5924.
Tubaishat, M. and S. Madria (2003). Sensor networks: an overview. Potentials, IEEE
22(2), 20–23.
Turner, D.B. (1994). Work Book of Atmospheric Dispersion Estimates: An Introduction to Dispersion Modelling. CRC Press. Boca Raton, USA.
Wang, N., M.H. Wang and N.Q. Zhang (2006). Wireless sensors in agriculture and
food industry: recent development and future perspective. Computers and Electronic in Agriculture 50(1), 1–14.
Werner-Allen, G., K. Lorincz, M. Ruiz, O. Marcillo, J. Johnson, J. Lees and M. Welsh
(2006). Deploying a wireless sensor network on an active volcano. IEEE/ACM
Transactions on Internet Computing 10(2), 18–25.
Williams, A. (1984). Indicator of piggery slurry odour offensiveness. Agricultural
Wastes 10, 15–36.
Xing, Y. (2006). Evaluation of commercial air dispersion models for livestock odour
dispersion simulation. Master thesis. Agricultural and Bioresource Engineering,
University of Saskatchewan. Saskatoon, Canada.
Yi, J. (2008). A peizo-sensor-based “smart tir” system for mobile robots and vehicles.
IEEE/ASME Transactions on Mechatronics 13(1), 95–103.
Yu, Z. (2010). Development of a livestock odour dispersion. Phd thesis. Agricultural
and Bioresource Engineering, University of Saskatchewan. Saskatoon,Canada.
Zadeh, L.A. (1965). Fuzzy sets. Information and Control 8(3), 338–353.
Zadeh, L.A. and R.E. Bellman (1977). Logical and fuzzy logics.. In: Modern Uses of
Multiple-Value Logics (Dunn and Epstein, Eds.). pp. 106–107. Reidel. Dordrecht,
USA.
98
Zahn, J.A., A.A. DiSpirito, Y.S. Do and B.E. Brooks (2001). Correlation of human olfactory response to airbone intensities of malodorous volatile organic compounds
emitted from swine effluent. Journal of Environmental Quality 30, 635–647.
Zahn, J.A., A.E. Tung and B.A. Roberts (2002). Continuous ammonia and hydrogen
sulfide emission measurements over a period of four seasons from a central missouri swine lagoon. In: 2002 American Society of Agriculture Engineers Annual
Meeting.. St. Joseph, USA. Paper No. 024080.
Zannetti, P. (1990). Air Pollution Modeling: Theories, Computational Methods and
Available Software. Computational Mechanics Publications. Boston, USA.
Zhang, Q., J.J.R. Feddes, I.K. Edeogu and X.J. Zhou (2002c). Correlation between
odour intensity assessed by human assessors and odour concentration measured
with olfactometers. Canadian Biosystems Engineering 44, 6.27–6.23.
Zhang, Q., J.J.R. Feddes, I.K. Edeogu and X.J. Zhou (2003). Correlation between
odour intensity assessed in n-butanol reference scale and odour concentration
measured with olfactometers. Canadian Biosystems Engineering 44, 6.27–6.32.
Zhang, Q., S.X. Yang, G.S. Mittal and S.J. Yi (2002). Prediction of performance indices and optimal parameters of rough rice drying with neural networks. Biosystems Engineering 83(3), 281–290.
Zhang, Z., J. Tong, D.H. Chen and Y.B. Lan (2008). Electronic nose with an air sensor
matrix for detecting beef freshness. Journal of Bionic Engineering 5(1), 67–73.
Zhao, Q., A. Swami and L. Tong (2006). The interplay between signal processing and
networking in sensor networks for military applications. IEEE Signal Processing
Magazine 23(4), 84–93.
Zhu, J. (1998). The influence of stability class on downwind odour concentration
predicted by air dispersion models. Journal of Agriculture Engineer Resources
A. 34(10), 1919–1931.
Zimmermann, H. (2001). Fuzzy Set Theory and Its Applications. Kluwer Academic
Publishers. Norwell, USA.
99
Zou, Z and L.B. Buck (2006). Combinatorial effects of odorant mixes in olfactory
cortex. Science 311(5766), 1477–1481.
100
Appendix: Selected Publications
Journal Papers
Published journal papers are attached after Page 102.
1. L. Pan and S.X. Yang: (2009). “An electronic nose network system for online
monitoring livestock farm odors,” IEEE/ASME Transactions on Mechatronics,
Vol. 14, No. 3, pp. 371-376.
2. R. Liu, L. Pan and S.X. Yang (2009). “A web-based collaborative system for
remote monitoring and analysis of livestock farm odours,” Journal of Environmental Informatics, Vol. 13, No. 2, pp. 75-85.
3. L. Pan, L. Qin, S.X. Yang and J. Shuai, (2008). “A neural network based
method for risk factor analysis of West Nile Virus,” Risk Analysis, Vol. 28, No.
2, pp. 487-496.
4. L. Pan and S.X. Yang, (2007). “Analyzing livestock farm odour using an adaptive neuro-fuzzy approach,” Biosystems Engineering, Vol. 97, No. 3, pp. 387393.
5. L. Pan, and S.X. Yang, (2007). “An new intelligent electronic nose for measuring
and analyzing livestock and poultry farm odours,” Environmental Monitoring
and Assessment, vol. 135, no. 1-3, pp. 399-408.
6. L. Pan, and S.X. Yang (Submitted). “A Neuro-dynamics Model for Odour Dispersion around Livestock Farms,” Computational Intelligence and Applications,
submitted in 2011.
101
Conference Papers
1. L. Pan, S.X. Yang, G.S. Mittal, S. Gregori, F. Wang, (2010). “A neurodynamics model for odour dispersion around livestock farms,” In Proc. IEEE
International Conference on Systems Man and Cybernetics, pp.843-848.
2. R. Liu; L. Pan; S.X.Yang, M.Q.-H. Meng, (2008). “A web based collaborative
portal for remote monitoring and analysis of livestock farm Odour,” In Proc.
IEEE/ASME International Conference on Advanced Intelligent Mechatronics,
pp. 371-376.
3. L. Pan, R. Liu, S. Peng, S.X. Yang, and S. Gregori, (2007). “Real-time monitoring system for odours around livestock farms,” In Proc. IEEE International
Conference on Networking, Sensing and Control, pp. 883-888.
4. L. Pan, R. Liu, S. Peng, Y. Chai and S.X. Yang, (2007). “An wireless electronic
nose network for odours around livestock farms,” In Proc. 14th International
Conference on Mechatronics and Machine Vision in Practice, pp. 211-216.
5. L. Pan and S.X. Yang (2007). “Analyzing livestock farm dour using a Neurofuzzy approach,” In Proc. International Conference on Intelligent Computing,
pp. 772-777.
6. R. Charles, Y. Krupin, J. Holstead, A. Trcka, L. Pan, and S.X. Yang, (2007).
“Development of a new electronic nose for odour measurement utilizing wireless
sensor networks,” In Proc. International Conference on Networking, Sensing
and Control , pp. 455-459.
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Short Papers
An Electronic Nose Network System for Online Monitoring
of Livestock Farm Odors
Leilei Pan and Simon X. Yang
Abstract—An electronic nose (e-nose)-based network system is developed for monitoring odors in and around livestock farms remotely. This
network is built from compact e-noses that are tailored to measure odor
compounds and environmental conditions such as temperature, wind speed,
and humidity. The e-noses are placed at various applicable locations in and
around the farm, and the collected odor data are transmitted via wireless
network to a computer server, where the data processing algorithms process and analyze the data. The developed e-nose network system enables
more effective odor management capabilities for more efficient operation
of odor control practice by providing consistent, comprehensive, real-time
data about the environment and odor profile in and around the livestock
farms. Experimental and simulation results demonstrate the effectiveness
of the developed system.
Index Terms—Electronic nose (e-nose), odor dispersion, odor monitoring, wireless sensor network.
I. INTRODUCTION
Odor emissions from livestock operations have caused significant
public concerns about the environment and human health. Odor control technologies are greatly impeded by the lack of science-based approaches to assess the effects of odor. With the current state of technology, the most commonly used methods to measure odor from livestock
facilities is human sniffing methods. Odor measurement technologies
such as olfactometry and scentometer use trained odor panelists to smell
air samples to determine the strength of an odor [1]. Both olfactometry
and scentometer methods are expensive to use and are susceptible to
the assessor’s personal bias. Therefore, unbiased automatic odor evaluating technologies are desirable. An electronic nose (e-nose) is such
a reliable alternative for objectively measuring livestock farm odors.
Now, e-noses have been developed for monitoring agricultural environment. In our previous study, an e-nose consisting of a sensor array
and an intelligent analysis system was developed. Experimental results proved its capability of producing objective and consistent odor
measurement [2].
However, an e-nose is not enough to provide a comprehensive odor
profile in and around the livestock facility. The impact area of odorous
gas emissions is difficult to determine because it continuously changes
with wind speed and direction. In addition, a modern livestock facility
is usually not a point odor source but rather an array of points (exhaust
fans), lines (side curtain opening), and areas (outdoor storage surfaces).
An e-nose can only measure the odor at one location; it cannot provide
an overall odor profile around livestock facilities that is necessary for
a comprehensive and efficient odor management strategy.
Manuscript received June 10, 2008; revised September 20, 2008. First
published March 10, 2009; current version published June 17, 2009. Recommended by Technical Editor H. Hashimoto. This work was supported in part
by Ontario Pork and in part by the Natural Sciences and Engineering Research
Council of Canada (NSERC).
The authors are with the School of Engineering, University of Guelph,
Guelph, ON N1G 2W1, Canada (e-mail: lpan@uoguelph.ca; syang@
uoguelph.ca).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TMECH.2009.2012850
Wireless communication technology has been widely applied to environmental observations [4], [9], habitat monitoring [3], medical diagnostic [10], [11], and various industries [12], [14]. Wireless network
enables remote data collection, and can provide localized measurements and detailed information that is hard to obtain through traditional
instrumentation [4].
Data analysis and display of the measured data and analysis results
in real time are an important issue in mechatronic system development. Many end users of the system such as livestock farmers do not
have sufficient knowledge of the complicated system and computer
skill to program the data analysis; therefore, user-friendly interface is
desirable [2].
In this research, a novel wireless e-nose network system is developed for online monitoring of livestock farm odors. The system utilizes
an e-nose network built from compact e-noses that can measure odor
compounds and environmental conditions such as temperature and humidity. The e-noses are placed at various applicable locations in and
around the farm; odor data measured by the e-noses are delivered via
a wireless network to the analysis station where several data analysis
algorithms process and analyze the data, display sensor signals in real
time, and model the odor dispersion around the farm. A software suite is
also developed to perform all the data analysis and support users’ odor
control practice. A novel intelligent odor dispersion model is proposed
for modeling the odor dispersion downwind of odor sources. By using
the wireless e-nose network system, real-time downwind odor measurement becomes possible. The proposed real-time odor monitoring
system would enable the remote measuring and location of livestock
farm odor plume. Experimental results show that the wireless e-nose
network is effective in remote monitoring and real-time analysis of
livestock farm odors. Computer simulations also demonstrate that the
proposed odor dispersion model is feasible in modeling odors around
livestock facilities.
In this paper, the design of the entire e-nose network is presented.
Section II first introduces the architecture of the proposed wireless enose network system. The development of each single e-nose is given
in details in Section II-A; Section II-B presents the setup of the wireless
network; several data processing and analysis approaches are covered
in Section II-C. Section II-D introduces the software suite developed
for the e-nose network system. Several advantages of the proposed
e-nose network system are finally addressed in Section III.
II. DEVELOPED WIRELESS ELECTRONIC NOSE NETWORK SYSTEM
In this study, an e-nose network system is developed for remotely
monitoring and analyzing livestock farm odors in real time. The development of the proposed wireless e-nose network system includes
following parts: the design of several simplified e-noses for measuring
odor components and environmental conditions, the network architecture design, the design of a software suite to process and analyze the
odor data, the development of data processing and analysis algorithms,
and the integration of the whole network system.
The architecture of the developed wireless e-nose network system
is illustrated in Fig. 1. Essentially, the wireless e-nose network is a
simple STAR network. STAR is a common network topology, with all
the sensor nodes communicating with a central control station (also
called sink or gateway).
The wireless e-nose network system consists of various hardware
components. In this section, the architecture of the wireless e-nose
1083-4435/$25.00 © 2009 IEEE
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IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 14, NO. 3, JUNE 2009
Fig. 2.
Fig. 1.
Developed sensor array.
Architecture of the developed wireless e-nose network.
TABLE I
SENSORS USED IN THE DEVELOPMENT OF THE E-NOSE
Fig. 3. Developed e-nose consists of the sensor array inside a chamber, a data
acquisition board, a mote processor, and a power supply.
network and the development of essential hardware components are
presented.
A. Development of the Electronic Noses
To be utilized in the wireless network, the e-noses should be compact
in size, energy efficient, and of low cost. The design toward the final enose for the wireless sensor network includes several steps, such as the
selection of sensors to measure odorous compounds most prevalent in
livestock odor, the incorporation of these sensors into a sensory array,
the design of a chamber to regulate gases toward the sensors, and the
acquisition of the sensor array data for wireless transfer.
1) Sensor Selection and Sensor Array: Each e-nose in the network
has a sensor array with four gas sensors to measure odor components in
ambient air, and two sensors to measure the environmental conditions,
i.e., a humidity sensor and a temperature sensor. The gas sensors are
selected based on previous extensive investigation and experiments,
as well as various sensors available for odor compound measurement.
These four sensors can measure most of the major gas compounds in
livestock farm odors. A section of this sensor list is shown in Table I.
The selected sensors are fixed in an array. A developed sensor array is
shown in Fig. 2.
2) Design of the Chamber: A chamber is designed to accommodate
the sensor array and regulate the flow of odorous air. The sensor array
is placed within this chamber, and the odorous air is directed toward
the array. Since the sensors have specific requirements on the airflow
rate for proper operation, a pump is used to control the airflow speed.
The chamber also contains a simple diffuser to disperse the incoming
odorous air or filtered clean air onto the sensor array. The sensor array
faces directly into the coming air so that the surface area of the array
that is exposed to the odorous air is maximized. This ensures best
measuring results and minimizes the possibility of attaining improper
readings. The chamber also has an input valve that is switched between
odorous and carbon filtered air ports, so that the filtered odorous air
can refresh the chamber and sensor when necessary.
3) Integration of the Electronic Nose: The analog sensor signals
collected by the e-nose are converted to 12-bit digital signals by a data
acquisition board (MDA300) right at the e-nose side. The multifunction
data acquisition board MDA300 can convert up to 11 channels of analog
inputs. The input analog signals range from 0 to 2.5 V, and the output
digital signals range from 0 to 4095. The digital signals are delivered
by a 2.4-GHz mote (MPR2400) to a gateway. Gas sensors and the air
pump are powered by a 12-V dc power supply. A developed e-nose
system is shown in Fig. 3. These small battery-powered e-noses are
placed in locations of interest around livestock facilities to collect odor
data. The e-nose network prototype consists of three e-noses at this
time, which can be easily expanded to accommodate more e-noses
when necessary. Usually, if an e-nose fails, other e-noses that cover the
area can also provide the missing information. For instance, if e-noses
A, B, and C all provide coverage of area G, when nose A fails, noses B
and C still provide information on area G. Therefore, the more e-noses
in a certain area, the more robust the wireless network system is to the
sensor failure. However, larger density of e-noses costs more, and large
redundant information could make wireless transmission slow.
IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 14, NO. 3, JUNE 2009
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where n is number of data points in the filter, k is the kth data point,
and x̄k is a simple moving average filter given by
x̄k =
Fig. 4.
Block diagram of the wireless network.
B. Electronic Nose Network
The odor data collected by the e-noses located in and around the farm
are delivered by a 2.4-GHz wireless LAN to the gateway. The gateway gathers the sensor signals and inputs them into the control center
via Ethernet. The block diagram of the wireless network is shown in
Fig. 4.
For the purposes of wireless transmission of sensor signals, in this
research, the MicaZ wireless network platform from Crossbow (Crossbow Technology, Inc., USA) is adopted. This wireless network platform has advantages of the small physical size, low cost, and low power
consumption, making it ideal for this odor monitoring application.
The small, low-power radio and processor board MPR2400 includes
a processor, radio, and battery. Power can be provided by any 3-V power
source, typically two AA batteries. The radio on the MPR module consists of a basic 2.4-GHz industrial, scientific and medical (ISM) band
transceiver that is compliant to IEEE 802.15.4/ZigBee protocol, an antenna, and collection of discrete components to configure the physical
layer characteristics, such as signal strength and sensitivity. The gateway MIB600 provides Ethernet connectivity for communication, and
allows remote access to the e-nose network data via Transmission Control Protocol (TCP)/IP. The wireless communication ability not only
enables sensor data and status information to be communicated across
the network, but also enables remote control of network nodes.
C. Data Processing and Analysis
Odor data collected by the e-noses need to be processed and analyzed
to identify rules and patterns in the data. Several signal processing
algorithms and pattern analysis techniques are used to analyze and
process sensor data.
1) Noise Reduction: Sensor signals generated by the e-nose usually
are contaminated by white noise. Noise reduction is the process of
removing noise from a signal. There are many noise reduction methods
to remove or reduce noise from sensor signals, such as commonly used
fast Fourier transforms, Kalman filters, and moving average filters. In
this research, moving average technology is adopted to reduce noises in
signals as it is fast in computing and easy to use in comparison to other
existing technologies. An exponentially weighted moving average filter
is used to minimize the noise [18]. The filter is given as
x̄k =
n
x̄k −1 +
n+1
n
xk
n+1
k
xi .
(1)
(2)
i = k −n + 1
This method applies weighting factors to the data points in the way
that the weights for earlier data decrease exponentially, thus giving
much more importance to recent observations while still not discarding
previous observations entirely.
2) Compensation for Signal Drift: Sensor signals are usually subjected to temporal variation, typically referred to as “long-term drift,”
due to temperature and humidity changes in the environment. This
drift may seriously influence calibration; therefore, drift compensation approaches are used to reduce the drift. These approaches include
mathematic methods based on curve fitting of the temporal variation of
the sensor signals, wavelet analysis, or statistic methods such as principal components analysis. In this study, a simple data preprocessing
algorithm is used to compensate the signal drift. This method compensates the variation of the conductance of the sensors that are caused
by environmental changes, and therefore reduces the variation of the
output electrical signals of the sensors. The general expression of the
compensation approach is given as
ri j =
Gi j − G0i
G0i
(3)
where G0i is the conductance of the sensor i in clean air (i.e., baseline
signal) and Gi j is the conductance of the sensor i in the presence of
odor j [8].
3) Modeling of Odor Dispersion: Accurately measuring odors in
and around the livestock farms has been an elusive goal of researchers
and livestock producers. Air dispersion models have been applied to
evaluate odor impacts around the livestock facilities and determine the
setback distance between the livestock facilities and local residents [6].
Various types of dispersion models have been developed to represent
different types of emission release scenarios. Commonly used dispersion models for livestock farm odor dispersion include numerical
advection–diffusion models, Gaussian models, and Lagrangian particle
trajectory theory. Gaussian dispersion models are used for steady-state
meteorology conditions, and therefore are not suitable for modeling dynamic odor dispersion on livestock farms [15]. Numerical approaches
and Lagrangian particle trajectory approaches are complicated, and require the high computational and memory capacity, and therefore are
not ideal for online odor analysis [5], [16]. As livestock farms have
special odor emissions, farmsteads, and odor management systems, an
ad hoc livestock farm odor dispersion model can greatly improve the
efficiency and accuracy of odor dispersion prediction.
A neural network model is proposed for modeling dynamic odor
dispersion around livestock farms. The neural dynamics of each neuron
is characterized by a shunting equation of an additive equation derived
from Hodgkin and Huxleys’s membrane equation [7]. A 2-D dynamic
odor dispersion model is proposed as
dC(Xi , t)
= −AC(Xi , t) + (B − C(Xi , t))
dt
([I(Xi , t)]+ +
N
wi j [C(Xj , t)]+ )
j=1
− (D + C(Xi , t))[I(Xi , t)]−
1−
1
n
(4)
where Xi = (x, y) is the location of the ith neuron, i = 1, 2, . . . , N ,
N is the total number of neighboring neurons of the ith neuron, x is the
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IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 14, NO. 3, JUNE 2009
downwind distance of the ith neuron to the odor source, while y is the
crosswind distance of the ith neuron to the odor source, C(Xi , t) is the
odor concentration at the ith neuron, and [I(Xi , t)]+ and [I(Xi , t)]−
are excitatory and inhibitory inputs, respectively, where [e]+ and [e]−
are linear-above-threshold functions defined as
[e]+ = max {e, 0}
[e]− = max {−e, 0} .
(5)
Parameters A, B, and D represent the passive decay rate, the upper and
lower bounds of the neural activity, respectively. Parameters B and D
are constants, and A can be a function of wind speed, i.e.,
A = qW (t)
(6)
where q is a positive constants and W (t) represents the wind speed at
time t. Therefore, parameter A represents the transportation effect of
airflow; the higher the airflow speed, the faster the odorous compounds
being dispersed.
The variable I(Xi , t) is external input to the ith neuron defined as
I(Xi , t) =
⎧
S(t),
⎪
⎪
⎪
⎪
⎨
−R(t),
⎪
⎪
⎪
⎪
⎩0,
if there is an odor source at
position Xi
if there is an odor reduction means
(7)
at position Xi
if there is no odor source or reduction
means at position Xi
where S(t) and R(t) are positive functions of time. The odor reduction
means include odor control practice operated by livestock producers,
dry deposition to the earth surface, and wet deposition by precipitation
(rain, fog, snow,
etc.).
N
In (4), item j = 1 wi j [C(Xj , t)]+ represents the interaction among
the neighbor neurons, where N is the total number of neighboring
neurons of the ith neuron and wi j is the connection weight from jth
neuron to the ith neuron, which is a function of distance defined as
wi j =
0,
u
,
di j
if di j > r0
if di j ≤ r0
(8)
where r0 is a positive constant and di j = |Xi − Xj | represents the
Euclidean distance between positions Xi and Xj in the state space.
Therefore, the neuron has only local lateral connections in a small
region (0, r0 ). Function u is a function of wind direction, which is
defined as
θ
(9)
u(θ) = −E cos
2
where θ is the angle between the wind direction and the position of
the neighbor neuron. When 0 ≤ θ ≤ 90, the neighbor position Xj is
downwind of Xi , the position being inspected; therefore, odor emissions are transported from position Xi to Xj , which causes a depletion
of odor concentration/intensity at position Xi ; when 90 < θ ≤ 180,
the neighbor position Xj is upwind of Xi ; therefore, odor emissions
are transported from position Xj to Xi , which results in a replenishment of odor concentration/intensity at position Xj ; when θ = 180 or
θ = 0, the position Xj is right on the centerline of the wind direction;
therefore, the odor concentration/intensity at Xi yields a maximum
replenishment/depletion effect.
The emission rate from the odor source, the environment conditions
(such as wind direction and speed), and farming activities (such as
spreading manure, applying odor reduction means) may vary with time.
The activity landscape of the neural network dynamically changes due
to the varying external inputs and the internal neural connections, and
therefore dynamically represents the changes of odor dispersion.
Fig. 5. Calculated odor dispersion from a point source. (a) Odor plume profile.
(b) Odor downwind along plume centerline. (c) Odor along crosswind direction.
An example of calculated odor dispersion from a point source is
shown in Fig. 5. The 2-D odor dispersion at receptor’s height is shown
in Fig. 5(a), downwind along plume centerline in Fig. 5(b), and along
crosswind direction in Fig. 5(c).
Previous research on odor dispersion indicated that the location of
the e-noses would influence the measurement results and the odor dispersion model. The odor dispersion plume is usually affected by the
landscape around the farm such as vegetation and buildings. For example, high crops in the field serve as windbreaks, which would greatly
reduce the odor dispersion [15]. A building could cause a downwash
effect, e.g., airflow forms a “cavity” with recirculating flow downwind
of the building, which would greatly change the odor dispersion close to
that building [17]. Therefore, it is important to make sure that the odor
information around these locations is available by deploying sensor
nodes to these locations.
D. Software Integration
In order to utilize the previously mentioned hardware, certain software packages are needed. The MDA300, the MICAz, and the MIB600
all run on TinyOS programming language. An application provided
with the hardware called MoteWorks is sufficient to set up a wireless
network for data transfer. This involved placing the gateway (MIB600)
in the same subnet as the client station and using a simple hub to
successfully place the device onto the network. Then, MoteWorks autoconfigures and acquires the IP address of the network. Once the
gateway device is active, the mote is programmed to interface with the
e-noses. Then, the wireless network system can be integrated and synchronized. An application software called MoteView from Crossbow
(Crossbow Technology, Inc., USA) can automatically detect the e-nose
and the gateway, and receive data from the e-nose through the wireless
network. The topology of the wireless network interface is shown in
Fig. 6.
The odor data collected by the e-nose network need to be processed
and analyzed for the purpose of supporting odor control decisions.
Hence, software suite is designed for real-time data capture, display,
and analysis. The software is running on a central analysis station,
and includes four main components: 1) a data acquisition system to
IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 14, NO. 3, JUNE 2009
Fig. 6.
375
Fig. 7.
Dynamic display console.
Fig. 8.
Odor analysis system.
Topology of the wireless network.
acquire the sensor signals that are transferred to the computer via the
wireless network; 2) a database that stores the odor data for future
applications; 3) an odor analysis system that identifies risk factors of
odor and calculates odor concentration and odor dispersion plume; and
4) a user interface tire that interacts with users and displays odor data
and reports analysis results.
1) Data Acquisition System: After analog sensor signals are converted into digital signals, the digital signals are sent wirelessly through
the wireless network to the gateway, which receives and collect the
digital signals. Then, the data acquisition system in the computer will
interface with the gateway and acquire the digital signals into the
database.
2) Database: Database plays an important role as a data storage,
management, and exchange center for various applications. In this
system, data structures (data records and files) are optimized to deal
with large amounts of data. A data storage system based on Microsoft
SQL Server 2005 database server is developed for storing sensor signals
and odor information. The stored odor data in the database can be
retrieved, managed, and processed through user interface.
3) User Interface Tire: Users interact with the control station directly through a series of user interfaces. These interfaces are within
the user interface layer. The user interfaces provide a user-friendly
environment and adequately support the operation undertaking. The
user interface layer provides graphical front-end pages to the database
records, analysis result reports, task management, and the other components. Microsoft C# environment is used for user interface development. The main interface is the first displayed page when users start the
application. Users log onto the system from this page to get the access
to other functional pages such as data display interface and analysis
system interface. To satisfy various requirements, the user interface
layer can provide a widely used standard for analyzing record data
and also accommodates applications developed in other programming
language environment.
The interface of data display system is shown in Fig. 7. This system provides online and offline display of the sensor signals. Several
display attributes can be selected by users, such as different update
interval, number of visible points and data range, average number display, etc. This system would be helpful for routine operation and various
studies.
4) Odor Analysis System: Odor analysis system is the key component of the software suite. The system utilizes the data analysis applications to identify rules and patterns in the data, to tell the users where is
odor, how strong the odor is, to give suggestion on odor control practice,
and to predict the resulted odor strength if any odor control means are
applied. The data analysis system has many subsystems: odor strength
analysis system calculates odor concentration/intensity using artificial
intelligent techniques; odor dispersion analysis system calculates odor
dispersion plume using odor dispersion models based on odor emission rates, and topological and meteorological information; risk factor
analysis module identifies significant risk factors that cause odor problems; and decision-making module assists users to make odor control
decisions based on analysis results and human expert knowledge. The
interface of odor analysis system is shown in Fig. 8.
This analysis system also provides decision support for farmer’s odor
control practice. In the analysis system, the sensor fusion algorithm
and odor dispersion algorithm calculate the odor dispersion, display
the odor plume dispersion in real time, and predict the odor map if
any environmental factors are changed or different control means are
applied. Based on odor plume prediction results, the analysis system
would provide suggestions on possible odor control practice based
on farmstead, topography, and meteorological data, such as when to
spread manure, change vent direction, and where to plant trees to block
odor. The odor dispersion analysis system provides an overall odor
mapping around live facilities that is necessary for an overall odor
management strategy. This will help the livestock producers to make
strategic funding decisions by investing only on efficient odor control
practice.
To validate the developed hardware and software system, a series
of experimental studies is conducted in laboratory using odorous gases
with single or multiple odor components. The results show that the
developed system is functional and effective in remote data collection,
wireless data transmission, and real-time display and analysis of odor
data. Simulation studies on the odor dispersion model show its feasibility to model the dynamic odor dispersion around livestock farms.
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IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 14, NO. 3, JUNE 2009
III. DISCUSSIONS
The proposed e-nose-based wireless network approach to automatically monitor livestock farm odors differs from any existing e-noses in
the following ways.
1) The developed wireless e-nose network system can provide multilocation odor monitoring while existing e-noses can only provide odor monitoring at a single location. Odor concentration
changes with source configuration (shape of the source, building geometry, etc.), landscape around the farm, and distance to
the source. The proposed wireless e-nose network system can
provide a more comprehensive odor plume profile than a single
e-nose system.
2) By using this wireless e-nose network system, users can monitor
the odor from a remote location such as inside a house or office,
so that they do not have to stay in the field, which makes odor
measurement easier in unfavorable weather.
3) The developed e-nose network system with multiple e-noses is
more robust to sensor failure and noise. One or more failed or
incorrect sensor reading could be corrected through the redundant
information from the other e-noses.
The developed wireless e-nose network system is a useful tool in remote monitoring of livestock farm odor, which will greatly improve the
efficiency of livestock farm odor reduction, and thus benefit livestock
industry. In addition, it would be used for prediction of upcoming odor
plume profile with weather information forecast, and planning upcoming odor management.
IV. CONCLUSION
The design of an e-nose-network-based livestock farm odor monitoring system was presented in this paper. The system is composed of
e-noses that can measure odor compounds and environmental factors.
The e-noses are deployed in and around the farm and odor data are
collected via wireless communication. The data processing algorithms
and odor dispersion algorithm in the analysis system calculate the odor
dispersion, display the odor plume dispersion in real time, and can also
predict upcoming odor plume with upcoming weather forecast.
The main contribution of this research include the following: 1) an
e-nose-based wireless network is developed for monitoring livestock
farm odors; 2) data acquisition and data processing software is developed for collecting and processing odor data from the wireless e-nose
network; 3) a neural-network-based odor dispersion model is proposed
for modeling odor dispersion downwind of the odor source in real time;
and 4) a data analysis software with user-friendly interface is developed
for assisting end users in making efficient odor management decisions.
The proposed e-nose network approach can automatically monitor
livestock farm odors, and have many potential industrial applications.
The developed wireless e-nose network system can perform remote
multipoint odor monitoring, and the integrated software suite provides
data analysis capabilities that would assist users’ decision making on
odor control practice. It enables more efficient odor control capabilities
by providing automated, robust, and comprehensive information about
the environment and odor profile in and around the livestock farms.
REFERENCES
[1] W. Powers and S. Bastyr, “Downwind air quality measurements from
poultry and livestock facilities,” Iowa State Univ., Ames, Animal Ind.
Rep., 2004.
[2] L. Pan and S. Y. Yang, “A new intelligent electronic nose system for
measuring and analysing livestock and poultry farm odours,” Environ.
Monitoring Assessment, vol. 135, no. 1–3, pp. 399–408, 2007.
[3] A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, and J. Anderson,
“Wireless sensor networks for habitat monitoring,” in Wireless Sensor
Networks. Norwell, MA: Kluwer, 2004, pp. 399–432.
[4] N. Wang, M. H. Wang, and N. Q. Zhang, “Wireless sensors in agriculture
and food industry: Recent development and future perspective,” Comput.
Electron. Agriculture, vol. 50, no. 1, pp. 1–14, 2006.
[5] M. M. Meerschaert, H. Scheffler, and C. Tadjeran, “Finite difference
methods for two-dimensional fractional dispersion equation,” J. Comput.
Phys., vol. 211, pp. 249–261, 2006.
[6] G. Y. Miller, R. G. Maghirang, G. L. Riskowski, A. J. Heber, M. J. Robert,
and M. E. T. Muyot, “Influence on air quality and odour from mechanically ventilated swine finishing buildings in Illinois,” Food, Agriculture
Environ., vol. 2, no. 2, pp. 353–360, 2004.
[7] A. L. Hodgkin, The Conduction of the Nervous Impulse. Liverpool,
U.K.: Liverpool Univ. Press, 1964.
[8] E. L. Hines, E. Llobet, and J. W. Gardner, “Electronic noses: A review of
signal processing techniques,” Proc. Inst. Electr. Eng.—Circuits Devices
Syst., vol. 146, no. 6, pp. 297–310, 1999.
[9] G. Werner-Allen, K. Lorincz, M. Ruiz, O. Marcillo, J. Johnson, J. Lees, and
M. Welsh, “Deploying a wireless sensor network on an active volcano,”
IEEE Internet Comput., vol. 10, no. 2, pp. 18–25, Mar./Apr. 2006.
[10] N. Koizumi, S. Warisawa, H. Hashizume, and M. Mitsuishi, “Continuous
path controller for the remote ultrasound diagnostic system,” IEEE/ASME
Trans. Mechatronics, vol. 13, no. 2, pp. 206–216, Apr. 2008.
[11] A. D. Greer, P. M. Newhook, and G. R. Sutherland, “Human–machine
interface for robotic surgery and stereotaxy,” IEEE/ASME Trans. Mechatronics, vol. 13, no. 3, pp. 355–361, Jun. 2005.
[12] J. Yi, “A piezo-sensor-based “smart tire” system for mobile robots and
vehicles,” IEEE/ASME Trans. Mechatronics, vol. 13, no. 1, pp. 95–103,
Feb. 2008.
[13] P. X. Liu, M. Q. H. Meng, P. R. Liu, and S. X. Yang, “An end-to-end transmission architecture for the remote control of robots over IP networks,”
IEEE/ASME Trans. Mechatronics, vol. 10, no. 5, pp. 560–570, Oct. 2005.
[14] P. Basset, A. Kaiser, D. Collard, and L. Buchailot, “Complete system
for wireless powering and remote control of electrostatic actuators by
inductive coupling,” IEEE/ASME Trans. Mechatronics, vol. 12, no. 1,
pp. 23–31, Feb. 2007.
[15] M. R. Beychok, Fundamentals of Stack Gas Dispersion, 4th ed. NewportBeach, CA: M. R. Beychok, 2005.
[16] N. P. Gao and J. L. Niu, “Modeling particle dispersion and deposition in
indoor environments,” Atmos. Environ., vol. 41, no. 18, pp. 3862–3876,
2007.
[17] E. Canepa, “An overview about the study of downwash effects on dispersion of airborne pollutants,” Environ. Modelling Softw., vol. 19, no. 12,
pp. 1077–1087, 2004.
[18] A. K. Palit and D. Popovic, Computational Intelligence in Time Series
Forecasting: Theory and Engineering Applications. New York: SpringerVerlag, 2005.
ISEIS
Journal of Environmental Informatics 13(2) 75-85 (2009)
Journal of
Environmental
Informatics
www.iseis.org/jei
A Web-Based Collaborative System for Remote Monitoring and Analysis of Livestock
Farm Odours
R. Liu, L. L. Pan, and S. X. Yang*
School of Engineering, University of Guelph, Guelph, Ontario N1G 2W1, Canada
Received 15 October 2008; revised 12 March 2009; accepted 1 April 2009; published online 10 June 2009
ABSTRACT. Monitoring and analysis of livestock farm environments require collection and management of large amount of data
from distributed farms. There is an increasing demand for collaboration among livestock producers, environment agencies and
governments. This paper presents a collaborative system for monitoring and analyzing livestock farm odours remotely, and for
enhancing the collaboration among users. This system utilizes a web-based portal application as the infrastructure for running
distributed applications. Livestock farm odour information is stored in central servers. Distributed users can access the data remotely,
submit odour data for analysis, receive analysis results through the Internet, exchange information, and discuss odour related topics on
public forums. This collaborative system provides a collaborative, robust, and user-friendly environment for distributed users to
efficiently manage and process the data records, share the analysis results and other information.
Keywords: collaborative web-based application, livestock farm odour, remote monitoring and analysis
1. Introduction
Odour emissions from livestock facilities have caused serious complaints from rural residents. The Ontario governments
have worked on setting up regulations and policies to limit
odorous air emissions from livestock farms. Environment agencies and researchers have made significant efforts to investigate and resolve the livestock farm odour problems (Janes et
al., 2004; Pan et al., 2007; Pan and Yang, 2007a, 2009).
In order to efficiently resolve the livestock farm odour
problem, there is an increasing demand for collaboration among livestock producers, consulting agencies and governments
(federal, provincial and local). Through collaboration, they can
share and make better use of their professional skills and expertise to serve the odour control problem. However, these users have different backgrounds, are responsible for different
tasks based on their roles, and work at different locations. It is
difficult to bring them together to achieve prompt and convenient information exchange and communications. High speed
Internet provides a way for remote communications and data
sharing. However, traditional HTML-based content only provides single public-facing web site. It is enough for small organizations to advertise their services or products, but it cannot satisfy the collaboration requirements of such a large-scaled service, management, industrial and research community.
*
Corresponding author. Tel.: +1 519 8244120; fax: +1 519 8360227.
E-mail address: syang@uoguelph.ca (S. X. Yang).
ISSN: 1726-2135 print/1684-8799 online
© 2009 ISEIS All rights reserved. doi:10.3808/jei.200900142
By using the traditional HTML-based web technologies, it requires hundreds or even thousands web sites for all the users
and activities in this odour monitoring and analysis task. It is
expensive and time consuming to keep up with the need to create and manage those web sites.
Due to the rapid development of platform-independent and
central processing technologies, web-based applications are
emerging to provide collaboration among distributed users. In
such a web-based application, the essential software and database are located on a central server and are accessible over networks. Therefore, users can upload and process their odour data through Internet web browser. Many web-based applications
have been developed in various areas, such as education (Chang et al., 2003; Jackson, 2003; Wang et al., 2004), information technology (Yu et al., 2003; Taguchi and Tokuda, 2005),
urban planning (Counsell, 2004; Babar and Gorton, 2004), and
environment monitoring (Zeng et al., 2007; Athanasiadia and
Mitkas, 2007; Carswell et al., 2008). Yu et al. (2003) developed a web-based application for 3D visualization and collaboration to enable production, dissemination and use of 3D imagery and geospatial information on a hierarchical level. Counsell (2004) developed a web-based application for collaboration on managing and maintaining large area urban modeling.
Taguchi and Tokuda (2005) designed a web application generator to generate data-intensive web applications by using diagrams that represent data-flow relationships among web components. Maglogiannis et al. (2006) presented a collaborative
web-based application for medical diagnosis over the Internet
via peer-to-peer distribution of electronic health records. Yu
and Cheng (2007) developed a web-based decision support system for slopeland hazard warning, which combines with GIS
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technology and can provide real-time monitoring of rainfall
via Internet Explorer. Despite broad applications of web collaboration technologies, none of them were developed for collaboration and cooperation among livestock producers and environment organizations. Zeng et al. (2007) proposed a web-based system for managing the information of urban water resource social renewability. An artificial neural network was
developed for analysis of the information and the evaluation
results were published via the Internet. Carswell et al. (2008)
introduced a web-based mobile system for monitoring fish species at risk. The fish data is stored in a central database and is
delivered to GPS-enabled hand-held devices so that professionals can access the data remotely and make decision immediately.
To date, however, the information exchange among livestock farmers, environment agencies and governments on resolving odour issues is still quite limited. The objective of this
study is to develop a web-based collaborative framework to assist the collaborations in resolving the livestock farm odour
problems. Collaboration, in dictionary, is defined as a process
that multiple people/organizations work together to achieve an
intersection of common goals (Oxford, 1989). Collaborative
software in information technology is an application designed
for people participated in a common task to reach their goals.
The proposed approach provides communication and data-sharing capability among distributed users, and thus enhances collaboration and cooperation among users with different backgrounds for more efficient odour management and decisionmaking. All the data collection, processing and analysis tools
are developed in a web-based application, and can be accessed simultaneously by multiple users. Users can communicate
with each other and share information from distributed locations. This proposed collaborative framework is built on Microsoft Framework 2.0 and Windows SharePoint system. Visual Studio 2005 is chosen as the development environment,
and Microsoft SQL server 2005 is used as the data repository.
2. The Proposed Approach
The proposed collaborative framework should provide essential collaboration capabilities for effective information sharing, data processing and management. This framework is developed for collecting, monitoring, processing, and analyzing
livestock farm odour data. This solution not only provides robust data management and data integration for the odour research community, but also brings added values to the experimental data. However, the development is complex. The following aspects are considered:
(1) Multiple user collaboration: In general, the collaborative framework should be able to serve multiple purposes, such
as user collaboration, data processing, and information sharing. The odour data that generated by a client acquisition system and data analysis results are shared by governments and
authorized agencies. Farmers can select environment agencies
to analyze their odour data, and grant necessary permissions
to chosen agencies to access their data. Farmers also have access to the analysis results and feedbacks from agencies. Ag-
76
encies process and analyze the data, generate result reports,
provide suggestions on odour control practice. Governments
review the odour data and analysis results, provide guidance
for farm operations, and enforce environmental regulations.
Governments also consult with agencies to revise or improve
environmental laws or regulations. Users can interact with each
other either by correspondence via private emails, or by discussions threads on the public discussion board. The proposed
collaborative framework provides a disciplined approach to
facilitate the collaboration among livestock farmers, agencies
and governments.
(2) Central data management and security requirement:
Generally, odour data are collected from separate farms and
stored in distributed computers. In order to analyze those distributed data and generate results, the data records need to be
sent into a central data server, where the data analysis system
can process numerous users’ requests simultaneously. However, in this way security issue is arisen, due to the sensitive and
private nature of the odour information. In order to solve the
security problem, separate data record libraries are established
on the central data server, and are assigned to different users.
Security settings and policies are specified for sensitive information. The collaborative framework authorizes different access privilege to different users, based on their roles and responsibilities. Only authorized users have access to the content
in the record library via role-based permission control; unauthorized users have no access to private contents unless the
owner of the record library grants them appropriate access privilege. For example, livestock farmers do not have access to
the data of other farmers unless they are granted access. This
feature enhances the data security.
(3) User-friendly interface: The user interface of the system provides a user-friendly and consistent environment for
users with different backgrounds and expertise. User interface
in computer science is a place for human-computer interaction.
It provides input and output means for users to access the computer system. Users can control the system by keystrokes with
the computer keyboard, or by movements and clicking of the
computer mouse; the system presents the users with graphical,
textural and auditory information. Considering most potential
users of the application are not computer experts, it is very important that the system should provide a user-friendly interface, adopting all necessary functionality that the users need
in operations and collaborations. This collaborative framework
is a platform-independent web-based application, which can
be run on different platforms, such as Windows, UNIX, and
Linux. Users do not need to have the same platform on their
computers. All users can access the real-time interactive collaborative system through a web browser. Moreover, all data acquisition applications are installed on the central server and
have online connection to the central server, so that individual
users do not have to process the data on their own computers.
These considerations make the proposed web-based application very easy to use, and also provide great convenience for
the system administrators to maintain and update the system,
since they only need to modify or tune applications on the central server. In addition to the access by desktop personal com-
R. Liu et al. / Journal of Environmental Informatics 13(2) 75-85 (2009)
puters, the interface also provides support for mobile device
access.
(4) Transparent workflow: The collaborative framework
is a workflow-driven application, supporting users to submit
different tasks for processing data records. Workflows represent the operational information of a work procedure, such as
how processes are structured, formalized, enforced, audited,
and how they are synchronized. With the robust, customizable
workflow framework, users can tell whether a process request
has been created, reviewed, processed, or updated, and also
receive the analyzed information. The central workflow-enabled processing functionality helps users to make effective decisions by providing all useful information in one central place.
3. Design of the Web Based Collaborative
Framework
The collaborative system is built on top of the wireless
sensor networks that are deployed on livestock farms to collect odour data. The architecture of the collaborative system is
shown in Figure 1. The system consists of three main components: client applications for wireless sensor networks; a central database server; and central application servers.
3.1. Client Applications for Wireless Sensor Networks
The wireless sensor networks are at the lowest level of
the collaborative system. They are deployed on livestock farms
to collect odour data. Each wireless sensor network has many
sensor nodes, where each node is an electronic nose (e-nose)
that is built of gas sensors and environmental sensors to measure odorous compounds and temperature and relative humidity. Sensor signals are delivered to and stored on a local computer for future processing. The detailed information about the
wireless sensor networks can be found in Pan and Yang (2009),
and the development of electronic noses is described in details
in Pan and Yang (2007a). The wireless sensor networks provide the collaborative system with remote odour monitoring
ability.
Client applications are developed to assist the data collection for the wireless sensor network. A client application includes three main components: a data acquisition module to
acquire the sensor signals transmitted to the computer via the
wireless network; a client-side database that stores the data,
and a data transfer web service that exchanges information between the client-side computer and the central database server.
3.1.1. Data Acquisition Module
The sensor signals collected on a farm are transmitted
through the wireless network to a gateway, which receives
and collects the digital signals. Then the data acquisition system in the computer interfaces with the getaway and gathers
the digital signals into the client-side database.
3.1.2. Client-Side Database
Database plays an important role as data storage, management, and exchange center. In this system, data structures
(records and files) are optimized to deal with very large amounts of data stored on a data storage server. A database based
on Microsoft SQL 2005 database Server 2005, is developed
for storing sensor signals and odour information. Besides database itself, there is also a data management system to store
and organize or pre-process the data. Database management
system is a complex set of software modules that help the organization, storage and retrieval of data in the database. It can
connect to the database, load data acquisition console, and display sensor signals dynamically.
The interface of data display system is shown in Figure 2.
It provides both on-line and off-line signal display options. Several display settings can be selected by users, such as different update interval, number of visible points and data range,
etc. This would be very helpful for routine operation and various studies.
Figure 1. Architecture of the collaborative web-based
application.
3.1.3. Data Transfer Web Service
The client application uses a web service to interface with central servers. The web service feeds data to or consumes
data from central servers. It provides remote accessibility for
the client application to integrate with the central servers.
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R. Liu et al. / Journal of Environmental Informatics 13(2) 75-85 (2009)
Figure 2. The dynamic monitoring console.
3.2. Central Database Server
The odour data collected by the wireless sensor networks
from farm terminals are shared among decision makers, such
as governments and environment agencies. The central database server provides data/information storage for the collaborative system. The proposed web-based application uses the
central SQL Server 2005 as central data repository. SQL Server 2005 provides enhanced safety and security, high performance and scalability, as well as a wide range of database service tools. The central database stores the data, and provides
the data to data processing services. Figure 3 shows the data
structure of the central database. The central database stores
different kinds of data sets and information such as real-time
odour data that are collected online by the gas sensors on livestock farms, historical data records, farm information and user
profiles, analysis results, and search index. With all these integrated database capabilities, the collaborative system can also
help the researchers to improve data analysis and research efficiency.
3.3. Central Application Servers
The architecture of the central application server is shown
in Figure 4. It includes three application servers: a collaboration server, an application server, and an active directory server. The collaboration server provides a platform for data and
information integration, sharing and exchange, as well as collaboration and cooperation ability among distributed end users.
The application server is used to host data processing and analysis services to provide core data processing, reporting and
content crawling functionality. All data processing features are
shared across application domains through web services. The
active directory server stores user information, and provides
user authentication and authorization functionality.
Livestock farmers, environment agencies, and government
users are the designed users of the application server. The
application server consists of two basic components: a data
processing tier, and a user collaboration interface. The collaboration interface is the front-end of the application server that
78
Figure 3. Data structure of the central database server.
Figure 4. Architecture of the central application server.
contains all the user interfaces and directly interacts with users. Data processing service communicates with the central
database server, and the user collaboration interface consumes
the data processing results. All the data processing and analysis modules are located in the data processing module. Data
processing service receives requests from users through the in-
R. Liu et al. / Journal of Environmental Informatics 13(2) 75-85 (2009)
Figure 5. Interface of the data processing services.
terface, looks up information in the database, analyzes the data, processes the requests, generates analysis result reports, and
then sends back result reports to presentation layer where the
result reports are displayed.
ledge. The detailed information about the odour dispersion analysis modules, risk factor analysis module, and decision making module can be found in Pan and Yang (2009). The report
generation module generates the analysis result reports and
provides report accessibility and management.
(1) Data Analysis and Reporting Module
The data analysis and reporting module provides a userfriendly environment and supports data processing operations.
This module combines data analysis services and result reporting services. Figure 5 illustrates the framework of the data analysis service that is built as an inter-operable middle-tier of
the collaborative system. The collaborative system utilizes the
data analysis services to identify rules and patterns in the data,
to inform the users where has odour and how strong the odour
is, to give suggestion on odour control practice, and to predict
the resulted odour strength if any odour control means are applied. The data analysis module has many sub-modules: odour
strength analysis module calculates odour concentration/intensity using artificial intelligent techniques; odour dispersion analysis module calculates odour dispersion plume using odour
dispersion models based on odour emission rates, topological
and meteorological information; risk factor analysis module
identifies significant risk factors that cause odour problems;
and decision making module assists users to make odour control decisions based on analysis results and human expert know-
Neural network methods are used in the data analysis module to explore the odour data pattern. Neural network algorithms mainly address the classification task. They can find nonlinear relationships among the input values of the predictable
attribute, by creating an internal classification and regression
models that are iteratively improved, based on the actual value (Haykin, 1999). Existing research demonstrates that neural
networks can be successfully applied to odor data analysis
(Janes et al., 2004, 2005; Pan et al., 2006; Pan and Yang,
2007b).
The odour model used in the data analysis module has
three layers as shown in Figure 6: the input layer, the hidden
layer and the output layer. The input nodes form the first layer
of the neural network, where each input node is mapped to one
sensor data input channel such as temperature, humidity or odour sensor data. The second layer is the hidden layer that has
hidden nodes, which receives input data from the input layer.
The output layer presents the predictable attributes. In this case,
our neural network has one single output attribute that is the
odour strength.
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R. Liu et al. / Journal of Environmental Informatics 13(2) 75-85 (2009)
The neural network is a feed-forward network. Before using the neural network to provide prediction on odour strength
value, it needs known data to tune the network, which is called the training process that is to find the best set of weights
for the neural network. The weights of the network are randomly initialized at the beginning. In every training iteration,
the network compares the actual value of the training case with the predicted value, and calculates the error at the outputs.
Then the weights of the network are adjusted using the error
back-ward propagation learning technique. The reason to choose neural network algorithms as the data analysis algorithm
is that neural networks can handle complex as well as large
amounts of training data efficiently.
f1
x1
Gas Sensor 1
x2
Gas Sensor 2
f2
f3
E  e2 / 2  (t  y ) 2 / 2
(5)
where t is the actual (desired) value of the output neuron for
the processed the training sample, and e is the error at the output neuron.
The weight adjusting equations are given as:
wijnew  wijold   f j (1  f j ) xi e hj ,
(6)
2
v new
 v old
j
j   (1  y ) f j e
(7)
y
x3
Gas Sensor 3
x4
nodes, and M is the number of hidden neurons. This neural
network updates the weights after a sample case is analyzed.
In order to minimize the training error, the least mean square
error E is used as the error function:
where η is the learning rate, and ejh is the error of j-th hidden
neuron given by:
Odour Strength
fj
Gas Sensor 4
x5
e hj  v j e
Temperature
x6
(8)
Humidity
fM
Input Layer
Hidden Layer
Output Layer
Figure 6. The structure of the neural network.
The processing unit in the neural network is a single neuron node. Each node processes the combination of weighted
sum of the input values into a single output value. The logistic
function g(a) is selected as the activation function for the hidden nodes, and the tahn function φ(a) is selected as the activation function for the output node, which are defined as:
g (a) 
ea  e a
ea  e a
(1)
 (a) 
1
1  e a
(2)
Therefore, the outputs of hidden neurons and the output
neuron are given as:
N
f j  g ( wij xi ) j = 1, 2, …, M
(3)
i 0
M
y   ( v j f j )
(4)
j 0
where y is the output of the neural network; fj is the output of
the j-th hidden node; wij is the weight between i-th input node
and the j-th hidden node; vj is the weight between the the j-th
hidden node and the output neuron, N is the number of input
80
The parameters of the neural network such as learning rate η and number of hidden nodes are determined by trial and
error. The value of η is usually set to a large value at the beginning stage of training and gradually decreases to avoid error oscillations. It allows the neural network to find out the optimum solution quickly.
The neural network model performs predictions for the
data analysis module and compares with the known data. By
tuning the model, the user is able to explore, query and check
the information that the analysis module provides. Once the
data analysis module is set up and well tuned, the system can
produce the reasonable analyzed results. This module uses the
integration services between data analysis and reporting in SQL
server 2005, providing predictive results in a flexible and scalable manner to the end users. Figure 7 shows an odour strength analysis interface using an artificial neural network approach. Figure 8 shows a comparison between the prediction
results and the actual measured values of odour strength,
where the predicted values match the actual ones very well.
The actual measured odour strengths range from 0 to 60 TD
(dilution to threshold), and the mean square error of the predicted odour strengths against the measured ones is 1.46. It demonstrates that the data analysis module produces accurate
predictions. Figure 9 shows an example of the result report. A
textual or graphic report is generated to show the patterns about the prediction. After the data analysis process is complete
and an accurate result is created, the application generates userfriendly reports. The report will be displayed on the browser
screen or be deployed to Microsoft Office. This reporting module allows the data analysis results to be easily delivered to
users in a simple format.
R. Liu et al. / Journal of Environmental Informatics 13(2) 75-85 (2009)
Figure 7. Odour strength analysis module.
Figure 8. A comparison between the predicted value and actual value of odour strength.
(2) Workflow Management
The sensor data processing requires the workflow management to manage the analyzing processes, record submission,
processing data, routing, etc. This central application server
implements a comprehensive workflow, by using Microsoft
Workflow Foundation (WFF) technology. This technology has
support for complex workflow functionality, rich user interface, long running processes and advanced multiple level custom actions. The collaborative system has the seamless supports for WFF. The solution is integrated with the user interactive functionality and is built up on it. The workflow integration feature can be used to provide custom workflow for
any custom business process/application. Users in research
community can collaborate on data records and manage data
processing tasks through particular web forms. Workflows help
users to adhere to consistent project processes.
(3) Personalization and Targeting
Personalization and targeting component is used to target
user specific information. This component primarily reads information from existing data sources, and retrieves changes to
user data in the parent data sources (i.e. Active Directory), and
then synchronizes with the system user profile database. The
system user profile database is used for managing user profiles. All the publishing interactions with user profiles, audiences and alerts are based on the proposed personalization and
targeting component. Personalization and content targeting
component are exposed as a functional web service, which
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R. Liu et al. / Journal of Environmental Informatics 13(2) 75-85 (2009)
Figure 9. Odour data analysis result report.
allows users to control the audience to whom the content is
targeted. Collaborative web site administrators (not necessarily
domain or local administrators) can easily create these audiences. These audiences are logical groups that satisfy some filtering rules (AD attribute values or custom attributes) in user profile database. It should be noted that personalization and targeting are not the same as security and restricting access to
content. Results management interface is an example of a content-specific functionality that controls the targeting of result
reporting to specified audience. The personalization and targeting feature provides functionality such as display a result
item (i.e. reports, announcements and etc) to authorized users
only. The different audience can be identified by looking up
the user roles.
(4) Security
All the applications and application servers within the collaborative system architecturally support Windows integrated
authentication. Users are authenticated against their credentials
and access rights as defined in the Active Directory server. All
the applications make use of these user credentials and access
rights to restrict specific content from being viewed by unauthorized users. Generally, the roles of a user (i.e. the Active
Directory group that they belong to) determine the access permissions that he has on the collaborative web site.
(5) User Interface
Users interact with the collaborative framework system
82
directly through a series of user interfaces. These interfaces
are within the web-based interface application. The interface
application provides front-end pages to the data record libraries, analysis result reports, discussion forums, task management, workflow management and other components. The homepage is the first displayed page when users start the application. Users log into the system from this page to get the access to other functional pages such as data record page and result reporting page. The security model of the sub-pages is inherited from the homepage so that it only needs one time login when browsing through the web pages. Figure 10 shows
the homepage of the system. Users can navigate through the
web site easily and find the information and content they need.
All task status can be monitored on the task status page.
The task status control shows the task status of submitted requests, and the service status control shows the status of pending service requests of the current user. Status bar shows the
completed percentage of the whole process. This page provides a way to track and close monitoring process status and
participants.
The public discussion forum page is a place for the users
to discuss topics of their interest and to interact with each other.
Livestock farmers can consult with agencies or governments
to improve their odour control operations; agencies can post
their comments and concerns, rural residents can deliver their
complaints about odour issue. The interface of the discussion
forum is shown in Figure 11.
R. Liu et al. / Journal of Environmental Informatics 13(2) 75-85 (2009)
Figure 10. Homepage of the collaborative portal.
4. Conclusions
A collaborative portal framework is developed for acquisition, transmission, integration, management, processing and
analysis of livestock odour data; and for providing a platform
to enhance collaboration in livestock farm odour management.
The user-friendly environment makes the application easy to
use. The centralized data storage and analysis tools and models enable easy maintenance and improvement for the application. The authentication and authorization features provide
enhanced security for the application. The web-based collaborative system is an efficient and convenient platform for collaboration among livestock producers, environment agencies,
and governments. It not only brings a data integration solution
to the research community but also increases the productivity
of all the participants and brings significant added values to
the data. The collaborative portal system eliminates the limitation on the livestock farm odour emission research by the Internet-accessible collaborative portal web site. The system delivers a collaborative interface to let users easily manage the
process and share the data. The interface links researchers together based on the same interests about the experimental data, topics or projects. Researchers are no longer required to be
in the same place working on the same project. The collaborative portal framework provides essential collaboration capabilities and dynamic, accurate, and robust environment information that promotes faster and more insightful decision making
on livestock farm odour control problems.
Acknowledgments. This work is supported by Ontario Pork and National Science Engineering Research Council (NSERC).
83
R. Liu et al. / Journal of Environmental Informatics 13(2) 75-85 (2009)
Figure 11. Public discussion forum.
References
Athanasiadia, I.N., and Mitkas, P.A. (2007). Knowledge discovery
for operational decision support in air quality management, J. Env.
Inform., 9(2), 100-107, doi:10.3808/jei.200700091.
Babar, M.A., and Gorton, I. (2004). Supporting architecture evaluation process with collaborative applications, Proceedings of
INMIC 2004 8th International Multitopic Conference, pp. 440446.
Carswell, J.D., Gardiner, K., Bertolotto, M., Rizzini, A., and Mandrak,
N. (2008). A web-based and mobile environmental management
system, J. Env. Inform., 12(1), 9-20, doi:10.3808/jei.200800119.
Chang, K.E., Sung, Y.T., Wang, K.Y., and Dai, C.Y. (2003). Web-Soc:
a socratic-dialectic-based collaborative tutoring system on the
Workd Wide Web, IEEE Transactions on Education, 46(1), 69-78,
doi:10.1109/TE.2002.808276.
Cheng, C.T., Chau, K.W., Gang, L., and Li, X.Y. (2005). Collaborative web application for flood control system of reservoirs,
Web Technologies Research and Development-APWeb, 3399, 364374, doi:10.1007/b106936.
Counsell, J. (2004). “Under construction”: the potential of a webbased application in managing and maintaining large area urban
modeling. Cooperative Design, Visualization, and Engineering,
3190, 148-155.
Haykin, S. (1999). Neural Networks: a Comprehensive Foundation,
Prentice Hall, NJ, USA.
Jackson, M.E. (2003). Portals: a strategic new direction to improve
access to information resources, VINE, 33(1), 33-36, doi:10.1108
/03055720310698443.
Janes, K.R., Yang, S.X., and Hacker, R.R. (2004). Single component
modelling of pig farm odour with statistical methods and neural
84
networks, Biosystems Eng., 88(3), 271-279, doi:10.1016/j.biosy
stemseng.2004.04.004.
Janes, K.R., Yang, S.X., and Hacker, R.R. (2005). Pork farm odour
modelling using multiple component multiple-factor analysis and
neural networks, Applied Soft Computing, 6(1), 53-61, doi:10.1016
/j.asoc.2004.10.006.
Maglogiannis, I., Delakouridis, C., and Kazatzopoulos, L. (2006).
Enabling collaborative medical diagnosis over the Internet via
peer-to-peer distribution of electronic health records, J.Medicine
Sys., 30, 107-116, doi:10.1007/s10916-005-7984-1.
Oxford University Press (1989). Oxford English Dictionary, 2nd Edition, Oxford University Press, UK.
Pan, L., Yang, S.X., Otten, L., and Hacker, R.R. (2006). Component
and factor analysis of pork farm odour using structural learning
with forgetting method, Biosystems Eng., 94(1), 85-93.
Pan, L., Yang, S.X., and DeBruyn, J. (2007). Factor analysis of downwind odours from livestock farms, Biosystems Eng., 96(3), 387397, doi:10.1016/j.biosystemseng.2006.10.017.
Pan, L., and Yang, S.X. (2007a). A new intelligent electronic nose
system for measuring and analyzing livestock and poultry farm odours, Environ. Monitoring Assess., 135(1-3), 399-408, doi:10.
1007/s10661-007-9659-5.
Pan, L., and Yang, S.X. (2007b). Analyzing livestock farm odour
using an adaptive neuro-fuzzy approach, Biosystems Eng., 97(3),
387-393, doi:10.1007/11816171.
Pan, L., and Yang, S.X. (2009). An electronic nose network system
for online monitoring livestock farm odors, IEEE Transactions on
Mechatronics, in press.
Taguchi, M., and Tokuda, T. (2005). Automatic generation of clientserver collaborative web applications from diagrams, Web Eng.,
3597, 609-611, doi:10.1007/11531371_82.
Wang, J.W., Zhang, J.M., Wei, X.P., and Wang, J. (2004). Collabora-
R. Liu et al. / Journal of Environmental Informatics 13(2) 75-85 (2009)
tive evaluation of engineering design concepts on the Web,
Proceedings of the 8th International Conference on Computer
Supported Cooperative Work in Design, pp. 224-229.
Yu, F.C., Chen, C.Y., Lin, S.C., Lin, Y.C, Wu, S.Y., and Cheung, K.W.
(2007). A web-based decision support system for slopeland hazard
warning, Environ. Monit. Assess., 127(1-3), 419-428.
Yu, H., Zhang, J., Wang, L., and Barksdale, J. (2003). A secure web
application: 3D visualization and collaboration. Proceeding of
Communications, Internet, and Information Technology, 408.
Zeng, W.H., Zhang, Y.J., Liu, J.L., Yang, Z.F. (2007). Web-Based
Geographic Information System for UWRSR Evaluations, J. Env.
Inform., 10(2), 75-81, doi:10.3808/jei.200700102.
85
Risk Analysis, Vol. 28, No. 2, 2008
DOI: 10.1111/j.1539-6924.2008.01029.x
A Neural Network-Based Method for Risk Factor Analysis
of West Nile Virus
Leilei Pan,1 Lixu Qin,1 Simon X. Yang,1 and Jiangping Shuai2 ∗
There is a lack of knowledge about which risk factors are more important in West Nile virus
(WNV) transmission and risk magnitude. A better understanding of the risk factors is of great
help in developing effective new technologies and appropriate prevention strategies for WNV
infection. A contribution analysis of all risk factors in WNV infection would identify those
major risk factors. Based on the identified major risk factors, measures to control WNV proliferation could be directed toward those significant risk factors, thus improving the effectiveness
and efficiency in developing WNV control and prevention strategies. Neural networks have
many generally accepted advantages over conventional analytical techniques, for instance,
ability to automatically learn the relationship between the inputs and outputs from training
data, powerful generalization ability, and capability of handling nonlinear interactions. In this
article, a neural network model was developed for analysis of risk factors in WNV infection.
To reveal the relative contribution of the input variables, the neural network was trained
using an algorithm called structural learning with forgetting. During the learning, weak neural connections are forced to fade away while a skeletal network with strong connections
emerges. The significant risk factors can be identified by analyzing this skeletal network. The
proposed approach is tested with the dead bird surveillance data in Ontario, Canada. The
results demonstrate the effectiveness of the proposed approach.
KEY WORDS: Neural network; risk factor analysis; structural learning with forgetting; West Nile virus
1. INTRODUCTION
cases of WNV human infection were reported in Ontario, Quebec, Manitoba, Saskatchewan, and Alberta
in 2005.(7–9,15)
Although WNV has been shown to infect many
other species of animals, such as horses, cats, bats,
and squirrels, it is believed that birds serve as primary reservoirs for WNV.(3,12,13) By feeding on infected birds, mosquitoes become infected, and then
further spread the virus to humans and other animals
through biting them.(1,3,6) Birds are considered as one
of the most important factors in the WNV transmission cycle.
Many researchers have used environmental factors to investigate WNV disease patterns. These environmental factors include metrology, topography, and
soil characteristics.(14–19) The WNV infections are also
influenced by climatic factors, such as temperature.
West Nile virus (WNV) is a mosquito-transmitted
disease that was first found in 1937 in the West
Nile district of Uganda and arrived in New York
City in 1999.(1,4,5) In Canada, the virus was first detected in 2001 in a wild bird in Ontario. In 2002,
six provinces detected the virus. In 2003, the virus
spread to seven provinces. In 2004 and 2005, however, only five provinces detected the virus. Confirmed
School of Engineering, University of Guelph, Guelph, Ontario,
N1G 2W1, Canada.
2 Public Health Agency of Canada, Guelph, Ontario, N1H 8J1,
Canada.
∗ Address correspondence to Simon X. Yang, School of Engineering, University of Guelph, Guelph, Ontario NIG 2WI,
Canada; tel: +1-519-824-4120; fax; +1-519-836-0227; syang@
uoguelph.ca.
1
487
C 2008 Society for Risk Analysis
0272-4332/08/0100-0487$22.00/1 488
Researchers have indicated that summer temperature
would influence the transmission of WNV. It is believed that the higher the summer temperature is, the
more quickly the mosquito develops from egg to adult
stage.(2,16–19) However, some researchers suggested
that winter temperature might also influence the infection of WNV, as in a mild winter, more mosquito
eggs can survive, resulting in a higher mosquito population early in the next year, and thus spread more
WNV in the coming spring and summer.(2,7,8) Further
investigation and modeling are needed to determine
the role of temperature, and identify if winter temperature patterns may be associated with WNV infection.
However, the WNV transmission system is very complicated, as many species of animals and humans interact with each other.(1,3,5,6) The role of temperature in
WNV disease spread and breakout is not adequately
understood. Understanding the nature of this interaction would help develop new technologies for WNV
prediction, prevention, and control.
The influence analysis of temperature-related risk
factors to WNV has been attracting much attention.
Some researchers used degree-days as the major temperature indicator,(16,19) while many other researchers
prefer to use mean monthly temperature as the temperature indicator.(17,18) The mathematical methods
employed include logistic regression analysis,(18,19)
principal component analysis,(17) and discriminate
analysis.(16) However, these statistical methods for
WNV infection have several weaknesses, for example, they require prior knowledge about the statistical
distributions in the data sources, and they are unable
to deal with vague and noisy data.(20)
Soft computing techniques, such as artificial neural networks (ANNs), have been successfully applied
to analysis of complex nonlinear systems where conventional statistical methods have difficulties. ANN
approaches are particularly suited for exploratory
analysis because of their capability to learn the association pattern among the inputs and outputs without any prior knowledge of the system being studied.(10) In addition, they can quickly adapt to changing situations, and are robust to inconsistent, incomplete, and noisy data. Therefore, ANN methods have
great potential in interpreting the nonlinear interactions between infection of WNV virus and the environment.(10,21) Bayesian networks have been used
in identifying input variables that have most significant influence to the outputs.(22,23,26) However, these
Bayesian approaches require an exhaustive search
over the model space for all the possible network
structures. Therefore, the time requirement for the
learning procedure increases super-exponentially as
Pan et al.
a function of the number of variables.(27,28) Thus they
are not a good choice for analyzing the model structures with large number of variables.
Fuzzy-logic-based methods can incorporate human experience and expert knowledge. However,
they do not have learning ability; thus fuzzy systems
have difficulty in analyzing complex systems without
prior knowledge of the system being investigated. To
overcome the limitations of fuzzy systems, adaptivenetwork-based fuzzy inference systems (ANFIS) that
incorporate neuroadaptive learning techniques into
the fuzzy logic models have been used for factor analysis, but their high computational cost makes them
unsuitable for large data sets with complex data structure.(24,25)
In order to investigate the influence of seasonal
temperature on WNV infection, in this article, a
neural-network-based approach that is able to extract
analytical information from WNV analysis models
was used. The main innovative aspect of this study
relies on the capability of finding out the significant
risk factors that contribute most to WNV infection
rates. A few comparative studies are also presented
in this article to show the effectiveness of the proposed
algorithm.
2. METHODS
A neural network model using a structural learning with forgetting (SLF) algorithm is proposed to
model and analyze the NWV infection. The contribution of monthly temperature to infections of
WNV is ranked in this study. Since the proposed
SLF algorithm is a modified typical back-propagation
learning (BPL) with forced forgetting of the connection weights, the basic idea of an SLF algorithm
is introduced after a brief description of the typical
BPL.
2.1. Data Sets and the Proposed WNV
Analysis Model
2.1.1. Data Sets
The data used for this study were collected
through the national WNV dead bird surveillance in
Ontario from 2002 to 2005. In each year, dead birds
were collected and tested in 33 health units within
the province. The dead birds tested include crows,
jays, magpies, and ravens. In the data, information
from each record includes the name of the health unit
where the dead birds were collected, the dates when
the infected dead birds were collected, the number of
A Neural Network-Based Method for Risk Factor Analysis of West Nile Virus
dead birds collected (in total of all species), and the
number of confirmed infected birds (in a total of all
species) from the health unit. The climatic information from 2002 to 2005 was collected from the website
of Environment Canada (http://www.ec.gc.ca). The location information, such as longitude, latitude, and elevation of each health unit, was also collected from
the spatial data of the Environment Canada website.
There are a total of 132 records in the data available for developing and validating the model. Approximately 75% were randomly selected for training the neural network model and the remaining 25%
of the total data sets were used for validating the
trained neural network structure. Data sets were randomly selected by using a MATLAB built-in function
“randperm(N),” which can generate a series of integral numbers ranging from 1 to N (where N is the
number of data records) in random order. These randomly ordered numbers were used as the index of
data records, so that the data records were also randomly ordered. Then the first 75% of data were used
as training data, and the remaining 25% were used as
testing data. This is equivalent to randomly selecting
75% of the data to be training data, since the data
records were randomly ordered.
2.1.2. Statistical Analysis
The infection number versus month is shown in
Fig. 1. Infected dead birds were found from May to
October during the study period (2002–2005), and the
maximum numbers of infected dead birds were found
in July for 2002 and 2003, and in August for 2004 and
2005.
Fig. 1. Number of infected dead birds collected in Ontario versus
month.
489
Fig. 2. Infection rate of dead birds collected in Ontario versus
month.
Merely analyzing the absolute number of positive
dead birds is not enough. The total number of dead
birds tested should also be considered because this
total number varied greatly among health units. This
study uses infection rate, which is the number of positive dead birds over the total number of dead birds
tested. The infection rate versus month is shown in
Fig. 2. The highest infection rates occur in August in
2002 and 2005, while in 2003 and 2004, highest infection rates happen in September.
The monthly mean temperature of Ontario is
shown in Fig. 3. July and August yield the highest mean
temperature of the year, while the lowest temperature
appears in January and the second lowest temperature
appears in February in most of years. By comparing
Figs. 1 and 2 with Fig. 3, we can assume that there is
Fig. 3. Mean monthly temperature of Ontario.
490
Pan et al.
Fig. 4. Performance of the statistical
multiple regression method.
a strong relationship between monthly mean temperature and infection rate of WNV. Generally, the infection number/rate increases as the temperature increases. However, both figures yield highly nonlinear
characteristics. Therefore, a WNV risk model using
conventional statistical algorithms may not be able to
accurately identify the risks. For instance, a statistical
multivariate regression method was performed to analyze the data. Standard diagnostics, such as normality
of observations and regression residuals, and independence of observations were performed to ensure the
goodness of fit using the method. A low-prediction
accuracy (r = 0.446, p< 0.05) was obtained on WNV
infection rate. The predicted results using multivariate regression are shown in Fig. 4.
2.1.3. Neural Network Architecture
A multifactor neural network model for WNV infection rate is proposed in this article. Since the data
were collected from May to October in each year, the
weather conditions during the data collection periods are considered, that is, this model considers the
monthly average temperatures of November and December of the previous year, and monthly average
temperatures from January to October of the current
year in which the dead bird surveillance data were
collected. The location (longitude, latitude, and elevation) of the health unit where the data were collected
is also considered as a factor that would influence the
infection rate of dead birds, and included in the neural
network model, because the location (longitude, latitude, and elevation) of the health unit may represent
predominant weather conditions such as temperature,
humidity, and other factors.
The proposed neural network model has an input layer, one hidden layer, and an output layer. The
model has N input neurons, where the inputs can be
risk factors such as monthly temperatures and topology information. The output is the WNV infection
rate. The neural network started with a fully connected network that had Q hidden units. Connections reflect the relation between input and output
attributes.(11)
The outputs at the hidden layer and the neural
network output are given as
⎧
N
⎪
⎪
⎪
hk = g
vki xi , i = 0, 1, . . . , N; k = 1, 2, . . . Q
⎪
⎪
⎨
i=0
(1)
Q
⎪
⎪
⎪
⎪
wkhk , k = 0, 1, . . . , Q,
⎪
⎩y = f
k=0
A Neural Network-Based Method for Risk Factor Analysis of West Nile Virus
where y is the output of the proposed network; hk
is the output of the kth hidden neuron; x i is the ith
input variable; v ki and wk are the connection weights
from the inputs to hidden layer and the hidden layer
to output layer, respectively; N is the number of input
neurons; and Q is the number of neurons in the hidden
layer. A nonlinear sigmoid function is used for the
activation function of the hidden layer, and a linear
function is used for the output layer, which are defined
as
⎧
x
−x
⎨g(x) = e − e
e x + e−x .
(2)
⎩
f (x) = x
The proposed neural network was trained using a
SLF method. A comparison to a typical BPL was also
conducted.
2.2. Typical BPL
The typical BPL is based on minimizing squared
errors,(10) whose error function E is defined as
1 2
1
e = (t − y)2 ,
(3)
2
2
where y is the predicted WNV infection rate, while t
is the collected WNV infection rate, and e is the error
at the network output.
In a typical BPL, the weight changes w B
k and
B
v ki are obtained by differentiating Equation (3) with
respect to the connection weight wk and v ki ,(10)
⎧
∂E
B
⎪
⎪
i = 0, 1, . . . , N;
⎪vki = −η
⎪
∂vki
⎪
⎨
= η 1 − h2k xi ekh ,
k = 1, 2, . . . , Q
(4)
⎪
⎪
⎪
∂E
⎪
⎪
⎩wkB = −η
= ηhke, k = 0, 1, . . . , Q
∂wk
E=
where η is the learning rate; and ekh is the error at the
kth hidden neuron, which is given as
ekh = wke,
k = 1, 2, . . . , Q.
(5)
2.3. SLF
A neural network learning algorithm called
structural learning with forgetting was proposed by
Ishikawa.(11) SLF is a modified typical BPL with
forced forgetting of the connection weights. Unlike
the typical BPL, in SLF a penalty term is added to the
criterion function to force the unnecessary connection weights decreasing to small values. The criterion
function in the learning with forgetting is given as
Ef = E + ε
491
Q
N |vki | +
i=0 k=1
Q
|wk| ,
(6)
k=0
where the first term E on the right side is the squared
error that is the same as the one in Equation (3) in
the typical BPL, the second term is the penalty item,
and ε is the forgetting rate.
Similar to the typical BPL, the weight change,
wk , and v ki are obtained by differentiating Equation (4) with respect to the connection weights w k and
v ki ,
⎧
∂E f
⎪
vki = −η
i = 0, 1, . . . , N;
⎪
⎪
⎪
∂vki
⎪
⎪
⎪
⎨ = v B − ηεsgn(vki ), k = 1, 2, . . . , Q
ki
(7)
⎪
∂E f
⎪
⎪
wk = −η
k = 0, 1, . . . , N
⎪
⎪
∂wk
⎪
⎪
⎩
= wkB − ηεsgn(wk),
B
where v B
ki and w k are the weight changes due
to the mean square error, which are the same as
those in Equation (4); and sgn(·) is the sign function.
The second terms on the right-hand side of Equation (7) show that the unnecessary connection weights
decrease continuously during the learning process.
Therefore, redundant connections can be eliminated
while only important connections remain in the resulting network.
3. RESULTS
In this section, the results using the proposed SLF
algorithm were analyzed. In order to demonstrate the
effectiveness of the proposed approach, the skeletal
neural network structure resulting from the SLF was
compared with the same neural network structure
trained using the typical BPL.
3.1. Analysis of Risk Factors
The WNV neural network model was trained by
the SLF algorithm. In neural network methodology,
parameters such as the learning rate, the forgetting
rate, and the number of hidden units are usually determined by trial and error.(10) Neural networks with different combinations of learning rate, forgetting rate,
and number of hidden neurons were tried, the training error and generalization error for each one were
computed, and the optimal parameter combination
with both small training error (to avoid underfitting)
and small generalization error (to avoid overfitting)
was selected. A computing program to implement the
492
Pan et al.
Fig. 5. Resulting structure of WNV model after trained using the typical BPL.
proposed algorithm is developed using MATLAB on
a computer with 2.1GHz CPU and 1GB RAM. In this
study, the neural network model has 15 input variables, and there are around 100 data sets for training the network. Both the SLF and typical BPL algorithms were run many times, and the learning rate η
= 0.01, the forgetting rate ε = 0.5, and the number of
hidden unit Q = 8 were chosen. Therefore, there were
137 adjustable weights and bias involved in training
the neural network.
The structure of the WNV neural network model
trained using the typical BPL is shown in Fig. 5, while
the resulted structure of the WNV model trained us-
Fig. 6. Resulted structure of WNV model after trained using SLF.
ing the SLF method is shown in Fig. 6. The solid lines
represent connection weights with absolute value
greater than 1, and the dashed lines represent connection weights with absolute value smaller than 1.
The strengths of the connections are represented by
the line width. When the connection weights are too
small (e.g., under a certain threshold value), the contribution of the input neurons to the output neurons is
negligible. To clearly compare the resulting structures,
a threshold θ = 0.1 was selected, for example, for a
connection with absolute value less than 0.1, there was
no line drawn. The resulting skeletal neural network
structure is shown in Fig. 7. Investigation of the con-
A Neural Network-Based Method for Risk Factor Analysis of West Nile Virus
493
Fig. 7. Resulting structure of WNV model after trained using SLF and after removing connections less than threshold θ = 0.01.
nection weights enables the extraction of significant
risk factors. Comparing the results from Fig. 7 with
those from Fig. 5 indicated that 13 factors, latitude,
longitude, elevation, monthly temperatures of previous December, current January to August and October, are relatively significant to the infection rate of
WNV. The remaining two factors, monthly temperatures of previous November and current September,
are relatively less important than these 13 factors.
These results show that winter temperature (represented by mean monthly temperatures of December
to February) does have an influence on WNV infection rate, while temperature in early spring and summer also affects the WNV infection rate. It is not surprising that the location factors (latitude, longitude,
and elevation) of the place where the measurement
was taken may be able to represent factors such as
terrain conditions, weather conditions, and other environmental conditions. It should be noted that risk
factors of WNV are not limited to the factors studied
in this research. Although this neural network model
only considers location factors and 12 monthly temperatures, other factors that are potentially associated
with WNV prevalence, such as ecological factors, for
instance, ecoregion and food source of birds, can also
be included in the neural network model once the data
are available.
3.2. Performance Analysis
By using the neural network approach, the accuracy of the prediction in testing can reach r = 0.597
(p < 0.05), while that using statistical nonlinear analysis is r = 0.446. The prediction accuracy of testing data
using the neural network approach is much higher
than using the statistical nonlinear regression method.
In order to analyze the performance of the SLF algorithm, the proposed neural network was trained by
both the typical BPL and the SLF algorithms. The performance of using the typical BPL is shown in Fig. 8,
and that of using the SLF is shown in Fig. 9. The performance is evaluated based on the prediction accuracy
of the test sets. The Pearson correlation coefficient
between the predicted infection rates and the experimental infection rates can reach r = 0.630 (p < 0.05)
using BPL, while it is r = 0.597 (p < 0.05) by using
SLF. This result shows that the neural network model
that was trained by BPL provided more accurate prediction than the SLF. This is because the SLF minimizes the total criterion Ef in Equation (6) instead of
the quadratic criterion E in Equation (3). However,
the difference in prediction accuracy is small, and by
using SLF, unnecessary connections fade away and a
skeletal network emerges, which enables identification of significant risk factors in the WNV infection
rate.
3.3. Validity of the Approach
One additional step was taken in order to ensure the validity of our proposed approach. One additional neural network model (NN2) was developed
by leaving out those identified unimportant factors.
If the performance of NN2 is degraded significantly
in comparison to that of NN1, which means those
494
Pan et al.
Fig. 8. Performance of the typical BPL.
Fig. 9. Performance of SLF.
A Neural Network-Based Method for Risk Factor Analysis of West Nile Virus
Table I. Performance of NN1a and NN2b
Type of Neural Network
NN1 (using multiple regression)
NN1 (trained using BPL)
NN1 (trained using SLF)
NN2 (trained using BPL)
495
4. CONCLUSION AND DISCUSSION
cr
d MSE(10−2 )
0.446
0.630
0.597
0.610
2.30
1.52
1.54
1.58
= NN model with inputs of longitude, latitude, elevation,
and monthly temperatures from previous November to current
October.
b NN2 = NN model only with the input neurons of longitude,
latitude, elevation, and average monthly temperatures of previous
December, current January to August, and October.
c r = correlation coefficient of predicted value and measured value.
d MSE = mean squared error.
a NN1
eliminated factors are actually important, then the
proposed methods are not effective in identifying significant risk factors. The new model NN2 has the same
structure as that of NN1, but with only 13 input neurons of latitude, longitude, elevation, temperatures of
previous December, current January to August, and
October. NN2 was trained using the typical BPL. In
order to ensure that the results could be compared
fairly as the attributes change, all the parameters, such
as learning rate η and hidden units Q, remained the
same as those of NN1, for example, η = 0.01, Q =
8. The performance of NN2 is shown in Table I. For
NN2, the correlation coefficient of predicted infection rate and tested infection rate was r = 0.610 (p
< 0.05), which was close to the results obtained from
NN1 trained with BPL. The prediction accuracy of
NN2 (with only 13 inputs: latitude, longitude, elevation, monthly mean temperatures of previous December, current January to August, and October) did not
decrease significantly from that of NN1 (with all 15 inputs: latitude, longitude, elevation, monthly average
temperatures of November, and December of the previous year, and monthly average temperatures from
January to October of the current year), which indicated that the rest of the factors in NN1 other than
these 13 inputs contributed little to the WNV infection rate. This result demonstrated that the proposed
SLF approach was effective in recognizing significant
risk factors associated with the WNV infection rate.
It should be noted that the proposed method is just
a data analysis method; the analysis result obtained
is largely influenced by the quality of data, factors
considered, and how the data were collected and processed. The validation of the result is subject to future
investigation by conducting specified surveillance and
studies on the WNV infection problem.
There is a need to gain more knowledge on the
risk factors and their influence on WNV activities and
transmission. This knowledge may help local public
health professionals to develop proper WNV risk control strategies and determine a risk control timeframe.
Scientists believe that seasonal temperature plays a
significant role in the emergence and resurgence of
WNV, but how and by how much seasonal temperature influences the WNV infection is still not clear.
In this article, a multifactor neural network model
was proposed based on dead bird surveillance data.
A neural network model with SLF was proposed to
model the WNV infection and analyze the associated
risk factors. After learning by the SLF algorithm, the
relative contributions of risk factors to the WNV infection rates were discovered. Testing results demonstrated the effectiveness of the proposed approach.
The finding that winter temperature also plays a
role in WNV activities deserves further discussion. It
is not clear that whether WNV is endemic or is an introduced epidemic, whether winter temperature influences WNV activities, there may be a possibility that
WNV might be endemic in Canada, although it may
also be interpreted that winter temperature may influence the habitat of migratory birds and mosquitoes.
It is suggested that more specific surveillance and
studies should be performed to investigate this
problem.
There are efforts to develop spatial risk assessment tools in Canada.(15) This study may also be used
as a spatial risk assessment method. By using a geographic information system, the SLF results can be
used to generate regional WNV risk index. This spatial risk index may be used by local public health professionals to target control and prevention areas.
ACKNOWLEDGMENTS
The authors thank the Public Health Agency of
Canada (PHAC) for partially funding this research
and providing WNV dead bird surveillance data and
spatial data of the study area.
REFERENCES
1. Hubalek, Z., & Halouzka, J. (1999). West Nile fever—A
reemerging mosquito-borne viral disease in Europe. Emerging Infectious Diseases, 5(5), 643–650.
2. Epstein, P. R. (2001). West Nile virus and the climate. Journal
of Urban Health, 78(2), 367–371.
3. Komar, N., Langevin, S., Hinten, S., Nemeth, N., Edwards,
E., Hettler, D., Davis, B., Bowen, R., & Bunning, M. (2003).
496
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
Pan et al.
Experimental infection of North American birds with the
New York 1999 strain of West Nile virus. Emerging Infectious
Diseases, 9(3), 311–322.
Novello, A. C. (2000). West Nile Virus in New York State: The
1999 outbreak and response plan for 2000. Viral Immunology,
13(4), 463–467.
Nash, D., Mostashari, F., Fine, A., Miller, J., O’Leary, D., Murray, K., Huang, A., Rosenberg, A., Greenberg, A., Sherman,
M., Wong, S., & Layton, M. (2001). Outbreak of West Nile
virus infection, New York City area, 1999. New England Journal of Medicine, 344(24), 1807–1814.
Turell, M. J., O’Guinn, M. L., Dohm, D. J., & Jones, J. W.
(2001). Vector competence of North American mosquitoes
(Diptera: Culicidae) for West Nile virus. Journal of Medical
Entomology, 38(2), 130–234.
Turell, M. J., Sardelis, M. R., & Dohm, D. J. (2001). Potential
North American vectors of West Nile virus. Annals New York
Academy of Sciences, 951, 317–324.
Nosal, B., & Pellizzari, R. (2003). West Nile virus. Practice
Public Health, 168(11), 1443–1444.
Flynn, F. (2005). West Nile virus 2005 Ontario update: Case
definitions, laboratory diagnostic test criteria. Ontario Medical Review, July–August.
Haykin, S. (1999). Neural Networks: A Comprehensive Foundation. NewYork: Prentice Hall.
Ishikawa, M. (1996). Structural learning with forgetting. Neural Networks, 9(3), 509–521.
Rogers, D. J., Myers, M. F., Tucker, C. J., Smith, P. F.,
White, D. J., Backenson, P. B., Eidson, M., Kramer, L. D.,
Bakker, B., & Hay, S. I. (2002). Predicting the distribution
of West Nile fever in North America using satellite sensor
data. Photogrammetric Engineering and Remote Sensing, 68,
112–114.
Jozan, M., Evans, R., McLean, R., Hall, R., Tangredi, B., Reed,
L., & Scott, J. (2003). Detection of West Nile virus infection in
birds in the United States by blocking ELISA and immunohistochemistry. Vector Borne and Zoonotic Disease, 3(3),
99–110.
Ruiz, M. O., Tedesco, C., McTighe, T. J., Austin, C., & Kitron,
U. (2004). Environmental and social determinants of human
risk during a West Nile virus outbreak in the greater Chicago
area, 2002. International Journal of Health Geographics, 3(1),
8–18.
Tachiiri, K., Klinkenberg, B., Mak, S., & Kazmi, J. (2006).
Predicting outbreaks: A spatial risk assessment of West Nile
virus in British Columbia. International Journal of Health Geographics, 5, 21–41.
Wontroba, P. (2003). Weather conditions and West Nile
virus. Bulletin of the American Meteorological Society, 84,
560–561.
17. Mongoh, M. N., Khaitsa, M. L., & Dyer, N. W. (2007). Environmental and ecological determinants of West Nile virus
occurrence in horses in North Dakota, 2002. Epidemiological
Infection, 135(1), 57–66.
18. Gibbs, S. E. J., Wimberly, M. C., Madden, M., Masour, J., &
Yabsley, M. J. (2006). Factors affecting the geographic distribution of West Nile virus in Georgia, USA: 2002–2004. VectorBorne and Zoonotic Diseases, 6(1), 73–82.
19. Kunkel, K. E., Novak, R., Lampman, R., & Gu, W. (2006).
Modeling the impact of variable climatic factors on the
crossover of Culex Restuans and Culex Pipiens (Diptera Culicidae) vectors of West Nile virus in Illinois. American Journal
of Tropical Medicine and Hygiene, 74, 168–172.
20. Benediktsson, J. A., Swain, P. H., & Ersoy, O. K. (1990). Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE Transactions
on Geoscience and Remote Sensing, 28(4), 540–552.
21. Williams, B. G., Rogers, D. J., Statton, G., Ripley, B., & Booth,
T. H. (1994). Statistical modeling of georeferenced data: Mapping tests distributions in Zimbabwe using climate and vegetation data. In B. D. Perry & J. W. Hansen (Eds.), Modelling
Vector-Borned and Other Parasitic Diseases (pp. 267–280).
Nairobi, Kenya: International Laboratory for Research on
Animal Diseases.
22. Djan-Sampson, P. O., & Sahin, F. (2004). Structural learning
of Bayesian networks from complete data using the scatter
search documents. Proceedings of 2004 IEEE International
Conference on Systems, Man and Cybernetics, 4, 3619–3624.
23. Langseth, H., & Nielsen, T. D. (2003). Fusion of domain
knowledge with data for structural learning in object oriented
domains. Journal of Machine Learning Research, 4, 339–368.
24. Liao, J., Er, M. J., & Lin, J. (2000). Application of a system
for the automatic generation of fuzzy neural networks. Engineering Applications of Artificial Intelligence, 13(3), 293–302.
25. Kelemen, A., Kozma, R., & Liang, Y. (2002). Neuro-fuzzy
classification for the job assignment problem. In Proceedings
of the IEEE International Joint Conference on Neural Networks (pp. 1831–1837). Heidelberg: Springer-Verlag.
26. Orme-Zavaleta, J., Jorgensen, J., D’ Ambrosio, B., Altendorf,
E., & Rossignol, P. A. (2006). Discovering spatio-temporal
models of the spread of West Nile virus. Risk Analysis, 26(2),
413–422.
27. Friedman, N., Nachman, I., & Peir, D. (1999). Learning
Bayesian network structure from massive datasets: The sparse
candidate algorithm. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (pp. 206–215).
Stockholm, Sweden.
28. Huang, Z. (2004). Bayesian computation for multilevel models: Some new methods with applications. Ph.D Thesis,
Columbia University. New York.
ARTICLE IN PRESS
BIOSYSTEMS ENGINEERING
97 (2007) 387– 393
Available at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/issn/15375110
Research Paper: SE—Structures and Environment
Analysing livestock farm odour using an adaptive
neuro-fuzzy approach
Leilei Pan, Simon X. Yang
School of Engineering, University of Guelph, Guelph, Ont., Canada, N1G 2W1
article info
In livestock farming, odour measurement and reduction are necessary for a cleaner
environment, lower health risks to humans, and higher quality of livestock production.
Article history:
There have been many studies on modelling of livestock farm odour by analysing the
Received 20 July 2006
chemical components in odorous air. It is suggested that the component analysis
Accepted 14 March 2007
approaches should be extended to factors such as temperature, relative humidity, and
Available online 2 July 2007
airflow speed.
In this paper, a neuro-fuzzy-based method for analysing odour generation factors to the
perception of livestock farm odour was proposed. The proposed approach incorporates
neuro-adaptive learning techniques into fuzzy logic method. Rather than choosing the
parameters associated with a given membership function by trail and error, these
parameters could be tuned automatically in a systematic manner so as to adjust the
membership functions of the input/output variables for optimal system performance. A
multi-factor livestock farm odour model was developed, and both numeric factors and
linguistic factors were considered. The proposed approach was tested with a livestock farm
odour database. The results demonstrated the effectiveness of the proposed approach in
comparison to a typical neural network model.
& 2007 IAgrE. Published by Elsevier Ltd. All rights reserved.
1.
Introduction
In the livestock industry, measurement and control of
offensive odours from livestock production facilities are very
important because of the requirement of environmental
conditions. Prediction of odour intensity is difficult due to
the complexity of the livestock farm odour system and
limited knowledge of the odours themselves (Zhang et al.,
2002a, 2002b). Many factors influence odour generation.
Research has indicated that environmental conditions such
as temperature and humidity (Powers & Bastyr, 2004; Schiffman et al., 1995), type of facilities (Janes et al., 2004), and farm
management practises affect odour generation (Zhang et al.,
2002a, 2002b). However, how and by how much a factor
influences livestock farm odour remains unclear (Schiffman
et al., 1995, 2001; Lunn & Van De Vyver, 1977; Powers & Bastyr,
2004; Pan et al., 2006). Because of a poor understanding of the
odour problem, existing odour control technologies are not
efficient.
Because of limitations in statistical modelling techniques,
most previous studies on modelling livestock farm odour
have concentrated on the modelling of the odour components
and have not considered other potential odour generation
factors, such as outdoor temperature, airflow speed, or type of
facilities. In statistical methods, odour system models are not
capable of incorporating non-numeric or subjective human
expert knowledge, such as environmental conditions, type of
livestock facilities. In addition, they are not well suited for
dealing with the vague and imprecise information that is
available in odour measurement and control.
Corresponding author.
E-mail address: syang@uoguelph.ca (S.X. Yang).
1537-5110/$ - see front matter & 2007 IAgrE. Published by Elsevier Ltd. All rights reserved.
doi:10.1016/j.biosystemseng.2007.03.012
ARTICLE IN PRESS
388
BIOSYSTEMS ENGINEERING
Nomenclature
Aij
p
ai
jth linguistic label of the ith input
aij, bij,
E
fp
i, j, p
ith Sugeno-type parameter of consequence of pth
rule
cij parameters of the bell-shaped membership
function Aij
mean-squared error
consequential output of the pth rule
index numbers
The rapid development of soft computing techniques, such
as neural networks (NNs) and fuzzy logic technologies, allows
the incorporation of those non-numeric contributing factors
and expert knowledge into livestock farm odour models.
Artificial neural networks (ANNs) have been used to predict
odour strength (Janes et al., 2004; Pan et al., 2006). It is widely
accepted that there are several advantages in using ANN
algorithms over the conventional techniques, including the
generalisation ability, the capability to handle nonlinear
interactions mathematically, as well as the ability to automatically learn the relationship that exits between the inputs
and outputs without any previous knowledge of the system
being studied. However, ANNs are not capable of properly
incorporating non-numeric or subjective human expert
knowledge (Haykin, 1999; Zhang et al., 2002a, 2002b; Sohn
et al., 2003).
Fuzzy logic utilises fuzzy sets and fuzzy reasoning to
provide a more human-like way of thinking in the computer
programming (Zadeh & Bellman, 1977). Fuzzy logic allows
intermediate values to be defined between conventional
evaluations such as true/false and high/low (Zadeh, 1965). It
can handle imprecise, ambiguous, and vague information,
and can incorporate human experience and expert knowledge. Fuzzy logic has been used in a wide range of
applications such as control systems, project management,
non-linear system models, expert systems, forecasting,
system identification and analysis (Zimmermann, 2001).
However, fuzzy logic does not have learning ability, thus it
is difficult to analyse complex systems without prior and
accurate knowledge on the system being analysed (Jang, 1993;
Castañeda-Miranda et al., 2006).
To overcome the limitations of NNs and fuzzy systems, in
this paper, an adaptive network-based fuzzy inference
system (ANFIS) approach is proposed to predict livestock
farm odour intensity. Two ANFIS models for livestock farm
odours were developed; both numeric data and non-numeric
data were incorporated in the odour models. Various comparative studies were also presented to demonstrate the
effectiveness of the proposed method.
2.
Methods
In this section, an adaptive neuro-fuzzy inference system
approach is proposed for analysing livestock farm odour. Two
ANFIS models are developed after a brief description of the
97 (2007) 387 – 393
m
n
Oqp
R
wp
wp
xi
mAij
number of membership functions for each input
number of inputs
pth output of the qth layer, q ¼ 1, 2, 3, 4, 5
Pearson correlation coefficient
firing strength of the pth rule
ratio of the pth rule’s firing strength to the sum of
all rules’ firing strength
ith input
membership function value of Aij
livestock farm odour data used for the analysis. Studies of the
ANFIS models are made in comparison to a multi-layer
feedforward NN model.
2.1.
Adaptive neuro-fuzzy inference systems
In common fuzzy logic systems, membership functions are
chosen by trail and error and are fixed once selected. Also,
rule structure is essentially predetermined by the user’s
interpretation of the characteristics of the variables in the
model. However, in practice, these models may not perform
satisfactorily due to limited knowledge of the system, and the
improper selection of membership parameters (Jang et al.,
1997).
The proposed ANFIS approach incorporates an adaptive
neural learning technique into the fuzzy logic model. The
parameters are tuned automatically during the learning so
the membership functions can well represent the non-linear
system being studied with an optimal performance.
The proposed ANFIS uses a Sugeno-type fuzzy relation
(Jang, 1993; Jang et al., 1997; Karray & De Silva, 2005), where
the fuzzy rules take the following form:
Rule 1 : IF x1 is A11 and x2 is A21... and xn is An1 ;
THEN f 1 ¼ a10 þ a11 x1 þ a21 x2 þ ::: þ a1n xn
Rule 2 :
IF x1 is A11 and x2 is A22... and xn is An1 ;
THEN f 1 ¼ a20 þ a21 x1 þ a22 x2 þ ::: þ a2n xn
..
.
Rule p :
IF x1 is A1m and x2 is A1m... and xn is A1m ;
p
p
p
p
THEN f p ¼ a0 þ a1 x1 þ a1 x2 þ ::: þ an xn ;
where xi, i ¼ 1; :::; n, is ith input linguistic variable in the
antecedent part of the rule; variable Aij is the jth linguistic
label (for instance, large, small, etc.) associated with xi.
Function fp is the consequent output of the rule, and
p
ai is the Sugeno parameter. A general ANFIS model is
shown in Fig. 1, which has n inputs and each input
variable has m membership functions, resulting in a total of
mn rules.
Layer 1: Fuzzification layer
In this layer, the crisp inputs xi, i ¼ 1,y,n, are fuzzified
through mapping into membership functions of the linguistic
labels Aij, j ¼ 1,y,m. The membership functions can be any
appropriate parameterised functions. In this paper, all the
ARTICLE IN PRESS
BIOSYSTEMS ENGINEERING
Layer 1
A11 (x1)
x1
A111
A
A122
A
A1m (x1)
AA1m2
A21 (x2)
x2
Layer 2
B211
A
Π
Layer 4
Layer 3
w1
Π
389
97 (20 07) 38 7 – 393
N
N
w1
Σ
w2
Layer 5
w1 f1
Σ
Odour
intensity
w3
Π
N
Π
N
Σ
A222
B
A2m (x2)
AB2m
Anl (xn)
xn
Anm (xn)
Bn11
A
Bn22
A
Π
wmn−1
Σ
wmn
wmn
wmn f mn
N
ABnm2
Fig. 1 – The structure of an adaptive neuro-fuzzy inference system (ANFIS) consists of a five-layer feedforward network.
membership functions are bell-shaped functions described by
mAij ðxi Þ ¼
1
1 þ ðxi cij =aij Þ2bij
i ¼ 1; 2; . . . ; n;
;
j ¼ 1; 2; . . . ; m n,
where aij, bij and cij are the parameters, n is the number of
inputs, and m is the number of membership functions of each
input. The bell-shaped functions vary accordingly when these
parameters are changed, and thus exhibit various forms of
membership functions on Aij. The output O1p of each node i in
layer 1 is the membership functions of Aij
O1p ¼ mAij ðxi Þ;
i ¼ 1; 2; . . . ; n; j ¼ 1; 2; . . . ; m;
p ¼ 1; 2; . . . ; m n,
where variable O1p represents the degree to which the given xi
associates with Aij.
Layer 2: Rule operation layer
In this layer, each rule node multiplies the incoming signals
and sends the product out. The output of each node in layer 2
represents the firing strength of a rule, which is given as
O2p ¼ wp ¼
n
Y
i¼1
mAij ðxi Þ;
i
cedent. Other t-norm operators can also be used as the node
function in this layer.
ji ¼ 1; 2; . . . ; m;
Layer 3: Normalisation layer
In this layer, the firing strengths of the fuzzy rules are
normalised. The output of each node in this layer is the ratio
of the ith rule’s firing strength to the sum of all rules’ firing
strength, and is calculated based on
wp
O3p ¼ wp ¼ Pmn
;
p¼1 wp
p ¼ 1; 2; . . . ; mn .
Layer 4: Consequent layer
In this layer, the values of the consequent are multiplied by
normalised firing strengths according to
O4p ¼ wp f p ;
p ¼ 1; 2; . . . ; mn .
Layer 5: Aggregation layer
The overall output of this layer is computed as a summation of all incoming signals:
P
X
p wp f p
wp f p ¼ P
.
O5 ¼
p wp
p
p ¼ 1; 2; . . . ; mn ,
where j1, j2,y,jn is a combination of m membership functions
from the n input variables; thus there are a total of mn
combinations, resulting in mn nodes in this layer. Each node
in fact performs an AND operation within the rule ante-
2.2.
Data sets
The proposed ANFIS approach was tested using a livestock
database. Data were collected in the fields downwind from
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four poultry, five dairy and six swine facilities in 2004.
Outdoor temperature, wind speed, distances to the odour
source, and odour dilution-to-threshold (D/T) intensity levels
perceived by human assessors were recorded and at the same
time, an electronic nose developed by Yang and his research
team in The University of Guelph took measurement at each
site (Pan & Yang, 2007). The electronic nose had 14 gas sensors
that were selected for detecting the gas compounds such as
hydrogen sulphide in livestock farm odour. The sensor signals
from the electronic nose represent the concentrations of gas
compounds in the air. The detailed measuring procedure is
described in the paper by Pan et al. (2007). The data of
temperature, type of facility and concentration of hydrogen
sulphide were used in odour generation modelling in this
study. Factors such as wind speed and distance to odour
source are not considered as they mainly influence downwind transportation of odour emissions. By using a statistical
multi-variant linear regression method, temperature and
concentration of hydrogen sulphide, being independent
variables and odour intensity being a dependent variable, a
low accuracy (r ¼ 0:40, Po0:01) was obtained on prediction of
odour intensity. It is not surprising that low accuracy was
achieved using this method, as livestock farm odour systems
are highly non-linear in character. There were a total of 64
samples in the data available for the development and testing
of models. Approximately 80% of the data points were
randomly selected training the NN, and the remaining 20%
were used for testing.
2.3.
Livestock farm models
In this section, three models are developed based on the
database mentioned above. The details of the three models
Layer 1
Layer 2
97 (2007) 387 – 393
are introduced below, two using an ANFIS approach and the
third using a NN.
2.3.1.
Model 1: an ANFIS model with two inputs
To estimate the odour intensity, an ANFIS model (model 1)
was designed. The model has 2 inputs: temperature and
concentration of hydrogen sulphide. Researchers have indicated that the concentration of hydrogen sulphide can act
as an indicator for livestock farm odour strength (Lunn & Van
De Vyver, 1977; Williams, 1984). The concentration of hydrogen sulphide was represented by the magnitude of the output
signal of a sensor that was selected for detecting hydrogen
sulphide. All the inputs are numeric and have to be fuzzified
in the fuzzification layer. The output of model 1 is the odour
intensity. Three membership functions are associated with
each input, thus resulting in 9 IF–THEN rules in the model.
Model 1 was trained by a back-propagation (BP) algorithm
(Haykin, 1999).
2.3.2.
Model 2: an ANFIS model with three inputs
In order to test the ability of the proposed approach of
incorporating linguistic information, another ANFIS model
(model 2) was developed. The model has 3 inputs: temperature, type of facilities, and concentration of hydrogen
sulphide. The type of facilities can generally represent such
measures as type of animals, farm activity, odour emission
rates, and other physical measures that may influence the
odour intensity. The output is the odour intensity. Model 2
also has 3 membership functions for each input, resulting in
27 IF–THEN rules in total. Model 2 is illustrated in Fig. 2. The
model was trained by BP algorithm.
Layer 3
Layer 4
Π
N
Σ
Π
N
Σ
Layer 5
Dairy
Type of
facility
Swine
A2
Poultry
Odour
intensity
Cold
Temperature
Π
N
Π
N
Π
N
Σ
Medium
2
Hot
B
Low
B1
H 2S
Σ
Medium
B 2
High
B2
Fig. 2 – An ANFIS model with three inputs and 27 IF–THEN rules for analysing livestock farm odour.
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BIOSYSTEMS ENGINEERING
Input layer
Hidden layer
Output layer
Odour
Membership
intensity
Fig. 3 – Structure of a neural network model for analysing
livestock farm odour.
3.
5
10
0
25
50
0
(b)
35
5
25
10
15
20
25
Temperature ˚C
Medium
50
40
High
Medium
Low
1
0.8
0.6
0.4
0.2
0
30
75 100 125 150 175 200 225 250
Concentration of H2S, ppm
1 Cold
.
08
0.6
0.4
0.2
0
0
15
20
25
Temperature, ˚C
Medium
1 Low
0.8
0.6
0.4
0.2
0
(a)
Results and analysis
In order to demonstrate the effectiveness of the proposed
approach, the odour intensity prediction results from both
ANFIS models were compared with that from NN model. In
neuro-fuzzy technology, parameters such as learning rate and
training epochs, and the number of membership functions for
each input are usually determined by trial and error. In this
study, the sensitivity of these parameters were investigated
and showed following results: (1) With each increment of the
training epoch, the accuracy of odour intensity prediction
improves. After 100 epochs, the curve of mean-squared error
tends to stabilise. (2) The number of membership functions
for each input reflects the complexity of ANFIS for tuning
parameters. If the number is too small, it is not enough to
approximate the complex relationship of data; if the number
is too large, it produces the redundancy for the non-linear
relationship of data. Three membership functions for each
input variable results in highest accuracy of odour intensity
prediction in this study. (3) The higher the learning rate, the
faster the network converges. However, if the learning rate is
too large, the network oscillates. In this study, a learning rate
of 0.1 was selected by trial and error.
Hot
30
35
40
High
75 100 125 150 175 200 225 250
Concentration of H2S, ppm
Fig. 4 – Comparison of initial membership functions (MF) for
temperature and for H2S before the learning (a) with final
membership functions after learning (b) using model 1.
squared error E ¼ 1:23 105 ) compared to that from model 3.
The result demonstrated that the proposed ANFIS model can
incorporate human experience and knowledge, which allows
prior knowledge to be embedded in the model, and this
improve the results of learning. The comparison of initial
membership functions before the learning with final membership functions after learning using model 1 is shown in
Fig. 4. The membership functions were all changed during the
learning. The results show that the proposed ANFIS approach
is able to tune the membership functions so as to improve
performance index.
3.2.
3.1.
0
Model 3: a neural network model
In order to analyse the effectiveness of the proposed
approach, a feedforward NN model was designed with an
input layer, one hidden layer, and an output layer. A threelayer NN is theoretically able to learn any non-linear relationship at a desired level of precision with sufficient number of
hidden neurons in the network architecture and through
sufficient learning. Fig. 3 illustrates the proposed network
structure for livestock farm odour. The model has two input
neurons, where the inputs are temperature and concentration of hydrogen sulphide. The output is the odour intensity.
The NN was trained by a typical BP algorithm, and the results
were compared with the results from models 1 and 2.
Membership
2.3.3.
Hot
Medium
1 Cold
0.8
0.6
0.4
0.2
0
Temperature
H2S
391
97 (20 07) 38 7 – 393
Performance of model 2
Performance of model 1
The performances of models were evaluated based on the
accuracy of odour intensity prediction and mean-squared
error of the predicted odour intensities. By using the proposed
ANFIS approach, the odour intensity prediction accuracy was
significantly improved (correlation coefficient r ¼ 0:946; mean
By incorporating type of facility, the prediction accuracy was
improved (r ¼ 0.976; E ¼ 1.06 1015) compared to those of
models 1 and 3. The comparison between initial membership
functions before the learning and final membership functions
after learning using model 2 is shown in Fig. 5. The
parameters of type of facilities were changed during the
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Membership
1
0.5
0
1
0.5
0
0
Dairy
Swine
Poultry
Dairy
Swine
Facilitytype
Poultry
Cold
Medium
Hot
5
0
25
(a)
Membership
1
0.5
0
15
20
25
Temperature, ˚C
High
Swine
Poultry
Dairy
Swine
Facility type
Poultry
Hot
Medium
5
10
15
20
25
Temperature, ˚C
Low
0
40
Dairy
0
1
0.5
0
35
50 75 100 125 150 175 200 225 250
Concentration of H2S, ppm
Cold
1
0.5
0
30
Medium
Low
1
0.5
0
(b)
10
25
Medium
50
30
35
40
High
75 100 125 150 175 200 225 250
Concentration of H2S, ppm
Fig. 5 – Comparison of initial membership functions (MF) for
temperature and for H2S before the learning (a) with final
membership functions after learning (b) using model 2.
learning. This result shows that type of facility does affect
odour intensity. The proposed ANFIS approach is able to
interpret linguistic variables such as type of facility and cloud
cover conditions, and therefore increase the accuracy of
odour intensity prediction.
Factors such as temperature and type of facility were
actually measured or known exactly before the analysis.
Their data were not fuzzy. However, by fuzzifying these data
and applying ANFIS, the predicted odour intensities yield
higher accuracy than those by using classical or crisp
methods (multi-variant linear regression or NN). By using
fuzzy sets and fuzzy reasoning, the proposed method is able
to break stiffness and inflexibility that lie in accurate and
precise classical or crisp logic, and make fuzzy logic more
‘‘soft’’ and dexterous.
3.3.
Performance of model 3
In this study, BP learning approach was run many times, a
learning rate of 0.05 and 5 hidden units were selected by trail
97 (2007) 387 – 393
and error to obtain a minimum mean-squared error. The
correlation coefficient of the predicted odour intensity and
perceived odour intensity was 0.522 by using NN technique
alone. The mean-squared error was 0.516. The predicted
odour intensities were not as accurate as those of models
1 and 2.
4.
Conclusion
The prediction of odour strength in and around livestock
facilities has been investigated by many researchers over past
decades. Prediction of the odour strength is difficult due to
limited knowledge on livestock farm odour itself. It is known
that a livestock farm odour system is a highly non-linear
system that is influenced by many odourous components and
various factors such as environmental conditions and type of
facilities. Statistical linear regression approaches are therefore no longer suitable for modelling livestock farm odour.
The rapid development of soft computing techniques provides more opportunities for improving accuracy of livestock
farm odour modelling.
An adaptive neuro-fuzzy-based approach, which incorporates neuro-adaptive learning techniques into fuzzy logic
method, was proposed to analyse livestock farm odour. The
proposed approach can incorporate non-numeric data and
subjective human expert knowledge, which allows prior
knowledge to be included in the model. In addition, the
membership functions can be tuned during the learning
according to input/output data to optimise performance. This
feature is important as it can avoid inappropriate predetermination of membership functions, thus minimising errors
resulting from the limited knowledge of the livestock farm
odour system. A livestock farm database collected by our
research team has been used in this study. The results show
that the proposed approach is effective and provides a much
more accurate odour prediction in comparison with a typical
multi-layer feedforward neural network.
R E F E R E N C E S
Castañeda-Miranda R; Ventura-Ramos E; Del Rocio Peniche-Vera
R; Herrera-Ruiz G (2006). Fuzzy greenhouse climate control
system based on a field programmable gate array. Biosystems
Engineering, 94(2), 165–177
Haykin S (1999). Neural Networks: A Comprehensive Foundation.
Prentice-Hall, Upper Saddle River, NJ
Janes K R; Yang S X; Hacker R R (2004). Single component
modelling of pig farm odour with statistical methods and
neural networks. Biosystems Engineering, 88(3), 271–279
Jang J S R (1993). ANFIS: adaptive-network-based fuzzy inference
systems. IEEE Transactions on Systems, Man, and Cybernetics,
23(3), 665–685
Jang J S R; Sun C; Mizutani E (1997). Neuro-Fuzzy and Soft
Computing: A Computational Approach to Learning and
Machine Intelligence. Prentice-Hall, NJ, America
Karray F; De Silva C (2005). Soft Computing and Tools of
Intelligent Systems Design: theory and Applications. Addison-Wesley, NJ, America
Lunn F; Van De Vyver J (1977). Sampling and analysis of air in pig
houses. Agriculture and Environment, 3(2-3), 159–169
ARTICLE IN PRESS
BIOSYSTEMS ENGINEERING
Pan L; Yang S X; DeBruyn J (2007). Factor analysis of downwind
odours from livestock farms. Biosystems Engineering, 96(3),
387–397
Pan L; Yang S X; Otten L; Hacker R R (2006). Component and
factor analysis of pork farm odour using structural learning
with forgetting method. Biosystems Engineering, 94(1),
85–93
Pan L; Yang S X (2007). A new intelligent electronic nose for
measuring and analyzing livestock and poultry farm odour.
Environmental Monitoring and Assessment, in press
doi:10.1007/s10661-007-9659-5.
Powers W; Bastyr S (2004). Downwind air quality measurements
from poultry and livestock facilities. Animal Industry Report
2004, Iowa State University
Schiffman S S; Bennett J L; Raymer J H (2001). Quantification
of odours and odorants from livestock operations in
North Carolina. Agricultural and Forest Meteorology, 108,
213–240
Schiffman S S; Sattely-Miller E A; Suggs M S; Graham B G (1995).
The effect of environmental odours emanating from commercial livestock operations on the mood of nearby residents.
Brain Research Bulletin, 37(4), 369–375
97 (20 07) 38 7 – 393
393
Sohn J H; Smith R; Yoong E; Leis J; Galvin G (2003). Quantification
of odors from piggery effluent ponds using an electronic nose
and an artificial neural network. Biosystems Engineering,
86(4), 399–410
Williams A G (1984). Indicators of Piggery Slurry Odour Offensiveness. Agricultural Wastes, 10, 15–36
Zadeh L A (1965). Fuzzy sets. Information and Control, 8(3),
338–353
Zadeh L A; Bellman R E (1977). Logical and fuzzy logics. In:
Modern Uses of Multiple-Value Logics (Dunn M; Epstein G,
eds), pp 106–107. Reidel, Dordrecht
Zhang Q; Feddes J; Edeogu I; Nyachoti M; House J; Small S; Liu C;
Mann D; Clark G (2002a). Odour production, evaluation and
control. Final Report for Project mlmmi 02-hers-03. Manitoba
Livestock Manure Management Initiative Inc., Winnipeg,
Canada
Zhang Q; Yang S X; Mittal G S; Yi S J (2002b). Prediction of
performance indices and optimal parameters of rough rice
drying with neural networks. Biosystems Engineering, 83(3),
281–290
Zimmermann H (2001). Fuzzy Set Theory and its Applications.
Kluwer Academic Publishers, Norwell, MA, USA
Environ Monit Assess (2007) 135:399–408
DOI 10.1007/s10661-007-9659-5
A new intelligent electronic nose system for measuring
and analysing livestock and poultry farm odours
Leilei Pan · Simon X. Yang
Received: 11 September 2006 / Accepted: 12 February 2007 / Published online: 24 March 2007
© Springer Science + Business Media B.V. 2007
Abstract This paper introduces a new portable
intelligent electronic nose system developed especially for measuring and analysing livestock and
poultry farm odours. It can be used in both laboratory and field. The sensor array of the proposed electronic nose consists of 14 gas sensors,
a humidity sensor, and a temperature sensor. The
gas sensors were especially selected for the main
compounds from the livestock farm odours. An
expert system called “Odour Expert” was developed to support researchers’ and farmers’ decision
making on odour control strategies for livestock
and poultry operations. “Odour Expert” utilises
several advanced artificial intelligence technologies tailored to livestock and poultry farm odours.
It can provide more advanced odour analysis than
existing commercially available products. In addition, a rank of odour generation factors is provided, which refines the focus of odour control
research. Field experiments were conducted
downwind from the barns on 14 livestock and
Supported by Ontario Pork, Natural Sciences and
Engineering Research Council (NSERC), and Ontario
Ministry of Agriculture and Food (OMAF) of Canada.
L. Pan · S. X. Yang (B)
School of Engineering, University of Guelph,
Guelph, Ontario N1G 2W1, Canada
e-mail: syang@uoguelph.ca
poultry farms. Experimental results show that
the predicted odour strengths by the electronic
nose yield higher consistency in comparison to
the perceived odour intensity by human panel.
The “Odour Expert” is a useful tool for assisting
farmers’ odour management practises.
Keywords Livestock and poultry farm odours ·
Electronic nose · Expert system ·
Neural network · Artificial intelligence
Introduction
In livestock and poultry farming, odour measurement and reduction are necessary for a cleaner
environment, lower health risks to humans, and
higher quality of livestock and poultry production.
However, efforts to remedy odours from livestock
and poultry production facilities have been impeded by the lack of science-based approaches
to assess odour control technologies and the true
effects of odour (Zhang et al. 2001). Livestock
and poultry farm odours are complex mixtures of
hundreds of compounds that interact with each
other; and the intensity of an odour is not a simple sum of intensities of all odorous compounds
within it (O’Neill and Philips 1992; Schiffman et al.
2001; Zahn et al. 2001). Besides, researchers have
indicated that many environmental factors such as
temperature, air flow speed, and relative humidity
400
Environ Monit Assess (2007) 135:399–408
also influence odour generation (Powers and
Bastyr 2004; Zahn et al. 2001).
To date, the most commonly used “instrument”
for measuring odour is still human nose. Trained
human panels have been used in most studies
in measuring odour strength and offensiveness
(Zhang et al. 2001, 2002). However, human panels
are expensive to use and are difficult to overcome the analyst’s personal bias and adaptation
to odour in odour measurement and evaluation.
Therefore, unbiased automatic odour evaluation
technologies are desirable.
Electronic noses are promising considering its
low operational cost, consistent and objective
readings. Electronic nose is an instrument which
is composed of an array of electronic gas sensors
and a signal-processing system, and is capable of
recognising simple or complex odours (Gardner
and Bartlett 1999). The output of an electronic
nose can be either the identity of chemical components of the odour, or a prediction of any
characteristic properties of the odour such as intensity and nuisance. Electronic nose technology
has proved a useful tool in the food industry,
medicine, and environmental monitoring (Barlett
et al. 1997; Loutfi 2006; Shaw et al. 2000; Schaller
et al. 1998; Singh et al. 1996). However, little
work has been done to use electronic nose for
agricultural environment monitoring (Hobbs et al.
1995; Hudon et al. 2000; Osarenren 2002; Powers
and Bastyr 2004; Qu et al. 2001). No commercially
available electronic noses were developed in particular for detecting livestock and poultry odours.
Moreover, most electronic noses developed previously can only be used in the laboratory, in
which the environmental condition is apparently
different from that in field. Therefore, the accuracy and reliability of the measurement are
Table 1 Partial list of the
sensors used for the
developed electronic nose
inevitably reduced. It is highly desirable to develop a new electronic nose for measuring livestock and poultry farm odours which have great
commercial potential.
In this paper, a new intelligent portable electronic nose was developed especially for measuring livestock and poultry farm odours in both
laboratory and field. An expert system was developed for assisting farmer’s odour management
operations. In order to test the developed electronic nose and expert system, field experiments
were conducted downwind of 14 swine, dairy, and
poultry farms.
Methods
The design of the intelligent electronic nose system includes the selection of special sensors to
measure odour compounds in and around the
livestock farms, the selection of a chamber with
a pump, the design of the electric circuits to acquire sensor signals to a computer, and software to
process and analyse the measured sensor signals.
Hardware design
Based on previous extensive investigation on
existing odour measurement technologies and
equipment, as well as on various sensors available
for measuring odour compounds, 16 sensors were
selected for detecting the compounds of livestock
and poultry farm odours and environment conditions. The 16 sensors include a temperature sensor, a humidity sensor, and 14 gas sensors that can
measure almost all the major gas compounds in
livestock and poultry farm odours. Some of the
16 sensors are listed in Table 1. More detailed
Name of sensor
Compounds to be detected
Material
7NH
TGS825
TGS813
TG826
....
MAX6605MXK-T
HIH-3610-001
Ammonia
Hydrogen-sulfide
Butane
Amine compounds
...
Temperature
Humidity
MMOS
Hybrid sensor
Tin dioxide (SnO2 )
Tungsten oxide
...
Semi-conductor
Thermoset polymer
Environ Monit Assess (2007) 135:399–408
401
Fig. 1 The sensor array in the chamber
Fig. 3 “Odour Expert” software suite
information about the sensors can be found in
Tian et al. (2005).
Since the sensors for odour compounds have
specific requirements on the flow rate when they
operate properly, a pump with “zero-link” valves
is used to intake ambient air into a chamber, such
that the flow rate is controlled by the pump. The
chamber is like the nasal cavity of human. The
sensor array is put inside the chamber. A photo
of the sensor array is shown in Fig. 1.
The output signals of sensors range from 0.1 to
10 mV; therefore, several electrical circuits were
designed to amplify these small signals to the
range accepted by the A/D (analog/digital) board.
In addition, the regulation buffer was designed to
transform the high output impedance of sensors
into low impedance to eliminate the effects of the
A/D board on the sensors, so that the sensors can
accurately measure the compounds in livestock
and poultry odours. The A/D converter has 32
channels to transform the analogue sensor signals
into digital signals, which are then sent to the
computer through a standard RS-232 serial port.
Only the first 16 channels are used as there are
only 16 sensors in the current design, leaving some
channels open for additional sensors in future
development.
User Interface
Help&Explain
Systems
Knowledge
Acquisition
Knowledge
Base
Analysis System
Electrical
Circuit
Power
Data Base
Chamber
Pump
Laptop
Fig. 2 The intelligent electronic nose with connection to a
laptop computer
Data
Acquisition
Ambient Air
Fig. 4 Architecture of the proposed expert system for
analysing livestock farm odour
402
The entire electronic nose with a connection to
a laptop computer is shown in Fig. 2. Ambient air
enter the chamber from the inlet port of the chamber; the pump is connected to the outside vacuum
outlet port of the chamber, so it can evacuate air
from inside the chamber at a certain flow rate
(0–5 l/min); the sensor array located inside the
chamber, sends the measured signals to the signal
amplifier and regulation buffer; the A/D interface
converts the analogue signals from the buffer to
digital signals and sends them to a laptop computer; and lastly the laptop computer processes
the measured sensor signals and calculated the
odour intensity.
Software design
The signals generated by the electronic nose need
to be processed and analysed. Hence, software
was designed for acquiring, storing, processing,
and analysing the signals. In this study, basic data
acquisition software was extended into a more
comprehensive expert system for livestock farm
odour management. The electronic nose system is
eventually used to evaluate various odour control
strategies and then make recommendations on
the use of those odour control techniques. These
Fig. 5 Interface of the
main system
Environ Monit Assess (2007) 135:399–408
recommendations are useful for livestock producers to efficiently control the odour, and could offer
new research directions in effective odour control
technique development.
An expert system called “Odour Expert” was
developed for supporting odour management
(Fig. 3). This expert system is not only able to
conduct odour measurement from livestock farms,
but also can analyse the chemical components
and generation factors to determine which component or factor contributes most to the strength
of odour, and then gives expert advice on what
control means to take in order to efficiently reduce
odour; moreover, this expert system is able to
forecast the effectiveness of odour control efforts
before those control means are applied.
The “Odour Expert” consists of the following
components: a data acquisition system for gathering data from the electronic nose, a database
based on MySQL for storing data, a knowledge
base for storing expert knowledge, a data analysis system for odour strength prediction and factor analysis, and a user interface to explain and
interpret analysis results to users. The architecture
of the proposed system is illustrated in Fig. 4. The
“Odour Expert” is coded using C# programming
language and MATLAB.
Environ Monit Assess (2007) 135:399–408
403
Fig. 6 Data acquisition
system
The natural language interface provides a userfriendly environment for researchers and livestock farm operators. The interface guides the
analysis procedure, interprets result to the users,
and provides qualitative advice on which odour
control approaches to take in order to most efficiently reduce odour level. Users can provide
their specific requirements on odour control
Fig. 7 Data dynamic
display system
operations; a list of potential control approaches
is shown on the interface. Users can select the
proposed potential control approach that they
are interested in, then the system predicts the
controlled odour level resulted from the selected
control approach. Figure 5 is the main interface
of the system. Pressing any of the buttons loads
the corresponding subsystem. The main interface
404
Environ Monit Assess (2007) 135:399–408
Fig. 8 Data management
system
looks clear and concise, therefore, easy for the
users to use.
The data acquisition system (Fig. 6) has 16
channels to receive the digital signals that are
transformed from the analog sensor signals by
A/D converter. MySQL based database is used for
storing odour data. The database is used by a data
dynamic display system (Fig. 7) which can display
Fig. 9 Data analysis
system
sensor signals in real time, and a data management
system (Fig. 8) by which the users can organise or
pre-process odour data.
A knowledge base was designed to store
expert knowledge. Expert knowledge used in the
analysis includes type of livestock facilities, odour
management methods, feeding techniques, and
constraints of factors such as upper and lower
Environ Monit Assess (2007) 135:399–408
405
Sensor Responses
means should be applied. The controlled odour
strength is forecasted if any control approach is
implemented. The analysis procedure is explicit
and easy to use.
Sensor #1
Sensor #2
Sensor #3
Experimental implementation
Sensor #4
Odour
D/T Level
Sensor #16
Downwind experiments were conducted on 14
Ontario livestock and poultry farms. Odour data
were collected and the developed electronic nose
was tested.
Environmental
Conditions
Temperature
Perceived
Predicted
60
Wind Speed
50
40
30
20
10
0
0
2
4
6
Case
8
10
12
60
Data Points
r=0.932
Best Linear Fit
50
40
Predicted (D/ T)
bounds of wind speed, temperature, distance
to the odour source and humidity. However, in
current state of design, only factor constraints are
considered. In future improvement, fuzzy rules
will be incorporated in the expert knowledge
for incorporating non-numeric odour generation
factors.
The data analysis system (Fig. 9) is the core
of the proposed expert system. It includes odour
strength prediction, factor contribution analysis,
and control results prediction. The analysis system
predicts odour strength based on sensor signals
from the electronic nose and expert knowledge
information from knowledge base, using artificial
intelligent methods. A three layer neural network analysis model is employed for analysing
the signals from the electronic nose (Fig. 10). The
input layer has 19 input neurons, where the inputs
are the signals from the 16 sensors, as well as
outdoor temperature, wind speed and distance
to the odour source. The output is the odour
strength. A typical back-propagation neural network algorithm is applied to train the datasets.
Significance of odour generation factors is ranked
by using statistical factor analysis method. This
rank can help users to evaluate the importance
of those factors and then decide which control
Output (D/ T)
Distance
Fig. 10 A neural network model for analysis of the signals
from the electronic nose
30
20
10
0
0
10
20
30
40
50
60
Perceived (D/T)
Fig. 11 Odour intensity prediction results with electronic
nose signals
406
Environ Monit Assess (2007) 135:399–408
60
data collected by panelists and the electronic nose
were entered into database. Outdoor temperature, wind speed and distance to the odour source
were recorded as well at each site. The detailed
measurement procedure is described in (Pan et al.
2007).
Predicted
Perceived
Output (D/ T)
50
40
30
Experimental results
20
10
0
0
2
4
6
Case
8
10
12
60
Data Points
r=0.809
Best Linear Fit
Predicted (D/ T)
50
40
30
20
10
0
0
10
20
30
40
Perceived(D/T)
50
60
Fig. 12 Odour intensity prediction results without electronic nose signals
Downwind experiments
Field measurements were conducted downwind
from 14 poultry, dairy and swine facilities, during a 7-week period between July and August,
2004. While at each facility, human assessors determined the odour strength in terms of odour
dilution-to-threshold ratio (D/T) using a portable
Nasal Ranger Field Olfactometer (St. Croix Sensory, Inc, Lake Elmo, MN, USA) (McGinley and
McGinley 2004), while the developed electronic
nose took measurement at the same time. All the
There were totally 64 datasets available collected
by the electronic nose available. Approximately
20% of the datasets were randomly selected for
testing and the remaining 80% were used for
training the neural network model. The neural
network model was trained by a typical backpropagation algorithm. The odour strength prediction result is shown in Fig. 11. By using
electronic nose, high accurate predictions were
achieved (r = 0.932).
The results with electronic nose signals were
compared to those from human assessors. These
datasets do not have the digital signals from the
electronic nose, only with environmental factors,
i.e. outdoor temperature, wind seed, and distance
to odour source. A three layer neural network
with three inputs and one output was used for the
datasets, and a typical back-propagation learning
algorithm was applied to train the datasets. The
prediction result is shown in Fig. 12. Low accurate predictions were obtained (r = 0.809) without electronic nose signal.
Comparison of predictions with electronic nose
signals to those without electronic nose signals
shows that sensor signals from the electronic nose
aid to odour strength evaluation, helps to achieve
higher accurate odour measurement. It demonstrates that the electronic nose provides accurate
responds to odour strength arising from poultry
and livestock farms.
Factor contribution rating was implemented by
using statistical factor analysis. From the results
Table 2 Correlations of factors to odour intensity
Factor
Correlation coefficient
p-value
Distance
Temperature
Wind speed
−0.3540
0.1707
0.0551
<0.01
0.01
0.4
Environ Monit Assess (2007) 135:399–408
407
35
Controlled
Original
30
Output (D/T)
25
20
15
10
5
0
0
2
4
6
Case
8
10
12
Fig. 13 Control results of livestock farm odour data (Distance > 100 m)
shown in Table 2, distance to the odour source is
the most significant factor to the odour strength,
outdoor temperature is less important, while wind
speed is least significant to the odour strength.
In order to investigate effectiveness of potential
control strategies, for instance, beginning with
the most significant factor, distances to the odour
sources were considered. Odour is diluted by dispersion as it is transported in the atmosphere. The
further it travels, the more it is diluted. Therefore,
adequate setback distances are a key in preventing
odour complaints. Distances to the odour source
were set back to larger than 100 m. Figure 13
shows the control result. Odour strengths were
effectively reduced.
Conclusion and discussion
A portable electronic nose with 14 gas sensors, a humidity sensor and a temperature sensor was successfully developed especially for objective measurement of livestock farm odours.
Experiments were conducted in field downwind
from 14 livestock and poultry farms. Experimental
results demonstrate that “Odour Expert” is a useful tool in supporting livestock and poultry farm
odour management, which greatly improves the
efficiency of livestock farm odour management
and thus benefit livestock industry. The electronic
nose is capable of producing accurate output with
greater consistency in odour measurement. It is
easier and cheaper to operate than using olfactometry or human panel. Thus it has good commercial potential.
However, an electronic nose can only evaluate
odour events on one point; it cannot provide an
overall odour mapping around livestock facilities
that is necessary for an overall odour management
strategy. It is suggested that an electronic nose
network combined with an air dispersion model is
possible to predict the downwind odour strengths
on the basis of odour emission rates, topography
and meteorological data. Therefore, it can locate
odour strength around livestock facilities in real
time, and help the development of an overall
odour management strategy.
References
Barlett, P. N., Elliot, J. M., & Gardner, J. W. (1997). Electronic noses and their application in the food industry.
Food Technology, 51(12), 44–48.
Hobbs, P. J., Misselbrook, T. H., & Pain, B. F. (1995).
Assessment of odours from livestock wastes by a photoionization detector, electronic nose, olfactometry
and gas chromatography-mass spectrometry. Journal
of Agricultural Engineering Research, 72, 291–298.
Hudon, G., Guy, C., & Hermia, J. (2000). Measurement of
odour intensity by an electronic nose. Journal of Air
and Wastes Management Association, 50, 1750–1758.
Gardner, J. W., & Barlett, P. N. (1999). Electronic noses:
Principals and applications. Oxford, UK: Oxford University Press.
Loutfi, A. (2006). Odour recognition using electronic
noses in robotic and intelligent systems. PhD thesis,
Institutionen för teknik, Örebro universitet, Örebro,
Swedish.
McGinley, M. A., & McGinley, C. M. Developing a credible odour monitoring program. In American Society
of Agricultural Engineers (ASAE) 2004 annual conference, Ottawa, Ontario, Canada (Paper no. 042201).
O’Neill, D. H., & Philips, V. R. (1992). A review of the control of odour nuisance from livestock buildings. Journal of Agricultural Engineering Research, 53(1), 23–50.
Osarenren, J. O. (2002). Computer aided electronic nose
for sensing odours in the dairy farms. In 2002 ASAE
annual international meeting/CIGR XVth world
congress, Chicago, IL, USA (Paper no. 024169).
Pan, L., Yang, S. X., & DeBruyn, J. (2007). Factor analysis
of downwind odours from livestock farms. Biosystems
Engineering, 96(3), 387–397.
Powers, W., & Bastyr, S. (2004). Downwind air quality
measurements from poultry and livestock facilities.
408
Animal industry report ASL-R1924. Ames, IA: Iowa
State University.
Qu, G., Feddes, J. J. R., Armstrong, W. W., Coleman, R.
N., & Leonard, J. J. (2001). Measuring odour concentration with an electronic nose. Transactions of the
ASAE, 44(6), 1807–1812.
Shaw, P. E., Rouseff, R. L., Goodner, K. L., Bazemore, R.,
Nordby, H. E., & Widmer, W. W. (2000). Comparison
of headspace GC and electronic sensor techniques for
classification of processed orange juices. Food Science
and Technology, 33, 331–334.
Schiffman, S. S., Bennett, J. L., & Raymer J. H. (2001).
Quantification of odours and odorants from swine operations in North Carolina. Agricultural and Forest
Meteorology, 108, 213–240.
Schaller, E., Bosset, J. O., & Escher, F. (1998). ‘Electronic
noses’ and their application to food. Food Science and
Technology, 31, 205–316.
Tian, F., Yang, S. X., & Dong, K. (2005). Circuit and noise
analysis of odorant gas sensors in an E-nose. Sensors,
5, 85–96.
Environ Monit Assess (2007) 135:399–408
Singh, S., Hines, E. L., & Gardner, J. W. (1996). Fuzzy
neural computing of coffee and tainted water data
from an electronic nose. Sensors and Actuators, 30(3),
190–195.
Zahn, J. A., DiSpirito, A. A., Do, Y. S., Brooks, B. E.,
Cooper, E. E., & Hatfield, J. L. (2001). Correlation of
human olfactory response to airborne concentrations
of malodorous volatile organic compounds emitted
from swine effluent. Journal of Environmental Quality, 30, 635–647.
Zhang, Q., Feddes, J. J. R., Edeogu, I., Nyachoti,
M., House, J., Small, S., et al. (2001). Odour
production, evaluation and control. Final report
for project MLMMI 02-hers-03. Winnipeg, Canada:
Manitoba Livestock Manure Management Initiative
Inc.
Zhang, Q., Feddes, J. J. R., Edeogu, I., & Zhou, X. J.
(2002). Correlation between odour intensity assessed
by human assessors and odour concentration measured with olfactometers. Canadian Biosystems Engineering, 44, 627–632
OUR KNOWLEDGE
IS YOUR SUCCESS
Rapportage ontwikkeling geur sensor voor het gebruik van geurmetingen bij
varkensstallen
Electronic determination of odour by olfactory with the Multi Gas Analyser (MGA)
Interpretation by an Artificial Neural Network
Inleiding
Geur meting is niet eenvoudig uitvoerbaar. Een exacte wetenschappelijk onderbouwde geurmeting moet
altijd worden uitgevoerd volgens de VDI richtlijnen. Deze geurmeting is tijdrovend, duur en heeft niet de
reproduceerbare nauwkeurigheid die een meetinstrument normaal heeft. De uitslagen van de geurmeting
kunnen nogal veranderen. Omdat er geen goede alternatieven op de markt zijn om eenvoudig
geurmetingen uit te voeren bij varkensstalen is in 2010-2011 in een project gekeken naar de haalbaarheid
van een elektronische geurmeting. Tevens is een geurmeetsysteem ontwikkeld dat speciaal gebruikt kan
worden bij een varkensstal.
Alternatieven voor het bepalen van geur(overlast)
Olfactometrie: VDI 3882
Verein Deutscher Ingenieure (VDI) (Vereniging van Duitse ingenieurs) is een organisatie van 135.000
ingenieurs en natuurwetenschappers. Opgericht in 1856 is VDI vandaag de grootste engineering
vereniging in West-Europa. De rol van de VDI in Duitsland is vergelijkbaar met dat van de American
Society of Civil Engineers (ASCE) in de Verenigde Staten. De VDI is geen unie. De VDI bevordert de
vooruitgang van de technologie en vertegenwoordigt de belangen van ingenieurs en engineering
bedrijven in Duitsland.
Intensiteit waarden
In (1992) wordt beschreven wat de geur intensiteit is. Omdat geur een subjectieve waarneming is wordt
met behulp van de Weber-Fechner wet een relatie gelegd tussen de concentratie gas in lucht en de
beleving hiervan.
Geur intensiteit kan worden onderverdeeld in de volgende categorieën op basis van intensiteit:
0
1
2
3
4
5
6
geen geur
zeer zwak (geur drempel)
Zwak
Onderscheiden
Sterk
zeer sterk
onaanvaardbare
Tabel 1 Intensiteit waarden
Deze waarden worden toegekend aan een geur door een geurpanel. De geur wordt een aantal keer
verdund en beoordeeld door het geurpanel. Na een serie beoordelingen kan de
intensiteit en geurgrens worden bepaald.
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Hedonische waarden
De hedonische waarde geeft de (on)aangenaamheid van een geur aan. De hedonische waarde is een
middel om de maat van geurhinder te voorspellen.
+4
+3
+2
+1
0
-1
-2
-3
-4
uiterst aangenaam
zeer aangenaam
aangenaam
enigszins aangenaam
Neutraal
enigszins
onaangenaam
Onaangenaam
zeer onaangenaam
uiterst onaangenaam
Tabel 2: Hedonische schaal
De hedonische waarde van een geur wordt doorgaans bepaald door een geurpanel en kan zowel in het
laboratorium als in het veld worden bepaald. De hedonische waarde van een geur hangt af van de sterkte
van een geur. Daarom wordt de hedonische waarde altijd gekoppeld aan een geuronderzoek waarbij
tevens de intensiteit in geureenheden wordt bepaald.
Een geur kan bij het betreden van een ruimte een hedonische waarde “onaangenaam” hebben. Door
enige tijd in de ruimte aanwezig te zijn kan de geur in de beleving naar de achtergrond van het bewustzijn
raken waarddor de geur enigszins onaangenaam of neutraal wordt beoordeeld. Omgekeerd zijn er
reukstoffen die in eerste instantie een aangename geur afgeven. Echter na enkele minuten werken deze
geurstoffen sterker in op ons reukorgaan waarbij de geur als zeer onaangenaam kan worden ervaren.
Wetsvoorstel geurhinder en veehouderij
Het rapport (2006) is naar aanleiding van het wetsvoorstel “geurhinder en veehouderij” geschreven. In dit
rapport wordt de relatie gelegd tussen geurhinder en de intensieve veehouderij in Nederland.
In tegenstelling tot de VDI richtlijnen wordt de omgeving ook meegenomen in het bepalen van de
geurhinder. Dit is gedaan omdat de indeling van het landschap ook invloed heeft op het bereik van geur.
Ook is aangetoond dat bewoners van een streek met intensieve landbouw veel minder last hebben van
geurhinder dan bewoners in een stedelijk gebied.
Om geurhinder aan te tonen wordt een sample genomen van de lucht. Deze wordt vervolgens in stappen
verdund tot de geurgrens met een geurpanel is bepaald. Als deze grens is bepaald kan de geurhinder
worden bepaald.
In het wetsvoorstel is vastgelegd hoeveel procent geurhinder in een bepaald gebied is toegestaan.
Conclusie
In beide alternatieven is de concentratie gas in lucht nodig. Volgens de VDI richtlijnen
kan voor verschillende gassen worden bepaald hoe groot de intensiteit en hedonische
waarde is. Dit is in tegenstelling met het wetsvoorstel want hierbij wordt de emissie
van veehouderij gezien als één soort lucht. In beide gevallen zijn geen parameters
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beschikbaar om met de concentratie de geurhinder te bepalen. Het voordeel van het wetsvoorstel is dat er
vaststaat wat de hoeveelheid geurhinder moet zijn om in overtreding te zijn. Omdat de VDI 3882 richtlijnen
zijn wordt dit niet aangegeven.
Aangenomen wordt dat de emissie van stallen verschillende concentraties stoffen kan bevatten. Daarom
wordt gekozen voor de VDI richtlijnen omdat in tegenstelling tot het wetsvoorstel de intensiteit van
verschillende stoffen kan worden bepaald.
Het project "Elektronische geurmeting”
Het meetonderzoek en testopstelling heeft als doel om met behulp van elektronische sensoren een
betrouwbare benadering te maken van de geur intensiteit en de hedonische waarde. Het project is
ingedeeld in 5 fasen.
Opzet / technische plan van aanpak
Om te komen tot de bepaling van de geurhinder en de intensiteit is het belangrijk dat de componenten uit
een gassamenstelling bekend zijn. Als deze componenten bekend zijn kan er vervolgens een koppeling
gemaakt worden naar de daadwerkelijke benadering. Schematisch gezien is de volgende opzet gebouwd:
Figuur 1: Schematische opzet van de MFCs, de permeatieovens en het neurale netwerk waarin de data
verwerkt wordt.
Fase 1: Uitwerken lab opstelling
Fase 1 is uitgewerkt. Er is een inventarisatie gemaakt van alle geurcomponenten. Deze componenten
worden gesimuleerd door gebruik van permeation tubes. De permeation tubes zijn
ondergebracht in 6 permeation ovens en uiteindelijk gekoppeld aan 1 systeem met
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een multi-array solid-state sensor
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Dit systeem is een multi-array sensor geplaatst in een temperatuur geconditioneerd ontwerp waarbij met
een klep nul-lucht kan worden toegevoerd en waarbij de uitkomsten van de sensor kunnen worden
verwerkt in een neuraal netwerk.
De stoffen die zijn geïnventariseerd zijn geëxtraheerd uit diverse wetenschappelijk publicaties. De meeste
publicaties handelen over de samenstelling van gasconcentraties uit varkensmest. We zijn gekomen tot
een classificatie van een top 10 aan componenten:
Methyl mercaptan
Phenol
Pentanoic Acid/Valeric
acid
Butyric Acid
Indole
Skatole
P-Cresol (4Methylphenol)
Acetic Acid
Ammoniak
Hydrogen Sulfide
Tabel 3: Overzicht van de meest aanwezige gascomponenten die aanwezig zijn in varkenslucht,
verkregen uit een literatuurstudie.
Naast deze componenten worden de grootheden temperatuur en RV op een traditionele wijze verwerkt
door respectievelijk een PID temperatuurregeling toe te passen op de sensor en de RV te compenseren
op de sensoren. Hiervoor zijn afzonderlijke testen uitgevoerd om de compensaties zo optimaal te laten
verlopen. Er is dus voor gekozen om de traditionele grootheden temperatuur en relatieve vochtigheid niet
in een neuraal net mee te laten analyser teneinde de resolutie van de overige componenten zo hoog als
mogelijk te houden.
Van alle componenten is vastgesteld wat de bovenste en de onderste geurgrenzen zijn. Dit is eveneens
verkregen uit literatuurstudies. Dezelfde componenten in verschillende onderzoeken laten nogal
uiteenlopende niveau’s zien. Uiteindelijk is gekozen voor de meest logische benaderingen.
Kunstmatig neuraal netwerk
Neurale netwerken zijn ontworpen naar analogie van het biologische brein, m.a.w. het zijn zeer
uitgebreide en onderling verbonden netwerken van neuronen. Kunstmatige neurale netwerken zijn toe te
passen in computersystemen. Een kunstmatig neuraal netwerk bestaat uit verscheidene (eventueel door
software gesimuleerde) meestal zeer eenvoudige processoren (neuronen) met een hoge mate van
onderlinge connectie waarover simpele scalaire berichten verzonden worden. De interactie tussen de
diverse onderling verbonden processoren waaruit het netwerk bestaat is bovendien adaptief, zodat
verbindingen tussen andere processoren in het neurale netwerk kunnen ontstaan, kunnen worden
versterkt, verzwakt of weer verbroken kunnen worden. Dit betekent dat een neuraal netwerk te 'trainen' is.
Het trainen van neurale netwerken gebeurt met backpropagation. Hierbij wordt een
willekeurig sample ingevoerd in het netwerk en wordt vervolgens afhankelijk van de
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Soorten netwerken
Bekende netwerken zijn de Multilayer Perceptron netwerken en de Generalized Feed Forward netwerken.
De multilayer perceptrons (MLPs) zijn netwerken die uit meerder lagen bestaan die de informatie
voorwaarts doorgeven. layered feed forward netwerken worden typisch getrained met statische
backpropagation (Verkregen informatie wordt terug het netwerk ingekoppeld). Dit type netwerken vind in
ontelbaar veel applicatie hun weg, bijzonder daar waar een statische classificatie patroon nodig is. De
voordelen van deze netwerken zijn dat ze eenvoudig zijn en dat ze vrijwel alle applicaties met alle inputsoutput structuren aankunnen. Het belangrijkste nadeel is dat de training van deze netwerken relatief
langzaam gaat en ze vereisen veel trainingsdata. (Meestal meer dan 3 keer meer training samples dan
netwerk gewichten.)
Generalized Feed Forward
Generalized feed forward netwerrken zijn een basis van de MLP netwerken, zodanig dat de verbindingen
in theorie kunnen overspringen naar elk andere laag of punt in een laag. In theorie kan een MLP network
alle problemen oplossen die een generalized feed forward netwerk ook kan oplossen. Echter in de praktijk
is een generalized feed forward network networks vaker sneller met de oplossing en wordt het problem
ook meer efficiënt opgelost.
Voor het netwerk wordt gekozen voor een Generalized Feed Forward Multilayer Perceptron Network. Het
voordeel van dit netwerk tegenover een standaard MLP netwerk is dat er minder trainingsdata nodig is.
Het nadeel van het netwerk is dat er meer parameters zijn dus dit kost extra geheugen wat in een
embedded oplossing beperkt is. De beperkte hoeveelheid trainingsdata is echter een zeer groot voordeel
omdat het verzamelen van de trainingsdata relatief veel tijd kost.
Topologie
Na een literatuurstudie over de toepassingen van kunstmatige neurale netwerken in gasmeet technieken
blijkt dat er vooral over twee soorten topologieën worden gebruikt.
De eerste topologie gaat over een enkelvoudig netwerk waar de sensor verhoudingen (ratio’s) direct in
het neurale netwerk worden ingevoerd. De ratio’s kunnen direct na de meting of alleen op een bepaald
tijdstip worden ingevoerd. Het is ook nog mogelijk om de ratio’s eerst te conditioneren. Omdat de sensor
bij alle soorten gassen een unieke stabilisatie en meettijd heeft kunnen ook alleen de unieke kenmerken
van deze periode worden ingevoerd. Deze topologie is in een wetenschappelijk onderzoek gebruikt om de
concentratie van één soort gas in lucht te bepalen.
MGA
Sensor
ratio’s
Conditionering
van
meetwaarden
Geconditioneerde
waarden
Classificatie en
concentratie
netwerk
Soort en concentratie
gas
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Figuur 2: Enkelvoudige topologie waarbij conditionering optioneel is
Bij een twee lagen topologie wordt eerst welke gassen zich er in de lucht bevinden en vervolgens wordt
hiervan de concentratie bepaald.
Sensor
ratio’s
MGA
Soorten
gassen
Classificatie
netwerk
Sensor
ratio’s
Concentratie
netwerk
Soorten
gassen
Concentratie
van gassen
Figuur 3: Twee lagen topologie
Desired Output and Actual Network Output
70
Output
60
50
Indole
40
P-Cresol
Butyric Acid
30
Indole Output
20
P-Cresol Output
10
Butyric Acid Output
0
-10
1
9
17
25
33
41
49
57
65
73
81
Exemplar
Figuur 4: Relatie tussen de gemeten waarden (ononderbroken lijnen) en de gewenste output
(stippellijnen)
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Performance
MSE
NMSE
MAE
Min Abs Error
Max Abs Error
r
Indole
126.2733405
0.597315442
8.371652246
0.017299954
45.54439157
0.782346579
P-Cresol
471.5152749
2.345525271
17.52764081
0.076797221
46.70068218
-0.159821897
Butyric Acid
199.1914232
1.068479668
11.04322773
0.034178076
32.69469265
0.62426319
Tabel 4: Bijbehorende afwijkingen van de gemeten waarden en de gewenste waarden.
Desired Output and Actual Network Output
70
Output
60
50
Indole
40
P-Cresol
Butyric Acid
30
Indole Output
20
P-Cresol Output
10
Butyric Acid Output
0
-10
1
3
5
7
9 11 13 15 17 19 21 23 25 27 29 31 33 35
Exemplar
Figuur 5: Relatie tussen de gemeten waarden (ononderbroken lijnen) en de gewenste output
(stippellijnen)
Performance
MSE
NMSE
MAE
Min Abs Error
Max Abs Error
r
Indole
29.94917093
0.110922855
3.988914929
0.247990571
20.28415467
0.951154943
P-Cresol
199.2941651
0.738126537
11.10540487
0.489905225
29.0716076
0.73565366
Butyric Acid
53.11927596
0.196738059
5.480077289
0.000933466
17.21323142
0.920110634
Tabel 5: Bijbehorende afwijkingen van de gemeten waarden en de gewenste waarden.
Grafische voorstelling en tabel van enkele resultaten uit het project van een training in
een neuraal netwerk.
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Permeatie-ovens
Een zeer speciaal aandachtspunt bij de opbouw van de opstelling waren de permeatie-ovens. De
belangrijkste vraag bij de opbouw was: Hoe maken we geurcomponenten waarmee we op ppb niveau
reproduceerbaar, relatief goedkoop en heel nauwkeurig kunnen kalibreren. EMS heeft naar aanleiding van
deze vraag zelf 6 verschillende ovens gebouwd. De nauwkeurigheid is factoren hoger dan de versies die
commercieel verkrijgbaar zijn. Met deze 6 ovens zijn allerleis soorten combinaties van stoffen
samengesteld waarmee kon worden gemeten. De software van de MFCs in combinatie met de permeatieovens is in staat om de concentratie van een geurcomponent tot op een concentraties van 1 tot 2 ppb te
regelen binnen een nauwkeurigheid van +-2.5%.
Figuur 6: Achterzijde van de permeatie-oven met flowcontrollers en gaswasflessen.
MGA
De MGA is een elektronisch systeem dat is opgebouwd en ontworpen op basis van een solid-state Array
sensor. Deze sensor heeft meerdere sensoren (receptoren) waarbij elke sensor verschillende
eigenschappen meegegeven kan worden. Dit kan gedaan worden door de aansturing van de sensoren
m.b.v. elektrische parameters te beïnvloeden. (Temperatuur, stroom, spanning, frequentie etc.) Een
kenmerk is dat deze sensordata door een mens moeilijk te interpreteren is. Echter voor een neuraal
netwerk is deze informatie goed te interpreteren. De MGA is geassembleerd opgebouwd en getest. Er is
een temperatuurregeling aangebracht om e.a. te stabiliseren en geen condensatie te laten plaatsvinden.
De output van de MGA bestaat uit een string met data die elke seconde wordt
afgedrukt naar een PC. Deze data wordt door het neurale netwerk gebruikt om de
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Figuur 7: Opgebouwde MGA voordat deze is ingebouwd in een (portable) kast voor in het veld.
Fase 2: Inleren lab opstelling
Het principe van werken met neurale netwerken is een uiterst geschikte oplossing voor geursensoren.
Het grote nadeel is dat er heel veel trainingsdata nodig moet zijn om alle mogelijke combinaties te trainen
en te testen. Een oplossing hiervoor hebben we gevonden in een afweging te maken door te kiezen voor
een lager resultaat in de uitkomsten, maar een hogere snelheid met inleren.
Het inleren is gebeurd door in stappen van verschillende concentraties stoffen aan de Multi Gas Analyser
aan te bieden. In eerste instantie is dit uitgevoerd in kleine stappen. Er zijn 2 stoffen aangeboden om
vervolgens te onderzoeken of het inleren van stoffen ook daadwerkelijk een significant resultaat
opleverde. Na enige verbeterslagen was dit optimaal en is begonnen om elke gascomponent in
verschillende stappen in te leren. Hierbij spelen reproductie van data, nauwkeurigheid van het systeem en
stabiele omgevingsfactoren een cruciale rol.
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Fase 3 / 4: Onderzoek inleren geurwaarde en \hedonische waarde lab / praktijk
Een belangrijke stap is de vertaalmethode om informatie uit een neuraal netwerk omgezet te krijgen naar
een intensiteit of een hedonische waarde. We vermelden er bij dat we het resultaat van de uitkomst uit de
elektronische neus zo dicht als mogelijk de intensiteit en de hedonische waarde willen laten benaderen
aan de methoden zoals beschreven in de VDI richtlijnen.
De methodiek om deze waarden te koppelen is als volgt opgezet: (Zeer kort samengevat daar de
onderliggende complexiteit te ver voert voor dit verslag)
De intensiteit is een logaritmische waarde. De sensoruitslag is eveneens een logaritme. Dit
betekend dat deze waarden min of meer lineair met elkaar te vergelijken zijn. Een intensiteit
met een waarde van 6 heeft een bijbehorende gasconcentratie in ppb / ppm. Deze waarde
gebruikt men in het inleren van het neurale netwerk en stelt men gelijk aan waarde 6. De
verhouding die hieruit voortkomt is een quasi lineaire benadering die ook in een aantal
onderzoeken als zodanig wordt bevestigd. (Kwantitatieve benadering).
De hedonische waarde is een vast getal per stof. In ons model berekenen we voor alle stoffen
een partiële hedonische waarde naar de verhouding van het aantal stoffen dat kwalitatief wordt
bepaald. Het totaal van deze partiële stoffen vormt de grondslag voor de optelsom van alle
partiële hedonische waarden en vormt zodoende de totale hedonische waarde van een
mengsel aan gassen. (Deels kwantitatief en kwalitatieve benadering).
Fase 5: Uitwerken resultaten, aanbrengen nieuw gevonden resultaten
Het resultaat van dit project is dat we een werkend systeem hebben gebouwd dat in het veld ook is
gebruikt en dat bestaat uit een kast waarin elektronica is verwerkt met daarin een zelfaanzuigende pomp,
een paar kleppen en een display. Deze kast werkt op 220VAC en stuurt elke seconde een datastring uit.
Daarnaast staat een PC die de datastring verwerkt. De data wordt verwerkt in een neuraal netwerk. Op de
PC is software geschreven waarmee de data kan worden geïnterpreteerd en visueel worden
weergegeven. Uiteindelijk wordt in een gassamenstelling bepaald welke van de eerder genoemde10
majeur aanwezige geurcomponenten aanwezig zijn. (Kwalitatieve bepaling). Daarna wordt voor elke
component ook bepaald wat de gasconcentratie is berekend naar genormaliseerde grootheden in ppbs.
(Kwantitatieve bepaling). Vanuit deze informatie wordt met behulp van een kleine database de intensiteit
en de hedonische waarde van een gas bepaald. De PC heeft een database waarbij de gevonden
meetwaarden ook opgeslagen worden. De meetinterval voor een meting is circa 15-45 minuten. Dit komt
omdat de sensoren opnieuw moeten worden gereinigd en hangt af van de concentratie van een
gassamenstelling. Gezegd moet worden dat er op dit moment geen systemen zijn voor het meten van
geur uit varkensmest die zo snel de geur kunnen bepalen.
Er is nog altijd een combinatie van een PC en een neuraal netwerk nodig. Echter de werking verloopt zeer
goed. De overeenkomsten met menselijke interpretatie van de beleving intensiteit en hedonische waarde
komt goed overeen.
Conclusie / aanbevelingen
Er is een systeem gebouwd waarmee geur op zowel kwantitatieve alsook kwalitatieve
alsook op kwantitatieve hoeveelheden beoordeelt. De intensiteit en de
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hedonische waarden worden bepaald. De relatie naar een menselijk geurpanel is niet gemaakt. Wel is de
verwachte output gecontroleerd met echte gasconcentraties in die in een labomgeving zijn aangeboden.
Daarin komt de uitslag overeen en is het systeem reproduceerbaar. Aanbevelingen voor verdere
verbetering zijn miniaturisatie, de opname van een neuraal netwerk in een kleinere processor, en een
mogelijke methode om de inleer-procedure / kalibratie procedure te versnellen of generiek te maken voor
alle sensoren uit eenzelfde serie. Verder mag de meting versneld worden om meer real-time data te
kunnen verkrijgen. De praktische uitvoering en het gebruik van deze sensor is onder handbereik, maar
hangt af van de vraag van de overheden en de handhavende instanties.
Figuur 8: Voorzijde met de 6 permeatie-ovens en bovenop de ingebouwde MGA sensor.
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Figuur 9: Programma voorbeeld van de MGA2Neural programma waarbij de intensiteit en de hedonische
waarde worden weergegeven op het scherm van de PC.
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