Impact of Acid Mine Drainage (AMD) on Water Quality

Water Air Soil Pollut
DOI 10.1007/s11270-008-9900-z
Impact of Acid Mine Drainage (AMD) on Water Quality,
Stream Sediments and Periphytic Diatom Communities
in the Surrounding Streams of Aljustrel Mining Area
(Portugal)
A. T. Luís & P. Teixeira & S. F. P. Almeida &
L. Ector & J. X. Matos & E. A. Ferreira da Silva
Received: 21 May 2008 / Accepted: 20 October 2008
# Springer Science + Business Media B.V. 2008
Abstract Aljustrel mining area is located in the
Iberian Pyrite Belt, one of the greatest concentrations
of massive sulphide deposits that extends from Lousal
(Portugal) to Aznalcóllar (Spain). The surrounding
streams, Roxo, Água Azeda and Água Forte, are
influenced by the erosion of the tailing deposits and
the input of acid mine drainage (AMD) from the
abandoned Aljustrel pyrite mines, recently reopened
in 2007. The purpose of this study was to understand
how these adverse conditions influenced the stream
sediments, water quality and periphytic diatom communities and establish the pre-restoration local conditions to judge the success of rehabilitation program
now under way. For stream sediments, the highest
metal concentration samples were found at sites F, G
and H. Arsenic, Cu, Fe, Pb and Sb detected concentrations, generally exceeded the probable effect
concentration values reaching level 4: the highest
toxicity level. In surficial water samples of AMD
affected sites (F, G and H), low pH values (1.5 to 3.5)
and high metal concentrations of As (6,837 μg L−1),
Cd (455 μg L −1 ), Cu (68,795 μg L −1 ), Fe
(1,262,000 μg L −1), Mn (19,451 μg L −1), Pb
(136 μg L−1), and Zn (264,377 μg L−1) were found.
In these sites, the diversity index (H′) for diatoms was
low (0.6 to 2.8) and the dominant taxa were Eunotia
exigua (site F, 33.5%) and Pinnularia acoricola
(abundances in sites: F, 86.8%; G, 88.5%; and H,
91.1%). In opposition, in less AMD impacted, H′ was
high (1.5 to 4.6) and low metal concentrations and
A. T. Luís : P. Teixeira : E. A. Ferreira da Silva (*)
Geobiotec–Geobiosciences, Technologies and Engineering,
Departamento de Geociências, Universidade de Aveiro,
Campus de Santiago,
3810-193 Aveiro, Portugal
e-mail: eafsilva@ua.pt
L. Ector
Department Environment and Agro-biotechnologies (EVA),
Public Research Center–Gabriel Lippmann,
Rue du Brill 41,
4422 Belvaux, Grand-duchy of Luxembourg
e-mail: ector@lippmann.lu
A. T. Luís
e-mail: anatluis@ua.pt
P. Teixeira
e-mail: a20240@alunos.ua.pt
S. F. P. Almeida
Geobiotec-Geobiosciences, Technologies and Engineering,
Departamento de Biologia, Universidade de Aveiro,
Campus de Santiago,
3810-193 Aveiro, Portugal
e-mail: salmeida@ua.pt
J. X. Matos
Assessor do Centro de Estudos Geológicos
e Mineiros de Beja,
Rua Frei Amador Arrais No 39 r/c, Apartado 104,
7801-902 Beja, Portugal
e-mail: joao.matos@ineti.pt
Water Air Soil Pollut
high pH were found. Achnanthidium minutissimum
was the dominant taxon in (abundances in sites: A,
76.1% and B, 24.39%). Canonical correspondence
analysis showed that spatial variation due to mine
influence was more important than seasonal variation,
which did not show any pattern.
Keywords Acid mine drainage (AMD) .
Aljustrel mining area . Diatoms . Stream sediments .
Surficial waters
1 Introduction
Mining activity exerts a significant influence in
energy and matter cycles of the natural environment,
so it is important to analyse the dispersion and
distribution of toxic elements mainly when their
concentrations seem anomalous to natural biochemical
background (Oliveira et al. 2002).
Acid mine drainage (AMD) is formed when pyritic
minerals are exposed to atmospheric, hydrological or
biological weathering (oxygen, water and chemoautotrophic bacteria) becoming oxidised and resulting in
sulphuric acid (low pH), dissolved metal ions, elevated
sulphate contents (Skousen et al. 1994), low alkalinity
and high conductivity. Arsenic, Cu, Fe, Mn, Pb, Zn
and sulphates are frequently found in high concentrations, and their solubility increases with acidity
(Dickson 1975; Stokes and Hutchinson 1975; Beamish
and Vanloon 1977; Almer et al. 1978; Van Dam et al.
1981; Harding and Boothroyd 2004). Dilution may
reduce metal concentrations while not markedly
influencing pH. At higher pH (>4.0) precipitation of
metal hydroxides can smother biota with precipitates
[the most visible structures are red-orange ferric
hydroxide (FeOH3) commonly referred to as yellow
boy], whereas at lower pH, the dissolved metals
toxicity can cross membranes (Van Ho et al. 2002).
The AMD effects on aquatic ecosystems are
twofold: (a) impacted communities experience lethal
levels of pH and metals, which lead to a decrease in
algal species richness and diversity (i.e. Mulholland et
al. 1986; Planas 1996; Verb and Vis 2000a, b); (b)
communities are restricted to tolerant organisms, which
are able to survive in these conditions. Alterations in
nutrient cycles and abiotic changes are supported for
these communities with large impact in biotic relations,
including extinction and succession of species and
groups of sensitive taxa (Kwandrans 2007).
Diatom’s metal response models are difficult to
establish because metal contamination is frequently
associated with acidic environments (Dixit et al.
1991). Several studies in metal-polluted rivers have
shown that diatoms respond to perturbation not only
at the community level through shifts in dominant
taxa (Gustavson and Wängberg 1995; Hirst et al.
2004) as also with changes in diversity (Leland and
Carter 1984; Medley and Clements 1998).
Therefore, the aims of this work are (a) to
characterise the water and stream sediments chemistry
of the AMD impacted streams, identifying the sources
and describing the dominant geochemical processes
occurring in the area, in order to access their impact
on diatom communities and (b) establish the prerestoration local conditions to judge the success of the
rehabilitation program now under way.
2 The Study Area
2.1 Environmental Setting
2.1.1 Aljustrel Geology
The Aljustrel region is characterised by a Palaeozoic
basement of the South Portuguese Zone and by a
modern sedimentary sequence of the Alto Sado
Tertiary Basin. The active NE–SW Messejana fault
defines the SE border of this basin, which is confined
to the northwestern block of the fault (Fig. 1).
The Messejana fault presents an Iberian dimension
and a senestral strike-slip movement of 2.5 km. Along
this major structure, a Jurassic dolerite is also
observed (Schermerhorn et al. 1987). The Aljustrel’s
Palaeozoic stratigraphic sequence is formed by the
following main units (Andrade and Schermerhorn
1971; Schermerhorn et al. 1987; Silva et al. 1997;
Leitão 1998; Matos 2005; Oliveira et al. 2006): Baixo
Alentejo Flysch Group, Mértola Formation (Upper
Visean)–shales and greywackes (flysch turbidites).
The Iberian Pyrite Belt (IPB), volcano–sedimentary
complex (VSC) (Upper Famennian–Upper Visean) is
represented by the following units: Paraiso Fm.–
siliceous shales, phyllites, tuffites, purple shales,
jaspers and cherts; felsitic/mine metavolcanics and
megacryst/green metavolcanics sequences–sericitic
Water Air Soil Pollut
Fig. 1 Geological setting of the Aljustrel mining region (adapted
from Matos 2005; Schermerhorn et al. 1987; Oliveira et al.
1992): 1 alluvium deposits; 2 undifferentiated Sado Tertiary
Basin sediments; Palaeozoic Basement (South Portuguese Zone/
Iberian Pyrite Belt): 3 Messejana Dolerite Dike (Middle
Jurassic); 4 Mértola Flysch Formation (Late Viséan); IPB
Volcano Sedimentary Complex (Late Famennian-Late Viséan) 5
Sediment, 6 Volcanic, 7 Algares and S. João outcropping ore
bodies; 8 Phyllite-Quartzite Group (Early Frasnian-Late Famennian). Mining waste: 9 brittle pyrite; 10 slag; 11 acid mine
drainage/leached materials; 12 evaporation tanks; 13 host
mineralization rocks; 14 urban areas; 15 acid water dams: BE
Estéreis, BAF Água Forte; 16 clean waters dams: BAC Águas
Claras, BAI Água Industrial; BMR Monte Ruas; 17 fault; 18
stream; 19 sample point. UTM coordinates in km
felsic volcanics, felsites, felsophyres, volcanic breccias, massive sulphides, feldspar megacryst volcanics
and lavas. The IPB lower unit, the sedimentary
Phyllite–Quartzite Group (Frasnian–Upper Famennian),
is not recognised in the Aljustrel area. The VSC
Aljustrel Anticlinorium is represented by a NW–SE
lineament (4.5 km length and 1.5 km across). Several
NW–SE thrust are identified, mainly in the short limbs
of the anticlines. The Aljustrel thrust is one of the main
structures and materialises the SW contact between the
Water Air Soil Pollut
VSC and the Flysch Group. Late variscan faults with
NNE–SSW direction produced important dextral
strike-slip movements, probably conjugated faults of
the Messejana fault. This system is represented from
SE to NW by the Esteval, the Azinhal, the Feitais, the
Represa, the Castelo and the Moinho faults.
2.1.2 The Aljustrel Mine
Located in the Alentejo province in the south of
Portugal, Aljustrel mine is one of the great IPB
mining sites, a world class volcanic-hosted massive
sulphides metalogenetic province (Barriga et al. 1997;
Carvalho et al. 1999; Matos and Martins 2006;
Oliveira et al. 2006; Relvas et al. 2006; Tornos
2006). In the Aljustrel mining site, six massive
sulphide orebodies are recognised: Moinho, Feitais,
Estação, Gavião, Algares and São João. The last two
were mined since Roman times (Leitão 1998; Silva et
al. 1997; Matos and Martins 2003, 2006). Moinho
deposit was exploited by the public Pirites Alentejanas
Company (PA) for copper until 1993.
The gossans and the supergene zones of the
Algares and São João orebodies were exploited
during roman era to 100 m deep (Domergue 1983).
rare chalcopyrite with sparse distribution in the iron
silicate matrix. The Roman slag had high concentrations of Pb, Cu, Zn, Fe, As and Sb elements.
Roman wastes are represented by in situ and
reworked slags and are located in the upper stream
sector of Água Forte stream.
Some of the mining infrastructures are unsafe and
potentially dangerous, like open pits, quarries, galleries and mining shafts very exposed to local urban
areas (Matos and Martins 2006). The walls of São
João open pit and Malpique and Moinho quarries
present significant geotechnical instability.
Other wastes are present in Aljustrel, with less
contaminant potential like host rocks, represented by
felsic well cleaved and coherent volcanics, siliceous,
purple and black shales, jaspers and cherts (volcano–
sedimentary complex host rocks). Roasted pyrite ore
has a very low expression confined to spotty
occurrences at Algares.
Presently, the EDM public company (the owner of
the Portuguese mines rehabilitation program) is
developing a rehabilitation program at mine-activityaffected areas, before 1990 (Martins 2005; Nero
2005; Matos and Martins 2006).
2.1.4 Aljustrel Landscape
2.1.3 Environmental Impact in the Aljustrel Area
Related with the Extractive Activities
As a result of thousand of years of pyrite ores exploitation
in Aljustrel, large areas are occupied by waste tailings
composed by Roman slag, pyrite ore (blocks and brittle
massive pyrite ore) and volcano–sedimentary complex
host rocks. Algares industrial area and São João sector
present the highest volumes of mine waste.
The small Mn-Fe exploitations are represented by
small open pits and associated ore tailings, usually
with less than 1 m thickness. The environmental
impact of these exploitations is not quantified but is
certainly locally significant. Part of these small mines
is used for illegal waste disposals.
The petrographic study of ore waste samples
allowed the identification of interstitial chalcopyrite,
sphalerite, galena, arsenopyrite and minor sulfossalts
in the massive pyrite ore. The pyrite ore present high
concentrations of Fe, Cu, Pb, Zn, Ag, Sb, Hg, Se, Co,
Au and Cd while the roasted pyrite ore shows high
concentrations of Au, Pb, Ag, Fe, Sb, Bi, Se, Cu, Zn
and Mo. Roman slag presents interstitial pyrite and
In the SE block of the Messejana fault, welldeveloped hills occur, locally controlled by differential erosion of outcropping Palaeozoic VSC units, e.g.
Algares and São João gossans, jasper and chert
horizons. The NW block of the Messejana fault is
characterised by a flat relief. Here, the Palaeozoic
basement is covered by the sediments of the Alto
Sado Tertiary Basin (up to 100 m thick).
The hydrographic network is well developed and
hierarchisied. From west to east, the local hydrographical basins are:
(a) Barranco do Farrôbo (BF)–stream affected by the
Sto. Antão new industrial area where the ore
processing plant, the main acid water dam
(Estéreis) and the Águas Claras clean water dam
are located. The Estéreis dam was considered for
the e-Ecorisk virtual model discharge, related with
a virtual dam rupture.
(b) Água Azeda (AA)–stream affected by the São
João mining sector (tailings and open pit) and by
the São João urban area.
Water Air Soil Pollut
(c) Água Forte (AF)–main damaged area, stream
affected by the Algares large brittle pyrite and
slag tailings. Extreme acid water dam (Água
Forte) and a clean water dam (Barragem de Água
Industrial) are present.
Sado River is the final destiny of the downstream
effluents of the Aljustrel mine. A virtual discharge of
AMD could affect the very sensible Sado ecosystem
characterised by marsh zones (Sado Natural Reserve).
The downstream mine area is clearly affected by
mining activity. Geochemical data are compatible
with a contamination diffusion model of the mine site.
3 Methods
3.1 Collecting and Processing Samples
One of the aims of this study was to provide data that
accurately represent the stream sediments and surface
water’s chemical contaminants concentrations in the
sampling system. The selected sampling sites were
located upstream and downstream of AMD input
influence.
Stream Sediment Samples These were sampled according to ASTM (1984) at eight sampling sites (Fig. 1):
three sites in Roxo stream (sites B, Roxo; C, Porto
Ferreira; and D, Jungeiros), one in Água Azeda stream
(site E, Água Azeda) and four in Água Forte stream (site
A, Águas Industriais Dam-upstream from the mine; F,
Água Forte; G, Ponte do Curval; and H, Porto de Beja).
For each sampling site, 3 kg of stream sediments
was collected, which was then oven-dried at a constant
temperature of 40°C to a constant weight. Samples
were disaggregated, sieved to <2 mm, crushed and
passed through a minus 80-mesh (177 μm).
Surface Water Samples These samples were collected
at the same sites of stream sediment samples (Fig. 1).
Sampling was carried out seasonally [in May and
December of 2005 (campaigns 1 and 2) and March
and June of 2006 (campaigns 3 and 4)]. In order to
assess the physical and chemical parameters of
surface water, a volume of 1 L was collected from
the surface, as close to the centre of the river as
possible. Water samples were returned in a cooled box to
the laboratory and stored at 4°C before the analyses were
conducted, according to Scalf (in Coleto Fiano and
Maestro Salmeron 1998) and according to the proposed
norms by EPA-Environmental Protection Agency
(1982), ASTM-American Society for Testing and
Materials (1984) and by German Chemists Association
(1980). In order to determine the concentrations of trace
elements, a volume of 250 mL was taken from each
sample and filtered through 0.45-μm Millipore membrane filters using an all-plastic pressurised filtration
system.
Diatom Samples The protocols proposed by Prygiel
and Coste (2000) were used for sampling, identifying
and quantifying diatoms. The epilithic samples were
obtained by scraping five boulders using a toothbrush
and the epiphytic by squeezing the plants from the
margins of the streams; the epipsammic were removed
from the sediment with a syringe. Following the
sampling protocol, the pools of stagnant water and
shaded sites were avoided. Each sample was split in
two, one kept alive (without preservation) and the other
preserved with formalin solution (5 %). From the first
sub-sample, an aliquot was cleaned using HNO3 (65 %)
and potassium dichromate (K2Cr2O7) at room temperature for 24 h, followed by several centrifugations
(1500 rpm) to wash the excess of acid. Permanent
slides were prepared using Naphrax®.
3.2 Analytical Methods
Stream Sediment Samples A multi-element analysis
using inductively coupled plasma with optical emission spectrometry (ICP-OES) was performed in an
accredited Canadian lab (ACME Anal. ISO 9002
Accredited Lab–Canada). For this, a 0.5 g portion of
each sample was loaded into hot (95°C) aqua regia
(HCl–HNO3–H2O) for 1 h. After dilution to 10 mL
with water, the solutions were analysed by ICP-OES
for 14 chemical elements: Al, As, Ca, Cd, Cu, Fe, K,
Mn, Na, Ni, Pb, S, Sb and Zn.
Surface Water Samples Temperature (T, °C), pH and
electrical conductivity (EC, μS cm−1, at 25°C) were
measured directly in the field with a WTW® Multiline
P4 SET. The chemical analysis of water samples was
carried out using ICP mass spectrometry for the
determination of 13 elements: As, Ca, Cd, Cl, Cu, Fe,
K, Mg, Mn, Na, Pb, SO42− and Zn). The determination of NH4+, NO3− and NO2− took place at IDAD
Laboratory at the University of Aveiro. A titrimetric
Water Air Soil Pollut
method 4500-NH3 (C) for NH4+ determination was
used; the adapted colorimetric method 4500-NO3− (E)
for NO3− and the colorimetric method 4500-NO2− (B)
for NO2− [all methods adapted from Clescerl et al.
(2000)]. HCO3− was determined by titration with
H2SO4 at 0.16 N in the Geochemistry Lab of the
Geosciences Department at University of Aveiro.
Diatoms For diatom observation on scanning electron
microscopy (JEOL-JSM 5400), a drop of the oxidised
sample was placed on a metal stub previously covered
with a thin carbon pellicle. The sample was dried at room
temperature (Almeida 1998). The stub with the sample
was then coated with a gold–palladium mixture.
Diatoms were identified and semi-quantified under the
light microscope (Leitz Biomed 20 EB) using a 100×
objective (N.A. 1.32). A total of about 400 valves were
counted in each sample. Taxonomy was based on
Germain (1981), Krammer and Lange-Bertalot (1986,
1988, 1991a, b), Simonsen (1987a, b, c), Round et al.
(1990) and Prygiel and Coste (2000).
3.3 Data Analysis
Stream Sediments and Surface Waters The used methodology to ordinate the results of stream sediments and
surface waters was principal component analysis (PCA).
Arsenic, Cd, Cu, Fe, Mn, Ni, Pb, Sb and Zn
potential toxicity in stream sediment samples was
estimated based on “Consensus-Based Sediment
Quality Guidelines”, developed by the Contaminated
Sediment Standing Team-CSST-in 2003. These guidelines can be applied not only to individual contaminants
but also to several metals and compounds to estimate
their combined toxic effects. The environmental sample
concentration is divided by the probable effect concentration (PEC), the concentration above which, according
to the consensus-based sediment quality guidelines,
adverse effects on benthic organisms are expected; the
resulting value is called PEC quotient (Table 2). The
guidelines list include threshold effect concentration
(TEC), the concentrations below which adverse effects
on benthic organisms are seldom observed, and PEC,
the concentration above which adverse effects on
benthic organisms are observed. The toxicity level
increases from level 1 to 4.
Diatoms The used methodology for species data
analysis was canonical correspondence analysis
(CCA), which is used to study diatom responses to
the environmental variables. The main purpose of this
method is treating the data as a whole, so that the
diatom community structure is highlighted and the
information is condensed in a simple and interpretable
way. CCA was performed using the computer Canoco
program (version 4.5; Ter Braak and Šmilauer 2002).
The used CCA original matrix was composed of 142
diatom taxa and 31 environmental variables. Species
data were square root transformed. One of the options
of this program is the progressive selection of
environmental variables, which chooses the set of
variables that better explain the species dispersion. The
statistical meaning of each variable is tested with a Monte
Carlo permutation test. The power of this test increases
with the number of permutations, so the maximum
number possible (999) in the program was chosen. Only
significant variables (P≤0.05) were included in the
analysis (Ter Braak and Šmilauer 2002).
Changes in diversity were checked by calculation of
Shannon–Wiener (H′) Index, which is widely used in
Almeida (1998), Butcher et al. (2003) and Reiss and
Kröncke (2005). This index is defined byP
s Shannon
(1948) (in Washington 1984) as: H 0 ¼ nNi log2 nNi ,
i¼1
where s is the number of species, ni the number of
specimens of i species and N total number of specimens.
4 Results
4.1 Stream Sediments
Descriptive statistics were used to characterise a group of
data and research the presence of anomalous structures.
The data matrix included 20 samples of stream
sediments and 14 active variables: Al, As, Ca, Cd, Cu,
Fe, K, Mn, Na, Ni, Pb, S, Sb and Zn. The explained
variance % and the cumulative variance % of PCA axes
are shown in Table 1. The first three axes explained
83.55% of the total inertia. The three axes retention
was based on an empirical criterion of eigenvalues >1
(Davis 1973, 1986): axis 1 explained S, Pb, Sb, Cu,
As, Fe, Zn and Cd in opposition to Ni and Mn; axis 2
explained Al, K and Na; and axis 3 explained Ca (Cd
and Zn are already explained by axis 1).
The variable projection on PCA’s first factorial
plane (had 71.62% of the matrix total information)
established groups of chemical elements according to
their affinity (Fig. 2).
Water Air Soil Pollut
Al
As
Ca
Cd
Cu
Fe
K
Mn
Na
Ni
Pb
S
Sb
Zn
Eigenvalues
% Explained variance
% Cumulative variance
Axis 1
Axis 2
Axis 3
0.03
−0.86
0.36
−0.63
−0.88
−0.80
−0.07
0.71
−0.34
0.76
−0.91
−0.92
−0.89
−0.69
6.83
48.79
48.79
−0.95
−0.02
−0.24
0.08
0.25
−0.03
−0.94
−0.38
−0.87
−0.43
−0.25
−0.22
−0.26
0.13
3.20
22.83
71.62
−0.02
−0.23
0.70
0.71
0.19
−0.26
−0.12
0.33
0.04
0.07
−0.06
−0.03
−0.03
0.63
1.67
11.93
83.55
In axis 1, Cu–Zn–Cd–As–Fe–S–Pb–Sb association
reflected the massive sulphide signature [pyrite (FeS2),
chalcopyrite (CuFeS2), esfalerite (ZnS), galena (PbS),
tetrahedrite (Cu12As4S13) and tenantite (Cu12 As4 S13)
and other less abundant minerals: bournonite
(CuPbSbS3), stanite (Cu2PbSbS4) and marcassite
(FeS2)] in opposition to manganese mineralisations
[hematite (Fe2O3) and pirolusite(MnO2)]. The samples
that contribute for this association were G1, G3, G4,
H1 and H4 (Água Forte stream). Axis 2 represents the
element–mineral association of the source rocks (Na–
K–Al) in the studied area and discriminates also three
groups of variables: Cu–Zn–Cd, As–Fe and S–Pb–Sb.
The consensus-based guidelines application, using
the PEC values is a new approach that estimates the
sediment potential toxicity for invertebrate test organisms
at sites where multiple contaminants are present. Table 2
shows the obtained results in the studied sites. The trace
elements were selected according to PCA results.
Many of the sediment samples collected in
Aljustrel streams contained metal concentrations that
exceeded criteria for benthic organisms. The highest
metal concentration samples were found at sites F, G
and H. Additional samples with high metal concentrations were found at sites C and D. Arsenic, Cu, Fe,
Pb and Sb detected concentrations generally exceeded
the PEC values reaching level 4, which is the highest
toxicity level.
Cadmium concentrations were lower than the TEC
value (0.99 mg kg−1). Only samples E1, G1, H1, D4
and H4 exceeded the TEC value and fall in level 2.
None exceeded the PEC value (5 mg kg−1). For Ni, all
samples were between levels 1 and 2 (except D1 and
B4), but none exceeded PEC. The examination of the
results for potential toxicity showed that samples
collected at sites F, G and H stood out in terms of
potential toxicity. Sites A and B showed the lowest
concentrations for all the trace metals. Sample A4 is
an exception (As and Fe concentrations exceeded the
PEC values). Site B is located in Roxo stream,
upstream from Aljustrel mining contaminated area,
and the concentrations of Mn and Ni revealed the
proximity of Mn mineralisations.
4.2 Surface Waters
Surface waters results (Fig. 3) showed large variation
in metal concentrations (sample H4 was excluded due
to presence of extremely high values): As, 1.7 to
6,837 μg L−1; Cd, 0.025 to 455 μg L−1; Cu, 1.4 to
68,795 μg L−1; Fe, 5 to 1,262,000 μg L−1: Mn, 0.025
to 19,451 μg L−1; Pb, 0.05 to 136 μg L−1; and Zn,
0.25 to 264377 μg L−1. The highest values occurred
at site H. The analysis of Fig. 3 revealed the presence
of outlier values, especially of Cd, Cu, Fe, Mn and
0,5
Factor 2 : 22,83%
Table 1 Correlations between variables and PCA axes (selected variables with values > |0.5|), eigenvalues, percentage of
explained variance and percentage of cumulative variance for
stream sediments
Cu
Zn
Cd
As
Fe
0,0
S
Pb
Sb
Ca
Mn
Ni
-0,5
Na
K Al
S, Sb; Pb
-1,0
-1,0
-0,5
0,0
0,5
1,0
Factor 1 : 48,79%
Fig. 2 Principal component analysis of stream sediments:
projection of the variables on the first factorial plane (axis 1/axis 2)
Water Air Soil Pollut
June 2006
March 2006
May 2005
Table 2 Seasonal and spatial variation of As, Cd, Cu, Fe, Mn, Ni, Pb, Sb and Zn in stream sediments of the studied sites and
comparison with TEC, MEC and PEC guideline values used for stream sediments evaluation
Stream Site
Background
values
As
Cd
Cu
Fe
Mn
Ni
Pb
Sb
Zn
26
0.3
65
38000
759
29
65
3.9
118
TEC
9.8 a)
0.99 a)
32 a)
20 000 b) 460 b)
23a)
36 a)
2 c)
120 a)
MEC
21.4 a)
3 a)
91 a)
30 000 b) 780 b)
36 a)
83 a)
13.5 c)
290 a)
PEC
33 a)
5 a)
150 a) 40 000 b) 1100b)
49 a)
130 a)
25 c)
460 a)
B1
RX
C1
D1
AZ
E1
F1
AF
G1
H1
B3
RX
C3
D3
AZ
E3
F3
AF
G3
AF (U) A4
B4
RX
C4
D4
F4
AF
G4
H4
17
92
21
51
168
1182
2490
18
295
42
110
704
1171
56
20
323
46
734
1045
1779
0.2
0.7
0.1
1.1
0.9
1.2
2.6
0.2
0.7
0.9
0.9
0.3
0.2
0.1
0.2
0.8
3
0.2
0.3
1.1
32
30
45
23
33
12
12
30
29
17
22
21
19
23
42
22
22
24
21
21
39
306
25
80
475
2339
2627
18
244
56
98
260
537
83
25
167
52
339
499
1176
2.5
17.8
2.0
6.2
30.1
270
311
0.9
8.3
1.9
5.0
17.2
36.2
3.6
1.7
9.3
3.2
36.0
44.0
97.0
82
390
81
765
393
580
937
91
460
386
392
216
227
128
108
536
1009
251
217
585
51
241
55
245
298
685
843
31
618
459
214
361
400
78
43
564
575
376
280
584
35600
56100
41500
28200
48600
142900
213300
33800
65500
26300
41400
109400
123600
44600
41800
205400
32300
124300
97100
149300
762
1182
1087
451
991
155
226
795
309
564
938
475
392
718
2113
340
738
806
508
360
Values in mg kg−1 (adapted from Consensus-Based Sediment Quality Guidelines Recommendations for Use & Application,
developed by the CSST 2003)
TEC threshold effect concentration, MEC midpoint effect concentration, PEC probable effect concentration), RX Roxo stream, AZ
Água Azeda stream, AF Água Forte stream, AF (U) Forte stream (upstream)
a
CBSQG (2000a) in CSST (2003)
b
Ontário (1993) in CSST (2003)
c
NOAA (1991) in CSST (2003)
≤ TEC
Level 1
> TEC
≤ MEC
Level 2
> MEC
≤ PEC
Zn. All variables showed positive asymmetry, except
Cl and pH (negative asymmetry).
Principal component analysis was applied to a
matrix of 28 individuals and 16 variables [Ca, Cl, K,
Mg, Na, HCO3−, SO42− (major elements), As, Cd, Cu,
Fe, Mn, Pb, Zn (trace elements), pH and conductiv-
Level 3
> PEC
Level 4
ity]. Table 3 shows the 16 coordinates for the four
axes that explained 88.89% of total variance. The first
factorial plane contains 49.88% of matrix total
information.
In axis 1, with an explained variance of 49.88%,
12 of the 16 active variables (conductivity, Mn, Cd,
Water Air Soil Pollut
Fig. 3 Box plots of As, Ca,
Cd, Cl, Cu, Fe, K, Mg, Mn,
Na, Pb, SO42−, Zn, pH and
conductivity (major elements and trace metals
expressed in μg L−1;conductivity values expressed
in μS cm−1) of surface
waters
10000000
1000000
100000
10000
1000
100
10
1
0.1
0.01
0.001
As
Ca
Cd
SO42−, Cu, Zn, Fe, Pb, As, Mg, pH and HCO3−) were
well represented. However, three of these variables
were also explained by two other axes: pH and HCO3−,
(also explained by axis 3) and Mg (by axis 2). The
variables Mn, Cd, SO42−, Cu, Zn, Fe, conductivity, Pb,
As and Mg were positively explained by axis 1 in
opposition to pH and HCO3−, negatively explained
showing that the high concentrations of trace metals
are related to acidic waters. Axis 2 explained Cl, Ca,
Na and Mg variables, while axis 3 explained HCO3−
and pH (already explained by axis 1). Variable K was
explained by axis 4. Figure 4 shows the variable
projection on the first factorial plane: 1/2.
4.3 Biological variables
4.3.1 Canonical Correspondence Analysis
Non-impacted sites (A and B) were removed from the
statistical analysis because their physical, chemical
and biological characteristics were very different from
other sites, which resulted in the clumping of the
other sites in the ordination diagram.
Cl
Cu
Fe
K
Mg
Mn
Na
Pb SO42- Zn
pH Cond.
The cumulative percentage variance of environment–species data for each axis was 17.6% (axis 1),
28.8% (axis 2), 39.8% (axis 3) and 48.8% (axis 4).
Axes 1 and 2 explained about 20% (19.8%) of the
variance in the species data and 28.8 % of the
environment–species relation. The total inertia was
3.32.
Axis 1 had a strong positive correlation with pH
and a negative correlation with Pb and Si. Axis 2 had
positive correlation with Mg and negative correlation
with HCO3− and K. Ca, Cl and Sr showed positive
correlation with axis 3.
Monte Carlo’s test with 999 permutations revealed
as significant variables: pH, Cd (correlated with axis
1), Ba, Ca, K, Mg, HCO3− and conductivity (correlated with axis 2).
Axis 1 is related to aquatic acidity (Fig. 5a).
Quadrants 1 and 4 show the sites with higher pH
(sites D and E) in contrast with quadrant 2 where sites
with lower pH (C, F and H) and high metal
concentrations are represented. Conductivity, SO42−,
Li and Ni variables are correlated with axis 2, which
represents the mineralisation gradient.
Water Air Soil Pollut
pH
Conductivity
HCO3−
SO42−
Ca
Cl
K
Mg
Na
As
Cd
Cu
Fe
Mn
Pb
Zn
Eigenvalues
Explained variance %
Cumulative variance %
Axis 1
Axis 2
Axis 3
Axis 4
−0.73
0.86
−0.51
0.94
0.19
−0.21
−0.22
0.52
−0.07
0.64
0.94
0.92
0.87
0.96
0.71
0.91
7.98
49.88
49.88
−0.15
0.45
−0.13
0.25
0.89
0.91
0.28
0.76
0.85
−0.33
−0.21
−0.35
−0.37
0.14
0.07
−0.21
3.77
23.54
73.41
0.51
0.11
0.57
0.01
0.24
0.27
−0.15
0.31
−0.25
0.47
0.00
0.15
0.29
−0.02
−0.40
−0.04
1.39
8.68
82.09
0.12
0.09
0.48
−0.09
−0.23
0.06
0.78
−0.11
0.19
−0.08
0.13
0.09
0.09
0.05
0.25
0.18
1.10
6.90
88.89
According to Fig. 5a sampling sites are well
discriminated despite substrate or seasonal variation.
This means that spatial variation is more important
than seasonal or substrate variation, making possible to
analyse diatom communities from different substrates
altogether because diatom communities are similar.
The relationship between environmental variables
and diatoms is shown in Fig. 5b. For interpretation of
diatom labels, see Tables 4, 5, 6 and 7 in the Appendix.
Figure 5b shows that all taxa in quadrant 2 are
correlated with low pH, high conductivity and high
concentrations of trace metals: Al, Cd, Co, Cu, Fe,
Mn, Ni, Sb and Zn. Most of this quadrant taxa belong
to campaign 3 (Table 6 in the Appendix). In
opposition, on quadrant 4 (mostly of the taxa
belonging to campaign 1), the represented taxa prefer
the pH and HCO3− highest values and the lowest
metal concentrations (Table 7 in the Appendix).
4.3.2 Species Diversity and Patterns between
Dominant Taxa, pH and Trace Metals
Seasonal and spatial variations of H′ for epiphytic,
epilithic and epipsammic communities are shown in
Fig. 6. No epipsammic or epiphytic samples were
found at site A, so measures are absent in the graph
(Fig 6a,c). Other sites did not have the three substrate
types, so they are missing as shown in Fig. 6 (e.g.
sites B and E in Fig. 6c).
The diversity index (H′) of epiphytic and epilithic
diatom samples decreased with proximity to the mine
(Fig. 6a,b). H′ of sites A (1.5 to 4.6) and B (2.6 to 4.6)
were higher than H′ of sites D (0.8 to 2), F (0.7 to 2.2)
and H (0.6 to 2.8). The epipsammic diatom samples
behaved inversely: H′ increased with increasing
proximity to the mine (Fig. 6c).
Considering the three substrates together it’s clear
that (a) campaign 1 Shannon–Wiener values (May of
2005) were generally the lowest; (b) the highest H′
values were found at sites A and B, the farthest from
the mines; (c) sites E and F had the lowest H′ values;
and (d) campaign 3 (March of 2006) showed the
highest H′ values.
Eunotia exigua and Pinnularia acoricola (Fig. 7)
were the dominant taxa in AMD most affected sites (F
and G).
Achnanthidium minutissimum (Fig. 7) was the
dominant taxon in AMD less affected sites (A and B).
1,0
Cl
Na
Ca
Mg
Impacted
sites
0,5
Factor 2 : 23,54%
Table 3 Correlations between variables and PCA axes (selected
variables with values > |0.5|); eigenvalues, percentage of
explained variance and percentage of cumulative variance for
surface waters
Cond
K
SO4
Mn
Pb
0,0
pH HCO3
Cd
Zn
As
Non
Impacted
sites
-0,5
Cu
Fe
-1,0
-1,0
-0,5
0,0
0,5
1,0
Factor 1 : 49,88%
Diatom diversity was calculated using the Shannon–
Wiener Index (H′) (Shannon 1948 in Washington
1984).
Fig. 4 Principal component analysis of surface waters:
projection of the variables on the first factorial plane (axis 1/
axis 2)
-1.0
Fig. 5 a CCA ordination of
the first two axes showing
scores for samples and environmental variables (sites
A and B were excluded).
Each sample is represented
by two letters and a number,
which represent the site
(from C to H)/type of sample (E epiphytic, R epilithic
and S epipsammic)/number
of campaign (from 1 to 4)
and environmental variables
are represented by vectors;
b CCA ordination of the
first two axes showing
scores for taxa and environmental variables. Taxa
labels consist of a four-letter
code (for further information see Appendix): the
species closest to the tip of
the arrow of an environmental variable were the
most correlated to it
1.0
Water Air Soil Pollut
1.0
-1.0
1.0
-1.0
-1.0
Although less dominant than the previous,
several other taxa were also numerically important
in the diatom communities: Brachysira vitrea,
Nitzschia hantzschiana, Navicula veneta, Nitzschia
1.0
fonticola and Nitzschia capitellata (Fig. 7) in sites C
and D.
It is important to know if there is no pattern
between these dominant taxa, pH and trace metals, so
Water Air Soil Pollut
Fig. 6 Shannon–Wiener
(H′) values for epiphytic (a),
epilithic (b) and epipsammic (c) samples during the
four sampling campaigns at
the studied sites (decreasing
mine distance from site
A to H)
(a)
5
4.5
Campaign 1
4
Campaign 2
3.5
Campaign 3
3
H' 2.5
Campaign 4
2
1.5
1
0.5
0
A
B
C
D
E
F
G
H
Sites
(b)
4.5
Campaign 1
4
Campaign 2
3.5
Campaign 3
3
H'
Campaign 4
2.5
2
1.5
1
0.5
0
A
B
C
D
E
F
G
H
E
F
G
H
Sites
(c)
4
Campaign 1
3.5
H'
Campaign 2
3
Campaign 3
2.5
Campaign 4
2
1.5
1
0.5
0
A
B
C
D
Sites
Pearson’s correlations were done, and they were
significant (P < 0.05) for H′/Cd (−0.40), H′/Pb
(−0.42) and H′/Zn (−0.41).
The following diagrams (Fig. 8a,b) show the
relation between the dominant taxa E. exigua and P.
acoricola and pH (only samples, where each taxon
appears, were projected in the graph).
E. exigua reached a 33.5% maximum at site F2.
Sites C, D and H showed over 5% relative abundance
(Fig. 8a). E. exigua is clearly acidophilous, reaching
its maximum abundance at pH 2 and revealed
unimodel-skewed curve to the left of the graph
(Fig. 8a). E. exigua increased its relative abundance
with decreasing pH. Samples with low pH [F2 (2.3),
Water Air Soil Pollut
F3 (2.6) and F4 (3.0) and C4 (3.4)] had higher metal
and sulphate concentrations and were those with
higher E. exigua relative abundances.
P. acoricola was abundant (>20 %) in a large
number of samples from metal-contaminated sites
(C3, F1, F2, F3, F4, G2, G3, G4, H2, H3 and H4)
reaching 90% maximum relative abundance at site H2
(Fig. 8b). P. acoricola is more abundant at sites with
pH around 2 to 3 (H2, H3 and H4), which are those
with high metals and sulphate concentrations.
5 Discussion
In this study, PCA showed that chemical characteristics of Aljustrel stream systems were influenced by
geology and AMD: (a) trace elements, pH, conductivity and sulphate concentrations were very high in
stream sediments and surface waters; (b) there was an
increase in drainage waters acidity and trace metal
concentrations when dissolution periods were coincident with strong rainfall during winter, spring and
autumn, which can be explained by metal lixiviation
processes; (c) the concentrations of trace elements
were higher at sites F, G and H (Água Forte stream);
and (d) sites A and B had the lowest metal
concentrations (the most upstream sites).
Surficial water of the three sub-basins show similar
global behaviour: at non-metal contaminated sites (A and
B), the water’s hydrochemical facies is mainly chloride
or bicarbonated, while at sites F, G and H, the water is
mainly sulphated and enriched in Mg. In general, most of
the surface water samples showed sulphated facies
characteristic of waters affected by AMD as result of
pyrite oxidation in tailings and waste rocks. A simplified
representation of this chemical process is given below:
reaction of pyrite with air and water,
2
þ
FeS2ðsÞ þ 7=2O2ðgÞ þ H2 Oð1Þ ! Fe2þ
ðaqÞ þ 2SO4ðaqÞ þ 2HðaqÞ
ð1Þ
in which the product is a ferrous sulphate and sulphuric
acid solution. The dissolved ferrous iron continues to
oxidise and hydrolyse when mine water is no longer in
contact with pyrite surfaces.
þ
Fe2þ
ðaqÞ þ 1=4O2ðgÞÞ þ 5=2H2ð1Þ ! FeðOHÞ3S þ 2HðaqÞ
ð2Þ
producing additional acidity (Nordstrom and Alpers
1999).
The severity of AMD (extreme acidity and high
amounts of sulphates and dissolved metals like Fe,
Al, Mn, Cu, Zn, Cd, Pb and As) is related with the
tailings and waste rocks mineralogy.
Some of the AMD elements may be transported
downstream as dissolved free ions, but others,
especially Fe and Al, can be quickly removed from
the water by precipitation as solid phases depending
on the physicochemical conditions along their
migration path. These precipitates play an important
role in heavy metals removal by adsorption and
coprecipitation.
In this study, surface waters and stream sediments
analyses showed a geochemical gradient from high
metal concentrations and low pH at the sites nearest to
the mining area (sites F, G and H), the most impacted
by AMD, to the sites furthest away from AMD
impacted areas (sites A and B), with low metal
concentrations and circumneutral environment. A lack
of seasonal variation was noted for surface water
results, probably due to the atypical climatic years of
the sampling, with registered high temperatures and
weak rainfall. The spatial gradient was the main
driving force for diatom community behaviour.
The conjugation of geochemical and diatomic data
highlights the fact that diatoms were strongly influenced by pH and metal concentrations variation
(Fig. 6b). Whitton and Satake (1996) mention that
diatoms of AMD environments (pH<3) belong mostly
to Pinnularia, Eunotia, Navicula and Nitzschia genera.
These results are also in accordance with those found
in Gerhardt et al. (2008).
In this study, substantial reduction on diatom diversity
and species richness was found in acidic environments
with high metal concentrations (sites F, G and H;
Fig. 6b), according to Hargreaves et al. 1975; Yan and
Stokes 1978; Van Dam 1981; Mulholland et al. 1986;
Stokes 1986; Eloranta 1988, 1990; Kwandrans 1993;
Verb and Vis 2000a, b, 2001, 2005; Sabater et al.
2003). Other researches combined information of
geochemical indicators and diatoms to assess pollution
[Lac Dufault (Canada)], where shifts in species
composition with metal pollution as well as morphological changes were noted (Cattaneo et al. 2004;
Couillard et al. 2004).
The most AMD-affected sites were dominated by
E. exigua (F, 33.5 %) and P. acoricola (F, 86.8%; G,
Water Air Soil Pollut
88.5%; H, 91.1%). This is in opposition to less AMDimpacted sites where A. minutissimum was the
dominant species (A, 76.1% and B, 24.39%).
Eunotia genus is well distributed in acidic waters
(Patrick 1977; Verb and Vis 2000b) as shown in this
study. The morphological variations of some species
of Eunotia are substantial (i.e. Steinman 1987), which
makes the taxonomy of this group very difficult.
Many of the typical taxa of acidic environments are
included in this genus: E. exigua, Eunotia tenella,
Eunotia levistriata and some varieties of Eunotia
septentrionalis.
E. exigua is acidobiont (Patrick 1977; Van Dam et
al. 1994) and is one of the common species in rivers
and lakes influenced by AMD, with pH<5 (Kwandrans
1993) in North America and Europe. Lessmann et al.
(1999) found this taxon in lignite-influenced lakes with
pH of 2 and 3 in Germany. E. exigua is α-mesosaprobiont and acidobiont according to Van Dam et al.
(1994) and shows high tolerance to contamination
(Baffico et al. 2004) and to large spectra of chemical
pollutants (Guasch et al. 1998) as seen in this study. In
the present study, samples with low pH and high metal
concentration had high relative abundances in E.
exigua, as in other studies (Warner 1971; DeNicola
2000; Passy 2006).
P. acoricola, also an abundant taxon in AMDaffected sites, was described by Hustedt (1935) as a
common taxon in acidic environments (pH<3.5),
extended all over the world: in Java, sulphuric
streams with pH of 2.8 to 3.0 (Carter 1972) and in
New Zealand in waters with pH below 1 (Cassie and
Cooper 1989). Negoro (1985) found this species well
distributed in acidic habitats in Japan, with pH variation
between 2 and 4 and temperatures of 20.5°C to 46.8°C;
Watanabe and Asai (1995) quoted this taxon also in
Japan, in sites of pH below 1.1; and Sabater et al.
(2003) found this taxon in an acid stream of
southwestern Spain, the Rio Tinto (same geological
unit: IPB, as Aljustrel). This taxon has a variable
morphology and resembles Pinnularia obscura
Krasske (Carter 1972), both recorded in other sites:
England (Hargreaves et al. 1975), North America
(Whitton and Diaz 1981) and South Africa (DeNicola
2000).
The dominant diatom, A. minutissimum, is abundant in a wide variety of habitats and conditions
(Beaver 1981; Verb and Vis 2000b). It has a much
wider range of tolerance to many environmental
Fig. 7 Scanning electron microscopy photographs (JEOL-JSM
5400) of dominant taxa. 1 External valvar view of Achnanthidium minutissimum (Kützing) Czarnecki; 2 internal valvar view
of Brachysira vitrea (Grunow) Ross in Hartley; 3 external
valvar view of Nitzschia capitellata Hustedt in A. Schmidt et
al.; 4 girdle view of Nitzschia hantzschiana Rabenhorst; 5
external valvar view of Navicula veneta Kützing; 6a external
valvar view of Pinnularia acoricola Hustedt; 6b internal valvar
view of Pinnularia acoricola Hustedt; 7 internal valvar view of
Eunotia exigua (Brébisson ex Kützing) Rabenhorst; internal
valvar view of Nitzschia fonticola Grunow in Cleve et Möller
parameters than other species of Achnanthidium and
is usually the only reported Achnanthidium species in
AMD-polluted streams (Ponader and Potapova 2007).
Some authors consider it a pioneer coloniser and
characteristic of disturbed conditions (Sabater et al.
1998; Sabater 2000), capable of invading open areas
due to changes in environmental conditions (Peterson
and Stevenson 1992; DeNicola 2000). It is very
common in pH below 5 according to Van Dam et al.
(1994) but may exist in media with widely variable
pH. Based on Young (1976) and Verb and Vis (2000a),
it can survive in sites of very low pH (3.0 to 3.5).
Other authors refer it as cosmopolitan being
present in waters with pH around 7 (neutrophilous)
but being able to tolerate waters of low pH and high
metal concentrations (e.g. Takamura et al. 1990;
Ruggiu et al. 1998; Ivorra et al. 1999; Cattaneo et
al. 2004).
Yoshiaki et al. (2004) showed that relative abundance of A. minutissimum had a raising tendency when
Cu, Zn and Pb concentrations were high. Therefore,
they concluded that A. minutissimum was tolerant to
heavy metals, probably due to its small size (also
concluded in Cattaneo et al. 1998), and it could be
used as heavy metal pollution bioindicator. In the
present study, we can consider A. minutissimum as
neutrophilous and avoiding high metal concentrations.
In AMD moderately influenced sites (C, D and E),
the dominant species were B. vitrea, N. hantzschiana,
N. veneta, N. fonticola and N. capitellata.
B. vitrea is common in oligosaprobic media (Van
Dam et al. 1994). It is considered an acidophilic taxon
(Monteith and Evans 2005; Van Dam et al. 1994) and
can stand pH oscillations in AMD-impacted waters
(Verb and Vis 2000a, b; Cattaneo et al. 2007), which
justify its dominance in this study in some AMD
impacted sites. Concerning this taxon’s optimum pH,
references are contradictory: for Dixit et al. (1991)
and Charles et al. (1989), this taxon is neutrophilic.
b
Water Air Soil Pollut
Water Air Soil Pollut
a) 40
b) 100
H2
F1
F2
High metal content
80
30
G3
G4
% PACO
% EEXI
High metal content
20
F3
F4
60
G2
H4
F2
F3
H3
40
C3
10
F4
20
C4
C2
H4
C3
C2
Low metal content
Low metal content
F1
H3
D2
C4
D3
0
D2
G1
D1
D3
E2 E3
0
0
2
4
6
8
0
2
pH
4
6
8
pH
Fig. 8 Variation of Eunotia exigua (EEXI) (a) and Pinnularia acoricola (PACO) (b) relative abundances (%) with pH
N. hantzschiana is very rare in mine-impacted sites
(Hofmann 1994), but it was sometimes identified as
dominant in sites just after the mine. Therefore, it is
capable of surviving in acidic sites. Based on Van
Dam et al. (1994), this taxon is found in mesotrophic
and oligosaprobic media. This author defines it as
neutrophilic taxon.
N. veneta, based on Van Dam et al. (1994), is an
alcaliphilic taxon. Related with trophic state, it is
eutrophic, and related with saprobic level, it is αmesosaprobic to polisaprobic. In this study, it is very
abundant in the least AMD-impacted sites (A and B),
which are those with the highest nutrient levels and
are closest to agricultural areas.
N. fonticola and N. capitellata are alcaliphilic taxa,
and based on Van Dam et al. (1994), the first one is
pollution sensitive while N. capitellata is pollution
tolerant. In the present study, these taxa are abundant
in moderately metal contaminated to very contaminated sites (C, F, G and H), which have low pH.
The H′ diversity index showed that epipsammic
samples had lower H′ values than epilithic and
epiphytic, which showed similar H′ values. However,
epipsamic samples were scarce, and we cannot
consider them as conclusive.
Canonical correspondence analysis showed axis 1
related with acidity and Pb (see also Verb and Vis
2000b, 2001), and it separated AMD-impacted sites
(quadrant 2) from non-impacted ones (quadrant 1).
Axis 2 represented the mineralisation due to dissolved
metallic ions, acidic runoff and mechanical dispersion
and divided chemical water facies: sulphated facies
(quadrant 3) from bicarbonated facies (quadrant 4).
6 Conclusions
Acid mine drainage is affecting, in several ways, the
Aljustrel mining area: extremely high concentrations
of trace elements in stream sediments and waters,
very different from background values; decrease of
diversity in diatom communities; elimination of
sensitive taxa; and shifts in taxonomic composition.
Decreasing AMD would minimise environmental
impact in this region.
Water pH influenced significantly not only the
composition of diatom assemblages but also other
environmental variables such as As, Cu, Fe, SO42−,
Mn, Pb, conductivity, etc; high acidity is mainly
correlated with high Fe and Al concentrations and less
with other metal concentrations (Hargreaves et al.
1975; Gross et al. 1998; Gross 2000) that leads to low
concentrations of total inorganic carbon and low
phosphorus concentrations, both affecting primary
production (Lessmann et al. 1999, 2000; Niyogi et
al. 1999).
Diatom communities of contaminated habitats can
differ from a community of a non-polluted habitat not
Water Air Soil Pollut
only in species composition but also on the tolerance
degree developed by species. Therefore, the relation
between metal pollution and diatom community composition is not simple (Foster 1982). Canonical correspondence analysis is a powerful method for examining the
multiple environmental variables influence on diatom
species distribution.
Understanding the ecology of diatoms is not
straightforward. In this study, some trends were noted
considering pH and some metals, but we must be
careful with hasty decisions. The media under study
have a very complex geochemistry, making difficult
the interpretation of the species’ ecological behaviour
and the multiple parameters isolation. Parallel laboratory studies can help to elucidate these problems
performing toxicity tests of single species with a
single varying factor. The metal uptake of acidophilic
microalgae in the lab, accomplished of the maintenance of the same conditions in the field (mesocosm
test), can also aid in the understanding of this
problem.
Appendix
Table 4 Diatom taxa from Quadrant 1 of CCA
Code
Name
CRAC
EALA
EMON
FPYG
LCOH
GTER
NAUR
NCLA
NDUB
NLIN
NNAN
PGIB
PSCA
SANG
Craticula accomoda (Hustedt) D.G. Mann
Entomoneis alata Ehrenberg
Eunotia monodon Ehrenberg
Fallacia pygmaea (Kützing) Stickle et D.G: Mann
Luticola cohnii (Hilse) D.G. Mann
Gomphonema tergestinum (Grunow) Fricke
Nitzschia aurariae Cholnoky
Nitzschia clausii Hantzsch
Nitzschia dubia W. M. Smith
Nitzschia linearis (Agardh) W.M. Smith
Nitzschia nana Grunow in Van Heurck
Pinnularia gibba Ehrenberg
Pinnularia subcapitata Gregory
Surirella angusta Kützing
Correspondence between each four-letter code and scientific
name
Table 5 Diatom taxa from Quadrant 2 of CCA
Code
Name
AEXG
Achnanthes exigua Grunow
ALAN=PTLA Achnanthes lanceolata (Bréb.) Grunow var.
lanceolata Grunow = Planothidum
lanceolatum (Brébisson ex Kützing)
Lange-Bertalot
APED
Amphora pediculus (Kützing) Grunow
BPAR
Bacillaria paradoxa Gmelin
CMOL
Caloneis molaris (Grunow) Krammer
COCE
Cyclotella ocellata Pantocsek
EEXI
Eunotia exigua (Brébisson in Kützing)
Rabenhorst
FSAP
Fistulifera saprophila (Lange-Bertalot et
Bonik) Lange-Bertalot
FCVA
Fragilaria capucina var. vaucheriae (Kützing)
Lange- Bertalot
FFAS
Fragilaria fasciculata (C.A. Agardh)
Lange-Bertalot
FSAX
Frustulia saxonica Rabenhorst
HAMP
Hantzschia amphioxys (Ehrenberg) Grunow in
Cleve et Grunow
LMUT
Luticola mutica (Kützing) D.G. Mann
LVEN
Luticola ventricosa (Kützing) D.G. Mann
NCPR
Navicula capitatoradiata Germain
NDIG
Navicula digitatoradiata (Gregory) Ralfs
NERI
Navicula erifuga Lange-Bertalot
NGRE
Navicula gregaria Donkin
NRCH
Navicula reichardtiana Lange-Bertalot
NVEN
Navicula veneta Kützing
NVRO=NROS Navicula viridula (Kütz.) Ehr. var. rostellata
(Kütz.) Cleve = Navicula rostellata Kützing
NACI
Nitzschia acicularis (Kützing) W. M. Smith
NCPL
Nitzschia capitellata Hustedt in A.
Schmidt et al.
NHAN
Nitzschia hantzschiana Rabenhorst
NINC
Nitzschia inconspicua Grunow
NMIC
Nitzschia microcephala Grunow in
Cleve et Möller
NIPM
Nitzschia perminuta (Grunow) M. Peragallo
NPRP
Nitzschia perspicua Cholnoky
NTHE
Nitzschia thermaloides Hustedt
PALP
Pinnularia alpina W. Smith
RSIN
Reimeria sinuata (Gregory) Kociolek et
Stoermer
STAN
Stauroneis anceps Ehrenberg
STKR
Stauroneis kriegeri Patrick
Correspondence between each four-letter code and scientific name
Water Air Soil Pollut
Table 6 Diatom taxa from Quadrant 3 of CCA
Code
Name
ADEL
Achnanthes delicatula (Kützing) Grunow in
Cleve & Grunow
Cocconeis placentula Ehrenberg
Diploneis oblongella (Naegeli) Cleve-Euler
Encyonema minutum (Hilse in Rabenhorst)
D. G. Mann
Fragilaria capucina Desmazières
Fragilaria tenera (W. Smith) Lange-Bertalot
Gomphonema angustum Agardh
Gomphonema gracile Ehrenberg
Gomphonema parvulum Kützing
Mayamaea atomus var. permitis (Hustedt)
Lange-Bertalot
Meridion circulare (Greville) C.A. Agardh
Navicula gibbula Cleve
Navicula insociabilis Krasske
Navicula subminuscula Manguin
Navicula tenelloides Hustedt
Neidium affine (Ehrenberg) Pfitzer
Nitzschia constricta (Kützing) Ralfs
Nitzschia dissipata (Kützing) Grunow
Nitzschia paleacea (Grunow) Grunow in van Heurck
Pinnularia acoricola Hustedt
Pinnularia borealis Ehrenberg
Pinnularia viridis (Nitzsch) Ehrenberg
Surirella minuta Brébisson
CPLA
DOBL
ENMI
FCAP
FTEN
GANT
GGRA
GPAR
MAPE
MCIR
NGBL
NINS
NSBM
NTEN
NEAF
NCOT
NDIS
NPAE
PACO
PBOR
PVIR
SUMI
Correspondence between each four-letter code and scientific name
Table 7 Diatom taxa from Quadrant 4 of CCA
Code
Name
AHIN
ADMI
Achnanthes hintzii Lange-Bertalot et Krammer
Achnanthidium minutissimum (Kützing)
Czarnecki
AVEN
Amphora veneta Kützing
BVIT
Brachysira vitrea Ross in Hartley
CMEN
Cyclotella meneghiniana Kützing
CAPH=CBAM Cymbella amphicephala Naegeli =
Cymbopleura amphicephala Krammer
FULN=UULN Fragilaria ulna (Nitzsch) Lange-Bertalot var.
ulna = Ulnaria ulna (Nitzsch) Compère
NPHY
Navicula phyllepta Kützing
NBIL
Nitzschia bilobata W. M. Smith
NHUN
Nitzschia hungarica Grunow
NPAL
Nitzschia palea (Kützing) W. Smith
NIPU
Nitzschia pusilla (Kützing) Grunow
NREC
Nitzschia recta Hantzsch in Rabenhorst
PKUT
Pinnularia kuetzingii Krammer
PMIC
Pinnularia microstauron (Ehrenberg) Cleve
Correspondence between each four-letter code and scientific
name
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