Factor analysis for the study of water resources
contamination due to the use of livestock
slurries as fertilizer
M. Vidal
a,*, A. LoÂpez
a, M.C. Santoalla
a, V. Valles
baEscuela PoliteÂcnica Superior, Universidad de Santiago de Compostela,
Campus universitario, 27002 Lugo, Spain
bUnite de Science du Sol. I.N.R.A., Site Agroparc, 84914 Avignon, Cedex 9, France
Accepted 14 September 1999
Abstract
We investigated the effects of slurry application on water quality in wells, pasture-drainage conduits and rivers. The parameters determined were electrical conductivity, dry residue, pH, dissolved O2, NO
ÿ 3, NO
ÿ 2, Cl
ÿ
, Na, PO3ÿ
4 organic matter content, COD, suspended solids, turbidity,
colour, Fe, Mn, Cu and Zn. The data were analysed by principal components analysis and cluster analysis. The ®rst axis extracted by principal components analysis of the samples-by-variables matrix represented the degree of dilution of the water strongly related with saline content; the second axis represented redox conditions, affecting organometallic component. In general, the positions of the samples in this factor space re¯ected the major contamination processes affecting water resources of that type (wells, conduits or rivers). In the case of river-water samples, cluster analysis showed that month of sampling had a clear effect, re¯ecting the importance of rainfall. Finally, in all three water resource types, contamination was most severe in the area of highest livestock density and highest frequency of slurry application.# 2000 Elsevier Science B.V. All rights reserved.
Keywords:Wells; Springs; Rivers; Slurries; Contamination; Principal components analysis; Cluster analysis
Agricultural Water Management 45 (2000) 1±15
*Corresponding author. Tel.:34-982-252231; fax:34-982-241835.
E-mail address: pvvidalb@lugo.usc.es (M. Vidal)
1. Introduction
Diffuse-source contamination of water resources as a result of the use of livestock wastes as fertilizers is increasingly recognized as an important problem (Melgar et al., 1997; Overcash et al., 1981; Peterson et al., 1971; Sauerbeck, 1987; CEC, 1978). This has promoted studies aimed at identifying contamination, its origin and the means or reduce it (Cheverry, 1992; ChreÂtien et al., 1974; Doyle et al., 1977; Thurman and Gerba, 1987).
Studies of such contamination typically require monitoring of a wide range of physical, chemical, microbiological and/or organoleptic parameters, which may be very difficult to interpret on the basis of parameter-by-parameter analyses. As a result, multivariate analyses are widely used in studies of this type (Kwan and Kowalski, 1987; Vandeginste and Van Dersel, 1978; Labrandero and Palou, 1980). Multivariate analyses, such as cluster analysis and principal components analysis (Knudson et al., 1977; Arambarri et al., 1981; Benzecri, 1982) reduce the dimensionality of the problem, simplifying and facilitating its interpretation without important loss of information.
In the present study, we monitored the physico-chemical properties of surface and subsurface water from three rural areas of Lugo (northwest Spain). Fertilization of pasture with livestock slurries is common in all the three areas, though livestock densities and the intensity of slurry use vary markedly. Our aim was to investigate the influence of slurry use on water quality.
2. Study area and methods
The study was performed in three rural areas (denominated A1, A2 and A3) (Fig. 1a) in the Province of Lugo (Galicia, northwest Spain). All three areas are predominantly agricultural, with high cattle and pig stocking densities and a large proportion of land area devoted to pastures and forage crops. The use of livestock slurry as fertilizer is widespread. Slurries are typically stored in open or closed ditches sometimes of small size and poor construction quality, generally close to the stables and/or agricultural dwellings. The nearness of the wells used to supply drinking water and flush slurry pits to these pollution sources may results in self-contamination (Coppenet, 1974; King and Le Bissonnais, 1992; Ndayegamiye and CoÃteÂ, 1989; Rail, 1986; Schofield, 1984; Thompson et al., 1987).
Since the principal aim of the study was to investigate the effects of slurry on water quality, it is important to characterize the different study areas in terms of livestock density, slurry storage capacity and slurry use (see ADAS, 1980; Bocchi and Tano, 1994; Decau and Salette, 1994; Destain and Raymond, 1983; Garwood and Tyson, 1977; Jarvis et al., 1987; Lecomte, 1980; Prins and Snijders, 1987; Schofield, 1984; Simon et al., 1992; Stevens et al., 1988; Thurman and Gerba, 1987; Wadman and Neetson, 1992). Table 1 shows a number of relevant indices for the three areas (data from AGACA, 1997). Table 2 summarizes soil fertility characteristics and inorganic fertilizer use in the three areas (data again from AGACA, 1997).
Mean temperature and monthly precipitation data for the study period are shown in Fig. 1b (data for the Nodar weather station, Lugo Province; Xunta de Galicia, 1997).
Samples were obtained monthly (about the 15th of each month), over the 12 month period December 1996±November 1997 inclusive, from a total of 44 sampling stations (12 drinking-water wells close to livestock farms; nine springs; nine subsurface conduits draining pasture habitually fertilized with slurry; 14 river stations). Sampling, sample handling and sample storage was in all cases in accordance with EC guidelines (CEC, 1979, 1980). For the purpose of the present study, wells and springs were considered as a single category.
Following the criteria proposed by Sarkar et al. (1986), a total of 19 physico-chemical parameters were determined, namely pH, electrical conductivity (EC), dissolved oxygen (O2), turbidity (by nephelometry), dry residue (DR), suspended solids (SS), oxidability
(organic matter, OM), chemical oxygen demand (COD), colour, Clÿ
, Na
, PO3ÿ
4 , NO
ÿ
2,
NOÿ
3, NH
4, Fe, Mn, Cu and Zn.
Dissolved oxygen, pH and turbidity were determined on site while all other parameters were analyzed in the laboratory. Samples were collected in Lamotte bottles and transferred to three different types of containers, namely: (a) 1 l polyethylene bottles most
Fig. 1. (a) Location map of study areas, (b) monthly rainfall and mean monthly temperature over the study period.
of which were used for analysis; (b) 100 ml polyethylene bottles containing 0.20 ml of ultrapure nitric acid Ð intended to reduce the absorption of elements on the container walls Ð which were used to determine heavy elements; and (c) the containers used in the on site determinations. As a rule, samples were stored refrigerated at 38C prior to analysis. Nitrate and nitrite were determined by UV absorption spectrophotometry at 220 and 425 nm, respectively. Ammonium was determined by using a selective electrode.
Fig. 1. (Continued).
Table 1
Mean characteristics of farms in the three study areas, as regards agricultural land use, livestock density and slurry usea
Area UAA (ha)
No. of cows
Cows (UAAÿ1)
No. of LLU
LLU (UAAÿ1)
Slurry (m3per month)
Slurry
(m3UAAÿ1monthÿ1)
ENSA
A1 27.0 35 1.3 45.1 1.7 90.2 3.3 10.0 A2 19.4 29 1.5 37.2 1.9 74.4 3.8 9.8 A3 18.2 41 2.3 53.9 3.0 107.8 5.9 13.0
aUAA Ð useful agricultural area, LLU Ð large livestock units, ENSA Ð expected number of slurry
applications per annum.
Chloride was quantified potentiometrically and phosphate by molecular spectroscopy (using ammonium molybdate after digestion in hot nitric acid). Iron, manganese, copper and zinc were measured by atomic absorption spectrometry. Organic matter was determined titrimetrically following oxidation with potassium permanganate. Finally, turbidity values are given in nephelometric units.
The data were analysed by principal components analysis (Knudson et al., 1977; Arambarri et al., 1981; BolarõÂn et al., 1982) and hierarchical cluster analysis, in both cases using the statistics package SPSS version 7.5.2. In cluster analysis, the distance measure was euclidean distance, and the clustering method was between groups linkage. Prior to the multivariate analyses, the values of the different parameters were normalized. Monthly rainfall depth was also included as an independent variable in these analyses for the purpose of evaluating its contribution to water runoff, leaching and contamination (Gracey, 1987; Lea, 1979).
3. Results and discussion
Descriptive statistics for the physico-chemical properties of the 528 water samples, classified by resource type (wells, conduits, rivers), are summarized in Table 3. In all three groups, high among-sample variability (i.e. wide range and high standard deviation) was apparent for the majority of parameters, including parameters indicative of salinity (EC, DR, Clÿ
, Na
and NOÿ
3), parameters indicative of organometallic content (COD, OM, Fe and Mn), colour, Cu and Zn. This high variability justifies the frequency with which contaminants exceed admissible thresholds for drinking water quality. The high variability in metal contents (Fe, Mn, Zn and Cu), particularly in the drainage conduits, reflects marked seasonal variation in redox conditions.
The results of multiple correlation analysis are summarized in Table 4. As can be seen, the groupings of variables showing significant correlation differ among the three resource types. In wells and conduits, similar groups of salinity-related variables are identified, whereas the groups identified in analysis of the river-water data are clearly different. Furthermore, the strong correlation observed between these parameters suggests a common origin for these groups of variables.
The results of bivariate correlation analyses (between EC, OM or monthly rainfall and the other variables) are likewise summarized in Table 4. Identification of variables correlated with monthly rainfall allows evaluation of the contribution of rainfall to runoff,
Table 2
Mean soil nutrient contents in farms of the three study areas, together with mean annual fertilizer inputs Area P available
(mg kgÿ1)
K available (mg kgÿ1)
N total (g kgÿ1)
OM (g kgÿ1)
VeAI (%) Mean annual inputs (kg haÿ1)
N P2O5 K2O
A1 27.4 217.0 3.0 60.2 20.22 66.0 87.6 103.3 A2 25.1 224.7 2.9 63.4 28.83 105.7 120.4 32.3 A3 43.7 357.7 2.3 52.1 6.09 115.9 103.4 17.6
Table 3
Descriptive statistics of the full data set, showing mean (X), standard deviation (SD), maxima, range and standard error of the mean (SEM) for each variable, considering wells and springs (W), pasture-drainage conduits (C) and rivers (R)
X SD Maxima Range SEM
W C R W C R W C R W C R W C R
pH 5.82 5.75 7.02 0.60 0.88 0.47 7.98 11.76 8.15 4.01 7.62 2.75 0.04 0.09 0.04 EC (ms cmÿ1) 238.89 920.00 105.00 0.13 8.19 0.03 850 852.0 200.0` 790.0 852.0 150.0 0.01 0.79 0.00
DR (mg lÿ1) 165.50 96.90 68.80 107.96 72.77 47.41 700.0 330.0 546.0 682.0 330.0 544.0 6.80 7.00 3.66
Clÿ
(mg lÿ1) 23.60 13.42 9.83 18.22 7.88 3.78 110.09 46.51 18.76 107.0 44.87 17.79 1.15 0.76 0.29
Na
(mg lÿ1) 12.43 7.37 6.46 8.14 2.61 2.23 41.08 18.40 13.41 43.08 15.20 11.61 0.51 0.25 0.17
NH
4 (mg l
ÿ1) 0.07 0.13 0.07 0.12 0.32 0.20 1.12 2.19 2.36 1.12 2.18 2.33 0.01 0.03 0.02
NOÿ
3 (mg l
ÿ1) 35.32 13.76 5.57 32.81 7.70 21.0 270.32 33.17 272.18 269.65 32.37 271.14 2.07 0.74 1.62
OM (mg lÿ1) 0.74 1.84 8.34 1.10 3.12 66.98 11.69 19.76 871.0 11.69 19.76 870.85 0.007 0.30 5.17
COD (mg lÿ1) 2.94 5.78 7.29 5.69 9.83 7.39 52.0 56.0 44.0 52.0 56.0 44.0 0.36 0.95 0.57
SS (mg lÿ1) 2.02 7.80 4.45 7.32 26.80 6.22 99.20 255.0 41.0 99.20 255.0 41.0 0.46 2.58 0.48
Turbidity (NTU) 0.51 1.48 1.51 1.18 3.51 1.72 14.40 27.0 13.70 14.35 27.0 13.59 0.07 0.34 0.13 PO3ÿ
4 (mg l
ÿ1) 0.19 0.21 0.14 0.28 0.32 0.24 2.63 2.26 1.84 2.62 2.25 1.83 0.02 0.03 0.02
O2(mg l
ÿ1) 5.93 6.43 9.75 2.38 2.12 7.36 11.42 10.50 11.2 10.71 9.43 9.85 0.15 0.20 0.57
Fe (mg lÿ1) 200.49 681.80 359.07 1505.91 4091.10 263.6 23212.8 42408.0 1478.70 23204.9 42403.3 1468.71 94.86 393.67 20.34
Mn (mg lÿ1) 66.55 116.10 38.74 84.94 221.88 44.36 559.98 1998.36 529.78 549.99 1988.37 519.79 5.35 21.35 3.42
Cu (mg lÿ1) 14.15 10.19 11.66 44.98 0.97 8.41 717.55 20.13 100.96 707.45 10.03 90.87 2.83 0.09 0.65
Zn (mg lÿ1) 25.04 22.56 18.61 55.50 34.18 12.50 809.72 339.56 79.66 799.73 329.57 69.68 3.50 3.29 0.96
Colour (Hazen) 2.82 13.23 31.65 10.27 24.15 24.92 150.0 150.0 150.0 150.0 150.0 150.0 0.65 2.32 1.92
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Table 4
Results of (1) multiple correlation analysis, and of bivariate analyses of correlation with (2) electrical conductivity (EC), (3) organic matter (OM), or (3) rainfall depth in the month of sampling (R)a
MC(1) EC(2) OM(3) R(4)
Wells CE-Cl±Na±NO3±DR Cl±Na±NO3-DR Cl±Na±NO3±NO2±NH4-EC-DR EC-Cl±NO3±NH4-DR
Colour-COD-OM-SS-NTU-Fe±Zn Colour-OM-COD-O2-pH SS-colour-COD-NTU-PO4±O2-pH Mn-pH-colour
Fe±Mn SS-PO4
NH4±O2
Conduits Cl±Na±NO3±NO2±NH4±DR-NTU-O2 pH Cl±Na±NO3±NO2±NH4-DR
Colour-COD-OM-NO2±NH4±Na±Fe±Mn±Zn-NTU-pH SS-colour-COD-NTU-PO4±O2-pH
Fe±Mn Fe±Mn±Zn
SS-PO4±NO2
Rivers EC-NO3-pH-Zn Na±NO3±NO2-DR Colour-OM-NTU Colour-COD-O2
Cl±Na±NO2±Fe-colour Fe±Mn±Zn-pH Fe±Zn Mn±pH
Colour-COD-SS-DR-NTU-Fe±Zn
aFor all sample types, column 1 shows intercorrelated variable groups; columns 2±4 show variables correlated with EC, OM or R respectively. In all cases
correlations are taken to be statistically signi®cant whenP< 0.05.
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1±15
leaching and contamination processes. In the case of drainage conduits, none of the variables considered was significantly correlated with monthly rainfall. In the case of wells, however, rainfall was significantly correlated (p< 0.05) with a number of salinity-related variables, while in the case of rivers rainfall was significantly corsalinity-related with colour, COD, O2, Mn and pH.
Principal components analyses (separate analyses for each sample-origin group) were performed with the correlation matrices. As pointed out by BolarõÂn et al. (1984), this approach favours definition of the principal components but generally means that the axes extracted explain a lower proportion of total variance. In each case, the factor matrix obtained by PCA was subjected to a varimax rotation, with the results summarized in Tables 5 and 6. It should be noted in order to facilitate interpretation of this Table that it does not show the dependent variables studied but rather the principal factors or components provided by the Principal Component Multivariate Analysis. For this reason, the percent variance accounted for by the two principal factors (F1 and F2) does not seem so low. The other factors Ð 19, as many as variables were analyzed Ð account for only small fractions of the overall variance, so they have been excluded from the interpretation.
The criterion used for the selection of number of eigenvalues was mean value. Only factors with eigenvalues >1 were considered in the interpretation. Plots of factor loadings are shown in Fig. 2.
Table 5
Summarized results of the three principal components analyses (for wells, conduits and rivers), showing loadings of the different variables on the ®rst two axes extracted, following varimax rotationa
Wells Drainage conduits Rivers
F1 F2 F1 F2 F1 F2
pH ± ± ± ± ± 0.71
EC 0.96 ± 0.51 ± ± 0.69
DS 0.95 ± 0.67 ± ± ±
Clÿ
0.93 ± 0.82 ± 0.63 ±
Na
0.90 ± 0.82 ± 0.63 ±
NH
4 ± ± 0.66 ± ± ±
NOÿ
2 ± ± 0.65 ± ± 0.57
NOÿ
3 0.86 ± ± ± ± ±
OM ± 0.67 0.55 0.76 ± ±
COD ± 0.65 0.50 0.72 0.76 ±
SS ± 0.74 ± 0.89 ± ±
Colour ± 0.75 0.68 ± 0.83 ±
Turbidity ± 0.85 0.81 ± 0.50 ±
O2 ± ± ± ± ± ±
PO3ÿ
4 ± 0.47 ± ± ± 0.50
Fe ± ± ± 0.89 0.63 ±
Mn ± ± ± 0.79 ± ±
Cu ± ± ± ± ± ±
Zn ± ± ± ± 0.53 ±
aOnly loadings > 0.5 are shown.
As in a previous study of water-quality data for wells and conduits (Vidal et al., 1997), we found that the first factor reflect degree of dilution by rainwater. Elements whose concentration is strongly dependent on dilution by rainfall (NOÿ
3, Cl
ÿ
, Na
, EC and DR) were plotted to the right of this axis, while elements whose concentrations are largely independent on dilution (notably heavy metals, with physico-chemical mechanisms of regulation) were plotted to the left. The second factor was found to reflect degree of aeration (i.e. redox conditions), with dissolved O2and NOÿ3 plotted towards the bottom, while the variables plotted towards the top included Fe and Mg (solubilized in reduced state) and Zn, NH
4 and OM.
The factor loadings for the wells data (Fig. 2a) and for the whole data set (Fig. 2d) are consistent with these factor interpretations. In both cases, note the clear separation of variates into two well-defined groups along axis I (the dilution axis). Likewise, the factor loadings for the conduit data (Fig. 2b) are consistent with these interpretations, though note that there is no clear separation of two groups of variates along axis I. However, the factor loadings for the river-water data are not consistent with the previous interpretations: in this case, factor I (F1) appears to reflect aeration, while there is no straight-forward interpretation of factor II (F2). This probably reflects the fact that river water quality is additionally dependent on other influences, notably drainage and waste discharge from urban areas.
In both Fig. 2a (wells) and Fig. 2b (conduits), dry residue (DR) is plotted to the right of axis I, along with the major elements (NOÿ
3, Cl
ÿ
, Na
and EC), suggesting that it is largely due to inorganic components.
Finally, the variates loading heavily on axis I in the wells-data analysis are those clearly associated with salinity (NOÿ
3, Cl
ÿ
, Na
, EC and DR), while the variates loading heavily on axis II in this analysis are all related to organic content (OM, COD, SS, colour, turbidity and phosphates) (Fig. 2a). By contrast, the variates loading heavily on axis I in the conduit analysis (DR, Clÿ
, Na
, NH
4, NO
ÿ
2, OM, COD, colour, NTU and pH) included organic and inorganic components, while the variates loading heavily on axis II in this analysis include Fe and Mn, OM, SS and COD (Fig. 2b).
Taken together, the above findings indicate that redox processes are the principal cause of the observed variability in the physico-chemical properties of wells and drainage conduits. This conclusion seems reasonable if it is borne in mind that wells and drainage conduits are more anoxic (i.e. reducing) environments than rivers. In Fig. 2, scatter along the aeration axis (axis II for wells and conduits, axis I for rivers) is most pronounced for drainage conduits and least pronounced for rivers (i.e. conduits > wells > rivers).
Table 6
Eigenvalues and variance explained (%) by the ®rst two axes extracted, following varimax rotation, in the principal components analyses of the data for wells, conduits and rivers
Wells Conduits Rivers
F1 F2 F1 F2 F1 F2
Eigenvalue 4.77 3.30 5.81 3.28 3.47 2.10 Percentage of variance 23.9 16.5 29.1 16.4 17.4 10.5 Cumulative percentage 23.9 40.4 29.1 45.5 17.4 27.9
Fig. 2. Plots of variables on the ®rst two axes extracted by principal components analysis (with varimax rotation) of the data for (a) wells, (b) conduits, (c) rivers and (d) all samples. Factor scores are normalized.
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However, mean ratios between NH
4, NO
ÿ
3 and NO ÿ
2 in samples from the different groups (see Table 7) suggest the existence of a reduction gradient running in the opposite direction (i.e. rivers > conduits > wells). The apparently greater reducing activity in rivers, reflected in the higher relative concentrations of NH
4, NO
ÿ
2, contradicts the evidently greater aeration of rivers. It thus seems likely that rivers receive an additional NH4 input, probably as a result of discharge of urban wastes. This hypothesis is
supported by the observed differences in NOÿ
3/Cl between upstream and downstream stretches of river: specifically, all three ratios were on average lower at downstream stations (data not shown), reflecting Clÿ
, Na
and NH 4 input.
Fig. 3a±d show plots of samples on the first two axes extracted by the corresponding principal components analysis, indicating the geographical area from which each sample was obtained (A1, A2 or A3). In general, samples plotted towards the right on axis I are those with higher salinity, while those plotted towards the top of axis II are those with more important organometallic component.
In the analyses of well samples (Fig. 3a) and of all samples (Fig. 3d), scatter was largely along axis I, reflecting differences in salinity, with this variable generally highest in A3 samples and lowest in A1 samples (A3 > A2 > A1). By contrast, there was little variability along axis II: some A3 sites were plotted slightly higher on this axis, and there was no appreciable difference in this regard between A1 and A2 sites. Thus contamination was in general most severe at A3 sites, in accordance with the fact that A3 has higher mean livestock density and higher mean annual slurry use per farm than A1 and A2.
A similar pattern was observed in the analysis of drainage conduit samples (Fig. 3b), except that A2 samples were generally those with highest salinity (A2 > A1 > A3). In the analysis of river-water samples (Fig. 3c), A1 sites likewise showed the highest organic contamination, while salinity was highest at A3 sites (A3 > A2 > A1).
The positions of the samples in the spaces defined by the first etape of principal components analysis were further investigated by cluster analysis (results not shown). In the case of the river-water data, samples were clearly clustered by month, indicating that seasonal variation in precipitation was an important discriminant factor.
In order to assess potential contamination by slurry, one can safely assume that any conditions under which application of the waste results in the soil's water storage capacity being exceeded will highly likely lead to pollution of water resources. The highest usable rate of this waste will essentially depend on the pedoclimatic conditions prevailing at the time of application. When the field capacity of the soil is overcome,
Table 7
2 ratios in samples from wells, conduits and rivers
Wells Conduits Rivers
Fig. 3. Plots of samples on the ®rst two axes extracted by principal components analysis (with varimax rotation) of the data for (a) wells, (b) conduits, (c) rivers and (d) all samples. In each case, samples from A1 sites are indicated by triangles, samples from A2 sites by circles, and samples from A3 sites by crosses.
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whether by application of an excessive slurry rate or because the application is followed by raining or irrigation to an extent resulting in the storage capacity of the soil being exceeded, draining inevitably appears. This in turn leads to contamination of the water by run-off of leached slurry in the rain or irrigation water.
On the other hand, the factors that control and lessen the polluting effect of the slurry are closely related to both the nitrate absorption capacity of the crop and the ammonium binding-adsorption capacity of the soil exchange complex. Similarly, the polluting effect of the slurry is lessened or at least delayed when, under specific weather and soil conditions, applied nitrogen is lost through denitrification and volatilization.
For this reason, based on the data of Table 1, applying slurry at a rate above 6 m3 AAUÿ1 per month over soil under near-saturation conditions is quite dangerous. In order to reduce losses through leaching by the liquid fraction of this waste, one must know the soil storage capacity Ð function of soil depth and storage capacity Ð at any time.
On the other hand, the infrastructure of slurry storage pits, near drinking water wells, will obviously affect water potability.
Based on the results of the bacteriological analyses performed in 12 of the wells and fountains sampled during the studied period, the wells were under strong bacterial contamination and their water was thus unfit for drinking over more than 80% of the period.
4. Conclusion
In all the three sample types studied (wells, conduits and rivers), mean NOÿ
3, Fe, Mn levels and colour exceeded the recommended limits for potability. However, the maximum values and standard deviations of Clÿ
, NOÿ phosphates, Cu and Zn indicate significant temporal variability in these parameters, with the result that potability limits for these components may be exceeded episodically. Considering well-water samples, monthly precipitation was significantly correlated (p< 0.05) with EC, Clÿ
, NOÿ
3, NH
4, pH, Mn and colour. Considering river-water samples, monthly precipitation was significantly correlated with colour, COD, O2, Mn
and pH.
Principal components analysis identified two factors that can be interpreted as reflecting saline and organometallic contamination respectively. In wells and conduits, saline contamination is more marked than organometallic contamination, while in rivers both types of contamination occur. Saline contamination is most marked in wells, conduits and rivers of A3, in which livestock density and frequency of slurry application are both higher than in the other two areas considered. Likewise, organic pollution is most marked in the rivers of A3.
Rivers show more sources of variability than wells and conduits, which is reflected in the greater scatter of samples on the two axes extracted by principal components analysis, reflecting concentration/dilution and redox conditions, respectively.
Finally, contamination levels in rivers are clearly influenced by month, reflecting the importance of dilution by rainfall.
Acknowledgements
This work was funded by the Xunta de Galicia (project no. XUGA-29104B94).
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