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Assessment of water quality of polluted lake using multivariate statistical techniques: A case study
ARTICLE IN PRESS Ecotoxicology and Environmental Safety 72 (2009) 301– 309
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Assessment of water quality of polluted lake using multivariate statistical techniques: A case study T.G. Kazi , M.B. Arain, M.K. Jamali, N. Jalbani, H.I. Afridi, R.A. Sarfraz, J.A. Baig, Abdul Q. Shah Center of Excellence in Analytical Chemistry, University of Sindh, Jamshoro 76080, Pakistan
a r t i c l e in fo
abstract
Article history: Received 30 October 2007 Received in revised form 21 February 2008 Accepted 23 February 2008 Available online 18 April 2008
Multivariate statistical techniques, cluster analysis (CA) and principal component analysis (PCA) were applied to the data on water quality of Manchar Lake (Pakistan), generated during 2005–06, with monitoring at five different sites for 36 parameters. This study evaluated and interpreted complex water quality data sets and apportioned of pollution sources to get better information about water quality and to design a monitoring network. The chemical correlations were observed by PCA, which were used to classify the samples by CA, based on the PCA scores. Three significant sampling locations—(sites 1 and 2), (site 4) and (sites 3 and 5)—were detected on the basis of similarity of their water quality. The results revealed that the major causes of water quality deterioration were related to inflow of effluent from industrial, domestic, agricultural and saline seeps into the lake at site 1 and also resulting from people living in boats and fishing at sites 2 and 3. & 2008 Elsevier Inc. All rights reserved.
Keywords: Physico-chemical parameters Water quality Cluster analysis Principal component analysis Manchar Lake (Pakistan)
1. Introduction Water quality is considered the main factor controlling health and the state of disease in both man and animals. Surface water quality in a region is largely determined both by natural processes (weathering and soil erosion) and by anthropogenic inputs (municipal and industrial wastewater discharge). The anthropogenic discharges constitute a constant polluting source, whereas surface runoff is a seasonal phenomenon, largely affected by climate within the basin (Singh et al., 2004; Vega et al., 1996). Niemi et al. (1990) reported human activities are a major factor determining the quality of the surface and groundwater through atmospheric pollution, effluent discharges, use of agricultural chemicals, eroded soils and land use. Environmental pollution, mainly of water sources, has become public interest. The underdeveloped countries have been suffering the impact of pollution due to disordered economic growth associated with the exploitation of natural resources. Large investigations have been carried out on anthropogenic contamination of ecosystems (Szymanowska et al., 1999; Issa et al., 1996). However, due to spatial and temporal variations in water quality (which are often difficult to interpret), a monitoring
Corresponding author. Fax: +92 222 771560.
E-mail addresses: [email protected] (T.G. Kazi), [email protected] (M.B. Arain), [email protected] (M.K. Jamali), [email protected] (N. Jalbani), [email protected] (H.I. Afridi), [email protected] (R.A. Sarfraz), [email protected] (J.A. Baig), [email protected] (A.Q. Shah). 0147-6513/$ - see front matter & 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.ecoenv.2008.02.024
program, providing a representative and reliable estimation of the quality of surface waters, is necessary (Dixon and Chiswell, 1996). These results are a large and complex data matrix comprised of a large number of physico-chemical parameters, which are often difficult to interpret and to draw meaningful conclusions. The application of different multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminate analysis (DA), helps in the interpretation of complex data matrices for a better understanding of water quality and ecological status of the study region. These techniques allow the identification of the possible sources that influence water systems and offers a valuable tool for reliable management of water resources as well as rapid solution for pollution problems (Reghunath et al., 2002; Simeonov et al., 2004). In Pakistan, drinking water comes from groundwater and surface water including rivers, lakes and reservoirs. The present free style way of disposal of agricultural, industrial and domestic effluents into natural water bodies results in serious surface and groundwater contamination. Run-off from agricultural land and saline seeps subject the most vulnerable water bodies to pollution and increased salinity, so the freshwater lakes are highly impacted. One example for this is the Manchar Lake, Pakistan’s largest freshwater lake. It is the main source of domestic water for the communities living around the lake. Groundwater in this vicinity is saline and is not suitable for drinking (WHO, 1998). The lake’s water in downstream areas is also important for farmers and fishermen, who depend on the lake for irrigation and fishery. As a result of extensive evaporation of water from the lake due to
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high temperature and low rain in this region, the increase of salts, heavy metals and other pollutants are responsible factors for the poor quality of the lake ecosystem. Until now there has been no systematic environmental study carried out for the Manchar Lake. So, there is no information available to enable us, to make valid comparison to the results of our study. The present study is a part of a comprehensive program conducted to evaluate the toxicological effects of contaminated water of Manchar Lake, which had caused up to 60 deaths, mostly of children in Hyderabad during 2004 (Siddiq, 2004). The objective of the present study is to analyze the 36 physico–chemical parameters in water samples for 2 years (2005–06) from the polluted lake, collected on monthly basis. The large data set obtained was subjected to the PCA and CA multivariate techniques to evaluate information about the similarities and dissimilarities present among the different sampling sites, to identify water quality variables for spatial dissimilarity, and to ascertain the influence of the pollution sources on the water quality parameters.
2. Experimental 2.1. Sampling site Manchar is the biggest shallow-water natural lake of Pakistan (Fig. 1) situated at a distance of about 18 km from Sehwan Sharif, Jamshoro district, Sindh (26130 N: 67160 E). It is a vast natural depression flanked by the Khirthar hills in the west, the Laki hills in the south and the river Indus in the east. The mean depth of Manchar Lake is approximately 2.5–3.75 m and it covers an area of
233 km2. Flood barriers were constructed in 1932 from its northern and northeastern boundaries. The human activities have been changing significantly the original regime of the lake over the last 50 years. The most important activities are construction and enlargement of the artificial channels linking the river to the lake and the construction of flood embankments to the north. The Main Nara Valley Drain (MNVD), brings agricultural, municipal, industrial and saline water, constitutes a constant polluting sources for the lake, whereas surface runoff is a seasonal phenomenon, and it has not been significant due to dry seasons in 2000–05. 2.2. Sample collection and pre-treatment The sampling network was designed to cover a wide range of determinates of key sites, which reasonably represent the water quality of the lake system, accounting for the tributary and inputs from wastewater drains that have impact on the water quality (Fig. 1). The first site is located in the area of entering main effluents from MNVD. Sites 2 and 3 which are located at the downstream side of lake, where mostly the boating occurs for fishing and local domestic waste also drains to this site as shown in Fig. 1. Site 3 has an outlet of lake water via canal, providing water to agricultural lands. Site 4 is near the hilly area from where mostly fresh water enters into the lake during rainy season. Site 5 is connected to Indus River and has dual aspects, for supply of fresh water when water is available in the Indus River and also as an output of lake water when the level of the Indus River is low. The samples were collected from 8:00 AM to 1:00 PM during the 2-year study. Water samples were collected using open water grab sampler (1.5 L capacity) equipped with a simple pull-ring
Fig. 1. Map of Manchar Lake and Pakistan.
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that allowed for sampling at various water depths (0–6 in and 424 in), from 5 to 7 sites of same station randomly. To evaluate the lake water quality, water samples were kept in a 2 L polyethylene plastic bottles cleaned with metal free soap, rinsed many times with distilled water and finally soaked in 10% nitric acid for 24 h, finally rinsed with ultrapure water. All water samples were stored in insulated cooler containing ice and delivered on the same day to laboratory and all samples were kept at 4 1C until processing and analysis (Clesceri et al., 1998). 2.3. Chemicals and reagents Ultrapure water obtained from ELGA lab water system (Bucks, UK) was used throughout the work. All chemicals and reagents were analytical grade, Merck (Darmstadt, Germany) and were checked for possible trace metal contamination. Standard solutions of all 16 elements were prepared by dilution of 1000 ppm certified standard solutions, Fluka Kamica (Buchs, Switzerland) of corresponding metal ions. Argon gas with 99.99% purity, used as sheath gas for the atomizer and for internal purge. 2.4. Analytical procedure Water quality parameters, their units and methods of analysis are summarized in Table 1. The temperature, pH, electrical conductivity (EC), salinity and DO of each water sample were measured at the sampling points by a mercury thermometer, digital pH, EC and DO meter, respectively. In laboratory the duplicate aqueous samples of about 1000 ml of each batch collected from five sampling sites, were filtered through polycarbonate filter (0.45 mm pore size) and the samples were divided into two parts. One part was used for analysis of anions and physico-chemical parameters, while second part treated with 2 ml of concentrated HNO3 for metal analysis. All water samples were analyzed for different physico-chemical parameters within 48 h, COD determined on the same sampling day while BOD have done promptly to avoid no changes in bacterial concentration. TS, TDS and TSS were determined gravimetrically at 105–110 1C. Total hardness and Ca hardness were measured by EDTA complexometry titration, the indicators are Eriochrome Black T and Murexide at pH 10 and 12, respectively (Eaton et al., 1995). Total alkalinity determined by acid titration using methyl-orange as endpoint and chloride by silver nitrate (AgNO3) titration, using potassium chromate (K2CrO4) solution as an indicator. The BOD5 was determined by the Winkler azide method and COD by the dichromate reflux method. PO4-P was measured by molybdateascorbic acid method, SO4 was determined spectrophotometrically by barium sulfate turbidity method, NH4-N measured with Nessler’s reagent and total nitrogen was determined by Kjeldahl’s method (Clesceri et al., 1998; AOAC, 1995). NO3-N and NO2-N were analyzed by brucine and diazotization methods, respectively (Kazi and Katz, 1987). Fluoride was measured using ion selective electrode (Clesceri et al., 1998). The acid-treated water samples were analyzed for the determination of major cations by further 20-time dilution with ultrapure water, Ca, Na and K were measured by flame photometry, while Mg was determined by the flame atomic absorption spectrometer (FAAS). For trace and toxic elements, the volume of water samples was reduced four-fold at 60 1C on an electric hot plate. Cu, Fe and Zn were determined by FAAS using an acetyleneair flame, while Al was determined by acetylene-nitrous oxide flame. Cd, Co, Cr, Mn, Ni and Pb were analyzed using electrothermal atomic absorption spectrometer (ETAAS), while As and Se were determined using hydride generation method (HGAAS). The quality of the analytical data was ensured through careful
303
Table 1 Water quality parameters associated with their abbreviations, units and analytical methods used Variables
Abbreviations
Units
Analytical methods
PH Electrical conductivity Salinity Total solids Total dissolved solids Total suspended solids Total hardness Calcium hardness Dissolved oxygen Biochemical oxygen demand Chemical oxygen demand Fluoride Chloride Total alkalinity Phosphate Sulphate Nitrite nitrogen Ammonical nitrogen Total kjeldahl nitrogen Nitrate nitrogen Sodium Potassium Calcium Magnesium Iron Aluminum Cadmium Lead Arsenic Chromium Nickel Cobalt Copper Manganese Zinc Selenium
pH EC Salinity TS TDS TSS T-Hard Ca-Hard DO BOD COD F Cl T-Alk PO4 SO4 NO2-N NH4-N TKN NO3-N Na K Ca Mg Fe Al Cd Pb As Cr Ni Co Cu Mn Zn Se
pH unit mS/cm % mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L
pH-meter Electrometric Electrometric Gravimetric Gravimetric Gravimetric Titrimetric Titrimetric Prob metod Winklar azide method Dichromate method Ion selective electrode Titrimetric Titrimetric Spectrophotometric Spectrophotometric Spectrophotometric Spectrophotometric Titrimetric Spectrophotometric Flame photometer Flame photometer Flame photometer FAAS FAAS FAAS ETAAS ETAAS Hydride generation AAS ETAAS ETAAS ETAAS FAAS ETAAS FAAS Hydride generation AAS
standardization, procedural blank measurements and triplicate samples. The ionic balance of each sample was within 75%. 2.5. Data treatment by statistical method All mathematical and statistical computations were made using Excel 2003 (Microsoft Offices) and STATISTICA 6 (StatSoft, Inc.s). Multivariate analysis of the lake water quality data set was performed through principal component and cluster analysis techniques (Liu et al., 2003). 2.5.1. Principal components analysis (PCA) PCA is designed to convert the original variables into new, uncorrelated variables (axes), called the principal components, which are linear combinations of the original variables. The new axes lie along the directions of maximum variance. PCA provides an objective way of finding indices of this type so that the variation in the data can be accounted for as concisely as possible (Sarbu and Pop, 2005). PCA provides information on the most meaningful parameters which describe the whole data set interpretation, data reduction and to summarize the statistical correlation among constituent in the water with minimum loss of original information (Helena et al., 2000). 2.5.2. Cluster analysis (CA) The CA technique is an unsupervised classification procedure that involves measuring either the distance or the similarity
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between the objects to be clustered. The resulting clusters of objects should then exhibit high internal (within cluster) homogeneity and high external (between clusters) heterogeneity. Hierarchical agglomerative clustering is the most common approach, which provides instinctive similarity relationships between any one sample and the entire data set, and is typically illustrated by a dendrogram (tree diagram) (McKenna, 2003). The dendrogram provides a visual summary of the clustering processes, presenting a picture of the groups and their proximity, with a dramatic reduction in dimensionality of the original data. The Euclidean distance usually gives the similarity between two samples and a distance can be represented by the difference between analytical values from the samples (Otto, 1998). In this study, hierarchical agglomerative CA was performed on the normalized data set by means of the Ward’s method, using squared Euclidean distances as a measure of similarity. In this research work PCA was applied to summarize the statistical correlation among components in the water samples. Concentration order among all physico-chemical parameters differ greatly and the statistical results should be highly biased by any parameter with high concentration. Therefore standardization (z-scale) was made on each chemical prior to the statistical analysis (Simeonov et al., 2004). Standardization tends to minimize the influence of difference on variance of variables and eliminates the influence of different units of measurements and renders the data dimensionless. The calculation was performed based on the correlation matrix of chemical components and the PCA scores were obtained from the standardized analytical data. Cluster analysis was applied to detect spatial similarity for grouping of sites under the monitoring network.
3. Results The basic statistics of lake water quality are based on 2160 total water samples (5 sampling sites 3 replications 6 sampling frequency 24 months) are summarized in Table 2, which gives the range, mean and the standard deviation of the results for each of the 36 parameters. The sampling sites were the grouping (dependent) variables, while all the measured parameters constituted the independent variables. The result of the PCA base on the correlation matrix of chemical components is expressed in Table 3, and station wise shown in Fig. 2. Three components of PCA analysis showed 97.6% of the variance in the data set of lake water. The eigenvectors classified the 36 physico-chemical parameters into three groups: in the first component all physico-chemical parameters, major, trace and toxic elements in lake water; the second component is loaded with trace and toxic metals while the third component shows TSS, major cations, TKN, NO3, NH4, PO4, Fe, Al, Cd, Pb, Cr, Co and Zn. Cluster analysis was applied on lake water quality data, to detect spatial similarity and dissimilarity for grouping of sampling sites (spatial variability) spread over the lake stretch. The resulted dendogram (Fig. 3), grouped all the five sampling sites into three statistically significant clusters, as sites (1–2) and (3–5) have low mutual dissimilarities as compared to site 4 has 28% of total dissimilarity. The analytical data set was standardized in order to compare the aspects of the variation of each physico-chemical parameters on different sampling sites as shown in Fig. 4. The TDS and EC during the annual season cycle showed no significant variations, presenting averages for all sampling sites. The range of TDS and conductivity in lake water was found in the range of 3580–4440 mg/L and 5.01–6.22 mS/cm, respectively. The major ions, F, Cl, NO3, SO4, PO4, Na, Ca, Mg and K were higher than their concentrations in several surface water bodies in the world
(Silva and Rezende, 2002). The range of Na was found in all the sampling site as 262–805 mg/L. The level of Fe and Al were found in the range of 1.52–7.21 and 0.91–4.27 mg/L, respectively, corresponding to 10–20-folds higher than WHO recommended values for drinking water. High concentrations of toxic elements (As, Cd and Pb), which were detected in 100% of the samples, ranged 35.0–171, 2.4–12.1, and 39.0–172 mg/L, respectively. The total phosphate and nitrogen values were found in the range of 0.262–0.719 and 44.9–128 mg/L, respectively, in lake water samples. Chloride concentrations ranged in all water samples found as 886–2210 mg/L. Low DO values, in the range of 2.5–7.4 mg/L, were found while high levels of COD and BOD were observed in all study sites. The range in BOD and COD of water samples collected from all sites were found to be 37.1–104.8 and 79–208 mg/L, respectively.
4. Discussion 4.1. Spatial similarity and site grouping Principal component analysis was applied to the normalized data sets (36 variables) separately for the five different sampling sites and demarcated by the CA technique to compare the compositional patterns among the analyzed water samples and to identify the factors that influence each one. The first component (PC1) accounted for over 78.6% of the total variance in the data set of the lake water, in other words, the physical parameters, major cations, anions and heavy metals in the solution demonstrate similar behavior in the lake water samples (Table 3). In a macroscopic point of view all the physico-chemical parameters behave similarly, i.e. high concentration of major elements as well as toxic metals in main body of whole lake except minor change at site 4, where the slight variation in pollution loading has some temporal effects. We have already mentioned that the wet season in this area is very short, and has little effect on values of variables at this site. The strong positive loading on BOD and COD were observed, whereas, a negative loading on DO. It is, thus, a group of purely organic pollution indicator parameters. This also suggests that the anthropogenic pollution, which is the major lake pollution problem, is mainly due to the discharge of wastewater as a regular source, throughout the year. The trend obtained was also supported by the analysis of the results on the raw data set. The second component (PC2), explaining 16.4% of the total variance has strong positive loadings for trace and toxic metals, thus basically represents the metals of pollution group. The third component (PC3) of PCA shows only 2.5% of the total variation has positive loading of major cations. The high values of Fe, Al and Pb are above the permissible limit of WHO values for drinking water (WHO, 2004). The result of CA base on the PCA scores is shown in Fig. 3. The dendogram clarifies the abnormality of the sampling sites 1 and 2, which make one group as cluster 1, which receive polluted effluents from non-point sources, i.e., from agricultural, industrial and domestic activities via MNVD as shown in Fig. 1. Besides cluster 1, the mutual dissimilarity among other sites made as cluster 2 (site 4) and cluster 3 (sites 3 and 5) correspond to relatively high pollution, low pollution and moderate pollution regions, respectively. It implies that for rapid assessment of water quality, only one site in each cluster may serve as good in spatial assessment of the water quality as the whole network. It is evident that the CA technique is useful in offering reliable classification of surface waters in the whole region and will make possible to adequately serve for spatial assessment in an optimal manner. Thus, the number of sampling sites and cost in the monitoring network will be reduced without loosing any
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Table 2 Range, mean and standard deviation (Std.) of water quality parameters at different sites of Lake Manchar during 2005– 06 Parameters
WHO limits
St1
St2
St3
St4
St5
pH
6.5– 8.5
Range Mean Std.
7.9– 8.7 8.08 0.18
7.4– 8.7 8.02 0.24
7.6– 8.7 8.17 0.26
8.0– 8.9 8.35 0.20
7.4– 8.7 8.10 0.27
EC (mS/cm)
1.5
Range Mean Std.
4.7– 10.8 6.22 1.37
4.60– 9.60 5.97 1.23
4.11– 9.21 5.58 1.53
3.87– 9.00 5.01 1.12
4.11– 9.05 5.47 1.09
Salinity (%)
0.1
Range Mean Std.
0.301– 0.656 0.386 0.080
0.276– 0.615 0.366 0.079
0.26– 0.60 0.351 0.094
0.202– 0.500 0.298 0.071
0.250– 0.562 0.334 0.071
TS (mg/L)
–
Range Mean Std.
4300– 9742 5753 1274
4088– 9128 5489 1161
4074– 8811 5293 1485
3319– 7813 4630 1066
3755– 7620 5027 1017
TDS (mg/L)
1000
Range Mean Std.
3362– 7136 4440 941
3224– 7269 4237 951
2978– 6435 4057 1021
2778– 5738 3579 750
2822– 6422 3842 855
TSS (mg/L)
–
Range Mean Std.
878.5– 2606 1113 371
863.9-1859 1035.1 271
716.9-2376 1052 531
468.9– 2246 850.9 410
622.7– 1939 985.2 311
Ca-Hard (mg/L)
100
Range Mean Std.
517.8– 1107 667.1 139
465.2– 1094 638.8 137
449.5– 1080 620.1 176
394.0– 910.4 535.9 115
437.4– 975.2 589.6 134
DO (mg/L)
–
Range Mean Std.
2.4– 5.4 3.7 0.87
3.4– 6.5 3.9 0.62
2.5– 6.8 4.3 1.33
4.8– 7.4 5.2 0.76
3.5– 7.2 4.7 1.25
BOD (mg/L)
6
Range Mean Std.
41.4– 92.03 58.44 15.53
45.9– 104.8 64.08 13.55
41.9– 100.2 61.46 13.86
37.1– 81.17 50.75 11.55
41.6– 97.93 56.49 14.17
COD (mg/L)
10
Range Mean Std.
100.1– 208.6 131.4 26.70
97.5– 196.9 120.9 28.63
93.7– 202.7 125.3 26.92
79.7– 178.3 106.2 23.69
84.5– 190.1 115.2 24.88
F (mg/L)
1.5
Range Mean Std.
0.55– 1.14 0.71 0.14
0.516– 1.084 0.68 0.14
0.50– 1.06 0.66 0.17
0.46– 0.92 0.57 0.12
0.47– 1.00 0.62 0.13
Cl (mg/L)
250
Range Mean Std.
1055– 2210 1410 279.6
999.9– 2097 1333 272.2
925.6– 2089 1285 334.6
885.9– 1759 1128 220.7
954.7– 2006 1223 262.2
T-Alk (mg/L)
200
Range Mean Std
132.7– 306.9 179.7 40.40
122.5– 294.9 171.2 39.04
117.2– 268.5 156.7 58.74
100.6– 236.0 141.1 31.67
119.5– 268.3 157.4 34.39
PO4 (mg/L)
–
Range Mean Std.
0.352– 0.716 0.52 0.11
0.322– 0.719 0.50 0.11
0.270– 0.694 0.48 0.14
0.277– 0.645 0.46 0.10
0.262– 0.601 0.42 0.09
SO4 (mg/L)
250
Range Mean Std.
144.9– 313.1 186.1 38.7
139.0– 288.09 176.70 37.4
24.78– 288.8 162.2 63.50
118.2– 245.5 150.4 33.97
125.6– 271.9 162.2 35.55
NO2-N (mg/L)
3
Range Mean Std.
2.41– 5.50 3.37 0.75
2.28– 5.11 3.22 0.71
2.37– 4.84 3.09 0.78
2.01– 4.21 2.68 0.58
2.11– 4.66 2.92 0.65
NH4-N (mg/L)
–
Range Mean Std.
5.85– 13.22 8.48 1.7
5.57– 12.6 8.04 1.7
5.48– 12.10 7.82 2.1
4.77– 10.36 6.82 1.4
5.36– 12.46 7.35 1.7
TKN (mg/L)
–
Range Mean Std.
58.7– 128.1 94.2 21.5
44.9– 117.5 86.7 27.5
55.5– 120.1 89.3 20.9
52.2– 105.4 74.7 16.7
52.7– 111.2 81.2 18.2
NO3-N (mg/L)
50
Range Mean Std.
5.03– 12.7 8.4 1.9
5.31– 11.3 8.2 2.1
5.59– 13.0 8.8 1.9
4.46– 11.0 7.2 1.6
4.72– 11.4 7.6 1.7
Na (mg/L)
200
Range Mean Std.
355.2– 804.7 474.5 109
327.2– 760.4 51.9 108
316.2– 758.3 433.6 136
262.4– 625.2 381.1 93.8
306.1– 720.8 418.4 100
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Table 2 (continued ) Parameters
WHO limits
St1
St2
St3
St4
St5
K (mg/L)
12
Range Mean Std.
18.1–41.2 23.9 5.1
17.1–39.2 22.5 4.7
17.8–35.7 21.6 5.2
14.5–33.1 18.9 4.3
15.2–36.9 20.5 4.6
Ca (mg/L)
100
Range Mean Std.
181–382 245.5 50.9
171–377 234.9 49.5
172–373 228.3 65.9
143–314 197.1 41.3
152–370 217.0 49.7
Mg (mg/L)
50
Range Mean Std.
129.1–291.0 167.2 38.8
119.1–268.6 157.7 35.7
120.0–280.0 154.3 48.5
106.8–257.9 134.0 32.5
105.6–248.7 145.6 33.0
Fe (mg/L)
0.3
Range Mean Std
1.68–6.88 3.4 1.7
1.71–5.52 3.0 1.2
1.58–6.25 2.8 1.3
1.52–7.21 3.0 1.7
1.59–5.28 2.6 1.3
Al (mg/L)
0.2
Range Mean Std.
1.01–4.27 2.1 1.1
0.91–3.86 1.92 0.93
0.967–4.06 1.87 0.96
1.06–3.92 2.01 0.96
0.931–3.83 1.93 0.95
Cd (mg/L)
3
Range Mean Std.
2.90–11.8 5.8 3.1
2.7–12.1 5.3 2.7
2.8–1.2 5.3 2.9
2.6–9.1 5.0 2.2
2.4–8.7 5.1 2.1
Pb (mg/L)
10
Range Mean Std.
43.1–172.2 89.4 46
43.7–171.2 82.3 43
39.2–170.7 82.0 44
40.4–161.4 81.2 41
43.7–142.2 77.2 34
As (mg/L)
10
Range Mean Std
43.2–171 86.1 47
35.4–150 83.8 38
42.7–140 77.4 34
42.1–158 80.2 38
39.3–141 76.5 34
Cr (mg/L)
50
Range Mean Std.
4.2–16.1 8.2 4.0
4.4–15.4 7.8 3.7
4.3–15.7 7.4 4.2
4.1–16.4 7.7 3.2
3.2–16.2 7.1 3.4
Ni (mg/L)
20
Range Mean Std.
20.2–76.4 38.1 18.1
15.1–58.5 36.0 15.2
17.2–67.4 33.4 16.2
15.2–61.5 34.6 15.2
17.1–58.5 32.7 14.1
Co (mg/L)
40
Range Mean Std.
23.0–88.0 42.97 21.4
19.0–84.0 39.32 21.2
17.0–77.0 38.30 17.4
19.0–73.0 38.03 17.2
21.0–64.0 36.06 14.8
Cu (mg/L)
2000
Range Mean Std.
11.2–40.2 21.1 10.2
11.6–36.1 19.2 8.74
9.4–40.6 17.2 10.4
9.1–36.3 18.7 9.24
8.2–38.4 18.3 10.1
Mn (mg/L)
100
Range Mean Std.
34.0–151.0 77.0 40.2
3.0–143.0 75.3 38.2
35.0–141.0 70.1 35.4
32.0–137.0 72.1 33.2
29.0–131.0 68.3 36.5
Zn (mg/L)
10
Range Mean Std.
377–1387 788 300
396–1580 731 415
340–1390 711 335
364–1455 721 356
361–4240 701 338
Se (mg/L)
10
Range Mean Std.
32.7–118 81.2 10.4
32.7–92.4 62.2 10.4
30.6–77.2 38.5 10.2
32.7–82.4 43.4 9.1
30.6–74.5 38.5 8.6
significance of the outcome. The same aspects are also reported by other researchers (Simeonov et al., 2004; Kim et al., 2005). 4.2. Chemistry of lake water The minimum and maximum values of all physico-chemical parameters of water samples collected from five sampling sites are presented in Table 2, the results are compared to the values of World Health recommended maximum permissible limits (WHO, 2004). 4.2.1. Physical parameters Air and water temperatures showed a very characteristic annual cycle, with higher values during the summer (30–49 1C),
and lower values in the winter season (12–28 1C). The pH values of collected water samples were within those defined by WHO guidelines of 6.5–8.5 (WHO, 2004). The high level of EC, due to significant amount of dissolved salt, was observed in all sites of lake under study. The annual rainfall in this basin is as little as o100 mm, so very little variation was obtained in values of conductivity in the rainy season. High conductivity in dry season represents water with high electrolyte concentration due to evaporation. The EC values exceeding the WHO (2004) guidelines (Table 2) for drinking water, the 12–15folds higher EC is attributed to the high salinity and high mineral content in all sampling sites. It also corresponds to the highest concentrations of dominant ions, which are the result of ion exchange and solubilisation in the aquifer (Sanchez-Perez and Tremolieres, 2003). The high level of major cations (Na, Ca and
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90
Table 3 Eigenvector and eigenvalues on the correlation matrixes of concentration of physico-chemical parameters in Lake Manchar
0.065 0.112 0.096 0.076 0.180 0.087 0.124 0.312 0.283 0.137 0.114 0.081 0.051 0.088 0.009 0.096 0.092 0.133 0.259 0.107 0.078 0.125 0.100 0.276 0.327 0.100 0.173 0.231 0.260 0.239 0.179 0.268 0.223 0.223 0.182 5.566 16.370 95.023
0.191 0.071 0.047 0.051 0.089 0.066 0.046 0.248 0.205 0.262 0.036 0.033 0.264 0.199 0.239 0.065 0.006 0.237 0.420 0.063 0.039 0.041 0.019 0.075 0.113 0.220 0.336 0.202 0.106 0.069 0.219 0.262 0.143 0.118 0.169 0.871 2.563 97.586
PC2 (16.37 %)
10
5 Site 4 Site 1
60 50 40 30 20 10 0 Site 5
0.188 0.186 0.188 0.190 0.172 0.189 0.185 0.024 0.120 0.176 0.186 0.190 0.185 0.169 0.188 0.188 0.189 0.178 0.132 0.186 0.190 0.184 0.188 0.145 0.098 0.183 0.166 0.152 0.142 0.157 0.170 0.135 0.152 0.162 0.172 26.742 78.653 78.653
Site 3
EC Salinity TS TDS TSS T-Hard Ca-Hard DO BOD COD F Cl T-Alk PO4 SO4 NO2-N NH4-N TKN NO3-N Na K Ca Mg Fe Al Cd Pb As Cr Ni Co Cu Mn Zn Se Eigenvalue Variability (%) Cumulative %
70
Site 4
PC3
Site 2
PC2
Site 1
PC1
80
Dissimilarity
Parameters
307
Fig. 3. Dendogram for cluster analysis based on the PCA score. The dissimilarity defined by Euclidean distance and the combination of cluster is based on Ward method.
The monitoring of oxygen concentration in aquatic system is an important subject (Galal-Gorchev et al., 1993), as the biological, chemical and physical processes involved in the increase or decrease of oxygen in lake are so numerous and complex that there is no model that can be used without a careful analysis of local characteristics. The highest value of COD was recorded at the sampling site 1 and lowest at sites 4 and 5. COD is widely used for determining waste concentration and is applied primarily to pollutant mixtures such as domestic sewage, agricultural and industrial waste. In the case of BOD, the higher values were observed at sites 2 and 3, due to local anthropogenic pollution produced by people who lived in boats day and night for fishing, and also addition of local domestic waste at this site. Total nitrogen and phosphate concentrations (Table 2) varied greatly as a result of lake water contamination resulting from domestic wastage and agricultural sources from the upstream agricultural areas where frequent use of the phosphate and nitrogen fertilizers are common. Our results are also consistent with those of the study on the Tiete River, which contains high values of nitrogen due to anthropogenic activities such as fertilizer usage, organic pollutants releases and discharge of water from domestic sources (Silva et al., 1999). The higher values of chloride in water samples exceeded the WHO proposed drinking water quality criteria (WHO, 2004). According to Versari et al. (2002), chloride concentrations higher than 200 mg/L are considered to be a risk for human health and may cause unpleasant taste of water.
0 Site 2 Site 5 Site 3
-5 -10
-5
0 PC1 (78.65 %)
5
10
Fig. 2. Scores of the first two principal components (PC1 and PC2) PC1 and PC2 explained 95.02% of the total variance.
Mg) and concentration of major anions Cl and SO4 in lake water increase EC, is consistent with other study (Zacheus and Martikainen, 1997). Alkalinity values varied between 0.20% and 0.66% among all sampling sites. This variation is similar to that for the variation of values of EC in under study water samples. A similar relationship was reported by Thomaz et al. (1992) for the Varzea Lake, Brazil.
4.2.2. Dissolved metals in lake The mean concentrations of elements in water samples collected in 2005–06 are presented in Table 2. The average levels showed that concentrations of major, trace and toxic elements due to anthropogenic contamination (domestic, industrial and agricultural wastes) in water do not vary among sampling sites. The trend obtained was also supported by the analysis of the results on the raw data of water samples. Among the 16 elements determined in Manchar Lake water except Cu, Cr, Co and Mn, other elements (Na, K, Ca, Mg, Fe, Al, Ni, As, Se, Pb and Cd) have higher values as compared to the permissible level of these elements in drinking water (WHO, 2004). These values of elements indicate that deterioration of the lake is mainly due to the input of agricultural and domestic waste. Metals generally had uniform distribution in all samples, and there was little seasonal variability. In the summer season at sampling site 4, there was a low level of elements due to dilution
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TDS
BOD
Alkalinity
COD
TKN
Standard Units
2
1
0
-1
-2 Site 1
Site 2
Na
K
Site 3 Ca
Site 4
Site 5 Fe
Mg
Al
Standard Units
2
1
0
-1
-2 Site 2
Site 1 Cd
Pb
Site 3 As
Site 4
Site 5
Se
2
Standard Units
1
of dissolved oxygen in understudy lake water was strongly correlated with the high Fe2+ concentrations in water samples (Barreto et al., 2005). The results of iron are also consistent with other studies on river water has Fe concentration in the range of 1.8–5.06 mg/L (Da Silva and Sacoman, 2001). Exchangeable Fe usually relates to the adsorbed metals on the sediment surface can be easily remobilized into the Lake water (Ikem et al., 2003). The major adverse effects of elevated concentrations of Fe are associated with aesthetic nuisances such as staining of laundry, unpleasant odor and taste. Some studies have found a significant relationship between the exposure to Al in water and an increased risk of Alzheimer’s disease (Gardner and Gunn, 1991). Prolonged exposure to Al, however, can cause systemic toxicity, mainly affecting the gastrointestinal tract and causing neurological and skeletal effects. The poisoning of Al in patients with chronic renal failure is also the most important clinical problems involving trace metal toxicity (Forbes and Hill, 1998). The concentration of As detected in all water samples were 3–15-fold higher than the permissible limit of As in drinking water (10 mg/L). The main adverse health effects of As are tracheae bronchitis, rhinitis, pharyngitis, shortness of breath and nasal congestions (Xia and Liu, 2004). Similarly, contamination of drinking water from As may also result in blackfoot disease (Liu et al., 2003; Tsai et al., 1998). The levels of Pb were found to be higher during 2-year lake water analyses, which are mostly higher than permissible limit of drinking water by WHO guidelines, indicating pollution in this lake. Adverse health effects of Pb include various cancers, adverse reproductive outcomes, cardiovascular and neurological diseases (Watt et al., 2000). Elevated concentrations of Cd can cause nausea, vomiting, salivation and renal failure as well as kidney, liver and blood damages (Ikem et al., 2002) suggested that high concentrations of Cd may even cause mutations. 4.3. Overview of the physico-chemical parameters of lake water
0
-1
-2 Site 1
Site 2
Site 3
Site 4
Site 5
Fig 4. Standard unit of chemical concentrations in water of the lake Manchar. The standard units is defined as z ¼ (xu)/S, where x is the raw concentrated data, u is the mean values and S is the standard deviation. (a) TDS, BOD, COD, Alkalinity and TKN (b) Major cations, (c) trace and toxic metals.
by entering of fresh water from large hills due to rainy season. The high level of Na was due to drainage from lands cultivated with rice and many salt seeps present in the upper basin tributaries that result in salt loading through MNVD (site 1) in the lake. The water of lake is frequently used for drinking by humans as well as animals, because the people have no other resources of drinking water. It has been reported that high consumption of salts, particularly NaCl, may be crucial for the development of hypertension and increases the risk for stroke, left ventricular hypertrophy, osteoporosis, renal stones and asthma. This study is consistent with other study that people residing in these localities have frequent renal stones and asthmatic problems (McCarthy, 2004). The concentrations of Al and Fe were found to be very high in water samples collected from different sampling sites. The high level of Fe in lake water samples are mainly due to the inflow of surface run off from hill torrents and agricultural wastes (agricultural and rocks), both are rich in Al and Fe. The low level
The data of the physico-chemical parameters were transformed into standard unit (z) to compare the aspects of the variation in water samples collected from different sites as shown in Fig. 4. Among the normalized data, except for BOD, all other parameters were found to be very high at sampling site 1, showing high pollution at this site; it is the main contributing source of pollution in the lake (Fig. 4a). The normalized concentrations for all the physico-chemical parameters are almost equivalent to their mean values above one standard deviation (1s). All physicochemical parameters show low normalized values at site 4 and 5 (below 0), whereas the values of these parameters are (o1s) at sites 2 and 3 (Fig. 4b). Se and As have similar behavior at sites 1 and 2, while other toxic metals (Pb, Cd) show moderate variations at sites 2 and 3 (Fig. 4c).
5. Conclusion In this study, different multivariate statistical techniques were used to evaluate variations in surface water quality of the Manchar Lake. Cluster analysis grouped five sampling sites into three clusters of similar water quality characteristics. Based on obtained information, it is possible to design a future, optimal sampling strategy, which could reduce the number of sampling sites and associated cost. Principle component analysis helped in identifying the factors or sources responsible for water quality variations. The main cause of degradation of the lake is the discharge of industrial, agricultural wastes and of municipal sewage water from the upper northern areas of Sindh, coming through MNVD (site 1). Fishing and boating activities were also
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among the major sources responsible for lake water quality deterioration. This study illustrates the usefulness of multivariate statistical techniques for the analysis and interpretation of complex data sets, identification of pollution sources and understanding variations in water quality for effective lake water management. The chemometric study enabled us to show similarities and differences among the lakes examined among variables that were not clearly visible from an examination of the analytical data in the tables. Interventions should be made to reduce anthropogenic discharges in the Lake basin; otherwise, high levels of pollution will greatly influence the population and will invite socio–economic disaster. These results should be considered for future planning in using the lake’s water for drinking purposes.
Acknowledgment The authors would like to thank the financial support from Higher Education Commission of Pakistan. References AOAC, 1995. Association of Official Analytical Chemists. Official Methods of Analysis, 16 ed. AOAC International, Gaithersburg, MD March 1998 revision. Barreto, W.J., Ribeiro, M.R., Solci, M.C., Scarminio, I.S., Nozaki, J., de Oliveira, E., Barreto, S.R.G., 2005. Interpretation of seasonal variation of metals and abiotic properties in a tropical lake using multivariate analysis and MINTEQA2 program. Anal. Sci. 21, 209–214. Clesceri, L.S., Greenberg, A.E., Eaton, A.D. (Eds.), 1998. Standard Methods for the Examination of Water and Wastewater, 20th ed. American Public Heath Association (APHA), American Water Works Association (AWWA) and Water Environment Federation (WEF), Washington, DC. Da Silva, A.M.M., Sacoman, L.B., 2001. Using chemical and physical parameters to define the quality of Pardo river water (Botucatu-sp-Brazil). Water Res. 5, 1609–1616. Dixon, W., Chiswell, B., 1996. Review of aquatic monitoring program design. Water Res. 30, 1935–1948. Eaton, D.A., Clesceri, L.S., Greenberg, A.E., 1995. Standard Methods, for 14 the Examination of Water and Wastewater, 19th ed. American Public Health Association, Washington, DC, p. 1015. Forbes, W.F., Hill, G.B., 1998. Is exposure to aluminum a risk factor for the development of Alzheimer disease?—yes. Arch. Neurol. 55, 740–741. Galal-Gorchev, H., Ozolins, G., Bonnefoy, X., 1993. Revision of the WHO guidelines for drinking water quality. Ann. Institutionali. Super Sanit. 29, 335–345. Gardner, M.J., Gunn, A.M., 1991. Bioavailability of Al from food and drinking water. In: Proceedings of the Royal Society Med. Round Table Series: Alzheimer’s Disease and the environment, London, UK. Helena, B., Pardo, R., Vega, M., Barrado, E., Fernandez, J.M., Fernandez, L., 2000. Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga river, Spain) by principal component analysis. Water Res. 34, 807–816. Ikem, A., Odueyungbo, S., Egiebor, N.O., Nyavor, K., 2002. Chemical quality of bottled waters from three cities in eastern Alabama. Sci. Total Environ. 285, 165–175. Ikem, A., Egiebor, N.O., Nyavor, K., 2003. Trace elements in water, fish and sediment from Tuskegee lake, southeastern USA. Water Air Soil Poll. 149, 51–75. Issa, Y.M., Elewa, A.A., Rizk, M.S., Hassouna, A.F.A., 1996. Distribution of some heavy metals in Qaroun lake and river Nile, Egypt, Menofiya. J. Agric. Res. 21, 733–746. Kazi, T.G., Katz, S.A., 1987. Spectrophotometric determination of nitrate nitrogen and nitrite nitrogen in sewage sludges. Spectrosc. Lett. 20, 509–517.
309
Kim, J.H., Kim, R.H., Lee, J., Cheong, T.J., Yum, B.W., Chang, H.W., 2005. Multivariate statistical analysis to identify the major factors governing groundwater quality in the coastal area of Kimje, South Korea. Hydrol. Process 19, 1261–1276. Liu, C.W., Lin, K.H., Kuo, Y.M., 2003. Application of factor analysis in the assessment of groundwater quality in a blackfoot disease area in Taiwan. Sci. Total Environ. 313, 77–89. McCarthy, M.F., 2004. Should we restrict chloride rather than sodium? Med. Hypotheses 63, 138–148. McKenna Jr., J.E., 2003. An enhanced cluster analysis program with bootstrap significance testing for ecological community analysis. Environ. Mode. Softw. 18, 205–220. Niemi, G.J., Devore, P., Detenbeck, N., Taylor, D., Lima, A., 1990. Overview of case studies on recovery of aquatic systems from disturbance. Environ. Manage. 14, 571–587. Otto, M., 1998. Multivariate methods. In: Kellner, R., Mermet, J.M., Otto, M., Widmer, H.M. (Eds.), Analytical Chemistry. Wiley/VCH, Weinheim, Germany, p. 916. Reghunath, R., Murthy, T.R.S., Raghavan, B.R., 2002. The utility of multivariate statistical techniques in hydrogeochemical studies: an example from Karnataka, India. Water Res. 36, 2437–2442. Sanchez-Perez, J.M., Tremolieres, M., 2003. Change in groundwater chemistry as a consequence of suppression of floods: the case of the Rhine floodplain. J. Hydrol. 270, 89–104. Sarbu, C., Pop, H.F., 2005. Principal component analysis versus fuzzy principal component analysis. A case study: the quality of Danube water (1985–1996). Talanta 65, 1215–1220. Siddiq, S., 2004. Annual Report 2003–04. Sustainable Development Policy Institute. /www.sdpi.orgS. Silva, M.A.L., Rezende, C.E., 2002. Behavior of selected micro and trace elements and organic matter in sediments of freshwater system in south–east Brazil. Sci. Total Environ. 292, 21–128. Silva, A.M., Novelli, E.L.B., Fascineli, M.L., Almeida, J.A., 1999. Impact of an environmentally realistic intake of water contaminants and superoxide formation on tissues of rats. Environ. Poll. 105, 243–249. Simeonov, V., Simeonova, P., Tsitouridou, R., 2004. Chemometric quality assessment of surface waters: two case studies. Chem. Eng. Ecol. 11, 449–469. Singh, K.P., Malik, A., Mohan, D., Sinha, S., 2004. Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti river (India): a case study. Water Res. 38, 3980–3992. Szymanowska, A., Samecka-Cymerman, A., Kempers, A.J., 1999. Heavy metals in three lakes in West Poland. Ecotoxicol. Environ. Saf. 43, 21–29. Thomaz, S.M., Lansac-Toha, F.A., Roberto, M.C., Esteves, F.A., Lima, A.F., 1992. Seasonal variation of some limnological factors of lagoa do Guarana, a Varzea lake of the high Rio Parana, State of Mato Grosso do Sul, Brazil. Rev. Hydrobiol. Trop. 25, 269–276. Tsai, S.M., Wang, T.N., Ko, Y.C., 1998. Cancer mortality trends in blackfoot disease endemic community of Taiwan following water source replacement. J. Toxicol. Environ. Health Part A 55, 389–404. Vega, M., Pardo, R., Barrado, E., Deban, L., 1996. Assessment of seasonal and polluting effects on the quality of river water by exploratory data analysis. Water Res. 32, 3581–3592. Versari, A., Parpinello, G.P., Galassi, S., 2002. Chemometric survey of Italian bottled mineral waters by means of their labelled physicochemical and chemical composition. J. Food Compos. Anal. 15, 251–264. Watt, G.C.M., Britton, A., Gilmour, H.G., Moore, M.R., Murray, G.D., Robertson, S.J., 2000. Public health implications of new guidelines for lead in drinking water: a case study in an area with historically high water lead levels. Food Chem. Toxicol. 38, 73–79. WHO, 1998. Guidelines for Drinking-water Quality, Addendum to Volume 2: Health Criteria and Other Supporting Information, 2nd ed. Geneva, Switzerland. WHO, 2004. Guidelines for Drinking-Water Quality, third ed. WHO, Geneva, Switzerland. Xia, Y., Liu, J., 2004. An overview of chronic arsenism via drinking water in PR China. Toxicology 198, 25–29. Zacheus, O.M., Martikainen, P.J., 1997. Physicochemical quality of drinking and hot waters in Finnish buildings originated from groundwater or surface water plants. Sci. Total Environ. 204, 1–10.
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Ecotoxicology and Environmental Safety journal homepage: www.elsevier.com/locate/ecoenv
Assessment of water quality of polluted lake using multivariate statistical techniques: A case study T.G. Kazi , M.B. Arain, M.K. Jamali, N. Jalbani, H.I. Afridi, R.A. Sarfraz, J.A. Baig, Abdul Q. Shah Center of Excellence in Analytical Chemistry, University of Sindh, Jamshoro 76080, Pakistan
a r t i c l e in fo
abstract
Article history: Received 30 October 2007 Received in revised form 21 February 2008 Accepted 23 February 2008 Available online 18 April 2008
Multivariate statistical techniques, cluster analysis (CA) and principal component analysis (PCA) were applied to the data on water quality of Manchar Lake (Pakistan), generated during 2005–06, with monitoring at five different sites for 36 parameters. This study evaluated and interpreted complex water quality data sets and apportioned of pollution sources to get better information about water quality and to design a monitoring network. The chemical correlations were observed by PCA, which were used to classify the samples by CA, based on the PCA scores. Three significant sampling locations—(sites 1 and 2), (site 4) and (sites 3 and 5)—were detected on the basis of similarity of their water quality. The results revealed that the major causes of water quality deterioration were related to inflow of effluent from industrial, domestic, agricultural and saline seeps into the lake at site 1 and also resulting from people living in boats and fishing at sites 2 and 3. & 2008 Elsevier Inc. All rights reserved.
Keywords: Physico-chemical parameters Water quality Cluster analysis Principal component analysis Manchar Lake (Pakistan)
1. Introduction Water quality is considered the main factor controlling health and the state of disease in both man and animals. Surface water quality in a region is largely determined both by natural processes (weathering and soil erosion) and by anthropogenic inputs (municipal and industrial wastewater discharge). The anthropogenic discharges constitute a constant polluting source, whereas surface runoff is a seasonal phenomenon, largely affected by climate within the basin (Singh et al., 2004; Vega et al., 1996). Niemi et al. (1990) reported human activities are a major factor determining the quality of the surface and groundwater through atmospheric pollution, effluent discharges, use of agricultural chemicals, eroded soils and land use. Environmental pollution, mainly of water sources, has become public interest. The underdeveloped countries have been suffering the impact of pollution due to disordered economic growth associated with the exploitation of natural resources. Large investigations have been carried out on anthropogenic contamination of ecosystems (Szymanowska et al., 1999; Issa et al., 1996). However, due to spatial and temporal variations in water quality (which are often difficult to interpret), a monitoring
Corresponding author. Fax: +92 222 771560.
E-mail addresses: [email protected] (T.G. Kazi), [email protected] (M.B. Arain), [email protected] (M.K. Jamali), [email protected] (N. Jalbani), [email protected] (H.I. Afridi), [email protected] (R.A. Sarfraz), [email protected] (J.A. Baig), [email protected] (A.Q. Shah). 0147-6513/$ - see front matter & 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.ecoenv.2008.02.024
program, providing a representative and reliable estimation of the quality of surface waters, is necessary (Dixon and Chiswell, 1996). These results are a large and complex data matrix comprised of a large number of physico-chemical parameters, which are often difficult to interpret and to draw meaningful conclusions. The application of different multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminate analysis (DA), helps in the interpretation of complex data matrices for a better understanding of water quality and ecological status of the study region. These techniques allow the identification of the possible sources that influence water systems and offers a valuable tool for reliable management of water resources as well as rapid solution for pollution problems (Reghunath et al., 2002; Simeonov et al., 2004). In Pakistan, drinking water comes from groundwater and surface water including rivers, lakes and reservoirs. The present free style way of disposal of agricultural, industrial and domestic effluents into natural water bodies results in serious surface and groundwater contamination. Run-off from agricultural land and saline seeps subject the most vulnerable water bodies to pollution and increased salinity, so the freshwater lakes are highly impacted. One example for this is the Manchar Lake, Pakistan’s largest freshwater lake. It is the main source of domestic water for the communities living around the lake. Groundwater in this vicinity is saline and is not suitable for drinking (WHO, 1998). The lake’s water in downstream areas is also important for farmers and fishermen, who depend on the lake for irrigation and fishery. As a result of extensive evaporation of water from the lake due to
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high temperature and low rain in this region, the increase of salts, heavy metals and other pollutants are responsible factors for the poor quality of the lake ecosystem. Until now there has been no systematic environmental study carried out for the Manchar Lake. So, there is no information available to enable us, to make valid comparison to the results of our study. The present study is a part of a comprehensive program conducted to evaluate the toxicological effects of contaminated water of Manchar Lake, which had caused up to 60 deaths, mostly of children in Hyderabad during 2004 (Siddiq, 2004). The objective of the present study is to analyze the 36 physico–chemical parameters in water samples for 2 years (2005–06) from the polluted lake, collected on monthly basis. The large data set obtained was subjected to the PCA and CA multivariate techniques to evaluate information about the similarities and dissimilarities present among the different sampling sites, to identify water quality variables for spatial dissimilarity, and to ascertain the influence of the pollution sources on the water quality parameters.
2. Experimental 2.1. Sampling site Manchar is the biggest shallow-water natural lake of Pakistan (Fig. 1) situated at a distance of about 18 km from Sehwan Sharif, Jamshoro district, Sindh (26130 N: 67160 E). It is a vast natural depression flanked by the Khirthar hills in the west, the Laki hills in the south and the river Indus in the east. The mean depth of Manchar Lake is approximately 2.5–3.75 m and it covers an area of
233 km2. Flood barriers were constructed in 1932 from its northern and northeastern boundaries. The human activities have been changing significantly the original regime of the lake over the last 50 years. The most important activities are construction and enlargement of the artificial channels linking the river to the lake and the construction of flood embankments to the north. The Main Nara Valley Drain (MNVD), brings agricultural, municipal, industrial and saline water, constitutes a constant polluting sources for the lake, whereas surface runoff is a seasonal phenomenon, and it has not been significant due to dry seasons in 2000–05. 2.2. Sample collection and pre-treatment The sampling network was designed to cover a wide range of determinates of key sites, which reasonably represent the water quality of the lake system, accounting for the tributary and inputs from wastewater drains that have impact on the water quality (Fig. 1). The first site is located in the area of entering main effluents from MNVD. Sites 2 and 3 which are located at the downstream side of lake, where mostly the boating occurs for fishing and local domestic waste also drains to this site as shown in Fig. 1. Site 3 has an outlet of lake water via canal, providing water to agricultural lands. Site 4 is near the hilly area from where mostly fresh water enters into the lake during rainy season. Site 5 is connected to Indus River and has dual aspects, for supply of fresh water when water is available in the Indus River and also as an output of lake water when the level of the Indus River is low. The samples were collected from 8:00 AM to 1:00 PM during the 2-year study. Water samples were collected using open water grab sampler (1.5 L capacity) equipped with a simple pull-ring
Fig. 1. Map of Manchar Lake and Pakistan.
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that allowed for sampling at various water depths (0–6 in and 424 in), from 5 to 7 sites of same station randomly. To evaluate the lake water quality, water samples were kept in a 2 L polyethylene plastic bottles cleaned with metal free soap, rinsed many times with distilled water and finally soaked in 10% nitric acid for 24 h, finally rinsed with ultrapure water. All water samples were stored in insulated cooler containing ice and delivered on the same day to laboratory and all samples were kept at 4 1C until processing and analysis (Clesceri et al., 1998). 2.3. Chemicals and reagents Ultrapure water obtained from ELGA lab water system (Bucks, UK) was used throughout the work. All chemicals and reagents were analytical grade, Merck (Darmstadt, Germany) and were checked for possible trace metal contamination. Standard solutions of all 16 elements were prepared by dilution of 1000 ppm certified standard solutions, Fluka Kamica (Buchs, Switzerland) of corresponding metal ions. Argon gas with 99.99% purity, used as sheath gas for the atomizer and for internal purge. 2.4. Analytical procedure Water quality parameters, their units and methods of analysis are summarized in Table 1. The temperature, pH, electrical conductivity (EC), salinity and DO of each water sample were measured at the sampling points by a mercury thermometer, digital pH, EC and DO meter, respectively. In laboratory the duplicate aqueous samples of about 1000 ml of each batch collected from five sampling sites, were filtered through polycarbonate filter (0.45 mm pore size) and the samples were divided into two parts. One part was used for analysis of anions and physico-chemical parameters, while second part treated with 2 ml of concentrated HNO3 for metal analysis. All water samples were analyzed for different physico-chemical parameters within 48 h, COD determined on the same sampling day while BOD have done promptly to avoid no changes in bacterial concentration. TS, TDS and TSS were determined gravimetrically at 105–110 1C. Total hardness and Ca hardness were measured by EDTA complexometry titration, the indicators are Eriochrome Black T and Murexide at pH 10 and 12, respectively (Eaton et al., 1995). Total alkalinity determined by acid titration using methyl-orange as endpoint and chloride by silver nitrate (AgNO3) titration, using potassium chromate (K2CrO4) solution as an indicator. The BOD5 was determined by the Winkler azide method and COD by the dichromate reflux method. PO4-P was measured by molybdateascorbic acid method, SO4 was determined spectrophotometrically by barium sulfate turbidity method, NH4-N measured with Nessler’s reagent and total nitrogen was determined by Kjeldahl’s method (Clesceri et al., 1998; AOAC, 1995). NO3-N and NO2-N were analyzed by brucine and diazotization methods, respectively (Kazi and Katz, 1987). Fluoride was measured using ion selective electrode (Clesceri et al., 1998). The acid-treated water samples were analyzed for the determination of major cations by further 20-time dilution with ultrapure water, Ca, Na and K were measured by flame photometry, while Mg was determined by the flame atomic absorption spectrometer (FAAS). For trace and toxic elements, the volume of water samples was reduced four-fold at 60 1C on an electric hot plate. Cu, Fe and Zn were determined by FAAS using an acetyleneair flame, while Al was determined by acetylene-nitrous oxide flame. Cd, Co, Cr, Mn, Ni and Pb were analyzed using electrothermal atomic absorption spectrometer (ETAAS), while As and Se were determined using hydride generation method (HGAAS). The quality of the analytical data was ensured through careful
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Table 1 Water quality parameters associated with their abbreviations, units and analytical methods used Variables
Abbreviations
Units
Analytical methods
PH Electrical conductivity Salinity Total solids Total dissolved solids Total suspended solids Total hardness Calcium hardness Dissolved oxygen Biochemical oxygen demand Chemical oxygen demand Fluoride Chloride Total alkalinity Phosphate Sulphate Nitrite nitrogen Ammonical nitrogen Total kjeldahl nitrogen Nitrate nitrogen Sodium Potassium Calcium Magnesium Iron Aluminum Cadmium Lead Arsenic Chromium Nickel Cobalt Copper Manganese Zinc Selenium
pH EC Salinity TS TDS TSS T-Hard Ca-Hard DO BOD COD F Cl T-Alk PO4 SO4 NO2-N NH4-N TKN NO3-N Na K Ca Mg Fe Al Cd Pb As Cr Ni Co Cu Mn Zn Se
pH unit mS/cm % mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L
pH-meter Electrometric Electrometric Gravimetric Gravimetric Gravimetric Titrimetric Titrimetric Prob metod Winklar azide method Dichromate method Ion selective electrode Titrimetric Titrimetric Spectrophotometric Spectrophotometric Spectrophotometric Spectrophotometric Titrimetric Spectrophotometric Flame photometer Flame photometer Flame photometer FAAS FAAS FAAS ETAAS ETAAS Hydride generation AAS ETAAS ETAAS ETAAS FAAS ETAAS FAAS Hydride generation AAS
standardization, procedural blank measurements and triplicate samples. The ionic balance of each sample was within 75%. 2.5. Data treatment by statistical method All mathematical and statistical computations were made using Excel 2003 (Microsoft Offices) and STATISTICA 6 (StatSoft, Inc.s). Multivariate analysis of the lake water quality data set was performed through principal component and cluster analysis techniques (Liu et al., 2003). 2.5.1. Principal components analysis (PCA) PCA is designed to convert the original variables into new, uncorrelated variables (axes), called the principal components, which are linear combinations of the original variables. The new axes lie along the directions of maximum variance. PCA provides an objective way of finding indices of this type so that the variation in the data can be accounted for as concisely as possible (Sarbu and Pop, 2005). PCA provides information on the most meaningful parameters which describe the whole data set interpretation, data reduction and to summarize the statistical correlation among constituent in the water with minimum loss of original information (Helena et al., 2000). 2.5.2. Cluster analysis (CA) The CA technique is an unsupervised classification procedure that involves measuring either the distance or the similarity
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between the objects to be clustered. The resulting clusters of objects should then exhibit high internal (within cluster) homogeneity and high external (between clusters) heterogeneity. Hierarchical agglomerative clustering is the most common approach, which provides instinctive similarity relationships between any one sample and the entire data set, and is typically illustrated by a dendrogram (tree diagram) (McKenna, 2003). The dendrogram provides a visual summary of the clustering processes, presenting a picture of the groups and their proximity, with a dramatic reduction in dimensionality of the original data. The Euclidean distance usually gives the similarity between two samples and a distance can be represented by the difference between analytical values from the samples (Otto, 1998). In this study, hierarchical agglomerative CA was performed on the normalized data set by means of the Ward’s method, using squared Euclidean distances as a measure of similarity. In this research work PCA was applied to summarize the statistical correlation among components in the water samples. Concentration order among all physico-chemical parameters differ greatly and the statistical results should be highly biased by any parameter with high concentration. Therefore standardization (z-scale) was made on each chemical prior to the statistical analysis (Simeonov et al., 2004). Standardization tends to minimize the influence of difference on variance of variables and eliminates the influence of different units of measurements and renders the data dimensionless. The calculation was performed based on the correlation matrix of chemical components and the PCA scores were obtained from the standardized analytical data. Cluster analysis was applied to detect spatial similarity for grouping of sites under the monitoring network.
3. Results The basic statistics of lake water quality are based on 2160 total water samples (5 sampling sites 3 replications 6 sampling frequency 24 months) are summarized in Table 2, which gives the range, mean and the standard deviation of the results for each of the 36 parameters. The sampling sites were the grouping (dependent) variables, while all the measured parameters constituted the independent variables. The result of the PCA base on the correlation matrix of chemical components is expressed in Table 3, and station wise shown in Fig. 2. Three components of PCA analysis showed 97.6% of the variance in the data set of lake water. The eigenvectors classified the 36 physico-chemical parameters into three groups: in the first component all physico-chemical parameters, major, trace and toxic elements in lake water; the second component is loaded with trace and toxic metals while the third component shows TSS, major cations, TKN, NO3, NH4, PO4, Fe, Al, Cd, Pb, Cr, Co and Zn. Cluster analysis was applied on lake water quality data, to detect spatial similarity and dissimilarity for grouping of sampling sites (spatial variability) spread over the lake stretch. The resulted dendogram (Fig. 3), grouped all the five sampling sites into three statistically significant clusters, as sites (1–2) and (3–5) have low mutual dissimilarities as compared to site 4 has 28% of total dissimilarity. The analytical data set was standardized in order to compare the aspects of the variation of each physico-chemical parameters on different sampling sites as shown in Fig. 4. The TDS and EC during the annual season cycle showed no significant variations, presenting averages for all sampling sites. The range of TDS and conductivity in lake water was found in the range of 3580–4440 mg/L and 5.01–6.22 mS/cm, respectively. The major ions, F, Cl, NO3, SO4, PO4, Na, Ca, Mg and K were higher than their concentrations in several surface water bodies in the world
(Silva and Rezende, 2002). The range of Na was found in all the sampling site as 262–805 mg/L. The level of Fe and Al were found in the range of 1.52–7.21 and 0.91–4.27 mg/L, respectively, corresponding to 10–20-folds higher than WHO recommended values for drinking water. High concentrations of toxic elements (As, Cd and Pb), which were detected in 100% of the samples, ranged 35.0–171, 2.4–12.1, and 39.0–172 mg/L, respectively. The total phosphate and nitrogen values were found in the range of 0.262–0.719 and 44.9–128 mg/L, respectively, in lake water samples. Chloride concentrations ranged in all water samples found as 886–2210 mg/L. Low DO values, in the range of 2.5–7.4 mg/L, were found while high levels of COD and BOD were observed in all study sites. The range in BOD and COD of water samples collected from all sites were found to be 37.1–104.8 and 79–208 mg/L, respectively.
4. Discussion 4.1. Spatial similarity and site grouping Principal component analysis was applied to the normalized data sets (36 variables) separately for the five different sampling sites and demarcated by the CA technique to compare the compositional patterns among the analyzed water samples and to identify the factors that influence each one. The first component (PC1) accounted for over 78.6% of the total variance in the data set of the lake water, in other words, the physical parameters, major cations, anions and heavy metals in the solution demonstrate similar behavior in the lake water samples (Table 3). In a macroscopic point of view all the physico-chemical parameters behave similarly, i.e. high concentration of major elements as well as toxic metals in main body of whole lake except minor change at site 4, where the slight variation in pollution loading has some temporal effects. We have already mentioned that the wet season in this area is very short, and has little effect on values of variables at this site. The strong positive loading on BOD and COD were observed, whereas, a negative loading on DO. It is, thus, a group of purely organic pollution indicator parameters. This also suggests that the anthropogenic pollution, which is the major lake pollution problem, is mainly due to the discharge of wastewater as a regular source, throughout the year. The trend obtained was also supported by the analysis of the results on the raw data set. The second component (PC2), explaining 16.4% of the total variance has strong positive loadings for trace and toxic metals, thus basically represents the metals of pollution group. The third component (PC3) of PCA shows only 2.5% of the total variation has positive loading of major cations. The high values of Fe, Al and Pb are above the permissible limit of WHO values for drinking water (WHO, 2004). The result of CA base on the PCA scores is shown in Fig. 3. The dendogram clarifies the abnormality of the sampling sites 1 and 2, which make one group as cluster 1, which receive polluted effluents from non-point sources, i.e., from agricultural, industrial and domestic activities via MNVD as shown in Fig. 1. Besides cluster 1, the mutual dissimilarity among other sites made as cluster 2 (site 4) and cluster 3 (sites 3 and 5) correspond to relatively high pollution, low pollution and moderate pollution regions, respectively. It implies that for rapid assessment of water quality, only one site in each cluster may serve as good in spatial assessment of the water quality as the whole network. It is evident that the CA technique is useful in offering reliable classification of surface waters in the whole region and will make possible to adequately serve for spatial assessment in an optimal manner. Thus, the number of sampling sites and cost in the monitoring network will be reduced without loosing any
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Table 2 Range, mean and standard deviation (Std.) of water quality parameters at different sites of Lake Manchar during 2005– 06 Parameters
WHO limits
St1
St2
St3
St4
St5
pH
6.5– 8.5
Range Mean Std.
7.9– 8.7 8.08 0.18
7.4– 8.7 8.02 0.24
7.6– 8.7 8.17 0.26
8.0– 8.9 8.35 0.20
7.4– 8.7 8.10 0.27
EC (mS/cm)
1.5
Range Mean Std.
4.7– 10.8 6.22 1.37
4.60– 9.60 5.97 1.23
4.11– 9.21 5.58 1.53
3.87– 9.00 5.01 1.12
4.11– 9.05 5.47 1.09
Salinity (%)
0.1
Range Mean Std.
0.301– 0.656 0.386 0.080
0.276– 0.615 0.366 0.079
0.26– 0.60 0.351 0.094
0.202– 0.500 0.298 0.071
0.250– 0.562 0.334 0.071
TS (mg/L)
–
Range Mean Std.
4300– 9742 5753 1274
4088– 9128 5489 1161
4074– 8811 5293 1485
3319– 7813 4630 1066
3755– 7620 5027 1017
TDS (mg/L)
1000
Range Mean Std.
3362– 7136 4440 941
3224– 7269 4237 951
2978– 6435 4057 1021
2778– 5738 3579 750
2822– 6422 3842 855
TSS (mg/L)
–
Range Mean Std.
878.5– 2606 1113 371
863.9-1859 1035.1 271
716.9-2376 1052 531
468.9– 2246 850.9 410
622.7– 1939 985.2 311
Ca-Hard (mg/L)
100
Range Mean Std.
517.8– 1107 667.1 139
465.2– 1094 638.8 137
449.5– 1080 620.1 176
394.0– 910.4 535.9 115
437.4– 975.2 589.6 134
DO (mg/L)
–
Range Mean Std.
2.4– 5.4 3.7 0.87
3.4– 6.5 3.9 0.62
2.5– 6.8 4.3 1.33
4.8– 7.4 5.2 0.76
3.5– 7.2 4.7 1.25
BOD (mg/L)
6
Range Mean Std.
41.4– 92.03 58.44 15.53
45.9– 104.8 64.08 13.55
41.9– 100.2 61.46 13.86
37.1– 81.17 50.75 11.55
41.6– 97.93 56.49 14.17
COD (mg/L)
10
Range Mean Std.
100.1– 208.6 131.4 26.70
97.5– 196.9 120.9 28.63
93.7– 202.7 125.3 26.92
79.7– 178.3 106.2 23.69
84.5– 190.1 115.2 24.88
F (mg/L)
1.5
Range Mean Std.
0.55– 1.14 0.71 0.14
0.516– 1.084 0.68 0.14
0.50– 1.06 0.66 0.17
0.46– 0.92 0.57 0.12
0.47– 1.00 0.62 0.13
Cl (mg/L)
250
Range Mean Std.
1055– 2210 1410 279.6
999.9– 2097 1333 272.2
925.6– 2089 1285 334.6
885.9– 1759 1128 220.7
954.7– 2006 1223 262.2
T-Alk (mg/L)
200
Range Mean Std
132.7– 306.9 179.7 40.40
122.5– 294.9 171.2 39.04
117.2– 268.5 156.7 58.74
100.6– 236.0 141.1 31.67
119.5– 268.3 157.4 34.39
PO4 (mg/L)
–
Range Mean Std.
0.352– 0.716 0.52 0.11
0.322– 0.719 0.50 0.11
0.270– 0.694 0.48 0.14
0.277– 0.645 0.46 0.10
0.262– 0.601 0.42 0.09
SO4 (mg/L)
250
Range Mean Std.
144.9– 313.1 186.1 38.7
139.0– 288.09 176.70 37.4
24.78– 288.8 162.2 63.50
118.2– 245.5 150.4 33.97
125.6– 271.9 162.2 35.55
NO2-N (mg/L)
3
Range Mean Std.
2.41– 5.50 3.37 0.75
2.28– 5.11 3.22 0.71
2.37– 4.84 3.09 0.78
2.01– 4.21 2.68 0.58
2.11– 4.66 2.92 0.65
NH4-N (mg/L)
–
Range Mean Std.
5.85– 13.22 8.48 1.7
5.57– 12.6 8.04 1.7
5.48– 12.10 7.82 2.1
4.77– 10.36 6.82 1.4
5.36– 12.46 7.35 1.7
TKN (mg/L)
–
Range Mean Std.
58.7– 128.1 94.2 21.5
44.9– 117.5 86.7 27.5
55.5– 120.1 89.3 20.9
52.2– 105.4 74.7 16.7
52.7– 111.2 81.2 18.2
NO3-N (mg/L)
50
Range Mean Std.
5.03– 12.7 8.4 1.9
5.31– 11.3 8.2 2.1
5.59– 13.0 8.8 1.9
4.46– 11.0 7.2 1.6
4.72– 11.4 7.6 1.7
Na (mg/L)
200
Range Mean Std.
355.2– 804.7 474.5 109
327.2– 760.4 51.9 108
316.2– 758.3 433.6 136
262.4– 625.2 381.1 93.8
306.1– 720.8 418.4 100
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Table 2 (continued ) Parameters
WHO limits
St1
St2
St3
St4
St5
K (mg/L)
12
Range Mean Std.
18.1–41.2 23.9 5.1
17.1–39.2 22.5 4.7
17.8–35.7 21.6 5.2
14.5–33.1 18.9 4.3
15.2–36.9 20.5 4.6
Ca (mg/L)
100
Range Mean Std.
181–382 245.5 50.9
171–377 234.9 49.5
172–373 228.3 65.9
143–314 197.1 41.3
152–370 217.0 49.7
Mg (mg/L)
50
Range Mean Std.
129.1–291.0 167.2 38.8
119.1–268.6 157.7 35.7
120.0–280.0 154.3 48.5
106.8–257.9 134.0 32.5
105.6–248.7 145.6 33.0
Fe (mg/L)
0.3
Range Mean Std
1.68–6.88 3.4 1.7
1.71–5.52 3.0 1.2
1.58–6.25 2.8 1.3
1.52–7.21 3.0 1.7
1.59–5.28 2.6 1.3
Al (mg/L)
0.2
Range Mean Std.
1.01–4.27 2.1 1.1
0.91–3.86 1.92 0.93
0.967–4.06 1.87 0.96
1.06–3.92 2.01 0.96
0.931–3.83 1.93 0.95
Cd (mg/L)
3
Range Mean Std.
2.90–11.8 5.8 3.1
2.7–12.1 5.3 2.7
2.8–1.2 5.3 2.9
2.6–9.1 5.0 2.2
2.4–8.7 5.1 2.1
Pb (mg/L)
10
Range Mean Std.
43.1–172.2 89.4 46
43.7–171.2 82.3 43
39.2–170.7 82.0 44
40.4–161.4 81.2 41
43.7–142.2 77.2 34
As (mg/L)
10
Range Mean Std
43.2–171 86.1 47
35.4–150 83.8 38
42.7–140 77.4 34
42.1–158 80.2 38
39.3–141 76.5 34
Cr (mg/L)
50
Range Mean Std.
4.2–16.1 8.2 4.0
4.4–15.4 7.8 3.7
4.3–15.7 7.4 4.2
4.1–16.4 7.7 3.2
3.2–16.2 7.1 3.4
Ni (mg/L)
20
Range Mean Std.
20.2–76.4 38.1 18.1
15.1–58.5 36.0 15.2
17.2–67.4 33.4 16.2
15.2–61.5 34.6 15.2
17.1–58.5 32.7 14.1
Co (mg/L)
40
Range Mean Std.
23.0–88.0 42.97 21.4
19.0–84.0 39.32 21.2
17.0–77.0 38.30 17.4
19.0–73.0 38.03 17.2
21.0–64.0 36.06 14.8
Cu (mg/L)
2000
Range Mean Std.
11.2–40.2 21.1 10.2
11.6–36.1 19.2 8.74
9.4–40.6 17.2 10.4
9.1–36.3 18.7 9.24
8.2–38.4 18.3 10.1
Mn (mg/L)
100
Range Mean Std.
34.0–151.0 77.0 40.2
3.0–143.0 75.3 38.2
35.0–141.0 70.1 35.4
32.0–137.0 72.1 33.2
29.0–131.0 68.3 36.5
Zn (mg/L)
10
Range Mean Std.
377–1387 788 300
396–1580 731 415
340–1390 711 335
364–1455 721 356
361–4240 701 338
Se (mg/L)
10
Range Mean Std.
32.7–118 81.2 10.4
32.7–92.4 62.2 10.4
30.6–77.2 38.5 10.2
32.7–82.4 43.4 9.1
30.6–74.5 38.5 8.6
significance of the outcome. The same aspects are also reported by other researchers (Simeonov et al., 2004; Kim et al., 2005). 4.2. Chemistry of lake water The minimum and maximum values of all physico-chemical parameters of water samples collected from five sampling sites are presented in Table 2, the results are compared to the values of World Health recommended maximum permissible limits (WHO, 2004). 4.2.1. Physical parameters Air and water temperatures showed a very characteristic annual cycle, with higher values during the summer (30–49 1C),
and lower values in the winter season (12–28 1C). The pH values of collected water samples were within those defined by WHO guidelines of 6.5–8.5 (WHO, 2004). The high level of EC, due to significant amount of dissolved salt, was observed in all sites of lake under study. The annual rainfall in this basin is as little as o100 mm, so very little variation was obtained in values of conductivity in the rainy season. High conductivity in dry season represents water with high electrolyte concentration due to evaporation. The EC values exceeding the WHO (2004) guidelines (Table 2) for drinking water, the 12–15folds higher EC is attributed to the high salinity and high mineral content in all sampling sites. It also corresponds to the highest concentrations of dominant ions, which are the result of ion exchange and solubilisation in the aquifer (Sanchez-Perez and Tremolieres, 2003). The high level of major cations (Na, Ca and
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Table 3 Eigenvector and eigenvalues on the correlation matrixes of concentration of physico-chemical parameters in Lake Manchar
0.065 0.112 0.096 0.076 0.180 0.087 0.124 0.312 0.283 0.137 0.114 0.081 0.051 0.088 0.009 0.096 0.092 0.133 0.259 0.107 0.078 0.125 0.100 0.276 0.327 0.100 0.173 0.231 0.260 0.239 0.179 0.268 0.223 0.223 0.182 5.566 16.370 95.023
0.191 0.071 0.047 0.051 0.089 0.066 0.046 0.248 0.205 0.262 0.036 0.033 0.264 0.199 0.239 0.065 0.006 0.237 0.420 0.063 0.039 0.041 0.019 0.075 0.113 0.220 0.336 0.202 0.106 0.069 0.219 0.262 0.143 0.118 0.169 0.871 2.563 97.586
PC2 (16.37 %)
10
5 Site 4 Site 1
60 50 40 30 20 10 0 Site 5
0.188 0.186 0.188 0.190 0.172 0.189 0.185 0.024 0.120 0.176 0.186 0.190 0.185 0.169 0.188 0.188 0.189 0.178 0.132 0.186 0.190 0.184 0.188 0.145 0.098 0.183 0.166 0.152 0.142 0.157 0.170 0.135 0.152 0.162 0.172 26.742 78.653 78.653
Site 3
EC Salinity TS TDS TSS T-Hard Ca-Hard DO BOD COD F Cl T-Alk PO4 SO4 NO2-N NH4-N TKN NO3-N Na K Ca Mg Fe Al Cd Pb As Cr Ni Co Cu Mn Zn Se Eigenvalue Variability (%) Cumulative %
70
Site 4
PC3
Site 2
PC2
Site 1
PC1
80
Dissimilarity
Parameters
307
Fig. 3. Dendogram for cluster analysis based on the PCA score. The dissimilarity defined by Euclidean distance and the combination of cluster is based on Ward method.
The monitoring of oxygen concentration in aquatic system is an important subject (Galal-Gorchev et al., 1993), as the biological, chemical and physical processes involved in the increase or decrease of oxygen in lake are so numerous and complex that there is no model that can be used without a careful analysis of local characteristics. The highest value of COD was recorded at the sampling site 1 and lowest at sites 4 and 5. COD is widely used for determining waste concentration and is applied primarily to pollutant mixtures such as domestic sewage, agricultural and industrial waste. In the case of BOD, the higher values were observed at sites 2 and 3, due to local anthropogenic pollution produced by people who lived in boats day and night for fishing, and also addition of local domestic waste at this site. Total nitrogen and phosphate concentrations (Table 2) varied greatly as a result of lake water contamination resulting from domestic wastage and agricultural sources from the upstream agricultural areas where frequent use of the phosphate and nitrogen fertilizers are common. Our results are also consistent with those of the study on the Tiete River, which contains high values of nitrogen due to anthropogenic activities such as fertilizer usage, organic pollutants releases and discharge of water from domestic sources (Silva et al., 1999). The higher values of chloride in water samples exceeded the WHO proposed drinking water quality criteria (WHO, 2004). According to Versari et al. (2002), chloride concentrations higher than 200 mg/L are considered to be a risk for human health and may cause unpleasant taste of water.
0 Site 2 Site 5 Site 3
-5 -10
-5
0 PC1 (78.65 %)
5
10
Fig. 2. Scores of the first two principal components (PC1 and PC2) PC1 and PC2 explained 95.02% of the total variance.
Mg) and concentration of major anions Cl and SO4 in lake water increase EC, is consistent with other study (Zacheus and Martikainen, 1997). Alkalinity values varied between 0.20% and 0.66% among all sampling sites. This variation is similar to that for the variation of values of EC in under study water samples. A similar relationship was reported by Thomaz et al. (1992) for the Varzea Lake, Brazil.
4.2.2. Dissolved metals in lake The mean concentrations of elements in water samples collected in 2005–06 are presented in Table 2. The average levels showed that concentrations of major, trace and toxic elements due to anthropogenic contamination (domestic, industrial and agricultural wastes) in water do not vary among sampling sites. The trend obtained was also supported by the analysis of the results on the raw data of water samples. Among the 16 elements determined in Manchar Lake water except Cu, Cr, Co and Mn, other elements (Na, K, Ca, Mg, Fe, Al, Ni, As, Se, Pb and Cd) have higher values as compared to the permissible level of these elements in drinking water (WHO, 2004). These values of elements indicate that deterioration of the lake is mainly due to the input of agricultural and domestic waste. Metals generally had uniform distribution in all samples, and there was little seasonal variability. In the summer season at sampling site 4, there was a low level of elements due to dilution
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TDS
BOD
Alkalinity
COD
TKN
Standard Units
2
1
0
-1
-2 Site 1
Site 2
Na
K
Site 3 Ca
Site 4
Site 5 Fe
Mg
Al
Standard Units
2
1
0
-1
-2 Site 2
Site 1 Cd
Pb
Site 3 As
Site 4
Site 5
Se
2
Standard Units
1
of dissolved oxygen in understudy lake water was strongly correlated with the high Fe2+ concentrations in water samples (Barreto et al., 2005). The results of iron are also consistent with other studies on river water has Fe concentration in the range of 1.8–5.06 mg/L (Da Silva and Sacoman, 2001). Exchangeable Fe usually relates to the adsorbed metals on the sediment surface can be easily remobilized into the Lake water (Ikem et al., 2003). The major adverse effects of elevated concentrations of Fe are associated with aesthetic nuisances such as staining of laundry, unpleasant odor and taste. Some studies have found a significant relationship between the exposure to Al in water and an increased risk of Alzheimer’s disease (Gardner and Gunn, 1991). Prolonged exposure to Al, however, can cause systemic toxicity, mainly affecting the gastrointestinal tract and causing neurological and skeletal effects. The poisoning of Al in patients with chronic renal failure is also the most important clinical problems involving trace metal toxicity (Forbes and Hill, 1998). The concentration of As detected in all water samples were 3–15-fold higher than the permissible limit of As in drinking water (10 mg/L). The main adverse health effects of As are tracheae bronchitis, rhinitis, pharyngitis, shortness of breath and nasal congestions (Xia and Liu, 2004). Similarly, contamination of drinking water from As may also result in blackfoot disease (Liu et al., 2003; Tsai et al., 1998). The levels of Pb were found to be higher during 2-year lake water analyses, which are mostly higher than permissible limit of drinking water by WHO guidelines, indicating pollution in this lake. Adverse health effects of Pb include various cancers, adverse reproductive outcomes, cardiovascular and neurological diseases (Watt et al., 2000). Elevated concentrations of Cd can cause nausea, vomiting, salivation and renal failure as well as kidney, liver and blood damages (Ikem et al., 2002) suggested that high concentrations of Cd may even cause mutations. 4.3. Overview of the physico-chemical parameters of lake water
0
-1
-2 Site 1
Site 2
Site 3
Site 4
Site 5
Fig 4. Standard unit of chemical concentrations in water of the lake Manchar. The standard units is defined as z ¼ (xu)/S, where x is the raw concentrated data, u is the mean values and S is the standard deviation. (a) TDS, BOD, COD, Alkalinity and TKN (b) Major cations, (c) trace and toxic metals.
by entering of fresh water from large hills due to rainy season. The high level of Na was due to drainage from lands cultivated with rice and many salt seeps present in the upper basin tributaries that result in salt loading through MNVD (site 1) in the lake. The water of lake is frequently used for drinking by humans as well as animals, because the people have no other resources of drinking water. It has been reported that high consumption of salts, particularly NaCl, may be crucial for the development of hypertension and increases the risk for stroke, left ventricular hypertrophy, osteoporosis, renal stones and asthma. This study is consistent with other study that people residing in these localities have frequent renal stones and asthmatic problems (McCarthy, 2004). The concentrations of Al and Fe were found to be very high in water samples collected from different sampling sites. The high level of Fe in lake water samples are mainly due to the inflow of surface run off from hill torrents and agricultural wastes (agricultural and rocks), both are rich in Al and Fe. The low level
The data of the physico-chemical parameters were transformed into standard unit (z) to compare the aspects of the variation in water samples collected from different sites as shown in Fig. 4. Among the normalized data, except for BOD, all other parameters were found to be very high at sampling site 1, showing high pollution at this site; it is the main contributing source of pollution in the lake (Fig. 4a). The normalized concentrations for all the physico-chemical parameters are almost equivalent to their mean values above one standard deviation (1s). All physicochemical parameters show low normalized values at site 4 and 5 (below 0), whereas the values of these parameters are (o1s) at sites 2 and 3 (Fig. 4b). Se and As have similar behavior at sites 1 and 2, while other toxic metals (Pb, Cd) show moderate variations at sites 2 and 3 (Fig. 4c).
5. Conclusion In this study, different multivariate statistical techniques were used to evaluate variations in surface water quality of the Manchar Lake. Cluster analysis grouped five sampling sites into three clusters of similar water quality characteristics. Based on obtained information, it is possible to design a future, optimal sampling strategy, which could reduce the number of sampling sites and associated cost. Principle component analysis helped in identifying the factors or sources responsible for water quality variations. The main cause of degradation of the lake is the discharge of industrial, agricultural wastes and of municipal sewage water from the upper northern areas of Sindh, coming through MNVD (site 1). Fishing and boating activities were also
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among the major sources responsible for lake water quality deterioration. This study illustrates the usefulness of multivariate statistical techniques for the analysis and interpretation of complex data sets, identification of pollution sources and understanding variations in water quality for effective lake water management. The chemometric study enabled us to show similarities and differences among the lakes examined among variables that were not clearly visible from an examination of the analytical data in the tables. Interventions should be made to reduce anthropogenic discharges in the Lake basin; otherwise, high levels of pollution will greatly influence the population and will invite socio–economic disaster. These results should be considered for future planning in using the lake’s water for drinking purposes.
Acknowledgment The authors would like to thank the financial support from Higher Education Commission of Pakistan. References AOAC, 1995. Association of Official Analytical Chemists. Official Methods of Analysis, 16 ed. AOAC International, Gaithersburg, MD March 1998 revision. Barreto, W.J., Ribeiro, M.R., Solci, M.C., Scarminio, I.S., Nozaki, J., de Oliveira, E., Barreto, S.R.G., 2005. Interpretation of seasonal variation of metals and abiotic properties in a tropical lake using multivariate analysis and MINTEQA2 program. Anal. Sci. 21, 209–214. Clesceri, L.S., Greenberg, A.E., Eaton, A.D. (Eds.), 1998. Standard Methods for the Examination of Water and Wastewater, 20th ed. American Public Heath Association (APHA), American Water Works Association (AWWA) and Water Environment Federation (WEF), Washington, DC. Da Silva, A.M.M., Sacoman, L.B., 2001. Using chemical and physical parameters to define the quality of Pardo river water (Botucatu-sp-Brazil). Water Res. 5, 1609–1616. Dixon, W., Chiswell, B., 1996. Review of aquatic monitoring program design. Water Res. 30, 1935–1948. Eaton, D.A., Clesceri, L.S., Greenberg, A.E., 1995. Standard Methods, for 14 the Examination of Water and Wastewater, 19th ed. American Public Health Association, Washington, DC, p. 1015. Forbes, W.F., Hill, G.B., 1998. Is exposure to aluminum a risk factor for the development of Alzheimer disease?—yes. Arch. Neurol. 55, 740–741. Galal-Gorchev, H., Ozolins, G., Bonnefoy, X., 1993. Revision of the WHO guidelines for drinking water quality. Ann. Institutionali. Super Sanit. 29, 335–345. Gardner, M.J., Gunn, A.M., 1991. Bioavailability of Al from food and drinking water. In: Proceedings of the Royal Society Med. Round Table Series: Alzheimer’s Disease and the environment, London, UK. Helena, B., Pardo, R., Vega, M., Barrado, E., Fernandez, J.M., Fernandez, L., 2000. Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga river, Spain) by principal component analysis. Water Res. 34, 807–816. Ikem, A., Odueyungbo, S., Egiebor, N.O., Nyavor, K., 2002. Chemical quality of bottled waters from three cities in eastern Alabama. Sci. Total Environ. 285, 165–175. Ikem, A., Egiebor, N.O., Nyavor, K., 2003. Trace elements in water, fish and sediment from Tuskegee lake, southeastern USA. Water Air Soil Poll. 149, 51–75. Issa, Y.M., Elewa, A.A., Rizk, M.S., Hassouna, A.F.A., 1996. Distribution of some heavy metals in Qaroun lake and river Nile, Egypt, Menofiya. J. Agric. Res. 21, 733–746. Kazi, T.G., Katz, S.A., 1987. Spectrophotometric determination of nitrate nitrogen and nitrite nitrogen in sewage sludges. Spectrosc. Lett. 20, 509–517.
309
Kim, J.H., Kim, R.H., Lee, J., Cheong, T.J., Yum, B.W., Chang, H.W., 2005. Multivariate statistical analysis to identify the major factors governing groundwater quality in the coastal area of Kimje, South Korea. Hydrol. Process 19, 1261–1276. Liu, C.W., Lin, K.H., Kuo, Y.M., 2003. Application of factor analysis in the assessment of groundwater quality in a blackfoot disease area in Taiwan. Sci. Total Environ. 313, 77–89. McCarthy, M.F., 2004. Should we restrict chloride rather than sodium? Med. Hypotheses 63, 138–148. McKenna Jr., J.E., 2003. An enhanced cluster analysis program with bootstrap significance testing for ecological community analysis. Environ. Mode. Softw. 18, 205–220. Niemi, G.J., Devore, P., Detenbeck, N., Taylor, D., Lima, A., 1990. Overview of case studies on recovery of aquatic systems from disturbance. Environ. Manage. 14, 571–587. Otto, M., 1998. Multivariate methods. In: Kellner, R., Mermet, J.M., Otto, M., Widmer, H.M. (Eds.), Analytical Chemistry. Wiley/VCH, Weinheim, Germany, p. 916. Reghunath, R., Murthy, T.R.S., Raghavan, B.R., 2002. The utility of multivariate statistical techniques in hydrogeochemical studies: an example from Karnataka, India. Water Res. 36, 2437–2442. Sanchez-Perez, J.M., Tremolieres, M., 2003. Change in groundwater chemistry as a consequence of suppression of floods: the case of the Rhine floodplain. J. Hydrol. 270, 89–104. Sarbu, C., Pop, H.F., 2005. Principal component analysis versus fuzzy principal component analysis. A case study: the quality of Danube water (1985–1996). Talanta 65, 1215–1220. Siddiq, S., 2004. Annual Report 2003–04. Sustainable Development Policy Institute. /www.sdpi.orgS. Silva, M.A.L., Rezende, C.E., 2002. Behavior of selected micro and trace elements and organic matter in sediments of freshwater system in south–east Brazil. Sci. Total Environ. 292, 21–128. Silva, A.M., Novelli, E.L.B., Fascineli, M.L., Almeida, J.A., 1999. Impact of an environmentally realistic intake of water contaminants and superoxide formation on tissues of rats. Environ. Poll. 105, 243–249. Simeonov, V., Simeonova, P., Tsitouridou, R., 2004. Chemometric quality assessment of surface waters: two case studies. Chem. Eng. Ecol. 11, 449–469. Singh, K.P., Malik, A., Mohan, D., Sinha, S., 2004. Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti river (India): a case study. Water Res. 38, 3980–3992. Szymanowska, A., Samecka-Cymerman, A., Kempers, A.J., 1999. Heavy metals in three lakes in West Poland. Ecotoxicol. Environ. Saf. 43, 21–29. Thomaz, S.M., Lansac-Toha, F.A., Roberto, M.C., Esteves, F.A., Lima, A.F., 1992. Seasonal variation of some limnological factors of lagoa do Guarana, a Varzea lake of the high Rio Parana, State of Mato Grosso do Sul, Brazil. Rev. Hydrobiol. Trop. 25, 269–276. Tsai, S.M., Wang, T.N., Ko, Y.C., 1998. Cancer mortality trends in blackfoot disease endemic community of Taiwan following water source replacement. J. Toxicol. Environ. Health Part A 55, 389–404. Vega, M., Pardo, R., Barrado, E., Deban, L., 1996. Assessment of seasonal and polluting effects on the quality of river water by exploratory data analysis. Water Res. 32, 3581–3592. Versari, A., Parpinello, G.P., Galassi, S., 2002. Chemometric survey of Italian bottled mineral waters by means of their labelled physicochemical and chemical composition. J. Food Compos. Anal. 15, 251–264. Watt, G.C.M., Britton, A., Gilmour, H.G., Moore, M.R., Murray, G.D., Robertson, S.J., 2000. Public health implications of new guidelines for lead in drinking water: a case study in an area with historically high water lead levels. Food Chem. Toxicol. 38, 73–79. WHO, 1998. Guidelines for Drinking-water Quality, Addendum to Volume 2: Health Criteria and Other Supporting Information, 2nd ed. Geneva, Switzerland. WHO, 2004. Guidelines for Drinking-Water Quality, third ed. WHO, Geneva, Switzerland. Xia, Y., Liu, J., 2004. An overview of chronic arsenism via drinking water in PR China. Toxicology 198, 25–29. Zacheus, O.M., Martikainen, P.J., 1997. Physicochemical quality of drinking and hot waters in Finnish buildings originated from groundwater or surface water plants. Sci. Total Environ. 204, 1–10.