Composite water samples were collected from different sites of Mangla reservoir, Pakistan, in premonsoon, monsoon, and postmonsoon seasons. The physicochemical parameters and trace/heavy metals were determined in all water samples. The results manifested significant seasonal variations among Co, Cr, Ni, and Pb and the metals exhibited highest contribution in premonsoon season except Mn. Principal component analysis (PCA) and cluster analysis (CA) revealed considerable anthropogenic intrusions in the reservoir. Probable risk associated with the metals levels on human health was also evaluated using hazard quotients (HQ) by ingestion and dermal routes for adults and children. It was noted that Cd, Co, Cr, Ni, and Pb (
Lakes are important and significant bodies in preserving freshwater, replenishing underground water, and adjusting local climate; consequently, they are considered one of the most versatile ecosystems in the world [
The Mangla Lake (Mirpur, Pakistan) was erected for hydroelectric power generation, irrigation, and flood control in 1967 across Jhelum River. Currently, the Lake water is also used for drinking and household purposes in adjoining areas. In this respect, the concentrations of trace metals in the Lake in different seasons were of great concern. The objectives of this study were
Mangla reservoir is one of the largest freshwater resources in Pakistan (Figure
Map of the study area showing sampling sites.
Composite water samples were collected from five sites in the Lake (as shown in Figure
The water quality parameters including temperature (T), hydrogen ion concentration (pH), dissolved oxygen (DO), and total dissolved solids (TDS) were measured in the field/on site: pH was measured using a digital pH meter (model: Martini Mi 180); DO was estimated by digital DO meter (model: Martini Mi 190); TDS were measured by a digital TDS meter (model: Jenway 470). Concentrations of the metals (Cd, Cr, Co, Cu, Fe, Mn, Ni, Pd, and Zn) were determined using a flame atomic absorption spectrophotometer (model: Shimadzu AA-670, Japan). Calibration line method was used for the quantification of metals. A reagent blank was analyzed to determine the contamination during processing/preserving of the water samples. All the measurements were made in triplicate. The reliability of the analytical data was ensured by using standard reference material (SRM-1643d) and the results are shown in Table
Certified versus measured concentrations (
Metal | Certified concentration | Measured concentration |
---|---|---|
Cd | 6.47 ± 0.37 | 6.09 ± 0.28 |
Cr | 18.53 ± 0.20 | 19.7 ± 0.24 |
Co | 25.00 ± 0.59 | 22.3 ± 0.41 |
Cu | 20.5 ± 3.8 | 19.2 ± 2.1 |
Fe | 91.2 ± 3.9 | 92.6 ± 3.5 |
Mn | 37.66 ± 0.83 | 36.1 ± 0.67 |
Ni | 58.1 ± 2.7 | 61.2 ± 2.1 |
Pb | 18.15 ± 0.64 | 19.7 ± 0.83 |
Zn | 72.48 ± 0.65 | 69.6 ± 0.94 |
Chemical reagents (AR grade, certified purity > 99.99%) used during chemical processing and analysis were purchased from E-Merck (Darmstadt, Germany). Doubly distilled water was used for the preparation of working standards from stock solution (1000 mg/L) and for the dilution of water samples whenever required [
Possible sources of the metals in water reservoir were identified by principal component analysis (PCA) and cluster analysis (CA). PCA was applied on the dataset after varimax normalized rotation. It yielded significant principal components (PCs) which showed the contribution of major sources to total pollution index [
Human beings are exposed to trace metals through three possible ways: direct ingestion, inhalation, and dermal contact. Oral intake and dermal absorption routes are most common for drinking water [
Risk evaluation related to the noncarcinogenic risks was quantified by computing hazard quotient (HQ). The HQ is a ratio of average intake of contaminants from exposure ways (oral intake/dermal) to the related reference dose (RfD) which was calculated using the following equation:
Significant noncarcinogenic risk is associated with HQ > 1. Hazard index (HI) was computed to determine the total possible noncarcinogenic risks posed by multipathways. The HI was computed by adding the HQs from all probable pathways as below:
The physical condition of a water body strongly influences the chemical and biological processes that occur in the water column and consequently its ecological and chemical status. The mineral constituents existing in water determine the aptness of water. Water quality parameters (
Descriptive statistics for trace metals (
Cd | Co | Cr | Cu | Fe | Mn | Ni | Pb | Zn |
|
pH | TDS | DO | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Premonsoon | |||||||||||||
Mean | 36.3 | 235 | 75.1 | 20.3 | 128 | 13.1 | 313 | 339 | 31.2 | 31.6 | 7.9 | 92.0 | 4.3 |
Median | 30.5 | 231 | 52.5 | 16.0 | 127 | 10.0 | 322 | 241 | 30.5 | 31.5 | 8.1 | 82.4 | 4.6 |
Max | 103 | 501 | 312 | 56.0 | 381 | 59.0 | 682 | 1501 | 76.0 | 32.5 | 8.3 | 229 | 4.9 |
Monsoon | |||||||||||||
Mean | 31.1 | 157 | 67.2 | 14.4 | 88.5 | 8.16 | 126 | 226 | 7.84 | 24.3 | 7.4 | 139 | 6.1 |
Median | 30.5 | 116 | 54.0 | 11.0 | 66.5 | 8.00 | 127 | 228 | 5.00 | 24.4 | 7.4 | 131 | 6.1 |
Max | 53.0 | 547 | 194 | 55.0 | 278 | 24.0 | 415 | 465 | 72.0 | 28.1 | 7.8 | 384 | 6.5 |
Postmonsoon | |||||||||||||
Mean | 18.3 | 103 | 21.0 | 13.7 | 109 | 18.9 | 124 | 129 | 11.7 | 13.8 | 6.4 | 79.4 | 6.5 |
Median | 17.0 | 99.0 | 17.0 | 12.5 | 78.5 | 18.0 | 116 | 124 | 10.5 | 13.8 | 6.4 | 77.0 | 6.5 |
Max | 40.0 | 254 | 63.0 | 30.0 | 454 | 43.0 | 405 | 267 | 31.0 | 14.2 | 6.8 | 139 | 7.4 |
Water quality criteria for drinking water | |||||||||||||
WHO (2008) | 3 | 40 | 50 | 2000 | 300 | 100 | 70 | 10 | 3000 | — | 6.5–8.5 | 1200 | — |
USEPA (2009), MCL | 5 | 100 | 1300 | 300 | 50 | 700 | 15 | 5000 | — | 6.5–8.5 | 500 | — | |
EC (1998) | 5 | 50 | 2000 | 200 | 20 | 10 | 100 | — | — | — | |||
Pak-EPA (2008) | 10 | 50 | 2000 | 500 | 20 | 50 | 5000 | — | 6.5–8.5 | 1000 | — | ||
Freshwater quality criteria for protection of aquatic life | |||||||||||||
USEPA (2006), CMC (acute) | 2 | 16 | 13 | 1000 | 470 | 65 | 120 | — | — | — | — | ||
USEPA (2006), CCC (chronic) | 2.5 | 11 | 9 | 1000 | 52 | 3 | — | — | — | — |
Total dissolved solid (TDS) is an important parameter as it can affect the taste of water. Concentrations of TDS are generally related to human activities such as urban water runoffs, municipal wastewater discharges, and agricultural activities in the catchments areas. The contents of TDS ranged from 86.3 to 229 mg/L, from 99.2 to 385 mg/L, and from 74.2 to 139 mg/L with mean values of 92.0, 139, and 79.4 mg/L in premonsoon, monsoon, and postmonsoon seasons, respectively. Water having TDS less than 1000 mg/L is generally considered fresh/acceptable [
The dissolved oxygen (DO) in surface water ranged from 4.2 to 4.9 mg/L (mean: 4.3 mg/L), from 6.0 to 6.5 mg/L (mean: 6.1 mg/L), and from 6.4 to 7.4 mg/L (mean: 6.5 mg/L) in premonsoon, monsoon, and postmonsoon seasons, respectively. DO concentration of greater than 5 mg/L is recommended to support the biota in aquatic ecosystem [
Measured values of pH in surface water were found to be within permissible limits of WHO [
During the study period, all trace metals did not show significant spatial variations (
Total concentrations of trace metals at different sampling sites.
Spatial distribution of total concentrations of the metals showed a decreasing trend from the sites near highly urban areas to the sites near suburban areas. The higher total concentrations were measured at sites S4 and S3 which are located at highly urbanized area (Mirpur city), while the lower total concentrations were found at sampling sites S1 and S2 which are close to the less populated areas of the Lake in all seasons. Moreover, the water samples collected near the Lake outlet had the lowest concentrations. It could be due to dilution and transfer of metals from water column towards the sediments [
The average concentrations of trace metals in all seasons are shown in Table
Most of the trace metals showed significant temporal/seasonal variations (
Average metal concentrations in water samples from Mangla Lake were also compared with the reported studies from different reservoirs in national and international studies (Table
Comparison of mean trace metals concentrations (
Cd | Co | Cr | Cu | Fe | Mn | Ni | Pb | Zn | Reference | |
---|---|---|---|---|---|---|---|---|---|---|
Mangla Lake | ||||||||||
Premonsoon | 36.3 | 75.1 | 235 | 20.3 | 128 | 13.1 | 313 | 339 | 31.2 | This study |
Mangla Lake | ||||||||||
Monsoon | 31.1 | 67.2 | 157 | 14.4 | 88.5 | 8.16 | 126 | 226 | 7.84 | This study |
Mangla Lake | ||||||||||
Postmonsoon | 18.3 | 21.0 | 103 | 13.7 | 109 | 18.9 | 124 | 129 | 11.7 | This study |
Kralkızı Dam Reservoir, Turkey | 0.036 | — | 22.06 | 2.83 | 58.63 | — | 15.75 | 2.56 | 5.02 | [ |
Dicle Dam Reservoir, Turkey | 0.030 | — | 18.58 | 2.12 | 62.07 | — | 15.86 | 1.84 | 4.12 | [ |
Batman Dam Reservoir, Turkey | 0.044 | — | 16.5 | ND | 57.66 | — | 15.96 | 1.56 | 4.09 | [ |
Danjiangkou Reservoir, China | 1.17 | 1.08 | 6.29 | 13.32 | 19.14 | 5.69 | 1.73 | 10.59 | 2.02 | [ |
Dil Deresi stream, Turkey | 8 | 21 | 42 | 37 | 4030 | — | — | 120 | 700 | [ |
Taihu Lake, China | 0.06 | — | 0.99 | 5.81 | — | — | 5.34 | 2.74 | 15.86 | [ |
Kanyaboli Lake, Kenya | 4.4 | 6.06 | 21.54 | 23.95 | — | 284.27 | 16.38 | 20.65 | 32.79 | [ |
Kainji Dam, Nigeria | — | 1.2 | 2.2 | 1.3 | 13 | 9 | 0.90 | 1.2 | 0.90 | [ |
Anthropogenic Lake in West Poland | 2.9 | 24 | 3.3 | 48 | 154 | 1230 | 4 | 111 | 207 | [ |
Legnica Lake in Southwest Poland | 1.72 | 65 | 1.1 | 29 | 6 | 670 | 68.2 | 0.21 | 204 | [ |
Lake Gilow, Poland | 0.58 | 27 | 0.9 | 48 | 6 | 390 | 76 | 0.5 | 167 | [ |
Mangla Lake, Pakistan | ||||||||||
Summer | 30 | 250 | 80 | 20 | 150 | 10 | 130 | 380 | 30 | [ |
Mangla Lake, Pakistan | ||||||||||
Winter | 30 | 160 | 70 | 20 | 130 | 20 | 110 | 340 | 30 | [ |
Rawal Lake, Pakistan | ||||||||||
Summer | 6 | 11 | 9 | 10 | 93 | 4 | — | 162 | 14 | [ |
Rawal Lake, Pakistan | ||||||||||
Winter | 25 | 204 | 97 | 17 | 76 | 13 | — | 223 | 22 | [ |
Khanpur Lake, Pakistan | 20 | 114 | 46 | 9 | 51 | 11 | — | 221 | 15 | [ |
Manchar Lake, Pakistan | ||||||||||
Summer | 6.14 | 41.0 | 8.23 | 20.4 | 3228 | 75.8 | 35.8 | 87.6 | 774 | [ |
Manchar Lake, Pakistan | ||||||||||
Winter | 4.22 | 34.7 | 6.84 | 18.2 | 2780 | 64.7 | 31.0 | 78.7 | 683 | [ |
Manchar Lake, Pakistan | 5.3 | 38.9 | 7.64 | 18.9 | 2960 | 72.6 | 35.0 | 82.4 | 730 | [ |
ND: not detected.
Average metal levels measured in the present study were compared with previously reported levels [
Metal concentrations in different seasons were compared with water quality guidelines for drinking water [
In this study, 100% of the water samples in monsoon and >90% samples in premonsoon and postmonsoon for Cd and Pb exceeded EC, WHO, USEPA, and Pak-EPA guidelines. About 58% of the samples for Cr in premonsoon and monsoon and >92% for Ni in all seasons were found to exceed WHO, EC, and Pak-EPA guidelines. Cobalt concentrations were higher than WHO guidelines in 96%, 86%, and 84% of samples in premonsoon, monsoon, and postmonsoon, respectively. However, the measured concentrations of Fe in more than 90% of samples were lower, whereas Cu, Mn, and Zn levels were found to be lower in 100% of the water samples than the national and international guidelines in all seasons. Thus Cd, Co, Ni, and Pb were potential pollutants in the reservoir and they might pose health risks for the local population. For example, Cd causes renal tubular dysfunction, bone fragility, kidney dysfunction, skeletal damage, and reproductive disorders; Co causes polycythemia, heart dysfunctions, thyroid alterations, testicular deterioration and wither, lesser development, and persistence of progenies; Ni may cause skin allergies, lung fibrosis, dermatitis, and cancer of the respiratory tract; and Pb may affect central nervous system especially in young children, kidney, and cardiovascular system [
Principal component analysis (PCA) was used to find out the plausible contributing sources of selected metals in water reservoir. This method allows identifying different groups of metals that correlate and thus can be considered as having a similar behavior and common origin [
Principal component loadings
PC1 | PC2 | PC3 | PC4 | |
---|---|---|---|---|
Eigenvalue | 1.85 | 1.47 | 1.23 | 1.22 |
Total variance (%) | 38.6 | 20.4 | 15.7 | 10.6 |
Cumulative variance (%) | 38.6 | 59.0 | 74.7 | 85.3 |
|
||||
Cd |
|
— | — | — |
Co |
|
— | — | — |
Cr | — |
|
— | — |
Cu | — | 0.35 |
|
0.25 |
Fe | 0.29 | — | — |
|
Mn | — | — | — |
|
Ni | 0.26 |
|
— | — |
Pb | — |
|
— | — |
Zn | — | 0.33 |
|
— |
Cluster analysis (CA) of the metal data was performed to explore the grouping of trace metals in surface water of Mangla Lake, Pakistan. It is shown as a dendrogram in Figure
Dendrogram showing clustering of trace metals in Mangla Lake.
Cluster analysis (CA) was also applied to the dataset to group the similar sampling sites (spatial variability). Spatial CA rendered a dendrogram (Figure
Dendrogram showing clustering of sampling sites in Mangla Lake.
Table
Hazard quotient (HQ) and hazard index (HI) for each metal in surface water of Mangla Lake.
Metal |
|
|
|
|
HI = |
|||
---|---|---|---|---|---|---|---|---|
( |
( |
Child | Adult | Child | Adult | Child | Adult | |
Premonsoon | ||||||||
Cd | 0.5 | 0.025 | 8.3 |
2.2 |
6.1 |
2.1 |
9.0 |
2.4 |
Co | 0.3 | 0.06 | 9.0 |
2.4 |
6.6 |
2.2 |
9.1 |
2.4 |
Cr | 3 | 0.075 | 2.9 |
7.5 |
8.4 |
2.9 |
3.7 |
1.0 |
Cu | 40 | 8 | 5.8 |
1.5 |
1.1 |
3.6 |
5.9 |
1.6 |
Fe | 700 | 140 | 2.1 |
5.5 |
3.9 |
1.3 |
2.1 |
5.6 |
Mn | 24 | 0.96 | 5.6 |
1.6 |
5.8 |
2.0 |
6.1 |
1.8 |
Ni | 20 | 5.4 | 1.5 |
4.7 |
9.8 |
3.3 |
1.5 |
4.8 |
Pb | 1.4 | 0.42 | 2.2 |
7.3 |
1.4 |
4.6 |
2.2 |
7.3 |
Zn | 300 | 60 | 1.2 |
3.1 |
1.3 |
4.5 |
1.2 |
3.2 |
|
||||||||
Monsoon | ||||||||
Cd | 0.5 | 0.025 | 7.2 |
1.9 |
5.3 |
1.8 |
7.7 |
2.1 |
Co | 0.3 | 0.06 | 6.0 |
1.6 |
4.4 |
1.5 |
6.1 |
1.6 |
Cr | 3 | 0.075 | 2.6 |
6.7 |
7.6 |
2.6 |
3.3 |
9.3 |
Cu | 40 | 8 | 4.1 |
1.1 |
7.6 |
2.6 |
4.2 |
1.1 |
Fe | 700 | 140 | 1.5 |
3.8 |
2.7 |
9.0 |
1.5 |
3.9 |
Mn | 24 | 0.96 | 3.5 |
1.0 |
3.6 |
1.2 |
3.8 |
1.1 |
Ni | 20 | 5.4 | 6.0 |
1.9 |
3.9 |
1.3 |
6.1 |
1.9 |
Pb | 1.4 | 0.42 | 1.5 |
4.9 |
9.1 |
3.1 |
1.5 |
4.9 |
Zn | 300 | 60 | 3.0 |
7.9 |
3.3 |
1.1 |
3.0 |
8.0 |
|
||||||||
Postmonsoon | ||||||||
Cd | 0.5 | 0.025 | 4.2 |
1.1 |
3.1 |
1.0 |
4.5 |
1.2 |
Co | 0.3 | 0.06 | 3.9 |
1.0 |
2.9 |
9.8 |
4.0 |
1.0 |
Cr | 3 | 0.075 | 8.1 |
2.1 |
2.4 |
8.0 |
1.0 |
2.9 |
Cu | 40 | 8 | 3.9 |
1.0 |
7.2 |
2.4 |
4.0 |
1.1 |
Fe | 700 | 140 | 1.8 |
4.7 |
3.3 |
1.1 |
1.8 |
4.8 |
Mn | 24 | 0.96 | 8.0 |
2.4 |
8.3 |
2.8 |
8.8 |
2.7 |
Ni | 20 | 5.4 | 6.0 |
1.9 |
3.9 |
1.3 |
6.0 |
1.9 |
Pb | 1.4 | 0.42 | 8.4 |
2.8 |
5.2 |
1.8 |
8.4 |
2.8 |
Zn | 300 | 60 | 4.5 |
1.2 |
4.9 |
1.7 |
4.5 |
1.2 |
In monsoon season,
As a whole, it was revealed that Cd, Co, Cr, Ni, and Pb could pose severe health effects to the inhabitants through ingestion pathway in the studied seasons, whereas the remaining metals through oral pathway and all the studied metals via dermal pathway could cause little/no health concerns. Consequently, distinctive consideration should be paid to manage these toxic elements and to support healthy aquatic ecosystem. Nonetheless, uncertainties related to methodological features, such as water and dermal contact factor (
Concentrations of trace metals (Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) in the water of the Mangla Lake, Pakistan, demonstrated significant seasonal variations. Irrespective of the sampling sites, the metal concentrations were lower in postmonsoon season compared to other seasons due to marked dilution effect. The measured concentrations of Cd, Co, Cr, Ni, and Pb were recorded higher than water quality guidelines. PCA and CA indicated that both geogenic and anthropogenic activities were contributing factors to metal abundance in the Lake. Cluster analysis grouped all sampling sites into three major clusters. Higher concentrations of metals were found near urban areas revealing that their concentrations had been strongly affected by anthropogenic influences. Thus, it was logical to conclude that the elevated concentrations of metals in Lake water were considerably due to direct discharge of untreated municipal/industrial wastes into the Lake. Human health risk was assessed using exposure risk assessment model which indicated Cd, Co, Cr, Ni, and Pb were the most prevalent pollutants causing noncarcinogenic concerns in all seasons and the oral ingestion was the major exposure pathway. Therefore, special attention should be paid to manage Cd, Co, Cr, Ni, and Pb in the study area and measures needed to be taken for sustaining the healthy aquatic ecosystem.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The research fellowship awarded by the Higher Education Commission (HEC), Pakistan, and Quaid-i-Azam University, Islamabad, Pakistan, to carry out this project is thankfully acknowledged. The authors are also grateful to the administration of Mangla Lake, Pakistan, for their assistance and help during the sampling campaigns.