This study investigates the spatial water quality pattern of seven stations located along the main Langat River. Environmetric methods, namely, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA), and the factor analysis (FA), were used to study the spatial variations of the most significant water quality variables and to determine the origin of pollution sources. Twenty-three water quality parameters were initially selected and analyzed. Three spatial clusters were formed based on HACA. These clusters are designated as downstream of Langat river, middle stream of Langat river, and upstream of Langat River regions. Forward and backward stepwise DA managed to discriminate six and seven water quality variables, respectively, from the original 23 variables. PCA and FA (varimax functionality) were used to investigate the origin of each water quality variable due to land use activities based on the three clustered regions. Seven principal components (PCs) were obtained with 81% total variation for the high-pollution source (HPS) region, while six PCs with 71% and 79% total variances were obtained for the moderate-pollution source (MPS) and low-pollution source (LPS) regions, respectively. The pollution sources for the HPS and MPS are of anthropogenic sources (industrial, municipal waste, and agricultural runoff). For the LPS region, the domestic and agricultural runoffs are the main sources of pollution. From this study, we can conclude that the application of environmetric methods can reveal meaningful information on the spatial variability of a large and complex river water quality data.
Multivariate analysis including correlation, multiple stepwise linear regression, and cluster analyses were applied to investigate the heavy metal concentrations (Cd, Cu, Fe, Ni, Pb, and Zn) in the different parts of bivalves and gastropods. It was also aimed to distinguish statistically the differences between the marine bivalves and the gastropods with regards to the accumulation of heavy metals in the different tissues. The different parts of four species of bivalves and four species of gastropods were obtained and analyzed for heavy metals. The multivariate analyses were then applied on the data. From the multivariate analyses conducted, there were correlations found between the soft tissues of bivalves and gastropods, but none was found between the shells and the soft tissues of most of the molluscs (except for Cerithidea obtusa and Puglina cochlidium). The significant correlations (P < 0.05) found between the soft tissues were further complemented by the multiple stepwise linear regressions where heavy metals in the total soft tissues were influenced by the accumulation in the different types of soft tissues. The present study found that the distributions of heavy metals in the different parts of molluscs were related to their feeding habits and living habitats. The statistical approaches proposed in this study are recommended for use in biomonitoring studies, since multivariate analyses can reduce the cost and time involved in identifying an effective tissue to monitor the heavy metal(s) bioavailability and contamination in tropical coastal waters.
Gadilam river basin has gained its importance due to the presence of Neyveli Lignite open cast mines and other industrial complexes. It is also due to extensive depressurization of Cuddalore aquifer, and bore wells for New Veeranam Scheme are constructed downstream of the basin. Geochemical indicators of groundwater were used to identify the chemical processes that control hydrogeochemistry. Chemical parameters of groundwater such as pH, electrical conductivity, total dissolved solids, sodium (Na(+)), potassium (K(+)), calcium (Ca(+)), magnesium (Mg(+)), bicarbonate (HCO(-)(3)), sulfate (SO(-)(4)), phosphate (PO(-)(4)), and silica (H(4)SiO(4)) were determined. Interpretation of hydrogeochemical data suggests that leaching of ions followed by weathering and anthropogenic impact controls the chemistry of the groundwater. Isotopic study reveals that recharge from meteoric source in sedimentary terrain and rock-water interaction with significant evaporation prevails in hard rock region.
As groundwater is a vital source of water for domestic and agricultural activities in Thanjavur city due to lack of surface water resources, groundwater quality and its suitability for drinking and agricultural usage were evaluated. In this study, 102 groundwater samples were collected from dug wells and bore wells during March 2008 and analyzed for pH, electrical conductivity, temperature, major ions, and nitrate. Results suggest that, in 90% of groundwater samples, sodium and chloride are predominant cation and anion, respectively, and NaCl and CaMgCl are major water types in the study area. The groundwater quality in the study site is impaired by surface contamination sources, mineral dissolution, ion exchange, and evaporation. Nitrate, chloride, and sulfate concentrations strongly express the impact of surface contamination sources such as agricultural and domestic activities, on groundwater quality, and 13% of samples have elevated nitrate content (>45 mg/l as NO(3)). PHREEQC code and Gibbs plots were employed to evaluate the contribution of mineral dissolution and suggest that mineral dissolution, especially carbonate minerals, regulates water chemistry. Groundwater suitability for drinking usage was evaluated by the World Health Organization and Indian standards and suggests that 34% of samples are not suitable for drinking. Integrated groundwater suitability map for drinking purposes was created using drinking water standards based on a concept that if the groundwater sample exceeds any one of the standards, it is not suitable for drinking. This map illustrates that wells in zones 1, 2, 3, and 4 are not fit for drinking purpose. Likewise, irrigational suitability of groundwater in the study region was evaluated, and results suggest that 20% samples are not fit for irrigation. Groundwater suitability map for irrigation was also produced based on salinity and sodium hazards and denotes that wells mostly situated in zones 2 and 3 are not suitable for irrigation. Both integrated suitability maps for drinking and irrigation usage provide overall scenario about the groundwater quality in the study area. Finally, the study concluded that groundwater quality is impaired by man-made activities, and proper management plan is necessary to protect valuable groundwater resources in Thanjavur city.
Water from 15 sampling stations in Tasik Chini (Chini Lake), Peninsular Malaysia were sampled for 12 months from September 2004 until August 2005 and analyzed for 11 metals including iron (Fe), aluminum (Al), manganese (Mn), barium (Ba), zinc (Zn), lead (Pb), copper (Cu), cadmium (Cd), nickel (Ni), chromium (Cr) and cobalt (Co). Results showed that the mean (min-max) metal concentrations (in micrograms per liter) in Tasik Chini waters for the 12 months sampling based on 15 sampling stations (in descending order) for Fe, Al, Mn, Ba, Zn, Pb, Cu and Cd were 794.84 (309.33-1609.07), 194.53 (62.37-665.93), 29.16 (16.68-79.85), 22.07 (15.64-29.71), 5.12 (2.224-6.553), 2.36 (1.165-4.240), 0.832 (0.362-1.443) and 0.421 (0.254-0.696) respectively. Concentration for three metals i.e. Ni, Cr and Co were too low and not detected by the graphite furnace Atomic Absorption Spectrophotometry (AAS). Comparison with various water quality standards showed that the mean metals concentration in surface water of Tasik Chini were low and within the range of natural background except for Fe and Al. In general, metal concentrations in Tasik Chini water varied temporally and spatially. The main factors influencing these metal concentrations in the water were the raining season and mining activities. Stations located at Tanjung Jerangking and Melai areas were the most effected due to those factors.
A study of the water quality changes of Chini Lake was conducted for 12 months, which began in May 2004 and ended in April 2005. Fifteen sampling stations were selected representing the open water body in the lake. A total of 14 water quality parameters were measured and Malaysian Department of Environment Water Quality Index (DOE-WQI) was calculated and classified according to the Interim National Water Quality Standard, Malaysia (INWQS). The physical and chemical variables were temperature, dissolved oxygen (DO), conductivity, pH, total dissolved solid (TDS), turbidity, chlorophyll-a, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solid (TSS), ammonia-N, nitrate, phosphate and sulphate. Results show that base on Malaysian WQI, the water in Chini Lake is classified as class II, which is suitable for recreational activities and allows body contact. With respect to the Interim National Water Quality Standard (INWQS), temperature was within the normal range, conductivity, TSS, nitrate, sulphate and TDS are categorized under class I. Parameters for DO, pH, turbidity, BOD, COD and ammonia-N are categorized under class II. Comparison with eutrophic status indicates that chlorophyll-a concentration in the lake was in mesotrophic condition. In general water quality in Chini Lake varied temporally and spatially, and the most affected water quality parameters were TSS, turbidity, chlorophyll-a, sulphate, DO, ammonia-N, pH and conductivity.
Multivariate statistical techniques such as multivariate analysis of variance (MANOVA) and discriminant analysis (DA) were applied for analyzing the data obtained from two rivers in the Penang State of Malaysia for the concentration of heavy metal ions (As, Cr, Cd, Zn, Cu, Pb, and Hg) using a flame atomic absorption spectrometry (F-AAS) for Cr, Cd, Zn, Cu, Pb, As and cold vapor atomic absorption spectrometry (CV-AAS) for Hg. The two locations of interest with 20 sampling points of each location were Kuala Juru (Juru River) and Bukit Tambun (Jejawi River). MANOVA showed a strong significant difference between the two rivers in terms of heavy metal concentrations in water samples. DA gave the best result to identify the relative contribution for all parameters in discriminating (distinguishing) the two rivers. It provided an important data reduction as it used four parameters (Zn, Pb, Cd and Cr) affording 100% correct assignations. Results indicated that the two rivers were different in terms of heavy metals concentrations in water, and the major difference was due to the contribution of Zn. A negative correlation was found between discriminate functions (DF) and Cr and As, whereas positive correlation was exhibited with other heavy metals. Therefore, DA allowed a reduction in the dimensionality of the data set, delineating a few indicator parameters responsible for large variations in heavy metal concentrations. Correlation matrix between the parameters exhibited a strong evidence of mutual dependence of these metals.
A field investigation was conducted at all water treatment plants throughout 11 states and Federal Territory in Peninsular Malaysia. The sampling points in this study include treatment plant operation, service reservoir outlet and auxiliary outlet point at the water pipelines. Analysis was performed by solid phase micro-extraction technique with a 100 microm polydimethylsiloxane fibre using gas chromatography with mass spectrometry detection to analyse 54 volatile organic compounds (VOCs) of different chemical families in drinking water. The concentration of VOCs ranged from undetectable to 230.2 microg/l. Among all of the VOCs species, chloroform has the highest concentration and was detected in all drinking water samples. Average concentrations of total trihalomethanes (THMs) were almost similar among all states which were in the range of 28.4--33.0 microg/l. Apart from THMs, other abundant compounds detected were cis and trans-1,2-dichloroethylene, trichloroethylene, 1,2-dibromoethane, benzene, toluene, ethylbenzene, chlorobenzene, 1,4-dichlorobenzene and 1,2-dichloro - benzene. Principal component analysis (PCA) with the aid of varimax rotation, and parallel factor analysis (PARAFAC) method were used to statistically verify the correlation between VOCs and the source of pollution. The multivariate analysis pointed out that the maintenance of auxiliary pipelines in the distribution systems is vital as it can become significant point source pollution to Malaysian drinking water.
Blast operations in the vicinity of residential areas usually produce significant environmental problems which may cause severe damage to the nearby areas. Blast-induced air overpressure (AOp) is one of the most important environmental impacts of blast operations which needs to be predicted to minimize the potential risk of damage. This paper presents an artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) for the prediction of AOp induced by quarry blasting. For this purpose, 95 blasting operations were precisely monitored in a granite quarry site in Malaysia and AOp values were recorded in each operation. Furthermore, the most influential parameters on AOp, including the maximum charge per delay and the distance between the blast-face and monitoring point, were measured and used to train the ICA-ANN model. Based on the generalized predictor equation and considering the measured data from the granite quarry site, a new empirical equation was developed to predict AOp. For comparison purposes, conventional ANN models were developed and compared with the ICA-ANN results. The results demonstrated that the proposed ICA-ANN model is able to predict blast-induced AOp more accurately than other presented techniques.
Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R(2)) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R(2) (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell's conjugate gradient as the training algorithm.
The present study deals with possible contamination of the soil by metal ions which have been affecting the environment. The concentrations of metal ions in 14 borehole samples were studied using the ICP-OES standard method. The degree of contamination was determined on the basis of single element pollution index (SEPI), combined pollution index (CPI), soil enrichment factor (SEF), and geo-accumulation index (Igeo). Geo-accumulation indices and contamination factors indicated moderate to strong contaminations for eight boreholes (BL-1, BL-2, BL-6, BL-8, BL-9, BL-10, BL-12, and BL-13) while the rest were extremely contaminated. Among all the boreholes, BL-3 and BL-11 demonstrated the highest level of Cd(II) and Pb(II) which were found the most polluted sites. The level of metal contamination was also compared with other countries. The development, variation, and limitations regarding the regulations of soil and groundwater contamination can be provided as a helpful guidance for the risk assessment of metal ions in developing countries.
Chenab River is one of the most important rivers of Punjab Province (Pakistan) that receives huge input of industrial effluents and municipal sewage from major cities in the Central Punjab, Pakistan. The current study was designed to evaluate the concentration levels and associated ecological risks of USEPA priority polycyclic aromatic hydrocarbons (PAHs) in the surface sediments of Chenab River. Sampling was performed from eight (n = 24) sampling stations of Chenab River and its tributaries. We observed a relatively high abundance of ∑16PAHs during the summer season (i.e. 554 ng g(-1)) versus that in the winter season (i.e. 361 ng g(-1)), with an overall abundance of two-, five- and six-ring PAH congeners. Results also revealed that the nitrate and phosphate contents in the sediments were closely associated with low molecular weight (LMW) and high molecular weight (HMW) PAHs, respectively. Source apportionment results showed that the combustion of fossil fuels appears to be the key source of PAHs in the study area. The risk quotient (RQ) values indicated that seven PAH congeners (i.e. phenanthrene, anthracene, fluoranthene, pyrene, benzo(a)pyrene, chrysene and benzo(a)anthracene) could pose serious threats to the aquatic life of the riverine ecosystem in Pakistan.
The approach of this paper is to predict the sand mass distribution in an urban stormwater holding pond at the Stormwater Management And Road Tunnel (SMART) Control Centre, Malaysia, using simulated depth average floodwater velocity diverted into the holding during storm events. Discriminant analysis (DA) was applied to derive the classification function to spatially distinguish areas of relatively high and low sand mass compositions based on the simulated water velocity variations at corresponding locations of gravimetrically measured sand mass composition of surface sediment samples. Three inflow parameter values, 16, 40 and 80 m(3) s(-1), representing diverted floodwater discharge for three storm event conditions were fixed as input parameters of the hydrodynamic model. The sand (grain size > 0.063 mm) mass composition of the surface sediment measured at 29 sampling locations ranges from 3.7 to 45.5%. The sampling locations of the surface sediment were spatially clustered into two groups based on the sand mass composition. The sand mass composition of group 1 is relatively lower (3.69 to 12.20%) compared to group 2 (16.90 to 45.55%). Two Fisher's linear discriminant functions, F 1 and F 2, were generated to predict areas; both consist of relatively higher and lower sand mass compositions based on the relationship between the simulated flow velocity and the measured surface sand composition at corresponding sampling locations. F 1 = -9.405 + 4232.119 × A - 1795.805 × B + 281.224 × C, and F 2 = -2.842 + 2725.137 × A - 1307.688 × B + 231.353 × C. A, B and C represent the simulated flow velocity generated by inflow parameter values of 16, 40 and 80 m(3) s(-1), respectively. The model correctly predicts 88.9 and 100.0% of sampling locations consisting of relatively high and low sand mass percentages, respectively, with the cross-validated classification showing that, overall, 82.8% are correctly classified. The model predicts that 31.4% of the model domain areas consist of high-sand mass composition areas and the remaining 68.6% comprise low-sand mass composition areas.
A computer-aided multivariate water quality index is developed based on partial least squares (PLS) regression. The index is termed as the partial least squares water quality index (PLS-WQI). Briefly, a training set was computationally generated based on the guideline of National Water Quality Standards for Malaysia (NWQS) to predict the water quality. The index is benchmarked with the well-established index developed by the Department of Environment, Malaysia (DOE-WQI). The PLS-WQI is a continuous variable with the value closer to I indicating good water quality and closer to V indicating poor water quality. Unlike other conventional indexing methods, the algorithm calculates the index in a multivariate manner. The algorithm allows rapid processing of a large dataset without tedious calculation; it can be an efficient tool for spatial and temporal routine monitoring of water quality. Although the algorithm is designed based on the guideline of NWQS, it can be easily adapted to accommodate other guidelines. The algorithm was evaluated and demonstrated on the simulated and real datasets. Results indicate that the algorithm is robust and reliable. Based on six parameters, the overall ratings derived are inversely correlated to DOE-WQI. When the number of parameter is increased, the overall ratings appear to provide better insights into the water quality.
The city of Dhaka has been ranked repeatedly as the most polluted, the most populous, and the most unbearable city in the world. More than 19.5 million inhabitants live in Dhaka, and the population growth rate of urban areas in Bangladesh is almost double that of rural areas. Rapid urbanization is one of the leading contributors to water pollution in Dhaka and could prevent the country from achieving sustainable development. Therefore, this study estimates respondents' willingness to pay (WTP) to improve water pollution management systems and identifies factors that influence WTP in Dhaka. This study employed the contingent valuation method (CVM) to estimate WTP of the respondents. Data were collected using a structured questionnaire with CVM questions, which was distributed to households in the study areas. The results revealed that 67% of the respondents are willing to pay for an improved water pollution management system, while 31.8% of households are unwilling to pay. The study also found that socio-economic factors (e.g., income and education) and perception significantly influence WTP. Therefore, this paper will provide directives for policymakers in developing an effective policy framework, as well as sensitize all stakeholders to the management of water pollution in Dhaka. The study suggests that social institutions, financial institutions, banks, non-government organizations (NGOs), insurance companies, and the government could provide effective outreach programs for water pollution management as part of their social responsibility.
The upper catchment region of the Baram River in Sarawak (Malaysian Borneo) is undergoing severe land degradation due to soil erosion. Heavy rainfall with high erosive power has led to a number of soil erosion hotspots. The goal of the present study is to generate an understanding about the spatial characteristics of seasonal and annual rainfall erosivity (R), which not only control sediment delivery from the region but also determine the quantity of material potentially eroded. Mean annual rainfall and rainfall erosivity range from 2170 to 5167 mm and 1632 to 5319 MJ mm ha-1 h-1 year-1, respectively. Seasonal rainfall and rainfall erosivity range from 848 to 1872 mm and 558 to 1883 MJ mm ha-1 h-1 year-1 for the southwest (SW) monsoon, 902 to 2200 mm and 664 to 2793 MJ mm ha-1h-1year-1 for the northeast (NE) monsoon and 400 to 933 mm and 331 to 1075 MJ mm ha-1 h-1 year-1 during the inter-monsoon (IM) period. Linear regression, Spearman's Rho and Mann Kendall tests were applied. Considering the regional mean rainfall erosivity in the study area, all the methods show an overall non-significant decreasing trend (- 9.34, - 0.25 and - 0.30 MJ mm ha-1 h-1 year-1, respectively for linear regression, Spearman's Rho and Mann Kendall tests). However, during SW monsoon and IM periods, rainfall erosivity showed a non-significant decreasing trend (- 25.45, - 0.52, - 0.40, and - 8.86, - 1.07, - 0.77 MJ mm ha-1 h-1 year-1, respectively) whereas in NE, monsoon season erosivity showed a non-significant increasing trend (14.90, 1.59 and 1.60 MJ mm ha-1 h-1 year-1, respectively). The mean erosivity density ranges from 0.77 to 1.38 MJ ha-1 h-1 year-1 and shows decreasing trend. Spatial distribution pattern of erosivity density indicates significantly higher occurrence of erosive rainfall in the lower elevation portion of the study area. The spatial pattern of mean rainfall erosivity trends (linear, Spearman's Rho and Mann Kendall) suggests that the study area can be divided into two zones with increasing rainfall erosivity trends in the northern zone and decreasing trends in the southern zone. These results can be used to plan conservation measures to reduce sediment delivery from localized soil erosion hotspots.
This study presents the use of a wavelet-based time series model to forecast the daily average particulate matter with an aerodynamic diameter of less than 10 μm (PM10) in Peninsular Malaysia. The highlight of this study is the use of a discrete wavelet transform (DWT) in order to improve the forecast accuracy. The DWT was applied to convert the highly variable PM10 series into more stable approximations and details sub-series, and the ARIMA-GARCH time series models were developed for each sub-series. Two different forecast periods, one was during normal days, while the other was during haze episodes, were designed to justify the usefulness of DWT. The models' performance was evaluated by four indices, namely root mean square error, mean absolute percentage error, probability of detection and false alarm rate. The results showed that the model incorporated with DWT yielded more accurate forecasts than the conventional method without DWT for both the forecast periods, and the improvement was more prominent for the period during the haze episodes.
The objective of this study is to delineate groundwater flowing well zone potential in An-Najif Province of Iraq in a data-driven evidential belief function model developed in a geographical information system (GIS) environment. An inventory map of 68 groundwater flowing wells was prepared through field survey. Seventy percent or 43 wells were used for training the evidential belief functions model and the reset 30 % or 19 wells were used for validation of the model. Seven groundwater conditioning factors mostly derived from RS were used, namely elevation, slope angle, curvature, topographic wetness index, stream power index, lithological units, and distance to the Euphrates River in this study. The relationship between training flowing well locations and the conditioning factors were investigated using evidential belief functions technique in a GIS environment. The integrated belief values were classified into five categories using natural break classification scheme to predict spatial zoning of groundwater flowing well, namely very low (0.17-0.34), low (0.34-0.46), moderate (0.46-0.58), high (0.58-0.80), and very high (0.80-0.99). The results show that very low and low zones cover 72 % (19,282 km(2)) of the study area mostly clustered in the central part, the moderate zone concentrated in the west part covers 13 % (3481 km(2)), and the high and very high zones extended over the northern part cover 15 % (3977 km(2)) of the study area. The vast spatial extension of very low and low zones indicates that groundwater flowing wells potential in the study area is low. The performance of the evidential belief functions spatial model was validated using the receiver operating characteristic curve. A success rate of 0.95 and a prediction rate of 0.94 were estimated from the area under relative operating characteristics curves, which indicate that the developed model has excellent capability to predict groundwater flowing well zones. The produced map of groundwater flowing well zones could be used to identify new wells and manage groundwater storage in a sustainable manner.
The occurrence and estrogenic activities of steroid estrogens, such as the natural estrone (E1), 17β estradiol (E2), and estriol (E3), as well as the synthetic 17α-ethynylestradiol (EE2), were investigated in eight sampling points along the Langat River (Malaysia). Surface water samples were collected at 0.5 m and surface sediment 0-5 cm from the river surface. Instrument analysis of steroid estrogens was determined by UPLC-ESI-MS with an ultra-performance liquid chromatograph (Perkin Elmer FX15) coupled to a Q Trap function mass spectrophotometer (model 3200: AB Sciex). Steroid estrogen concentrations were higher in the Langat River sediments than those in its surface water. In surface water, E1 was not detected in any sampling point, E2 was only detected in two midstream sampling points (range 0-0.004 ng/L), E3 in three sampling points (range 0-0.002 ng/L), and EE2 in four sampling points (range 0-0.02 ng/L). E1 and E2 were detected in sediments from all sampling points, E3 in five sampling points, while EE2 only in one midstream sample (3.29E-4 ng/g). Sewage treatment plants, farming waste, and agricultural activities particularly present midstream and downstream were identified as potential sources of estrogens. Estrogenic activity expressed as estradiol equivalents (EEQs) was below 1 ng/L in all samples for both surface water and sediment, indicating therefore a low potential estrogenic risk to the aquatic environment. Although the health risks are still uncertain for drinking water consumers exposed to low levels of steroid estrogen concentrations, Langat River water is unacceptable for direct drinking purposes without treatment. Further studies of endocrine disruptors in Malaysian waters are highly recommended.
The spatial distributions of Na, Mg, K, Ca, Cr, Fe, Ni, Cu, Zn, As, Se and Pb in Hemibagrus sp. from Selangor River and a reference site were determined with inductively coupled plasma-mass spectrometer, in comparison to the levels in their surrounding water body and sediments. The results demonstrated significant differences in elemental accumulation pattern in different fish tissues originated from both sites. The variations observed were mainly subjected to their metabolic activities, and also the influence of the surrounding medium. In general, the liver tends to accumulate higher concentration of metals followed by the gills, and muscle tissues. The data also indicate associations between the concentrations of metal contaminants measured in the fish and the levels observed at the sites. The concentrations of hazardous metals As, Se and Pb in all the studied tissues reflect the influence of anthropogenic inputs. This suggests the potential utility of widely available Hemibagrus sp. as a valuable bioindicator of metal pollution in environmental monitoring and assessment.