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Concentration of eight heavy metals in surface and groundwater around Dhaka Export Processing Zone (DEPZ) industrial area were investigated, and the health risk posed to local children and adult residents via ingestion and dermal contact was evaluated using deterministic and probabilistic approaches. Metal concentrations (except Cu, Mn, Ni, and Zn) in Bangshi River water were above the drinking water quality guidelines, while in groundwater were less than the recommended limits. Concentration of metals in surface water decreased as a function of distance. Estimations of non-carcinogenic health risk for surface water revealed that mean hazard index (HI) values of As, Cr, Cu, and Pb for combined pathways (i.e., ingestion and dermal contact) were >1.0 for both age groups. The estimated risk mainly came from the ingestion pathway. However, the HI values for all the examined metals in groundwater were <1.0, indicating no possible human health hazard. Deterministically estimated total cancer risk (TCR) via Bangshi River water exceeded the acceptable limit of 1 × 10 for adult and children. Although, probabilistically estimated 95th percentile values of TCR exceeded the benchmark, mean TCR values were less than 1 × 10. Simulated results showed that 20.13% and 5.43% values of TCR for surface water were >1 × 10 for adult and children, respectively. Deterministic and probabilistic estimations of cancer risk through exposure to groundwater were well below the safety limit. Overall, the population exposed to Bangshi River water remained at carcinogenic and non-carcinogenic health threat and the risk was higher for adults. Sensitivity analysis identified exposure duration (ED) and ingestion rate (IR) of water as the most relevant variables affecting the probabilistic risk estimation model outcome.

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Groundwater evaluation indices, multivariate statistical techniques, and geostatistical models are applied to assess the source apportionment and spatial variability of groundwater pollutants at the Lakshimpur district of Bangladesh. A total of 70 groundwater samples have been collected from wells (shallow to deep wells, i.e., 10-375 m) from the study area. Groundwater quality index reveals that 50 % of the water samples belong to goodquality water. The degrees of contamination, heavy metal pollution index, and heavy metal evaluation index present diversified results in samples even though they show significant correlations among them. The results of principal component analysis (PCA) show that groundwater quality in the study area mainly has geogenic (weathering and geochemical alteration of source rock) sources followed by anthropogenic source (agrogenic, domestic sewage, etc.). Cluster analysis and correlation matrix also supported the results of PCA. The Gaussian semivariogram models have been tested as the best fit models for most of the water quality indices and PCA components. The results of semivariogram models have shown that most of the variables have weak spatial dependence, indicating agricultural and residential/domestic influences. The spatial distribution maps of water quality parameters have provided a useful and robust visual tool for decision makers toward defining adaptive measures. This study is an implication to show the multiple approaches for quality assessment and spatial variability of groundwater as an effort toward a more effective groundwater quality management.