4.7 Article

Industrial metal pollution in water and probabilistic assessment of human health risk

期刊

JOURNAL OF ENVIRONMENTAL MANAGEMENT
卷 185, 期 -, 页码 70-78

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ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jenvman.2016.10.023

关键词

Water pollution; Health risk analysis; Deterministic approach; Probabilistic approach; Monte-Carlo simulation

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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 x 10(-4) for adult and children. Although, probabilistically estimated 95th percentile values of TCR exceeded the benchmark, mean TCR values were less than 1 x 10(-4). Simulated results showed that 20.13% and 5.43% values of TCR for surface water were >1 x 10(-4) 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. (C) 2016 Elsevier Ltd. All rights reserved.

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