An integrated GIS-based approach in assessing carcinogenic risks via food-chain exposure in arsenic-affected groundwater areas
Journal
Environmental Toxicology
Journal Volume
25
Journal Issue
2
Pages
113-123
Date Issued
2010
Author(s)
Abstract
This study presented an integrated GIS-based approach for assessing potential carcinogenic risks via food-chain exposure of ingesting inorganic arsenic (As) in aquacultural tilapia, milkfish, mullet, and clam in the As-affected groundwater areas. To integrate spatial information, geographic information system (GIS) was adopted to combine polygon-shaped features of aquacultural species with cell-shaped features of As contamination in groundwater. Owing to sparse measured data, Monte Carlo simulation and sequential indicator simulation were used to characterize the uncertainty of assessed parameters. Target cancer risks (TRs) of ingesting As contents at fish ponds were spatially mapped to assess potential risks to human health. The analyzed results reveal that clam farmed at the western coastal ponds and milkfish farmed at the southwestern coastal ponds have high risks to human health, whereas tilapia cultivated mainly at the inland ponds only has high risks at the 95th percentile of TR. Mullet in general has low risks to human health. Moreover, to decrease risks, this study suggests reducing the use of As-affected groundwater at clam and milkfish ponds due to high bioconcentration factor (BCF) of clam and inorganic As accumulation ratio of milkfish. The integrated GIS-based approach can provide fishery administrators with an effective management strategy at specific fish ponds with high risks to human health. © 2009 Wiley Periodicals, Inc.
Subjects
Aquaculture; Arsenic; GIS; Target cancer risks; Uncertainty
Other Subjects
As content; Bioconcentration factor; Carcinogenic risk; Effective management; GIS; Human health; Inorganic arsenic; Measured data; Monte Carlo Simulation; Potential risks; Sequential indicator simulations; Spatial informations; Target cancer risks; Agriculture; Arsenic; Bioinformatics; Chemical contamination; Computer simulation; Fish ponds; Geographic information systems; Groundwater; Groundwater pollution; Health; Lakes; Monte Carlo methods; Risk assessment; Targets; Uncertainty analysis; Health risks; arsenic; carcinogen; ground water; arsenic; carcinogen; aquaculture; arsenic; bioaccumulation; cancer; carcinogen; data set; environmental risk; food chain; GIS; groundwater pollution; health risk; Monte Carlo analysis; numerical model; parameterization; pollution exposure; public health; teleost; uncertainty analysis; aquaculture; arsenic poisoning; article; bioaccumulation; cancer risk; cell shape; clam; coastal waters; exposure; fish; food chain; geographic information system; health hazard; ingestion; milkfish; Monte Carlo method; mullet; nonhuman; pond; priority journal; risk assessment; risk reduction; Tilapia; water contamination; animal; bivalve; environmental exposure; environmental monitoring; food chain; human; metabolism; methodology; risk factor; theoretical model; water pollutant; Bivalvia; Tilapia; Animals; Aquaculture; Arsenic; Bivalvia; Carcinogens, Environmental; Environmental Exposure; Environmental Monitoring; Fishes; Food Chain; Geographic Information Systems; Humans; Models, Theoretical; Risk Assessment; Risk Factors; Water Pollutants, Chemical
Type
journal article