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dc.contributor.authorDong, Leien_US
dc.contributor.authorLiu, Jingen_US
dc.contributor.authorDu, Xien_US
dc.contributor.authorDai, Chaoen_US
dc.contributor.authorLiu, Renzhien_US
dc.identifier.citationDong, L., Liu, J., Du, X., Dai, C., & Liu, R. (2018). Simulation-based risk analysis of water pollution accidents combining multi-stressors and multi-receptors in a coastal watershed. Ecological Indicators, 92, 161-170. doi: 10.1016/j.ecolind.2017.06.018en_US
dc.description.abstractAs environmentally important ecosystems, coastal watersheds are vulnerable to abrupt water pollution events. Previous researches of accidental pollution in coastal watersheds mostly focused on oil leakages from mobile sources, pipelines, or stationary sources; however few considered chemical pollution accidents from terrestrial stationary sources. The present paper uses one-dimensional and MIKE 21 convection-diffusion models to simulate chemical transport processes in coastal rivers and in near-shore seas. Based on the simulation results, a Coastal Accidental Water Pollution Risk Analysis (CAWPRA) method is proposed. CAWPRA considers acute pollution risk passing from upstream to downstream, and to estuary and near-shore sea areas in a coastal watershed. The method is applied to Beihai City, a typical coastal area of rising economic development, to provide a risk assessment based on simulations of water accidental pollution from terrestrial stationary sources. A leakage event of hydrochloric acid from Wenkehaiyang Co. Ltd is taken as an example. Water quality simulation results indicate that after 6, 12, 18, and 24 h the peak concentrations of hydrochloric acid would be 3.72, 0.91, 0.75, and 0.68 mg/l, respectively, and accordingly, the areas of coastal water exceeding the seawater quality standard would be 5.56, 4.92, 9.71, and 45.01 km2, respectively. Hydrochloric acid would reach Dangjiang Mangrove Nature Reserve a time 14 h after the leakage event first occurred. The resultant map indicates high, medium, and low risk sub-areas. The map is useful at identifying the most risky sources and the most vulnerable receptors for the Beihai coastal watershed.en_US
dc.relation.ispartofEcological Indicatorsen_US
dc.rights© 2017 Elsevier Ltd. All rights reserved.en_US
dc.subjectEngineering::Civil engineeringen_US
dc.titleSimulation-based risk analysis of water pollution accidents combining multi-stressors and multi-receptors in a coastal watersheden_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Civil and Environmental Engineeringen_US
dc.subject.keywordsEnvironmental Risk Assessmenten_US
dc.subject.keywordsNumerical Simulationen_US
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