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Ecological risk assessment and priority setting for typical toxic pollutants in the water from Beijing-Tianjin-Bohai area using Bayesian matbugs calculator (BMC)

克丽舍恩 环境科学 环境化学 北京 甲氧氯 污染物 风险评估 毒理 杀虫剂 化学 生态学 地理 中国 生物 有机化学 考古 计算机科学 计算机安全
作者
Wei He,Ning Qin,Xiangzhen Kong,Wenxiu Liu,Weihong Wu,Qing He,Chen Yang,Yu-Jiao Jiang,Qingmei Wang,Bin Yang,Fu-Liu Xu
出处
期刊:Ecological Indicators [Elsevier]
卷期号:45: 209-218 被引量:36
标识
DOI:10.1016/j.ecolind.2014.04.008
摘要

• A novel platform, namely BMC, was developed for ecological risk assessment. • The water bodies in Hebei had the greatest risk followed by Tianjin and Beijing. • DDT metabolites and HCH isomers posed remarkable ecological risk in BTB areas. • BMC is a friendly, easily, efficient tool to set the controlling priority of PTSs. A novel platform, named the Bayesian matbugs calculator (BMC), was developed to select the best SSD model, assess ecological risk at high-, mid- and low-levels of the 95% credible interval (CI), and to set the priority of toxic substances. The BMC platform was applied to the ecological risk assessment and priority setting of 32 toxic substances, including polycyclic aromatic hydrocarbons (PAHs) and organochlorine pesticides (OCPs), in the water from the Beijing-Tianjin-Bohai (BTB) area of northern China. The results showed that most of the studied PAH and OCP compounds have a high-level ecological risk with potential affected fraction (PAF) > 10 −3 except for benzo(a)anthracene, pyrene, chrysene and β-hexachlorocyclohexane (β-HCH). The Yongdinghe River, Yongdingxinhe River, and Guanting Reservoir had the highest multiple substance combined PAF (msPAF) at mid-level, whereas the Qingshuihe River had the lowest msPAF, ranging from 2.91 × 10 −7 to 1.15 × 10 −1 at various levels. On the basis of ecological risk at the high level of 95% CI, the priorities for PAHs and OCPs were anthracene, chrysene, benzo(a)pyrene, δ-HCH, p,p′-dichlorodiphenyldichloroethane (p,p′-DDD), heptachlor epoxide, endosulfan sulfate, methoxychlor, and endosulfan II. The BMC platform can be concluded to be a friendly, accessible, efficient tool to select the best SSD model, calculate relevant indicators, assess ecological risks with uncertainty, and to set the priority of toxic substances.
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