首页> 中文期刊> 《海洋学报:英文版》 >Use of QSAR and SSD methods on deriving predicted no-effect concentrations in seawater and sediment for ten individual parent-and alkyl-PAHs and a case study on the assessment of their ecological risks from the Dalian Bay, China

Use of QSAR and SSD methods on deriving predicted no-effect concentrations in seawater and sediment for ten individual parent-and alkyl-PAHs and a case study on the assessment of their ecological risks from the Dalian Bay, China

         

摘要

Parent and alkylated polycyclic aromatic hydrocarbons(alkyl-PAHs),which are a class of important toxic components of crude oil especially in the marine environment,exhibit adverse effects on aquatic life and potentially pose a human health risk.However,the lack of chronic toxicity data is one of the hindrances for alkylPAHs when assessing their ecological risks.In this study,predicted no-effect concentrations(PNECs)in seawater and marine sediment for ten parent-and alkyl-PAHs were derived by applying species sensitivity distributions(SSDs)and quantitative structure-activity relationships(QSARs).The local area,Dalian Bay,where an oil-spilled accident happened in 2010,was chosen as a case site to assess ecological risks for ten PAHs in surface seawaters and marine sediments.Their PNECs in seawater and sediment for protecting aquatic organisms in marine ecosystems were calculated and recommended in the range of 0.012-2.79μg/L and 48.2-1337 ng/g(dry weight),respectively.Overall,the derived PNECs for the studied PAHs in seawater and marine sediment were comparable to those obtained by classical methods.Risk quotient results indicate low ecological risks to ecosystems for ten parent-and alkyl-PAHs in surface seawaters and surface sediments from the Dalian Bay.These findings provide a first insight into the PNECs and ecological risks of alkyl-PAHs,emphasizing the role of the computational toxicology in ecological risk assessments.The use of QSARs has been identified as a valuable tool for preliminarily assessing ecological risks of emerging pollutants,being more predictable of real exposure scenarios for risk assessment purposes.

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