title: Ecological risk assessment of nonylphenol in coastal waters of China based on species sensitivity distribution model.
abstract: Nonylphenol (NP) is an endocrine disruptor and causes feminization and carcinogenesis in various organisms. Consequently, the environmental distribution and ecological risks of NP have received wide concern. China accounts for approximately 10% of the total NP usage in the world, but the water quality criteria of NP have not been established in China and the ecological risks of this pollutant cannot be properly assessed. This study thus aims to determine the predicted no effect concentration (PNEC) of NP and to assess the ecological risks of NP in coastal waters of China with the PNEC as water quality criteria. To obtain the HC5 (hazardous concentration for 5% of biological species) and PNEC estimates, the species sensitivity distributions (SSDs) models were built with chronic toxicity data of NP on aquatic organisms screened from the US Environmental Protection Agency (USEPA) ECOTOX database. The results showed that the PNEC for NP in freshwater and seawater was 0.48 μg L(-1) and 0.28 μg L(-1), respectively. The RQ (risk quotient) values of NP in coastal waters of China ranged from 0.01 to 69.7. About 60% of the reported areas showed a high ecological risk with an RQ value ≥ 1.00. NP therefore exists ubiquitously in coastal waters of China and it poses various risks to aquatic ecosystems in the country. This study demonstrates that the SSD methodology can provide a feasible tool for the establishment of water quality criteria for emergent new pollutants when sufficient toxicity data is available.
authors.affiliation: College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, PR China.
authors.forename: Pei
authors.initials: P
authors.lastname: Gao
authors.affiliation: College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, PR China. Electronic address: zhengyan@ouc.edu.cn.
authors.forename: Zhengyan
authors.initials: Z
authors.lastname: Li
authors.affiliation: Department of Process Engineering and Applied Science, Dalhousie University, Halifax, NS B3J 2X4, Canada.
authors.forename: Mark
authors.initials: M
authors.lastname: Gibson
authors.affiliation: College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, PR China.
authors.forename: Huiwang
authors.initials: H
authors.lastname: Gao
mesh_terms: D000818:Animals; D059001:Aquatic Organisms / Q000378:metabolism*; D002681:China; D017753:Ecosystem; D004784:Environmental Monitoring; D005618:Fresh Water / Q000032:analysis*; D015386:Hazardous Substances / Q000032:analysis; D008954:Models, Biological; D008956:Models, Chemical; D010636:Phenols / Q000032:analysis* / Q000378:metabolism; D018570:Risk Assessment; D012623:Seawater / Q000032:analysis*; D014874:Water Pollutants, Chemical / Q000032:analysis* / Q000378:metabolism; D060753:Water Quality
chemical_list: D015386:Hazardous Substances; D010636:Phenols; D014874:Water Pollutants, Chemical; C025256:nonylphenol
keywords: Coastal water; Ecological risk assessment; Nonylphenol; Risk quotient; Species sensitivity distribution