title: Risk of endocrine disruption to fish in the Yellow River catchment in China assessed using a spatially explicit model.
abstract: The global water availability assessment (GWAVA) model, incorporating regional water abstractions and reservoir information, was used to model the human-sourced steroid estrogens estrone (E1) and estradiol (E2) in the Yellow River catchment (China). The river flows in the main stem were calibrated using gauged flows. Following a review of Chinese data on estrogen discharge from a range of sewage treatment plants, low, median, and high discharge rates were identified and used as best, expected, and worst-case scenarios, respectively. For any given location, the temporal variation of modeled estrogen levels was summarized using the mean and upper 90th percentile, which is where the model predicts 90% of values would be below this concentration. The predicted means and 90th percentiles for E1 were comparable to previous E1 measurements reported in the river. For the whole catchment, only 19% (mean value) of the river system by length was predicted to exceed 1 ng/L E2 equivalents (EEQs) using expected estrogen sewage discharge. Only 3% of the network by length was predicted to exceed the dangerously high 10 ng/L EEQ when considering 90th percentile concentrations. The highest exposures were in the Fen and Wei tributaries. Endocrine disruption risk from estrogens was predicted to be minimal in the main stem. Only in the worst-case discharge scenario and 90th percentile predicted concentrations were the most downstream river reaches of the main stem predicted to be at risk. Reservoirs appeared to be helpful in reducing estrogen concentrations thanks to longer water residence facilitating biodegradation.
authors.affiliation: Hefei University of Technology (Xuancheng Campus), Anhui, People's Republic of China.|Centre for Ecology and Hydrology, Wallingford, Oxfordshire, United Kingdom.
authors.forename: Xiaowei
authors.initials: X
authors.lastname: Liu
authors.affiliation: Centre for Ecology and Hydrology, Wallingford, Oxfordshire, United Kingdom.
authors.forename: Virginie
authors.initials: V
authors.lastname: Keller
authors.affiliation: Centre for Ecology and Hydrology, Wallingford, Oxfordshire, United Kingdom.
authors.forename: Egon L
authors.initials: EL
authors.lastname: Dumont
authors.affiliation: State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, People's Republic of China.
authors.forename: Jianghong
authors.initials: J
authors.lastname: Shi
authors.affiliation: Centre for Ecology and Hydrology, Wallingford, Oxfordshire, United Kingdom.
authors.forename: Andrew C
authors.initials: AC
authors.lastname: Johnson
mesh_terms: D000818:Animals; D001673:Biodegradation, Environmental; D002681:China; D052244:Endocrine Disruptors / Q000032:analysis*; D004967:Estrogens / Q000032:analysis; D005399:Fishes / Q000378:metabolism*; D006801:Humans; D008962:Models, Theoretical; D018570:Risk Assessment; D045483:Rivers / Q000737:chemistry*; D012722:Sewage / Q000737:chemistry; D014874:Water Pollutants, Chemical / Q000032:analysis*
chemical_list: D052244:Endocrine Disruptors; D004967:Estrogens; D012722:Sewage; D014874:Water Pollutants, Chemical
keywords: Flow modeling; GWAVA; Risk assessment; Steroid estrogens