title: A Bayesian approach of high impaired river reaches identification and total nitrogen load estimation in a sparsely monitored basin.
abstract: In this study, a modeling framework based on the theory of SPAtially Referenced Regression On Watershed attributes (SPARROW) model was developed to identify impaired river reaches with respect to total nitrogen (TN) and estimate the TN sources in the Xin'anjiang River basin, which had limited monitoring sites. A Bayesian approach was applied to estimate the mean values and uncertainties of parameters, including land use export coefficients and in-stream attention rates. Based on the parameters, the midranges (25-75 %) of annual TN concentrations were assessed by the model and 4.5 % of river reaches in the basin were found to be with higher impaired probabilities (namely [TN] > 1.5 mg/l) than other reaches. The amount and yields of TN discharged from diffuse sources were estimated for each county in the basin. The results suggested that Tunxi City had the highest TN yields from farm land and population, while the highest TN yields in Huangshan City were from tea plantations. The outcomes of this study will guide the implementation of practical management measures to reduce TN loads.
authors.affiliation: Tianjin Key Laboratory of Water Resources and Environment, Tianjin Normal University, Tianjin, 300387, China.
authors.forename: Xue
authors.initials: X
authors.lastname: Li
authors.affiliation: Key Laboratory of Pollution Process and Environmental Criteria of Ministry of Education and Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China. fengjf@nankai.edu.cn.
authors.forename: Jianfeng
authors.initials: J
authors.lastname: Feng
authors.affiliation: Great Lakes Institute of Environmental Research, University of Windsor, Windsor, ON, Canada.
authors.forename: Christopher
authors.initials: C
authors.lastname: Wellen
authors.affiliation: College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China.
authors.forename: Yuqiu
authors.initials: Y
authors.lastname: Wang
mesh_terms: D001499:Bayes Theorem; D002681:China; D004784:Environmental Monitoring / Q000379:methods*; D008962:Models, Theoretical; D009584:Nitrogen / Q000032:analysis*; D010758:Phosphorus / Q000032:analysis; D045483:Rivers / Q000737:chemistry*; D035501:Uncertainty; D014874:Water Pollutants, Chemical / Q000032:analysis*
chemical_list: D014874:Water Pollutants, Chemical; D010758:Phosphorus; D009584:Nitrogen
keywords: Non-point source pollution; Tea plantation; Uncertainty analysis