Huihang Sun: State Key Lab of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
Yu Tian: State Key Lab of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China. Electronic address: hit_tianyu@163.com.
Lipin Li: State Key Lab of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
Yu Zhuang: State Key Lab of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
Xue Zhou: State Key Lab of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
Haoran Zhang: State Key Lab of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
Wei Zhan: State Key Lab of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
Wei Zuo: State Key Lab of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China.
Chengyu Luan: Harbin Institute of Technology National Engineering Research Center of Urban Water Resources Co., Ltd., Harbin Institute of Technology, Harbin 150090, China.
A comprehensive understanding of nutrient transport patterns and clarification of pollutant sources' load contributions are critical prerequisites for developing scientific pollution control strategies in complex river basins. Here, we focused on the Minjiang River Basin (MRB) and employed the Soil and Water Assessment Tool (SWAT) model to systematically investigate the nitrogen (N) and phosphorus (P) loads from both point and non-point sources. Results revealed that the key source areas of N and P pollution in the MRB were predominantly located along the riverbanks, influenced by a combination of sediment, precipitation, agricultural activities such as fertilization. Our analysis indicated that soil nutrient loss, fertilization, and livestock farming were the major contributors to N and P inputs, accounting for over 70 % of the total input, followed by rural residential and urban point sources. Based on the identification of non-point source pollution as the primary load source, a multi-objective optimization was conducted using response surface methodology (RSM) coupled with the non-dominated sorting genetic algorithm-II (NSGA-II), resulting in the identification of optimal best management practices (BMPs) that achieve a reduction of 40.04 % in N load, 39.22 % in P load, and a net economic benefit of -1.13 billion yuan per year. Compared to the RSM and automated optimization results, the proposed management measures exhibited significant improvements in N and P load reduction and net benefits. Overall, the findings provide important insights for formulating agricultural management policies in the MRB and offering valuable implications for pollution management in other complex river basins.
