基于水环境质量提升的农业非点源污染关键源区识别OACHSSCD

Agricultural non-point source pollution(ANPS)has become a significant contributor to watershed water pollution.And the scientific identification of critical source areas(CSAs)constitutes a crucial aspect of water environment management.Existing studies primarily identify CSAs based on pollution intensity,yet often fail to guarantee actual pollution reduction efficacy in environmental engineering practices.Therefore,building upon pollution-intensity screening,this study introduced the"mitigation-reduction ratio"to quantify a new indicator of sub-basin management potential and thereby constructed an optimized framework for CSAs identification.Taking Wangkuai Reservoir in the Beijing-Tianjin-Hebei region as a case study,we calibrated the SWAT model using spatial,attribute,and water quality data from 2010 to 2023(NSE＞0.5,MAPE＜0.1,and R2＞0.65).Furthermore,a fast non-dominated sorting approach was employed to identify sub-basins exhibiting consistently high pollution levels across various hydrological year scenarios.An improved radar chart method was then applied to evaluate the integrated pollution reduction effects after implementing measures such as fertilizer reduction and vegetative buffer strips.Ultimately,CSAs with both high pollution levels and high mitigation potential were identified.The results indicate that 46%of the sub-basins are high pollution risk areas,among which sub-basins 1,8,12,13,28,32,36,and 37 were identified as CSAs.Compared with the traditional unit area load method,this method reduces treatment area by 3.31%,increases pollution removal by 4.17%and 22.00%,and enhances mitigation potential by 0.83 and 0.98,achieving dual optimization of cost and environmental benefits.This study provides technical support for ANPS control in other watersheds with similar climatic and topographic characteristics.