Identification of Critical Source Areas for Non-Point Source Pollution Control: A Simulation-Based Mixed-Integer Multi-Objective Non-Linear Programming Approach

作     者：Chen, Guihuang Tan, Qian Wang, Shuping 

作者机构：Guangdong Provincial Key Laboratory of Water Quality Improvement and Ecological Restoration for Watersheds Institute of Environmental and Ecological Engineering Guangdong University of Technology Guangzhou510006 China Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education School of Ecology Environment and Resources Guangdong University of Technology Guangzhou510006 China College of Water Resources and Civil Engineering China Agricultural University Beijing100083 China 

出 版 物：《SSRN》 

年 卷 期：2023年

核心收录：

主　　题：Nonlinear programming 

摘      要：Proper identification of critical source areas (CSAs) is crucial for improving the efficiency of pollutant reduction and the economic viability of best management practices (BMPs) aimed at controlling non-point source pollution (NPSP). Different identification criteria, referring to different identification ratios, scales, and methods, affect the determination of CSAs, and then affect the spatial layout of BMPs. Nevertheless, few studies have optimized the BMPs placement in CSAs determined by different identification criteria and selected appropriate identification factors by comparing the effectiveness of BMPs schemes. To address this issue, a simulation-optimization method called simulation-based mixed-integer multi-objective non-linear programming (SMIMONLP) model was proposed by coupling Soil and Water Assessment Tool (SWAT) with mixed-integer multi-objective non-linear programming (MIMONLP). The method was presented to optimize BMPs placement in CSAs identified by different combinations of identification ratios, methods, and scales, while appropriate identification factors were selected by comparing the effectiveness of these BMPs optimization schemes. The effectiveness of these BMPs optimization schemes were evaluated by pollutant reduction, economic cost, and the satisfaction level of the decision maker with the objective function. The developed method was applied to a real case study in the Luan River Basin of North China. By comparing the economic cost and pollutant reduction of BMPs optimization schemes based on CSAs determined by different identification criteria, the results revealed that the economic cost difference of BMPs schemes could reach up to 5.9 times, 5.7 times, and 5.3 times due to the impact of CSAs identification ratio, scale and method, respectively. When other identification factors were the same, with the increase of pollutant reduction, the scenarios with a larger identification ratio, or using the Load Per Region Area Index (LPRAI) iden