面源污染最佳管理措施多目标协同优化配置研究进展

随着点源污染逐渐得到有效控制,面源污染逐渐成为我国多数地区影响水环境质量安全的主要因素。推广实施最佳管理措施(Best Management Practices, BMPs)被认为是控制面源污染的有效途径。受到区域种植制度、耕作方式、政策以及经济成本等因素的影响,导致流域尺度配置BMPs存在一定的困难,特别是随着流域空间尺度的变化,会进一步加大BMPs配置难度,使得BMPs的配置工作变为了一项多目标决策优化问题,即如何在有限的成本投入下,实现水环境质量改善的目标。需要在不同空间尺度下对流域BMPs进行多目标协同优化配置。从面源污染关键源区识别、BMPs削减效率评估以及BMPs多目标协同优化模拟3个方面对面源污染BMPs多目标协同优化配置研究进行了综述。结果表明:1)包含地块尺度和流域尺度的多尺度模型耦合系统的构建,将是实现关键源区精准识别的有效途径;2)BMPs削减效率对水质改善响应的滞后性、不确定性、时空异质性、污染物形态转换风险等均是今后BMPs削减效率评估中需要重点解决的关键问题;3)建立流域污染物负荷削减量与水质改善之间的非线性响应关系,并以此为基础将BMPs组合数据库、成本数据库以及基于进化算法的的优化配置方案进行耦合,进而构建多目标决策支持系统,以获取BMPs空间优化配置方案以及多目标成本-效益最优曲线。

A review: multi-objective collaborative optimization of best management practices for non-point sources pollution control

With point source pollution gradually being controlled, non-point source pollution has become a threat for water quality in most regions of China. Best management practices (BMPs) have been regarded as the most effective way to control non-point source pollution. However, the effectiveness of regional cropping systems, cultivation methods, policies, and economic costs lead to difficulties in BMP allocation at the watershed scale. In particular, the difficulties further increase with the changes at spatial scale. As a result, the allocation of BMPs has been moved to a multi-objective decision optimization program, to achieve water quality improvement targets under limited inputs. Therefore, there is a need for multi-objective collaborative optimization of BMPs at different spatial scales. Herein, we reviewed the current research pertaining to multi-objective collaborative optimization of BMPs for non-point source pollution with respect to three aspects: identify the critical source areas (CSAs) of non-point source pollution, assessment of BMPs cutting efficiency, and imitate multi-objective collaborative optimization of BMPs. The results indicate that: i) building multi-scale model coupling system, including land scale and watershed scale, would be the most efficient way to accurately identify CSAs; ii) reducing time lag, uncertainty, and spatial and temporal heterogeneity as well as the risk of pollution to improve water quality will be the key to the cutting efficiency of BMPs; iii) building a nonlinear response relation between watershed pollutant reduction and water quality improvement is essential. The BMP database, cost database, and scientific allocation schemes based on evolutionary algorithm (EA) can be combined to build a multi-scale decision supporting system. The allocation scheme of BMPs and the optimum curve of multi-scale cost-effectiveness can then be acquired.