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研究报告

基金项目：国家自然科学基金面上项目（No.51779066）；国家自然科学基金青年项目（No.52000022）；国家重点研发计划课题（No.2018YFC0408001）；中国博士后科学基金面上项目（No.2018M631935）

摘要：针对河流水污染应急响应过程中污染源排放历史迟知、未知的问题，结合多种群遗传算法和自适应遗传算法，利用一维河流水质模型和水质监测数据，研究建立了基于改进遗传算法的河流水污染定量源反演方法，实现了对河流污染源排放历史的识别与重构.将该方法应用于美国特拉基河流的3个不同流量下的示踪剂实验中，对示踪剂排放历史进行定量源反演分析.结果表明：IGA算法对高、中、低不同流量下的3次示踪剂实验均可以很好的重构和识别示踪剂排放历史，对于实际河流水污染源反演分析的误差均在可接受范围内.IGA算法在河流水污染源反演分析中具有一定的可靠性和稳定性，可为河流水污染精准溯源与治理提供科学的技术支撑.

Abstract：Since the discharge history of pollution source is hard to master for the first time, an IGA based pollution source inversion method is developed to identify the release history of an unknown pollution source occurred upstream by integration with multi-population and adaptive Genetic Algorithms using one-dimensional river water quality model and related water quality monitoring data. The developed method is then applied to three trace experiments under various river discharges in order to identify the release history of the pollution source in Truckee River, America. The results demonstrate that the developed IGA based pollution source inversion method can significantly identify the release histories of all three trace experiments. Errors of the inversion results for three trace experiments are all within acceptable limits. Meanwhile, the developed IGA based pollution source inversion method also can guarantee the reliability and stability of the pollution source inverse, obtain satisfactory release histories under all three race experiments with different river discharges, and provide scientific and technical supports for accurate traceability and management of river environmental pollution.

Key words：surface river tracer experiment one-dimensional water quality model pollution source inversion improved genetic algorithm