摘要
为快速实现流域突发水污染追溯,构建基于云边终协同架构的流域云溯源体系。将云端的全流域二维水动力水质预报结果与终端的水质监测数据进行实时过程对比,实现事故识别。边缘端在事故预警后进行事故区域多污染情景的水质快速模拟,形成基于源-质响应的情景数据库,通过对比分析监测信息和污染情景模拟序列的过程拟合度,确定污染源筛查范围。将该方法应用于长江重庆段突发水污染溯源研究,结果表明:在一定的污染情景下,排污企业A、B对下游监测断面1类污染过程的纳什效率系数分别为0.91和0.87,对2类污染过程的纳什效率系数分别为0.84和0.95,表明企业A、B均为可能的事故源,且造成1类污染过程的源头更可能是企业A,造成2类污染过程的源头更可能是企业B。
In order to quickly realize the traceability of sudden water pollution in river basin, a cloud source identification system in river basin based on the cloud-edge-terminal collaboration architecture was constructed. By comparing the two-dimensional hydrodynamic water quality prediction results of the whole basin in the cloud with the water quality monitoring data of the terminal in real-time, sudden water pollution accidents could be identified. After the accident warning, rapid water quality simulation of multi pollution scenario in accident area was conducted at the edge end to form a scenario database based on source-quality response. By comparing and analyzing the process fitting degree of monitoring information and pollution scenario simulation sequence, the scope of pollution source screening was determined. The method was applied to the source identification of sudden water pollution in Chongqing section of the Yangtze River. The results show that under a certain pollution scenario, the Nash-Sutcliffe efficiency coefficient of pollutant discharge enterprises A and B for class 1 pollution process of downstream monitoring section are 0.91 and 0.87 respectively, and for class 2 pollution process are 0.84 and 0.95 respectively. It indicates that enterprises A and B are possible accident sources, and the source of class 1 pollution process is more likely to be enterprise A, and the source of class 2 pollution process is more likely to be enterprise B.
作者
彭虹
周文婷
张万顺
夏函
张潇
王浩
PENG Hong;ZHOU Wenting;ZHANG Wanshun;XIA Han;ZHANG Xiao;WANG Hao(School of Water Resources and Hydropower Engineering,Wuhan University,Wuhan 430072,China;School of Resource and Environmental Sciences,Wuhan University,Wuhan 430079,China;State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin,China Institute of Water Resources and Hydropower Research,Beijing 100038,China)
出处
《水资源保护》
CAS
CSCD
北大核心
2022年第1期176-181,204,共7页
Water Resources Protection
基金
国家自然科学基金(41877531)。
关键词
突发水污染
水污染溯源
云边终协同架构
水动力水质预报
源-质响应
情景数据库
sudden water pollution
water pollution source identification
cloud-edge-terminal collaboration architecture
hydrodynamic water quality prediction
source-quality response
scenario database
