Wet weather behavior of unregulated pollutants and pathogens in combined sewer network

合流制污水管网中不受管制的污染物和病原体的潮湿天气行为

基本信息

批准号：
16360262
负责人：
FURUMAI Hiroaki
金额：
$ 9.54万
依托单位：
the University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2004
资助国家：
日本
起止时间：
2004 至 2006
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-16360262/
关键词：
Combined Sewer Overflow Pipe Sediment Pathogens Virus Distributed Model Antibiotics Quinone profile Pollutant Runoff Analysis 医薬品リアルタイムコントロール模擬降雨 PCR

项目摘要

The aim of this research project was to unveil the wet weather behavior of unregulated micropollutants and pathogens derived from combined sewer overflow (CSO). Detailed field survey was conducted to characterize the diurnal variation of the pollutant loads, particle size distribution, quinone profile of suspended microbes, health related microorganisms including viruses and micropollutants such as antibiotics. The diurnal variation was categorized into several patterns dependent on the pollutant source. The variation of antibiotics concentration seemed to reflect the metabolism and excretion characteristics of each antibiotic.To investigate the phenomenon of resuspension of pipe sediment under wet weather condition, the rainwater runoff was simulated by pouring clean water into an upstream manhole from a water wagon. Loads of bacteria in the downstream pipe exhibited a different varying pattern from that of viruses.A mathematical modeling was conducted to evaluate the sediment distribution in a combined sewer network and then a protocol to distinguish the priority pipes for cleaning was proposed. A model simulation revealed that cleaning of pipes which specifically accumulated sediments was effective for reducing the CSO pollutant load.Algorithm of sewer system operation to minimize the CSO pollutant load was developed. A simple auto-regression model for on-line water quality prediction was modified with referring a measured water quality value in an upstream pipe. Water flow prediction model was refined to provide a better simulation of CSO occurrence at medium- and small-size rainfall events. Future application of the algorithm to a real time control system for CSO management was discussed.

该研究项目的目的是揭示来自合流下水道溢流 (CSO) 的不受监管的微污染物和病原体在潮湿天气中的行为。进行了详细的现场调查，以表征污染物负荷、粒径分布、悬浮微生物的醌谱、包括病毒在内的健康相关微生物和抗生素等微污染物的日变化。根据污染物源的不同，日变化可分为几种模式。抗生素浓度的变化似乎反映了每种抗生素的代谢和排泄特征。为了研究潮湿天气条件下管道沉积物的再悬浮现象，通过从水车向上游沙井倒入清水来模拟雨水径流。下游管道中的细菌负荷表现出与病毒不同的变化模式。通过数学模型来评估组合下水道网络中的沉积物分布，然后提出了区分优先清洁管道的协议。模型模拟表明，清洁专门积聚沉积物的管道对于减少CSO污染物负荷是有效的。开发了下水道系统运行算法以最小化CSO污染物负荷。参考上游管道的水质测量值，修改了用于在线水质预测的简单自回归模型。完善水流预测模型，更好地模拟中小规模降雨事件中CSO的发生。讨论了该算法在 CSO 管理实时控制系统中的未来应用。