POLLUTION MECHANISM OF WET-WEATHER PUMPING DISCHARGE IN SEPARATED STORMWATER DRAINAGE SYSTEMS BASED ON ARTIFICIAL NEURAL NETWORK

WEI Qing; CHEN Yongqi; XIE Yifan; LIN Jingying; YIN Hailong

doi:10.13205/j.hjgc.202312006

Volume 41 Issue 12

Dec. 2023

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WEI Qing, CHEN Yongqi, XIE Yifan, LIN Jingying, YIN Hailong. POLLUTION MECHANISM OF WET-WEATHER PUMPING DISCHARGE IN SEPARATED STORMWATER DRAINAGE SYSTEMS BASED ON ARTIFICIAL NEURAL NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(12): 54-60,181. doi: 10.13205/j.hjgc.202312006

Citation:

WEI Qing, CHEN Yongqi, XIE Yifan, LIN Jingying, YIN Hailong. POLLUTION MECHANISM OF WET-WEATHER PUMPING DISCHARGE IN SEPARATED STORMWATER DRAINAGE SYSTEMS BASED ON ARTIFICIAL NEURAL NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(12): 54-60,181. doi: 10.13205/j.hjgc.202312006

Citation:

WEI Qing, CHEN Yongqi, XIE Yifan, LIN Jingying, YIN Hailong. POLLUTION MECHANISM OF WET-WEATHER PUMPING DISCHARGE IN SEPARATED STORMWATER DRAINAGE SYSTEMS BASED ON ARTIFICIAL NEURAL NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(12): 54-60,181. doi: 10.13205/j.hjgc.202312006

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POLLUTION MECHANISM OF WET-WEATHER PUMPING DISCHARGE IN SEPARATED STORMWATER DRAINAGE SYSTEMS BASED ON ARTIFICIAL NEURAL NETWORK

doi: 10.13205/j.hjgc.202312006

WEI Qing^1,2,
CHEN Yongqi^1,2,
XIE Yifan³,
LIN Jingying^1,2,
YIN Hailong^{1,2
,
,}

1. College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China;
2. State Key Laboratory of Pollution Control and Resource Utilization Research, Tongji University, Shanghai 200092, China;
3. School of the Environment, Tsinghua University, Beijing 100084, China

Received Date: 2023-11-15
Available Online: 2024-03-08

Abstract

Abstract

Wet-weather discharges from stormwater pumping stations in separate stormwater drainage systems contribute to the deterioration of river water quality. Understanding the pollution mechanism of pumping discharge is the key to alleviating wet-weather pollution and improving water quality. To identify the dominant factors for controlling wet-weather discharge pollution, this study employed back propagation neural network (BPNN) and radial basis neural network (RBNN) models to establish the nonlinear relationships between 11 input parameters (i.e., rainfall and stormwater network operation) and the wet-weather discharge pollution from two pumping stations in separate stormwater drainage systems in downtown Shanghai. The findings indicated that RBNN performs better in simulating wet-weather pumping discharge pollution than BPNN. For water quality indicators of COD, NH₃-N, and SS, the RBNN achieved a higher performance of 15.6% to 31.9%, 12.3%~18.3%, and 12.6%~53.9%, respectively than BPNN, measuring by average absolute error, root mean square error, and average percentage error, respectively. Coefficients of determination were improved by 3.1%~5.4% when comparing RBNN with BPNN. Moreover, the importance ranking of input parameters using RBNN identified five key parameters, including time duration between two consecutive pump operation events, wet-well water level for starting the pumps, peak rainfall intensity, antecedent dry-weather periods, and wet-well level for stopping the pumps. Actions should be taken to investigate the sewage sources illicitly discharging into storm drains and then correct these illicit discharges. These actions can eliminate the sewage accommodation and the corresponding accumulated pollutant deposits within the storm pipes on dry-weather days, which are inappropriately flushed into the receiving waters during the operation of wet-weather storm pumps. Therefore, the wet-weather discharge pollution would be obviously alleviated. Optimizing the in-pipe water level to stop stormwater pumps, can also aid in reducing the wet-weather discharge pollution.

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References(14)

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