PROFILING ON POLLUTION OF URBAN DRAINAGE PUMPING OUTFLOW DURING WET WEATHER

TAN Qiong

doi:10.13205/j.hjgc.202311012

Volume 41 Issue 11

Nov. 2023

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2023 > 41(11): 64-68

Lu Sufen, Song Bo, Meng Dongliu, Yu Yuanyuan, Huang Yufei, Yuan Zhennan, Zhong Xuemei. HEAVY METAL CONTENTS IN SOIL AND VEGETABLES OF A TAILING SPILL IN HUANJIANGAND HUMAN HEALTH RISKS ASSESSMENT[J]. ENVIRONMENTAL ENGINEERING , 2015, 33(10): 130-134. doi: 10.13205/j.hjgc.201510029

Citation:

TAN Qiong. PROFILING ON POLLUTION OF URBAN DRAINAGE PUMPING OUTFLOW DURING WET WEATHER[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(11): 64-68. doi: 10.13205/j.hjgc.202311012

Citation:

TAN Qiong. PROFILING ON POLLUTION OF URBAN DRAINAGE PUMPING OUTFLOW DURING WET WEATHER[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(11): 64-68. doi: 10.13205/j.hjgc.202311012

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PROFILING ON POLLUTION OF URBAN DRAINAGE PUMPING OUTFLOW DURING WET WEATHER

doi: 10.13205/j.hjgc.202311012

TAN Qiong

Shanghai Water Planning and Design Research Institute, Shanghai 200233, China

Received Date: 2023-08-16
Available Online: 2023-12-25

Abstract

Abstract

Unsupervised learning with K-means clustering is used to identify pollution characteristics of urban drainage pumping outflow during wet weather. Indicators including pumping station asset property and behavior data are chosen and then profiled for over 200 pumping stations of Shanghai downtown area. It shows that these pumping stations are classified into 4 clusters including low-frequency high-concentration, high-frequency low-concentration, high-frequency high-pollution, and medium-frequency medium-pollution, and the last 2 clusters are of higher priority for pollution control measures. The method used to profile pumping stations shows reasonable results and is of great value for policymakers to deploy drainage quality improving and efficiency enhancing measures.
- drainage pumping outflow,
- wet weather pollution,
- drainage profile,
- unsupervised learning

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References(10)

References

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