Predicting Regional Wastewater Treatment Plant Discharges Using Machine Learning and Population Migration Big Data

Yu, Jiang1,2; Tian, Yong2; Jing, Hao2,3; Sun, Taotao2,4; Wang, Xiaoli2,5; Andrews, Charles B.2,6; Zheng, Chunmiao2,7

2023-03-01

10.1021/acsestwater.2c00639

ACS ES&T WATER   影响因子和分区

2690-0637

Quantifying the temporal variation of wastewater treatment plant (WWTP) discharges is essential for water pollution control and environment protection in metropolitan areas. This study develops an ensemble machine learning (ML) model to predict discharges from WWTPs and to quantify the contribution of extraneous water (mixed precipitation and infiltrated groundwater) by leveraging the power of ML and population migration big data. The approach is applied to predict the discharges at 265 WWTPs in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China. The major conclusions are as follows. First, the ensemble ML model provides an efficient and reliable way to predict WWTP discharges using data easily accessible to the public. The predicted treated sewage amount increased from 20.4 x 106 m3/day in 2015 to 24.5 x 106 m3/day in 2020. Second, the predictors, including daily precipitation, average precipitation of past proceeding days, daily temperature, and population migration, play different roles in predicting different city's discharges. Finally, mixed precipitation and infiltrated groundwater account for, on average, 1.6 and 10.3% of total discharges from WWTPs in the GBA. This study represents the first attempt to bring population migration big data into data-driven environmental engineering modeling and can be easily extended to predict other environmental variables of concern.

Guangdong-Hong Kong-Macao Greater Bay Area wastewater treatment plants machine learning Bayesian model averaging population migration infiltrated groundwater

ESCI

英语

通讯

National Natural Science Foundation of China["41890852","42071244"] ; Shenzhen Science and Technology Innovation Commission[20200925174525002]

Environmental Sciences & Ecology ; Water Resources

Environmental Sciences ; Water Resources

WOS:000945432600001

AMER CHEMICAL SOC

Web of Science

1.Peking Univ, Inst Water Sci, Coll Engn, Beijing 100871, Peoples R China
2.Southern Univ Sci & Technol, Sch Environm Sci & Engn, State Environm Protect Key Lab Integrated Surface, Shenzhen 518055, Peoples R China
3.Harbin Inst Technol, Sch Environm, Harbin 150001, Peoples R China
4.Shenzhen Acad Environm Sci, Shenzhen 518172, Peoples R China
5.Univ Hong Kong, Dept Civil Engn, Pok Fu Lam, Hong Kong 999077, Peoples R China
6.SS Papadopulos & Associates Inc, Rockville, MD 20852 USA
7.EIT Inst Adv Study, Ningbo 315200, Peoples R China

Yu, Jiang,Tian, Yong,Jing, Hao,et al. Predicting Regional Wastewater Treatment Plant Discharges Using Machine Learning and Population Migration Big Data[J]. ACS ES&T WATER,2023,3(5).

Yu, Jiang.,Tian, Yong.,Jing, Hao.,Sun, Taotao.,Wang, Xiaoli.,...&Zheng, Chunmiao.(2023).Predicting Regional Wastewater Treatment Plant Discharges Using Machine Learning and Population Migration Big Data.ACS ES&T WATER,3(5).

Yu, Jiang,et al."Predicting Regional Wastewater Treatment Plant Discharges Using Machine Learning and Population Migration Big Data".ACS ES&T WATER 3.5(2023).