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Pollution loads in the middle-lower Yangtze river by coupling water quality models with machine learning

文献类型：期刊论文

作者Huang, Sheng2,3,4; Xia, Jun1,3,4; Wang, Yueling1; Wang, Gangsheng3,4; She, Dunxian3,4; Lei, Jiarui2
刊名WATER RESEARCH
出版日期2024-10-01
卷号263页码:13
关键词Water quality model Machine learning Pollution loads The Yangtze River Anthropogenic activities Gated recurrent unit
ISSN号0043-1354
DOI10.1016/j.watres.2024.122191
产权排序4
英文摘要Pollution control and environmental protection of the Yangtze River have received major attention in China. However, modeling the river's pollution load remains challenging due to limited monitoring and unclear spatiotemporal distribution of pollution sources. Specifically, anthropogenic activities' contribution to the pollution have been underestimated in previous research. Here, we coupled a hydrodynamic-based water quality (HWQ) model with a machine learning (ML) model, namely attention-based Gated Recurrent Unit, to decipher the daily pollution loads (i.e., chemical oxygen demand, COD; total phosphorus, TP) and their sources in the Middle-Lower Yangtze River from 2014 to 2018. The coupled HWQ-ML model outperformed the standalone ML model with KGE values ranging 0.77-0.91 for COD and 0.47-0.64 for TP, while also reducing parameter uncertainty. When examining the relative contributions at the Middle Yangtze River Hankou cross-section, we observed that the main stream and tributaries, lateral anthropogenic discharges, and parameter uncertainty contributed 15, 66, and 19% to COD, and 58, 35, and 7% to TP, respectively. For the Lower Yangtze River Datong cross-section, the contributions were 6, 69, and 25% for COD and 41, 42, and 17% for TP. According to the attention weights of the coupled model, the primary drivers of lateral anthropogenic pollution sources, in descending order of importance, were temperature, date, and precipitation, reflecting seasonal pollution discharge, industrial effluent, and first flush effect and combined sewer overflows, respectively. This study emphasizes the synergy between physical modeling and machine learning, offering new insights into pollution load dynamics in the Yangtze River.
WOS关键词COEFFICIENTS
资助项目Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23040502] ; National Natural Science Foundation of China[52339002] ; National Natural Science Foundation of China[41890823] ; China Scholarship Council ; National University of Singapore Start-up Grant[A-0009542-00-00]
WOS研究方向Engineering ; Environmental Sciences & Ecology ; Water Resources
语种英语
WOS记录号WOS:001288248000001
出版者PERGAMON-ELSEVIER SCIENCE LTD
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; China Scholarship Council ; National University of Singapore Start-up Grant
源URL[http://ir.igsnrr.ac.cn/handle/311030/209116]  
专题陆地水循环及地表过程院重点实验室_外文论文
通讯作者Xia, Jun; Wang, Gangsheng
作者单位1.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
2.Natl Univ Singapore, Dept Civil & Environm Engn, Singapore 117578, Singapore
3.Wuhan Univ, Inst Water Carbon Cycles & Carbon Neutral, Wuhan 430072, Peoples R China
4.Wuhan Univ, State Key Lab Water Resources Engn & Management, Wuhan 430072, Peoples R China
推荐引用方式
GB/T 7714		 Huang, Sheng,Xia, Jun,Wang, Yueling,et al. Pollution loads in the middle-lower Yangtze river by coupling water quality models with machine learning[J]. WATER RESEARCH,2024,263:13.
APA Huang, Sheng,Xia, Jun,Wang, Yueling,Wang, Gangsheng,She, Dunxian,&Lei, Jiarui.(2024).Pollution loads in the middle-lower Yangtze river by coupling water quality models with machine learning.WATER RESEARCH,263,13.
MLA Huang, Sheng,et al."Pollution loads in the middle-lower Yangtze river by coupling water quality models with machine learning".WATER RESEARCH 263(2024):13.

入库方式： OAI收割

来源：地理科学与资源研究所

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