Water Quality Evaluation and Pollution Source Apportionment of Surface Water in a Major City in Southeast China Using Multi-Statistical Analyses and Machine Learning Models

Yu Zhou, Xinmin Wang, Weiying Li (), Shuyun Zhou and Laizhu Jiang
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Yu Zhou: College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
Xinmin Wang: College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China
Weiying Li: State Key Laboratory of Pollution Control and Resource Reuse, Tongji University, Shanghai 200092, China
Shuyun Zhou: Jiangsu Yinyang Stainless Steel Pipe Co., Ltd., Wuxi 214000, China
Laizhu Jiang: Fujian Qingtuo Special Steel Technology Research Co., Ltd., Fuzhou 350000, China

IJERPH, 2023, vol. 20, issue 1, 1-16

Abstract: The comprehensive evaluation of water quality and identification of potential pollution sources has become a hot research topic. In this study, 14 water quality parameters at 4 water quality monitoring stations on the M River of a city in southeast China were measured monthly for 10 years (2011–2020). Multiple statistical methods, the water quality index (WQI) model, machine learning (ML), and positive matrix factorisation (PMF) models were used to assess the overall condition of the river, select crucial water quality parameters, and identify potential pollution sources. The average WQI values of the four sites ranged from 68.31 to 77.16, with a clear trend of deterioration from upstream to downstream. A random forest-based WQI model (WQI RF model) was developed, and the results showed that Mn, Fe, faecal coliform, dissolved oxygen, and total nitrogen were selected as the top five important water quality parameters. Based on the results of the WQI RF and PMF models, the contributions of potential pollution sources to the variation in the WQI values were quantitatively assessed and ranked. These findings prove the effectiveness of ML in evaluating water quality, and improve our understanding of surface water quality, thus providing support for the formulation of water quality management strategies.

Keywords: water quality index (WQI); machine learning; parameter selection; positive matrix factorization (PMF); source apportionment (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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