Assessing water quality in the Pearl River for the last decade based on clustering: Characteristic, evolution and policy implications

Jiang Wu; Shu-Peng Cheng; Ling-Yan He; Yi-Chu Wang; Yao Yue; Hui Zeng; Nan Xu

doi:10.1016/j.watres.2023.120492

Assessing water quality in the Pearl River for the last decade based on clustering: Characteristic, evolution and policy implications

Water Res. 2023 Oct 1:244:120492. doi: 10.1016/j.watres.2023.120492. Epub 2023 Aug 14.

Authors

Jiang Wu¹, Shu-Peng Cheng², Ling-Yan He³, Yi-Chu Wang⁴, Yao Yue⁵, Hui Zeng¹, Nan Xu¹

Affiliations

¹ Key Laboratory for Urban Habitat Environmental Science and Technology, Peking University Shenzhen Graduate School, Shenzhen 518055, China.
² School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
³ Key Laboratory for Urban Habitat Environmental Science and Technology, Peking University Shenzhen Graduate School, Shenzhen 518055, China. Electronic address: hely@pku.edu.cn.
⁴ College of Water Sciences, Beijing Normal University, Beijing 100875, China.
⁵ State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China.

PMID: 37598570
DOI: 10.1016/j.watres.2023.120492

Abstract

The Pearl River (PR) is China's second-largest river, playing a crucial role in regulating and supplying water in the southeast. However, for the last decade, the PR has been experiencing water quality deterioration due to population growth, rapid economic development, and diverse human activities, particularly in its delta areas. This study analyzed the characteristics and evolution of eight water quality variables, including pH values (pH), water temperature (WT), dissolved oxygen (DO), five-day biochemical oxygen demand (BOD₅), permanganate index (PI), total phosphorus (TP), ammonia nitrogen (NH₃N), and fluoride (F^-), which were monitored monthly at 16 water quality monitoring stations from January 2009 to August 2019. Overall, annual average BOD₅ and F^- concentrations met Class I water quality standards, while TP and NH₃N conformed to lower standards. The cluster results showed noticeable differences for parameter grouping (DO-organic parameters-nutrient and solutes), seasonal variation (wet and dry), and water quality status (contaminated-remediating-fine). The Water Quality Index (WQI) ranged from 8.3 ("very poor") to 91.7 ("excellent") in the entire basin from 2009 to 2019, and NH₃N-DO based WQImins were identified using the All-Subsets Linear Regression method. The fitting results of the Generalized Additive Models displayed that the deviance explained by natural factors ranged from 37.2% to 61.3%, while the socioeconomic explanation exceeded 70%. The WQImin component evolution (2003-2019) of Shenzhen River estuary, the most important part of the PR estuary, agreed with key parameter variations in heterogeneous clusters in the entire basin. Moreover, Shenzhen's water quality remediation applications indicated that reasonable-efficient-powerful efforts and support from governments could accelerate recovery. For the management departments, consistent measures should be strictly enforced, and elaborate regionalized management based on clusters could be attempted to maintain excellent water quality.

Keywords: Cluster; Parameter selection; Pearl River; River management; Water quality assessment; Water quality index.

MeSH terms

China
Cluster Analysis
Environmental Monitoring / methods
Humans
Nitrogen / analysis
Oxygen / analysis
Phosphorus / analysis
Rivers
Water Pollutants, Chemical* / analysis
Water Quality*

Substances

Water Pollutants, Chemical
Phosphorus
Nitrogen
Oxygen