» Articles » PMID: 38633700

Spatiotemporal Pattern of Coastal Water Pollution and Its Driving Factors: Implications for Improving Water Environment Along Hainan Island, China

Overview
Journal Front Microbiol
Specialty Microbiology
Date 2024 Apr 18
PMID 38633700
Authors
Affiliations
Soon will be listed here.
Abstract

In the context of human activities and climate change, the gradual degradation of coastal water quality seriously threatens the balance of coastal and marine ecosystems. However, the spatiotemporal patterns of coastal water quality and its driving factors were still not well understood. Based on 31 water quality parameters from 2015 to 2020, a new approach of optimizing water quality index (WQI) model was proposed to quantitatively assess the spatial and temporal water quality along tropical Hainan Island, China. In addition, pollution sources were further identified by factor analysis and the effects of pollution source on water quality was finally quantitatively in our study. The results showed that the average water quality was moderate. Water quality at 86.36% of the monitoring stations was good while 13.53% of the monitoring stations has bad or very bad water quality. Besides, the coastal water quality had spatial and seasonal variation, along Hainan Island, China. The water quality at "bad" level was mainly appeared in the coastal waters along large cities (Haikou and Sanya) and some aquaculture regions. Seasonally, the average water quality in March, October and November was worse than in other months. Factor analysis revealed that water quality in this region was mostly affected by urbanization, planting and breeding factor, industrial factor, and they played the different role in different coastal zones. Waters at 10.23% of monitoring stations were at the greatest risk of deterioration due to severe pressure from environmental factors. Our study has significant important references for improving water quality and managing coastal water environment.

References
1.
Zhao J, Zhao X, Chao L, Zhang W, You T, Zhang J . Diversity change of microbial communities responding to zinc and arsenic pollution in a river of northeastern China. J Zhejiang Univ Sci B. 2014; 15(7):670-80. PMC: 4097376. DOI: 10.1631/jzus.B1400003. View

2.
Cheng K, Chan S, Lee J . Remote sensing of coastal algal blooms using unmanned aerial vehicles (UAVs). Mar Pollut Bull. 2020; 152:110889. DOI: 10.1016/j.marpolbul.2020.110889. View

3.
Wang H, Bouwman A, Van Gils J, Vilmin L, Beusen A, Wang J . Hindcasting harmful algal bloom risk due to land-based nutrient pollution in the Eastern Chinese coastal seas. Water Res. 2023; 231:119669. DOI: 10.1016/j.watres.2023.119669. View

4.
Chi Z, Zhu Y, Li H, Wu H, Yan B . Unraveling bacterial community structure and function and their links with natural salinity gradient in the Yellow River Delta. Sci Total Environ. 2021; 773:145673. DOI: 10.1016/j.scitotenv.2021.145673. View

5.
Uddin M, Nash S, Rahman A, Olbert A . A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches. Water Res. 2022; 229:119422. DOI: 10.1016/j.watres.2022.119422. View