Tracing the potential pollution sources of the coastal water in Hong Kong with statistical models combining APCS-MLR

Lili Liu; Zhou Tang; Ming Kong; Xin Chen; Chunchun Zhou; Kai Huang; Zhiping Wang

doi:10.1016/j.jenvman.2019.05.066

Tracing the potential pollution sources of the coastal water in Hong Kong with statistical models combining APCS-MLR

J Environ Manage. 2019 Sep 1:245:143-150. doi: 10.1016/j.jenvman.2019.05.066. Epub 2019 May 28.

Authors

Lili Liu¹, Zhou Tang², Ming Kong³, Xin Chen², Chunchun Zhou², Kai Huang², Zhiping Wang⁴

Affiliations

¹ State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, School of Resource and Environmental Engineering, East China University of Science and Technology, Shanghai, 200237, China; Shanghai Academy of Environmental Sciences, Shanghai, 200233, China. Electronic address: lilyliu@ecust.edu.cn.
² State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, School of Resource and Environmental Engineering, East China University of Science and Technology, Shanghai, 200237, China.
³ Nanjing Institute of Environmental Sciences, Ministry of Environmental Protection, No. 8 Jiang Wang Miao Street, Nanjing, 210042, China.
⁴ School of Environment Science and Technology, Shanghai Jiao Tong University, 200240, Shanghai, China.

PMID: 31150905
DOI: 10.1016/j.jenvman.2019.05.066

Abstract

In this study, variety of statistical methods were performed to reveal the spatiotemporal distribution characteristics of pollutants and parsing pollution sources of the coastal water in Hong Kong. The temporal-spatial distribution characteristics of the water pollution were various among the three distinct areas, which might be ascribed to the different dominant pollution sources. Cluster and network analysis showed preliminary pollution sources in these areas, and also indicated the temporal characteristics of Deep Bay water pollution, which could divided into two parts before and after 2010. According to the principal component analysis/factor analysis results, three factors in Deep Bay, Tolo Harbour and Victoria Harbour could explained 68.72%, 54.87% and 72.28% of the total variances, respectively. The contribution rate of different pollution source on water quality variables in each area had calculated by absolute principal component score-multiple linear regression model. The contribution rate was roughly ranked as: point source pollution > non-point source pollution > overland runoff > river input. It is the first time to combine multivariate statistical methods, network analysis and regression model to profoundly analyze spatiotemporal variation of seawater quality and parsing the pollution sources. This novel analysis method can provide reference for the water quality evaluation and management of other water bodies.

Keywords: APCS-MLR model; Coastal water; Hong Kong; Network analysis; Water quality.

MeSH terms

Environmental Monitoring*
Hong Kong
Models, Statistical
Rivers
Water Pollutants, Chemical*
Water Pollution

Substances

Water Pollutants, Chemical