Insights into the long-term pollution trends and sources contributions in Lake Taihu, China using multi-statistic analyses models

Lili Liu; Yongcheng Dong; Ming Kong; Jian Zhou; Hanbin Zhao; Zhou Tang; Meng Zhang; Zhiping Wang

doi:10.1016/j.chemosphere.2019.125272

Insights into the long-term pollution trends and sources contributions in Lake Taihu, China using multi-statistic analyses models

Chemosphere. 2020 Mar:242:125272. doi: 10.1016/j.chemosphere.2019.125272. Epub 2019 Nov 2.

Authors

Lili Liu¹, Yongcheng Dong², Ming Kong³, Jian Zhou⁴, Hanbin Zhao², Zhou Tang², Meng Zhang², Zhiping Wang⁵

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 Institute of Pollution Control and Ecological Security, Shanghai, 200092, China.
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, Nanjing, 210042, China.
⁴ Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China.
⁵ School of Environment Science and Technology, Shanghai Jiao Tong University, Shanghai, 200240, China. Electronic address: wangzply@sjtu.edu.cn.

PMID: 31896182
DOI: 10.1016/j.chemosphere.2019.125272

Abstract

Eutrophication pollution seriously threatens the sustainable development of Lake Taihu, China. In order to identify the primary parameters of water quality and the potential pollution sources, the water quality dataset of Lake Taihu (2010-2014) was analyzed with the water quality index (WQI) and multivariate statistical analysis methods. Principle component analysis/factor analysis (PCA/FA) and correlation analysis screened out five significant water quality indicators, i.e. potassium permanganate index (COD_Mn), total nitrogen (TN), total phosphorus (TP), chloride ion (Cl^-) and dissolved oxygen (DO), to represent the whole datasets and evaluate the water quality with WQI. Since northwestern of Lake Taihu was the most heavily polluted area, the parameters of the water quality were analyzed to further explore the potential sources and their contributions. Five potential pollution sources of northwestern lake were identified, and the contribution rate of each pollution source was calculated by the absolute principal component score-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models. In brief, the PMF model was more suitable for pollution source apportionment of the northwestern lake, and the contribution rate was ranked as agricultural non-point source pollution (26.6%) > domestic sewage discharge (23.5%) > industrial wastewater discharge and atmospheric deposition (20.6%) > phytoplankton growth (16.0%) > rainfall or wind disturbance (13.4%). This study might provide useful information for the optimization of water quality management and pollution control strategies of Lake Taihu.

Keywords: Absolute principal component score-multiple linear regression model; Lake Taihu; Positive matrix factorization model; Source apportionment; Water quality.

MeSH terms

China
Data Interpretation, Statistical
Environmental Monitoring / methods*
Environmental Monitoring / statistics & numerical data
Eutrophication
Factor Analysis, Statistical
Lakes / chemistry*
Linear Models
Models, Statistical*
Multivariate Analysis
Principal Component Analysis
Water Pollutants, Chemical / analysis*
Water Quality*

Substances

Water Pollutants, Chemical