Using Principal Components Analysis and IDW Interpolation to Determine Spatial and Temporal Changes of Surface Water Quality of Xin'anjiang River in Huangshan, China.

Wenjie Yang,Aijun Lin,Dong Wang,Yue Zhao,Li He,Huihui Wu

doi:10.3390/ijerph17082942

Abstract

This study was aimed at assessing the spatial and temporal distribution of surface water quality variables of the Xin’anjiang River (Huangshan). For this purpose, 960 water samples were collected monthly along the Xin’anjiang River from 2008 to 2017. Twenty-four water quality indicators, according to the environmental quality standards for surface water (GB 3838-2002), were detected to evaluate the water quality of the Xin’anjiang River over the past 10 years. Principal component analysis (PCA) was used to comprehensively evaluate the water quality across eight monitoring stations and analyze the sources of water pollution. The results showed that all samples could be analyzed by three main components, which accounted for 87.24% of the total variance. PCA technology identified important water quality parameters and revealed that nutrient pollution and organic pollution are major latent factors which influence the water quality of Xin’anjiang River. It also showed that agricultural activities, erosion, domestic, and industrial discharges are fundamental causes of water pollution in the study area. It is of great significance for water quality safety management and pollution control of the Xin’anjiang River. Meanwhile, the inverse distance weighted (IDW) method was used to interpolate the PCA comprehensive score. Based on this, the temporal and spatial structure and changing characteristics of water quality in the Xin’anjiang River were analyzed. We found that the overall water quality of Xin’anjiang River (Huangshan) was stable from 2008 to 2017, but the pollution of the Pukou sampling point was of great concern. The results of IDW helped us to identify key areas requiring control in the Xin’anjiang River, which pointed the way for further delicacy management of the river. This study proved that the combination of PCA and IDW interpolation is an effective tool for determining surface water quality. It was of great significance for the control of water pollution in Xin’anjiang River and the reduction of eutrophication pressure in Thousand Island Lake.

Highlights

With the acceleration of China’s industrialization and urbanization, a large number of toxic and harmful pollutants have been discharged into surface water bodies, posing a direct andInt
principal component analysis (PCA) reduces the dimensionality of a multivariate data set to a small number of independent principal components
PCA was conducted on 18 water quality indexes for eight monitoring points in the

Summary

Introduction

With the acceleration of China’s industrialization and urbanization, a large number of toxic and harmful pollutants have been discharged into surface water bodies, posing a direct andInt. With the acceleration of China’s industrialization and urbanization, a large number of toxic and harmful pollutants have been discharged into surface water bodies, posing a direct and. Res. Public Health 2020, 17, 2942; doi:10.3390/ijerph17082942 www.mdpi.com/journal/ijerph. Res. Public Health 2020, 17, 2942 potentiallypersistent threat to their ecological environment and human health [1,2]. Contaminants in water can cause acute or chronic poisoning in humans through direct drinking or pose serious health risks to humans through sewage irrigation [3]. 190 million people fall sick due to water pollution, and 60,000 people die from diseases caused by water pollution in China [4]. It has been reported that people’s mental health can be improved in the absence of water pollution [5]. It is urgent to evaluate the comprehensive water quality, understand the situation of water pollution, and identify the main pollution sources, to protect water resources and control water pollution [6,7]

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