Spatiotemporal Analysis of Water Quality Using Multivariate Statistical Techniques and the Water Quality Identification Index for the Qinhuai River Basin, East China

Xiaoxue Ma,Hong Yang,Na Li,Chang Gong,Lachun Wang

doi:10.3390/w12102764

Abstract

Monitoring water quality is indispensable for the identification of threats to water environment and later management of water resources. Accurate monitoring and assessment of water quality have been long-term challenges. In this study, multivariate statistical techniques (MST) and water quality identification index (WQII) were applied to analyze spatiotemporal variation in water quality and determine the major pollution sources in the Qinhuai River, East China. A rotated principal component analysis (PCA) identified three potential pollution sources during the wet season (mixed pollution, physicochemical, and nonpoint sources of nutrients) and the dry season (nutrient, primary environmental, and organic sources) and they explained 81.14% of the total variances in the wet season and 78.42% of total variances in the dry season. The result of redundancy analysis (RDA) showed that population density, urbanization, and wastewater discharge are the main sources of organic pollution, while agricultural fertilizer consumption and industrial wastewater discharge are the main sources of nutrients such as nitrogen and phosphorus. The water quality of the Qinhuai River basin was determined to be mainly Class III (slightly polluted) and Class IV (moderately polluted) based on WQII. Temporally, the change trend of WQII showed that water quality gradually deteriorated between 1990 and 2005, improved between 2006 and 2010, and then deteriorated again. Spatially, the WQII distribution map showed that areas with more developed urbanization were relatively more polluted. Our results show that MST and WQII are useful tools to help the public and decision makers to evaluate the water quality of aquatic environment.

Highlights

Water is a very important resource, playing a crucial role for domestic use, agricultural and industrial development, recreation, or other purposes
Multivariate statistical techniques and water quality identification index (WQII) were applied in water quality assessment in the Qinhuai River, East China
The technique of principal component analysis (PCA) has successfully extracted total nitrogen (TN), total phosphorous (TP), NH4 + −N, dissolved oxygen (DO), CODcr, CODMn, and BOD5 as the most important parameters used for water quality assessment

Summary

Introduction

Water is a very important resource, playing a crucial role for domestic use, agricultural and industrial development, recreation, or other purposes. Water ecosystems are affected by both natural and anthropogenic processes. Anthropogenic activities are primarily comprised of domestic and municipal wastewater [4], industrial and agricultural effluents [4,5], water diversion projects [6], and others. Regular monitoring campaigns and evaluations of water quality are helpful in preventing water pollution and applying remedial measures [7,8]. These sampling networks provide a large volume of physical, chemical, and biological water quality parameters, which has increased over time [9]. 4, 18, and 20 parameters were utilized to evaluate water quality in the Chillán

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Conclusion