Spatiotemporal Analysis of Water Quality Using Multivariate Statistical Techniques and the Water Quality Identification Index for the Qinhuai River Basin, East China
1
College of Urban Resources and Environment, Jiangsu Second Normal University, Nanjing 210013, China
2
Signal Processing in Earth Observation (SiPEO), Technical University of Munich (TUM), 80333 Munich, Germany
3
School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210093, China
4
Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC), School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
5
Department of Geography and Environmental Science, University of Reading, Whiteknights, Reading RG6 6 AB, UK
6
School of Environmental Science and Engineering, Yancheng Institute of Technology, Yancheng 224051, China
7
Suqian Hydrological and Water Resources Management Bureau of Jiangsu Province, Suqian 223800, China
*
Authors to whom correspondence should be addressed.
Water 2020, 12(10), 2764; https://doi.org/10.3390/w12102764
Received: 11 September 2020 / Revised: 29 September 2020 / Accepted: 1 October 2020 / Published: 4 October 2020
(This article belongs to the Special Issue The Impact of Climate Change and Human Activities on Aquatic Environments)
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.
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Keywords:
comprehensive water quality identification index; multivariate techniques; source apportionment; Qinhuai River
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MDPI and ACS Style
Ma, X.; Wang, L.; Yang, H.; Li, N.; Gong, C. Spatiotemporal Analysis of Water Quality Using Multivariate Statistical Techniques and the Water Quality Identification Index for the Qinhuai River Basin, East China. Water 2020, 12, 2764.
AMA Style
Ma X, Wang L, Yang H, Li N, Gong C. Spatiotemporal Analysis of Water Quality Using Multivariate Statistical Techniques and the Water Quality Identification Index for the Qinhuai River Basin, East China. Water. 2020; 12(10):2764.
Chicago/Turabian StyleMa, Xiaoxue; Wang, Lachun; Yang, Hong; Li, Na; Gong, Chang. 2020. "Spatiotemporal Analysis of Water Quality Using Multivariate Statistical Techniques and the Water Quality Identification Index for the Qinhuai River Basin, East China" Water 12, no. 10: 2764.
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