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Integrated Application of Multivariate Statistical Methods to Source Apportionment of Watercourses in the Liao River Basin, Northeast China

Author

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  • Jiabo Chen

    (National & Local United Engineering Laboratory of Petroleum Chemical Process Operation, Optimization and Energy Conservation Technology, Liaoning Shihua University, Fushun 113001, China
    Institute of Eco-Environmental Sciences, Liaoning Shihua University, Fushun 113001, China)

  • Fayun Li

    (National & Local United Engineering Laboratory of Petroleum Chemical Process Operation, Optimization and Energy Conservation Technology, Liaoning Shihua University, Fushun 113001, China
    Institute of Eco-Environmental Sciences, Liaoning Shihua University, Fushun 113001, China)

  • Zhiping Fan

    (National & Local United Engineering Laboratory of Petroleum Chemical Process Operation, Optimization and Energy Conservation Technology, Liaoning Shihua University, Fushun 113001, China
    Institute of Eco-Environmental Sciences, Liaoning Shihua University, Fushun 113001, China)

  • Yanjie Wang

    (National & Local United Engineering Laboratory of Petroleum Chemical Process Operation, Optimization and Energy Conservation Technology, Liaoning Shihua University, Fushun 113001, China
    Institute of Eco-Environmental Sciences, Liaoning Shihua University, Fushun 113001, China)

Abstract

Source apportionment of river water pollution is critical in water resource management and aquatic conservation. Comprehensive application of various GIS-based multivariate statistical methods was performed to analyze datasets (2009–2011) on water quality in the Liao River system (China). Cluster analysis (CA) classified the 12 months of the year into three groups (May–October, February–April and November–January) and the 66 sampling sites into three groups (groups A, B and C) based on similarities in water quality characteristics. Discriminant analysis (DA) determined that temperature, dissolved oxygen (DO), pH, chemical oxygen demand (COD Mn ) , 5-day biochemical oxygen demand (BOD 5 ), NH 4 + –N, total phosphorus (TP) and volatile phenols were significant variables affecting temporal variations, with 81.2% correct assignments. Principal component analysis (PCA) and positive matrix factorization (PMF) identified eight potential pollution factors for each part of the data structure, explaining more than 61% of the total variance. Oxygen-consuming organics from cropland and woodland runoff were the main latent pollution factor for group A. For group B, the main pollutants were oxygen-consuming organics, oil, nutrients and fecal matter. For group C, the evaluated pollutants primarily included oxygen-consuming organics, oil and toxic organics.

Suggested Citation

  • Jiabo Chen & Fayun Li & Zhiping Fan & Yanjie Wang, 2016. "Integrated Application of Multivariate Statistical Methods to Source Apportionment of Watercourses in the Liao River Basin, Northeast China," IJERPH, MDPI, vol. 13(10), pages 1-27, October.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:10:p:1035-:d:81145
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    References listed on IDEAS

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    1. Jiabo Chen & Jun Lu, 2014. "Effects of Land Use, Topography and Socio-Economic Factors on River Water Quality in a Mountainous Watershed with Intensive Agricultural Production in East China," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-12, August.
    2. Hong Yao & Xin Qian & Hailong Gao & Yulei Wang & Bisheng Xia, 2014. "Seasonal and Spatial Variations of Heavy Metals in Two Typical Chinese Rivers: Concentrations, Environmental Risks, and Possible Sources," IJERPH, MDPI, vol. 11(11), pages 1-19, November.
    3. Baoling Duan & Fenwu Liu & Wuping Zhang & Haixia Zheng & Qiang Zhang & Xiaomei Li & Yushan Bu, 2015. "Evaluation and Source Apportionment of Heavy Metals (HMs) in Sewage Sludge of Municipal Wastewater Treatment Plants (WWTPs) in Shanxi, China," IJERPH, MDPI, vol. 12(12), pages 1-12, December.
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