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Effects of Land Use, Topography and Socio-Economic Factors on River Water Quality in a Mountainous Watershed with Intensive Agricultural Production in East China

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  • Jiabo Chen
  • Jun Lu

Abstract

Understanding the primary effects of anthropogenic activities and natural factors on river water quality is important in the study and efficient management of water resources. In this study, analysis of Variance (ANOVA), Principal component analysis (PCA), Pearson correlations, Multiple regression analysis (MRA) and Redundancy analysis (RDA) were applied as an integrated approach in a GIS environment to explore the temporal and spatial variations in river water quality and to estimate the influence of watershed land use, topography and socio-economic factors on river water quality based on 3 years of water quality monitoring data for the Cao-E River system. The statistical analysis revealed that TN, pH and temperature were generally higher in the rainy season, whereas BOD5, DO and turbidity were higher in the dry season. Spatial variations in river water quality were related to numerous anthropogenic and natural factors. Urban land use was found to be the most important explanatory variable for BOD5, CODMn, TN, DN, NH4+-N, NO3−-N, DO, pH and TP. The animal husbandry output per capita was an important predictor of TP and turbidity, and the gross domestic product per capita largely determined spatial variations in EC. The remaining unexplained variance was related to other factors, such as topography. Our results suggested that pollution control of animal waste discharge in rural settlements, agricultural runoff in cropland, industrial production pollution and domestic pollution in urban and industrial areas were important within the Cao-E River basin. Moreover, the percentage of the total overall river water quality variance explained by an individual variable and/or all environmental variables (according to RDA) can assist in quantitatively identifying the primary factors that control pollution at the watershed scale.

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  • 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.
    • Handle: RePEc:plo:pone00:0102714
    DOI: 10.1371/journal.pone.0102714
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    References listed on IDEAS

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    2. Turuganti Venkateswarlu & Jagadeesh Anmala, 2024. "Importance of land use factors in the prediction of water quality of the Upper Green River watershed, Kentucky, USA, using random forest," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(9), pages 23961-23984, September.
    3. Yichun Xie & Chao Liu & Shujuan Chang & Bin Jiang, 2022. "Urban Sustainability: Integrating Socioeconomic and Environmental Data for Multi-Objective Assessment," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    4. Se-Rin Park & Sang-Woo Lee, 2020. "Spatially Varying and Scale-Dependent Relationships of Land Use Types with Stream Water Quality," IJERPH, MDPI, vol. 17(5), pages 1-22, March.
    5. Qinghui You & Na Fang & Lingling Liu & Wenjing Yang & Li Zhang & Yeqiao Wang, 2019. "Effects of land use, topography, climate and socio-economic factors on geographical variation pattern of inland surface water quality in China," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-14, June.
    6. Mehdi Aalipour & Naicheng Wu & Nicola Fohrer & Yusef Kianpoor Kalkhajeh & Bahman Jabbarian Amiri, 2023. "Examining the Influence of Landscape Patch Shapes on River Water Quality," Land, MDPI, vol. 12(5), pages 1-15, May.
    7. Md Mamun & Kwang-Guk An, 2021. "Application of Multivariate Statistical Techniques and Water Quality Index for the Assessment of Water Quality and Apportionment of Pollution Sources in the Yeongsan River, South Korea," IJERPH, MDPI, vol. 18(16), pages 1-23, August.
    8. Quan Wang & Xianhua Wu & Bin Zhao & Jie Qin & Tingchun Peng, 2015. "Combined Multivariate Statistical Techniques, Water Pollution Index (WPI) and Daniel Trend Test Methods to Evaluate Temporal and Spatial Variations and Trends of Water Quality at Shanchong River in th," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-17, April.
    9. Pingyang Liu & Juan M. Moreno & Peiying Song & Elona Hoover & Marie K. Harder, 2016. "The Use of Oral Histories to Identify Criteria for Future Scenarios of Sustainable Farming in the South Yangtze River, China," Sustainability, MDPI, vol. 8(9), pages 1-24, August.
    10. Se-Rin Park & Soon-Jin Hwang & Kyungjin An & Sang-Woo Lee, 2021. "Identifying Key Watershed Characteristics That Affect the Biological Integrity of Streams in the Han River Watershed, Korea," Sustainability, MDPI, vol. 13(6), pages 1-15, March.
    11. 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.

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