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Identification of the Relative Importance of Factors Affecting the Spatial Variability of River Water Quality

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  • Jiyu Seo

    (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University, Busan 48513, Korea)

  • Jeongeun Won

    (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University, Busan 48513, Korea)

  • Jeonghyeon Choi

    (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University, Busan 48513, Korea)

  • Sangdan Kim

    (Department of Environmental Engineering, Pukyong National University, Busan 48513, Korea)

Abstract

Understanding the temporal and spatial variability of water quality is important in order to establish effective customized management strategies for polluted aquatic ecosystems. Although various water quality management methods have been proposed based on insights into river water pollution factors through physically based modeling or statistical techniques, it is difficult to find studies that analyze the relative importance of these water pollution factors in a relatively large watershed using a step-by-step methodology. In this study, the spatial variability of river water quality is analyzed using time-averaged river water quality data collected from 40 sites in the Nakdong river basin, located on the Korean Peninsula. We focused on biological oxygen demand, total suspended solids, total nitrogen, and total organic carbon. A two-step exhaustive search approach was used to find a linear model that best links the various factors of the watershed with the average river water quality. The optimal model was selected by applying cross-correlation analysis and Bayesian inference. Through the process of finding the optimal statistical model, the major factors that have the most influence on river water quality were identified by analyzing the factors affecting river water quality, their levels of influence, and their levels of uncertainty. Identifying a set of processes provides insight into the key factors influencing spatial variability in average stream water quality conditions. We were able to identify the relative influences and uncertainties of the hydrological, climatic, topographical, and geological characteristics of the watershed on the spatial variability of river water quality. The proposed spatial variability model of average river water quality can be used to predict river water quality responses to future climate change, land use pattern change, and soil management strategy change.

Suggested Citation

  • Jiyu Seo & Jeongeun Won & Jeonghyeon Choi & Sangdan Kim, 2021. "Identification of the Relative Importance of Factors Affecting the Spatial Variability of River Water Quality," Sustainability, MDPI, vol. 13(20), pages 1-19, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:20:p:11319-:d:655524
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    References listed on IDEAS

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    1. 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.
    2. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    3. Marina Valentukevičienė & Lina Bagdžiūnaitė-Litvinaitienė & Viktoras Chadyšas & Andrius Litvinaitis, 2018. "Evaluating the Impacts of Integrated Pollution on Water Quality of the Trans-Boundary Neris (Viliya) River," Sustainability, MDPI, vol. 10(11), pages 1-19, November.
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