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Modeling of Agricultural Nonpoint-Source Pollution Quantitative Assessment: A Case Study in the Mun River Basin, Thailand

Author

Listed:
  • Zhonghe Zhao

    (Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China)

  • Kun Liu

    (College of Agronomy, Inner Mongolia Agricultural University, Hohhot 010019, China)

  • Bowei Yu

    (Institute of Environment and Sustainable Development, Agricuture of Chinese Academy of Agricultural Sciences, Beijing 100081, China)

  • Gaohuan Liu

    (Jiangsu Center for Collaborative Innovation, Geographical Information Resource Development and Application, Nanjing 210023, China
    Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

  • Youxiao Wang

    (School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China)

  • Chunsheng Wu

    (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

Abstract

Agricultural nonpoint-source pollution (ANPSP) is a key cause of global environmental problems. However, the estimation of ANPSP, based on agricultural land use type, crop management, and attenuation of pollutants with distance, is lacking. Using the Mun River Basin as an example, this study established quantitative response relationships between subbasin flows and hydrological and water quality parameters. A good matching of the monitored sections and the control area based on flow relationships was achieved. By determining flow paths and flow distances, the overland and in-river transport attenuations of ANPSP were clarified. The overland and in-river transport and attenuation parameters were also quantified. The land use distribution and structure were further refined through crop management, which included crop types and crop rotation (monocropping or double cropping). Based on the above procedures, quantitative relationships among land use pattern, crop management, attenuation of pollutants with distance, and river water quality were established and used to construct six kinds of regression models. Among these models, the best modeling results were obtained when the parameters of water quality, land use structure, crop management, and soil nutrient attenuation were included. The modeling accuracy in the dry season increased from 0.398 to 0.881 when information about attenuation with distance and crop management was included. Similarly, the modeling accuracy in the wet season increased from 0.365 to 0.727. This study’s findings indicate that the constructed water quality model is effective and has significance for the quantitative determination of ANPSP.

Suggested Citation

  • Zhonghe Zhao & Kun Liu & Bowei Yu & Gaohuan Liu & Youxiao Wang & Chunsheng Wu, 2023. "Modeling of Agricultural Nonpoint-Source Pollution Quantitative Assessment: A Case Study in the Mun River Basin, Thailand," Sustainability, MDPI, vol. 15(13), pages 1-16, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10325-:d:1183256
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    References listed on IDEAS

    as
    1. Zhonghe Zhao & Gaohuan Liu & Qingsheng Liu & Chong Huang & He Li, 2018. "Studies on the Spatiotemporal Variability of River Water Quality and Its Relationships with Soil and Precipitation: A Case Study of the Mun River Basin in Thailand," IJERPH, MDPI, vol. 15(11), pages 1-19, November.
    2. Zhonghe Zhao & Gaohuan Liu & Qingsheng Liu & Chong Huang & He Li & Chunsheng Wu, 2018. "Distribution Characteristics and Seasonal Variation of Soil Nutrients in the Mun River Basin, Thailand," IJERPH, MDPI, vol. 15(9), pages 1-18, August.
    3. Bahman Amiri & Kaneyuki Nakane, 2009. "Modeling the Linkage Between River Water Quality and Landscape Metrics in the Chugoku District of Japan," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(5), pages 931-956, March.
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    Cited by:

    1. Zhenyu Qi & Yuezhou You, 2024. "The Impact of the Rural Digital Economy on Agricultural Green Development and Its Mechanism: Empirical Evidence from China," Sustainability, MDPI, vol. 16(9), pages 1-23, April.

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