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Potential Risk Identification of Agricultural Nonpoint Source Pollution: A Case Study of Yichang City, Hubei Province

Author

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  • Jinfeng Yang

    (Institute of Plant Nutrition, Resources and Environment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Xuelei Wang

    (Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment, Beijing 100094, China)

  • Xinrong Li

    (Institute of Plant Nutrition, Resources and Environment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Zhuang Tian

    (Institute of Plant Nutrition, Resources and Environment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Guoyuan Zou

    (Institute of Plant Nutrition, Resources and Environment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Lianfeng Du

    (Institute of Plant Nutrition, Resources and Environment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Xuan Guo

    (Institute of Plant Nutrition, Resources and Environment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

Abstract

Potential risk identification of agricultural nonpoint source pollution (ANPSP) is essential for pollution control and sustainable agriculture. Herein, we propose a novel method for potential risk identification of ANPSP via a comprehensive analysis of risk sources and sink factors. A potential risk assessment index system (PRAIS) was established. The proposed method was used to systematically evaluate the potential risk level of ANPSP of Yichang City, Hubei Province. The potential risk of ANPSP in Yichang City was 18.86%. High-risk areas account for 4.95% and have characteristics such as high nitrogen and phosphorus application rates, large soil erosion factors, and low vegetation coverage. Compared with the identification results of the Diffuse Pollution estimation with the Remote Sensing (DPeRS) model, the area difference of the same risk level calculated by the PRAIS was reduced by 33.9% on average. This indicates that PRAIS has the same level of accuracy as the DPeRS model in identifying potential risks of ANPSP. Thus, a rapid and efficient identification system of potential risks of regional ANPSP was achieved.

Suggested Citation

  • Jinfeng Yang & Xuelei Wang & Xinrong Li & Zhuang Tian & Guoyuan Zou & Lianfeng Du & Xuan Guo, 2023. "Potential Risk Identification of Agricultural Nonpoint Source Pollution: A Case Study of Yichang City, Hubei Province," Sustainability, MDPI, vol. 15(23), pages 1-14, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16324-:d:1288263
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    References listed on IDEAS

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    1. Ying Chen & Binbin Lu & Chongyu Xu & Xingwei Chen & Meibing Liu & Lu Gao & Haijun Deng, 2022. "Uncertainty Evaluation of Best Management Practice Effectiveness Based on the AnnAGNPS Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1307-1321, March.
    2. Shen, Zhenyao & Hong, Qian & Chu, Zheng & Gong, Yongwei, 2011. "A framework for priority non-point source area identification and load estimation integrated with APPI and PLOAD model in Fujiang Watershed, China," Agricultural Water Management, Elsevier, vol. 98(6), pages 977-989, April.
    3. Yanrong Lu & Chen Wang & Rongjin Yang & Meiying Sun & Le Zhang & Yuying Zhang & Xiuhong Li, 2023. "Research on the Progress of Agricultural Non-Point Source Pollution Management in China: A Review," Sustainability, MDPI, vol. 15(18), pages 1-14, September.
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