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

Jinfeng Yang, Xuelei Wang, Xinrong Li, Zhuang Tian, Guoyuan Zou, Lianfeng Du and Xuan Guo ()
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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

Sustainability, 2023, vol. 15, issue 23, 1-14

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.

Keywords: agricultural nonpoint source pollution; index system; potential risk identification; critical source region (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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