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While significant progress has been made in controlling point source pollution, agricultural non-point source pollution (AGNPSP) has emerged as a major contributor to global water pollution, posing a severe threat to ecological quality. According to China’s Second National Pollution Source Census, AGNPSP constitutes a substantial proportion of water pollution, making its mitigation a critical challenge. Identifying AGNPSP risk zones is essential for targeted management and effective intervention. This study focuses on Yongchuan District, a representative hilly–mountainous area in the Yangtze River Basin. Applying the landscape ecology “source–sink” theory, we selected seven natural factors influencing AGNPSP and constructed a minimum cumulative resistance model using remote sensing post-processing data. An attempt was made to classify the “source” and “sink” landscapes, and ultimately conduct a risk assessment of AGNPSP in Yongchuan District, identifying the key areas for AGNPSP control. Key findings include: 1. Vegetation coverage is the most significant natural factor affecting AGNPSP. 2. Extremely high- and high-risk zones cover 90% of Yongchuan, primarily concentrated in the central and southern regions, indicating severe AGNPSP pressure that demands urgent management. 3. The levels of ammonia nitrogen and total phosphorus in the typical sections are related to the risk levels of the corresponding sections. Consequently, the risk level of AGNPSP directly correlates with the pollutant concentrations measured in the sections. This study provides a robust scientific basis for AGNPSP risk assessment and targeted control strategies, offering valuable insights for pollution management in Yongchuan and similar regions.
Details
Data processing;
Water pollution;
Soil erosion;
Remote sensing;
Agricultural land;
Mountainous areas;
Nonpoint source pollution;
Risk assessment;
Mountain regions;
Pollution sources;
Risk levels;
Phosphorus;
Research methodology;
Point source pollution;
Prevention;
Watersheds;
Ammonia;
Landscape ecology;
Mountains;
Land use;
Pollution
1 State Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; [email protected] (Y.L.); [email protected] (G.D.); [email protected] (Y.Z.); [email protected] (Y.Y.), State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, No. 8, Da Yang Fang, An Wai, Chao Yang District, Beijing 100012, China; [email protected] (R.Y.); [email protected] (M.S.); [email protected] (L.Z.)
2 State Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; [email protected] (Y.L.); [email protected] (G.D.); [email protected] (Y.Z.); [email protected] (Y.Y.)
3 State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, No. 8, Da Yang Fang, An Wai, Chao Yang District, Beijing 100012, China; [email protected] (R.Y.); [email protected] (M.S.); [email protected] (L.Z.)