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| Spatio-temporal evolution characteristics of agricultural non-point source pollution load and identification of key source areas |
| Received:December 31, 2025 |
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| KeyWord:non-point source pollution;improved export coefficient model;topographic modification;rainfall;random Forest;stable hotspots |
| Author Name | Affiliation | E-mail | | YANG Chen | College of Earth Sciences, Hebei GEO University, Shijiazhuang 050031, China
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China | | | GAO Maofang | Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China | gaomaofang@caas.cn | | YANG Qiyan | College of Earth Sciences, Hebei GEO University, Shijiazhuang 050031, China | | | LI Shilei | Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China | | | WANG Tianli | Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China | | | LI Jiawen | College of Earth Sciences, Hebei GEO University, Shijiazhuang 050031, China
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China | | | MA Dingyu | College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China | | | ZHANG Yijie | Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China | | | WANG Qizhi | Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China | |
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| Abstract: |
| Aiming at the problem that traditional static assessment methods struggle to distinguish between perennial high-risk pollution areas and accidental outbreak areas, this study intends to reveal the spatiotemporal dynamic characteristics and environmental driving mechanisms of agricultural non-point source pollution at the microscale. Taking Haoping Town in the Danjiangkou Reservoir area as the study area, an improved export coefficient model integrated with rainfall-topography correction was established. The hotspots frequency index was introduced to identify stable critical source areas, and correlation analysis combined with the random forest model was adopted to determine the influencing factors of the spatiotemporal differentiation of non-point source pollution. The results showed that：from 2019 to 2024, the pollution loads of TN and TP in Haoping Town both exhibited a trend of rising first, then falling and rising again. The interannual fluctuation was mainly driven by rainfall, and land use and livestock and poultry breeding were always the main sources of TN and TP pollution loads. The spatial distribution presented an agglomeration pattern of“high in the central area and low in the north and south”, with high-load areas mainly concentrated in the central regions such as Guanyinmiao Village. The stable critical source areas shared by TN and TP were identified to account for 16.1% of the town′ s total area, of which 94.4% was cultivated land, characterized by sustained high emissions. The volatile critical source areas were significantly driven by rainfall and relatively scattered in spatial distribution. The driving force analysis shows that slope is the key factor for pollution differentiation within cultivated land, and its relative importance to TN is higher than that to TP. For stable hotspots, source reduction and soil and water conservation measures should be implemented, together with enhanced resource utilization of livestock and poultry manure. For unstable hotspots, process interception strategies such as constructing ecological buffer zones should be adopted. |
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