海河流域农业面源污染潜在风险识别方法

在综合分析农业面源污染风险源汇因子的基础上，筛选出影响海河流域农业面源污染的8个主要因子（年降水量、溶解态面源污染物入河系数、吸附态面源污染物入河系数、年植被覆盖度、坡度、土壤可侵蚀性因子、农田氮表观平衡量和农田磷表观平衡量），建立了农业面源污染潜在风险识别指标体系，采用多因子综合分析法对海河流域农业面源污染潜在风险等级进行评价，并与DPeRS模型风险识别结果进行偏差分析.结果表明，海河流域有61.91%的区域存在农业面源污染潜在风险，集中分布在流域的中部和南部地区，高风险区主要分布在北京市东南部、天津市中部、流域山东段东北部和河南段南部等区域；与DPeRS模型识别结果对比验证，显示同一风险等级面积相差不超过12%，且高风险级别面积相差仅为0.12%，97.17%以上的区域均为偏差小或无偏差，表明该识别方法具有与DPeRS模型法同等水平的农业面源污染潜在风险识别精准度，可实现区域农业面源污染潜在风险的快速、高效识别.

An identification method of potential risk for agricultural non-point source pollution in the Haihe River Basin

Based on a comprehensive analysis of the source and sink factors of agricultural non-point source pollution risk, eight main factors leading to the agricultural non-point source pollution, which were annual precipitation, dissolved non-point source pollutant inflow coefficient, granular non-point source pollutant inflow coefficient, annual vegetation coverage, slope, soil erodibility factor, nitrogen and phosphorus balance of farmland, were identified. Moreover, an identification index system of the potential risk for agricultural non-point source pollution was established. The multi-factor comprehensive analysis method was used to evaluate the risk of agricultural non-point source pollution in the Haihe River Basin. The results were compared with the risk identification results of Diffuse pollution estimation with remote sensing (DPeRS) model using deviation analysis. It showed that 61.91% of the Haihe River Basin had potential risks of agricultural non-point source pollution. It was concentrated in the central and southern regions of the basin. Among which were high-risk areas including the southeast of Beijing, the central part of Tianjin, the northeastern part of Shandong and the southern part of the Henan within the basin. Comparing the results with that of DPeRS model, the area of the same risk level regions differed by no more than 12%, and the area of high-risk regions differed by only 0.12%, and there were little or no deviation for more than 97.17% of the regions. It was shown that the identification method had the same accuracy as the DPeRS model method for identifying potential risks of agricultural non-point source pollution, making rapid and efficient identification of potential risks of regional agricultural non-point source pollution possible.