State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China.
Environ Pollut. 2021 Aug 15;283:117117. doi: 10.1016/j.envpol.2021.117117. Epub 2021 Apr 10.
Soil erosion contributes greatly to nonpoint source pollution (NSP). We built a coastal NSP risk calculation method (CNSPRI) based on the Revised Universal Soil Loss Equation (RUSLE) and geospatial methods. In studies on the formation and transport of coastal NSP, we analysed the pollution impacts on the sea by dividing subbasins into the sea and monitoring the pollutant flux. In this paper, a case study in the Yellow River Delta showed that the CNSPRI could better predict the total nitrogen (TN) and total phosphorus (TP) NSP risks. The value of the soil erodibility factor (K) was 0.0377 t h·MJ·mm, indicating higher soil erodibility levels, and presented an increased trend from the west to the east coast. The NSP risk also showed an increased trend from west to east, and the worst status was found near the Guangli River of the south-eastern region. The contributions of the seven influencing factors to CNSPRI presented an order of vegetation cover > rainfall erosivity > soil content > soil erodibility > flow > flow path > slope. The different roles of source and sink landscapes influenced the pollutant outputs on a subbasin scale. Arable land and saline-alkali land were the two land-use types with the greatest NSP risks. Therefore, in coastal zones, to reduce NSP output risks, we should pay more attention to the spatial distribution of vegetation cover, increase its interception effect on soil loss, and prioritize the improvement of saline-alkali land to reduce the amount of bare land.
土壤侵蚀是造成非点源污染（NSP）的主要原因。我们基于修正的通用土壤流失方程（RUSLE）和地理空间方法，建立了一种沿海 NSP 风险计算方法（CNSPRI）。在研究沿海 NSP 的形成和运移时，我们通过将子流域划分为海域和监测污染物通量来分析污染对海洋的影响。本文以黄河三角洲为例的研究表明，CNSPRI 可以更好地预测总氮（TN）和总磷（TP）NSP 风险。土壤可蚀性因子（K）的值为 0.0377 t·h·MJ·mm，表明土壤的可蚀性水平较高，并且从西向东呈增加趋势。NSP 风险也呈现出从西向东增加的趋势，在东南部的广利河附近情况最严重。七个影响因素对 CNSPRI 的贡献顺序为植被覆盖度>降雨侵蚀力>土壤含量>土壤可蚀性>流量>流径>坡度。源和汇景观的不同作用影响了子流域尺度上的污染物输出。耕地和盐碱地是 NSP 风险最大的两种土地利用类型。因此，在沿海地区，为了降低 NSP 输出风险，我们应该更加关注植被覆盖的空间分布，增加其对土壤流失的截留作用，并优先考虑改善盐碱地，减少裸地面积。
