College of Water Sciences, Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing Normal University, Beijing, 100875, China.
College of Water Sciences, Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, Beijing Normal University, Beijing, 100875, China.
Environ Res. 2022 Nov;214(Pt 2):113892. doi: 10.1016/j.envres.2022.113892. Epub 2022 Jul 19.
Identification of critical source areas (CSAs) for non-point source (NPS) pollution is of great significance for environment governance and prevention. However, the CSAs are generally characterized as great spatial dispersion, and spatially heterogeneous precipitation has a great influence on the spatial distribution of nutrient yields. Therefore, we identify the CSAs for nutrient yields in an agricultural watershed of Northeast China at hydrological response units (HRUs) scale based on the Soil and Water Assessment Tool (SWAT), assess the impacts of spatially heterogeneity of precipitation on the identification of the CSAs, analyze the sensitivity of nutrient yields to precipitation by scenarios analysis method, and further identify priority management areas (PMAs) that have poor ability to retain nutrients. The results showed that the CSAs for nutrient yields identified by uniform precipitation showed greater fluctuation range and coverage area than actual precipitation; the major prevention areas of total nitrogen (TN) yield were mainly distributed in regions nearby main stem of lower reaches, while that of total phosphorus (TP) yield were mostly located in urban area nearby outlet of the watershed; the identification of the PMAs significantly decreased the CSAs for TN yield, whereas that for TP yield was no significant difference with the CSAs. This study could provide scientific guidance for the NPS pollution governance and prevention.
识别非点源（NPS）污染的关键源区（CSAs）对于环境治理和防治具有重要意义。然而，CSAs 通常具有很大的空间分散性，空间异质的降水对养分产量的空间分布有很大影响。因此，我们基于土壤和水评估工具（SWAT），在水文响应单元（HRU）尺度上识别中国东北地区农业流域的养分产量 CSAs，评估降水空间异质性对 CSAs 识别的影响，通过情景分析方法分析养分产量对降水的敏感性，并进一步识别保留养分能力较差的优先管理区（PMAs）。结果表明，均匀降水识别的养分产量 CSAs 的波动范围和覆盖面积大于实际降水；总氮（TN）产量的主要防治区主要分布在下游干流附近地区，而总磷（TP）产量的主要防治区则主要位于流域出口附近的城区；PMAs 的识别显著减少了 TN 产量的 CSAs，而 TP 产量的 CSAs 与 TN 产量的 CSAs 没有显著差异。本研究可为 NPS 污染治理和防治提供科学指导。
