College of Resources and Environment, Southwest University, Chongqing 400715, China; National Base of International S&T Collaboration on Water Environmental Monitoring and Simulation in TGR Region, Chongqing 400715, China.
State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China.
Sci Total Environ. 2023 Jun 1;875:162543. doi: 10.1016/j.scitotenv.2023.162543. Epub 2023 Mar 5.
Although physical models at present have made important achievements in the assessment of non-point source pollution (NPSP), the requirement for large volumes of data and their accuracy limit their application. Therefore, constructing a scientific evaluation model of NPS nitrogen (N) and phosphorus (P) output is of great significance for the identification of N and P sources as well as pollution prevention and control in the basin. We considered runoff, leaching and landscape interception conditions, and constructed an input-migration-output (IMO) model based on the classic export coefficient model (ECM), and identified the main driving factors of NPSP using geographical detector (GD) in Three Gorges Reservoir area (TGRA). The results showed that, compared with the traditional export coefficient model, the prediction accuracy of the improved model for total nitrogen (TN) and total phosphorus (TP) increased by 15.46 % and 20.17 % respectively, and the error rates with the measured data were 9.43 % and 10.62 %. It was found that the total input volume of TN in the TGRA had declined from 58.16 × 10 t to 48.37 × 10 t, while the TP input volume increased from 2.76 × 10 t to 4.11 × 10 t, and then decreased to 4.01 × 10 t. In addition Pengxi River, Huangjin River and the northern part of Qi River were high value areas of NPSP input and output, but the range of high value areas of migration factors has narrowed. Pig breeding, rural population and dry land area were the main driving factors of N and P export. The IMO model can effectively improve prediction accuracy, and has significant implications for the prevention and control of NPSP.
虽然物理模型目前在评估非点源污染（NPSP）方面取得了重要成果，但对大量数据的需求及其准确性限制了其应用。因此，构建 NPS 氮（N）和磷（P）输出的科学评价模型，对于识别 N 和 P 源以及流域污染防治具有重要意义。本研究考虑了径流量、淋溶量和景观截留量等条件，在经典输出系数模型（ECM）的基础上构建了输入-迁移-输出（IMO）模型，并利用地理探测器（GD）识别了三峡库区（TGRA）NPSP 的主要驱动因素。结果表明，与传统的输出系数模型相比，改进模型对总氮（TN）和总磷（TP）的预测精度分别提高了 15.46%和 20.17%，与实测数据的误差率分别为 9.43%和 10.62%。研究发现，TGRA 的 TN 总输入量从 58.16×10t 下降到 48.37×10t，TP 输入量从 2.76×10t 增加到 4.11×10t，然后下降到 4.01×10t。此外，澎溪河、黄金河和七北部地区是 NPSP 输入和输出的高值区，但迁移因子高值区的范围已经缩小。养猪业、农村人口和旱地面积是 N 和 P 输出的主要驱动因素。IMO 模型可以有效提高预测精度，对 NPSP 的防治具有重要意义。
