Output characteristics and driving factors of non-point source nitrogen (N) and phosphorus (P) in the Three Gorges reservoir area (TGRA) based on migration process: 1995–2020

非点源污染 环境科学 地表径流 污染 水文学(农业) 拦截 富营养化 流域 环境工程 氮气 相关系数 营养物 数学 生态学 地理 化学 统计 生物 工程类 岩土工程 地图学 有机化学
作者
Shaojun Tan,Deti Xie,Jiupai Ni,Lei Chen,Chengsheng Ni,Wei Ye,Guangyao Zhao,Jing’an Shao,Fangxin Chen
出处
期刊:Science of The Total Environment [Elsevier BV]
卷期号:875: 162543-162543 被引量:29
标识
DOI:10.1016/j.scitotenv.2023.162543
摘要

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 × 104 t to 48.37 × 104 t, while the TP input volume increased from 2.76 × 104 t to 4.11 × 104 t, and then decreased to 4.01 × 104 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.
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