Xueting Wang, Lei Wu, Yongkun Luo, Yimu Liu, Ruowen Wang
{"title":"黄河流域非点源污染精确估算与治理的综合出口流系数模型。","authors":"Xueting Wang, Lei Wu, Yongkun Luo, Yimu Liu, Ruowen Wang","doi":"10.1038/s41598-025-99552-1","DOIUrl":null,"url":null,"abstract":"<p><p>The export coefficient model (ECM) remains widely applied in estimating agricultural Non-point Source Pollution (NPSP) due to its simplicity, minimal parameter requirements, and relatively high accuracy. However, its reliance on empirical export coefficient (EC) limits its ability to accurately quantify pollutant loads in large and complex watersheds. This necessitates the development of more advanced approaches for improved pollutant load estimation. To overcome these challenges, the EC-ICM integrates environmental factors with optimized EC values for various land use types, enhancing its adaptability for watershed management. Empirical EC values were derived using genetic algorithm (GA) and Latin hypercube sampling, then improved EC and corrected EC were employed to estimate pollutant discharge and water inflow across land uses. The model incorporates multiple factors-such as surface runoff, topographic influence, landscape interception, soil erosion, pollutant production, water leaching, and cost-distance-allowing for more accurate NPSP load assessments. Pollution factors were classified using the natural breaks method, with Entropy Weight method determining weights for a comprehensive multi-factor evaluation and risk-level assignment. Compared to ECM optimized solely with GA, the EC-ICM demonstrates improved accuracy, reducing the relative error of total nitrogen and total phosphorus by 9.66% and 6.68%, respectively. Land use contributes the highest share of TN loads, particularly from cropland and grassland, followed by livestock and population sources. TP loads are primarily attributed to livestock and poultry farming, followed by land use and population sources. The Longdong Loess Plateau, responsible for approximately 12% of total NPSP loss, is identified as a high-risk area. Targeted zoning management strategies based on risk analysis prioritize these high-risk regions, providing practical recommendations for pollution control and comprehensive watershed environmental management. Future research can further explore the impact of improving temporal resolution, future climate change and combining hydrodynamic models on the ability to simulate the amount of pollutants entering the river.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"26302"},"PeriodicalIF":3.9000,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277455/pdf/","citationCount":"0","resultStr":"{\"title\":\"Integrated export instream coefficient model for accurate nonpoint source pollution estimation and management in the Yellow River Basin.\",\"authors\":\"Xueting Wang, Lei Wu, Yongkun Luo, Yimu Liu, Ruowen Wang\",\"doi\":\"10.1038/s41598-025-99552-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The export coefficient model (ECM) remains widely applied in estimating agricultural Non-point Source Pollution (NPSP) due to its simplicity, minimal parameter requirements, and relatively high accuracy. However, its reliance on empirical export coefficient (EC) limits its ability to accurately quantify pollutant loads in large and complex watersheds. This necessitates the development of more advanced approaches for improved pollutant load estimation. To overcome these challenges, the EC-ICM integrates environmental factors with optimized EC values for various land use types, enhancing its adaptability for watershed management. Empirical EC values were derived using genetic algorithm (GA) and Latin hypercube sampling, then improved EC and corrected EC were employed to estimate pollutant discharge and water inflow across land uses. The model incorporates multiple factors-such as surface runoff, topographic influence, landscape interception, soil erosion, pollutant production, water leaching, and cost-distance-allowing for more accurate NPSP load assessments. Pollution factors were classified using the natural breaks method, with Entropy Weight method determining weights for a comprehensive multi-factor evaluation and risk-level assignment. Compared to ECM optimized solely with GA, the EC-ICM demonstrates improved accuracy, reducing the relative error of total nitrogen and total phosphorus by 9.66% and 6.68%, respectively. Land use contributes the highest share of TN loads, particularly from cropland and grassland, followed by livestock and population sources. TP loads are primarily attributed to livestock and poultry farming, followed by land use and population sources. The Longdong Loess Plateau, responsible for approximately 12% of total NPSP loss, is identified as a high-risk area. Targeted zoning management strategies based on risk analysis prioritize these high-risk regions, providing practical recommendations for pollution control and comprehensive watershed environmental management. Future research can further explore the impact of improving temporal resolution, future climate change and combining hydrodynamic models on the ability to simulate the amount of pollutants entering the river.</p>\",\"PeriodicalId\":21811,\"journal\":{\"name\":\"Scientific Reports\",\"volume\":\"15 1\",\"pages\":\"26302\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-07-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12277455/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Reports\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41598-025-99552-1\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Reports","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41598-025-99552-1","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Integrated export instream coefficient model for accurate nonpoint source pollution estimation and management in the Yellow River Basin.
The export coefficient model (ECM) remains widely applied in estimating agricultural Non-point Source Pollution (NPSP) due to its simplicity, minimal parameter requirements, and relatively high accuracy. However, its reliance on empirical export coefficient (EC) limits its ability to accurately quantify pollutant loads in large and complex watersheds. This necessitates the development of more advanced approaches for improved pollutant load estimation. To overcome these challenges, the EC-ICM integrates environmental factors with optimized EC values for various land use types, enhancing its adaptability for watershed management. Empirical EC values were derived using genetic algorithm (GA) and Latin hypercube sampling, then improved EC and corrected EC were employed to estimate pollutant discharge and water inflow across land uses. The model incorporates multiple factors-such as surface runoff, topographic influence, landscape interception, soil erosion, pollutant production, water leaching, and cost-distance-allowing for more accurate NPSP load assessments. Pollution factors were classified using the natural breaks method, with Entropy Weight method determining weights for a comprehensive multi-factor evaluation and risk-level assignment. Compared to ECM optimized solely with GA, the EC-ICM demonstrates improved accuracy, reducing the relative error of total nitrogen and total phosphorus by 9.66% and 6.68%, respectively. Land use contributes the highest share of TN loads, particularly from cropland and grassland, followed by livestock and population sources. TP loads are primarily attributed to livestock and poultry farming, followed by land use and population sources. The Longdong Loess Plateau, responsible for approximately 12% of total NPSP loss, is identified as a high-risk area. Targeted zoning management strategies based on risk analysis prioritize these high-risk regions, providing practical recommendations for pollution control and comprehensive watershed environmental management. Future research can further explore the impact of improving temporal resolution, future climate change and combining hydrodynamic models on the ability to simulate the amount of pollutants entering the river.
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