黄河中游集约化农区附加面源污染物排放量的降尺度核算OACHSSCD
Downscaling Accounting of Additional Non-point Source Pollution Emissions from Intensive Agricultural Areas in the Middle Reaches of the Yellow River Basin
针对黄河中游集约化农区附加面源污染精细化核算的需求,本研究以渭河流域为例,通过耦合土地利用数据,对农村人口和畜禽养殖数据进行空间分配,将区县尺度的统计数据离散到 1 km×1 km 的栅格尺度,并结合排污系数与差异化去除率,实现了 2000-2022 年农村生活污水和畜禽养殖两类附加面源污染物(TN、TP)的高分辨率排放量核算.研究表明:(1)农村生活污水 TN、TP 高排放区位于流域东南部;畜禽养殖源 TN、TP 高排放区主要集中于流域西部;农村居民点扩张区与规模化养殖聚集区成为污染热点区域.(2)两类污染源栅格尺度的 TN、TP信息熵(H)均高于区县尺度,表明栅格尺度能够揭示出更加精细的污染分布特征.以 TN 为例,2022 年,农村生活污水栅格尺度的 HTN=13.76,区县尺度的 HTN=5.15;畜禽养殖源栅格尺度的 HTN=14.42,区县尺度的 HTN=5.61.(3)2022 年,两类污染源栅格和区县尺度的热点区域表现出高度重叠的空间分布特征,栅格尺度的 TN、TP 热点区域面积均占重叠面积的 70%以上,远高于区县尺度的 32.6%～44.5%,表明栅格尺度在揭示空间细节与污染物高值聚集区方面更具优势.因此,本研究提出的降尺度核算方法显著提升了附加面源污染物排放的空间异质性表征能力,研究结果可为黄河中游集约化农区的精准治污与本地化水质响应机理模型的高效耦合提供科学依据和数据支撑.
Aiming at the demand for fine accounting of additional non-point source pollution in the intensive agricultural areas in the middle reaches of the Yellow River,this study takes the Weihe River Basin as a case study.By integrating land use data,rural population and livestock farming data were spatially allocated,enabling the disaggregation of county-level statistical data to a 1 km×1 km grid scale.Coupled with emission coefficients and differentiated removal rates,the study achieved high-resolution estimations of two major categories of additional non-point source pollutants—total nitrogen(TN)and total phosphorus(TP)—from rural domestic sewage and livestock farming over the period 2000-2022.The re-sults show that:(1)High-emission areas of TN and TP from rural domestic sewage were primarily located in the south-eastern part of the basin,whereas those from livestock farming were mainly concentrated in the western region.Areas with expanding rural settlements and intensive livestock farming clusters emerged as pollution hotspots.(2)The information entropy(H)of TN and TP at the grid scale was higher than that at the county scale for both pollution sources,indicating that the grid scale better captures the spatial heterogeneity of pollutant distribution.For example,the information entropy of TN from rural domestic sewage at the grid scale was 13.76 in 2022,significantly higher than 5.15 at the county scale.Similarly,for livestock farming,the grid-scale HTN reached 14.42,compared to 5.61 at the county scale.(3)In 2022,the hotspot areas identified at both grid and county scales exhibited a high degree of spatial overlap for both pollution sources.Over 70%of the total hotspot area was captured at the grid scale,which was substantially higher than the range of 32.6%-44.5%at the county scale.This suggests that the grid scale offers greater advantages in revealing spatial details and identifying zones of high pollutant concentration.Therefore,the downscaling accounting method proposed in this study significantly enhances the ability to characterize the spatial heterogeneity of additional non-point source pollutant emis-sions.The results of this study can provide scientific basis and data support for the efficient coupling of explicit pollution control and localized water quality response mechanism modeling in intensive agricultural areas in the middle reaches of the Yellow River.
余严飞;王小溪;李佳一;闵敏
河南大学黄河文明与可持续发展研究中心暨黄河文明省部共建协同创新中心,河南 开封 475001河南大学黄河文明与可持续发展研究中心暨黄河文明省部共建协同创新中心,河南 开封 475001河南大学黄河文明与可持续发展研究中心暨黄河文明省部共建协同创新中心,河南 开封 475001河南大学黄河文明与可持续发展研究中心暨黄河文明省部共建协同创新中心,河南 开封 475001
资源环境
栅格尺度附加面源污染排污系数法信息熵冷热点分析
grid scaleadditional non-point source pollutionpollutant discharge coefficient methodinformation entro-pyhotspot and coldspot analysis
《生态与农村环境学报》 2026 (6)
783-794,12
国家自然科学基金青年项目(42101285,42201051)国家资助博士后研究人员计划B档(GZB20240196)河南省青年人才托举工程项目(2025HYTP007)中国博士后科学基金面上资助项目(2023M730945)河南省科技攻关项目(242102321120,242102320234)河南省高等学校重点科研项目(24A170009,23A170017)
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