摘要
我国畜禽年产污量近40亿t,其有效治理是推动畜牧业绿色转型与国家“无废城市”建设的关键环节。针对当前研究在畜禽粪污全国产量精准量化、时空分布格局等方面的不足,本研究构建了统一的畜禽产污核算体系,融合多源养殖数据与乘性预测模型,量化我国畜禽产污总量,系统分析其时空分异特征,并预测未来10年的演变趋势。结果表明:2023年我国畜禽产污总量达36.4亿t(粪污产量占65.4%,尿污产量占34.6%),呈现显著的物种主导与空间集聚双重特征。物种贡献上,牛和猪是绝对主导,分别占39%和36.7%;牛主导粪污排放(贡献率>40%),猪主导尿污产生(贡献率>55%),反映出草饲畜种与集约化养殖的生物学分异。空间分布上,西南、华北、西北地区形成高负荷连绵带(占我国产污总量的50.8%),其中,内蒙古的牛羊、四川的猪、新疆的其他大牲畜、河南的家禽是核心贡献源。产污预测结果显示,2033年我国畜禽总产污将增至46亿t,年均增长2.7%,未来增量主要来源于牛粪污(较2023年增加41.1%)和家禽粪污(较2023年增加42.4%);相反,其他大牲畜粪污产量将显著下降(较2023年降低38.1%),映射出农业机械化对传统役用牲畜的替代效应。建议我国未来的畜禽产污治理,在类别上需聚焦牛、猪两大主导物种,同时关注反刍动物与家禽产污的快速增长;在空间上需重点锚定西南、华北、西北三大热点区域。研究结果可为区域联防联控、优化养殖模式等为核心的区域差异化治理政策提供重要支撑。
China′s annual livestock and poultry waste output approaches 4.00×109 t.Effective management of this waste is critical for advancing the green transformation of the livestock industry and supporting the national‘waste-free city’initiative.To address existing research gaps in accurately quantifying national livestock and poultry manure production and understanding its spatiotemporal distribution patterns,this study establishes a unified accounting system for livestock and poultry waste generation.By integrating multi-source farming data with multiplicative prediction models,it quantifies China′s total livestock and poultry waste output,systematically analyses its spatiotemporal variation characteristics,and forecasts future trends over the next decade.Results indicate that in 2023,China′s total livestock and poultry waste production reached 3.64×109 t,with fecal waste accounting for 65.4%and urine waste for 34.6%,exhibiting dual characteristics of significant species dominance and spatial concentration.In terms of species contribution,cattle and pigs were primary contributors,accounting for 39%and 36.7%of total waste,respectively.Cattle contributed more than 40%of fecal waste emissions,while pigs contributed over 55%of urine waste,reflecting biological and production-system differences between grass-fed livestock and intensive farming systems.In terms of spatial distribution,a continuous belt of high-load areas extended across Southwest,North,and Northwest regions,collectively accounting for 50.8%of China′s total livestock manure output.Major contributors within this belt included cattle and sheep in Inner Mongolia,pigs in Sichuan,other large livestock in Xinjiang,and poultry in Henan.Waste generation projections indicate that China′s total livestock manure output will increase to 4.60×109 t by 2033,with an average annual growth rate of 2.7%.Future increases will be driven primarily by cattle manure(up 41.1%from 2023)and poultry manure(up 42.4%from 2023).Conversely,manure production from other large livestock will decline significantly(down 38.1%from 2023),reflecting the substitution effect of agricultural mechanization on traditional draft animals.Future livestock and poultry waste management should focus on two dominant species,cattle and pigs,while also addressing the rapid increase in waste production from ruminants and poultry.Spatially,efforts should concentrate on three key hotspot regions:Southwest,North China,and Northwest China.These findings provide essential support for formulating regionally differentiated governance policies centered on coordinated prevention and control,as well as optimized farming models.
作者
马晨辉
徐亚
郑景华
陈丰
刘玉强
窦飞
张兴林
钱璨
MA Chenhui;XU Ya;ZHENG Jinghua;CHEN Feng;LIU Yuqiang;DOU Fei;ZHANG Xinglin;QIAN Can(State Key Laboratory of Environmental Criteria and Risk Assessment,Research Institute of Solid Waste Management,Chinese Research Academy of Environmental Sciences,Beijing 100012,China;College of Environmental Science and Engineering,Liaoning Technical University,Fuxin 123000,China;Gansu Provincial Key Laboratory for Industrial Waste Recycling and Regulation,Gansu Ecological Environment Science Design and Research Institute,Lanzhou 730070,China)
出处
《环境科学研究》
北大核心
2026年第2期437-448,共12页
Research of Environmental Sciences
基金
中央引导地方科技发展资金(No.24ZYQA025)
国家重点研发计划项目(No.2023YFC3708902)
黄河流域生态保护和高质量发展联合研究项目(No.2022YRUC010303)。
关键词
粪污
尿污
时空分布
产污预测
fecal waster
urine waste
spatiotemporal distribution
pollution prediction
