2010-2022年陕西省人为源大气氨排放清单及未来减排潜力  

Emission Inventory of Atmospheric Ammonia from Anthropogenic Sources in Shaanxi Province from 2010 to 2022 and Future Emission Reduction Potential

作　　者：高可心 马正权 郑怡 贾宏涛[1,2] 努尔麦麦提·艾尔肯 麦麦提·斯马义 王国航 黄梓豪 苏麦耶·亚森 玛依努尔·莫沙 苏麦亚·乌斯曼 GAO Kexin;MA Zhengquan;ZHENG Yi;JIA Hongtao;NUERMAIMAITI Aierken;MAIMAITI Simayi;WANG Guohang;HUANG Zihao;SUMAIYE Yasen;MAYINUER Mosha;SUMAIYA Wusiman(College of Resources and Environment,Xinjiang Agricultural University,Urumqi 830052,China;Xinjiang Key Laboratory of Soil and Plant Ecological Processes,Xinjiang Agricultural University,Urumqi 830052,China)

机构地区：[1]新疆农业大学资源与环境学院,新疆乌鲁木齐830052 [2]新疆农业大学,新疆土壤与植物生态过程重点实验室,新疆乌鲁木齐830052

出　　处：《环境科学研究》2024年第8期1680-1693,共14页Research of Environmental Sciences

基　　金：新疆维吾尔自治区重点实验室开放课题(No.23XJTRZW13);新疆维吾尔自治区“天池英才”引进计划-青年博士项目;环境模拟与污染控制国家重点联合实验室(北京大学)开放基金项目(No.22K02ESPCP)。

摘　　要：为了解陕西省人为源氨(NH_(3))的排放特征,基于排放因子法和“自上而下”的县级活动水平收集方法,建立2010-2022年陕西省及其地级市的人为源NH_(3)排放清单,运用STIRAT模型、基于LSTM滑动窗口的BP神经系统模型和情景模拟法,预测2023-2035年人为源和畜禽养殖的NH_(3)排放变化趋势,量化分析不同发展结构和排放控制措施对NH_(3)排放影响的差异性。结果表明:①2022年陕西省NH_(3)排放总量为24.48×10^(4)t,畜禽养殖和氮肥施用是该省NH_(3)排放的主要贡献者,贡献率分别为63.2%和19.5%,其他人为源贡献率为17.3%。2022年陕西省人为源NH_(3)排放量最高和最低的城市分别为榆林市和铜川市,对全省排放总量的贡献率分别为23.7%和2.1%。②2010-2022年陕西省人为源NH_(3)排放总量呈明显下降趋势,年均下降率为2.9%,NH_(3)排放量时间变化趋势主要受畜禽养殖活动的影响。③2023-2035年陕西省NH_(3)排放总量在粗放发展状态下呈增长态势,2035年排放量较2020年增长约110%。基于2020年控制政策下的基准发展模式,人为源和畜禽养殖NH_(3)排放量均于2030年左右达到高峰(分别为33.01×10^(4)和18.90×10^(4)t),与“碳达峰”时间一致,其2035年排放量较2020年分别增加39.9%和18.3%;在绿色发展情景下,2035年人为源NH_(3)排放量较2020年下降14.42×10^(4)t;在GAINS模型建立的政策加强(PR)减排情景下,2035年人为源和畜禽养殖的NH_(3)排放量较2020年分别下降36.4%和16.9%~37.8%。研究显示,当前NH_(3)排放控制措施支持陕西省2030年前完成“碳达峰”目标,但之后的减排效果不明显;PR情景假设中,通过建设低排放房舍、粪便覆盖储存和低NH_(3)施用等多种减排措施相互组合可以提高减排效果,从而达到重点控制畜禽养殖源NH_(3)排放的目的。In order to understand the characteristics of ammonia(NH_(3))emissions from anthropogenic sources in Shaanxi Province,an anthropogenic NH_(3)emission inventory for Shaanxi Province and its sub-municipalities(counties)from 2010 to 2022 was established based on the emission factor method and the‘top-down’county-level activity data collection method.The STIRAT model,BP neural system model based on LSTM sliding window,and scenario simulation method were used to predict the trend of NH_(3)emissions from anthropogenic sources and livestock farming from 2023 to 2035.The differences in the impact of different development structures and emission control measures on NH_(3)emissions were quantitatively analyzed.The results showed that:(1)In 2022,the total NH_(3)emissions in Shaanxi were 24.48×10^(4)t.Livestock farming and nitrogen fertilizer application were the main contributors to NH_(3)emissions,contributing 63.2%and 19.5%,respectively,and the contributions of other anthropogenic sources was 17.3%.The cities with the highest and lowest NH_(3)emissions from anthropogenic sources in Shaanxi in 2022 were Yulin and Tongchuan,accounting for 23.7%and 2.1%of the total emissions in the province,respectively.(2)The total anthropogenic NH_(3)emissions from Shaanxi during 2010-2022 showeda significant downward trend,with an average annual decrease rate of 2.9%,and the temporal trend of NH_(3)emissions was mainly influenced by livestock and poultry farming activities.(3)From 2023 to 2035,the total NH_(3)emissions in Shaanxi will continue to increase in the state of rough development,and the emissions in 2035 will increase by about 110%compared with that in 2020.Based on the benchmark development model with the 2020 control policy,NH_(3)emissions from total sources and livestock farming sources will reach their peak around 2030(33.01×10^(4)and 18.90×10^(4)t),consisting with the‘carbon peak’time.Their emissions in 2035 will increase by about 39.9%and 18.3%.Under the scenario of green development,the anthropogenic NH_(3)emis

关 键 词：NH_(3)排放清单 STIRPAT情景模拟 BP神经网络 NH3减排潜势 

分 类 号：X511[环境科学与工程—环境工程]