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作 者:Xinran Zhang Shasha Yin Xuan Lu Yali Liu Tiantian Wang Binglin Zhang Zhuo Li Wenju Wang Mengdi Kong Keying Chen
机构地区:[1]College of Chemistry,Zhengzhou University,Zhengzhou 450001,China [2]School of Ecology and Environment,Zhengzhou University,Zhengzhou 450001,China [3]Research Institute of Environmental Sciences,Zhengzhou University,Zhengzhou 450001,China
出 处:《Journal of Environmental Sciences》2025年第4期604-621,共18页环境科学学报(英文版)
基 金:supported by the 2020 National Supercomputing Zhengzhou Center Innovation Ecosystem Construction Technology Project(No.201400210700);Zhengzhou PM2.5 and O3 Collaborative Control and Monitoring Project(No.20220347A).
摘 要:Recently,the transportation sector in China has gradually become the main source of urban air pollution and primary driver of carbon emissions growth.Considering air pollutants and greenhouse gases come from the same emission sources,it is necessary to establish an updated high-resolution emission inventory for the transportation sector in Central China,themost polluted region in China.The inventory includes on-road mobile,non-roadmobile,oil storage and transportation,and covers 9 types of air pollutants and 3 types of greenhouse gases.Based on the Long-range Energy Alternatives Planning System(LEAP)model,the emissions of pollutants were predicted for the period from2020 to 2035 in different scenarios.Results showed that in 2020,emissions of SO_(2),NO_(x),CO,PM_(10),PM_(2.5),VOCs,NH_(3),BC,OC,CO_(2),CH_(4),and N_(2)O in Henan Province were 27.5,503.2,878.6,20.1,17.4,222.1,21.5,9.4,2.9,92,077.9,6.0,and 10.4 kilotons,respectively.Energy demand and pollutant emissions in Henan Province are simulated under four scenarios(Baseline Scenario(BS),Pollution Abatement Scenario(PA),Green Transportation Scenario(GT),and Reinforcing Low Carbon Scenario(RLC)).The collaborative emission reduction effect is most significant in the RLC scenario,followed by the GT scenario.By 2035,under the RLC scenario,energy consumption and emissions of SO_(2),NO_(x),CO,PM_(10),PM_(2.5),VOCs,NH_(3),CO_(2),CH_(4),and N_(2)O are projected to decrease by 72.0%,30.0%,55.6%,56.0%,38.6%,39.7%,51.5%,66.1%,65.5%,55.4%,and 52.8%,respectively.This study provides fundamental data support for subsequent numerical simulations.
关 键 词:Emission inventory Transport sector LEAP model Co-benefit emission reduction Scenario analysis
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