Emission Characteristics Of Air Pollutants From Road Mobile Sources In The Sichuan Basin And Their Changes During The COVID-19 Outbreak

In early 2020,the COVID-19 pandemic broke out and led to the lockdown in Wuhan,Hubei province.From January 23rd,strict control measures were taken to prevent the spread of viruses all over the country.On January 25,Sichuan province launched the"first-level response to major public health emergency".Following the emergency regulations,the local government implemented strict control measures,such as traffic control,gathering forbidding,community closure,etc.In this paper,the COPERT(Computer Programme to Calculate Emissions from Road Transport)model was used to simulate on-road vehicle emissions in the Basin area of Sichuan Province,including Bazhong,Chengdu,Dazhou,Deyang,Guangan,Guangyuan,Leshan,Luzhou,Meishan,Mianyang,Nanchong,Neijiang,Suining,Yaan,Yibin,Ziyang and Zigong,in 2019.A gridded traffic flow data were induced as proxy data to produce emission data with high spatial resolution by allocating the results from COPERT to the grids.The characteristics of the on-road vehicle emissions in the study area were analyzed.The impact of COVID-19 on the emissions was evaluated by comparing the difference between the emissions in February of 2019 and 2020,and the primary causes were studied.The main findings of this study were listed below.(1)In 2019,the on-road vehicle emissions of NO_X,CO,NH₃,SO₂,PM_2.5,PM₁₀ and VOCs in the basin area of Sichuan province were 207342.2,286075.1,3773.0,447.3,9933.3,14179.6 and 54978.1 tons,respectively.(2)The emissions from passenger cars accounted for the highest proportion of the NH₃,CO,VOCs and SO₂ emissions,while the emissions from heavy-duty trucks accounted for the highest proportion of the NO_X and PM_2.5 emissions.In addition,the heavy-duty vehicles contributed the second largest part of CO,SO₂,VOCs and NH₃ emissions.Passenger cars and light commercial trucks were the secondary important sources of PM_2.5 emissions.And the contribution of heavy-duty trucks and motorcycles to CO and VOCs emissions were also important.(3)From the perspective of spatial distribution,the proportions of PM₁₀ emissions from heavy-duty trucks and passenger cars in Bazhong,Meishan,Nanchong,Suining,Yibin,Mianyang,Neijiang and Ziyang were similar,ranging from 45.6%to 55.1%and 30.1%to59.1%,respectively.In all cities,NO_X emissions were mainly contributed by heavy-duty trucks,accounting for 65.3%to 79.3%.The contribution of light commercial trucks and medium-duty trucks was relatively low in all cities.And the contribution of medium-duty trucks was the lowest in all cities.For most cities,motorcycles and heavy-duty trucks were the main contributors of VOCs emissions,accounting for up to 38.5%and 29.7%,respectively.(4)As to the detailed characteristics of high-resolution distribution,taking NO_X as an example,the high emission areas were distributed in the central areas of cities and along inter-city highways.(5)Due to the COVID-19,the on-road vehicle emissions in the study area decreased in February 2020,compared to those in February 2019.The emissions of PM_2.5,PM₁₀,CO,NH₃,NO_X,SO₂ and VOCs decreased by 169.6,242.0,4883.0,64.4,3539.1,7.6 and 938.4 tons,respectively.Among all the cities in the study area,Chengdu exhibited the most year-over-year changes,followed by Nanchong.And the changes were the smallest in Bazhong.(6)Taking NO_X as an example as well,the year-over-year reduction of emissions were higher in the areas further away from the city center.In some urban areas,NOx emissions showed an increasing trend,which was most significant in Chengdu.This might be caused by the low frequency of the usage of public transportation due to the impact of COVID-19.(7)There was a strong correlation between the traffic flow index and the number of employees and GRP(Gross regional product)in February 2019.And a strong correlation between the year-over-year changes of traffic flow index and the number of employees in February 2020 was also observed.The number of employees is directly related to the transportation demand,which caused the strong correlation.Similarly,GRP represented the activity level of economic actions and could explain the strong correlation to emissions and changes of emissions.In practical application,the research results of this paper have a certain reference value for regional On-road vehicle emission reduction,air pollution warning and motor vehicle operation management.