| The improvement of ambient air quality strongly depends on emission control measures.Chinese government has formulated and issued a series of air pollutant emission control measures(hereinafter referred to as“control measures”).These control measures can be roughly divided into three categories:the first is aimed at industries with large pollutant emissions,the second is long-term control measures,and the third is emergency control measures.Industries with large pollutant emissions mainly include thermal power,steel,petrochemical,cement,nonferrous metals and chemical industry.Long-term control measures are not limited to a certain industry,but comprehensive and systematic overall emission reduction control.In addition,the emergency control measures taken to ensure the air quality during major political and economic activities or to deal with emergencies will directly or indirectly have a certain impact on the air quality.The impact of control measures on air quality is concerned by scholars and policy makers.The traditional method of studying the impact of control measures on air quality is“bottom-up”.The basic idea is to adjusting the anthropogenic emission inventory(hereinafter referred to as“inventory”)according to control measures,get the air quality simulation results after adjusting the inventory,and then compare with the simulation results without adjusting the inventory,so as to obtain the impact of control measures on air quality.The“bottom-up”method has very clear control path,so it is widely used.However,it is undeniable that there are some uncertainties in this method,especially in the study of emergency control measures Therefore,it is very urgent and necessary to construct a more accurate evaluation method for the impact of control measures on air quality.In the first part of this paper,the“bottom-up”method is used to further demonstrate the impact of the adopted and proposed control measures of thermal power plants in Beijing-Tianjin-Hebei region on the air quality in this region and the contribution of thermal power plants to air pollution in Beijing.The results show:1)the two-way coupled WRF-CMAQ model can capture the temporal and spatial evolution of PM2.5 in Beijing-Tianjin-Hebei region(NMB=19.6-26.6%);2)the implemented control measures can reduce the annual average concentration of PM2.5,PM10,NO2 and SO2 in Beijing by 5.3-6.3%;3)the proposed control measures can further improve the air quality in Beijing in winter,and reduce the concentration of PM2.5,PM10,NO2 and SO2 in Beijing in winter by 8.6-14.8%;4)the contribution of thermal power plants in Beijing-Tianjin-Hebei region to air pollutants in Beijing in winter are 23.8%(PM2.5),24.0%(PM10),23.0%(NO2),23.1%(SO2)and 37.6%(CO),respectively;5)the response of air quality in other cities in Beijing-Tianjin-Hebei region to these control measures is similar;6)there is a high correlation between the spatial distribution of PM2.5 concentration reduction and the spatial distribution of thermal power plants in this area.This part of the study demonstrates that the“bottom-up”method is very interpretable in studying the impact of control measures on air quality.In the second part of this paper,the evaluation method of the impact of“top-down”control measures on air quality is preliminarily constructed and evaluated.The method mainly includes two kinds,one for long-term control measures and the other for emergency control measures.The basic idea of this method is to obtain the change of air quality under joint action of various factors,then eliminate the influence of meteorological factors,model uncertainty and other factors on the results,and finally obtain the influence of control measures on air quality.The core problem of this method is to assimilate the model results with monitoring data to obtain accurate simulation results.In this study,the change of PM2.5 concentration in the Yangtze River Delta in winter of 2019 compared with that in winter of 2016 was used to construct an evaluation method for log-term control measures.It is found that:1)the concentration of PM2.5 in the Yangtze River Delta in winter of 2019 is significantly lower than that in winter of2016,especially in large cities in the Yangtze River Delta,such as Shanghai(?29%),Hangzhou(?26%),Nanjing(?29%)and Hefei(?22%);2)meteorological factors aggravate PM2.5 pollution in Yangtze River Delta(about 12?g/m3,15%);3)there is a high correlation between the spatial distribution of PM2.5 concentration reduction and the spatial distribution of pollutant emission in this area.In addition,this study uses the emergency control measures adopted at the G20 Summit in Hangzhou in 2016 to construct an evaluation method for emergency control measures.The study found that:1)during the G20 Summit in Hangzhou in 2016,the concentration of PM2.5 in Hangzhou,where the G20 Summit was held,decreased most significantly,exceeding50%;2)due to the implementation of emergency control measures,implementation efficiency and local emissions,the PM2.5 concentration in other cities in the Yangtze River Delta region is not as low as that in Hangzhou,such as Shanghai(?26%),Nanjing(?33%)and Hefei(?24%);3)using the ratio of PM2.5 concentration reduction under emergency control measures and long-term control measures in Hangzhou,the emission reduction potential of PM2.5 in other cities in the Yangtze River Delta region is derived.In the third part of this paper,the evaluation method constructed in the second part is optimized,and the optimized evaluation method is used to study the impact of epidemic control measures on air quality in early 2020.On this basis,the impacts of the comprehensive promotion of electric vehicles on the air quality(NO2 and PM2.5)in cities at prefecture level and above in China was predicted.In order to curb the spread of COVID-19,the Chinese government has implemented a comprehensive lockdown measures.One of the main measures of the comprehensive lockdown was traffic restriction.This is an unprecedented geoengineering experiment for vehicle emission controls.It was found that there was a linear relationship between traffic flow and surface concentrations of NO2 and PM2.5(correlation coefficient=0.491-0.626).Using the estimated linear relationship to build a model,it can be inferred that the comprehensive promotion of electric vehicles can reduce PM2.5 concentrations by 30-70%in the most areas of Central and Southeast China.Due to the existence of the industrial emissions around Beijing and Tianjin,their PM2.5 concentrations decreased slightly(10-20%).Meanwhile,the potential declines of NO2 in the different regions were 40-90%.This result provides a direction for the formulation of China's atmospheric environment improvement policy,and provides favorable evidence for the transition to renewable energy. |
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