Modeling Analysis Of Spatiotemporal Variation And Source Of Atmospheric Particle Number Concentration In Typical Cities Of China

Particle number concentration（PNC）is an important parameter for evaluating the environment,climate,and health effects of particulate matter.A good understanding of PNC is essential to control atmospheric ultrafine particles and protect public health.It is difficult to reflect the spatial distribution and long-term change trend of PNC through the short-term observation study which is limited to a single point.At present,China urgently needs model methods to study the spatiotemporal variations and sources of PNC.In this paper,the binary nucleation mechanism is added to the UCD/CIT air quality model.The PNC of Beijing and Shanghai in 2013 and 2017（January,April,July,and October respectively represent winter,spring,summer,and autumn）and their spatiotemporal variations and source contributions were simulated and analyzed by using binary and ternary nucleation mechanisms respectively.The changes and driving factors of PNC in response to the implementation of the Clean Air Action Plan（CAA）were further analyzed.The main findings are as follows:（1）We evaluated the simulation results of PNC and analyzed the spatiotemporal distribution.The average value simulated by the UCD/CIT model of the binary and ternary nucleation mechanism can better simulate PNC.Except for January（Beijing and Shanghai）and July（Beijing）,the model reproduces more than 50%of the hourly PNC within a factor of 2 in other months and basically all of the hourly PNC within a factor of 10.The average is obviously overestimated for January（Beijing NMB=2.34,Shanghai NMB=1.88）and October（Beijing NMB=1.05,Shanghai NMB=0.95）,while the simulation results for April（Beijing NMB=0.02,Shanghai NMB=0.36）and July（Beijing NMB=-0.43,Shanghai NMB=-0.03）are relatively good.The overestimation of Beijing in January is due to the overestimation of particle number emissions from residential sources,while the overestimation of Shanghai in January is due to too much nucleation contribution,and the overestimation of NH₃ emissions affecting nucleation.The total PNC（Ntot）had significantly higher values in southeastern Beijing/southwestern Shanghai than in northwestern Beijing/northeastern Shanghai.It can be seen that the simulation of nucleation needs to be improved as the nucleation mode PNC（Nnuc）is greatly underestimated（NMB:-0.05～-0.98）and overestimated（NMB:0.56～44.72）.The higher value of aitken mode and accumulation mode PNC（Nait+Nacc）in Beijing appears in winter(regional average in January:16575 cm^-3),and the lower value appears in summer(July:4388 cm^-3);The higher value in Shanghai appears in spring(regional average in April:7255 cm^-3),and the lower value appears in autumn(October:3984 cm^-3).Nnuc has an opposite spatial pattern to that in Nait and Nacc.It stems from the distribution characteristics of anthropogenic pollution in Beijing and Shanghai.Nnuc is higher in relatively clean regions and lower in polluted regions.（2）The source characteristics of PNC in different modes were analyzed.In 2017,the residential source is the largest source of Ntot in Beijing,contributing more than 32%in each season（except for 27%in April）.This is inconsistent with the conclusion that traffic sources make the largest contribution in the observation study,which is mainly due to the overestimation of non-combustion sources in residential sources.But the largest source of Ntot in Shanghai is nucleation,which contributes more than 36%（except for 16%in July）.For nucleation mode（Nnuc）with the smallest particle size,nucleation is the most important source of Beijing and Shanghai,contributing more than 90%（except for 41%in July of Beijing.It is because there are other nucleation mechanisms such as organic nucleation in summer of Beijing,which leads to underestimation of nucleation in the model）.For aitken mode（Nait）,the largest source of Beijing is residential,which contributes more than 42%.Shanghai’s largest source in January（38%）and April（66%）came from nucleation,July from traffic（29%）,and October from residential（36%）and traffic（35%）.For accumulation mode（Nacc）,the largest source in Beijing and Shanghai is the industry,which contributes more than 33%（except for 24%from Beijing in January,and the source of traffic in Shanghai also contributes 34%in January）.The spatial distributions of PNC from different sources are related to their emissions.Except that the spatial distribution of power plants and industrial sectors is the same in each month,there are certain differences in the spatial distribution and absolute values of different sources in different seasons.For residential and traffic,higher PNC values appear in January and October,while lower values appear in April and July.The spatial distribution of PNC generated by the simulation of nucleation in Beijing and Shanghai in January>April>October>July.However,the Nnuc observed by Beijing monitoring stations in July>April>January>October（mainly because new particle formation events are frequent in spring and summer）.The overestimation of Nnuc in January and the underestimation of Nnuc in July lead to the model can’t well reflecting the seasonal characteristics of PNC generated by nucleation.（3）The PNC change characteristics from 2013 to 2017 were analyzed,and the contributions of meteorology/emissions to PNC change were quantitatively assessed.Observational data show that the implementation of CAA has resulted in a decrease in PNC in Beijing and Shanghai.The UCD/CIT model can basically simulate the variation of PNC.The simulated annual mean values of Ntot,Nnuc,Nait,and Nacc are all decreasing.The reduction of emissions in Beijing and Shanghai has a larger contribution to the reduction of PNC,contributing 42%-91%in each mode.Meteorology contributes less（0-20%）,and in Nnuc,meteorology is a negative contribution,that is,the meteorology contributed to the increase of PNC of nucleation mode(Beijing increase 1353 cm^-3,Shanghai increase 173 cm^-3).For the emission of different pollutants,in Beijing and Shanghai,SO₂ has the greatest impact on both Ntot and Nnuc.SO₂ emissions decreased the most after the implementation of CAA,resulting in a decrease of PNC from nucleation.Therefore,Ntot and Nnuc decrease significantly.The decrease of NO_X and NH₃ lead to the increase of Ntot,especially the increase of Nnuc.For the emission of different pollution sources,residential sources in Beijing/Shanghai have the largest reduction,with the contribution to the reduction of PNC accounting for 39.3%/43.8%,followed by nucleation with 32.2%/39.9%,followed by industry and traffic with 22.7%/5.1%and3.9%/10.3%respectively.The PNC from power plants is relatively low,but it increases a little in Shanghai in 2017.In Beijing,the intensity of high number concentration pollution day decreased(the observed average value of PNC decreased from 24027 cm^-3 to 18119 cm^-3,and the simulated average value decreased from 50493 cm^-3 to 16569 cm^-3)while the frequency of its occurrence increased（observation from 15.5%to 27.1%,simulation from 4.7%to 15.9%）.Model results indicate that the number of high number concentration pollution days due to nucleation increased from 5 in 2013 to 7 in 2017.This is consistent with the conclusions of some current observational studies.Although total PNC is trending downward due to the implementation of CAA,new particle formation events may occur more frequently as a result of emission controls reducing condensation sinks.