# Simulating Aerosol Size Distribution and Mass Concentration with Simultaneous Nucleation, Condensation/Coagulation, and Deposition with the GRAPES–CUACE

• Corresponding author: Chunhong ZHOU, zhouch@cma.gov.cn
• Funds:

Supported by the National Key Project of the Ministry of Science and Technology of China (2016YFC0203306), National Natural Science Foundation of China (91544232), National Science and Technology Support Program of China (2014BAC16B03), and China Meteorological Administration Innovation Team Fund for Haze–Fog Monitoring and Forecasts

• doi: 10.1007/s13351-018-7116-8
• A coupled aerosol–cloud model is essential for investigating the formation of haze and fog and the interaction of aerosols with clouds and precipitation. One of the key tasks of such a model is to produce correct mass and number size distributions of aerosols. In this paper, a parameterization scheme for aerosol size distribution in initial emission, which took into account the measured mass and number size distributions of aerosols, was developed in the GRAPES–CUACE [Global/Regional Assimilation and PrEdiction System–China Meteorological Administration (CMA) UnifiedAtmospheric Chemistry Environment model]—an online chemical weather forecast system that contains microphysical processes and emission, transport, and chemical conversion of sectional multi-component aerosols. In addition, the competitive mechanism between nucleation and condensation for secondary aerosol formation was improved, and the dry deposition was also modified to be in consistent with the real depositing length. Based on the above improvements, the GRAPES–CUACE simulations were verified against observational data during 1–31 January 2013, when a series of heavy regional haze–fog events occurred in eastern China. The results show that the aerosol number size distribution from the improved experiment was much closer to the observation, whereas in the old experiment the number concentration was higher in the nucleation mode and lower in the accumulation mode. Meanwhile, the errors in aerosol number size distribution as diagnosed by its sectional mass size distribution were also reduced. Moreover, simulations of organic carbon, sulfate, and other aerosol components were improved and the overestimation as well as underestimation of PM2.5 concentration in eastern China was significantly reduced, leading to increased correlation coefficient between simulated and observed PM2.5 by more than 70%. In the remote areas where bad simulation results were produced previously, the correlation coefficient grew from 0.35 to 0.61, and the mean mass concentration went up from 43% to 87.5% of the observed value. Thus, the simulation of particulate matters in these areas has been improved considerably.
• Fig. 1.  Variations of nucleation rate and condensation rate according to sulfate production rate. The curve with hollow circles is for nucleation rate, the curve with triangles is for condensation rate, and the curve with solid circles is for gas sulfate concentration.

Fig. 2.  Number size distributions of aerosols at (a) Beijing and (b) Lin’an. The black solid line denotes observation, the blue dashed one denotes experiment OLD, and the red dotted one denotes experiment NEW.

Fig. 3.  Number concentration ratio between that diagnosed from mass size distribution and the observed. The dots are the ratios of each sample in each bin, the blue solid line is for the average ratio, and the red line is the baseline.

Fig. 4.  Time series of hourly OC concentration at (a) Beijing and (b) Lin’an. The blue, red, and green solid lines are for observation (Obs.), the NEW experiment, and the OLD experiment, respectively. The data are plotted at 3-h intervals.

Fig. 5.  Aerosol area size distributions at (a) Beijing and (b) Lin’an with the aerosol mass concentration of 100 μg m–3. The red solid line is for the NEW experiment and the blue dashed one is for the OLD experiment.

Fig. 6.  Scatter plots for the simulated and observed PM2.5 concentration (μg m–3) for (a, b) whole China, (c, d) eastern China, and (e, f) western (remote areas of) China. Obs. is for observation and model is for simulation. Left (right) panels are for the OLD (NEW) experiment.

•  [1] Adams, P. J., and J. H. Seinfeld, 2002: Predicting global aerosol size distributions in general circulation models. J. Geophys. Res., 107, 4370.. [2] Andreae, M. O., and D. Rosenfeld, 2008: Aerosol–cloud–precipitation interactions. Part 1. The nature and sources of cloud-active aerosols. Earth-Sci. Rev., 89, 13–41.. [3] Berner, A., S. Sidla, Z. Galambos, et al., 1996: Modal character of atmospheric black carbon size distributions. J. Geophys. Res., 101, 19,559–19,565.. [4] Deshler, T., M. E. Hervig, D. J. Hofmann, et al., 2003: Thirty years of in situ stratospheric aerosol size distribution measurements from Laramie, Wyoming (41°N), using balloon-borne instruments. J. Geophys. Res., 108, 4167.. [5] Dusek, U., G. P. Frank, L. Hildebrandt, et al., 2006: Size matters more than chemistry for cloud-nucleating ability of aerosol particles. Science, 312, 1375–1378.. [6] Fuchs, N. A., and C. N. Davies, 1964: Aerosol mechanics. (Book Reviews: The Mechanics of Aerosols). Science, 146, 1033–1034.. [7] Fuchs, N. A., and A. G. Sutugin, 1971: High-dispersed aerosols. Topics in Current Aerosol Research, G. M. Hidy, and J. R. Brock, Eds., New York, Elsevier, 38–47. [8] Gao, J., J. Wang, S.-H. Cheng, et al., 2007: Number concentration and size distributions of submicron particles in Jinan urban area: Characteristics in summer and winter. J. Environ. Sci., 19, 1466–1473.. [9] Gelbard, F., and J. H. Seinfeld, 1980: Simulation of multicomponent aerosol dynamics. J. Colloid Interface Sci., 78, 485–501.. [10] Gong, S. L., L. A. Barrie, and J.-P. Blanchet, 1997: Modeling sea-salt aerosols in the atmosphere: 1. Model development. J. Geophys. Res., 102, 3805–3818.. [11] Gong, S. L., L. A. Barrie, J.-P. Blanchet, et al., 2003: Canadian aerosol module: A size-segregated simulation of atmospheric aerosol processes for climate and air quality models. 1. Module development. J. Geophys. Res., 108, 4007.. [12] Han, X., M. Zhang, J. Gao, et al., 2014: Modeling analysis of the seasonal characteristics of haze formation in Beijing. Atmos. Chem. Phys., 14, 10231–10248.. [13] Huang, R. J., Y. Zhang, C. Bozzetti, et al., 2014: High secondary aerosol contribution to particulate pollution during haze events in China. Nature, 514, 218–222.. [14] Jacobson, M. Z., 1997: Numerical techniques to solve condensational and dissolutional growth equations when growth is coupled to reversible reactions. Aerosol Sci. Technol., 27, 491–498.. [15] Jacobson, M. Z., and R. P. Turco, 1995: Simulating condensatio-nal growth, evaporation, and coagulation of aerosols using a combined moving and stationary size grid. Aerosol Sci. Technol., 22, 73–92.. [16] Jacobson, M. Z., R. P. Turco, E. J. Jensen, et al., 1994: Modeling coagulation among particles of different composition and size. Atmos. Environ., 28, 1327–1338.. [17] Kang, H. Q., B. Zhu, and S. G. Fan, 2009: Size distributions and wet scavening properties of winter aerosol particles in north suburb of Nanjing. Climatic Environ. Res., 14, 523–530. . (in Chinese) [18] Ku, B. K., and D. E. Evans, 2012: Investigation of aerosol surface area estimation from number and mass concentration measurements: Particle density effect. Aerosol Sci. Technol., 46, 473–484.. [19] Kulmala, M., A. Laaksonen, L. Pirjola, 1998: Parameterizations for sulfuric acid/water nucleation rates. J. Geophys. Res., 103, 8301–8307.. [20] Kulmala, M., H. Vehkamäki, T. Petäjä, et al., 2004: Formation and growth rates of ultrafine atmospheric particles: A review of observations. J. Aerosol Sci., 35, 143–176.. [21] Lang, F. L., W. Q. Yan, Q. Zhang, et al., 2013: Size distribution of atmospheric particle number in Beijing and association with meteorological conditions. China Environ. Sci., 33, 1153–1159. (in Chinese) [22] Li, J. X., Y. Yin, P. R. Li, et al., 2015: Aircraft measurements of the vertical distribution and activation property of aerosol particles over the Loess Plateau in China. Atmos. Res., 155, 73–86.. [23] Liu, Z. R., B. Hu, Q. Liu, et al., 2014: Source apportionment of urban fine particle number concentration during summertime in Beijing. Atmos. Environ., 96, 359–369.. [24] Liu, Z. R., Y. S. Wang, B. Hu, et al., 2016: Source appointment of fine particle number and volume concentration during severe haze pollution in Beijing in January 2013. Environ. Sci. Pollut. Res., 23, 6845–6860.. [25] Meng, Z. Y., D. Dabdub, and J. H. Seinfeld, 1998: Size-resolved and chemically resolved model of atmospheric aerosol dynamics. J. Geophys. Res., 103, 3419–3435.. [26] Nenes, A., S. N. Pandis, and C. Pilinis, 1998: ISORROPIA: A new thermodynamic equilibrium model for multiphase multicomponent inorganic aerosols. Aquat. Geochem., 4, 123–152.. [27] Park, R. J., and S.-W. Kim, 2014: Air quality modeling in East Asia: Present issues and future directions. Asia-Pac. J. Atmos. Sci., 50, 105–120.. [28] Peng, J. F., M. Hu, Z. B. Wang, et al., 2014: Submicron aerosols at thirteen diversified sites in China: Size distribution, new particle formation and corresponding contribution to cloud condensation nuclei production. Atmos. Chem. Phys., 14, 10249–10265.. [29] Seigneur, C., A. B. Hudischewskyj, J. H. Seinfeld, et al., 1986: Simulation of aerosol dynamics: A comparative review of mathematical models. Aerosol Sci. Technol., 5, 205–222.. [30] Shampine, L. F., and C. W. Gear, 1979: A user’s view of solving stiff ordinary differential equations. SIAM Rev., 21, 1–17. doi: 10.1137/1021001. [31] Shen, X. J., J. Y. Sun, Y. M. Zhang, et al., 2011: First long-term study of particle number size distributions and new particle formation events of regional aerosol in the North China Plain. Atmos. Chem. Phys., 11, 1565–1580.. [32] Shen, X. J., J. Y. Sun, X. Y. Zhang, et al., 2015: Characterization of submicron aerosols and effect on visibility during a severe haze–fog episode in Yangtze River delta, China. Atmos. Environ., 120, 307–316.. [33] Slinn, W. G. N., 1982: Predictions for particle deposition to vegetative canopies. Atmos. Environ., 16, 1785–1794.. [34] Slinn, W. G. N., 1984: Precipitation scavenging. Atmospheric Science and Power Production. D. Randerson, Ed., Office of Scientific and Technical Information, Ork Ridge, USA, 466–532. [35] Stockwell, W. R., P. Middleton, J. S. Chang, et al., 1990: The second generation regional acid deposition model chemical mechanism for regional air quality modeling. J. Geophys. Res., 95, 16343–16376.. [36] Tsang, T. H., and A. Rao, 1988: Comparison of different numeri-cal schemes for condensational growth of aerosols. Aerosol Sci. Technol., 9, 271–277.. [37] Venzac, H., K. Sellegri, P. Villani, et al., 2009: Seasonal variation of aerosol size distributions in the free troposphere and residual layer at the puy de Dôme station, France. Atmos. Chem. Phys., 9, 1465–1478.. [38] Wang, H., X. Y. Zhang, S. L. Gong, et al., 2010: Radiative feedback of dust aerosols on the East Asian dust storms. J. Geophys. Res., 115, D23214.. [39] Wang, H. L., B. Zhu, L. J. Shen, et al., 2014: Number size distribution of aerosols at Mt. Huang and Nanjing in the Yangtze River Delta, China: Effects of air masses and characteristics of new particle formation. Atmos. Res., 150, 42–56.. [40] Wang, Y. S., L. Yao, L. L. Wang, et al., 2014: Mechanism for the formation of the January 2013 heavy haze pollution episode over central and eastern China. Sci. China Earth Sci., 57, 14–25.. [41] Wang, Y. Q., X. Y. Zhang, J. Y. Sun, et al., 2015: Spatial and temporal variations of the concentrations of PM10, PM2.5 and PM1 in China. Atmos. Chem. Phys., 15, 13585–13598.. [42] Warren, D. R., and J. H. Seinfeld, 1985: Simulation of aerosol size distribution evolution in systems with simultaneous nucleation, condensation, and coagulation. Aerosol Sci. Technol., 4, 31–43.. [43] Willeke, K., and K. T. Whitby, 1975: Atmospheric aerosols: Size distribution interpretation. J. Air Pollut. Contr. Assoc., 25, 529–534.. [44] Wu, C. Y., and P. Biswas, 1998: Study of numerical diffusion in a discrete-sectional model and its application to aerosol dynamics simulation. Aerosol Sci. Technol., 29, 359–378.. [45] Wu, Z. J., M. Hu, P. Lin, et al., 2008: Particle number size distribution in the urban atmosphere of Beijing, China. Atmos. En-viron., 42, 7967–7980.. [46] Xu, H. H., Y. S. Wang, T. X. Wen, et al., 2007: Size distributions and vertical distributions of metal elements of atmospheric aerosol in Beijing. Environ. Sci., 26, 675–679.. [47] Xu, P. J., W. X. Wang, L. X. Yang, et al., 2011: Aerosol size distributions in urban Jinan: Seasonal characteristics and variations between weekdays and weekends in a heavily polluted atmosphere. Environ. Monit. Assess., 179, 443–456.. [48] Yue, D. L., M. Hu, Z. B. Wang, et al., 2013: Comparison of particle number size distributions and new particle formation between the urban and rural sites in the PRD Region, China. Atmos. Environ., 76, 181–188.. [49] Zhang, L. M., S. L. Gong, J. Padro, et al., 2001: A size-segregated particle dry deposition scheme for an atmospheric aerosol module. Atmos. Environ., 35, 549–560.. [50] Zhang, X. Y., Y. Q. Wang, X. C. Zhang, et al., 2008: Carbonaceous aerosol composition over various regions of China during 2006. J. Geophys. Res., 113, D14111.. [51] Zhang, X. Y., Y. Q. Wang, T. Niu, et al., 2012: Atmospheric aerosol compositions in China: Spatial/temporal variability, chemical signature, regional haze distribution and comparisons with global aerosols. Atmos. Chem. Phys, 12, 779–799.. [52] Zhang, Y., C. Seigneur, J. H. Seinfeld, et al., 1999: Simulation of aerosol dynamics: A comparative review of algorithms used in air quality models. Aerosol Sci. Technol., 31, 487–514.. [53] Zhang, Y., R. C. Easter, S. J. Ghan, et al., 2002: Impact of aerosol size representation on modeling aerosol–cloud interactions. J. Geophys. Res., 107, 4558.. [54] Zhang, Y. M., X. Y. Zhang, J. Y. Sun, et al., 2014: Chemical composition and mass size distribution of PM1 at an elevated site in central East China. Atmos. Chem. Phys., 14, 12237–12249.. [55] Zhou, C. H., S. L. Gong, X. Y. Zhang, et al., 2012: Towards the improvements of simulating the chemical and optical properties of Chinese aerosols using an online coupled model—CUACE/Aero. Tellus B, 64, 18965.. [56] Zhou, C. H., X. Zhang, S. Gong, et al., 2016: Improving aerosol interaction with clouds and precipitation in a regional chemi-cal weather modeling system. Atmos. Chem. Phys., 16, 145–160.. [57] Zhong, J. T., X. Y. Zhang, Y. Q. Wang, et al., 2017: Relative contributions of boundary-layer meteorological factors to the explosive growth of PM2.5 during the red-alert heavy pollution episodes in Beijing in December 2016. J. Meteor. Res., 31, 809–819..
###### 通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

## Simulating Aerosol Size Distribution and Mass Concentration with Simultaneous Nucleation, Condensation/Coagulation, and Deposition with the GRAPES–CUACE

###### Corresponding author: Chunhong ZHOU, zhouch@cma.gov.cn;
• 1. State Key Laboratory of Severe Weather/Key Laboratory of Atmospheric Chemistry of China Meteorological Administration, Chinese Academy of Meteorological Sciences, Beijing 100081
• 2. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
• 3. Jiangsu Collaborative Innovation Center of Climate Change, Nanjing 210093
Funds: Supported by the National Key Project of the Ministry of Science and Technology of China (2016YFC0203306), National Natural Science Foundation of China (91544232), National Science and Technology Support Program of China (2014BAC16B03), and China Meteorological Administration Innovation Team Fund for Haze–Fog Monitoring and Forecasts

Abstract: A coupled aerosol–cloud model is essential for investigating the formation of haze and fog and the interaction of aerosols with clouds and precipitation. One of the key tasks of such a model is to produce correct mass and number size distributions of aerosols. In this paper, a parameterization scheme for aerosol size distribution in initial emission, which took into account the measured mass and number size distributions of aerosols, was developed in the GRAPES–CUACE [Global/Regional Assimilation and PrEdiction System–China Meteorological Administration (CMA) UnifiedAtmospheric Chemistry Environment model]—an online chemical weather forecast system that contains microphysical processes and emission, transport, and chemical conversion of sectional multi-component aerosols. In addition, the competitive mechanism between nucleation and condensation for secondary aerosol formation was improved, and the dry deposition was also modified to be in consistent with the real depositing length. Based on the above improvements, the GRAPES–CUACE simulations were verified against observational data during 1–31 January 2013, when a series of heavy regional haze–fog events occurred in eastern China. The results show that the aerosol number size distribution from the improved experiment was much closer to the observation, whereas in the old experiment the number concentration was higher in the nucleation mode and lower in the accumulation mode. Meanwhile, the errors in aerosol number size distribution as diagnosed by its sectional mass size distribution were also reduced. Moreover, simulations of organic carbon, sulfate, and other aerosol components were improved and the overestimation as well as underestimation of PM2.5 concentration in eastern China was significantly reduced, leading to increased correlation coefficient between simulated and observed PM2.5 by more than 70%. In the remote areas where bad simulation results were produced previously, the correlation coefficient grew from 0.35 to 0.61, and the mean mass concentration went up from 43% to 87.5% of the observed value. Thus, the simulation of particulate matters in these areas has been improved considerably.

Reference (57)

/