Aerosol microphysical processes are essential for the next generation of global and regional climate and air quality models to determine particle size distribution. The contribution of organic aerosols (OAs) to particle formation, mass, and number concentration is one of the major uncertainties in current models. A new global–regional nested aerosol model was developed to simulate detailed microphysical processes. The model combines an advanced particle microphysics (APM) module and a volatility basis set (VBS) OA module to calculate the kinetic condensation of low-volatility organic compounds and equilibrium partitioning of semi-volatile organic compounds in a 3-D framework using global–regional nested domain. In addition to the condensation of sulfuric acid, the equilibrium partitioning of nitrate and ammonium, and the coagulation process of particles, the microphysical processes of the OAs are realistically represented in our new model. The model uses high-resolution size bins to calculate the size distribution of new particles formed through nucleation and subsequent growth. The multi-scale nesting enables the model to perform high-resolution simulations of the particle formation processes in the urban atmosphere in the background of regional and global environments. By using the nested domains, the model reasonably reproduced the OA components obtained from the analysis of aerosol mass spectrometry measurements through positive matrix factorization and the particle number size distribution in the megacity of Beijing during a period of approximately a month. Anthropogenic organic species accounted for 67 % of the OAs of secondary particles formed by nucleation and subsequent growth, which is considerably larger than that of biogenic OAs. On the global scale, the model well predicted the particle number concentration in various environments. The microphysical module combined with the VBS simulated the universal distribution of organic components among the different aerosol populations. The model results strongly suggest the importance of anthropogenic organic species in aerosol particle formation and growth at polluted urban sites and over the whole globe.
The increased concentrations of atmospheric aerosol particles caused by anthropogenic activities have become an important scientific issue due to their substantial climate forcing and health effects (Twomey, 1977; Albrecht, 1989; Charlson et al., 1992; Donaldson et al., 2002; Tsigaridis et al., 2006; IPCC, 2013) on global and regional scales. These effects depend on aerosol size, composition, and mixing state. The direct influence of aerosols on climate is their scattering of solar radiations largely determined by the aforementioned key properties of aerosols (IPCC, 2013). The indirect effects of aerosols involve their ability to affect cloud microphysical properties and precipitation processes by serving as cloud condensation nuclei (CCN), which are highly dependent on CCN number concentrations (Dusek et al., 2006). Ultrafine particles, despite having a lower mass concentration, have a larger health effect because of their ability to easily penetrate the body and their higher number concentrations (Delfino et al., 2005; Kumar et al., 2014). Therefore, it is crucial to gain deep insight into the life cycle of aerosol particles and quantify their sources not only in mass concentration but also in their number concentration.
There are two sources of atmospheric aerosols: direct emissions from primary sources and secondary formation processes (Seinfeld and Pandis, 2006). Mineral dust particles over desert regions and sea salt particles over oceans are the two major natural sources contributing to particle mass and number concentration regionally (Textor et al., 2006). Anthropogenic activities (e.g., fossil fuel combustion and biomass burning) can directly emit particles and they have been the most significant contributors to the aerosols since the industrial revolution (IPCC, 2013). The physical and chemical properties of these primarily emitted particles can be modified by condensation, coagulation, and chemical reactions in the atmosphere (Seinfeld and Pandis, 2006). In addition, new particle formation (NPF) has been reported to be an important contributor to aerosol particles in various global environments (Holmes, 2007; Yu et al., 2008; Yu and Luo, 2009; Kulmala et al., 2013). Field observation studies have also demonstrated that NPF can significantly increase CCN number concentrations (Kuang et al., 2009; Wiedensohler et al., 2009; Yue et al., 2011). Thus, it is necessary to reasonably represent primary emissions, their microphysical aging, and the new particle formation process in 3-D models.
During the past two decades, numerous models have incorporated a microphysical module to describe particle formation processes (e.g., Binkowski and Shankar, 1995; Jacobson, 1997; Stier et al., 2005; Bergman et al., 2012). However, large uncertainties still exist due to the unclear complication of processes and the relevant mechanisms. Intercomparison and evaluation of global aerosol models indicate that the constraint of size-resolved primary emissions and improved understanding of secondary formations are required to improve the ability of the model to simulate particle number size distribution (PNSD) (Mann et al., 2014). Spracklen et al. (2005) found that the size distribution assumption has a large impact on particle number concentrations in the boundary layer. A comparison between the simulations and single particle soot photometer measurements suggested that the model that was employed had a large bias in simulating the number size distribution of black carbon particles (Reddington et al., 2013). Considerable improvements in the simulation of the particle number concentration and aerosol optical properties were achieved by using an optimized size distribution of primary particles in a polluted atmosphere over areas with large emissions (Zhou et al., 2012, 2018). However, much work remains to reduce the uncertainty associated with primary emissions, especially over primary particles-dominated regions (e.g., China) in terms of particle number concentration.
The main source of uncertainty in simulating NPF at regional and global scales can be attributed to the fact that the nucleation mechanism and particle growth rates are unexplained. Although sulfuric acid has been identified as a major component and plays a central role in nucleation (Yu and Turco, 2001; Boy et al., 2005; Kirkby et al., 2011), it could not alone explain the NPF rates (Wang et al., 2013; Kulmala et al., 2013). Recent studies have revealed that certain organic vapors are involved in particle nucleation (Metzger et al., 2010; Zhang et al., 2012; Yao et al., 2018) and contribute considerably to particle growth (Kulmala and Kerminen, 2008; Tröstl et al., 2016). Thus, there is no doubt that a reasonable representation of organic aerosols (OAs) is crucial for aerosol models to realistically simulate NPF and particle growth. However, it is still an open question as to which organic species are possibly involved in new particle formation process. Even the chemical composition and the sources of OAs are still uncertain because they contain a large number of compounds (Goldstein and Galbally, 2007). To date, OAs are still the least understood of the components of aerosols (Kanakidou et al., 2005; Hallquist et al., 2009). Clearly, OA representation is the major uncertainty contributing to the large knowledge gap in elucidating particle formation processes.
In recent years, much progress has been made in simulating the formation of OAs and secondary organic aerosols (SOAs). The two product (2P) model recommended by Odum et al. (1996) has been widely used in 3-D models to describe the SOA formation process empirically. The volatility basis set (VBS) approach was recently developed to represent the oxidation of primary OAs (POAs) and SOAs and the partitioning of OAs in different volatilities between the gas phase and aerosol phase (Donahue et al., 2006). Many regional models have used VBS to simulate OAs and SOAs (Shrivastava et al., 2008; Fountoukis et al., 2011; Ahmadov et al., 2012; Zhao et al., 2016; Han et al., 2016). However, the application of VBS in global models is limited because of the large number of tracers required and the uncertainty of the involved parameters (Farina et al., 2010; Hodzic et al., 2016). There are even fewer applications of this unified framework in 3-D global aerosol models to calculate the processes of particle formation. Among the second phase AeroCom aerosol microphysical models, the simplified parameterization and the 2P method are the most common schemes used to represent SOAs (Mann et al., 2014). Recently, some models have incorporated VBS in their microphysical module to simulate the aerosol microphysical formation process. Patoulias et al. (2015) developed a new aerosol dynamics model with VBS and explored the contribution of SOAs with various volatilities to particle growth in different stages; however, the 3-D modeling was not presented. By assuming equilibrium partitioning for all volatility bins, Gao et al. (2017) implemented VBS in an aerosol microphysics model and examined the effect of semi-volatile SOAs (SV-SOAs) on the composition, growth, and mixing state of particles. Their box model simulation suggested that the volatility of organic compounds simulate rather different mixing states from those simulated by the coagulation process alone in the scheme treating the primary emissions of organics as non-volatile. Matsui (2017) represented aerosol size distribution with a 2-D sectional method in a global aerosol model coupled with the VBS scheme, but the size-bin resolution is insufficient to accurately resolve the growth of new particles.
To our knowledge, there are few 3-D modeling studies using VBS to account for both (1) the kinetic condensation of low-volatility organics and re-evaporation of semi-volatile organics and (2) the size-resolved kinetics of the mass transfer for new particles. In addition, particle formation in the polluted atmosphere was not well understood (Kulmala et al., 2016; Wang et al., 2017; Chu et al., 2019). Over urban areas in northern China, observation and modeling studies have indicated that anthropogenic SOAs contribute a larger fraction to OAs than those of biogenic SOAs and play an significant role in particle formation (Yang et al., 2016; Guo et al., 2020; Han et al., 2016; Lin et al., 2016). Simultaneously calculating both anthropogenic and biogenic SOAs in microphysical models with a high resolution is crucial to resolve the particle formation processes over urban areas. Furthermore, the previous studies focusing on the sensitivity of particle number concentration to primary emissions were based on models without considering the detailed microphysics of organic species (e.g., Spracklen et al., 2006; Chang et al., 2009; Chen et al., 2018; Zhou et al., 2018). Therefore, it is urgently needed to establish a 3-D modeling framework of VBS with an aerosol microphysics module with high size-bin resolution to simulate the particle number size distribution and explore the uncertainties associated with the treatment of primary emissions.
In our previous work, a regional model with detailed microphysical processes has been developed to improve the simulation of NPF during summer in Beijing (Chen et al., 2019). In this study, we extend our work to the global scale and by doing so establish a new aerosol model by coupling a VBS OA scheme with a particle microphysics module in a global–regional nested model. The model performance was evaluated against the measurements at a tower and a dataset collected from published papers. In addition, the model's sensitivity to the size distribution of primary emissions and volatility distribution of POAs were explored to elucidate and quantify the associated uncertainties. This new modeling framework can provide a useful tool to simulate the aerosol microphysical process on both global and regional scales. The description of the model and its development method are introduced in Sect. 2. The experiments setup and model input are detailed in Sect. 3. The observed data used for evaluating model performance are described in Sect. 4. The model results and simulation analysis are presented in Sect. 5. Finally, the conclusions and discussions are summarized in Sect. 6.
The host model employed in this study is the Atmospheric Aerosol and Chemistry Model developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP-AACM). The IAP-AACM is a 3-D atmospheric chemical transport model treating chemical and physical processes for gases and aerosols on global and regional scales using a multi-scale domain-nesting technique (Wang et al., 2001; Li et al., 2012; Chen et al., 2015). The meteorological parameters inputted into the IAP-AACM were simulated by the global version of the Weather Research and Forecasting (WRF) model (Skamarock et al., 2008; Zhang et al., 2012). The IAP-AACM model has been successfully used to explore mercury transport (Chen et al., 2015) and simulate the global and regional distribution of gaseous pollutants and aerosol components (Du et al., 2019; Wei et al., 2019). The calculation of some modules in the model has also been recently optimized (Wang et al., 2017, 2019). The model calculates 3-D advection (Walcek and Aleksic, 1998), turbulent diffusion (Byun and Dennis, 1995), gas-phase chemical reactions (Zaveri and Peters, 1999), dry deposition at the surface (Zhang et al., 2003), aqueous reactions in the cloud and wet scavenging (Stockwell et al., 1990), and heterogeneous chemical processes (Li et al., 2012). The partition of nitric acid and ammonia into particle phase to form nitrate and ammonium is simulated using a thermodynamic equilibrium model (Nenes et al., 1998). The model calculates the online emission of dimethyl sulfide (Lana et al., 2011), sea salt (Athanasopoulou et al., 2008), and dust (Wang et al., 2000; Luo and Wang, 2006). The simulation results of the IAP-AACM have been evaluated against a comprehensive observation dataset and compared with other model results. The model exhibited good performance in reproducing global aerosol components (Wei et al., 2019).
The advanced particle microphysics (APM) module is an aerosol module that uses
the sectional method to represent particle number size distribution. The APM
module has been coupled to several 3-D models, such as Goddard Earth
Observing System-Chemistry model (Yu and Luo, 2009), Weather Research and
Forecasting-Chemistry model (Luo and Yu, 2011), and the Nested Air Quality
Prediction Modeling System (NAQPMS; Chen et al., 2014). In APM, there are
two types of aerosol particles: secondary particles (SPs) and primary
particles (PPs) with a secondary species coating. The definitions of SPs and
PPs in our model differ from those of secondary aerosol and primary aerosol
commonly used in the community. SPs indicate that they originate from the
nucleation and the subsequent growth of newly nucleated particles whereas
PPs originate from direct emission. PPs include dust particles, sea salt
particles, organic carbon (OC) particles, and black carbon (BC) particles.
The APM module has a high size-bin resolution to accurately describe the
formation and growth processes of SPs (composed of sulfate, nitrate,
ammonium, and organic compounds). SPs are represented by 40 size bins from
0.0012 to 12
The basic microphysical processes in the APM module include nucleation,
condensation or evaporation, coagulation, and thermodynamic equilibrium
partition. The nucleation scheme is ion-mediated nucleation (IMN) (Yu, 2006,
2010), which is physically based and constrained by laboratory data and has
predicted reasonable distributions of global nucleation (Yu et al., 2008).
Because of very low saturation vapor pressure, the condensation of
To reproduce the formation and evolution of OAs, a 1.5-D VBS approach (Koo et
al., 2014) based on 1-D VBS framework but accounting for changes in the
oxidation state and volatility of OAs in the 2-D VBS space is coupled to the
model. Both secondary and primary OAs are distributed in five volatility
bins ranging from
In this VBS module, gas-phase organic compounds can be aged by extremely
reactive hydroxyl (OH) radicals and other oxidants. Volatile organic
precursors of SOAs in this study include compounds with a terminal olefin
carbon bond (
In our previous work, the VBS module was combined with APM to improve the
simulation of NPF process in our regional model (NAQPMS
The tracers associated with aerosol microphysical processes in the
IAP-AACM
The newly added tracers for simulation in microphysical processes.
In this study, we used two nested modeling domains for a 1-year simulation in
2010, with the first domain covering the whole globe at 1
The IAP-AACM
As pointed out in Sect. 1, the size distribution of primarily emitted particles can directly influence the number concentration of PPs through emission and indirectly change the number concentration of SPs through coagulation-scavenging SPs and competing for condensable gases with SPs. In addition, POA volatility distribution can influence the concentration of OAs and the microphysical behavior of OAs and thus the particle number concentration. For these reasons, the sensitivity experiments involving the size distribution of primarily emitted particles, including BC and POC, and the volatility distribution of POAs were designed to investigate the impacts of these factors on the particle number concentration. One base experiment and four sensitivity experiments were used in our study. Table 2 lists the experiments and their corresponding parameters used in this study. In the base experiment, the volatility distributions of POAs from vehicles and biomass burning were based on values from chamber studies (May et al., 2013a, b, c); the factors of other POA emissions were from the estimations of Robinson et al. (2007). The low-volatility POA (LV_POA) experiment and high-volatility POA (HV_POA) experiment used the lower and upper quartiles of the POA volatility distribution factors (May et al., 2013a, b, c; Robinson et al., 2007), respectively. The geometric mean diameters of POC in the OCD0.5 experiment and BC and POC in the PPD0.5 experiment were set as half of the values used in the base experiment.
Sensitivity experiments and their description. The “Primary size” column refers to the geometric mean diameter values (nm) assumed for primary carbonaceous aerosol emissions. The “Volatility distribution” column refers to the coefficients of POA distributed to the volatility bins for vehicles, other anthropogenic, and biomass burning, respectively. Coefficients for different sources are separated by semicolons and different bins (from the lowest to the highest) by commas.
The hourly observation of OAs and particle number size distribution (PNSD) in
Beijing was used to evaluate the model performance in a typical urban
environment. The observation site was located at the Tower Branch of the
Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS)
(39
The simulated OA concentration was compared with the results of the PMF
analysis of the HR-AMS measurements before evaluating the simulated PNSD in
Beijing. Here, HOA and OOA components obtained through PMF analysis were
compared with the simulation results assuming they are primary and
secondary components of OAs (i.e., POAs and SOAs), respectively. Because they
were affected by local emission sources (e.g., traffic emissions and cooking
emissions), the observed values for OAs were not representative at ground
level. Therefore, only HOAs and OOAs at 260 m were used for comparison. The
third-domain results at 0.11
Comparison of the simulated and the observed
During the past decades, many field observations have been conducted to study the characteristics of PNSD in Beijing (Wehner et al., 2004; Wu et al., 2007, 2008; Wang et al., 2015). The NAQPMS model has been used to explain the evolution of PNSD in winter in Beijing (Chen et al., 2017). However, 3-D modeling studies on these issues are still limited (Kulmala et al., 2016; Wang et al., 2016). In the current study, the observed PNSD at 260 m was used to evaluate the model performance. Figure 2 presents the comparison of the simulated PNSD with the observations. The model realistically reproduced the evolution of PNSD at 260 m at the measurement site. In the observation, there are five cycles of conversion from clean days to pollution days. Once the pollution episode was over, an obvious new particle formation event occurred, such as the events on 3, 12, 19, and 25 September. When the pollution level increased, the PNSD shifted to the large diameter side. The model successfully captured the NPF events and the growth of particles in the aforementioned pollution episode. Because the atmosphere at higher level is not susceptible to local sources, the observation at 260 m was more representative than that at the ground level. The number concentration of particles from 100 to 685 nm was accurately reproduced, with a normalized bias less than 40 % and a correlation coefficient of 0.70. The consistency between the simulation and observation suggests good model performance in producing a reasonable number concentration of regional aerosol particles, especially in the climate-relevant size range. However, the number concentration of particles from 15 to 25 nm was overestimated. On one hand, the measurements have analytical errors (Du et al., 2017). On the other hand, the model also has several uncertainties. First, the model used the monthly mean emissions and therefore could not simulate the diurnal variation of traffic emissions. In addition, the size distribution of primary emissions did not meet the assumed lognormal distribution. For example, traffic sources emit smaller particles than do industrial sources (Paasonen et al., 2013; Kumar et al., 2014). Second, the nucleation scheme also has some uncertainties (Zhang et al., 2010; Yu et al., 2018). Nevertheless, the main features of NPF events and the growth of particles were captured by the model. Generally, our model produced the aerosols of the real atmosphere and the simulation results were reasonable.
The reasonable performance of our model in simulating OA components and PNSD gives us the confidence to further analyze the composition of newly formed particles through nucleation and subsequent growth (i.e., SPs) in our model. Figure 3a shows the simulated mean contribution of sulfate, nitrate, ammonium, and OAs to the mass concentration of SPs in September. Figure 3b displays the contribution of LV anthropogenic OAs (LV-AOAs), LV biogenic OAs (LV-BOAs), SV biogenic OAs (SV-BOAs), and SV anthropogenic OAs (SV-AOAs) to the mass concentration of OAs in SPs. OAs were the major component of SPs, followed by sulfate, nitrate, and ammonium (Fig. 3a). Among the components of OAs in SPs, AOAs accounted for 67 %, substantially larger than the 33 % of BOAs, suggesting the dominant role of AOAs in particle growth. In terms of volatility, LV-OAs comprised 67 %, of which LV-AOAs was responsible for 50 % and LV-BOAs for 17 %. Our model calculated the gas-phase concentration of LV-SOGs and its kinetic condensation on size-resolved SPs. The large fraction of LV-AOAs in the OAs of SPs indicates their critical role in the growth of SPs. Furthermore, LV-AOAs are an indicator of aged atmosphere and its large contribution to OAs suggested the influence of regional transport of OAs and precursors of OAs from surrounding areas to Beijing. The aging and growth during the lifetime of SPs in the atmosphere could greatly enhance their regional impact. In addition to the local emissions of OA precursors (Guo et al., 2014), our results also highlight the importance of regional sources of OA precursors in the growth of new particles.
There are two important characteristics of OAs that influence particle growth
and particle number concentration: (1) the concentration of OAs and (2) the
condensation behavior of OAs. The concentration of OAs is dependent on OA
sources and sinks. The condensation behavior of OAs is closely related to the
separation of POAs and SOAs and their volatility distribution. Therefore,
these properties of OAs are given as the background to discuss global and
regional particle number concentration. Figure 4 presents the surface
distribution of OC concentration and the fraction of secondary OC (SOC) in
the base experiment. In our model, OAs are formed by primary emissions and the
partitioning of gas-phase species onto preexisting OAs. Therefore, the
distribution of OC is well correlated with the amount of primary emissions
and SOA precursors. Globally, high concentrations of OC are located in
continental regions with large emissions. Over China and India, the OC
concentration can exceed 10
Surface layer horizontal spatial distributions of organic carbon concentrations
The discrepancy over China can be explained by the following reasons: (1) the time difference between the simulation and observation could lead to
this discrepancy; (2) the OC emissions over China had large uncertainties
(Zhao et al., 2013; M. Li et al., 2017) and the emissions (3.54 Tg C yr
In Fig. 4b, it can be seen that SOC dominated most regions of the globe with
the fraction above 70 %. Over China, POC dominated the eastern regions
whereas SOC dominated the western regions. In the eastern regions, the
higher primary emissions led to the lower fraction of SOC in OC even though
the SOC concentrations are higher than those in western regions. In the VBS
in our model, there are three pathways forming SOAs, i.e., the oxidation of POAs and the
oxidation products of VOCs and IVOCs from anthropogenic and biogenic sources. The
simulations of most previous studies (Kanakidou et al., 2005; Tsigaridis et
al., 2014) have indicated that biogenic SOAs (BSOAs) are dominant over the
global scale because their major sources are from the oxidation of biogenic
VOCs. However, our simulations (Fig. 5) indicated that anthropogenic SOAs
(ASOAs) are as important as the biogenic, especially over areas with large
anthropogenic emissions. Over some areas in India and eastern China, ASOA
concentrations can exceed 7
Surface layer horizontal spatial distributions of ASOA
concentrations
The volatility distribution of SOAs is a factor controlling not only the mass
concentrations of OAs but also the size distributions of aerosol particles
through microphysical processes. The VBS framework can simulate the
volatility distribution of OAs in five saturation concentration bins. Riipinen et al. (2011) suggested that approximately half of the
condensing mass needs to be distributed proportional to the aerosol surface
area to explain the observed aerosol particle growth. The condensation of
this part of the OAs is governed by gas-phase concentration rather than the
equilibrium vapor pressure, which is how our model calculates the growth of
LV-SOAs to particles. The volatility distribution of SOAs is an important
factor affecting the global and regional distribution of particle number
concentrations. Figure 6 shows the surface layer spatial distributions of
SV-SOA and LV-SOA concentrations. Globally, high SV-SOA concentrations are
mainly located in the continental source regions. By contrast, LV-SOA
distribution is more homogeneous and its contribution to SOA is lower in
source regions. The differences between spatial distributions of SV-SOAs and
LV-SOAs are mainly caused by different formation processes. The major source
of SV-SOAs is the oxidation of VOCs and its distribution is almost consistent
with the source areas of VOCs. However, LV-SOAs are formed from the further
oxidation of SV-SOGs. Multi-generation aging processes enable LV-SOAs to form
both in the source regions and downwind regions. The continental areas with
higher emissions have a lower contribution of LV-SOAs. In downwind regions,
LV-SOAs have a higher concentration than SV-SOAs. Even over source areas,
such as North America and Europe, LV-SOAs also have a higher concentration
than SV-SOAs. These results indicate that the multi-generation aging of
OA in the VBS produce a higher concentration of LV-SOAs and consequently the
wider spread of OAs, which has a large impact on the role of OAs in particle
formation processes. Over China, SV-SOAs have a concentration of 3–10
Surface layer horizontal spatial distributions of SV-SOA
concentrations
Figure 7 displays the simulated surface layer horizontal spatial distributions of annual mean number concentrations of CN10 and the fraction
of CN10 that is secondary. The observed CN10 values provided in Table S1 are
also illustrated in Fig. 7a and c for comparison. High concentrations of
CN10 in the surface layer are located in the regions with large
anthropogenic emissions (Fig. 7a). The highest concentrations of annual mean
CN10 are over central eastern China and the Sichuan basin, and their values
can be larger than 10 000 cm
Surface layer horizontal spatial distributions of annual mean number
concentrations of CN10
Comparison of simulated and observed annual mean number
concentrations of particle condensation larger than 10 nm at 34 sites
listed in Table S1. The solid carmine line shows a
Both SPs formed through nucleation and subsequent growth and direct emission of PPs can contribute to atmospheric particle number concentration. It is important to quantify the contribution of these two sources in different parts of the globe. In Fig. 7b, it can be seen that SPs are dominant in most parts of the globe except for regions with large primary emissions, such as eastern China, India, and southern Africa. The low contribution of SPs in these regions is due to the strong scavenging of secondary particles by primary particles and the low nucleation rate caused by the competition of PPs for condensable gases. Although secondary aerosol species are high in these regions (Fig. 6), they tend to act as coating species on PPs rather than form new particles; thus, PPs are dominant. This spatial pattern is consistent with the results of a previous study (Yu and Luo, 2009). However, the fractions of SPs in CN10 are lower than those in CN3 reported by Yu and Luo (2009) because of the dominant contribution of secondary nucleation to particles that are 3 to 10 nm in diameter. A boundary from northeast to southwest China can be seen to separate the areas dominated by SPs from that by PPs over China (Fig. 7d). This phenomenon is also caused by the large difference in emissions between the western region and eastern region of the country.
In addition to particle number concentration, the mixing state of aerosols is also necessary to evaluate aerosol impacts on climate. The VBS framework treats the emitted OAs with diverse volatilities and thus allows them to be partitioned among different aerosol particles through condensation. In addition, the evolving volatility due to oxidation in the atmosphere makes the microphysical behavior of POAs different from that of the non-volatile POAs. In our model, semi-volatile organics are temperature-driven and partitioned through the equilibrium assumption while low-volatility species are kinetically condensed on the particles. Figure 9 presents the fraction of organic species residing in aerosols of different types (i.e., SPs, sea salt, dust, BC, and OC) defined in our model. Most of the organic species reside in OC, SPs, and BC particles, suggesting the intense mixing of anthropogenic aerosol species (Fig. 9). In the Southern Hemisphere, the fractions of organic species residing in SPs are above 30 %, larger than that of OC particles. In the Northern Hemisphere, organic species mainly reside in OC particles because of the higher concentration of POAs and the subsequent partition. The fractions of organic species residing in SPs are lower, but still considerable, indicating the important role of organic species in forming particles over the whole globe. Because of differences in emissions and the associated microphysical processes, there are distinct spatial variations of organic species distribution among different continents. Over the United States, 30 %–40 % of OAs reside in SPs. By contrast, this fraction is below 20 % over China. In China, significant differences also exists between the western and eastern regions. The dominant contribution of semi-volatile species to OAs (Fig. 6) and their partition proportional to LV-OAs lead to a higher fraction of organic species residing in OC particles over eastern China. The mixing of natural aerosols and organic species is also demonstrated in Fig. 9. Over most areas of the globe, 15 % of organic species are distributed in dust particles, which could greatly modify the properties of the dust particles and thus their climate forcing over these regions (Huang et al., 2019).
Surface layer horizontal spatial distributions of the fraction of organic species that reside in SPs
Previous studies have indicated that organic species are the major
components of aerosols (e.g., Zhang et al., 2007; Jimenez et al., 2009) and
low-volatility organic species can greatly enhance the growth of new
particles (e.g., Yu, 2011; Tröstl et al., 2016). Our results also
indicated the substantial distribution of organic species in SPs. For this
reason, the contribution of LV-SOGs to the growth of SPs was analyzed. Figure 10 shows the ratio of LV-SOGs to
The ratio of LV-SOGs to
In the VBS, POAs are treated as volatile species and allowed to be aged by
oxidation in the atmosphere; thus, it is necessary to explore the
uncertainties associated with this treatment of volatility distribution. In
addition, the size distribution of POAs and the associated microphysical
processes are also modified because of this treatment. Therefore, the
sensitivity of particle number concentration to the volatility of POAs and
the assumed size distribution of PPs are discussed here. Figure 11 displays
the change ratio of CN10 number concentrations in the LV_POA
experiment, HV_POA experiment, PPD0.5 experiment, and OCD0.5
experiment relative to that in the base experiment. Overall, CN10 concentrations
changed little when POA volatilities were in the inter-quartile range of
measurements (Fig. 11a and b). When using the low-volatility distribution of
POAs, PPs number concentrations were increased by 5 %–10 % over most areas in the Northern Hemisphere; the concentrations decreased by 5 %–10 % when the high volatility distribution of POAs was used. By contrast, SP number concentrations only exhibited minor changes over the areas with the strongest emissions. Because of the dominant contribution of SPs, CN10 had no clear change in most regions of the globe. By contrast, the size distribution of emitted PPs has a large influence on the CN10 concentration. When the median diameter of BC and OC was set as half the size used in the base experiment, concentrations of CN10 increased by 50 %–150 % over the areas with large emission sources of BC and OC, which were too high to match the observations shown in Fig. 7a. For example, the CN10 in the PPD0.5
experiment was greatly overestimated when compared with the observed
concentration (12 000 cm
Relative change of number concentrations of CN10 in the
Laboratory studies and field observations have highlighted the importance of
the sources of OAs, volatility distribution, and the microphysical behavior
of organic species in particle formation processes; however, OA processes
are still poorly represented and this lack of knowledge contributes
considerably to model uncertainties in simulating aerosol microphysical
properties. In this study, a new global–regional nested aerosol model was
developed to simulate detailed microphysical processes in the real
atmosphere. The new model combines the APM module and a VBS module to
simulate the microphysical processes of OAs. In the model, the OAs in the
lowest volatility bin are treated as non-volatile or low-volatility species
and their condensation is simulated using the kinetic method. The OAs in
other volatility bins are simulated using equilibrium partitioning. Using
this framework, both the condensation of secondary inorganic species (i.e.,
sulfuric acid, nitrate, and ammonium) and the condensation of organic
species with various volatilities (i.e., low-volatility organic compounds
and semi-volatile organic compounds) are simultaneously simulated, which
represents a major advantage of our new model. The concentration of
low-volatility organics is separately calculated and the condensation of
Compared with most models in the second phase of AeroCom (Tsigaridis et al., 2014; Mann et al., 2014) and recently developed new models (e.g., Yu, 2011; Patoulias et al., 2015; Gao et al., 2017), our model includes more comprehensive sources of SOA, especially anthropogenic SOAs, by using the VBS framework. In addition, allowing POAs to evaporate and re-condense onto the particles makes its microphysical behavior resemble that of SOAs and therefore gives new meaning to the POA–SOA split, which has a substantial effect on global CCN formation (Trivitayanurak and Adams, 2014). The flexible framework of APM combined with VBS produces the different distribution of organic species in aerosols (i.e., the mixing state of OAs), which has been found to cause substantial differences in the radiative effects of aerosols (Zhu et al., 2017). Box model analyses revealed that LV-SOAs have a large fraction in the growing nucleation mode particles (Pierce et al., 2011). The comprehensive thermodynamic–kinetic approach treating the condensation and the partitioning of organic species that originated from biogenic and anthropogenic sources enable us to investigate the full role of organic species in the growth of new particles, which is vital for understanding the formation processes of particles relevant to radiative forcing and clouds (Shrivastava et al., 2017).
The model with three nested domains was applied to simulate the aerosol components and PNSD in the megacity of Beijing during a period of approximately 1 month. The simulation results were evaluated by observations at a high level on the IAP tower, which is more representative than ground level observations on the regional scale. The simulated BC and OA components agreed well with the PMF analysis of the AMS measurements. The evolution of PNSD and NPF events were also nicely reproduced by the model. Our modeling analyses revealed that AOAs account for the largest part of the OAs of SPs and thus contribute considerably to the growth of SPs in Beijing. Molteni et al. (2018) indicated that highly oxygenated organic compounds that formed from anthropogenic VOCs can substantially contribute to NPF in urban areas. Observations in Beijing suggested that anthropogenic VOCs are major constituents of SOAs (Ding et al., 2014; Yang et al., 2016). For the first time, the contribution of AOAs to new particles was quantified because of the mixing state our model resolved in our study. Although the exact role of AOAs in NPF is not perfectly clear, our study explicitly demonstrated the critical role of AOAs in NPF in Chinese megacities, which can help elucidate the mechanism of more frequent occurrences of NPF events than theoretical prediction in a polluted atmosphere (Kulmala et al., 2017; Chu et al., 2019). Comparisons with the observations collected from published data revealed that the model well reproduced the annual mean concentration of the observed OC at continent sites in America and the spatial pattern of OC in China. Because of the re-evaporation and oxidation of POAs and the additional emission of IVOCs, ASOAs become dominant in SOAs over POA source areas. At sites in different environments over the globe, the model produced reasonable CN10 concentrations within a factor of 2 of observed values for most of the sites. We noted that LV-SOGs, especially anthropogenic SOAs, have a large contribution to new particle growth over areas with intense anthropogenic emissions, such as eastern China. The global simulation of Kelly et al. (2018) indicated that including large anthropogenic SOA sources could achieve results consistent with observations over mid-latitudes of the Northern Hemisphere. Simulation over East Asia also indicated that most of the OAs were from anthropogenic sources (Matsui et al., 2014). Together with these studies, our modeling results further provided the direct evidence of AOAs in particle formation processes not only in Chinese megacities but also in other regions influenced by anthropogenic sources on the global scale.
Sensitivity analyses indicated that CN10 concentration changed only a little in the regions with the highest POA emissions and had no clear change in most regions of the globe when POA volatilities were in the inter-quartile range of measurements. Although the size distribution of primary emitted particles has a large effect on the simulation of CN10, as suggested by other studies (e.g., Spracklen et al., 2006; Chang et al., 2009; Zhou et al., 2018), the simulation of the base experiment had better agreement with the observations than the sensitivity experiments and the conclusions will not be changed. Even so, the importance of the size distribution of primary emitted particles should be emphasized. Global model results in a related study suggested the high sensitivity of CCN to the assumed emission size distribution (Lee et al., 2013). Recently, Xausa et al. (2018) found that using size-segregated primary particle number emissions can help make the number concentration of accumulation mode particles closer to the measurements. The current simulation indicates to the importance of parameterization of the size distribution of emitted OC particles after considering their re-evaporation and condensation. Therefore, the primary emissions must be constrained both in their size distribution and volatility. In addition, the simulated properties of OAs were also determined by the parameters of the VBS module, the emissions inventory and meteorological fields inputted into the model, and the physicochemical processes in the model. Although our model provided reasonable calculations comparable with the available observations and model results of other authors, it has room for further improvements in the future. For example, the size-resolved emissions of anthropogenic PPs may be used as the model input to reduce the associated uncertainties. The fixed parameters in VBS make it difficult to represent the real formation pathway of SOAs and capture the response of SOAs to emission changes (Jo et al., 2019). More accurate parameterizations considering the key physicochemical dependencies should be incorporated to update the VBS module in our model. More nucleation schemes may also be implemented into the model to investigate the influence of nucleation schemes on the aerosol number concentrations because the uncertainties from the nucleation scheme remain large (Dunne et al., 2016). Aqueous-phase formation processes of SOAs have an evident influence on the particle properties and total SOA mass (Ervens et al., 2011), and these processes can close the gap between simulations and observations (Lin et al., 2014). Finally, the description of aerosol microphysical processes should be refined by including the aqueous formation of SOAs in our model.
All of the observations in this research are provided in the paper. The simulation data can be made available from the authors upon request (chenxsh@mail.iap.ac.cn, zifawang@mail.iap.ac.cn).
The supplement related to this article is available online at:
XC developed the model, performed the simulations and analysis, and prepared the manuscript with contributions from all co-authors. FY provided the code for the APM module and modified the manuscript. WY coupled the VBS module and modified the manuscript. YS, WD, and JZ provided the observation data in Beijing and modified the manuscript. HC prepared the emission data and modified the model code. YW, LW, HD, ZhW, QW, JL, and JA modified the manuscript. ZiW guided the study and modified the manuscript.
The authors declare that they have no conflict of interest.
The authors acknowledge the support of the National Key R&D Program of China, the National Natural Science Foundation of China, and the National Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (EarthLab). We also thank the reviewers for providing constructive comments and suggestions to improve our manuscript.
This research has been supported by the National Key R&D Program of China (grant nos. 2017YFC0209801 and 2017YFC0209805) and the National Natural Science Foundation of China (grant nos. 41705108, 41907200, and 41907201).
This paper was edited by Gordon McFiggans and reviewed by two anonymous referees.