The Community Multiscale Air Quality model (CMAQ v5.0.1), coupled with a modified SAPRC-11, was used in this study. Model configurations were largely based on that used by Hu et al. (2016) as summarized below. Firstly, SAPRC-11 was expanded for a more detailed treatment of isoprene oxidation and for tracking dicarbonyl products (glyoxal and methylglyoxal) from different groups of major precursors (Ying et al., 2015). Secondly, SOA from isoprene epoxydiols (IEPOX), methacrylic acid epoxide (MAE), and dicarbonyls through surface-controlled irreversible reactive uptake was added (Hu et al., 2017; Li et al., 2015; Liu et al., 2020; Ying et al., 2015). Thirdly, the heterogeneous formation of secondary nitrate and sulfate from NO2 and SO2 reactions on the particle surfaces (Ying et al., 2014) was added, which is an important source of secondary inorganic aerosol (Zheng et al., 2015) and improves model estimates of nitrate and sulfate (Qiao et al., 2018; Shi et al., 2017). Fourthly, SOA yields were corrected for vapor wall loss (Zhang et al., 2014). Impacts of the above updates on model performances have been extensively discussed in the cited work and are not further investigated in the current study.

The SOA module mostly follows Pankow et al. (2015). Two types of SOA as traditionally treated in CMAQ were considered: the “semivolatile” (SV) portion that formed via equilibrium absorption partitioning of SVOCs and the “nonvolatile” (NV) portion that includes the oligomers and SOA formed via direct oxidation of aromatics at low NOx. SOA from dicarbonyls, IEPOX, and MAE was formed by irreversible reactive uptake and categorized as NV SOA in the current model as well. Some studies investigated SOA from glyoxal, methylglyoxal, and IEPOX using detailed reactions and reversible pathways in models or observed as reversible processes in chamber experiments, leading to a relatively lower SOA yield compared to the surface-controlled irreversible uptake (Lim et al., 2013; Knote et al., 2014; Galloway et al., 2009; El-Sayed et al., 2018; Budisulistiorini et al., 2017). The nonvolatile assumption used in this paper allows an upper-limit estimation of the importance of these additional SOA formation pathways. POA was treated as nonvolatile and nonreactive. The mass distribution of SVOCs between the gas phase and particle phase follows the equation 1Kp,i=FiM⋅Ai, where Kp,i (m3 µg-1) is the gas–particle partitioning constant for compound i, Fi (µg m-3) is the concentration of species i in the particle phase, Ai (µg m-3) is the concentration of species i in the gas phase, and M (µg m-3) is the total mass concentration of the absorbing organic phase (i.e., OPM). The gas–particle partitioning constant Kp,i is dependent on the chemical composition of the OPM. Pankow (1994) derived Kp,i for SVOCs partitioning into an absorbing organic phase as 2Kp,i=RT106MW‾ξipL,io, where pL,io (atm) is the SVP of the pure compound i at temperature T (K), ξi is the activity coefficient of species i in the absorbing organic phase, MW‾ (g mol-1) is the average molecular weight of the OPM, R (8.314 J mol-1 K-1) is the gas constant, and 106 is used to convert the units to cubic meters per microgram.

There are 12 lumped SVOCs generated by oxidation of alkanes, alkenes, and aromatics under different NOx conditions and 8 NV organic products as listed in Tables S1 and S2 in the Supplement. More details about the lumped precursors such as formation conditions (“high” and “low” NOx), lumping species and method, and yields from parent VOCs can be found in Carlton et al. (2010) and are summarized in the Supplement. Activity coefficients of SVOCs were calculated based on the composition of the OPM using the UNIFAC method, with carbon number (nc), functional groups, and energy interaction parameters assigned to both SV and NV compounds (Pankow et al., 2015). The UNIFAC model is one of the most commonly used models with which activity coefficients of condensed organics and their interactions with water can be estimated. This method has been adopted to investigate the impacts of nonideality and water partitioning into OPM on SOA for different precursors in box models (Seinfeld et al., 2001; Bowman and Melton, 2004) and CTMs (Pankow et al., 2015; Kim et al., 2019). In the current model, POA was assumed to have a bulk composition of 10 categories of surrogate species (Table S3), as used by Li et al. (2015). POA is also involved in the calculation of activity coefficients for the organic–water mixture. Detailed information about the surrogate species, including their structures and properties, can be found in Li et al. (2015) and references therein.

In addition to organic compounds, water partitioning into OPM is enabled according to Eqs. (1) and (2). In such a case, the absorbing phase in Eq. (1) includes both organic aerosol and water associated with OPM. As water condenses in the absorbing organic phase, it will further alter the molar fraction of each composition, the activity coefficient of SVOCs, and the SV SOA mass concentrations as a result. In the current model, we assumed no interactions between the inorganic and organic phases.

Based on the κ–Köhler theory with linearly additive hygroscopic behavior of each component of the mixed particle, ALWorg is related to the hygroscopicity parameter for the organic mixture (κorg) by Eq. (3) (Petters and Kreidenweis, 2007): 3ALWorg=ρwVorgκorgaw1-aw, where ρw is the density of water (assumed to be 1 g cm-1), Vorg is the volume concentration of organics, and aw is the water activity (assumed to be the same as RH). Since ALWorg in this study is calculated mechanistically using the partitioning theory, κorg can be estimated by rearranging Eq. (3): 4κorg=ALWorgρwVorg×1-awaw. Vorg can be estimated from the modeled mass concentration of OA, assuming the density of OA to be 1.2 g cm-3 (Li et al., 2019).

In many studies, κorg is assumed to increase linearly with the oxidation state of OA, expressed as the O:C ratio (Massoli et al., 2010; Duplissy et al., 2011; Lambe et al., 2011). The correlation of κorg and the O:C ratio in nine representative cities was evaluated during January and July of 2013 with the reduced major axis regression method (Ayers, 2001). The O:C ratio was calculated using Eq. (5): 5O:C=∑i=1nfi(O:C)i, where fi and (O:C)i are the molar fraction and O:C ratio of organic aerosol component i, respectively. For POA, a fixed molar fraction and composition were assumed following Li et al. (2015). For SOA, the O:C ratio was calculated by using the ratio of organic matter to organic carbon (OM:OC) following Simon and Bhave (2012): 6O:C=1215(OM:OC)-1415. The OM:OC ratio of each SOA component follows Pankow et al. (2015) as shown in Tables S1 and S2.

The simulation domain has a horizontal resolution of 36 km × 36 km (100×100 grids) and a vertical structure of 18 layers up to 21 km, which covers eastern China as shown in Fig. S1 in the Supplement. Anthropogenic emissions were generated from the Multi-resolution Emission Inventory for China (MEIC; Zhang et al., 2009) v1.0 at a resolution of 0.25∘ × 0.25∘ (http://www.meicmodel.org, last access: 1 March 2019) for China and from the Regional Emission inventory in ASia version 2 (REAS2; Kurokawa et al., 2013) at a resolution of 0.25∘ × 0.25∘ (http://www.nies.go.jp/REAS/, last access: 1 March 2019) for the rest of the domain. Biogenic emissions were generated by the Model for Emissions of Gases and Aerosols from Nature (MEGAN) v2.1, with the leaf area index (LAI) from the 8 d Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product (MOD15A2) and the plant function types (PFTs) from the Global Community Land Model (CLM 3.0). Open biomass burning emissions were generated from the Fire INventory from the National Center for Atmospheric Research (FINN; Wiedinmyer et al., 2011). Dust and sea salt emissions were generated online during CMAQ simulations. The total emissions of major SOA precursors and their spatial distributions are shown in Table S4 and Fig. S2. Meteorological fields were generated using the Weather Research and Forecasting (WRF) model v3.6.1 with initial and boundary conditions from the National Centers for Environmental Prediction Final (NCEP FNL) Operational Model Global Tropospheric Analyses dataset. More details about the model application can be found in Hu et al. (2016).

Four scenarios are investigated in this study. The base case (BC) applies the default secondary organic aerosol module of CMAQ v5.0.1. In this case, no water partitioning into OPM is considered. Lumped semivolatile products from the oxidation of various precursors partition into a single organic phase, which is considered as an ideal mixture of POA and SOA with γorg=1. The water case (C1) includes water partitioning into OPM, which is again considered as an ideal solution (γorg=1 and γH2O=1). The UNIFAC case (C2) considers the interaction between organic constituents with UNIFAC-calculated activity coefficients (γorg≠1) but does not allow water partitioning into OPM. The combined case (C3) allows both water partitioning and interactions between all constituents (including water and organics) using UNIFAC-calculated activity coefficients (γorg≠1 and γH2O≠1). The results of the BC and C3 are used to examine the overall impacts of water partitioning into OPM and polarity of organics on SOA and ALWorg, as shown in Sect. 3.1–3.4. The separate influences of those two processes on SOA from C1 and C2 are discussed in Sect. 3.5.

The meteorological inputs and emissions have been used in several previous publications. Model performance on meteorological parameters (temperature and RH), gaseous species, and gas and aerosol concentrations have been extensively evaluated (Hu et al., 2016, 2017; Qiao et al., 2018; Shi et al., 2017). A summary of the model performance related to this study is provided below. Observed meteorological data were obtained from the National Climatic Data Center (ftp://ftp.ncdc.noaa.gov/pub/data/noaa, last access: 15 February 2019). Observations of OC at two urban locations, Beijing (Cao et al., 2014; Wang et al., 2015) and Guangzhou (Lai et al., 2016), and of OA in Beijing (Sun et al., 2013) during January of 2013 as well as surface PM2.5 observations from the China National Environmental Monitoring Center (http://113.108.142.147:20035/emcpublish/, last access: 10 May 2015) at several monitoring sites during July of 2013 were used to evaluate model estimates of aerosols. Details of measurement methodology and uncertainties of observations are listed in the corresponding references.

Temperature and RH are the two meteorological factors that affect SOA formation. Table 1 lists model statistics of mean observation (OBS), mean prediction (PRE), mean bias (MB), gross error (GE), and correlation coefficient (R) based on WRF and observations at monitoring sites located in eight subregions of the domain (Fig. S1) during January and July of 2013. The benchmarks for the MM5 model (another meteorology model) of 4–12 km horizontal resolution suggested by Emery et al. (2001) are also listed in the table. Details of monitoring sites in the eight subregions are listed in Table S5. Overall, WRF tends to underestimate both temperature and RH. The model shows better agreement with observed temperature as R is higher than that of RH. Both temperature and RH are well captured by the model in the YRD, the Pearl River Delta (PRD), and the central regions of China (the major regions of eastern China). In these regions, MB and GE of temperature are -1.2–0.7 and 1.8–2.6 K, respectively, which are -11.8 %–5.6 % and 9.2 %–16.8 % for RH, respectively.

Model estimates of daily organic carbon (OC) from case C3 are compared with measurements at monitoring sites in Beijing and Guangzhou in January of 2013 (Fig. 1a). The factors used to convert SOA to OC (SOC) are listed in Tables S1 and S2. OC from POA (POC) is directly predicted by the model. Generally, the ratio between modeled and observed OC concentration falls in the range of 1:2 to 2:1, with an R value of 0.7. The model tends to underestimate OC on high-concentration days. Overall, the mean fractional bias (MFB) and mean fractional error (MFE) of OC are -0.20 and 0.27, which are within the criteria (MFB ≤± 0.6; MFE ≤ 0.75) suggested by the EPA (2007). The bias in OC might be due to underestimated POA emissions and underpredicted SOA in CMAQ from missing precursors (Hu et al., 2017; B. Zhao et al., 2016). No significant differences in OC are observed in C3 compared to BC (not shown), likely due to the low-biased SOA predicted in the current model that limits the impact of ALWorg on SOA formation.

The underestimation of SOA can be seen from Fig. 1b as well. CMAQ can well capture the observed diurnal variation in OA in Beijing during wintertime except for the underestimates of peak values. The correlation coefficient of modeled to observed OA is 0.55. We find a 25 % underestimate of OA on average. Better agreement between the model and observations is shown on nonpolluted days (daily averaged concentration less than 75 µg m-3). The MFB and MFE of polluted days are -0.38 and 0.64, respectively, which are worse than those of nonpolluted days (-0.26 for MFB and 0.52 for MFE). The overall MFB and MFE of OA during January are -0.28 and 0.54, respectively, which are within the criteria (MFB ≤± 0.6; MFE ≤ 0.75) suggested by the EPA (2007). Again, no apparent changes in SOA or OA are observed between case C3 and BC (not shown) since POA is predicted to be the primary contributor to OA in Beijing in winter in the current model, with an averaged SOA/POA ratio of 0.12. This ratio is much lower than the field observation of about 0.45–1.94 (Zhao et al., 2019; Sun et al., 2013, 2016). The bias might be due to the missing SOA converted by partitioning and aging of semivolatile POA as well as oxidation from intermediate volatile organic compounds (IVOCs) and VOC oxidation products. Those pathways are shown to be important for SOA formation by modeling, field, and chamber studies (Hodzic et al., 2010; Jimenez et al., 2009; Murphy et al., 2017; Robinson et al., 2007; Shrivastava et al., 2008; Tkacik et al., 2012; Zhao et al., 2014; B. Zhao et al., 2016).

A sensitivity test was performed by using the newest CMAQ model version 5.3.1, which includes all the above processes in the aerosol module. The SOA/POA ratio in Beijing is improved greatly in winter, at 0.83. However, high uncertainties still exist in the emissions of the involved precursors and characterization of SOA formation through these processes, requiring further constraints by observations. Their influences on SOA due to water partitioning into OPM and nonideality of the organic–water mixture will be evaluated in a future study.

Due to the lack of observed OC and OA in July of 2013, model performances are evaluated by comparing predicted and observed PM2.5 at ground sites (Fig. S1) as an alternative, as shown in Fig. S3. Generally, the model can well reproduce the diurnal variation in PM2.5 in most regions. Predicted PM2.5 on highly concentrated days is biased low, especially in the North China Plain (NCP). The NCP has the highest PM2.5, ranging from 60 to 300 µg m-3. The bias in modeled PM2.5 is significant in cities in the northwest. This might be due to missing dust emissions in the current inventory (Hu et al., 2016). To further evaluate the model performance, the averaged MFB and MFE of modeled PM2.5 are plotted against observations of each site as shown in Fig. S4. The criteria of MFB and MFE followed recommendations by Boylan and Russell (2006). Our model performs well since most of the predictions meet the criteria, and a large fraction (>58 %) meets the goal. The averaged MFB and MFE across all the sites are -0.28 and 0.39, respectively, indicating slight underestimation of PM2.5 by the model.

The spatial distribution of SOA varies greatly in the two seasons. In winter, SOA are relatively high in the eastern SCB and the central and eastern provinces of Shandong, Henan, Anhui, and Hubei (Figs. 2 and S5). Monthly averaged SOA concentrations in the above areas are up to 25 and 15–20 µg m-3, respectively. Anthropogenic emissions such as dicarbonyl products from the oxidation of xylene and toluene (Hu et al., 2017) are the major sources of SOA (Fig. S6). In summer, surface SOA is high in the northeast, the NCP, and the YRD. The highest SOA occurs in Shanghai and Jiangsu provinces as well as the coastal area of the Yellow Sea, with values of ∼9–16 µg m-3 at the surface and ∼20–25 mg m-2 in the column (col-SOA) of the atmosphere below 21 km (Fig. S5). In contrast to winter SOA, a significant fraction of summer SOA originates from biogenic emissions (Fig. S7). Anthropogenic SOA is highly concentrated in the coastal areas of the Yellow Sea and Bohai Bay.

The combined effects of water partitioning into OPM and nonideality on SOA formation also exhibit strong seasonal variation. In winter, SOA is slightly decreased by 1.5 µg m-3 (10 %–20 %) on average at the surface (Fig. 2) and less than ∼1 mg m-2 (20 %) in the column (Fig. S5) over high-SOA regions where anthropogenic sources dominate. We show later that the decrease in SOA is mainly due to the large activity coefficients of SVOCs, which decrease Kp,i. In summer, higher temperature and RH promote water partitioning and SOA formation so that SOA apparently increases over the entire domain, with the highest enhancement of 2–4 µg m-3 (20 %–50 %) at the surface (Fig. 2) and 4–6 mg m-2 (30 %–60 %) in the column (Fig. S5) over the YRD and the coastal area of the Yellow Sea. Anthropogenic SOA dominates the total change in winter as shown in Fig. S6. In summer, the increase in SOA is attributed to biogenic sources in inland areas and anthropogenic sources over the ocean (Fig. S7).

Regional distribution of ALWorg is similar to the change in SOA as shown in Fig. 3. In winter, a maximum averaged concentration of 3.0 µg m-3 for ALWorg occurs in the high-SOA region, where significant changes in SOA also occurs. In other areas, the averaged concentration of ALWorg is about 0.5–1.5 µg m-3. Overall, the average ratio of ALWorg to SOA is about 0.1–0.3 in winter. In summer, water partitioning into OPM mostly occurs in the eastern coastal area at the surface, where a significant increase in anthropogenic SOA (such as those from toluene and xylenes) is observed. This might be due to the high polarity of anthropogenic SVOCs (having more -COOH groups) that absorb more water. In the coastal area, the averaged concentration of ALWorg is about 5–7 µg m-3, with an ALWorg/SOA ratio of 0.5–1.0. Over land, the averaged concentration of ALWorg is about 1–3 µg m-3 (ALWorg/SOA ratio of 0.2–0.5) in northeastern and eastern China. Water partitioning is mostly associated with biogenic SOA originated from isoprene and monoterpene oxidation that produces SVOCs with abundant OH groups.

Based on the column concentrations of ALWorg and the ALWorg/SOA ratio (Fig. S8), more ALWorg must have occurred in winter in the southern and southwestern regions at higher levels, where a significant increase in col-SOA occurs (Fig. S5). The averaged col-ALWorg/col-SOA ratio in the high-SOA area is 0.1–0.3. In summer, the ALWorg must be high at higher altitudes over the central regions in China. The maximum col-ALWorg is about 7 mg m-2 over the YRD, with a col-ALWorg/col-SOA ratio of about 0.3.

Since ALWorg is determined in C3, the values of κorg can be estimated from the modeled ALWorg, OA, and RH using Eq. (4). Nine representative cities were selected to investigate the relationship of κorg and O:C and its seasonal variation as shown in Figs. S9 and S10. The results of all the cities in winter and summer are merged and analyzed as shown in Fig. 4. Pairs of κorg and O:C data are grouped into 10 O:C bins, and the averaged κorg in each bin is then calculated. Overall, the estimated O:C ratio is within the range of 0.2–0.8. The averaged κorg in each O:C bin is less than 0.1 in winter, with the highest value in Guangzhou. As more ALWorg is formed in summer, the averaged κorg also increases greatly, with the highest value of 0.35 in Beijing. The linear correlation between κorg and O:C shows significant spatial and seasonal variations. For example, the slope of κorg–O:C is 70 %–90 % smaller in winter than in summer in northern cities such as Shenyang, Beijing, Zhengzhou, and Xi'an. However, in Guangzhou, the slope is 83% higher in winter than in summer. In Chengdu, the slope is quite similar in both seasons. Overall, the slope of κorg vs. O:C in the nine cities is 0.16 in winter and 0.40 in summer. Most of the fitted linear correlations of the individual cities fall outside of the range of 0.18–0.37 suggested in previous studies (Duplissy et al., 2011; Lambe et al., 2011; Massoli et al., 2010; Chang et al., 2010), indicating that the hygroscopicity of organic aerosol cannot be simply represented by a single parameter such as the O:C ratio (Rickards et al., 2013). In both seasons, κorg approaches zero and negative values as O:C decreases, which might be due to the linear regression of κorg and O:C. To avoid this, an exponential fitting of the two variables is performed so that κorg falls in the range of (0, 1) and is positively correlated with O:C. In this case, the fitted correlations are κorg=1-exp⁡(-(O:C/1.88)2.29) and κorg=1-exp(-(O:C/1.06)4.50) for January and July of 2013, respectively.

The impacts on aerosol optical depth (AOD) and aerosol radiative forcing (ARF) are further investigated. Figure 5 shows the monthly averaged AOD at 550 nm in January and July of 2013. It is calculated by summarizing the product of the model-estimated extinction coefficient of fine particles (bext,i) multiplied by the thickness (HLi) in each layer: 7AOD=∑i=1Nbext,i×HLi, where N is the number of layers. There are two methods to estimate the aerosol extinction coefficient in CMAQ v5.0.1. One is based on the Mie theory and the predicted aerosol component concentrations (bext,m), and the other is based on correlation with the IMPROVE monitoring network, which considers the impacts of hygroscopicity of different aerosol components (bext,r; Malm et al., 1994). AOD calculated with the two types of extinction coefficient are denoted as AODm and AODr, respectively.

In Fig. 5, a clear pattern of high AODr in the SCB and the NCP and low AODr in western China is observed in both winter and summer, which is consistent with previous studies (He et al., 2016, 2019; Luo et al., 2014). An identical trend in AODm is shown in Fig. S11. The monthly averaged AODr ranges from 1.0 to 3.2 in January and from 0.3 to 0.9 in July. AODm is lower than AODr, falling within the range of 0.7–2.2 in January and 0.3–0.6 in July. The model significantly overestimates AOD in January but agrees better with observations from MODIS, where AOD is high in July (Fig. S12). The bias in the predicted AOD might be partially due to the empirical equation applied in the calculation of AOD in CMAQ (Wang et al., 2009; Liu et al., 2010) and partially due to the uncertainties of fine AOD over land from the MODIS data (Wang et al., 2009; Levy et al., 2010). The increase in AOD due to ALWorg shows a strong spatial and seasonal pattern. In winter, there are no significant changes in AODr across the whole domain due to insignificant changes in SOA. In summer, AODr increases significantly in the YRD and the adjacent areas by up to 10 %.

ARF represents the change in the radiative flux at the top of the atmosphere due to aerosols. An offline version of the Shortwave Radiative Transfer Model for GCMs (RRTMG_SW) was used to calculate the direct radiative effect of aerosols on shortwave radiation (Iacono et al., 2008). Generally, fine aerosols exhibit cooling effects on the shortwave radiation in both winter and summer over the entire domain as shown in Fig. 6. This impact is much stronger in the areas where AOD is high (Fig. 5). ARF is highest in Shandong in winter and in the coastal area near Jiangsu province in summer, amounting to about -5 and -6 W m-2, respectively. In winter, no significant changes in ARF are observed in eastern China (Fig. 6b). This is likely attributed to an insignificant contribution of SOA to PM2.5 in winter compared to other components with cooling effects, such as sulfate. In summer, SOA is an important component of PM2.5 (20 %–60 %), and the effects of water partitioning on shortwave radiation are relatively stronger. An enhancement in the cooling effects of ARF of up to 15 % occurs near the YRD region, where AOD significantly changes as well.

Meteorological conditions and SOA precursors affect the impacts of water partitioning on SOA. To better illustrate the dependency of SOA on temperature, RH, and SVP of SVOCs, an offline calculation of SOA formation was performed in two representative cities (Jinan, in the NCP, during winter and Nanjing, in the YRD, during summer) when the daily maximum SOA increases occurred. We assume temperature (T) and water vapor mixing ratio (QV) to be within the range of X‾±σ, where X‾ and σ are the mean and standard deviation, respectively, calculated based on WRF predictions at each location. We choose 10 evenly distributed values for T and QV within the range of X‾±σ. The temperature dependence parameter of SVP (ΔH/R) is also scaled separately by 0.2, 0.8, 1.4, and 2.0 for all the SVOCs. As shown in Fig. 7, SOA exhibits a negative correlation with T and a positive correlation with QV in both cities. SOA is more sensitive to QV under cold conditions (Jinan) and to temperature under hot conditions (Nanjing). When the temperature is fixed, the sensitivity of SOA to ΔH/R is different in the two cities. We find more changes in SOA across ΔH/R in Jinan. This is attributed to the temperature correction factor (ζcorr) of Kp in CMAQ as defined below: 8ζcorr=Kp,TrefKp,T=TrefTexp⁡ΔHR1Tref-1T, where Tref is the reference temperature (298 K), and T is temperature. According to Fig. 7, the range of T is 265–274 K in Jinan and 300–307 K in Nanjing. The deviation of temperature from the reference value (298 K) is greater in Jinan than in Nanjing. Therefore, the unit change in ΔH/R causes greater variations of ζcorr and thus Kp in Jinan. As a result, SOA is more sensitive to ΔH/R. The impacts of SVP estimation on SOA are more significant in winter than in summer.

The impacts of water partitioning into OPM and nonideality of the organic–water mixture on SOA are in opposite directions. Water partitioning increases SOA by ∼10 %–20 % in winter and ∼30 %–80 % in summer in most areas of the domain (Fig. 8). This is because the molecular weight of water is quite small and will reduce the mole-averaged molecular weight of the OPM (MW‾) in Eq. (2) (Pankow et al., 2015). The reduced MW‾ further increases Kp and promotes mass transfer of SVOCs from the gas phase to OPM. On the other hand, by considering nonideality of the organic–water mixture, activity coefficients of SVOCs are usually greater than 1.0 in this study, leading to a decrease in Kp. As a result, the total SOA concentration is reduced by ∼10 %–20 % in winter and ∼10 %–50 % in summer (Fig. 9). Overall, the final impacts are the combined consequences of the two processes. In winter, the increase in SOA caused by water partitioning is offset by the decrease in SOA due to the polarity of SVOCs in most areas of the domain, resulting in slight decreases in SOA. In summer, the effect of water partitioning overcomes that of SVOC polarity, so the total SOA loading increases. This further leads to an enhanced attenuation of shortwave solar radiation and cooling of the atmosphere.