China has experienced rapid urbanization and industrialization in recent years, which has led to significant growth in concentration of PM2.5 (particulate matter with aerodynamic diameter less than 2.5 µm) in Chinese megacities, particularly in Beijing (the capital of China) and the surrounding area (Zhang et al., 2015, 2016; Wang et al., 2014). Extensive studies consistently show high PM2.5 levels in winter due to increased coal combustion for heating and a stable atmospheric boundary layer (Sun et al., 2014, 2016; Z. Liu et al., 2015; Ma et al., 2017). The frequency and concentration of PM2.5 pollution negatively impacts human health and atmospheric visibility and results in economic losses (Gao et al., 2015; Lelieveld, et al., 2015; Zhang et al., 2015). The Chinese government has implemented a series of policies to improve air quality, and, as a result, the annual average PM2.5 concentration in Beijing decreased by ∼20 % from 2013 to 2017 (Sun et al., 2016; Zheng et al., 2018; Beijing Environment Protection Agency, 2018; Chinese State Council, 2013). Despite these improvements, PM2.5 concentrations in Beijing still regularly exceed the Chinese National Ambient Air Quality Standards (CNAAQS, 35 µg m-3 annual average) (Sun et al., 2016; Zhang et al., 2016; Beijing Environment Protection Agency, 2018).

Sulfate is one of the most important components of PM2.5, representing 10 %–30 % of PM2.5 mass in eastern China (Huang et al., 2014; Zhang et al., 2013; Wang et al., 2014; Sun et al., 2016; Shi et al., 2017). Recent observations show that the sulfate mass fraction of PM2.5 increases during haze pollution periods, indicating that sulfate is a key driver for severe haze events (Wang et al., 2014, 2016; Cheng et al., 2016; G. Li et al., 2017). Previous simulations have shown that most models fail to predict severe haze pollution in Beijing, at least in part because of sulfate underestimation (Jiang et al., 2013; Park et al., 2014; Pozzer et al., 2012). Globally, sulfate production is dominated by the gas-phase oxidation of SO2 by OH and aqueous-phase oxidation of S(IV) (SO2 ⚫ H2O + HSO3-+SO32-) by H2O2, O3, and O2 catalyzed by transition metal ions (TMIs) in cloud droplets (Calvert and Stockwell, 1983; Schwartz, 1987; Harris et al., 2013; Alexander et al., 2012; Chen et al., 2018). Heterogeneous sulfate production, which refers to aqueous-phase oxidation of S(IV) (SO2 ⚫ H2O + HSO3- + SO32-) on the surface of and/or within the bulk preexisting aerosols, is generally thought to be minor due to the low liquid water content of aerosols (<10-9 cm3 cm-3) compared to clouds (10-8 to 10-6 cm3 cm-3) (Jacob, 2000). However, recent studies have shown that traditional gas- and aqueous-phase chemistry in cloud droplets cannot explain rapid sulfate production observed during haze, suggesting missing sulfate formation mechanisms on aerosols in the models (Zheng et al., 2015; D. Chen et al., 2016; Zhang et al., 2015; Wang et al., 2014; Cheng et al., 2016; Huang et al., 2014). These missing sulfate formation mechanisms on aerosols include heterogeneous oxidation of SO2 by NO2 (Cheng et al., 2016; Wang et al., 2016, 2018; Gao et al., 2016; Zhang et al., 2015) and O2 catalyzed by TMIs (G. Li et al., 2017; Li et al., 2011) and via a free radical chain mechanism (Huie and Neta, 1984; Hung and Hoffman, 2015). The importance of these heterogeneous reactions remains highly uncertain, due in part to uncertainties regarding the aerosol liquid water content, pH, and ionic strength, all of which impact heterogeneous reaction rates (Herrmann et al., 2015; Cheng et al., 2016). In particular, ambient aerosol pH cannot be directly measured, and thus represents a large source of uncertainty (Hennigan et al., 2015).

Previous studies have calculated a large range of aerosol pH values (3.4–7.8) in Beijing using a thermodynamic model (ISORROPIA-II) (Fountoukis and Nenes, 2007). He et al. (2018) noted that the large differences in calculated aerosol pH depended on whether they assume aerosols exist in a stable (Wang et al., 2016; He et al., 2018) or metastable state (Liu et al., 2017; Guo et al., 2017; He et al., 2018). A stable-state assumption allows for the simultaneous existence of solid and aqueous phases, while the metastable-state assumption allows for the existence of aqueous phase only by assuming salts to be supersaturated in aerosols (Fountoukis and Nenes, 2007). Aerosol pH values simulated assuming a stable state are near neutral (pH of 7), much higher than when assuming a metastable state (pH of 4–5) during haze events in China (Guo et al., 2017; Liu et al., 2017; He et al., 2018). However, a recent study calculated an aerosol pH of around 4.6 during Chinese haze events for both the stable and metastable assumptions in ISORROPIA-II after fixing coding errors that impacted the stable-state assumption (Song et al., 2018). This casts doubt on the existence of neutral aerosol pH during Chinese haze events and thus the importance of the NO2 oxidation pathway, which is not important under acidic conditions. Other factors, such as an underestimate of NH3 emissions (Zhang et al., 2018) and Ca2+ concentrations in aerosols (Shen et al., 2016) and the lack of consideration of organic acids in aerosol pH calculations (Wang et al., 2018), add additional uncertainties in estimates of aerosol pH and thus sulfate production rates and mechanisms in Chinese haze events.

The oxygen isotopic composition Δ17O (≈ δ17O - 0.52 × δ18O) of secondary sulfate (Δ17O(SO42-)) reflects the relative importance of different oxidation mechanisms in sulfate production because some of the oxidants transfer unique oxygen isotope signatures to the sulfate oxidation product (Savarino et al., 2000). Sulfate production via S(IV) oxidation by O3 and H2O2 leads to positive Δ17O (SO42-) values of 9.8 ‰ and 0.7 ‰, respectively, while all other oxidants lead to Δ17O(SO42-) at or near 0 ‰ (Table S1 in the Supplement) (Vicars and Savarino, 2014; Savarino and Thiemens, 1999; Lee and Schwartz, 1983; Holt et al., 1981; Dubey et al., 1997). Primary sulfate, both natural (dust and sea salt) and anthropogenic (coal and oil combustion), also has Δ17O(SO42-) values equal to 0 ‰ (Dominguez et al., 2008; Lee et al., 2001). Once formed, sulfate in the atmosphere does not undergo further isotope exchange. Surface observations around the world show that Δ17O(SO42-) ranges from 0 ‰ to 6 ‰ (Alexander et al., 2005, 2012; Q. Chen et al., 2016; Dominguez et al., 2008; Jenkins and Bao, 2006; Lee et al., 2001; Lee and Thiemens, 2001; McCabe et al., 2006; Patris et al., 2007; He et al., 2018; Ishino et al., 2017). Due to the large positive enrichment of sulfate formed from O3 oxidation (Δ17O(SO42-) = 9.8 ‰) and the strong pH dependence of this aqueous-phase reaction, Δ17O(SO42-) is highly sensitive to pH.

In this work, we implement four heterogeneous reactions for sulfate formation (via H2O2, O3, NO2, and TMIs) into a global chemical transport model (GEOS-Chem) and compare the model results with observations of sulfate and SO2 concentrations and Δ17O(SO42-) in Beijing from October 2014 to January 2015. During this time period, Beijing held the Asia-Pacific Economic Cooperation (APEC) meeting from 5 to 11 November 2014. During and before APEC, SO2 emissions in Beijing and its surrounding regions decrease due to strict emission controls to improve air quality (Zhang et al., 2016; J. G. Liu et al., 2015). This paper is organized as follows. Section 2 describes the model simulations and the observations, and the method used to calculate heterogeneous sulfate production rates. Section 3 discusses model results with and without heterogeneous sulfate production considered in comparison with observed concentration and Δ17O(SO42-). Section 4 discusses and explains the differences between our results and observations. Section 5 summarizes the main conclusions.

The model results demonstrate that implementation of heterogeneous sulfate formation pathways on aerosols reduces modeled low biases in both the concentration and oxygen isotopic signature of sulfate (Δ17O(SO42-)) and suggests that TMI catalysis dominates heterogeneous sulfate production. However, the model is still biased low in both metrics, indicating that the model is still underestimating sulfate production rates. The modeled underestimate in Δ17O(SO42-) reveals an underestimate in the O3 oxidation pathway. Since the relative contribution of heterogeneous sulfate formation pathways are sensitive to aerosol pH, we investigated the influence of aerosol pH using a series of sensitivity simulations with prescribed aerosol pH values between 2 and 8 (Table 1). Figure 5 shows modeled sulfate concentrations produced from different oxidation pathways and resulting Δ17O(SO42-) in Beijing averaged over HPP assuming different aerosol pH values. Heterogeneous sulfate formation represents over 50 % of total sulfate formation when pH > 5 and when pH < 4. At aerosol pH < 4, heterogeneous sulfate formation is dominated by the TMI pathway because of the high solubility of Fe and Mn at low pH (Guieu et al., 1994; Mackie et al., 2005). At aerosol pH > 5, heterogeneous sulfate formation is dominated by the O3 pathway because the solubility of SO2 increases with increasing pH and because the S(IV) partitioning shifts to favor SO32- at higher pH. The aqueous-phase rate constant for SO32- + O3 is almost 4 orders of magnitude faster than for HSO3- + O3 (Table 2). Heterogeneous sulfate formation by NO2 also increases with increasing pH due to the increased solubility of SO2 at high pH, and represents 15 %–30 % of total heterogeneous sulfate production between pH 6 and 8. In contrast to our results, previous results (Cheng et al., 2016; Wang et al., 2016) suggested that NO2 oxidation is more important than O3 oxidation at high pH values. The difference is due to assumed O3 concentrations used in the rate calculations. The aforementioned studies assumed an O3 concentration of 1 ppb relative to an NO2 concentration of 66 ppb. In our model simulations, average O3 and NO2 concentrations are 9 and 85 ppb, respectively. Modeled O3 concentrations are similar to the observations during the measurement period (Fig. S6). Heterogeneous sulfate production is lower (20 %) when pH 4–5 because of the low solubility of Fe and Mn and low concentrations of SO32- in this pH range. We note that while the pH-dependence of S(IV) partitioning is well known, the pH-dependency of metal solubility is more uncertain.

Anthropogenic SO2 emissions in China have been reduced sharply since 2009 due to the stringent pollution control measures implemented (Zheng et al., 2018; Van der A et al., 2017; Krotkov et al., 2016). Compared with 2010, anthropogenic SO2 emissions were reduced by about 50 % in 2015 (Krotkov et al., 2016; Zheng et al., 2018; Van der A et al., 2017). However, NH3 and NMVOC emissions in China remained stable during 2010–2017 due to the absence of effective mitigation measures in current policies (Zheng et al., 2018). The emission changes may affect the abundance of species that influence cloud and aerosol pH and further influence sulfate production rates and the contribution of each sulfate formation pathway. However, other studies using observations between 2014 and 2016 (Liu et al., 2017; Song et al., 2018) found a similar pH range to that calculated here, suggesting that a modeled low bias in aerosol pH is not likely to be the source of the modeled discrepancy in Δ17O(SO42-).

Figure 5 also shows that model results with mean aerosol pH > 5 would result in a high bias in Δ17O(SO42-) due to the increasing importance of the O3 oxidation pathway at higher pH values, effectively providing an observational constraint for typical aerosol pH < 5 in Beijing haze. However, modeled Δ17O(SO42-) calculated at the aerosol pH of 4.2-4.7, as estimated by recent studies (Guo et al., 2017; Song et al., 2018) represents a low bias relative to the observations. A joint comparison of observations and model results with both sulfate concentrations and Δ17O(SO42-) suggests an average aerosol pH between 5 and 6 in Beijing during the study period, which is higher than bulk aerosol pH calculations in our model calculations and in previous studies (Guo et al., 2017; Song et al., 2018).

Thermodynamic pH calculations in ISORROPIA II are based on the assumption of internally mixed aerosols and do not directly consider aerosol alkalinity (carbonate). Fresh dust is initially alkaline (calcium carbonates). This alkalinity is partially depleted (carbonate is converted to CO2) upon uptake of the acid gases HNO3, SO2, and H2SO4 (Fairlie et al., 2010; Usher et al., 2003). Due to the high pH of alkaline dust, uptake of SO2 would be followed by heterogeneous oxidation by O3 (Fairlie et al., 2010; Ullerstam et al., 2003) and NO2 (Zhao et al., 2018) if there is enough aerosol liquid water to promote aqueous-phase oxidation. A recent modeling study by Uno et al. (2017) using a previous version of GEOS-Chem explicitly includes uptake of HNO3, SO2, and H2SO4 on alkaline dust aerosols as described in Fairlie et al. (2010). Their model calculated that sulfate from uptake of SO2 and H2SO4 on alkaline dust in Beijing during our measurement period represents on average 3 % of total sulfate abundance. Most of this sulfate formation on alkaline dust was from uptake of H2SO4 (70 %–80 %, Fairlie et al., 2010), with the remaining fraction (20 %–30 %) from uptake of SO2, followed by oxidation to sulfate. On average, less than 1 % of sulfate in Beijing is formed on alkaline dust via the uptake and oxidation of SO2 in the model.

At pH values representative of alkaline dust (pH = 7–8), sulfate formed via oxidation of SO2 on alkaline dust would be dominated by O3, leading to a relatively high Δ17O(SO42-) value of 6 ‰ (Fig. 5). In order to account for the difference in the median Δ17O(SO42-) observed (1.0 ‰) and modeled (0.5 ‰), sulfate formation from the uptake and oxidation of SO2 on alkaline dust would need to account for an average of 9 % of total sulfate abundance during our measurement period. This fraction is higher than that calculated in Uno et al. (2017), which did not include anthropogenic dust. In our model, anthropogenic dust accounts for 28 % of total dust in Beijing (Fig. S7), and natural dust mostly originates from the Gobi Desert in southwestern Mongolia and the Badain Jaran Desert in northern China (Zhang et al., 2003, 2016). The anthropogenic dust is not abundant enough to explain the difference between the results of Uno et al. (2017) and amount of sulfate production on alkaline dust required to explain the observed Δ17O(SO42-). Due to uncertainties in processes such as the RH dependence of the uptake of SO2 on alkaline dust, the importance of this pathway could be underestimated in Uno et al. (2017). If this process does account for the difference in modeled and observed Δ17O(SO42-), then NO2 oxidation may be slightly more important than indicated in Fig. 3. Our calculations (Fig. 5) suggest that NO2 oxidation accounts for 23 % of heterogeneous sulfate formation at aerosol pH values between 7 and 8. If sulfate formation on alkaline dust accounts for 9 % of total sulfate formation and NO2 oxidation accounts for 23 % of this sulfate formation pathway, then NO2 oxidation can account for up to 2 % of total heterogeneous sulfate formation in Beijing, which still suggests NO2 oxidation is a minor pathway during wintertime Beijing haze events.

In addition to a model underestimate of the ozone oxidation pathway, a model underestimate of the H2O2 oxidation pathway may explain part of the modeled low bias in Δ17O(SO42-). The average modeled H2O2 concentrations during HPP and CP underestimate the observations (Ye et al., 2018) by up to an order of magnitude (Fig. S3c). Mao et al. (2013) proposed a HO2-Cu-Fe catalytic mechanism for H2O2 production in the aerosol phase. In their mechanism, the uptake of O2 and subsequent heterogeneous reactions with Cu and Fe will lead to production of H2O2, when the molar ratio of dissolved Cu to Fe was >0.1. Ye et al. (2018) found that the molar ratio of dissolved Cu to Fe is >0.1 during moderately polluted days, suggesting the uptake of HO2 radicals on particles might be an important source of H2O2 during the winter in Beijing. We note, however, that an underestimate of modeled H2O2 cannot explain all of the discrepancy in Δ17O(SO42-), as sulfate formed from H2O2 oxidation (0.7 ‰) is lower than the observed mean Δ17O(SO42-) of 0.9 ‰. All other oxidation pathways yield Δ17O(SO42-) = 0 ‰ and cannot explain the model's low bias in Δ17O(SO42-).

An increase in sulfate abundance of 9 % from formation on alkaline dust is not enough to explain the remaining model underestimate in sulfate concentrations (-40 %) and SOR (-28 %) during HPP. Two recent studies suggested that hydroxymethane sulfonate (HMS) may be present in Chinese haze events and measured as sulfate via ion chromatography and HR-AMS (Moch et al., 2018; Song et al., 2019). HMS is formed via aqueous-phase oxidation via nucleophilic attack of HSO3- and SO32- on HCHO. Moch et al. (2018) suggested that in-cloud formation of HMS could explain the low model bias in sulfate concentrations, while Song et al. (2019) suggested that heterogeneous formation of HMS on aerosol surfaces can explain up to one-third of the low model bias in their study. In the present study, as the organics were removed from the sample matrix prior to Δ17O(SO42-) analysis (Geng et al., 2013), suggesting that measured Δ17O(SO42-) does not have significant contributions from HMS. This will depend on the efficiency of HMS removal in He et al. (2018); previous work suggests that one of the two organic removal techniques employed (H2O2 oxidation) does not remove HMS under atmospheric conditions (Munger et al., 1984, 1986). However, the presence of HMS in the sample matrix, if any, would lower measured Δ17O(SO42-), and any correction for this would increase the observed Δ17O(SO42-) and further increase the discrepancy between modeled and observed Δ17O(SO42-).

We have used a combination of observations and modeling of sulfate and SO2 concentrations and Δ17O(SO42-) to quantify sulfate production mechanisms in Beijing. We focus on the period of 17 October 2014–20 January 2015, when 10 heavy pollution periods (HPPs) were defined with observed PM2.5 concentrations > 150 µg m-3. The standard model simulation that only includes primary sulfate and sulfate formation from gas-phase oxidation by OH and in-cloud oxidation by H2O2 and O3 underestimates mean sulfate concentration by 65 % and Δ17O(SO42-) by 50 % during HPPs. Adding in-cloud oxidation catalyzed by transition metal ions (TMIs) and heterogeneous oxidation by H2O2, O3, NO2, and TMIs on aerosols can improve the model simulation of sulfate abundance and Δ17O(SO42-), with the model biases decreasing from -65 % to -40 % for sulfate and from -50 % to -5 % for Δ17O(SO42-) during HPP. Modeled heterogeneous sulfate production accounts for around 20 % of total sulfate production. The model predicts that the TMI-catalyzed oxidation dominates heterogeneous sulfate production under the calculated aerosol pH of ≤5; however, this reaction is highly uncertain due to limitations in our ability to assess modeled dissolved Fe(III) and Mn(II) concentrations. The modeled Δ17O(SO42-) is still biased low compared to observations, suggesting an underestimate of sulfate production by O3 oxidation. We hypothesize that sulfate aerosol production by O3 on externally mixed alkaline dust aerosol can explain at least part of the remaining discrepancy in Δ17O(SO42-). The Δ17O(SO42-) observations suggest that a fractional sulfate contribution of just 9 % originating from SO2 oxidation on alkaline dust aerosol can explain the model discrepancy in Δ17O(SO42-). We calculate that sulfate formation on alkaline dust is dominated by O3 oxidation (74 %), followed by NO2 oxidation (23 %). The Δ17O(SO42-) observations combined with our model calculations indicate only a minor (2 %) role of heterogeneous sulfate formation via NO2 oxidation of SO2. Future studies will examine the impact of these heterogeneous S(IV) oxidation mechanisms on the regional and global sulfur budgets.