3.1 Nocturnal heterogeneous N₂O₅ reaction at Wangdu

Figure 1Time series of concentrations of N₂O₅, ClNO₂, NO₃ production rates, the steady-state lifetimes of N₂O₅, and related gas and aerosol data at Wangdu from 21 June to 9 July 2014. N₂O₅ and ClNO₂ are 1 min averaged data, whereas the NO_x, O₃, and NO₃ production rates and τ(N₂O₅) are given as 5 min averages. The data for S_a and fine particulate ${\mathrm{NO}}_{\mathrm{3}}^{-}$ are in 10 and 30 min time resolutions, respectively. The data gaps were caused by technical problems, calibrations, or instrument maintenance.

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Figure 1 illustrates the time series of NO_x, O₃, N₂O₅, ClNO₂, particulate ${\mathrm{NO}}_{\mathrm{3}}^{-}$, S_a, the calculated production rate of NO₃, and the lifetime of N₂O₅ observed at Wangdu between 20 June and 9 July 2014. The abundance of NO_x and O₃ was observed at nighttime (20:00 to 05:00 local time); with average nighttime mixing ratios of 21 and 30 ppbv, respectively. The elevated nighttime NO_x and O₃ levels led to the active production of NO₃, with an average nighttime production rate of NO₃ ($=k_{{\mathrm{O}}_{\mathrm{3}}+{\mathrm{NO}}_{\mathrm{2}}}$[NO₂][O₃]) of 1.7 ppv h⁻¹ and a maximum level of 8.3 ppv h⁻¹ for the entire campaign. Even with the rapid production of NO₃ and the high NO₂ level at night, the observed N₂O₅ concentrations were typically low (i.e., the average nighttime concentration of 34±14 pptv). The low N₂O₅ value is consistent with the short steady-state lifetime of N₂O₅ (τ(N₂O₅)) for the study period, ranging from 0.1 to 10 min, suggesting that the direct loss of N₂O₅ via heterogeneous reaction and/or indirect loss of N₂O₅ via decomposition to NO₃ (i.e., reactions of NO₃ with NO and VOCs) were rapid in this region. The good correlation between nighttime levels of ClNO₂ and fine particulate ${\mathrm{NO}}_{\mathrm{3}}^{-}$ (the products of heterogeneous reactions of N₂O₅ via Reactions R3 and R4, respectively) – with a coefficient of determination (r²) of greater than 0.6 on 10 out of 13 nights (with full CIMS measurement) – provides field evidence of active N₂O₅ heterogeneous uptake processes in this region.

3.2 Estimation of N₂O₅ uptake coefficient and ClNO₂ production yield

The consistent trends and clear correlation between ClNO₂ and the ${\mathrm{NO}}_{\mathrm{3}}^{-}$ could be used to quantify N₂O₅ heterogeneous uptake following the method described by Phillips et al. (2016). The uptake coefficient of N₂O₅, γ(N₂O₅), was estimated based on the production rate of ClNO₂ (pClNO₂) and the nitrate formation rate ($p{\mathrm{NO}}_{\mathrm{3}}^{-}$) from the following Eq. (4). The pClNO₂ and $p{\mathrm{NO}}_{\mathrm{3}}^{-}$ were determined from the linear fit of the increase in ClNO₂ and total ${\mathrm{NO}}_{\mathrm{3}}^{-}$ (sum of HNO₃ and particulate ${\mathrm{NO}}_{\mathrm{3}}^{-}$) with time, while [N₂O₅] is the mean concentration of N₂O₅ for the specified duration.

The yield of ClNO₂ was determined from the regression analysis of ClNO₂ versus total ${\mathrm{NO}}_{\mathrm{3}}^{-}$ (Wagner et al., 2012; Riedel et al., 2013). The slope (m) from the regression plot was fitted into Eq. (5) to obtain the ϕ.

The concentrations of ClNO₂, N₂O₅, total ${\mathrm{NO}}_{\mathrm{3}}^{-}$, and other related data used for this analysis were averaged or interpolated into 10 min. This analysis assumes (1) that the air mass is stable, vertical mixing is limited and losses of ClNO₂ and total ${\mathrm{NO}}_{\mathrm{3}}^{-}$ are insignificant within the duration of analysis; and (2) that N₂O₅ heterogeneous uptake is a dominant source of total soluble nitrate during the night rather than the gas homogeneous production or nitrate production from the preceding daytime.

The limitation of this method is that it cannot predict the γ(N₂O₅) with negative changes in the concentrations of ClNO₂ or total ${\mathrm{NO}}_{\mathrm{3}}^{-}$, which may be a result of differences in the origin or age of the air mass. In accordance with this limitation and with the assumption (1) above, we carefully select plumes during the nighttime that meet the following criterium for the analysis: shorter periods of data, usually between 1.5 and 4 h, with concurrent increases in ClNO₂ and total ${\mathrm{NO}}_{\mathrm{3}}^{-}$. The plume age, represented by the ratios of NO_x to NO_y, was relatively stable (change <0.1 min⁻¹), and no drastic changes were seen in other variables such as wind conditions, the particle surface area, RH, or temperature. Typically, the air masses in the selected cases can be influenced by the emissions from the nearby village or urban area, coal-fired power plants, and biomass burning activities in the region prior to arrival at the site (see Tham et al., 2016). Hence the concentration of NO in the plume must be relatively constant (change of NO∕NO₂ ratio <0.1 min⁻¹), as the presence of a transient NO plume may affect the concentration of N₂O₅, which can bias the estimation of γ(N₂O₅). Figure 2 shows two examples of relatively constant conditions of relevant chemical composition and environmental variables, together with a plot of ClNO₂ versus total ${\mathrm{NO}}_{\mathrm{3}}^{-}$ for the night. Our previous analysis showed that the nighttime vertical mixing is limited at the ground site of Wangdu (Tham et al., 2016), and will likely not affect the analysis of ClNO₂ and total ${\mathrm{NO}}_{\mathrm{3}}^{-}$. It should also be noted that contribution of total ${\mathrm{NO}}_{\mathrm{3}}^{-}$ from other sources, like the reaction of OH with NO₂ and the oxidation of VOCs by NO₃, can bias the values predicted for γ(N₂O₅) and ϕ.

Figure 2Example of the accumulation of ClNO₂ and total nitrate (particulate ${\mathrm{NO}}_{\mathrm{3}}^{-}$ + HNO₃) concentrations during the relatively constant condition of relevant chemical compositions and environmental variables observed for (a) Plume 1 on 20–21 June 2014 and (c) Plume 7 on 29–30 June 2014. Scatter plots of ClNO₂ versus particulate ${\mathrm{NO}}_{\mathrm{3}}^{-}$ + HNO₃ to estimate the ClNO₂ yield (ϕ) for these two cases are shown in (b) and (d).

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To check the validity of assumption (2) above, we also calculated the production rate of ${\mathrm{NO}}_{\mathrm{3}}^{-}/{\mathrm{HNO}}_{\mathrm{3}}$ via reaction of OH + NO₂ ($=k_{\mathrm{OH}+{\mathrm{NO}}_{\mathrm{2}}}$[OH][NO₂]) and NO₃ + VOC (=Σ_ik_i[VOC_i][NO₃], where VOC_i=C₂H₆, C₃H₆, C₃H₈, HCHO, CH₃OH, C₂H₄O, CH₃C(O)CH₃), as shown in the average diurnal profiles of related species in Fig. 3. It is clear that particulate ${\mathrm{NO}}_{\mathrm{3}}^{-}$ was the dominant species during the nighttime at Wangdu, while the nighttime gas-phase HNO₃ is only 7 % (on average) of the total ${\mathrm{NO}}_{\mathrm{3}}^{-}$ (Fig. 3b). The strong correlation between ClNO₂ and nitrate during the night indicates that the heterogeneous process of N₂O₅ was the dominant source of particulate nitrate. Moreover, the production rate of HNO₃, as calculated from the gas-phase reactions of OH + NO₂ and NO₃ + VOC, shows a decreasing trend towards the night (Fig. 3c), and the combination of these rates on average is less than one-third of the average $p{\mathrm{NO}}_{\mathrm{3}}^{-}$, which was determined from the slope of nighttime particulate ${\mathrm{NO}}_{\mathrm{3}}^{-}$ in Fig. 3b. The increase in nighttime ${\mathrm{NO}}_{\mathrm{3}}^{-}$ was also accompanied by an increase in ammonium (${\mathrm{NH}}_{\mathrm{4}}^{+}$), which suggests that the repartition process to form ammonium nitrate was efficient, thus limiting the release of HNO₃ (Fig. 3d). These results support the validity of the above assumptions and the determination of uptake and yield in this analysis.

Figure 3Diurnal variations in (a) N₂O₅ and ClNO₂; (b) particulate ${\mathrm{NO}}_{\mathrm{3}}^{-}$ and gas-phase HNO₃ (c) gas-phase production rate of ${\mathrm{NO}}_{\mathrm{3}}^{-}/{\mathrm{HNO}}_{\mathrm{3}}$ via reaction of OH + NO₂ and NO₃ + VOC; and (d) concentrations of ${\mathrm{NH}}_{\mathrm{4}}^{+}$ in relation to particulate ${\mathrm{NO}}_{\mathrm{3}}^{-}$. The time indicated in the x axis is the local time and the shaded areas (yellow) represent the daytime.

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Table 1N₂O₅ uptake coefficients and ClNO₂ production yields from 10 selected plumes at Wangdu during the summer of 2014. The uncertainty (±) in the γ(N₂O₅) and ϕ was estimated by the geometric mean of uncertainty from the scattering of the data plots and uncertainty from the measurement of N₂O₅, ClNO₂, aerosol surface area, and total ${\mathrm{NO}}_{\mathrm{3}}^{-}$.

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With these methods and selection criteria, we can derive γ(N₂O₅) and ϕ for 10 different nighttime plumes in 8 out of 13 nights with the full CIMS measurement. Table 1 shows the estimated N₂O₅ uptake coefficients and ClNO₂ yields at Wangdu together with the errors that account for the scattering of data in the analysis and uncertainty from the measurement of N₂O₅, ClNO₂, aerosol surface area, and total ${\mathrm{NO}}_{\mathrm{3}}^{-}$. The estimated γ(N₂O₅) values ranged from 0.005 to 0.039, with a mean value of 0.022. A large variability was found in ϕ (range: 0.06 to 1.04). The relatively larger γ(N₂O₅) and ϕ values observed on the night of 20–21 June are consistent with the observation of the highest ClNO₂ concentration, whereas the lower γ(N₂O₅) and ϕ values on the night of 28–29 June explain the observation of the elevated N₂O₅ and small ClNO₂ mixing ratios (cf. Fig. 1). The observed γ(N₂O₅) and ϕ values at Wangdu were compared with literature values derived from previous field observations in various locations in North America, Europe, and China, as summarized in Fig. 4 and Table 2. The variable values of γ(N₂O₅) in this study fall in the range of γ(N₂O₅) (<0.001 to 0.11) and ϕ (0.01–1.38) reported around the world. The values are also within the range of N₂O₅ uptake coefficients and ClNO₂ yields determined in regions of China (γ(N₂O₅) =0.004–0.103; ϕ=0.01–0.98), which are in the middle to upper end of the values reported around the world. The observed significant ClNO₂ concentrations and high yields of ϕ here, consistent with other studies at inland sites (cf. Table 2), also point to the fact that ClNO₂ production can be efficient in regions far from the oceanic source of chloride and further highlight the important role of anthropogenic chloride emissions in the chlorine activation process and the next day's photochemistry. The question that arises here is what drives the large variability in the γ(N₂O₅) and ϕ at Wangdu.

Figure 4Comparison of the field-observed N₂O₅ uptake coefficient and ClNO₂ yield from previous studies. Stick bars represent the range of the reported values, and cube represent the median or average values reported in these measurements. The corresponding references are listed in Table 2.

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Table 2Summary of field-observed N₂O₅ uptake coefficient and ClNO₂ yield from previous studies.

n.a. = no information available

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3.3 Factors that control the N₂O₅ uptake coefficient

Heterogeneous uptake of N₂O₅ is governed by various factors, including the amount of water and the physical and chemical characteristics of the aerosols (Chang et al., 2011; Brown and Stutz, 2012). To gain better insight into the factors that drive the N₂O₅ heterogeneous uptake, the determined γ(N₂O₅) values were compared with those predicted from complex laboratory-derived parameterizations, and their relationships with the aerosol water content and aerosol compositions observed at Wangdu were examined. The parameterization of N₂O₅ uptake coefficient derived from Bertram and Thornton (2009; γ_B&T) assumed a volume-limited reaction of N₂O₅ on mixed aerosols and considered the bulk amount of nitrate, chloride, and water in the aerosol as the controlling factors, which can be expressed by Eq. (6):

where A is an empirical pre-factor calculated from the volume of aerosol (V), S_a, $c_{{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}}$, and Henry's law coefficient of N₂O₅ ($A=\mathrm{4}/c_{{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}}\times V/S_{\mathrm{a}}\times H_{\mathrm{aq}})$; $k=\mathrm{1.15}\times {\mathrm{10}}^{\mathrm{6}}-(\mathrm{1.15}\times {\mathrm{10}}^{\mathrm{6}}{)}^{\exp (-\mathrm{0.13}[{\mathrm{H}}_{\mathrm{2}}\mathrm{O}\left]\right)}$; $k_{\mathrm{R}\mathrm{3}}/k_{\mathrm{R}\mathrm{2}\mathrm{b}}=\mathrm{0.06}$; and $k_{\mathrm{R}\mathrm{4}}/k_{\mathrm{R}\mathrm{2}\mathrm{b}}=\mathrm{29}$. The concentration of aerosol liquid water ([H₂O](l)) used in this study was estimated from the Extended Aerosol Inorganics Model (E-AIM) model IV with inputs of measured bulk aerosol composition of ${\mathrm{NH}}_{\mathrm{4}}^{+}$, Na⁺, ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$, ${\mathrm{NO}}_{\mathrm{3}}^{-}$, and Cl⁻ (http://www.aim.env.uea.ac.uk /aim/model4/model4a.php, last access: 16 August 2018; Wexler and Clegg, 2002), and the V∕S_a was taken from the field measurement at Wangdu. It should be noted that the parameterization and calculation here assumes an internal mixing of the aerosol chemical species, and the size distribution of [H₂O], [${\mathrm{NO}}_{\mathrm{3}}^{-}$], and [Cl⁻] in aerosols was not considered due to lack of measurement information. The uptake process would vary with size and mixing state of the particles, thus the predicted γ values here may be biased as a result, but represents an average over bulk aerosols. The γ_B&T also does not account for the suppression of γ(N₂O₅) from the organics, but it is frequently used with the parameterization formulated by Anttila et al. (2006), who treated the organic fraction in the aerosols as a coating, as given in Eq. (7) (e.g., Morgan et al., 2015; Phillips et al., 2016; Chang et al., 2016). The net uptake of N₂O₅ onto an aqueous core and organic coating (γ_B&T+Org) can be determined by Eq. (8).

Here, the H_org is the Henry's law constant of N₂O₅ for organic coating; D_org is the solubility and diffusivity of N₂O₅ in the organic coating of thickness L; and R_c and R_p are the radii of the aqueous core and particle, respectively. The particle radius R_p was determined from the measured median radius of the particle surface area distribution. The L was calculated from the volume ratio of the inorganics to total particle volume following the method in Riemer et al. (2009) with the assumption of a hydrophobic organic coating (density, 1.27 g cm⁻³) on the aqueous inorganic core (with a density of 1.77 g cm⁻³). The aqueous core radius R_c was calculated by subtracting the L from R_p. The H_orgD_org is equal to 0.03×H_aqD_aq, where H_aq=5000 M atm⁻¹ and $D_{\mathrm{aq}}={\mathrm{10}}^{-\mathrm{9}}$ m² s⁻¹ (Chang et al., 2011, and references therein). In addition, Evans and Jacob (2005) proposed a simpler parameterization of N₂O₅ uptake on sulfate aerosol (γ_E&J) as a function of temperature and RH, as given by Eq. (9).

Figure 5 illustrates a comparison of field-derived N₂O₅ uptake coefficients with the values computed from the above parameterizations. The computed γ_B&T (red circle) ranged from 0.046 to 0.094 and was consistently higher than the field-derived γ(N₂O₅) by up to a factor of 9. By accounting for the effects of organic coating on the N₂O₅ uptake coefficient via Eqs. (7) and (8), the calculated N₂O₅ uptake coefficients (green circle in Fig. 5) are significantly underestimated. Note that only six cases were available to compute the γ_B&T+Org due to the limited organic aerosols data for the study period. The N₂O₅ uptake coefficients computed from the parameterization suggested by Evans and Jacob (2005) are generally consistent with the field-derived γ(N₂O₅) (as shown by blue circles). The different results from these parameterizations may suggest more complex aerosol composition, mixing states, and other physicochemical properties in the real ambient atmosphere than in the aerosol sample used in the laboratory study.

Figure 5Comparison of field-derived N₂O₅ uptake coefficients with values computed from different parameterizations. The dashed line represents 1:1, and the error bars show the uncertainty in γ(N₂O₅) derived from the field.

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Figure 6Relationship between field-derived γ(N₂O₅) and (a) aerosol water content (mol per volume of aerosol); (b) nitrate concentration per volume of aerosol; (c) particulate nitrate concentration (µmol m⁻³ of air); (d) H₂O to ${\mathrm{NO}}_{\mathrm{3}}^{-}$ molar ratio; (e) Cl⁻ to ${\mathrm{NO}}_{\mathrm{3}}^{-}$ molar ratio; (f) organic-to-sulfate mass ratio (data from aerosol mass spectrometer); (g) concentration of ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$ (µmol m⁻³ of air); and (h) amount of water in aerosol multiplied by the volume-to-surface-area ratio. Color code represents the ambient RH, and the value in the box is the best correlation coefficient obtained from curve fittings.

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Figure 7Scatter plots for (a) yield derived from the field versus yield calculated from the parameterization, using k_R4∕k_R3 of 483 (recommended by Bertram and Thornton, 2009; solid circle) and 836 (recommended by Behnke et al., 1997; pink open circle). Error bars represent the uncertainty in field-derived ϕ, and the black dotted line represents the 1:1 ratio; (b) field-derived yield versus aerosol water content; (c) field-derived yield versus chloride; and (d) field-derived yield versus CH₃CN∕CO. The red dotted lines show the quadratic fitting line of the data.

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We then examined the relationships of the field-derived γ(N₂O₅) with RH, water content, and aerosol compositions, as illustrated in Fig. 6. It can be seen in Fig. 6a that the γ(N₂O₅) has a clear correlation with the aerosol water content (r²=0.88; p<0.01, t test). The strong dependence of γ(N₂O₅) on the aerosol water content was observed for RH ranging from 40 % to exceed 90 % or [H₂O] from 10 mol L⁻¹ to above 40 mol L⁻¹. This pattern is different to the trends observed in laboratory studies for N₂O₅ uptake onto aqueous sulfate and malonic acid aerosols, in which the γ(N₂O₅) strongly increases with humidity at RH below 40–50 % but becomes insensitive above this threshold (e.g., Hallquist et al., 2003; Thornton et al., 2003). The γ(N₂O₅) at Wangdu shows a decreasing trend with the concentration of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ per volume of aerosol (see Fig. 6b), which is similar to the results from the previous laboratory studies (Bertram and Thornton, 2009; Griffiths et al., 2009). However, we do not think that [${\mathrm{NO}}_{\mathrm{3}}^{-}$] is the dominant limiting factor for N₂O₅ uptake at this site – as seen in the consistency of the γ(N₂O₅) data points with the change in RH (in the color code of Fig. 6b), the increasing trend of γ(N₂O₅) with the concentration of particulate nitrate in the air (cf. Fig. 6c), and the positive dependency of γ(N₂O₅) on the molar ratio of [H₂O] ∕ [${\mathrm{NO}}_{\mathrm{3}}^{-}$] (cf. Fig. 6d) – which reflect that the N₂O₅ uptake is more sensitive to the aerosol water content than to the ${\mathrm{NO}}_{\mathrm{3}}^{-}$, at least up to a [H₂O] : [${\mathrm{NO}}_{\mathrm{3}}^{-}$] ratio of 20. The increase in ambient particulate nitrate is probably due to the faster N₂O₅ heterogeneous reaction. The N₂O₅ uptake does not show an increasing trend with the chloride-to-nitrate molar ratio, a pattern demonstrated in the laboratory result (Bertram and Thornton, 2009), but rather a decrease for high [Cl⁻] ∕ [${\mathrm{NO}}_{\mathrm{3}}^{-}$] ratios, and it also correlates with differences in RH (cf. Fig. 6e). There is a lack of correlation of γ(N₂O₅) with the [Org] : [${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$] ratio observed in the ratio range of 0.5–1.2, indicating that the suppression of organics on the N₂O₅ uptake may be insignificant at Wangdu. These results are in line with the parameterization comparison results shown in Fig. 5, which reveal that the variation in the N₂O₅ uptake at Wangdu is not driven by the chemical properties of aerosols like ${\mathrm{NO}}_{\mathrm{3}}^{-}$, Cl⁻, and organics but rather that the RH or the aerosol water content plays a defining role in the N₂O₅ heterogeneous uptake.

The response of N₂O₅ uptake to the changes in RH is consistent with the changes in the sulfate (${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$) concentrations (see Fig. 6g), which mainly determine the hygroscopicity of the aerosols and were found to be responsible for the particle growth at Wangdu (Wu et al., 2017). The hygroscopic growth of aerosols inferred by the RH (water uptake) can affect the amount of water in the aerosol and the volume-to-surface-area ratio ([H₂O]V∕S_a). The good positive correlation of γ(N₂O₅) with [H₂O]V∕S_a (see Fig. 6h) suggests that the increased volume of aerosol, in particular the layer of aerosol water content, could allow efficient diffusion of N₂O₅ and solvation of N₂O₅ into ${\mathrm{H}}_{\mathrm{2}}{\mathrm{ONO}}_{\mathrm{2}}^{+}$ and ${\mathrm{NO}}_{\mathrm{3}}^{-}$ for further aqueous reactions, whereas a smaller volume of aerosol (less water content) may be easily saturated by N₂O₅ and then diffuse the N₂O₅ out of the aerosol, limiting the solvation of the N₂O₅ process and restricting N₂O₅ uptake. These results are consistent with several laboratory studies which have demonstrated that an increase in RH enhanced the particle aqueous volume and increased the bulk reactive N₂O₅ uptake on aqueous sulfate and organic acids (e.g., malonic, succinic, and glutaric acids) containing aerosols (Thornton et al., 2003; Hallquist et al., 2003). The increase in RH can also lower the viscosity of the aqueous layer in organic-containing aerosols, leading to greater diffusivity of N₂O₅ within the aerosol water layer, which ultimately increases the N₂O₅ uptake (Gržinić et al., 2015).

The strong dependency of N₂O₅ uptake upon the RH has not been clearly demonstrated in other field measurements. Field observations of γ(N₂O₅) in North America and Europe did not show any significant direct dependence of γ(N₂O₅) on RH but were strongly influenced by the aerosol composition (refer to the descriptions in Table 2). For instance, the flight measurements in Texas and London showed the independence of γ(N₂O₅) at RH from 34 % to 90 %, but the γ(N₂O₅) were generally controlled by the amount of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ and/or organic compounds (Brown et al., 2009; Morgan et al., 2015). A comparison of ground measurements in Seattle and Boulder showed variations in γ(N₂O₅) at various H₂O(l) levels in the two places, but the RH alone was insufficient to describe their observed γ(N₂O₅) variability, and the organic composition of the aerosols was determined to have a dominant influence on γ(N₂O₅) (Bertram et al., 2009). Another study from a mountainous site in Germany reported no significant correlation of γ(N₂O₅) with aerosol compositions and only a weak dependence on humidity (Phillips et al., 2016). However, field measurements at Jinan and Mt. Tai in northern China during the same season also showed a positive relationship of γ(N₂O₅) with an RH between 43 % and 72 % and aerosol water content of 31–65 mol L⁻¹, respectively (X. Wang et al., 2017; Z. Wang et al., 2017). The results of this study and previously reported results in the region may suggest that RH and aerosol water content are the important limiting factors for the N₂O₅ heterogeneous process in polluted northern China. In summary, the more complex parameterizations considering nitrate, chloride, and the organic coating cannot fully represent the variation in γ(N₂O₅) at Wangdu; instead, a simple parameterization that accounts only for temperature and RH appears to explain the variation in γ(N₂O₅) at Wangdu. It would be of great interest to determine whether such a phenomenon can be found in other places.

3.4 Factors that affect the ClNO₂ production yield

In addition to the uptake coefficients, the factors that influence the branching yield of ClNO₂ from the N₂O₅ heterogeneous uptake were also assessed. Figure 7a shows the scatter plot of the ϕ calculated from Eq. (3) versus the ClNO₂ yield derived from the Wangdu field data from Eq. (5) in Sect. 3.2. Generally, the ϕ_param. shows less variability but was obviously overestimated relative to the field-determined ϕ. Such a discrepancy has also been observed elsewhere (Thornton et al., 2010; Mielke et al., 2013; Riedel et al., 2013), including our recent observations in an urban site (Jinan) and a mountaintop site (Mt. Tai) in northern China, where the parameterized ϕ would be overestimated by up to 2 orders of magnitude (X. Wang et al., 2017; Z. Wang et al., 2017). Further analysis by linking the field-derived ClNO₂ yields with the aerosol water content (Fig. 7b) and the Cl⁻ content (Fig. 7c) show a weak positive correlation (r²=0.36) and a weak negative trend (r²=0.20), respectively (from quadratic fitting). The weak correlations reflect that the ClNO₂ yield is not solely controlled by the amount of water and chloride in the aerosol, as defined in the parameterization (see Eq. 3), and/or the existence of other nucleophiles that can compete with Cl⁻ in Reactions (R3) and (R4). The aqueous concentration of Cl⁻ in the present study is relatively higher than previous laboratory studies (e.g., Bertram and Thornton, 2009; Roberts et al., 2009), and might not be fully involved in the Reaction (R4); for example, the possible effect of the nonuniform distribution of chloride within the aerosol. It might contribute to the overestimation and less variability in ϕ predicted from the parameterization (Riedel et al., 2013) and the positive relationship of field-derived ϕ with [H₂O] (see Fig. 7b) might also imply that the increase in water content could increase the availability of the aerosol Cl⁻, thus prompting the Reaction (R4) to increase the ClNO₂ production yield.

An interesting observation from Wangdu is that the field-derived ϕ shows a decreasing trend (r²=0.58 from quadratic data fitting) with the ratio of acetonitrile to carbon monoxide (CH₃CN∕CO), which is an indicator of biomass burning emission (Christian et al., 2003; Akagi et al., 2011), as illustrated in Fig. 7d. The ϕ decreased at larger CH₃CN∕CO ratios, which corresponds to higher [Cl⁻] concentrations per volume of aerosol (cf. Fig. 7c) because biomass burning emits a significant level of chloride particles. The observations here may suggest that the ϕ is likely “suppressed” in air masses influenced by biomass burning, which were frequently observed during the study period (Tham et al., 2016), and is consistent with the recent field observation of much lower concentrations of ClNO₂ during the bonfire event in Manchester compared to that after the event (Reyes-Villegas et al., 2017) and with a laboratory experiment which demonstrated that only a small amount (∼10 %) of reacted N₂O₅ was converted to ClNO₂ on the biomass-burning aerosols (Ahern et al., 2018). Another laboratory study showed that the ClNO₂ yield can be reduced by as much as 80 % in the presence of aromatic organic compounds like phenol and humic acid in the aerosol (Ryder et al., 2015), and previous studies in China reported abundant humic-like substances (e.g., aromatic organic compounds) in aerosols with a large contribution from biomass burning (Fu et al., 2008). Therefore, the frequently observed influence of biomass burning at the Wangdu site during the campaign could in part explain the lower ϕ values and the discrepancy between observation and parameterization. More studies are needed to investigate the effects of biomass burning emissions on the heterogeneous process.