Heterogeneous N₂O₅ uptake coefficient and production yield of ClNO₂ in polluted northern China: roles of aerosol water content and chemical composition

Heterogeneous uptake of dinitrogen pentoxide (N₂O₅) and production of nitryl chloride (ClNO₂) are important nocturnal atmospheric processes that have significant implications for the production of secondary pollutants. However, the understanding of N₂O₅ uptake processes and ClNO₂ production remains limited, especially in China. This study presents a field investigation of the N₂O₅ heterogeneous uptake coefficient (γ(N₂O₅)) and ClNO₂ production yield (ϕ) in a polluted area of northern China during the summer of 2014. The N₂O₅ uptake coefficient and ClNO₂ yield were estimated by using the simultaneously measured ClNO₂ and total nitrate in 10 selected cases, which have concurrent increases in the ClNO₂ and nitrate concentrations and relatively stable environmental conditions. The determined γ(N₂O₅) and ϕ values varied greatly, with an average of 0.022 for γ(N₂O₅) (±0.012, standard deviation) and 0.34 for ϕ (±0.28, standard deviation). The variations in γ(N₂O₅) could not be fully explained by the previously derived parameterizations of N₂O₅ uptake that consider nitrate, chloride, and the organic coating. Heterogeneous uptake of N₂O₅ was found to have a strong positive dependence on the relative humidity and aerosol water content. This result suggests that the heterogeneous uptake of N₂O₅ in Wangdu is governed mainly by the amount of water in the aerosol, and is strongly water limited, which is different from most of the field observations in the US and Europe. The ClNO₂ yield estimated from the parameterization was also overestimated comparing to that derived from the observation. The observation-derived ϕ showed a decreasing trend with an increasing ratio of acetonitrile to carbon monoxide, an indicator of biomass burning emissions, which suggests a possible suppressive effect on the production yield of ClNO₂ in the plumes influenced by biomass burning in this region. The findings of this study illustrate the need to improve our understanding and to parameterize the key factors for γ(N₂O₅) and ϕ to accurately assess photochemical and haze pollution.

1 Introduction

The nocturnal heterogeneous reaction of dinitrogen pentoxide (N₂O₅) with aerosols is a loss pathway of NO_x and a source of aerosol nitrate and gas-phase nitryl chloride (ClNO₂) (Brown et al., 2006; Osthoff et al., 2008; Thornton et al., 2010; Sarwar et al., 2014) and thereby has important implications for air quality (e.g., Li et al., 2016; Tang et al., 2017). The process begins with the accumulation of gas-phase nitrate radical (NO₃) after sunset via the oxidation of nitrogen dioxide (NO₂) by O₃ and further reaction of NO₃ with another NO₂, yielding N₂O₅. The accommodation of N₂O₅ on the aqueous surface of the aerosol (Reaction R1) and reaction with liquid water (H₂O) leads to the formation of a protonated nitric acid intermediate (${\mathrm{H}}_{\mathrm{2}}{\mathrm{ONO}}_{\mathrm{2}}^{+}$) and a nitrate (${\mathrm{NO}}_{\mathrm{3}}^{-}$) (Reaction R2; Thornton and Abbatt, 2005; Bertram and Thornton, 2009).

$$\begin{array}{}\text{(R1)}& {\displaystyle }& {\displaystyle }{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}\left(\mathrm{g}\right)⟷{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}\left(\mathrm{aq}\right)\text{(R2)}& {\displaystyle }& {\displaystyle }{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}\left(\mathrm{aq}\right)+{\mathrm{H}}_{\mathrm{2}}\mathrm{O}\left(\mathrm{l}\right)⟷{\mathrm{NO}}_{\mathrm{3}}\left(\mathrm{aq}\right)+{\mathrm{H}}_{\mathrm{2}}{\mathrm{ONO}}_{\mathrm{2}}^{+}\left(\mathrm{aq}\right)\end{array}$$

The ${\mathrm{H}}_{\mathrm{2}}{\mathrm{ONO}}_{\mathrm{2}}^{+}$ will proceed by reacting with another H₂O to form an aqueous nitric acid (HNO₃; Reaction R3). If chloride (Cl⁻) is present in the aerosols, the ${\mathrm{H}}_{\mathrm{2}}{\mathrm{ONO}}_{\mathrm{2}}^{+}$ will undergo another pathway to produce a nitryl chloride (ClNO₂) through Reaction (R4), which is a dominant source of highly reactive chlorine radicals in the troposphere (e.g., Riedel et al., 2012a, 2014).

$$\begin{array}{}\text{(R3)}& {\displaystyle }& {\displaystyle }{\mathrm{H}}_{\mathrm{2}}{\mathrm{ONO}}_{\mathrm{2}}^{+}\left(\mathrm{aq}\right)+{\mathrm{H}}_{\mathrm{2}}\mathrm{O}\left(\mathrm{l}\right)\to {\mathrm{H}}_{\mathrm{3}}{\mathrm{O}}^{+}\left(\mathrm{aq}\right)+{\mathrm{HNO}}_{\mathrm{3}}\left(\mathrm{aq}\right)\text{(R4)}& {\displaystyle }& {\displaystyle }{\mathrm{H}}_{\mathrm{2}}{\mathrm{ONO}}_{\mathrm{2}}^{+}\left(\mathrm{aq}\right)+{\mathrm{Cl}}^{-}\left(\mathrm{aq}\right)\to {\mathrm{ClNO}}_{\mathrm{2}}+{\mathrm{H}}_{\mathrm{2}}\mathrm{O}\left(\mathrm{l}\right)\end{array}$$

The heterogeneous loss rate of N₂O₅ (k(N₂O₅)_het) and the ClNO₂ production rate (p(ClNO₂)) are fundamentally governed by the probability of N₂O₅ lost upon collision with particle surface area in a volume of air (i.e., uptake coefficient, γ(N₂O₅)) and the ClNO₂ yield (ϕ), which is defined as the branching ratio between the formation of HNO₃ via Reaction (R3) and ClNO₂ via Reaction (R4). Assuming that the gas-phase diffusion to the aerosol surfaces is negligible, their relationship can be described by Eqs. (1) and (2), in which $c_{{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}}$ is the average molecular speed of N₂O₅ and S_a is the aerosol surface area.

$$\begin{array}{}\text{(1)}& {\displaystyle }& {\displaystyle }k({\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}{)}_{\mathrm{het}}={\displaystyle \frac{\mathrm{1}}{\mathrm{4}}}{c}_{{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}}\mathit{\gamma }({\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}})S_{\mathrm{a}}\text{(2)}& {\displaystyle }& {\displaystyle }p\left({\mathrm{ClNO}}_{\mathrm{2}}\right)=k\left({\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}{)}_{\mathrm{het}}\right[{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}]\mathit{\varphi }\end{array}$$

N₂O₅ uptake has been shown in the laboratory to be susceptible to changes in the water content, chloride, nitrate, and organic particle coatings in aerosols (e.g., Mentel et al., 1999; Bertram and Thornton, 2009; Tang et al., 2014). The presence of liquid water on the aerosols allows for the accommodation of N₂O₅ (Reaction R1) and acts as a medium for the solvation process of N₂O₅ (Reaction R2). It has been found that N₂O₅ uptake is significantly enhanced in humid conditions than in dry conditions (e.g., Hallquist et al., 2003; Bertram and Thornton, 2009; Gržinić et al., 2015). Higher loading of ${\mathrm{NO}}_{\mathrm{3}}^{-}$ in the aerosol can dramatically decrease N₂O₅ uptake by reversing the solvation/ionization process of N₂O₅, shifting the equilibrium in Reaction (R2) to the left to reproduce N₂O₅, which can be diffused out of the aerosol (known as the “nitrate suppression” effect). The rate of the reversible reaction of Reaction (R2) (i.e., ${\mathrm{H}}_{\mathrm{2}}{\mathrm{ONO}}_{\mathrm{2}}^{+}$ with ${\mathrm{NO}}_{\mathrm{3}}^{-}$) was documented to be 30 to 40 times faster than the reaction of ${\mathrm{H}}_{\mathrm{2}}{\mathrm{ONO}}_{\mathrm{2}}^{+}$ with liquid water in Reaction (R3) (Bertram and Thornton, 2009; Griffiths et al., 2009). The presence of Cl⁻ in the aerosol, in contrast, can enhance the reactive uptake because Cl⁻ reacts effectively with ${\mathrm{H}}_{\mathrm{2}}{\mathrm{ONO}}_{\mathrm{2}}^{+}$ (in Reaction R4), thus negating the nitrate suppression effect by shifting the equilibrium in Reaction (R2) to the right (Finlayson-Pitts et al., 1989; Bertram and Thornton, 2009). The uptake of N₂O₅ can also be hindered by the presence of organics, because the organic coating layer on the aerosol could lower the liquid water content and/or limit the surface activity, thus suppressing the accommodation of N₂O₅ (e.g., Cosman et al., 2008; Gaston et al., 2014).

As for the ClNO₂ yield from N₂O₅ heterogeneous reactions, it was found to be dependent on the fate of ${\mathrm{H}}_{\mathrm{2}}{\mathrm{ONO}}_{\mathrm{2}}^{+}$ and thus on the relative amount of Cl⁻ and water content (Benhke et al., 1997; Roberts et al., 2009; Bertram and Thornton, 2009). Therefore, ϕ can be expressed by the following equation (Eq. 3).

$$\begin{array}{}\text{(3)}& {\displaystyle }\mathit{\varphi }\text{param.}={\displaystyle \frac{\mathrm{1}}{\frac{k_{\mathrm{R}\mathrm{3}}\left[{\mathrm{H}}_{\mathrm{2}}\mathrm{O}\right]}{k_{\mathrm{R}\mathrm{4}}\left[{\mathrm{Cl}}^{-}\right]}+\mathrm{1}}}\end{array}$$

Roberts et al. (2009) and reference therein reported that the coefficient rate of k_R4 is about 450–836 times faster than that of k_R3, indicating that ${\mathrm{H}}_{\mathrm{2}}{\mathrm{ONO}}_{\mathrm{2}}^{+}$ proceeds more favorably via Reaction (R4), even with a small amount of Cl⁻. However, some laboratory experiments have suggested that the presence of halides (i.e., bromide), phenols, and humic acid may significantly reduce the ϕ (e.g., Schweitzer et al., 1998; Ryder et al., 2015).

The parameterization of γ(N₂O₅) and ϕ as a function of the aerosol water content and aerosol chemical composition, derived based on the findings of the laboratory studies mentioned above (e.g., Antilla et al., 2006; Bertram and Thornton, 2009; Davis et al., 2008), have recently been compared with the ambient observations in different environments (Morgan et al., 2015; Phillips et al., 2016; Chang et al., 2016; Z. Wang et al., 2017; McDuffie et al., 2018). Large discrepancies were observed between the γ(N₂O₅) and ϕ values determined in the field and the laboratory parameterizations derived with pure or mixed aerosol samples, and the differences can be up to 1 order of magnitude. Several reasons have been proposed for the discrepancies between the parameterization and observation values, including the failure of parameterization to account for (1) the complex mixture of organic composition (Bertram et al., 2009; Mielke et al., 2013); (2) the “real” nitrate suppression effect (Riedel et al., 2012b; Morgan et al., 2015); (3) the varying mixing states of the particles (Ryder et al., 2014; X. Wang et al., 2017); and (4) bulk or surface reactions on different particles (e.g., Gaston and Thornton, 2016). These results suggest a lack of comprehensive understanding of the N₂O₅ uptake and ClNO₂ production yield in various atmospheric environments around the world.

Most of the previous field studies of N₂O₅ uptake and ClNO₂ production have been conducted in the United States of America (US) and Europe (Brown et al., 2009; Chang et al., 2016). Direct field investigation of the N₂O₅ heterogeneous processes in China is very limited. Pathak et al. (2009, 2011) analyzed the aerosol composition and suggested that the accumulation of fine ${\mathrm{NO}}_{\mathrm{3}}^{-}$ aerosol downwind of Beijing and Shanghai was due to significant N₂O₅ heterogeneous reactions. Wang et al. (2013) linked the observed ${\mathrm{NO}}_{\mathrm{3}}^{-}$ with the precursors of N₂O₅ (i.e., NO₂ and O₃) in urban Shanghai and suggested that the N₂O₅ heterogeneous uptake dominated ${\mathrm{NO}}_{\mathrm{3}}^{-}$ formation on polluted days. However, field measurements of N₂O₅ and ClNO₂ were not available until recently at several sites in southern and northern China (Tham et al., 2014; Z. Wang et al., 2017).

In the summer of 2014, a field campaign was carried out to investigate ClNO₂ and N₂O₅ at a semirural ground site at Wangdu in polluted northern China (Tham et al., 2016). Elevated levels of ClNO₂ up to 2070 pptv (1 min average), but relatively low values of N₂O₅ (1 min average maximum of 430 pptv), were observed on most nights at this site, and heterogeneous processes have been shown to have a significant effect on the following day's radical and ozone production at the site (Tham et al., 2016). Yet, the factors that drive the N₂O₅ heterogeneous uptake and ClNO₂ production yield remain unclear. In this study, we further analyze the dataset to investigate this topic. We first derive values for γ(N₂O₅) and ϕ from the regression analysis of ClNO₂ and the total nitrate (HNO₃ and particulate ${\mathrm{NO}}_{\mathrm{3}}^{-}$) dataset and then compare the values obtained in the field with various parameterizations derived from the laboratory studies. With the aid of the aerosol composition and meteorological measurements, we illustrate the factors that drive or influence the variations in γ(N₂O₅) and ϕ at Wangdu. The values for γ(N₂O₅) and ϕ obtained here are also compared with field results from the literature to provide an overview of the N₂O₅–ClNO₂ heterogeneous process observed in various environments around the world.

2 Methods

The measurement site (38.66^∘ N, 115.204^∘ E) is located at a semirural area in Wangdu county of Hebei province, in the northern part of China. The Wangdu site is situated within agricultural land but is bounded by villages and towns. Beijing (the national capital) is about 170 km to the northeast, Tianjin is about 180 km to the east, Shijiazhuang is about 90 km to the southwest, and Baoding is ∼33 km to the northeast. Dozens of major coal-fired power stations are also located in the region. During the study period, frequent biomass burning activity was observed in the surrounding regions. Analysis of the air masses' back trajectories showed that the sampling site was frequently affected by these surrounding anthropogenic sources. Details on the sampling site and the meteorological conditions during the campaign can be found in Tham et al. (2016).

In this study, N₂O₅ and ClNO₂ were measured with an iodide chemical ionization mass spectrometer (CIMS), with which the N₂O₅ and ClNO₂ were detected as the iodide-cluster ions of I(ClNO₂)⁻ and I(N₂O₅)⁻, similar to those outlined by Kercher et al. (2009). The detection principles, calibration, and inlet maintenance were described in detail in our previous studies (T. Wang et al., 2016; Tham et al., 2016). The CIMS measurement at the Wangdu site was performed from 20 June to 9 July 2014. A corona discharge ion source setup (for generation of iodide primary ions) was used in the CIMS measurement from 20 to 26 June 2014 with detection limits of 16 pptv for N₂O₅ and 14 pptv for ClNO₂ (3σ; 1 min averaged data) but was replaced by a radioactive ion source from 27 June 2014 until the end of the study with detection limits of 7 pptv for N₂O₅ and 6 pptv for ClNO₂ (3σ; 1 min averaged data). The overall uncertainty in the CIMS measurement was estimated to be ±25 %, with a precision of 3 %.

The present study was supported by other auxiliary measurements of aerosol, trace gases, and meteorological parameters, and the detailed instrumentation for these measurements has been listed in a previous paper (Tham et al., 2016). Trace gases including NO, NO₂, O₃, SO₂, CO, and total odd nitrogen (NO_y) were measured with online gas analyzers (Tan et al., 2017). A gas aerosol collector coupled to an ion chromatography system was used to measure the ionic compositions of PM_2.5, including ${\mathrm{NO}}_{\mathrm{3}}^{-}$, Cl⁻, ${\mathrm{SO}}_{\mathrm{4}}^{\mathrm{2}-}$, ${\mathrm{NH}}_{\mathrm{4}}^{+}$, and gas-phase HNO₃ (Dong et al., 2012). The particle surface area concentrations (S_a) were calculated based on the wet ambient particle number size distribution by assuming spherical particles. In brief, the dry-state particle number size distribution was measured with a mobility particle size spectrometer (covering mobility particle diameters of 4 to 800 nm) and an aerodynamic particle size spectrometer (for aerodynamic particle diameters 0.8 to 10 µm). The wet particle number size distribution as a function of the relative humidity (RH) was calculated from a size-resolved kappa-Köhler function determined from realtime measurement of a high humidity tandem differential mobility analyzer (Hennig et al., 2005; Liu et al., 2014). It should be noted that the major uncertainty in S_a calculation was the assumption and application of κ at different size ranges, leading to an overall uncertainty of ±19 %.

Hydroxyl radicals (OH) were measured with the laser-induced fluorescence technique (Tan et al., 2017). Volatile organic compounds (VOCs) including methane, C₂–C₁₀ hydrocarbons, formaldehyde, and oxygenated hydrocarbons and acetonitrile (CH₃CN) were measured with a cavity ring-down spectrometer, an online gas chromatograph equipped with a mass spectrometer and a flame ionization detector, a Hantzsch fluorimetric monitor, and a proton-transfer-reaction mass spectrometer, respectively (Yuan et al., 2010; Wang et al., 2014). Meteorological data including the wind profile, RH, and temperature were measured with an ultrasonic anemometer and a weather station on a 20 m high tower. Detailed descriptions of these instrumentation and measurement techniques at Wangdu can be found in previous publications (e.g., Y. Wang et al., 2016; Min et al., 2016; Tham et al., 2016; Tan et al., 2017).

3 Results and discussion

3.1 Nocturnal heterogeneous N₂O₅ reaction at Wangdu

Figure 1 Time 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.

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.

$$\begin{array}{}\text{(4)}& {\displaystyle }\mathit{\gamma }\left({\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}\right)={\displaystyle \frac{\mathrm{2}\left(p{\mathrm{ClNO}}_{\mathrm{2}}+p{\mathrm{NO}}_{\mathrm{3}}^{-}\right)}{C_{{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}}{S}_{\mathrm{a}}\left[{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}\right]}}\end{array}$$

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.

$$\begin{array}{}\text{(5)}& {\displaystyle }\mathit{\varphi }={\displaystyle \frac{\mathrm{2}m}{\mathrm{1}+m}}\end{array}$$

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 2 Example 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).

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 3 Diurnal 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.

Table 1 N₂O₅ 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 ${\mathrm{NO}}_{\mathrm{3}}^{-}$.

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 4 Comparison 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.

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

n.a. = no information available

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):

$$\begin{array}{}\text{(6)}& {\displaystyle }{\mathit{\gamma }}_{\mathrm{B}\mathrm{\&}\mathrm{T}}=Ak\left(\mathrm{1}-{\displaystyle \frac{\mathrm{1}}{\left(\frac{k_{\mathrm{R}\mathrm{3}}\left[{\mathrm{H}}_{\mathrm{2}}\mathrm{O}\right]\left(\mathrm{l}\right)}{k_{\mathrm{R}\mathrm{2}\mathrm{b}}\left[{\mathrm{NO}}_{\mathrm{3}}^{-}\right]}\right)+\mathrm{1}+\left(\frac{k_{\mathrm{R}\mathrm{4}}\left[{\mathrm{Cl}}^{-}\right]}{k_{\mathrm{R}\mathrm{2}\mathrm{b}}\left[{\mathrm{NO}}_{\mathrm{3}}^{-}\right]}\right)}}\right),\end{array}$$

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).

$$\begin{array}{}\text{(7)}& {\displaystyle }& {\displaystyle }{\mathit{\gamma }}_{\mathrm{Org}}={\displaystyle \frac{\mathrm{4}RT{H}_{\mathrm{org}}{D}_{\mathrm{org}}{R}_{\mathrm{c}}}{C_{{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}}L{R}_{\mathrm{p}}}}\text{(8)}& {\displaystyle }& {\displaystyle \frac{\mathrm{1}}{{\mathit{\gamma }}_{\mathrm{B}\mathrm{\&}\mathrm{T}+\mathrm{Org}}}}={\displaystyle \frac{\mathrm{1}}{{\mathit{\gamma }}_{\mathrm{B}\mathrm{\&}\mathrm{T}}}}+{\displaystyle \frac{\mathrm{1}}{{\mathit{\gamma }}_{\mathrm{Org}}}}\end{array}$$

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).

$$\begin{array}{ll}{\displaystyle }& {\displaystyle }{\mathit{\gamma }}_{\mathrm{E}\mathrm{\&}\mathrm{J}}=(\mathrm{2.79}\times {\mathrm{10}}^{-\mathrm{4}}+\mathrm{1.3}\times {\mathrm{10}}^{-\mathrm{4}}\times \mathrm{RH}-\mathrm{3.43}\times {\mathrm{10}}^{-\mathrm{6}}\\ \text{(9)}& {\displaystyle }& {\displaystyle }\times {\mathrm{RH}}^{\mathrm{2}}+\mathrm{7.52}\times {\mathrm{10}}^{-\mathrm{8}}\times {\mathrm{RH}}^{\mathrm{3}})\times {\mathrm{10}}^{(\mathrm{4}\times {\mathrm{10}}^{-\mathrm{2}}\times (T-\mathrm{294}\left)\right)}\end{array}$$

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 5 Comparison 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.

Figure 6 Relationship 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.

Figure 7 Scatter 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.

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.

4 Summary and conclusions

We present an in-depth analysis of the N₂O₅ uptake coefficient and ClNO₂ yield in a polluted northern China environment during the summer of 2014. Large variations in the levels of γ(N₂O₅) and ϕ were observed during the study, ranging from 0.005 to 0.039 and from 0.06 to 1.04, respectively. A comparison between the γ(N₂O₅) values derived from the field and the parameterizations that considered the nitrate and chloride levels and the hydrophobic organic coating showed poor agreement, suggesting more complex influences of ambient aerosol properties at the site than with the pure or mixed samples used in the laboratory. The γ(N₂O₅) values at Wangdu were found to have a clear dependence on the RH and the aerosol water content, a phenomenon that was found in laboratory experiments but has not been observed in previous field studies in the US or Europe. The parameterization that explicitly considers the dependence on RH showed better agreement with the field-derived γ(N₂O₅) compared to the more complex formulation that considers the aerosol composition. The ClNO₂ yield estimated from the parameterization is generally overestimated when compared to the field-derived values. The observed ϕ was found to be suppressed in the air masses influenced by biomass burning even though abundant aerosol chloride was present. The results of this study point to the need for more field and laboratory studies to obtain realistic parameterization of the heterogeneous processes of N₂O₅ and ClNO₂ to better simulate the ozone and aerosol production in air quality models in regions of China with high NO_x emissions.