The significant contribution of HONO to secondary pollutants during a severe winter pollution event in southern China

Nitrous acid (HONO) can strongly affect atmospheric photochemistry in polluted regions through the production of hydroxyl radicals (OHs). In January 2017, a severe pollution episode occurred in the Pearl River Delta (PRD) of China, with maximum hourly PM_2.5, ozone, and HONO levels reaching 400 µg m⁻³, 150 ppb, and 8 ppb, respectively, at a suburban site. The present study investigated the sources and processes generating such high HONO concentrations and the role of HONO chemistry in this severe winter episode. Four recently reported HONO sources were added to the Community Multiscale Air Quality (CMAQ) model, including RH-dependent (relative humidity) and light-enhancing effects on heterogeneous reactions, photolysis of particulate nitrate in the atmosphere, and photolysis of HNO₃ and nitrate on surfaces. The revised model reproduced the observed HONO and significantly improved its performance for O₃ and PM_2.5. The model simulations showed that the heterogeneous generation on surfaces (with RH and light effects) was the largest contributor (72 %) to the predicted HONO concentrations, with the RH-enhancing effects more significant at nighttime and the light-enhancing effects more important in the daytime. The photolysis of total nitrate in the atmosphere and deposited on surfaces was the dominant HONO source during noon and afternoon, contributing above 50 % of the simulated HONO. The HONO photolysis was the dominant contributor to HO_x production in this episode. With all HONO sources, the daytime average O₃ at the Heshan site was increased by 24 ppb (or 70 %), compared to the simulation results without any HONO sources. Moreover, the simulated mean concentrations of TNO₃ (HNO₃+ fine particle ${\mathrm{NO}}_{\mathrm{3}}^{-}$) at the Heshan site, which was the key species for this haze formation, increased by about 17 µg m⁻³ (67 %) due to the HONO chemistry, and the peak enhancement reached 55 µg m⁻³. This study highlights the key role of HONO chemistry in the formation of winter haze in a subtropical environment.

1 Introduction

Nitrous acid (HONO) can significantly affect atmospheric photochemistry through its photolysis producing hydroxyl radicals (OHs) (Reaction R1) and subsequent reactions of OH with other gases (Alicke et al., 2003; Kleffmann et al., 2005). OH radicals oxidize volatile organic compounds (VOCs) and convert nitric oxide (NO) into nitrogen dioxide (NO₂) without consuming ozone (O₃), leading to the generation of O₃ (Reactions R2 to R6). The oxidation of oxides of nitrogen (${\mathrm{NO}}_x=\mathrm{NO}+{\mathrm{NO}}_{\mathrm{2}}$), sulfur dioxide (SO₂), and VOCs by OH and O₃ also produce secondary aerosols, which are the key components of haze (e.g., Pathak et al., 2009, 2011; Cao et al., 2012).

$$\begin{array}{}\text{(R1)}& {\displaystyle }& {\displaystyle }\mathrm{HONO}+\mathit{\text{hv}}\to \mathrm{NO}+\mathrm{OH}\text{(R2)}& {\displaystyle }& {\displaystyle }\mathrm{OH}+\mathrm{VOC}\to {\mathrm{RO}}_{\mathrm{2}}+{\mathrm{H}}_{\mathrm{2}}\mathrm{O}\text{(R3)}& {\displaystyle }& {\displaystyle }{\mathrm{RO}}_{\mathrm{2}}+\mathrm{NO}\to {\mathrm{NO}}_{\mathrm{2}}+{\mathrm{HO}}_{\mathrm{2}}+\mathrm{OVOC}\text{(R4)}& {\displaystyle }& {\displaystyle }{\mathrm{HO}}_{\mathrm{2}}+\mathrm{NO}\to {\mathrm{NO}}_{\mathrm{2}}+\mathrm{OH}\text{(R5)}& {\displaystyle }& {\displaystyle }{\mathrm{NO}}_{\mathrm{2}}+\mathit{\text{hv}}\to \mathrm{NO}+{\mathrm{O}}^{\mathrm{3}}\mathrm{P}\text{(R6)}& {\displaystyle }& {\displaystyle }{\mathrm{O}}^{\mathrm{3}}\mathrm{P}+{\mathrm{O}}_{\mathrm{2}}\to {\mathrm{O}}_{\mathrm{3}}\end{array}$$

The impact of HONO on atmospheric photochemistry varies under different environmental conditions. In general, the effect of HONO is more significant in polluted conditions than clean conditions. For example, calculations constrained by field measurements of HONO have suggested that the contributions of HONO photolysis to the daytime HO_x (${\mathrm{HO}}_x=\mathrm{OH}+{\mathrm{HO}}_{\mathrm{2}}$) production can reach 56 % in an urban area (Ren et al., 2003) and 87 % at a roadside (Yun et al., 2017), which are higher than the contributions in rural and forested areas (30 % to 40 %) (Acker et al., 2006; Kleffmann et al., 2005). Simulations by chemistry transport models which considered major sources of HONO showed that the maximum enhancements of O₃ concentrations due to HONO were mostly less than 10 ppb in the US and other Western countries (Sarwar et al., 2008; Czader et al., 2012; Li et al., 2010; Goncalves et al., 2012), but more significant impacts have been reported in China due to more intense NO_x and VOC emissions. For example, the reported maximum hourly O₃ enhancement can be more than 30 ppb in Beijing (Li et al., 2011; Xu et al., 2006) and up to 25 ppb in Hong Kong (Zhang et al., 2016). The previous studies mostly focused on summertime. Limited attention has been paid to the winter season when the observed HONO concentrations can also be high (Hou et al., 2016; Li et al., 2018a; Wang et al., 2016; Xu et al., 2015).

The source and formation mechanisms of HONO are still not fully understood. Most previous studies suggest that a heterogeneous reaction of NO₂ on the surface is dominant, especially at night (Li et al., 2012; J. Wang et al., 2017; Zhang et al., 2016). In the daytime, the long-known gas-phase reaction of NO and OH explains less than 10 % of the daytime HONO production (Sarwar et al., 2008; Li et al., 2010; Zhang et al., 2016). Other daytime sources include direct traffic emissions (Kurtenbach et al., 2001; Liang et al., 2017), humidity- and light-dependent heterogeneous generation (Finlayson-Pitts et al., 2003; Ndour et al., 2008; Monge et al., 2010), soil emissions (Oswald et al., 2013; Su et al., 2011; Meusel et al., 2018), photolysis of particle nitrate in the atmosphere (Ye et al., 2016b, 2017), and photolysis of deposited HNO₃ and nitrate on the ground (Ye et al., 2016a; Zhou et al., 2011).

During 4–8 January 2017, a severe winter air pollution event occurred in the Pearl River Delta (PRD), a region long known to suffer from photochemical pollution due to its fast industrialization (Chan and Yao, 2008; Wang et al., 1998, 2003; T. Wang 2017; Xue et al., 2014; Zhang et al., 2008; Zheng et al., 2010). During this multiday episode, an hourly peak value of ∼150 ppb for O₃ and ∼400 µg m⁻³ for PM_2.5 were observed, and the HONO levels reached 8 ppb, with an average value of 2.9 ppb at night (18:00 to 06:00 LT) and 2.4 ppb in daytime (07:00 to 17:00 LT). The HONO values were among the highest ever reported in China (Bernard et al., 2016; Huang et al., 2017; Li et al., 2012, 2018a; Qin et al., 2009; Su et al., 2008; J. Wang et al., 2017, 2013; Xu et al., 2015). It is of great interest to find out how such a high level of HONO was produced and what impact it had on the radical levels and secondary pollutants during this severe winter pollution event.

The present study utilizes the Community Multiscale Air Quality (CMAQ) model with up-to-date HONO sources, including those in the original CMAQ model (gas-phase generation, heterogeneous reaction, and vehicle emissions) and four newly added sources (RH-enhancing effects on heterogeneous reactions, light-enhancing effects on heterogeneous reactions, photolysis of particulate nitrate in the atmosphere, and photolysis of HNO₃ and nitrate adsorbed on surfaces). The updated model was then used to analyze the contributions of different HONO sources and also to quantify the contributions of HONO to secondary pollutants during this severe winter pollution event. Our study reveals the very large impact of HONO on winter-time chemistry at this subtropical site.

2 Materials and methods

2.1 Model description

2.1.1 CMAQ model configurations and inputs

CMAQ version 5.1 with the updated carbon bond 2005 e51 (CB05e51) gas mechanism and AERO6 aerosol mechanism (Appel et al., 2017) was used in this study. One-way triple nesting domains were used with their horizontal resolutions being 36, 12, and 4 km, respectively, and the innermost domain (domain 3) covers the PRD region (Fig. 1). These domains are based on a Lambert projection with two true latitudes of 25 and 40^∘ N. The objective simulation period was 4 to 8 January 2017, with 6 days before as a spin-up time.

Figure 1 Model domains and locations of the monitoring sites. The green star represents the Heshan site and red dots represent official monitoring sites in the PRD region from the Ministry of Ecology and Environment of China.

The Weather Research and Forecasting (WRF) Model version 3.7 was applied to generate the meteorological fields for the CMAQ simulations. The physical options used in the WRF model were the Lin microphysics scheme (Lin et al., 1983), the Rapid Radiative Transfer Model for GCMs (RRTMG) shortwave and longwave radiation scheme (Mlawer and Clough, 1998; Mlawer et al., 1997), Noah land surface scheme (Chen and Dudhia, 2001), YSU PBL scheme (Hong et al., 2006), and Kain–Fritsch cumulus scheme (Kain, 2004). To improve the meteorological modeling performance, the nudging was performed using the NCEP Automated Data Processing (ADP) data (ds351.0 and ds461.0) and surface observation data from China Meteorological Administration (Zhang et al., 2016). Table S1 in the Supplement summarizes the statistical performance for the meteorological predictions.

The anthropogenic emission input was generated based on three emission inventories covering different regions. For the PRD region, a local emission inventory with high resolution for 2010 (Pan et al., 2014) was used. For other regions in China, the emission data for 2013 were from Ma et al. (2017). For other Asian countries, the INTEX-B dataset (Zhang et al., 2009) was used. HONO emissions from transportation sources were calculated based on the HONO ∕ NO_x ratios and NO_x emissions from the transportation sources in the anthropogenic emission inventory. The HONO ∕ NO_x ratios were set as 0.8 % and 2.3 % for gasoline and diesel engines, respectively (Kurtenbach et al., 2001; Gutzwiller et al., 2002). Natural biogenic emissions were estimated by the Model of Emissions of Gases and Aerosols from Nature (MEGAN) (Guenther et al., 2006).

2.1.2 Parameterization of HONO sources

In addition to the direct anthropogenic emissions, the default CMAQ model has two more HONO sources, including the gas-phase homogeneous reaction of NO and OH (Reaction R7) and the heterogeneous reactions of NO₂ on surfaces (Reaction R8) (Sarwar et al., 2008). The heterogeneous formation of HONO on the particle, urban, and leaf surfaces was estimated with a reaction rate $k=\mathrm{5}\times {\mathrm{10}}^{-\mathrm{5}}\times$ (S∕V) as measured by Kurtenbach et al. (2001) under dark conditions with a relative humidity (RH) of 50 %.

$$\begin{array}{}\text{(R7)}& {\displaystyle }& {\displaystyle }\mathrm{NO}+\mathrm{OH}\to \mathrm{HONO}\text{(R8)}& {\displaystyle }& {\displaystyle }\mathrm{2}{\mathrm{NO}}_{\mathrm{2}}+{\mathrm{H}}_{\mathrm{2}}\mathrm{O}\stackrel{\text{surface}}{⟶}\mathrm{HONO}+{\mathrm{HNO}}_{\mathrm{3}}\end{array}$$

In the present study, we incorporated four additional HONO sources, as described below.

1. RH-enhancing effects on the heterogeneous reaction of NO₂ on surfaces

   The default heterogeneous reaction rate was based on measurements at a relative humidity of 50 %. However, previous field and lab studies found that surface adsorbed water played a key role in the heterogeneous conversion and the reaction rate was highly dependent on the RH level (Finlayson-Pitts et al., 2003; Stutz et al., 2004; Qin et al., 2009). The laboratory measurements over an RH range of 0 % to 80 % conducted by Finlayson-Pitts et al. (2003) showed that the heterogeneous conversion rate increased much faster when RH≥50 % than that when RH<50 %. Based on this result, the RH dependence of the heterogeneous reaction was considered through scaling the default reaction rate by a factor of f_RH in this study, as shown in the following equation:

   $$\begin{array}{}\text{(1)}& {\displaystyle }{k}_{\text{het}}=\mathrm{5}\times {\mathrm{10}}^{-\mathrm{5}}\times f_{\text{RH}}\times \left(\mathrm{S}/\mathrm{V}\right)\end{array}$$
   $$f_{\text{RH}}=\left\{\begin{array}{l}\mathrm{RH}/\mathrm{50}\begin{array}{cc}& \left(\mathrm{RH}<\mathrm{50}\right)\end{array}\\ \mathrm{RH}/\mathrm{10}-\mathrm{4}\begin{array}{cc}& \left(\mathrm{50}\le \mathrm{RH}<\mathrm{80}\right)\end{array}\\ \mathrm{4}\begin{array}{cc}& \left(\mathrm{RH}\ge \mathrm{80}\right).\end{array}\end{array}\right.$$

2. Light-enhancing effects on the heterogeneous reaction of NO₂ on surfaces

   The default reaction rate coefficient for the heterogeneous reaction was based on measurements under dark conditions. However, it has been reported that sunlight significantly boosts the heterogeneous generation of HONO (Ndour et al., 2008; Monge et al., 2010; Stemmler et al., 2007). To consider the photo-enhancing effect, we applied a higher reaction rate at daytime (Li et al., 2010; Czader et al., 2012), as shown in the following equation:

   $$\begin{array}{}\text{(2)}& {\displaystyle }{k}_{\text{het}}=\mathrm{1}\times {\mathrm{10}}^{-\mathrm{3}}\times {\displaystyle \frac{\text{light intensity}}{\mathrm{400}}}\times \left(\mathrm{S}/\mathrm{V}\right),\end{array}$$

   where light intensity means the total downward irradiance at the surface, measured in watts per meter squared (W m⁻²).

3. Photolysis of particulate nitrate in the atmosphere

   Evidence from recent aircraft observations and laboratory measurements suggested that particulate nitrate in the atmosphere can undergo photolysis to produce HONO and NO₂ (Reaction R9) (Ye et al., 2016b, 2017).

   $$\begin{array}{}\text{(R9)}& p{\mathrm{NO}}_{\mathrm{3}}\to \mathrm{0.67}\text{HONO}+\mathrm{0.33}{\mathrm{NO}}_{\mathrm{2}}\end{array}$$

   Ye et al. (2017) reported the photolysis rates ranging from $\mathrm{6.2}\times {\mathrm{10}}^{-\mathrm{6}}$ to $\mathrm{5.0}\times {\mathrm{10}}^{-\mathrm{4}}$ s⁻¹, with a median of $\mathrm{8.3}\times {\mathrm{10}}^{-\mathrm{5}}$ s⁻¹ at noontime tropical conditions. The reported value was much higher than the photolysis rate of gaseous HNO₃ ($\sim \mathrm{7}\times {\mathrm{10}}^{-\mathrm{7}}$ s⁻¹) under the typical tropical noontime conditions (Finlayson-Pitts and Pitts, 2000; Ye et al., 2017). Similar to the methodology of Sarwar et al. (2008), the photolysis rate of particulate nitrate was estimated as the following equation:

   $$\begin{array}{}\text{(3)}& {\displaystyle }{J}_{\mathrm{PNO}\mathrm{3}}={\displaystyle \frac{\mathrm{8.3}\times {\mathrm{10}}^{-\mathrm{5}}}{\mathrm{7}\times {\mathrm{10}}^{-\mathrm{7}}}}\times J_{\mathrm{HNO}\mathrm{3}-\mathrm{CMAQ}},\end{array}$$

   where J_HNO3−CMAQ is the photolysis rate of gaseous HNO₃ calculated online in CMAQ.

4. Photolysis of HNO₃ and nitrate deposited on surfaces

   Field observations and lab studies also indicated that the photolysis of HNO₃ and nitrate deposited on the surface could be an important daytime HONO source (Reaction R10) (Zhou et al., 2003, 2011; Baergen and Donaldson, 2013). Ye et al. (2016a) tested the photolysis of HNO₃ and nitrate deposited on various natural and artificial surfaces in the laboratory, and reported the photolysis rates ranging from $\mathrm{6}\times {\mathrm{10}}^{-\mathrm{6}}$ to $\mathrm{3.7}\times {\mathrm{10}}^{-\mathrm{4}}$ s⁻¹, with a median of $\mathrm{3.4}\times {\mathrm{10}}^{-\mathrm{5}}$ s⁻¹.

   $$\begin{array}{}\text{(R10)}& \mathrm{\text{deposited}}\mathit{\_}{\mathrm{HNO}}_{\mathrm{3}}/\text{nitrate}\to \mathrm{0.67}\text{HONO}+\mathrm{0.33}{\mathrm{NO}}_{\mathrm{2}}\end{array}$$

   This reaction was incorporated into the CMAQ model by assuming deposited HNO₃ and nitrate on surfaces to equal the accumulation of dry deposition since the last precipitation event, referring to the method of Sarwar et al. (2008). The photolysis rate of deposited HNO₃ and nitrate was estimated as the following equation:

   $$\begin{array}{}\text{(4)}& {\displaystyle }{J}_{\mathrm{DNO}\mathrm{3}}={\displaystyle \frac{\mathrm{3.4}\times {\mathrm{10}}^{-\mathrm{5}}}{\mathrm{7}\times {\mathrm{10}}^{-\mathrm{7}}}}\times J_{\mathrm{HNO}\mathrm{3}-\mathrm{CMAQ}}.\end{array}$$

Table 1 Model performance of the default and revised CMAQ model for O₃, PM_2.5, and NO₂ at 56 monitoring sites in the PRD region.

^a The NO₂ observation data were adjusted based on the method of Zhang et al. (2017): NO_2 obs = ${\mathrm{NO}}_{\mathrm{2}\text{obs}}^{\ast }$ × $\frac{{\mathrm{NO}}_{\mathrm{2}\text{sim}}}{{\mathrm{NO}}_{\mathrm{2}\text{sim}}+{\mathrm{NO}}_{z\text{sim}}-{\text{Nitrate}}_{\text{sim}}}$, where ${\mathrm{NO}}_{\mathrm{2}\text{obs}}^{\ast }$ is the measured concentration of NO₂ by the catalytic conversion technique; NO_2 sim, NO_z sim, and Nitrate_sim are the simulated concentrations of NO₂, NO_z, and nitrate.

2.1.3 Simulation cases

Eight simulations were conducted considering different HONO sources, including the following.

• NO_HONO: without any HONO sources

• G: gas-phase homogeneous reaction (G)

• GE: G + vehicle emissions (E)

• GEH (CMAQ default): GE + heterogeneous reactions under dark conditions with a RH of 50 % (H)

• GEHR: GEH + RH-enhancing effects on heterogeneous reactions (R)

• GEHRL: GEHR + light-enhancing effects on heterogeneous reactions (L)

• GEHRLP: GEHRL + photolysis of particulate nitrate in the atmosphere (P)

• GEHRLPD (CMAQ revised): GEHRLP + photolysis of HNO₃ and nitrate deposited on the surface (D)

2.2 Observation data

Field observations of HONO and other major air pollutants were conducted at the Heshan site (22^∘42${}^{\prime }{\mathrm{50}}^{\prime \prime }$ N, 112^∘55${}^{\prime }{\mathrm{17}}^{\prime \prime }$ E) in the PRD region (Fig. 1). Hourly HONO concentration was measured using a Long Path Absorption Photometer (LOPAP) (QUMA, Model LOPAP^®-03) (Heland et al., 2001). The same instrument was employed by our group in several previous field campaigns (Zha et al., 2014; Xu et al., 2015; Liang et al., 2017; Yun et al., 2018). The reader referred to these sources (e.g., Yun et al., 2018) for description of measurement principle. Following our previous practice, the instrument background was determined with synthetic air four times a day, and calibrations with a nitrite solution standard were conducted every 3 days. The time resolution of this instrument was 10 min. The detection limit was 7 ppt with an accuracy of ±20 %. The sample inlets were placed at the roof of a four-floor building, at a height of about 15 m above the ground.

Figure 2 Observed and simulated HONO mixing ratios by the default and revised CMAQ model at the Heshan site during 4–8 January 2017.

Other instruments whose data are used in the present paper have been summarized in Yun et al. (2018) with references provided for each instrument. Briefly, PM_2.5 concentrations were determined by a multiangle absorption photometer (Thermo Scientific, Model 5012). Sulfate, nitrate, and ammonium in PM_2.5 were measured by a gas and aerosol collector coupled with ion chromatography (GAC-IC) system. Gas HNO₃ concentrations were also measured by this GAC-IC system. O₃ concentrations were measured by a UV photometric analyzer (Thermo Scientific, Model 49i). NO₂ concentrations were measured using a chemiluminescence instrument (Thermo Scientific, Model 42i) coupled with a photolytic converter (Droplet Measurement Technologies, model BLC). The sample inlets for these instruments were placed at the same height as LOPAP.

Additionally, hourly PM_2.5, O₃, and NO₂ observation data at 56 official monitoring sites (Fig. 1) in the PRD region were obtained from the Ministry of Ecology and Environment (MEE). It should be noted that NO₂ concentrations in the national network were measured using the catalytic conversion method, which overestimates NO₂, especially during the period with active photochemistry and at the location away from the primary emission sources (Xu et al., 2013). The NO₂ observation data were adjusted based on the method of Zhang et al. (2017) (also see footnote of Table 1).

Figure 3 Average diurnal variations of contributions of different HONO sources to the simulated HONO mixing ratios at the Heshan site for 4–8 January 2017. The HONO sources include gas-phase homogeneous reaction of NO and OH (G), vehicle emissions (E), heterogeneous reactions under dark conditions with an RH of 50 % (H), RH-enhancing effects on heterogeneous reactions (R), light-enhancing effects on heterogeneous reactions (L), photolysis of particulate nitrate in the atmosphere (P), and photolysis of HNO₃ and nitrate deposited on the surface (D).

3 Results and discussion

3.1 Observed pollution in this winter episode

During 4–8 January 2017, a severe pollution episode was observed in the PRD region. As shown in Fig. S1, the episode average PM_2.5 concentrations observed at the Heshan site reached 142 µg m⁻³, which is nearly twice the respective standard of China (75 µg m⁻³ for daily average PM_2.5 concentration). The hourly PM_2.5 concentration peaked at 382 µg m⁻³, which was among the highest PM_2.5 concentrations reported so far in the PRD region (Tan et al., 2009; Wang et al., 2012; Yue et al., 2015). O₃ levels were also high, with a peak of ∼150 ppb. Figure S2 presented the spatial distribution of observed PM_2.5 and O₃ on the two heaviest polluted days (5 and 6 January) based on the interpolation of the observation data at the 56 official monitoring sites. It can be seen that the whole western PRD region suffered from severe pollution. On 5 January, the highest PM_2.5 pollution occurred in Foshan city and its surrounding area, with a peak of 448 µg m⁻³. On 6 January, the pollution expanded to wider areas.

3.2 Evaluation of model performance

Figure 2 compared the simulated HONO mixing ratios by the default CMAQ model (GEH) and the revised CMAQ model (GEHRLPD) with the observations at the Heshan site for 4–8 January. For the HONO simulation, the default CMAQ significantly underestimated the levels, with a normalized mean bias (NMB) of −71.6 %. The maximum underestimation was 6 ppb in the early morning of 5 January. The simulated HONO levels in the afternoon were lower than 0.1 ppb in the default CMAQ model. By considering the four additional HONO sources, the model performance was improved considerably, with an NMB of −22.5 %. The simulated average daytime HONO mixing ratios increased from 0.37 to 1.44 ppb, and the simulated nighttime HONO values increased from 1.10 to 2.63 ppb. The revised model reproduced the HONO diurnal variation. Underestimation was found on the mornings of 5–6 and 8 January. One possible reason was that some HONO sources were not considered in this study. For example, previous studies have proposed that deposited HONO can be reserved in dew water on the ground or vegetation during the night and re-released when the dew water evaporates in the morning (He et al., 2006). Here we have simply estimated HONO released from dew in the morning on 5 January during 00:00–08:00 LT using the method described in He et al. (2006). The total HONO dry deposition was $\mathrm{1.28}\times {\mathrm{10}}^{-\mathrm{6}}$ moles m⁻². At 09:00 LT, if 80 % of accumulated HONO was emitted to the surface model layer (about 0–30 m), the total released HONO amount would be 0.76 ppb. This suggests that dew evaporation may be a considerable source, but other sources may also exist. Overestimation was seen on the nights of 4 and 7 January, which was possibly related to some missing HONO sinks, e.g., the uptake of HONO on the ground surface (VandenBoer et al., 2014). Additionally, the overestimation of NO₂ could also partially explain the overestimation of HONO on the night of 7 January.

Figure 4 Average diurnal variations of OH and HO₂ mixing ratios simulated in the NO_HONO and GEHRLPD cases at the Heshan site during 4–8 January 2017.

Figure 5 Average diurnal variations of contributions of different reactions to HO_x generation at the Heshan site.

The inclusion of the four additional HONO sources also changed the model performance for other air pollutants. As shown in Fig. S1, the O₃ prediction by the default CMAQ model was not satisfactory, especially for the peak value. For example, on 6 January, the observed maximum 1 and 8 h O₃ mixing ratios at the Heshan site reached 147 and 125 ppb, respectively, exceeding the respective standard of China (∼93  and 75 ppb for maximum 1 and 8 h O₃ concentrations, respectively). The simulated levels by the default CMAQ model were just 64 and 52 ppb, respectively, which failed to predict the noncompliance of O₃ for this episode. In contrast, the revised model was better to reproduce the magnitude. For the episode average, the NMB values decreased from −32.9 % to 5.5 % for the maximum 8 h O₃. Therefore, it is crucial to include these additional HONO sources into air quality models. For NO₂, the revised model could reproduce its temporal variation in general, although underestimation was seen for some peak NO₂ values. This underprediction could also partially explain the underestimation of HONO peak values. For PM_2.5, improvements also can be seen, especially for the peak values. For the sulfate, nitrate and ammonium components in PM_2.5, as shown in Fig. S3, the revised model performed well in reproducing their temporal variations, with correlation coefficients (R values) of 0.5, 0.7, and 0.7, respectively. Compared to the default CMAQ model, the NMB values decreased from −29.8 % to −10 %, −53.8 % to −41.2 %, and −32.9 % to −14.1 % for fine particle sulfate, nitrate and ammonium, respectively. Underestimation could be seen for particle nitrate, especially on 5 January. The possible reason was underestimation of the emissions of NH₃ or other alkaline species (e.g., Ca, K, Na), which led to gas HNO₃ not being converted to particle nitrate sufficiently. The model performance for total nitrate (TNO₃ = gaseous HNO₃ + fine particle nitrate) was satisfactory, with an NMB of −3.2 % and R of 0.8.

Regionally, as shown in Table 1, the simulation results of the revised CMAQ model were in better agreement with the observations, with the NMBs decreasing from −33.3 % to 0.7 %, −8.1 % to 4.7 %, and 20.6 % to 9.4 % for 8 h maximum O₃, hourly average PM_2.5, and hourly average NO₂ values, respectively.

We have conducted a sensitivity test to compare the simulations with those considering more recent emission by linearly adjusting the emissions based on the ratio of 2017 emissions to 2010 emissions for China reported in a very recent paper (Zheng et al., 2018), but no improvements were indicated for HONO, O₃, and PM_2.5 at the Heshan site (Fig. S4). Despite the uncertainty, we think that the current emission setting is acceptable for simulations of the present case.

The above results indicated the satisfactory performance of the revised CMAQ model to simulate HONO and other major pollutants. It could be used to analyze the formation and impact of HONO in this episode.

Figure 6 Temporal variation of observed and simulated O₃ mixing ratios in the NO_HONO and GEHRLPD cases at the Heshan site during 4–8 January 2017.

Figure 7 Spatial distributions of O₃ mixing ratios at 15:00 LT simulated in the NO_HONO and GEHRLPD cases and their difference for domain 3.

3.3 Contributions of different sources to HONO concentrations

Figure 3 presents the contributions of different HONO sources to the HONO mixing ratios at the Heshan site. Heterogeneous generation on surfaces (including RH and light effects, H + R + L) was the largest contributor, representing 72 % of the average predicted HONO for the whole day. It contributed up to 81 % of the average predicted HONO values during the nighttime (18:00 to 06:00 LT) and ∼52 % during the daytime (07:00 to 17:00 LT). These findings on the dominant contribution from heterogeneous generation were similar to those in the previous modeling studies (Sarwar et al., 2008; Zhang et al., 2016) and field measurements (Li et al., 2012; J. Wang et al., 2017). High relative humidity and solar radiation were two important driving factors for the formation of the high HONO concentrations in this pollution episode. During the nighttime and early morning (18:00 to 08:00 LT), RH-enhancing effects on the heterogeneous generation were significant, due to high relative humidity (70 % to 90 %) (Fig. S5). The heterogeneous generation of HONO had a ∼2-fold increase by considering RH-enhancing effects, compared to that under the uniform relative humidity of 50 %. From 09:00 LT to the afternoon, light-enhancing effects became important, contributing approximately 25 % to HONO levels, due to strong radiation with a maximum of ∼450 W m⁻² (Fig. S5). In addition to enhancing the heterogeneous generation, the existence of light also increased HONO values through the photolysis of particle nitrate in the atmosphere and total nitrate deposited on the surface. Due to the high total nitrate concentrations in this pollution episode, these two photolysis sources were dominant during noon and afternoon (11:00 to 17:00 LT), contributing 31 % and 36 % to the HONO levels, respectively. Vehicle emissions contributed approximately 8 % of the daily average HONO values. The gas-phase homogeneous reaction was the smallest source, contributing approximately 3 % and 7 % of the daily and daytime average HONO values, respectively. Our model simulations suggest that the three additional light-dependent sources (L + P + D) could be an important part of missing daytime sources for the present case.

Figure 8 Temporal variation of observed and simulated total nitrate, sulfate, and ammonium concentrations in the NO_HONO and GEHRLPD cases at the Heshan site during 4–8 January 2017.

Figure 9 Spatial distributions of average TNO₃ concentrations simulated in the NO_HONO and GEHRLPD cases and their difference for domain 3 during 4–8 January 2017.

3.4 Impacts of HONO chemistry on HO_x and O₃

The HO_x (OH+HO₂) radical plays a key role in the atmospheric photochemical process. The photolysis of HONO can produce OH radicals, and a fast conversion exists between OH and HO₂ radicals. Therefore, the photolysis of HONO can affect the abundance of HO_x radicals. Figure 4 presents the average diurnal OH and HO₂ variations based on the simulations with and without the HONO sources (NO_HONO vs. GEHRLPD). The diurnal pattern and magnitude of the simulated OH and HO₂ mixing ratios in the GEHRLPD case were comparable to the previous observations in the PRD region and other areas (Lu et al., 2012; Mao et al., 2010). Compared to the results of the NO_HONO simulation, the daytime average OH and HO₂ values were increased by 175 % and 336 %, respectively. The integrated reaction rate (IRR) analysis tool available in CMAQ was utilized to explore the contribution of HONO photolysis to HO_x radical production relative to other HO_x radical sources in the surface layer (∼0 to 30 m), including the reaction of H₂O with O (¹D) which comes from the photolysis of O₃, HCHO photolysis, H₂O₂ photolysis, and the reaction of O₃ with alkenes and biogenic VOCs. As shown in Fig. 5, HONO photolysis was the dominant HO_x source during the daytime at the Heshan site. The daytime average contribution of HONO photolysis to HO_x production was approximately 74 %, and the contribution during the morning could be above 90 %. With the increasing of O₃ concentrations from sunrise to afternoon, the contribution of the O₃ photolysis began to increase and reached a peak of 33.4 % at 14:00 LT. Compared to the reported results for summer in the previous studies (Czader et al., 2012; Mao et al., 2010; Tham et al., 2016; Xue et al., 2016; Li et al., 2018c), the contribution of HONO photolysis to HO_x was much larger in the present study, mainly due to the high HONO concentrations in this winter episode. For example, Xue et al. (2016) and Li et al. (2018c) reported daytime average contributions of less than 30 % from HONO photolysis at two sites in Hong Kong. Tham et al. (2016) showed a contribution of less than 50 % in the morning at a site in the North China Plain. In these cases, the HONO levels were relatively low, with the peak HONO levels ranging among 1–3 ppb. The high HONO concentrations at Heshan increased the contribution of HONO photolysis to the formation of HO_x in this winter pollution episode. A similar high contribution can be found in other winter studies with high HONO concentrations, e.g., Elshorbany et al. (2010).

The enhanced HO_x formation due to HONO chemistry could increase O₃ concentrations. As shown in Fig. 6, the simulated daytime average O₃ mixing ratio at the Heshan site in the GEHRLPD case was approximately 24 ppb (70 %) higher than that in the NO_HONO case, with a peak increase up to 60 ppb. The O₃ enhancement was much higher than the previous simulation results for the summer cases (Sarwar et al., 2008; Li et al., 2010; Zhang et al., 2016). In addition to higher HONO concentrations, another possible reason was that the VOC ∕ NO_x ratio was lower in winter than that in summer, due to less biogenic VOC emissions and more NO_x emission from fuel combustion in winter. For example, Zou et al. (2015) reported VOC ∕ NO_x values higher than 10:1 in summer and about 5:1 for winter based on 1-year observation in Guangzhou, which is about 60 km from the Heshan site. The lower VOC ∕ NO_x ratio increased the sensitivity of O₃ concentrations to HO_x changes through Reactions (2) to (4). Therefore, the enhancement of O₃ concentrations was more significant (Li et al., 2018b). Figure 7 presents the spatial distribution of simulated surface O₃ concentrations at 15:00 LT in the NO_HONO and GEHRLPD cases. With HONO sources, the increase of the regional average O₃ mixing ratio at 15:00 LT was 34 % (∼17 ppb). The above results indicated the significant impacts of HONO chemistry on the atmospheric oxidation capacity and O₃ pollution in the PRD region during this heavy winter episode.

3.5 Impacts of HONO chemistry on secondary inorganic aerosol formation

Secondary inorganic aerosols, including nitrate, sulfate, and ammonium, contributed approximately 50 % to PM_2.5 concentrations during this episode (Fig. S6). Among them, nitrate was the dominant component, with peak concentrations reaching ∼110 µg m⁻³, which was much higher than the sulfate and ammonium concentrations. Particle nitrate can be generated through the partition of HNO₃ to particle phase. The major formation pathway of HNO₃ is the oxidation of NO₂ by OH during the daytime (Reaction R11). HNO₃ can also be produced via heterogeneous reactions of N₂O₅ on the particle surface at night (Reactions R12 to R14). Therefore, HONO chemistry can accelerate the formation of nitrate through the enhancement of atmospheric oxidation capacity (HO_x and O₃).

$$\begin{array}{}\text{(R11)}& {\displaystyle }& {\displaystyle }\mathrm{OH}+{\mathrm{NO}}_{\mathrm{2}}\to {\mathrm{HNO}}_{\mathrm{3}}\text{(R12)}& {\displaystyle }& {\displaystyle }{\mathrm{O}}_{\mathrm{3}}+{\mathrm{NO}}_{\mathrm{2}}\to {\mathrm{NO}}_{\mathrm{3}}\text{(R13)}& {\displaystyle }& {\displaystyle }{\mathrm{NO}}_{\mathrm{3}}+{\mathrm{NO}}_{\mathrm{2}}\to {\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}\text{(R14)}& {\displaystyle }& {\displaystyle }{\mathrm{N}}_{\mathrm{2}}{\mathrm{O}}_{\mathrm{5}}+{\mathrm{H}}_{\mathrm{2}}\mathrm{O}\to \mathrm{2}{\mathrm{HNO}}_{\mathrm{3}}\end{array}$$

Considering the uncertainty of the partition of gaseous HNO₃ to particle nitrate (see Sect. 3.2), we assessed the impact of HONO chemistry on the sum of HNO₃ and fine particle ${\mathrm{NO}}_{\mathrm{3}}^{-}$ (TNO₃). As shown in Fig. 8, the average TNO₃ concentrations at the Heshan site increased by about 17 µg m⁻³ (67 %), and the peak enhancement reached 55 µg m⁻³ at 10:00 LT on 6 January, when the observed PM_2.5 and nitrate concentration was at its respective highest level. Figure 9 presents the distribution of simulated average surface TNO₃ concentrations in the NO_HONO and GEHRLPD case. With HONO sources, the regional average TNO₃ concentrations were increased by 8.4 µg m⁻³ for the entire episode. The absolute enhancement was more significant in the areas with higher aerosol concentrations. The highest increase for TNO₃ was above 25 µg m⁻³ for the episode average. Meanwhile, the HONO chemistry also accelerated the formation of other secondary inorganic aerosols. Particle sulfate can be generated through the partition of H₂SO₄ to the particle phase. The major formation pathways of H₂SO₄ include the gaseous oxidation of SO₂ by OH and the aqueous oxidation of S(IV) by O₃, H₂O₂, and other oxidants. The partition of gaseous NH₃ to particle ammonium was based on the ${\mathrm{H}}_{\mathrm{2}}{\mathrm{SO}}_{\mathrm{4}}-{\mathrm{HNO}}_{\mathrm{3}}-{\mathrm{NH}}_{\mathrm{3}}$ thermodynamic equilibrium. The simulated enhancements of the average sulfate and ammonium concentrations at the Heshan site were 32 % and 33 %, respectively. The above results indicated that the HONO chemistry also aggravated the particulate pollution during this episode.

4 Conclusions

This study has identified the major contributors to the observed high HONO levels during a severe winter pollution episode and highlighted the importance of HONO chemistry in the combined photochemical and haze pollution in a subtropical region. Including up-to-date HONO sources in the widely used CMAQ model significantly improved its capability in simulating ambient concentrations of HONO and other major pollutants (e.g., O₃ and PM_2.5). The model simulations suggested a predominant contribution from NO₂ heterogeneous reactions enhanced by humidity and solar radiation. The high HONO concentration significantly increased the atmospheric oxidation capacity and the levels of ozone and secondary aerosols, especially total nitrate. This study highlights the key role of HONO chemistry in the formation of winter haze in a subtropical environment and indicates the critical need to include and update HONO sources in regional air quality models in order to predict ozone and other secondary pollutants during heavy pollution events in southern China and similar regions. Additional efforts are needed to improve the current representation of HONO sources, such as evaporation of dew and a more accurate simulation of deposited nitrate.