This study compared the performance of the BT09 and YU20 aerosol schemes within the CAMx model during snow-covered periods (Fig. S3). The YU20 scheme demonstrated significant improvements in the statistical indicators for N₂O₅ and ClNO₂ compared to the BT09 scheme (Table S4). For N₂O₅, the mean bias (MB) decreased from 114.62 to 69.66, the normalized mean bias (NMB) from 1.92 to 1.16, and the root mean square error (RMSE) from 201.39 to 139.03, while the IOA increased from 0.49 to 0.61. Similarly, for ClNO₂, the MB decreased from -29.28 to -16.08, the NMB from -0.28 to -0.16, the RMSE from 100.66 to 99.80, with the index of agreement (IOA) from 0.81 to 0.84. The YU20 scheme is a set of parameter values derived from local observation data in China, making it more representative of the evolution of atmospheric chemical species in the region. Given the above enhancements, the YU20 and BT09 schemes were selected for snow-covered and snow-free regions, respectively, in the subsequent scenario simulations.

The parameters γ(N₂O₅) and ϕ(ClNO₂) are critical parameters for understanding N₂O₅ heterogeneous chemistry on aerosol surfaces. The BT09 scheme is believed to overestimate the γ(N₂O₅) (Wagner et al., 2013) and ϕ(ClNO₂) (McDuffie et al., 2018a). Yang et al. (2022) further demonstrated that incorporating anthropogenic chlorine and biomass burning emissions into the YU20 parameterization effectively enhanced γ(N₂O₅) values, emphasizing the advantage of localized schemes for improving model accuracy (Yang et al., 2022). However, Xie et al. (2025) found that the BT09 scheme outperformed YU20 at the Wangdu station in Hebei province of China under snow-free conditions using the WRF-CAMx model. Similarly, Dai et al. (2020) evaluated the emission of PCl from the South China Sea and also found that the BT09 aerosol scheme better captured the peak value of ClNO₂ compared to the YU20 scheme.

In contrast, our study found that YU20 performed better than BT09 during snow-covered conditions. This improvement can be attributed to the significant impact of atmospheric humidity changes under snow cover conditions, along with an increase in anthropogenic chlorine emissions (e.g., Cl₂, PCI, HCI, and HOCI) during winter in high-latitude regions. These factors together drove changes in species concentrations (chloride ions, and nitrate ions) on aerosol surfaces during the observation period, directly affecting the γ(N₂O₅) and ϕ(ClNO₂) values in the N₂O₅ aerosol schemes (BT09 or YU20).

A comparison of atmospheric N₂O₅ and ClNO₂ concentrations before and after model modification is presented in Fig. 3. In the Y0 scenario, N₂O₅ concentrations averaged 141.81 ± 148.62 pptv, with a peak value of 540.37 pptv. In the Y3 scenario with modified modeling, N₂O₅ concentrations ranged from 0.0 to 513.03 pptv, with an average value of 33.58 ± 143.92 pptv. While the MB of N₂O₅ decreased from 64.10 to 56.53 pptv, the NMB from 0.98 % to 0.87 %, and the RMSE from 123.17 to 118.39 pptv, the IOA remained at 0.66 (Table 3). This suggests no significant improvement in model accuracy, likely due to the relatively low re-emission rate of N₂O₅ deposited on the ground surface, which has a minimal impact on atmospheric N₂O₅ concentrations.

In contrast, the revised model significantly improved the simulation of ClNO₂, particularly in capturing peak concentrations. In Y0 scenario, the average ClNO₂ concentrations is 7.73 ± 6.96 pptv with a peak value of 35.59 pptv. In contrast, the Y3 scenario showed an expanded range of 0.07 to 503.30 pptv and an increased average concentration of 110.30 ± 117.45 pptv. Additionally, the MB of ClNO₂ improved from -105.78 to 2.66 pptv, the NMB increased from -0.93 % to 0.02 %, and the RMSE decreased from 170.26 to 88.28 pptv. The IOA also improved significantly, rising from 0.39 to 0.86 (Table 3). These results demonstrate that incorporating chlorine emissions and surface N₂O₅ chemical processes into the CAMx model substantially enhanced the simulation accuracy for ClNO₂, while the impact on N₂O₅ remained limited. In addition, we further examine the effects of the revised model on secondary pollutants (PM_2.5 and O₃) at this site, which are also pronounced. For PM_2.5 the MB improves from -10.56 to -1.39 µg m⁻³, and the IOA increases from 0.68 to 0.91; for O₃, the MB decreases from 19.42 to 13.91 µg m⁻³, while the IOA improves from 0.72 to 0.77 (Table S5). These findings underscore that the updated parameterization not only refines ClNO₂ simulation but also yields significant improvements in simulating secondary aerosol and ozone formation.

In northern regions of China, residential heating typically extends from early November through March of the following year. During this period, coal combustion releases substantial quantities of particulate chloride (PCl) and HCl into the atmosphere (Liu et al., 2018). To assess the impact of model modifications, we further examined the spatial concentration differences and ratios of PCl, HCl, N₂O₅, and ClNO₂ between Y0 and Y3 (Fig. 4). The spatial concentration differences (e.g., Y3–Y0) are used to quantify the magnitude of changes in simulated pollutant concentrations between different scenarios, while contribution ratios (e.g., (Y3–Y0) / Y3) are employed to assess the relative contributions of model modifications. For PCl and HCl, peak spatial concentrations in the Y0 scenario reached 0.43 µg m⁻³ and 76.96 pptv, respectively. In the Y3 scenario, these ranges increased to 0–0.97 µg m⁻³ for PCl and 0–77.79 pptv for HCl. The spatial differences in PCl concentrations between the Y0 and Y3 scenarios ranged from 0 to 0.59 µg m⁻³, with the most significant differences simulated in Heilongjiang and Liaoning provinces, where the contribution rate was approximately 72 %. Meanwhile, the spatial differences in HCl concentrations between the Y0 and Y3 scenarios ranged from 0 to 8.21 pptv with the highest contribution rate was 99.20 % in Heilongjiang province. Regarding N₂O₅ and ClNO₂, the N₂O₅ concentration peaked at 310.65 pptv in the Y0 scenario, while the maximum spatial concentration increased to 343.29 pptv in the Y3 scenario. The spatial differences in N₂O₅ between the Y0 and Y3 scenarios ranged from -24.04 to 0.0 pptv (Fig. 4k) with a maximum contribution ratio of 44.87 %, indicating net consumption of N₂O₅. ClNO₂ concentrations peaked at 116.82 pptv in the Y0 scenario and increased to 179.69 pptv in the Y3 scenario. Spatial differences of ClNO₂ between the Y0 and Y3 scenarios ranged from 0 to 173.49 pptv (Fig. 4o) with the contribution rate of ClNO₂ concentrations exceeding 90 % in most simulated regions. These findings highlight the significant impact of incorporating anthropogenic chlorine emissions and revised chlorine chemical reaction mechanisms on regional ClNO₂ concentrations.

To isolate the contribution of anthropogenic chlorine emissions, the Y0 and Y2 scenarios were established. In the Y2 scenario, the spatial concentrations of PCl and HCl ranged from 0.0 to 0.95 µg m⁻³ and 0.0 to 77.60 pptv, respectively (Fig. 5b and f). The spatial differences in PCl between the Y0 and Y2 scenarios ranged from 0.0 to 0.58 µg m⁻³ (Fig. 5c), with an average contribution ratio of 61.02 %. Similarly, the spatial differences in HCl concentrations between the Y0 and Y2 scenarios ranged from 0 to 30.56 pptv, with the highest contribution rate of 99.11 % simulated in Heilongjiang Province, China. Notably, the peak ClNO₂ concentration in the Y2 scenario reached 166.82 pptv, with a maximum difference of 151.67 pptv. In most regions of northeastern China, ClNO₂ contributions exceeded 50 %.

For snow-free conditions, Liu et al. (2018) first utilized the ACEIC chlorine emission inventory in the Community Multi-scale Air Quality (CMAQ) modeling system and found that anthropogenic chlorine emissions contributed approximately 100 pptv to ClNO₂ concentrations, with a contribution range of 30 %–50 %. Similarly, Hong et al. (2020) using the CMAQ model with ACEIC inventory, further reported that the maximum contribution of chlorine emissions to ClNO₂ ranged from 50 to 100 pptv in Liaoning Province, corresponding to a contribution rate of 20 %–50 %. In contrast, the simulated contribution concentration range in Jilin and Heilongjiang Provinces was only 10–50 pptv, with a corresponding contribution rate of 10 %–20 %. However, under snow-covered conditions in this study, simulations using the CAMx model and an updated ACEIC inventory yielded ClNO₂ concentrations ranging from 0 to 151.67 pptv, with a contribution range of 50 %–90 %. This suggests that snow cover with higher albedo plays a significant role in the photochemistry of the ClNO₂ generation. Additionally, differences in air quality models and simulation periods may also contribute to the variations observed among these studies.

The existing air quality models typically underestimate ClNO₂ concentrations compared to observed values, suggesting the presence of unknown sources (Wang et al., 2022). To address this gap, we further incorporated two new ground surface chemical reactions including ClNO₂ production and consumption reactions (Table 1) into a 3D air quality model to investigate the contribution of these unknown ClNO₂ sources.

Figure 6 presents the simulated spatial distributions of monthly mean, nighttime monthly mean, and daytime monthly mean ClNO₂ concentrations, along with their corresponding contribution ratios from ground surface chemistry. The spatial differences in ClNO₂ between the Y2 and Y3 scenarios ranged from 0.0 to 38.96 pptv, with the contribution ratios of up to 48.20 % simulated in Heilongjiang, Jilin, and the northeastern regions of Liaoning Province (Fig. 6c and d). The higher albedo of snow can facilitate the surface photochemical reactions and lead to rapid ClNO₂ photolysis (Chen et al., 2019), which explains significant differences in the concentration of ClNO₂between daytime and nighttime. During the daytime, ClNO₂ concentrations in both the Y2 and Y3 scenarios ranged from 0 to 70.60 pptv. The maximum spatial difference in ClNO₂ between the Y2 and Y3 scenarios was 7.51 pptv, with monthly average contribution ratios reaching up to 23.02 % in the eastern regions of Heilongjiang and Jilin Provinces. In contrast, nighttime ClNO₂ concentrations exhibited an accumulation trend. Scenarios Y2 and Y3 simulated maximum nighttime ClNO₂ concentrations of 271.07 and 310.04 pptv, respectively. The nighttime spatial maximum difference (72.14 pptv) was approximately ten times greater than that simulated during the day, with a contribution ratio of around 92 % in Heilongjiang and Jilin Provinces.

The flux exchange between ground surface chemical processes and the atmosphere is a critical component of atmospheric chemistry. Pollutants deposited on surfaces (soil, snow, vegetation, and buildings) can also undergo heterogeneous chemical reactions.

Snow, in particular, provides a larger reaction interface for heterogeneous chemical while low temperatures influence chemical equilibria and increase atmospheric moisture content, favoring gas deposition processes (McNamara et al., 2021). Consequently, higher ClNO₂ and N₂O₅ concentrations are observed during winter compared to summer (Xia et al., 2021), primarily due to elevated N₂O₅ levels enhancing ClNO₂ production, while lower winter temperatures favor the equilibrium shift of the N₂O₅–NO₃ reaction toward N₂O₅ formation. Additionally, longer winter nights in high latitudes facilitate N₂O₅ accumulation under dark conditions (Wagner et al., 2013).

The ClNO₂ yield and flux variations serve as key indicators for assessing the significance of surface heterogenous chemical processes. During the snow-covered periods, the φ(ClNO₂) values are higher compared to snow free periods, ranging from 0.065 to 1.00 during most winter months in the Northern Hemisphere (Table 4). Notably, over snow surfaces, Wang et al. (2020) and Jeong et al. (2023) reported the φ(ClNO₂) values even exceeding 0.80 during winter.

From the perspective of ClNO₂ flux variations, McNamara et al. (2021) conducted vertical gas profile observations and snow chamber experiments in Kalamazoo, Michigan, reporting a daily averaged ClNO₂ flux of 3 × 10⁷ molec. cm⁻² s⁻¹ over snow-covered surfaces, compared to a negative flux of -24 × 10⁷ molec. cm⁻² s⁻¹ over snow-free surfaces. Jeong et al. (2023) further integrated a one-dimensional atmospheric boundary layer model with snow modules to investigate the vertical distribution and impact of urban snow cover on ClNO₂ emissions in Kalamazoo, Michigan. Their findings showed that the ClNO₂ flux was positive on snow-covered nights, averaging 3.7 × 10⁸ molec. cm⁻² s⁻¹, but turned negative on snow-free nights, averaging -(2.8 × 10⁹) molec. cm⁻² s⁻¹. These findings indicate that under snow-covered conditions, the flux of ClNO₂ is positive, whereas under snow-free conditions, it turns negative. Furthermore, the positive flux of ClNO₂ during nighttime snow conditions is significantly higher than the daily average flux.

As mentioned above, snow-covered ground surfaces exhibit higher φ(ClNO₂) values and larger positive ClNO₂ fluxes (Jeong et al., 2023; Kulju et al., 2022; McNamara et al., 2021), particularly at night. In this study, we further integrated the surface chemistry module into the 4D Eulerian model of CAMx, establishing a link for flux exchange between the ground surfaces and the atmosphere. This integration provides a bridge to explore the spatiotemporal variations in ClNO₂ concentration and allows for a more nuanced quantification of the impacts of chlorine chemistry on atmospheric oxidizing capacity and regional air quality.

According to previous studies (Lu et al., 2014; Zhu et al., 2022), the incorporation of chlorine chemical reactions can enhance the AOC. The spatial concentrations of oxidizing species (OH, HO₂, and RO₂) in scenarios Y0, Y1, Y2, and Y3 are shown in (Fig. S5). The contributions of anthropogenic chlorine emissions and N₂O₅ heterogeneous chemistry under different conditions are illustrated in Fig. S6. Note that Y3–Y0, Y2–Y1, and Y3–Y2 represent contributions from N₂O₅ heterogeneous chemistry, aerosol surface heterogeneous chemistry, and ground surface heterogeneous chemistry, denoted as Chl_het_N₂O₅_a+g, Het_N₂O₅_a, and Het_N₂O₅_g, respectively.

In the Y3–Y0 scenario, the maximum OH concentration difference reached 3.69 × 10⁵ cm⁻³ with a maximum relative contribution of 7.59 %. This result is consistent with Wang et al. (2020), who reported a 6 % contribution of anthropogenic chlorine emissions to the AOC in Northeast China for 2014 (Wang et al., 2020). The spatial concentration difference in RO₂ ranged from -(4.70 × 10⁶) to 0.17 × 10⁶ cm⁻³ with a maximum relative contribution of 6.56 %. The HO₂ concentration was more regionalized where it primarily concentrated in coastal areas of the Beijing-Tianjin-Hebei region and the Bohai Sea, with the range of -(10.96 × 10⁵) to 7.95 × 10⁵ cm⁻³ and a relative contribution of approximately 3 % in Northeast China.

In the Y2–Y1 scenario, the spatial OH concentration difference ranged from -(0.15 × 10⁴) to 1.88 × 10⁴ cm⁻³ with a maximum relative contribution of 6.34 %. The maximum HO₂ concentration difference (9.04 × 10⁵ cm⁻³) exceeded that simulated in the Y3–Y0 scenario. Furthermore, the RO₂ difference range -(6.98 × 10⁸) to 0.29 × 10⁸ cm⁻³ was also larger than in the Y3–Y0 scenario.

In the Y3–Y2 scenario, the OH concentration difference ranged from -(0.12 × 10⁴) to 3.63 × 10⁴ cm⁻³, and the contribution regions were primarily concentrated in Heilongjiang Province and the four eastern regions of Inner Mongolia with a maximum relative contribution of 7.37 %. The maximum HO₂ contribution was 5.98 %, while the RO₂ contribution was the smallest at 1.99 %. The results illustrate that under snow cover conditions, chlorine chemistry generally promotes OH generation, inhibits RO₂ production, and has varying effects on HO₂ production, inhibiting it in Chifeng city while promoting it in other regions of Northeast China (Fig. S6).

The greater spatial distribution differences simulated in Y2–Y1 compared to Y3–Y0 can be attributed to the CB6r2h-lts chemical mechanism, which includes 22 gas-phase chlorine reactions, eight of which Reactions (R8–R15) directly impact the generation and removal of OH, HO₂, and RO₂. The analysis reveals that the Y2–Y1 scenario had the most significant impact, followed by Y3–Y0, while the contribution of Y3–Y2 was relatively minor. R8HOCl+hv=Cl+OHR9OH+HCl=ClR10OH+FMCl=Cl+COR11FMCl=Cl+CO+HO2R12ClO+MEO2=Cl+FORM+HO2R13Cl+ETHA=HCl+0.991AlD2+0.991XO2H+0.009XO2N+RO2R14Cl+PRPA=HCl+ACET+0.97XO2H+0.03XO2N+RO2R15Cl+ISOP=FMCl+ISPD+0.96XO2H+0.04XO2N+RO2 Where FMCL denotes formyl chloride (HC(O)Cl); MEO₂ represents the methyl peroxy radical; AlD₂ refers to acetaldehyde and higher aldehydes; XO₂H indicates NO-to-NO₂ converting peroxy radicals from HCs; XO₂N signifies NO-to-organic nitrate converting peroxy radicals; ISPD represents Isoprene product/Isoprene-derived peroxy radical; FORM denotes formaldehyde; ACET indicates acetone; and ISOP represents isoprene.

Chlorine emissions and the associated chlorine chemical reactions significantly influence AOC, the formation of particulate matter, and ozone levels. Figure S7 presents the spatial concentration distributions of these species with concentration ranges of 0–128.64 µg m⁻³ for PM_2.5, 0–16.94 µg m⁻³ for PNH₄, 0–13.06 µg m⁻³ for PSO₄, and 0–47.53 µg m⁻³ for PNO₃, respectively. Figure 7 exhibited the differences and contribution ratios of scenarios (Y3–Y0), (Y2–Y1), and (Y3–Y2) on PM_2.5 and its components (PNH₄, PSO₄, and PNO₃), respectively.

In the Y3–Y0 scenario, chlorine chemistry suppressed PM_2.5 formation in the eastern regions of Northeast China with a maximum suppression of 1.94 µg m⁻³. Conversely, chlorine chemistry promoted PM_2.5 formation in most regions of Heilongjiang, Jilin, and Liaoning Provinces with a significant increase of 3.65 µg m⁻³ and a maximum relative contribution of 15.34 %. The simulation results show that chlorine addition promoted the formation of PNH₄ (increased by 0.73 µg m⁻³) and PSO₄ (increased by 0.37 µg m⁻³) which were primarily concentrated in urban areas such as Harbin and Changchun. In contrast, PNO₃ was suppressed, with a minimum difference value of -1.78 µg m⁻³ and a corresponding relative contribution of 23.77 %.

In the Y2–Y1 scenario, PM_2.5 was inhibited in the eastern regions of Heilongjiang and Liaoning Provinces, while an increase was simulated in certain areas of Jilin Province, with concentration differences ranging from -2.64 to 2.16 µg m⁻³ and a maximum contribution of 3.52 %. Aerosol surface heterogeneous chemistry inhibited PNH₄ and PNO₃ formation across most of the central and northeastern regions, with maximum relative contributions of 3.14 % and 26.25 %, respectively. The spatial concentration difference in PSO42- was not significant in the Northeast region with the maximum value of 0.09 µg m⁻³.

In the Y3–Y2 scenario, the formations of PM_2.5, PNO₃, PNH₄, and PSO42- were enhanced, with maximum differences of 0.18, 0.16, 0.05, and 0.01 µg m⁻³, respectively. Compared to Y3–Y0 and Y2–Y1, the spatial concentration difference in Y3–Y2 was relatively minor for these species.

In the Y3–Y2 scenario, the impact on particulate matter is relatively small, primarily due to two factors: (1) the concentration of re-emitted species was low, and the species involved were limited; and (2) highly oxidizing species such as HCl and ClNO₂ in rapidly decomposed into other chlorides within the snow, preventing their release into the atmosphere as oxidants and leading to a net loss of chlorine. Additionally, an analysis of ClNO₂ photolysis in snow cover revealed a maximum consumption value of 2.88 pptv, with a monthly average maximum concentration of 38.96 pptv on the ground, accounting for approximately 7.40 % (Fig. S8).

The anthropogenic chlorine emissions and N₂O₅ heterogeneous chemistry can significantly enhance ozone levels. Previous studies have quantified this effect: Wang et al. (2020) reported that anthropogenic chlorine emissions increased the maximum daily 8 h average (MDA8) O₃ concentration by 1.70 ppbv while Yi et al. (2021) found a 6.70 ppbv increase in winter MDA8 O₃ concentration in the Yangtze River Delta (YRD) region due to updates in chlorine chemistry processes. Furthermore, Yang et al. (2022) demonstrated that incorporating the anthropogenic chlorine emissions with biomass burning emissions and updating the parameterization of heterogeneous N₂O₅ and Cl related chemistries, including adjustments of γ(N₂O₅) and ϕ(ClNO₂), led to a 4.5 ppbv increase in the MDA8O₃ concentration over China (Yang et al., 2022). In this study, the maximum difference in monthly mean MDA8 O₃reached 3.41 ppbv (Fig. 8a), which is smaller than the value simulated (4.74 ppbv) in the Y2–Y1 scenario. This difference is primarily attributed to the adding of chlorine emissions, which suppresses atmospheric oxidation. For example, the maximum difference concentrations of RO₂ and HO₂ in Y2–Y1 are 0.29 × 10⁸ and 9.04 × 10⁵ cm⁻³, respectively, while in Y3–Y0, they are 0.17 × 10⁶ and 7.95 × 10⁵ cm⁻³, respectively (Fig. S6). The maximum contribution concentration of surface heterogenous processes Y3–Y2 to ozone was 1.30 ppbv (Fig. 8c).

To fully understand the dynamic exchange processes between the atmosphere and the ground surfaces, it is essential to quantify the mass of chemical species deposited on various surfaces, such as snow, vegetation, buildings, and soil, which provide sufficiently large reaction interfaces. Incorporating these deposition processes into the air quality models will offer a more comprehensive understanding of ground surface-atmosphere interactions and their impact on atmospheric chemistry. However, the current ground surface chemistry module remains relatively simplified, primarily focusing on the re-emission of species into the atmosphere from predefined reactions. To more accurately represent the full impact of surface chemistry on atmospheric processes, further development and refinement of the ground surface module are necessary.