Implementation of HONO into the chemistry-climate model CHASER (V4.0): roles in tropospheric chemistry

. Nitrous acid (HONO) is an important atmospheric gas given its contribution to the cycles of NO x and HO x , but its role in global atmospheric photochemistry is not fully understood. This study implemented three pathways of HONO formation in the chemistry-climate model CHASER (MIROC-ESM) to explore three physical phenomena: gas-phase kinetic reactions (GRs), direct emission (EM), and heterogeneous reactions on cloud/aerosol particles (HRs). We evaluated the simulations by the atmospheric measurements from the OMI (Ozone Monitoring Instrument), EANET (Acid Deposition Monitoring Network 15 in eastern Asia) / EMEP (European Monitoring and Evaluation Programme) ground-based stationary observations, observations from the ship R/V Mirai , and aircraft-based measurements by ATom1 (atmospheric tomography) and EMeRGe-Asia-2018 (Effect of Megacities on the Transport and Transformation of Pollutants on the Regional to Global scales). We showed that the inclusion of the HONO chemistry in the modeling process reduces the model bias against the measurements for PM 2.5 , NO 3– /HNO 3 , NO 2 , OH, O 3 , and CO, especially in the lower troposphere and the North Pacific (NP) region. 20 We found that the retrieved global abundance of tropospheric HONO was 1.4 TgN. Of the three 63% and 26% to the net production, respectively. We also observed that, on the surfaces contributed larger amounts of HONO (51%) than those on the cloud surfaces (12%). The model exhibited significant negative biases for daytime HONO in the Asian off-coast region, compared with the airborne measurements by EMeRGe-Asia-2018, indicating the existence of unknown daytime HONO sources. Strengthening of aerosol uptake of NO 2 near surface 25 and in the middle troposphere, cloud uptake, and direct HONO emission are all potential yet-unknown HONO sources. The most promising daytime source for HONO found in this study is the combination of enhanced aerosol uptake of NO 2 and surface-catalyzed HNO 3 photolysis, which can also remedy the model bias for NO 2 and O 3 during EMeRGe. We also found that the simulated HONO abundance and its impact on NO x -O 3 chemistry are sensitive to the yield of the heterogeneous conversion of NO increases CH 4 lifetime (13%) and tropospheric CO abundance (8%). HRs on the surfaces of cloud particles, which have been neglected in previous modeling studies, are the main drivers of these impacts. This effect is particularly salient for the substantial reductions of levels of OH (40–67%) and O 3 (30–45%) in the NP region during summer, given the significant 35 reduction of NO x level (50–95%). In contrast, HRs on aerosol surfaces in China (Beijing) enhance OH and O 3 winter mean levels by 600–1700% and 10–33%, respectively, with regards to their minima in winter. Furthermore, sensitivity simulations reveal that the heterogeneous formation of HONO from NO 2 and heterogenous photolysis of HNO 3 coincides in the real atmosphere. Nevertheless, the global effects calculated in the combined case, which most captured the measured daytime HONO level, still reduce tropospheric oxidizing capacity. Overall, our findings suggest that a global model that does not 40 consider HONO heterogeneous mechanisms (especially photochemical heterogeneous formations) may erroneously predict the effect of HONO in remote areas and polluted regions. net the heterogeneous processes involving ice and cloud particles are more significant globally. We found that the HONO chemistry, including the heterogeneous processes on cloud particles, can reduce the troposphere's oxidizing capacity on a global scale. It should be underlined that this finding is rather unexpected and contrasts with the increasing oxidation capacity previously reported for polluted areas. This new finding may also affect climate change mechanisms and, as a result, may influence its mitigation policies. However, the tendencies and magnitudes of HONO’s global effects are still debated along with the effort regarding daytime HONO formation mechanisms. In capturing HONO measurement during EMeRGe campaign, the combined case enhancing NO 2 aerosol uptake and implementing heterogeneous photolysis of HNO 3 (maxST+JANO3-B) in the reduction for global tropospheric oxidizing capacity. case, the effect magnitude is smaller for CH 4 lifetime, but those for the NO x -O 3 -CO chemistry are stronger compared with the calculation in the STD case. Overall, our results prove that a global model without heterogeneous HONO formation, especially photochemical heterogeneous HONO formations, can bias the overall impacts of HONO on tropospheric photochemistry as it neglects the photochemical effects of HONO in remote areas and underestimates them in polluted regions.


Introduction
Nitrous acid (HONO) is an important atmospheric gas as it participates in the cycles of nitrogen oxides (NOx = NO + NO2) and radical chemistry (OH, HO2, and RO2) (Kanaya et al., 2007;Ren et al., 2013;Whalley et al., 2018). Researchers have 45 suggested to include the HONO chemistry in atmospheric chemistry models for more accurate simulations of oxidative substances (Jacob, 2000;Li et al., 2011). Despite the empirical evidences have indicated that the HONO concentrations in urban environments can reach 14 ppbv at night and can reach several hundred pptv throughout the day (Appel et al., 1990;Febo et al., 1996;Kanaya et al., 2007;Lee et al., 2016;Tan et al., 2017;Whalley et al., 2018), the HONO formation mechanism remains unclear. More specifically, the mechanisms of the HONO daytime sources have recently attracted considerable 50 attention of researchers (Kleffmann et al., 2003;Li et al., 2014;VandenBoer et al., 2013;Xue et al., 2021a,b;Ye et al., 2018).
The only homogeneous reaction known to produce HONO in the troposphere is the direct combination of OH and NO (R2). Note that the major loss of HONO occurs via photolysis (R1) in the atmosphere at 300-405 nm: (R1) HONO + h  OH + NO (300 nm < λ< 405 nm) (R2) NO + OH + M  HONO + M 55 (R3) HONO + OH  NO 2 + H 2 O Moreover, the photolysis of HONO (R1) has attracted considerable attention in the literature as a critical source of OH radicals in the polluted urban atmosphere (e.g., Calvert et al., 1994;Harris et al., 1982;Jenkin et al., 1988;Platt and Perner, 1980). The OH level at sunrise can be increased by a factor of 5 due to the photolysis of HONO, with the regional daily maximum O3 level increasing by 8% (Jenkin et al., 1988). Besides the direct loss via photolysis, the reaction of HONO with 60 OH (R3) may also contribute to the daytime loss of HONO (Burkholder et al., 1992).
Notably, some night-time measurements hinted on the heterogeneous sources of HONO from aerosol surfaces. For instance, Harrison and Kitto (1994) have provided evidence about the HONO source from high concentration episodes of > 10 ppbv NO2 for grassland in eastern England (Harrison and Kitto, 1994). Two reactions have been widely suggested to been proven to be first-order with NO2 and H2O in reaction chamber studies (Sakamaki et al., 1983, Jenkin et al., 1988. The second process was evaluated by using laboratory surfaces (Sakamaki et al., 1983, Jenkin et al., 1988 and by using field observations in the presence of high O3 and when NO2 was the dominant form of NOx (Kessler and Platt, 1984). As a result, the second process was proposed as a peculiarly important source of HONO in the urban atmosphere (Ammann et al., 1998;Gerecke et al., 1998). In the past two decades, researchers have investigated the heterogeneous NO2 reactivity on vegetated, 70 aqueous, sea salt, carbonaceous, and soot surfaces (Acker et al., 2001(Acker et al., , 2006Arens et al., 2001;Kleffmann and Wiesen, 2005;Kleffmann et al.,1998;Lammel and Cape, 1996;Lee et al., 2016;Notholt et al., 1992;Reisinger, 2000;Rubio et al., 2002;Stutz et al., 2002). In our model, these two processes are simplified as NO2  0.5 HONO + 0.5 HNO3 (R4) and NO2  HONO (R5).
Also, some modeling studies have reported overestimations of HONO over remote areas, indicating the HONO release 75 from or deposition in snow (Chu et al., 2000;Fenter and Rossi, 1996;Kerbrat et al., 2010), partitioning to cloud water (Bongartz et al. 1994;Cape et al., 1992;Harrison and Collins, 1998;Mertes and Wahner, 1995), and deliquescent aerosol surfaces (Harrison and Collins, 1998). The loss process occurs via the reaction HONO + H2O  NO -+ H3O + , simplified in our model as HONO  NO (R6) for surfaces of liquid and aqueous sulfate aerosols.
The natural sources of HONO include plant-foliar cuticles or soil biological crust (Hayashi and Noguchi, 2006;Oswald 80 et al., 2013;Porada et al., 2019;Su et al., 2011), with estimated global total emission of 0.69 Tg yr −1 HONO-N (Porada et al., 2019). Given the widespread occurrence of nitrite-fertilized soil in natural environment, highly acidic soils are arguably the strong sources of HONO and OH (Su et al., 2011). This potentially important source has been likely overseen by many previous modeling studies at both global and regional scales. Soil emissions could sustain the daytime HONO budget at relatively low aerosol concentrations (Lu et al., 2018). Anthropogenic activities can also directly emit HONO through incomplete 85 combustion, as vehicles, for instance, can yield as high concentrations as 7 ppb (Kirchstetter et al., 1996;Kurtenbach et al., 2001). In regional air quality models, HONO sources from vehicles and vessels are often given at 0.8-2.3% of NOx emissions level, given the differences between gasoline and diesel vehicle types (e.g., Aumont et al., 2003;Kurtenbach et al., 2001;Li et al., 2011;Zhang et al., 2016).
Many field observational studies reported unknown HONO sources during the day, and various mechanisms have been 90 proposed as efficient daytime HONO formation mechanisms. The photolysis of particle-phase NO3 -(aNO3 -) < 300 nm has been previously suggested as a supplemental NOx source (Romer et al., 2018) and can be the efficient HONO production mechanism during the daytime in an aqueous environment with low pH and the presence of OH scavengers (Benedict et al., 2017a;Benedict et al., 2017b;Scharko et al., 2014;Ye et al., 2018). Another study addressed the altitudes below 300 m, where HONO deposited onto the ground surface at night and further proposed to be a significant reservoir for HONO during the day 95 (VandenBoer et al., 2013). Such a parameter for ground surfaces in a global model is somewhat uncertain. Moreover, the HONO source from ground surfaces may only affect the lower boundary layer while insignificantly contributing to the tropospheric HONO budget (Ye et al., 2018;Zhang et al., 2009). Furthermore, the particle-phase NO3photolysis can occur on both ground and aerosol surfaces (HNO3 + h  HONO) with a 2-orders-of-magnitude faster rate than its rate in the gas phase (Lee et al., 2016;Lu et al., 2018). Photolysis of ortho-nitrophenols, photoexcited NO2 gas reaction (HO2×H2O + NO2 100  HONO), photosensitized heterogeneous conversion of NO2 on grounds are all potential daytime HONO sources (Jorba et al., 2012;Lee et al., 2016;Li et al., 2014), yet the mechanisms are complicated, and their efficiency are merely evaluated for ground-based observation.
Many scholars have scrupulously addressed the effects of HONO in polluted regions as well. For instance, HONO-induced enhancements in winter daytime HOx (up to >200% for OH) and O3 (6-12%) over urban sites in China have been reported (Li 105 et al., 2011;Lu et al., 2018;Zhang et al., 2016). A box modelling study analysed the detailed budget of HONO in London and found that HONO chemistry increased OH by 20% during the day (Lee et al., 2016). A global modelling study found increments for OH and O3 across the globe and throughout the troposphere, with a maximum of 30 ppb O3 in Eastern Asia and slight NO2 increment, although the results were evaluated with only ground-based data (Jorba et al., 2012). However, enhanced O3 levels in response to additional OH production from the HONO photolysis only occur in high-NOx regions, although they 110 can be decreased in some areas under low NOx conditions (Jorba et al., 2012). At the same time, another 3D modeling study used a constant occurrence ratio for HONO as 0.02 of NOx and reported similar patterns for O3 changes regarding HONO chemistry (Elshorbany et al., 2012). The NOx reduction effects that follow the NO2 conversion are suggested to be more critical over the oceans than over continental regions, with up to 20% NOx reduction and 5%-20% HNO3 enhancement over ocean regions of the lower troposphere (Martin et al., 2003). 115 As H2O is required for the uptake of NO2 on surfaces, wet surfaces have been broadly recommended as favoured surfaces for NO2 uptake. Therefore, cloud droplets can be an important surface for heterogeneous reactions of NO2 because they are ubiquitous in the troposphere. Heterogeneous reactions by clouds can have a similar impact as aerosol particles on tropospheric O3 and OH levels (Christopher et al., 2019). However, this aspect has been overlooked many times in previous studies, leading to potential underestimating (or even dismissing) the potential effects over remote environments. 120 This study introduces HONO chemistry into the global atmospheric chemistry model CHASER-V4.0 for the first time.
The standard model configuration uses basic mechanisms of HONO chemistry, while various sensitivity cases implement other potential HONO sources are also conducted to force simulation into an agreement with the observed HONO values. The main idea for the HONO inclusion is to elaborate the model simulation for tropospheric oxidative substances while focusing on aerosol and cloud processes. The model includes the detailed online calculation of O3-HOx-NOx-CH4-CO coupling and 125 oxidation of non-methane hydrocarbons (NMHCs) (Sudo et al., 2002) and heterogeneous processes for N2O5, HO2, and RO2 radicals (Ha et al., 2021;Sekiya and Sudo, 2014;Sekiya et al., 2018;Sudo and Akimoto, 2007). In Section 2, we describe the approach, including the model description and configuration. In Section 3.1, our model is evaluated by the available observations for atmospheric species, including observations from aircraft/ship, ground stations, particularly addressing the roles of the HRs. In addition, simulated daytime HONO was verified with aircraft measurements for an Asian off-coast region. 130 Section 3.2 presents the model results for HONO distributions, verification for global effects to TCO with OMI satellite, global HONO impacts including different effects from each pathway and a discussion on the uncertainty of the calculated effects.
Finally, Section 4 effectively represents the summary and concluding remarks.

Global chemistry model 135
This study applied the global chemistry model CHASER (MIROC-ESM) (Sudo et al., 2002(Sudo et al., , 2007Watanabe et al., 2011), which considers the detailed photochemistry in the troposphere and stratosphere. The chemistry component of the model, based on CHASER-V4.0, retrieves the concentrations of 94 total species and 258 chemical reactions (57 photolytic, 180 kinetic, and 21 heterogeneous reactions on tropospheric aerosol and cloud surfaces and polar stratospheric clouds) (Table 1) (Ha et al., 2021;Morgenstern et al., 2017;Sekiya et al., 2018). In this study, the newly added HONO system includes three pathways of HONO formation and interactions: (1) gas-phase formation via the 145 NO + OH reaction (R2), the photolysis of HONO (R1), and the reaction of HONO with OH (R3), hereafter denoted as GRs; (2) HONO direct emissions estimated from anthropogenic-and soil-NOx emissions (hereafter denoted as EM); and (3) the HONO conversion from NO2 (R4, R5) and its loss on liquid/ice surfaces and aqueous aerosols (R6), which is hereafter denoted as HRs.
The heterogeneous photolysis of HNO3 (HNO3 + h  HONO), which was suggested as an efficient HONO source at 150 daytime (Lee et al., 2016;Zhou et al., 2011), is investigated in Chapter 3 as sensitivity cases in the effort of making simulation for daytime HONO compatible with measurement. This photolysis is simplified using a factor multiply with HNO3 photolysis in the gas phase (HNO3 + h  OH + NO2) (see Sect. 3.1.2). The light-dependent gas-phase HONO source (HO2×H2O + NO2  HONO) (Li et al., 2014) is not investigated in this study. However, a simple gas-phase reaction, HO2 + NO2  HONO + O2 (Burkholder et al., 2015), was introduced, but it did not successfully preserve the total reactive nitrogen chemistry (NOy); 155 hence, it was omitted in this study.

Experimental setup
The Global Emissions Initiative (GEIA) inventory (http://www.geiacenter.org/) is applied to quantify the soil NOx emissions (6 TgN yr -1 ) and anthropogenic NOx emissions (45 Tg N yr -1 ). Since this broadly applied inventory is not currently available for HONO, this study tentatively imposes the HONO direct emissions based on the above NOx emission inventory through a 160 constant factor of 0.1 (10% of NOx emissions). This assumption (soils + combustion) leads to a global HONO soil-emission estimate of about 0.6 TgN yr -1 , which is equivalent to the estimate from Porada et al. (2019), and it suggests that the anthropogenic emission for HONO is 4.5 TgN yr -1 . For HONO from exhaust sources, this factor (10%) is considerably higher than the previously reported estimate of 0.7%, derived for combustion (Xue et al., 2021b) or 0.8-2.3% for on-road vehicles (Aumont et al., 2003;Kurtenbach et al., 2001;Li et al., 2011) and 3%-6% for commercial aircraft (Lee et al., 2011). This 165 factor intends to show the apparent potential impacts of direct HONO sources on the troposphere. The photolysis reaction HONO + h  OH + NO (300 nm < λ< 405 nm) (R1) is employed with the wavelength-dependent cross-sections following the recent study of Burkholder et al. (2015). 170 The kinetic of homogeneous reactions NO + OH + M  HONO + M (R2) and HONO + OH  NO2 + H2O (R3) is applied with the low and high-pressure-limit rate constants, which are temperature dependent, as suggested in the aforementioned study.
In CHASER, the heterogeneous chemistry of interest is simplified as a first-order chemical loss in the aerosol phase for a species transferred from the gas phase. The rate of this pseudo loss is combined, and the first-order-loss rate for heterogeneous 175 processes is calculated by using the Schwartz theory (Jacob, 2000;Schwartz, 1986), being simply treated with the mass transfer limitations in addition to the reactive uptake coefficient ( ) (Ha et al., 2021). Note that only surface reactions are considered in CHASER, and there is no bulk particle reaction for the HR scheme.
The uptake coefficient parameter ( ) is defined as the net probability that a molecule X undergoing a gas-kinetic collision with a surface is taken up onto the surface. An average uptake coefficient for NO2 (R4) of 10 -4 (10 -6 -10 -3 ) for the conversion 180 of aqueous aerosols and clouds has been previously suggested (Jacob, 2000;Kleffmann et al., 1998;Li et al., 2018;Lu et al., 2018). The NO2 uptake by organic carbon aerosols has been reported to have similar coefficient values (Salgado-Muñoz and Rossi, 2002). The uptake coefficient for fresh black carbon is highly efficient and equals 3×10 -3 (Ammann et al., 1998;. The parameters for the uptake coefficients of R4 applied in the CHASER model are shown in Table 2. As previous studies have noted, the fast initial uptake of NO2 is observed on soot with an uptake coefficient in the range 185 of 10 -1 -10 -4 (Ammann et al., 1996(Ammann et al., , 1998. However, it rapidly decreased to ~10 -7 over 5 min (Kleffmann et al., 1999) to < 4×10 -8 for 5-day aged surfaces (Saathoff et al., 2001). In organic soot, γ is in the range of 10 -4 -10 -6 (Al- Abadleh et al., 2000;Ammann et al., 1996;Arens et al., 2001;Salgado-Muñoz et al., 2002). In CHASER, the NO2 conversion on organic carbon and soot (R5) was tentatively applied with uptake coefficients of 10 -4 and 3×10 -4 , respectively, which also falls within the previously suggested range (10 -6 -10 -3 ) considering the higher efficiency for soot (Table 2). 190 Also, previous laboratory experiments have introduced a wide range for the uptake coefficient of HONO by (R6), that is, 3.7×10 -3 at 178 K to 1.3×10 -5 at 200 K for the ice surface (Fenter and Rossi, 1996;Chu et al., 2000) and 4×10 -3 -4×10 -2 at 278 K (Mertes and Wahner, 1995) or 0.03-0.15 at 297 K (Bongartz et al. 1994) for liquid water surfaces. In the aerosol flow reactor experiment on deliquescent sodium chloride and ammonium sulfate droplets at 279 K, the HONO reactive uptake coefficient of 0.0028 for 85% relative humidity has been previously obtained (Harrison and Collins, 1998). In CHASER, the 195 aforementioned reference values for HONO uptake on ice, liquid clouds, and aqueous sulfate are simply averaged to be used as a heterogeneous loss of HONO (R6) in the atmosphere (

Simulations
In this study, two main simulations, OLD and STD, and three sensitivity simulations (Table 3, no. 2-4), were conducted 200 to isolate the distinct impacts of each pathway of the HONO chemistry for different surface types considered in the model (Table 3). The OLD simulation is run with the base model configuration without any HONO chemistry, with a heterogeneous scheme containing eight reactions on N2O5 (N2O5  2HNO3), HO2 (HO2  0.5 H2O2 + 0.5 O2), and RO2 (RO2  inert products) (Ha et al., 2021). The control case (STD) considers all three types of HONO sources: direct emissions (EM), gasphase reactions (GRs), and heterogeneous reactions (HRs). To quantify the effects of each mechanism using Eq. (1), two 205 sensitivity cases (GR, GR+HR) intentionally implement GRs (R1, R2, R3) into the OLD case and HRs (R1, R2, R3, R4, R5, R6) into the GR case, respectively. GR+HR (cld) is another sensitive case like GR+HR, with HRs on aerosols excluded to investigate the different effects of clouds and aerosols. Eq. (1) determines the effects of each mechanism on atmospheric species i (i = OH, O3, NOx, CO) by concentration differences of i in two relevant cases being compared to that in the OLD case. 210 where Case1i and Case2i are the concentrations of i in two separate cases: GR and OLD cases for the pure effects by the gaseous mechanism; GR+HR and GR cases for the effects of heterogeneous mechanisms; STD and GR+HR cases for the HONO emissions effects; and GR+HR(cld) and GR cases for the effects of heterogeneous reactions that exclusively occur on ice and cloud particles. 215

Observation data for model evaluation
We evaluated the OLD, STD, and sensitivity simulations with ground-based, ship-based, aircraft, and satellite measurements.
The observational information and locations of the surface site and ship/aircraft tracks for the observations used in this study are summarized in Table 4, Figure 1, Figure 6, and Figure S6. 220 Simulated tropospheric column ozone was also evaluated by using tropospheric column O3 (TCO) derived from the OMI (Ozone Monitoring Instrument) spaceborne observations (https://daac.gsfc.nasa.gov/). To this end, the monthly data from 45 stations during 2010-2016 were used to verify aerosol surface concentrations (sulfate, nitrate) and trace gases (HNO3, NOx, O3) in the Acid Deposition Monitoring Network in eastern Asia (EANET: https://www.eanet.asia/). We also used the European Daytime HONO concentrations were also analyzed by using the DLR-HALO aircraft (Operator Deutsches Zentrum für Luft-und Raumfahrt -High-Altitude and Long-Range research aircraft) measurements made during the EMeRGe-Asia (Effect of Megacities on the Transport and Transformation of Pollutants on the Regional to Global scales) campaign in March and April 2018, over an off-coast region between Korea (including the Jeju Island as the part of the domain), Taiwan, and the 235 Philippines (http://www.iup.uni-bremen.de/emerge/home/). The measuring time falls in the range of 0:00 UTC to 9:00 UTC, around 8:00 to 17:00 in local time (UTC+8). Verification with EMeRGe data helps explore the daytime HONO chemistry mechanisms in the free troposphere.
A model bias for each species was calculated as the difference between the simulated and observed concentrations, as shown in Eq. (2), where N is the total number of data points used in the calculation.  Table S1). In (d), numbers show flight tracks.

Cloud fraction and surface area density for cloud and aerosols
In this study, besides NO2 conversion onto clouds and aqueous particles (R4), the losses of HONO onto the ice and liquid clouds (R6) are also included. Therefore, for accurate simulations of HRs, we need to examine the cloud distribution. The CHASER model applies the common cloud maximum-random overlap assumptions (MRAN) in the radiation and cloud microphysics schemes as other general circulation models to estimate the distribution of the cloud fraction. The verification 255 by using the satellite observation data ISCCP D2, CALIPSO-GOCCP, and reanalysis data JRA55 generally revealed good correlation, whereas notable (10-20%) underestimation for the entire troposphere was yet salient. During June -July -August (JJA), CHASER's cloud fraction is likely overestimated for the lower troposphere of the North Pacific (NP) region (10-20% compared to JRA55 reanalysis data). This finding indicates that thorough scrutiny of any impacts in this region is highly required (see the discussion in Section 3.2). Note that more detailed information for cloud verification for CHASER has been 260 provided by Ha et al. (2021).
The heterogeneous processes by clouds and aerosol particles are parameterized by using surface area density (SAD) estimations alongside the cloud fraction and aerosol concentration. During DJF, the simulated total SAD is attributed to all types of aerosols. However, for JJA, liquid clouds and sulfate aerosols are the principal SAD sources. This is a peculiarly visible pattern for the northern polar and mid-latitude maritime regions. The performance for aerosol SAD in our model is in 265 line with the earlier report by Thornton et al. (2008), except for sea salt density, which was very low in our model (up to 2 µm 2 cm -3 ) compared to their work (up to 75 µm 2 cm -3 ). This disagreement may be ascribed to the two models' different size distributions for sea salt. The calculated SAD for the liquid cloud is two orders of magnitude higher than SAD for ice cloud and total aerosols. Liquid cloud SAD maximized at ~ 800 hPa in the tropical convective systems and over the midlatitude storm tracks, reaching ~ 50,000 µm 2 cm −3 at the surface of the North Pacific region in JJA. Sulfate aerosols dominate above 270 600 hPa for the Northern Hemisphere (~ 20 µm 2 cm −3 ) among the total aerosol surface area, followed by organic carbons and soil dust (~ 10 µm 2 cm −3 in JJA). At the surface layer, sulfate aerosols are prevalent in DJF for the Chinese region (> 1,000 µm 2 cm −3 ), north-eastern U.S. (~ 500 µm 2 cm −3 ), and North Pacific region in JJA (~ 250 µm 2 cm −3 ). SAD for soil dust dominates in desert regions, with annual average values > 100 µm 2 cm −3 . Organic carbon (OC) is dominant in winter over biomass burning regions such as China (up to 1,000 µm 2 cm −3 ) and South Africa (up to 800 µm 2 cm −3 ). For the Chinese region, 275 SAD for black carbon (BC) can reach 600 µm 2 cm −3 in DJF and 75 µm 2 cm −3 in India. The total-aerosol SAD for the northern high-latitude and mid-latitude oceans is ~ 75 µm 2 cm −3 , consistent with the estimation by Thornton et al. (2008). The distribution of SAD is discussed in more detail in an earlier report (Ha et al., 2021).

Daytime concentrations of HONO and other atmospheric species
This section evaluated CHASER-based HONO estimates using the HONO measurements collected during the EMeRGe 280 campaign off-coast eastern Asia in spring 2018 (Andrés Hernández et al., 2021). This is the first global HONO modelling work using EMeRGe as the validation source. The HONO measurements in the free troposphere could provide essential information on the underlying gas-phase and heterogeneous HONO formation mechanisms as most current HONO measurements were conducted in the surface air. The daytime HONO concentration was retrieved from the aircraft-borne limb measurements using the HALO mini-DOAS (differential optical absorption spectroscopy) instrument, in which the absorbed 285 UV light (310-440 nm) by HONO was detected (Hüneke et al., 2017). The mini-DOAS's measurement method relies on near-UV/VIS/IR skylight spectroscopy in nadir and limb geometry. Data evaluation consists of three steps: (1) retrieval of slant column densities (SCDs) of trace gases by the DOAS method (Platt and Stutz, 2008), (2) forward radiative transfer modeling for each measurement using McArtim (Deutschmann et al., 2011); and (3) retrieval of concentration through a new scaling method for UV/VIS data (Stutz et al., 2017;Hüneke et al., 2017;Werner et al., 2017;Kluge et al., 2020;Rotermund et al., 290 2021).
Additional sensitivity runs were conducted to explore potential HONO sources during the daytime (Table 5). The ratR4+CLD case is run in an attempt to produce more HONO from heterogeneous sources by altering the HONO: HNO3 yield ratio in (R4) to 0.9: 0.1, and γ γ γ γliq. increased a hundred-fold (10 -4  10 -2 ). The main idea here is to evaluate whether the missing HONO source was sensitive to cloud uptake in this region or not. The maxST case maximized the uptake coefficients (γ-295 values) of NO2 on organic and black carbons to 0.1 (R4, R5), to estimate the separate role of soot uptake under daytime conditions (George et al., 2005;Monge et al., 2010;Ndour et al., 2008), which could achieve an unrealistically high -value of 10 −1 (Ammann et al., 1998;Kalberer et al. 1999). In three other runs (JANO3-A, JANO3-B, JANO3-C), the photolysis of aerosol nitrate / adsorbed HNO3 on the ground and other surfaces (NO3 -/HNO3) were examined, simply as HNO3 + hν  HONO (R7)). These heterogeneous photolyses of HNO3 were previously proposed as potential HONO sources at day (Lee et 300 al., 2016;Scharko et al., 2014;Zhou et al., 2011). Because aerosol nitrate and aqueous surfaces are ubiquitous in the atmosphere, the photolysis (R7) is simply set for the gaseous HNO3 species to occur in particular model spatial grids exposing ground surfaces and sufficient surface area density for aerosols and clouds. The photolysis (R7) is taken at a faster rate by two orders of magnitude than the gas-phase photolysis rate of HNO3 (HNO3 + hν  OH + NO2) (Zhou et al., 2011) and presumably yield 100% HONO to access the maximum effects by this photolysis (Lee et al., 2016). This setting allows (R7) not only to 305 occur at the surfaces of particles but also in the gas and bulk phases. However, in this test, (R7) generally refers to surfacecatalyzed photolysis or heterogeneous photolysis of HNO3. The JANO3-A case investigated the photolysis of adsorbed HNO3 on ground surfaces by implementing (R7) for the first vertical layer (z=1). The JANO3-B explored photolysis of nitrate particles and adsorbed HNO3 gas on both grounds and aerosol surfaces, applying (R7) for model grid cells with the SAD of 10 -6 ~ 10 -4 cm 2 cm -3 (100 to 10,000 µm 2 cm -3 ) to use 10 -4 cm 2 cm -3 threshold to exclude cloud surfaces (Sect. 3.1.1). The 310 JANO3-C case examines (R7) for regions present of all particles with SAD ≥ 10 -7 cm 2 cm -3 (10 µm 2 /cm 3 ). The SAD of 10 -6 and 10 -7 cm 2 cm -3 is supposed to be the threshold for continental aerosols. The maxST and JANO3-B / JANO3-C cases are also combined in two additional cases (maxST+JANO3-B and maxST+JANO3-C, respectively), given the contrary effects on NO2-O3-CO chemistry of these cases might be neutralized, is discussed next. Other tests examined the possible HONO sources from aviation crafts (AIRC), amplified emissions (EMx8), amplified homogeneous HONO formation (R2) (GRx8), which 315 descriptions were listed in the supplement Table S3.
The correlation coefficient (R) and model biases against EMeRGe for HONO are shown in Table S4 Figure 2(a) shows the vertical average score (cruising altitudes ± 500 m) for the measured (blacks) and simulated HONO concentrations in STD (reds) and those results of sensitivity cases. The measured daytime HONO concentration was close to 325 the boundary layer (below 1,000 m) over Taiwan, averaged at 115 ppt, and was peaked at ~250 ppt. Also, the HONO concentration decreased up to 9,000 m (± 500 m), with mean values dropping from 70 ppt (2,000 m ± 500 m) to < 20 ppt (5,000 m ± 500 m) and < 10 ppt above. These measured HONO values for this Asian coastal region are surprisingly high, which range from 10-115 ppt for 2,000 m ± 500 m altitudes, compared to Wang's report of < 100 ppt (maximum) and < 30 ppt (4 daytime hours means) for 1,500-2,000 m altitudes measured by a MAX-DOAS at a station nearby the HONO source 330 . This indicates that the source of HONO during EMeRGe might relate to other mechanisms than emission sources. In this study, the simulated HONO concentration in the STD case significantly underestimated the observations. They reached only 30-70 ppt at 1,000 m and nearly zero from 2,000 m upward (Figure 2(a): red versus black triangles for the simulation and the observations, respectively). These discrepancies indicate a significant unknown HONO source during the daytime, although the proposed heterogeneous HONO formation mechanisms were incorporated in our model. This finding 335 adds another instance of evidence about missing HONO sources in the polluted boundary layer and free troposphere (e.g., Kleffmann et al., 2003;Li et al., 2014;VandenBoer et al., 2013;Xue et al., 2021a;Ye et al., 2018). HONO from HNO3 photolysis (adsorbed on ground and aerosol surfaces for continental regions excluding cloud surface) 6 JANO3-C Add (R7) (SAD ≥ 10 µm 2 cm -3 ) Similar to JANO3-B but using a larger SAD threshold 7 maxST+ JANO3-B 8 9: and 8 ;: (R4, R5) = 0.1 Add (R7) (100 < SAD < 10,000 µm 2 cm -3 ) Combination of maxST and JANO3-B cases 8 maxST+ JANO3-C 8 9: and 8 ;: (R4, R5) = 0.1 Add (R7) (SAD ≥ 10 µm 2 cm -3 ) Combination of maxST and JANO3-C cases In Figure 3, which shows model discrepancies, the measured NO2 below 3,000 m (±500 m) close to land is captured well 340 in the model (Figure 3(a,e,i): magentas and greens), with 34% of the data being quite close for NO2 (±70 ppt) ( Figure S7 we identified the NO2 uptake on sulfate nearly as important through a parallel test (not shown). Especially, the heterogeneous photolysis of HNO3 can provide a large HONO amount near Taiwan, South-Korea, Japan (in JANO3-B and JANO3-C cases) and a small HONO amount for the route bound to the Philippines (in JANO3-C case) for the altitudes below 2,500 m ( Figure   3(d,h): greens, oranges, blues). during the cruises bounding to South Korea (up to 40 ppt) and the Philippines (up to 20 ppt) (Figure 3(m,p): small greens and blues left of the vertical line). These overabundances might hint on the deep stratospheric intrusion in springtime that caused imperfect downward mixing fluxes (Lin et al., 2012;Stohl et al., 2003;Trickl et al., 2014). This excessive NO2 and the corresponding missing HONO are also sensitive to the AIRC and GRx8 cases (Figure 3(n,q)), indicating that aircraft-exhaust of HONO could adjust the HONO/NO2 ratio and more homogeneous HONO production might contribute more, given the high 360 abundances of oxidizing substances at these altitudes. The possibility of emission of aviation-induced particles on which NOx to HONO conversion could reach 45% (Meilinger et al., 2005) can support the need for NO2 reduction and HONO formation for this height across EMeRGe's near-land domains. Moreover, the surface-catalyzed photolysis of HNO3 in the JANO3-C runs can serve as an efficient source and greatly reduce the model negative bias for HONO at 6,000 m, although the NO2 reproduction is too strong (because less NOx is removed via deposition of HNO3), which turns the model bias for NO2 become 365 overestimates (Figure 3(s)).
The model underestimation for HONO is also associated with the concurrent underestimation of NO2, which was observed more often at the altitudes of > 1000 m. The likely erroneous NO2 concentrations of ~1.8 ppb (1,000 m) and ~220 ppt (3,000 m) across Taiwan, linked with HONO of as high as 290 ppt (1,000 m) and 140 ppt (3,000 m), were not reproduced ( Figure 3(e,i)). These likely inadequate NO2 abundances can be partially alleviated through the enhanced HONO/NOx emission 370 ratio and more efficient NOx recycling process in the ratR4+CLD cases, respectively (Figure 3(g,j)). Here, the missing HONO is largely supplemented only at ~1,000 m over the marine environment, namely, during the Taiwan-Japan cruise, when we identified more products for HONO on cloud in the ratR4+CLD case. (Figure 3(e,g): red-orange diamonds). At ~6,000 m, small deficits of 60 ppt NO2 corresponding to ~10 ppt HONO (Figure 3(p): oranges and magentas) might correspond to lightning NOx emissions (Sudo et al., 2002) and stratospheric sources. Some homogeneous mechanisms at ~6,000 m as in the 375 GRx8 could be effective (Figure 3(n,o)). Moreover, the heterogeneous photolysis of HNO3 in the JANO3-C case can be an effective HONO supplement above 5,500 m (Figure 3(s)), while this photolysis can act as a NO2 production mechanism at any altitudes.
In general, the upper limit for the aerosol uptake coefficients (maxST case) may be applicable for the lowest cruising altitudes, which induced the increase of modelled HONO levels during both daytime and nighttime ( Figure S10). The 380 photolysis of adsorbed HNO3 on ground surfaces implemented in the JANO3-A case is impractical to be a source for HONO during EMeRGe, as this case only provided a mild HONO amount at a thin surface layer (<500 m; not shown). Fortunately, the surface-catalyzed photolysis of HNO3 in JANO3-B and JANO3-C cases can remedy the model-measurement discrepancies, i.e. RHONO>0.6 and model bias for HONO was reduced from -112 ppt (STD) to -22 ppt (JANO3-B) and -18 ppt (JANO3-C) for 0-500 m (Table S4). The HONO source from this photolysis of HNO3 is sufficient for continental and near-land regions. 385 In particular, the photolysis of HNO3 adsorbed on particles with smaller SAD (JANO3-C case) can be responsible for the 500 -3,000 m atmosphere around Philipines and South Korea and at higher altitudes where robust solar radiance can enhance the HNO3 photolysis. In the combined cases (maxST+B and maxST+C), HONO production was boosted, and the estimated NO2/O3 concentrations were best captured for 2,000 -5,000 m (±500 m) (Figure 2(a,b,d): black diamonds vs orange circles).
Furthermore, as discussed in Sect. 3.2.3, the sensitivity cases including combined cases can change the global tropospheric 390 effects differently.
These discussions demand further exploitation of this model towards efficient HONO formation mechanisms, especially elaborating the combined HONO production mechanisms from enhanced NO2 aerosol-uptakes and HNO3 photolysis.
Moreover, it requires (1) better emission inventories for South-East Asia and East Asia, the lightning source for NOx and HOx, and aviation-induced aerosols, (2) improvements in the model's vertical mixing and stratospheric air mass transport. 395 The remaining drawbacks in reproducing HONO and other atmospheric species (NO2, O3, CO) by model urges further elucidation of more efficient HONO formation mechanisms. To this end, one needs (1) to provide better emission inventories for anthropogenic sources of pollutants from Southeast Asia and East Asia, lightning-produced NOx and HOx, and aviationinduced aerosols, (2) to improve the vertical mixing and air mass transport from the stratosphere, (3) to apply other potential HONO formation mechanisms and NOx recycling processes alongside the selection and algorithm development among the 400 sensitivity tests provided here, especially for the maxST and JANO3-B runs; and (4) to simulate the lower and upper limits for the uptake coefficients of NO2 on aerosols and clouds.   The model performance of the free troposphere was evaluated through the atmospheric tomography (ATom1) aviation in August 2016 for NO2, OH, CO, and O3. The STD run reconstructed the chemical field observed in ATom1 with moderate or strong positive correlations for NO2, OH, CO,and O3 (RNO2=0.730,RO3=0.751,ROH=0.579,RCO=0.659 ; Table S2). For the NP region, the model correlations for these species were slightly lower (RNO2=0.621, RO3=0.609, ROH=0.407,RCO=0.596 ; Table  420 S2). The R values for NO2 and CO were consistently higher in the STD run than those in the OLD run, while for OH and O3, the R values are only improved for the NP region (Table S2). Figure 4 shows measured (grey) and simulated (red and black) NO2, O3, OH, CO concentrations and the effects of including HONO in the simulation for the NP region (flight #2 on August 3 rd ). Figure 5 displays vertical profiles of the model biases in STD vs OLD cases and photochemical effects by each HONO formation mechanism. Here, the data in all flights or 425 in the NP region are classified based on the air pressure from 1000-200 hPa (±50 hPa) and separated into nine bins. In the NP region, the OLD run (black lines) tends to overestimate NO2, O3, OH, but it underestimates CO at the lower troposphere, whereas the unsteady discrepancies at the upper layer are visible (Figure 4(a,b,e,f)). All four species tended to be underestimated near the tropopause (300-400 hPa) and to be overestimated in the lower stratosphere ( Figure 5(e-h). The HONO inclusion in STD run (red lines) has reduced NO2, OH, O3 and has increased CO levels, thereby, dwindling the model 430 biases for NO2, OH, and CO in the NP region except near the tropopause ( Figure 5(e,g,h)).
In the NP region, the surface NO2 level is reduced under the effects of HONO uptake on clouds (Figure 4 GRs also affect OH, O3, and CO, whereas the effects manifest in the upper troposphere rather than in the lower troposphere. This is likely the most influential factor that increases OH and O3 levels at these high altitudes (Figure 4 texts are the percentage reduction in model bias). Note that these biases are very pronounced near-surface (~1000 hPa) in the NP region (51.7% for NO2 and 77.3% for OH). To capture the patterns identified by observations in the upper troposphere, 445 except the NP region, more robust increases for NO2 and OH levels are still required (Figure 5(a,c)). At these altitudes, NOx and HOx sources from lightning (Brune et al., 2021) or aviation can be relevant, as discussed in Section 3.1.5.
Overall, the comparisons between the model and Mirai and ATom1 may indicate that the HRs on cloud surfaces are the main contributing factor to the marine boundary's photochemistry, whose effects emerged during the ATom1 flights in the marine atmosphere. GRs and aerosol HRs have a stronger impact on atmospheric chemistry at higher altitudes than the near-450 surface layer. Also, their effects should be enhanced through the additional NOx and HONO sources to reconcile the model simulations with the observations.

plotted. In (c-d, g-h), changes in concentrations by GRs (blue bars), EM (red), HRs on clouds (orange), and HRs on aerosols (green)
are plotted. Vertical blue and grey columns reflect the data for the regions with air pressure P > 500 hPa.

Figure 5: Vertical profile of model bias against aerial ATom1 data (a-h) and changes by HONO chemistry (l-s) for NO2, O3, OH, and CO (from left to right columns). Biases in OLD (black lines) and STD (red lines) runs are calculated for all flights (a-d) and NP region (e-h). The red numerical texts are the relative reductions (%) of the bias in the STD run compared to that in the OLD run. Changes by GRs (blue), HRs on clouds (orange), HRs on aerosols (green), and EM (red) are calculated for all flights (i-o) and NP region (p-s).
The simulations also compared with the research vessel (R/V) Mirai's observation in the western Pacific Ocean for O3 and 465 CO. The interpolation of model results for six cruises across Japan-Alaska region (40° N-75° N, 140° E-150° W) in 7-8-9/2015-2017 (summer), Indonesia-Australia region (5°-25° S, 105-115° E), and Indonesia-Japan region (10-35° N, 129°-140° E) are provided for the period of 12/2015 -1/2016. All the measured and simulated data were provided, whereas the data for the NP region (40°-60° N) were analyzed separately, as discussed in Section 3.2. More detailed information about the R/V Mirai can be found in Kanaya et al. (2019). Furthermore, the model evaluation with Mirai for the OLD run can be found in 470 Ha et al. (2021). Table 6 shows correlation coefficients, which indicate that the STD simulation for CO and O3 agree well with Mirai (R =~0.6). However, these correlation coefficients are slightly worsened compared with the OLD case. Although the HONO inclusion mostly reduces the model bias for CO, especially in the NP region (-16.158 to -4.948 ppb), the model bias for O3 is increased. The model biases exhibit negative trend for both CO and O3 in the OLD case. This simulation pattern for O3 in the 475 NP region is in line with the OMI comparison (Sect. 3.1.1). This finding seemingly indicates an insufficient downward mixing process of O3 in the free-troposphere or inconsistent surface deposition (Ha et al., 2021;Kanaya et al., 2019). However, the CO underestimations in the NP region might mark the inadequate CO emission in the HTAP inventory in CHASER (Ha et al., 2021). In Figure 6(a,c), overestimations of CO and O3 are visible along Japan-Indonesia-Australia (Track-2) during the low episodes in December/January. Here, the larger model biases might account for the model's insufficient halogen chemistry 480 (Kanaya et al., 2019;Ha et al., 2021). Figure 6 shows the model's percentage discrepancies for O3 from Mirai's data, except those from HONO concentrations interpolated for these regions. The underestimated simulations of O3 are enlarged, especially in the Japan -Alaska region, being driven by the reduction effects in the STD case. In another way, these effects weaken the O3 overestimates across the land areas, namely, over the region near Japan and Indonesia-Australia. Moreover, the higher HONO levels are identified for these off-shore data with up to 1.4 ppb abundances (Figure 6 The effects of the HONO chemistry along the R/V Mirai tracks exhibit various trends for each mechanism. Figure 7(b,d) illustrates O3 and CO changes triggered by the HONO gas reactions (GRs), uptakes (HRs), and emission (EM). The gaseous reactions (blue bars) have mostly increased CO levels due to the reduced OH and O3 levels ( Figure S14). The gaseous 490 mechanisms cause some reductions at the peak CO level because higher OH level from HONO photolysis near land domain or extra OH flux from stratosphere near 60 °N latitude could dilute CO. Furthermore, O3 level is slightly increased due to GRs north of 60° N, as GRs is a source for NO2 thus enhance O3 formation at these high latitudes ( Figure S14). O3 level is often decreased in the NP region since minor NO2 increase and stronger OH reduction are seen for this region ( Figure S14). The change tendencies in O3 near land areas are varied (T2, T3) because the vertical effects to NOx and OH are stronger during 495 DJF for this region ( Figure S14). HRs, largely consuming NO2, reduced O3 (as large as 8 ppb), and increased CO (~10 ppb) levels (Figure 7(b,d): orange+green). HRs, (particularly HRs on cloud surfaces, shown by orange bars), exerted the strongest contribution to the calculated changes in O3 and CO among the three HONO pathways. This predominant cloud effect was also prominent in the previous comparisons, especially EMEP (Figure 8(ii,jj): blue), thereby, indicating substantial effects of clouds at the mid-latitudes where the cloud SAD is higher ( Figure S1). HRs on aerosols (green bars) have minor contributions 500 during all cruises, despite they caused a marked increase in the O3 concentrations off-coast Japan (track #3). It should be noted that this is not enough to explain the simulation bias with regard to the measurements. The additional HONO from direct emission (red bars) has mainly increased O3 and has reduced CO concentrations, especially near land (latitude < 50° N). This finding resonates with the comparison for continental stations (Figure 8(cc,ff,ii,jj): oranges). The overall effects of the HONO chemistry along Mirai cruises tend to reduce O3 and increase CO levels. For the NP region, CO level increase and OH revel 505 reduction also can ameliorate the model performance. The improved model performance is evidenced from the comparison of the simulation with aircraft data as well ( Figure 5(g,h)). Thus, the strengthened underestimation of O3 concentration in the NP region is unlikely driven by HR on cloud particles (Sect 3.1.1 and Ha et al., 2021). It is rather related to the inconsistencies in the surface deposition of ozone. These inconsistencies are supported by empirical evidence as the negative bias in this comparison turns neutral or positive for the aircraft measurements in the same region ( Figure 5(f): at 1000 hPa). The latter 510 pattern is shown in the next section of this study as well.

NOx, O3, and other atmospheric species at ground-based stations
The effects of HONO chemistry in the continental near-surface layer of East Asia and Europe were also investigated. To 525 this end, we conducted model comparisons versus EANET and EMEP stationary observations for mass and gaseous concentrations of PM2.5, SO4 2-, NO3 -, HNO3, NOx, O3, and CO (CO for EMEP only). Table 7 shows the correlation coefficients (R) and model biases for each species in the OLD and STD cases. The OLD simulation has its fair correlations and RMSEs with observation for SO4 2-(R(EANET) = 0.56, R(EMEP) = 0.63), NO3 -(R(EANET) = 0.36, R(EMEP) = 0.71), and HNO3 (R(EANET) = 0.18, R(EMEP) = 0.12), which are in line with other atmospheric chemistry models' R and RMSE values against 530 EANET and EMEP (Brian et al., 2017), as also discussed in Ha et al. (2021). The ground-based observations in the period 2010-2016 revealed the slightly decreasing NOx for moderate NOx concentrations, as well as PM2.5, and aerosols (Figure 8(e,h) and Figure S4(e,g,h,i)). These decreasing trends were not captured by our simulations that used the high emission scenario for the EDGAR/HTAP-2008 inventory. Note that NOx and PM2.5 concentrations are generally underestimated in the model (OLD), especially in high-NOx regions (Figure 8(b,e,h) and Figure   S4(a,d,g)) with model's averaged biases of -0.8 ppb NOx for EMEP and -4 ppb NOx for EANET (Table 7). These 540 underestimations were stronger during winter, particularly for the high-NOx regions. It is possible that complex domestic sources could lead to diluted emissions for the simulations' moderate horizontal resolution (~2.8°). Higher model resolutions, such as 1.1°, 0.56° or even higher, could remedy such effects (Sekiya et al., 2018).
In the STD case, including HONO photochemistry, the negative biases of NOx in the model have adversely enhanced due to the NO2 loss processes (bias OLDSTD: -3.997-4.358 ppb for EANET (Table 7)). These processes also suppressed the NOx seasonality observed at most sites (Figure 8(b,h), red lines). The lack of seasonality was driven by the substantial loss of NO2 on the surfaces of atmospheric particles during winter. For EANET's low NOx and EMEP stations, this huge NO2 loss is 555 attributed to cloud surfaces (Figure 8(ee,hh): blue bars). However, NO2 uptake by aerosols has a comparable contribution effect to the cloud effect in high-NOx environments such as Jinyunshan (Figure 8(bb), grey bars). Namely, nearly half of the NO2 was converted to HNO3 in R4 (Figure 8(aa,dd,gg)) without an efficient recycling process, leading to an overall removal of NOx. This lack of NOx could be the main driver for the seasonal NOx deterioration and the exacerbated overestimations of HNO3 by simulations. 560 The STD O3 simulation exhibited moderate and strong positive correlations with EANET and EMEP observations, 0.595 and 0.707, respectively (Table 7). The model improvements for SO4 2-, NO3 -, PM2.5, and HNO3 were minor. However, the model improvement for O3 was considerable, with a bias reduction of ~67% for EMEP and ~74% for EANET (Table 4). In the STD case, too little NOx is left from its heterogeneous loss, causing a net O3 chemical destruction (because lacking oxygen atom from NO2 photolysis), which in turn, reduces the model overestimates for O3 in the OLD case (Table 7; Figure 8(c,f,i), red 565 versus blue lines). However, further improvements in the chemical scheme are necessary to reproduce the O3 measurements better; namely, a larger O3 reduction for the summer and a reduced effect in simulated O3 for the winter might alleviate the undesired effects. A delayed minimum from summer (as observed) to early winter (calculated in OLD and STD runs) causing opposite seasonality for O3 was prominent for the low-NOx EANET stations (Figure 8(f)). The effects of HONO chemistry on the mean OH levels were small, although it showed slight increases for OH's minima ( Figure S4(j,k,l)). Thus, due to the 570 apparent O3 reduction for EMEP stations, CO was increased. Despite the reductions in NOx and O3 levels being exaggerated during winter, the increment in CO reconciles the model's underestimation of CO high peaks in spring (Figure 8(j)), thereby strongly dwindling the bias for CO by ~59% (Table 4). However, the CO concentrations during summer should be reduced in the STD case to capture the measurement. This finding might indicate inadequate HONO emissions for the EMEP stations The break-down scrutinies for aerosols and clouds effects for the ground-based stations (EANET/EMEP) also reveal the vast role of cloud-uptakes in the HONO impacts on NO3aerosols, NOx, O3, and CO (Figure 8 and Figure S16: blue bars), while the HONO impacts on HNO3, PM2.5, and SO4 2aerosols were governed by aerosol-uptakes and HONO emission (grey and yellow bars).
The existing ill-reproduction in NOx's seasonality and overestimations for HNO3 might be amended by an explicit 580 inventory for direct NOx emissions and an efficient NOx-recycle process. Such mechanism via HNO3 uptakes on soot surfaces (HNO3NO2) was also tested in this study using the uptake coefficients range from 3×10 -5 -4.6×10 -3 (Lary et al., 1997;Akimoto et al., 2019). Unfortunately, this heterogeneous HNO3 conversion could not solely serve as a productive NOx recycling process in the EANET/EMEP stations (not shown). Among the sensitivity cases discussed in the EMeRGe comparison, the alternated HONO : HNO3 (0.9 : 0.1) product ratio of (R4) (ratR4 case) showed a good remedy for NOx at 585 EMEP stations (Figure S11(g): brown vs black). For the EANET sites, the photolysis of adsorbed HNO3 on ground surfaces (JANO3-A case) avoiding NOx removal via HNO3 can remedy the NOx seasonality issue for these ground-based stations ( Figure S11(a,d): greens vs blacks). However, the photolysis of adsorbed HNO3 on grounds and aerosol/cloud surfaces (JANO3-B and JANO3-C cases) is not an effective NOx recycling for EANET/EMEP measurements, leaving only slight differences in surface NOx levels compared to STD case ( Figure S11(a,d,g): cyans and oranges vs reds). However, the 590 heterogeneous photolysis of HNO3 increased O3 and OH at the high NOx regions instead of O3 reduction in the STD case ( Figure S11(b,c): cyans and oranges vs blues), which brought reconciliation to the underestimates for O3 peak in springs (Figure S11(e): dotted cyan vs dotted blacks), although the runs with HNO3 photolysis still not capture the O3 minimum in summer. Only the ratR4 case can capture the O3 minimum in summer among the sensitivity cases, which indicate the need for stronger NOx recycle processes for these ground-based stations. 595 Table 7: Model comparison of different species with observations at the EMEP and EANET stations. Three-sigma-rule outlier detection is applied for each station before calculating correlation coefficients R. NOx data are filtered once more using the twosigma-rule. R and bias of the STD run are shown as bold if improved compared to the OLD run. Units for model biases for PM2.5, SO4 2-, NO3 -: µm m -3 ; HNO3, NOx, O3, CO: ppb.

Global HONO distribution and burden 610
This section sheds light on the global HONO distribution computed for the STD case. The surface HONO concentration is peaked over the geographical region that embraces China, with seasonal mean levels up to 2.8 ppbv during summer and 7.8 ppbv during winter (Figure 9(a,b)). The winter peak agrees with observations for a large industrial region in the Yangtze River Delta of China (Zheng et al., 2020). The high concentrations of HONO are also identified in other industrial regional clusters: Northeast America (seasonal mean up to 0.5-1 ppbv), India (up to 1-3 ppbv), forest regions, especially the extra-tropic 615 evergreen forest in Europe (up to 1-3 ppbv), and Africa (up to 0.5-1 ppbv). Over the ocean, HONO levels remain at 10-30 pptv in the coastal regions and below 10 pptv far off the coast. The simulated HONO distribution is in line with a previous study (Elshorbany et al., 2012) despite the peaks over polluted Chinese areas are markedly higher in our model (tenfold) The overestimation associated with the soot uptake in our model has been previously neglected. The highest HONO concentrations (10-30 pptv) in the free troposphere (at 2500 m) were simulated over Africa's biomass burning region during wet months 620 (JJA) (Figure 9(c,d)), which could arise due to the NO2 uptake on aerosols, originated from this wildfire source.
In the model, HRs and EM are the main contributors to HONO at the surface layer ( Figure 10) by providing efficient HONO formation and promoting gas reaction (R2). Of the various surfaces provided for HRs in our model, liquid/ice cloud particle surfaces are supposed to catalyze significant photochemical effects in remote regions. This phenomenon has not been previously addressed in detail. The uptake of liquid/ice cloud particles either increases HONO formation via (R4) for the 625 tropical and southern oceans or reduce it via (R6) along 60 °S in DJF and Artic in JJA (Figure 10(a,b)). Besides cloud particles, HRs on aqueous aerosols also produce HONO in a continental atmosphere rich in sulfate, dust, and soot particles ( Figure   10(c,d)). EM included in the model has sharply increased the HONO level over deserts (Sahara, Arabian), grasslands (South Africa, South America), boreal, and agricultural land (West Europe, Australia). This finding agrees well with another study, based on spaceborne observations for HONO in wildfire plumes (Theys et al., 2020) and along ship tracks in the marine 630 boundary layer (Figure 10(e,f)). Table 8 summarizes the global sources and sinks of tropospheric HONO quantified by CHASER. The simulations indicate that GRs contribute only 11% of the HONO net production. HRs and EM produce more significant HONO (63% and 26% HONO net production, respectively). The pyrogenic HONO emission estimated in this study might be underestimated as HONO/NOx emission ratio could be enhanced by up to 1 at extratropic evergreen forests (universally 0.1 in this study (STD)) 635 (Theys et al., 2020). For large metropolitan areas such as those in China, HRs and EM have also been reported as the two most significant contributors to HONO formation, at ~59% and 26-29%, respectively (Li et al., 2011;Zhang et al., 2016). Of the various surfaces provided for HRs, aerosols represent a more effective HONO formation site (~51.2%) compared with ice and clouds, as they are contributing only 11.8% to HONO production. Moreover, the HONO loss through photolysis (R1) and (R3) is estimated to be 1.4 TgN in our model. HONO production calculated in sensitivity cases (Sect. 3.1.2) are recorded in Table S5 (last column), and the spatial distributions are plotted in Figure S12. The small supplement by the photolysis of adsorbed HNO3 on ground surfaces (JANO3-A case) to surface HONO concentration and tropospheric HONO burden (1.40  1.45 TgN) is consistent with the discussion for EmeRGe campaign. In the JANO3-B and JANO3-C cases, tropospheric HONO burden is increased to 2.02 TgN and 2.93 645 TgN, respectively, mainly remaining for the lower troposphere (~600 hPa) ( Figure S12(g,h)). Compared to HNO3 photolysis, the enhanced aerosol-uptake (maxST case) produces HONO more extensively over the source region and in the winter hemisphere where there is no photolysis (Figure S12(a-d)). Therefore, the maxST case does not produce enough HONO during EMeRGe (worse than JANO3 cases). However, the global HONO burden in maxST case is added to 7.79 TgN, which might be because we set the enhanced aerosol uptake of NO2 for all environments. The combined cases (maxST+JANO3-B or 650 maxST+JANO3-C), although appropriately approaching daytime HONO production as well as NO2 and O3 levels during EMeRGe, incredibly escalate the global HONO burden to 12.64 and 17.13 TgN, respectively (Table S5), more via the enhanced aerosol-uptake setting that could reach the upper troposphere ( Figure S12(k,l)). However, it might be more realistic if HONO production stayed at the lower troposphere (Eshorbany et al., 2012). Further work for the combined cases to be standard cases, the amplified NO2 conversion on aerosols should be confined in high SAD regions (Kalberer et al., 1999;Stadler et al., 2000), 655 or at daytime only (Notholt et al., 1992;Stemmler, 2007). P denotes chemical production, S denotes source (emission + chemical production), and L denotes loss. The numbers in parentheses represent the portion of each pathway to the total HONO net production. Bold lines show the most significant contributing mechanisms to the HONO burden. 660

Global effects on tropospheric column ozone
Comparison between simulation and satellite OMI observation for tropospheric column ozone (TCO) can be examined as a global effect for ozone. In Figure 11, the STD run with HONO inclusion improves the overall tropospheric column ozone 670 (TCO) distribution observed by the OMI, especially at the mid-latitudes. Figure S3 indicates a TCO reduction when HONO chemistry is included in the STD case (red lines vs green lines). Although HONO photolysis (R1) is a source of OH, supposedly increasing the tropospheric oxidizing capacity, the calculation in STD case shows the OH and O3 increases only occur at the surface of polluted sites (Figure 12(a,e)). The NO2 conversion to HONO and HNO3 (R4) becomes a NOx's removal pathway at remote regions, thus restricting the formation of O3 and OH for the larger part of the troposphere (lacking oxygen atom from 675 NO2 photolysis). Figure 11 shows that the O3-reducing effects of HONO chemistry greatly reduced the model overestimates in the OLD simulation for the general Northern Hemisphere and polluted regions such as China. However, in the NP region, the inclusion of HONO only reduces the model overestimates during the insignificant episodes of TCO (autumn to early winter) while extending the underestimates for TCO for the rest of the year ( Figure S3(b)). These O3 underestimates in the NP region are also visible for the modelled surface air versus the measurements during the Mirai cruises (Figure 6(a,b)). Notably, 680 these underestimates for O3 could hold up to 400 hPa, as seen in comparison with the ATom1 flights ( Figure 5(f): 400-900 hPa). This phenomenon can be related to the stratospheric downward transport and insufficient vertical mixing, as discussed in Sections 3.1.3 and 3.1.4, for comparisons in the NP region's surface air and free troposphere. Although the HONO level in STD remains < 10 ppt for this area (Figure 9), the O3 reducing effects exacerbate the model discrepancy. The HONO photochemistry is unlikely to be the primary driver of this phenomenon as the ozone simulation is improved over the continents 685 when the HONO photochemistry is included.
In the combined sensitivity case maxST+JANO3-B (Sect. 3.1.2), O3 was further reduced than the STD case. The reduction in TCO might be due to the enhanced NO2 uptake on aerosols leading to more substantial O3 formation restriction. The maxST+JANO3-B case showed better harmony with OMI for the regions of TCO overestimations (Figure 11(g,h,k)), especially the annual mean (g panel). However, the underscores of TCO, including the NP region, were worsened. These 690 results indicated that the reduction for O3 by HONO chemistry is reasonable, and the combined cases such as maxST+JANO3-B can be plausible, although the estimated reduction degree should be reduced by elaborating the maxST's reactive conditions.

Implication of HONO on the tropospheric photochemistry
In this section, the global impact of HONO photochemistry is elucidated. To this end, Table 8 summarizes the HONO budget and the contribution of each pathway to the HONO photochemical cycle. Table 9 describes its consequences for the lifetime of CH4, and the budgets of NOx, O3, and CO. The gaseous reactions of HONO tend to increase the abundance of NOx, O3, and 700 CO (+1.01%, +0.15%, +0.44%, respectively) and CH4 lifetime (+0.36%) in the troposphere. Without heterogeneous and direct emissions, the relatively low HONO formation by gaseous reactions (11% of the total net HONO production; Table 8) did not cause any significant effects on NOx, O3, and CO in the troposphere.
Heterogeneous reactions that produce HONO are the most salient contributing factors to tropospheric chemistry, thereby, decreasing the tropospheric oxidizing capacity and increasing the CH4 lifetime by 15% and CO abundance by 10%. HRs also 705 reduces NOx level by 23% and O3 level by 6%, respectively ( Table 9). The global HONO distribution from Figure 12 is mainly caused by the HR formation of HONO. Here, the reducing effects for NOx levels, with consequences for OH and O3 level reductions by heterogeneous reactions, are significant at mid-to-high latitudes during summer. More specifically, in DJF along 60 °S and the Arctic and NP oceans during JJA, which amounted to about a -100% reduction in NOx level at the surface (-60% in OH and -40% in O3) (Figure 12(a-f): blue areas). These reductions in NOx, OH, and O3 levels extend up to 400 hPa at high 710 N/S latitudes (Figure 12(k, l)). All these reduction effects for NOx, OH, and O3 levels are due to the removal of HONO on ice and cloud particles (R6) (Figure 10(a, b): blue fields). On the one hand, it accelerates the conversion of NO2 to HONO and ultimately strengthen its deposition by particulate nitrate (R4) (Figure S13(a)). On the other hand, HRs occurring on aerosol surfaces lead to increments in OH and O3 near the surface of polluted regions during winter. These are the main contributors to the regional photochemical effects over China, Western Europe, and East U.S. regions in winter (up to -74% in NOx, +1500% 715 OH, +48% in O3, Figure 12(a,c,e)). However, these OH and O 3 levels increase are only accumulated in the surface layer (only small red areas at ~1000 hPa in Figure 12(i,m)). Compared to HRs on aerosol, HRs on clouds exhibit twofold effectiveness when reducing tropospheric NOx level (-15% versus -8%) and cause threefold effects on the tropospheric oxidation capacity (+11.5% in CH4 lifetime, -4.6% in O3, +7% in CO), compared with HRs on aerosol (+3.5% CH4 lifetime, -1.5% O3, +2.6% CO) (Table 9). 720 Given the direct emissions of HONO (~10% of NOx emission inventory), the surface NOx, O3, and OH concentrations are generally enhanced in the STD case compared to the GR+HR case. They induce the concentration modification for NOx (+1.77%), O3 (+0.97%), CO (-1.63%), and a significant reduction (-2.3%) in the CH4 lifetime (Table 9). Remarkable enhancements for NOx (up to +198%), OH (+243%), and O3 (+24%) (Figure 12(b,d,f): red fields) were identified for the cropland and shrubland/forest regions in Australia, South America, and South Africa during JJA, and the boreal vegetation 725 prevailing at mid-high latitudes in Europe, North America, and the polluted Chinese region in DJF (up to +748% OH) ( Figure   12(a): red fields). NOx and OH were elevated in these mid-latitude regions because of the enhanced HONO photolysis (R1) by the additional HONO source. However, OH, NOx, and O3 levels were reduced near the surface of the Northern Hemisphere's land during summer (up to -47% OH, -82% NOx, and -15% O3) (Figure 12(b,d,f)). The latter phenomenon is similar to the heterogeneous cloud effects for the high latitudes discussed above. 730 Overall, the inclusion of the three HONO processes (gas phase, aerosol and cloud uptakes, direct emission) causes -20% in NOx, -5% in O3, +8% in CO, and a significant increase of +13% CH4 lifetime in the troposphere (Table 9). Figure 13 highlights the consequences of HOx, NOx, and O3 for the Chinese and NP regions. NOx level reduction accumulates in the Arctic and Antarctic during summer, especially over the NP ocean (reducing NOx level by 60-90%, Figure 13(i)). These reductions in NO2 and HONO concentrations are due to their uptake onto ice and clouds in these regions. However, these 735 reducing effects cause further reductions in OH and O3 levels for a larger part of the troposphere. As NOx is essential in regulating O3 and OH in the troposphere, a reduction of NOx level increases the HO2/OH ratio (due to the HO2 + NO  OH + NO2 reaction), which restrains the formation of OH and ultimately of O3. Moreover, a NO2-deficit environment directly affects the O3 level as NO2 is a primary source of an oxygen atom that engages in the formation of O3. Thus, in summer, both OH and O3 levels are drastically reduced over the NP region (35-67% for OH, 30-43% for O3, Figure 13(g,k)), and CO level 740 is increased by 18% in this region (Figure 12(h)).
The significant impacts of HONO photochemistry are especially relevant over Eastern China in winter, which may reduce NOx level by 48-78% (Figure 12(c)) due to the uptake of NO2 onto aqueous aerosols. At the surface, OH level is enormously increased as a result of HONO photolysis (R1), heterogeneous NO2 conversions (R4, R5), and additional direct emissions ( Figure 13(a,b)). The corresponding increase in O3 level was only identified at the surface of the Beijing region during winter, 745 with +28.8% caused by HRs on aerosols (Figure 13(e)). For Beijing with high NOx emissions, VOC-limited O3 chemistry is likely the driving mechanism (Liu et al., 2010). The vast increases in OH and O3 levels over Beijing in winter are basically in line with the present knowledge of HONO photochemistry (e.g., Lu et al., 2018). Elshorbany et al. (2012) also reported an increase in OH (2-5×10 6 molecules cm -3 ) and O3 (0.3-0.5 ppbv) concentrations over polluted regions in China during winter.
Compared with Elshorbany's work, the increases in OH and O3 concentrations in our model are higher due to the different 750 HONO mechanisms applied in the two models, simply an averaged HONO/NOx ratio (0.02) in their model. In particular, our newly added heterogeneous reactions on cloud particles (R4) caused significant reductions in OH, NOx, and O3 levels in the NP region during summer, which their model does not cover. The overall reductions in tropospheric oxidizing capacity due to HONO photochemistry are in line with the expected response to heterogeneous processes (Liao et al., 2003;Martin et al., 2003) and agree with those previously reported for other HRs (HO2, N2O5, and RO2) previously reported (Ha et al., 2021) 755 (Table 9). Our findings indicate that a global model without heterogeneous processes for HONO would neglect the significant changes in OH and O3 concentrations in remote areas and, thus, will underestimate the potential effects in polluted regions.
The estimated global effects of HONO chemistry in the STD case is the abatement of global tropospheric oxidizing power, despite surface OH and O3 levels being increased at polluted sites. The reduction tendency in global OH and O3 contrasts with other modelling studies (e.g. Elshorbany et al., 2012;Jorba et al., 2012;Lee et al., 2016;Zhang et al., 2021). Some discussions 760 on the tendency of HONO's global effects are addressed here. The amplified aerosol uptake of NO2 (maxST case) further reduces an unrealistic degree of global NOx abundance (-55.4%) and tropospheric oxidizing capacity, leading to 14.5 years for a global CH4 lifetime. The ratR4+CLD case introducing an approach to recycle NOx led to lowered global effects of HONO (only 8.57% NOx is reduced globally, CH4 lifetime = 9.6 years). The photolysis of adsorbed HNO3 on ground surfaces (JANO3-A case) still reduces global abundances of OH and O3 (Table S5). The ground-surface HNO3 photolysis in the JANO3-A case 765 causes only minor changes for a thin surface layer which is in line with other studies (Ye et al., 2018;Zhang et al., 2009). In JANO3-B and JANO3-C cases, a recycling process for NOx via HNO3 photolysis is expected. However, only the JANO3-C case shows an increment in global NOx and O3 (+29% and +16.1%, respectively), leading to only 5.4 years for the global CH4 lifetime, which is impractical. This is because that simplified approach and maximum thresholds for the phase HNO3 photolysis are used. The combined cases maxST+JANO3-B lead to more convincing effects (CH4 lifetime was 10.2 years; Table S5), 770 which held the same tendencies as those calculated in the STD case. However, validating this combined case is only conducted for the daytime environment during EMeRGe (Sect. 3.1.2) and for TCO at northern mid-latitudes with OMI ( Figure 11). In conclusion, we suggest the global effects tendency is towards tropospheric oxidizing capacity reduction, although further elaboration for enhanced aerosol-uptakes of NO2 and surface-catalyzed photolysis of HNO3 can change the effect magnitude.
The implication of HONO chemistry in a bottom-up approached global model such as CHASER securely needs intense 775 examination on possible HONO sources and profound evaluations with observed HONO in the troposphere.

Conclusion
The HONO photochemical processes, including (a) the gas-phase reaction involving HONO, (b) direct HONO emission from combustion and soil crust, as well as (c) heterogeneous processes involving HONO; all were added to the chemistry-climate model (CHASER) for the first time by this study. We found that the model biases are reduced against the EANET/EMEP stationary observations for PM2.5, NO3 components, O3, and CO concentrations when the HONO photochemistry is included. 795 The HONO inclusion mostly reduces the overestimations of OMI-based TCO by simulations given its reducing effects for O3, which notably includes the geographical region that embraces China. Although the simulation underestimations of surface O3 in the NP region is associated with the inconsistent surface deposition or vertical fluxes (c.f. from the stratosphere) becoming strong, the model still stands out with NO2, OH, and CO improvements in this region, compared with the observations made during Mirai and ATom1. We also compared the measurements during the EMeRGe flights off the coastal region of East Asia 800 and discerned good agreement between the measured and calculated NO2, O3, and CO profiles. However, the model does not reflect the influence of the Chinese river delta regions, as the large reductions in air masses affected by land emissions were identified.
In the model, the tropospheric abundance for HONO is 1.4 TgN and is made by 26% from the direct emission and 63% by HRs, in which HRs on clouds cause 11.8% and HRs on aerosols cause 51.2%. The HONO concentrations over the continents 805 range from 30 ppt to 7 ppb and are maximized due to HRs over eastern China during winter. Only 5-10 ppt of HONO can be transported up to ~2000 m, indicating that its impacts remain mainly in the planetary boundary layer. We argue that these simulated HONOs may underestimate the actual concentrations off-coast of eastern Asia in spring 2018. The unknown daytime HONO concentrations of up to 200 ppt measured in the boundary layer and free troposphere during the EMeRGe campaign are not reproduced by the STD simulation but moderately captured by the combined simulation, which enhanced aerosol-810 uptakes of NO2 and heterogeneous photolysis of HNO3 (maxST+JANO3-B). However, the enhancement for NO2 uptakes on aerosols should be confined to particular environments to eliminate the effect exaggeration. Moreover, a further improvement of the model performance for the HONO photochemistry requires (1) the revised model's emission inventory with the emission sources of NOx and CO from South-Eastern and Eastern Asia, (2) the lighting-related NOx module is upgraded, and (3) the vertical mixing and downward fluxes from the stratosphere to be elaborated. 815 One or more renoxification mechanisms converting HNO3 into NOx should be added to the model to overcome the observed and simulated NOx seasonality mismatches. Shifting the product ratio towards more HONO and less HNO3 in reaction R4 can also provide more HONO and mitigate the deteriorated representation of NOx seasonality. The sensitivity tests also suggest that more robust aerosol processing in polluted areas and less HNO3 product in R4 can further reduce O3 level in summer, reducing the bias against measurements. The photolysis of adsorbed HNO3 on ground surfaces (JANO3-A case) can 820 also serve as a recycling process for NOx at Asian ground-based sites (EANET).
Of the three HONO sources, HRs produced the most prominent effects on the tropospheric photochemistry: reducing OH, NOx, O3, and increasing CO levels in the troposphere, leading to a +13.05% longer CH4 lifetime, and -20.4% less NOx, -5.03% less O3, and an increased CO (+8.36%) abundance. In winter near the surface, gas-phase reactions involving HONO and NO2 conversions on soot induce significant photochemical effects over eastern Chinese with changes of -60% in NOx, +1700% in 825 OH, +33% in O3. During summer, HRs on ice and cloud particles could cause significant changes of -67% in OH, -45% in O3, -75% in NOx, and +17% in CO in the NP region. Albeit the more significant contribution of aerosols' heterogeneous reactions to the net HONO production, the heterogeneous processes involving ice and cloud particles are more significant globally. We found that the HONO chemistry, including the heterogeneous processes on cloud particles, can reduce the troposphere's oxidizing capacity on a global scale. It should be underlined that this finding is rather unexpected and contrasts with the 830 increasing oxidation capacity previously reported for polluted areas. This new finding may also affect climate change mechanisms and, as a result, may influence its mitigation policies. However, the tendencies and magnitudes of HONO's global effects are still debated along with the effort regarding daytime HONO formation mechanisms. In capturing HONO measurement during EMeRGe campaign, the combined case enhancing NO2 aerosol uptake and implementing heterogeneous photolysis of HNO3 (maxST+JANO3-B) still results in the reduction for global tropospheric oxidizing capacity. In this case, 835 the effect magnitude is smaller for CH4 lifetime, but those for the NOx-O3-CO chemistry are stronger compared with the calculation in the STD case. Overall, our results prove that a global model without heterogeneous HONO formation, especially photochemical heterogeneous HONO formations, can bias the overall impacts of HONO on tropospheric photochemistry as it neglects the photochemical effects of HONO in remote areas and underestimates them in polluted regions. by the German Research Foundation (Deutsche Forschungsgemeinschaft; DFG) HALO-SPP 1294. The contributions from BS and KP were supported via the DFG grants PF 384/16,PF 384/17 und PF 384/19. 865