Atmospheric Measurements at the Foot and the Summit of Mt. Tai-Part II: HONO Budget and Radical (ROx + NO3) Chemistry in the Lower Boundary Layer

Chaoyang Xue , Can Ye , Jörg Kleffmann, Wenjin Zhang, Xiaowei He , Pengfei Liu , Chenglong Zhang , Xiaoxi Zhao , Chengtang Liu , Zhuobiao Ma, Junfeng Liu , Jinhe Wang, Keding Lu, Valéry Catoire, Abdelwahid Mellouki , Yujing Mu 3* 1 Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China 2 Laboratoire de Physique et Chimie de l’Environnement et de l’Espace (LPC2E), CNRS–Université Orléans–CNES, Cedex 10 2, Orléans 45071, France 3 Centre for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China 4 Institut de Combustion Aérothermique, Réactivité et Environnement, Centre National de la Recherche Scientifique (ICARECNRS), Cedex 2, Orléans 45071, France 15 5 Physical and Theoretical Chemistry, University of Wuppertal, Gaußstrasse 20, 42119 Wuppertal, Germany 6 State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China 7 School of Municipal and Environmental Engineering, Co-Innovation Centre for Green Building of Shandong Province, Shandong Jianzhu University, Jinan 250101, China 20 8 Environmental Research Institute, Shandong University, Qingdao, Shandong 266237, China 9 University of Chinese Academy of Sciences, Beijing 100049, China

On a global scale, OH controls atmospheric oxidation. As the detergent in the troposphere, OH can oxidize most trace gases, including inorganic (SO2, NO2, etc.) and organic compounds (VOCs, etc.), and determines the lifetime of greenhouse gases 60 (e.g., CH4). Besides the fast conversion of HO2 to OH (R-1) as part of the radical propagation cycle, primary OH (radical initiation) mainly originates from photolysis reactions, including O3 ((R-2) to (R-4)), HONO (R-5), HCHO ((R-6) to (R-9), and (R-1)), and H2O2 (R-10), and the ozonolysis of alkenes (not shown in detail here). In particular, HONO photolysis is reported to be an important or even the major OH source in the lower atmosphere of polluted regions, with a contribution of 20 -90% (Alicke et al., 2003;Elshorbany et al., 2009;Kleffmann et al., 2005;Platt et al., 1980;. However, 65 this process still needs more global quantifications due to the incomplete understanding of HONO formation and its vertical distribution in the atmosphere (Kleffmann, 2007). A state-of-art summary of the reported HONO sources can be found in our recent study . Besides, other oxidants can also be of importance on a regional scale. For example, NO3 radical could be a major oxidant in forests (vegetation shadows slow down its photolysis) or in the nocturnal boundary layer at high O3 regions (Brown and Stutz, 80 2012). Formed by the reaction of NO2 + O3 (R-11), high NO3 levels usually occur at night, concerning its very rapid photolysis during the daytime (R-12) and (R-13). Moreover, high NO3 concentrations are only observed for high O3 and medium NOx concentrations in the absence of significant levels of NO caused by reaction (R-15). Like OH, NO3 also has high reactivity with various trace gases (Brown and Stutz, 2012;Mellouki et al., 2021). For example, NO3 reacts with NO2 to form N2O5 (R-14), which can undergo hydrolysis on wet surfaces or clouds to produce HNO3 (or NO3 -) (R-16) or decomposition back to NO3 85 + NO2 (R-17). NO3 can also react with various organic compounds to form secondary organic aerosol (SOA). For instance, NO3 reacts with isoprene (C5H8), leading to significant organic nitrates (e.g., alkyl nitrates) production (Rollins et al., 2009). In the past decade, particle pollution, such as PM2.5, is going down while O3 pollution is increasing in many cities of China 95 (Han et al., 2020;Sun et al., 2016Sun et al., , 2019, especially in the North China Plain (NCP), where there exists a large population (>330 million) and air pollution in this region becomes a major environmental risk for public health. This raises effort in exploring the NOx-VOCs-O3 chemistry. Meanwhile, high O3 indicates an enhanced atmospheric oxidizing capacity; that is, elevated OH and NO3 levels are expected. However, OH and NO3 levels, as well as their production (e.g., HONO photolysis or NO2 + O3) or loss (e.g., to oxidize primary pollutants) in the high-O3 region of the NCP, by far, are very few 100 reported Suhail et al., 2019). Herein, we provided the first HONO measurements at the foot of Mt. Tai (in Tai'an city, a typical urban site), followed by measurements at the summit of Mt. Tai in the summer of 2018. Data from the summit station was presented in the companion paper, in which daytime HONO formation and its role in the atmospheric oxidizing capacity at the summit level were studied. In this paper, coupled with the box model, the HONO budget and the radical chemistry at the ground level were explored and discussed. 105

Measurement Site
In the summer (from late May to July) of 2018, a comprehensive field campaign was conducted to understand the atmospheric oxidizing capacity and O3 pollution in Tai'an, a city in the middle of the NCP. Measurements were conducted both at the 110 ground level (the foot of Mt. Tai, 150 m a.s.l.) and the summit level (the summit of Mt. Tai, 1534 m a.s.l., 36.23°N, 117.11°E).
The foot station was inside Shandong College of Electric Power (36.18°N, 117.11°E), which represents a typical urban site.
Inside the campus (about 50 ha) frequent traffic was not observed, but it sometimes occurred on the urban roads nearby. Tai'an city has a population of about 5.6 million and is about 60 km south of Jinan city (the capital city of Shandong province, population: ~8.7 million). Mt. Tai locates in the north part of Tai'an city. Locations of these two stations on the map could be 115 found in the companion ACP paper (entitled "Atmospheric Measurements at the Foot and the Summit of Mt. Tai -Part I: HONO Formation and Its Role in the Oxidizing Capacity of the Upper Boundary Layer").

Instrumentation
HONO mixing ratios were continuously measured by the LOPAP technique (LOPAP-03, QUMA GmbH, Germany) (Heland et al., 2001;Kleffmann et al., 2006) from 29 th May to 8 th July 2017 at the foot station, followed by measurements at the summit 120 station from 9 th to 31 st July 2017. At the foot station, NO-NO2-NOx, O3, CO, and SO2 were online measured by a series of Thermo Fisher Scientific instruments (42i, 49i, 48i, and 43i, respectively). Because chemiluminescence techniques were reported to overestimate the NO2 level caused by other NOy interference, we furtherly corrected the measured NO2 with a family constraint in a model run (see Section 3.1.2). VOCs (56 species) and OVOCs (15 species) were measured by a homemade GC-FID instrument (Liu et al., 2016) and the USEPA DNPH-HPLC method , respectively. Gas-125 phase H2O2 was measured by a monitor based on the wet chemical method (AL2021, Aerolaser GmbH, Germany), and details about the used instrument can be found in Ye et al. (2018). Water-soluble ions (i.e., NO3 -, SO4 2-, Cl -, Na + , K + , Ca 2+ , etc.) of PM2.5 were collected on Teflon filters every two hours at a sampling flow of 100 L min -1 and analyzed by an ion chromatograph .
Meteorological parameters, including atmospheric temperature (T), pressure (p), relative humidity (RH), wind direction (WD), 130 wind speed (WS), and solar irradiance (Ra) were continuously measured by an auto meteorological station. J(NO2) was measured by a 4-π filter radiometer (Metcon GmbH, Germany). 10-min and hourly-average data (except for PM2.5) were used for the following analysis (time series and static description) and model simulations, respectively. PM2.5 measurement was obtained from the Tai'an monitoring station (200 m east of the foot station), and only hourly-average data was available. Other J-values used in this study, including J(HONO), J(O( 1 D)), J(H2O2), J(HCHO)rad, and J(HNO3), are calculated by the box model 135 based on trigonometric SZA function (MCM default photolysis frequency calculation, see Jenkin et al. (1997)) and scaled by the measured J(NO2). For instance, J(HONO) = J(HONO)model*J(NO2)measured/J(NO2)model.

Box Model and Constraints
The Framework for 0-D Atmospheric Modeling, F0AM v4.0 (available at https://github.com/AirChem/F0AM) developed by 140 Wolfe et al. (2016) was used to explore the HONO budget and the radical chemistry. The used chemical mechanism was MCM v3.3.1, which could be obtained from http://mcm.leeds.ac.uk/MCMv3.3.1/home.htt. Note that the present F0AM model could also be run with family constraints (see details in Wolfe et al. (2016)), such as the NOy family, Cly family, etc., and hence it allows us to correct for interferences of the NO2 measurement by the chemiluminescence method (see Section 3.1.2).
The model was constrained by the measured J(NO2), T, RH, P, VOCs, OVOCs, and all the other measured inorganic species, 145 including the corrected NO2 by the family constraint. Continuous VOCs measurement was available from 12 th June to 6 th July, and hence box model simulations were performed during this period. While J(NO2) measurement was available from 16 th June, J(NO2) from 12 th to 16 th June was estimated through the high quadratic correlation (R 2 = 0.96, Figure S1) between J(NO2) and solar irradiance. https://doi.org/10.5194/acp-2021-531 Preprint. Discussion started: 30 July 2021 c Author(s) 2021. CC BY 4.0 License. Table 1 shows the description of different model scenarios. A base case (Sce-0) with all the measured parameters as constraints was run to simulate radicals' concentrations and their production/loss rates. The family constraint was used in this scenario to correct for interferences of NO2 measurements (Section 3.1.2). Meanwhile, the role of HONO in radical chemistry was also explored by several model sensitivity tests with reducing or increasing the constrained HONO.

Model Scenarios 150
With the simulated OH and the corrected NO2 from the base case, we could further explore the HONO budget. Three model 155 scenarios were conducted to assess the potential contributions of different HONO sources, including one with only the default model source (Sce-1), and one with all the six additional sources, including direct emission, the dark and the photo-enhanced NO2 uptake on the aerosol and ground surfaces and nitrate photolysis (Sce-2). In Sce-3, photo-enhanced NO2 uptake on the ground surface was reduced by a factor of 10, aerosol-derived sources (NO2 uptake on the aerosol surface or particulate nitrate photolysis) were significantly enhanced to test whether the aerosol-derived sources could well explain the observations. 160  Figure 1 shows the meteorological parameters measured at the foot station. During the campaign, it was generally sunny except 165 slightly rainy (<10 mm) on 9 th , 10 th , 13 th , and 28 th and heavy rainy (~100 mm) at night of 25 th /26 th June. Ambient temperature was normally around 25 º C at night and around 30 º C during the daytime, except for rainy days when the temperature was relatively low. The relative atmospheric humidity (RH) was high (up to 80%) on those rainy or cloudy days and low (around 40%) on other days. Campaign-averaged temperature and RH were 27.5 º C and 46.6%, respectively (Table 2). Air mass observed at this site was originated from multiple directions, including west, south, and east, which can be obtained from the 170 wind rose plot ( Figure S2). Wind speed was generally low, with an average of about 2 m s -1 .

Scenarios
Constraints Objectives   was typically lower than O3. Consequently, a relative low NO was frequently found, whose concentration was generally lower than 1 ppbv, except for some fresh plumes with higher NO concentrations inside. The two primary pollutants, CO and SO2, were generally lower than 0.5 ppmv and 5 ppbv, respectively, except for several polluted events, within which CO and SO2 185 reached around 2 ppmv and around 35 ppbv, respectively. However, all the primary pollutants, including NO, CO, and SO2, showed poor correlations with HONO (R = 0.49, 0.44, and 0.13, respectively), implying the minor role of direct emission in HONO formation. The measured hourly PM2.5 varied from 10 to 66 µg m -3 , with an average of 29 µg m -3 . The correlation of PM2.5 and HONO was very low (R = 0.06), suggesting a minor role of aerosol-derived sources in HONO formation. More discussion on the HONO budget is presented in Section 3.2. 190 Compared to other previous summertime measurements worldwide (Table 3), the measured HONO level at this site is similar to some measurements in China, such as Beijing in 2007 (Hendrick et al., 2014), Beijing in 2008 (Hendrick et al., 2014) and Guangzhou in 2006 (Yang et al., 2014); in Europe, such as Milan in 1998 (Alicke et al., 2002) and Rome in 2001 (Acker et 195 al., 2006); and in North America, such as New York in 2001 (Ren et al., 2003) and Colorado in 2011 (Vandenboer et al., 2013).
Besides, it is lower than measurements in cities during polluted periods, such as Jinan in 2016 (Li et al., 2018) (Villena et al., 2011), and Mexico in 2003, but higher than recent measurements near European cities, including Forschungszentrum Karlsruhe , Forschungszentrum Jülich (Elshorbany et al., 2012), suburban Paris (Michoud et al., 2014), and Cyprus (Meusel et al., 200 2016). It is noteworthy that the measured HONO at the foot station is significantly higher than that observed at the summit station in the same summer, indicating possibly different roles and formation paths of HONO at these two stations.

NO2 Interference and Correction
NO2 measured by the chemiluminescence method suffers from the interference of other NOy species (Villena et al., 2012), primarily including inorganic species such as (measured) HONO, (non-measured) HNO3, HNO4, N2O5, and NO3, peroxyacyl nitrates (PANs, RC(O)OONO2), organic nitrates (RONO2), and peroxy nitrates (ROONO2), etc. The sum of the latter two was 210 defined here as organic nitrates * . Hence, the measured NO2 is the sum of real NO2 and those interference species. HONO was measured and subtracted from the measured NO2, and we defined NO2 * = the measured NO2 -HONO. As NO2 is the most important HONO precursor, we used the family constraint (NO2 * = NO2 + HNO4 + 2N2O5 + NO3 + PANs + organic nitrates * ) in the base case (Sce-0) to separate each species from NO2 * . In the term of PANs, PAN, PPN, and MPAN (MCM names, see https://doi.org/10.5194/acp-2021-531 Preprint. Discussion started: 30 July 2021 c Author(s) 2021. CC BY 4.0 License. their structures at http://mcm.leeds.ac.uk/MCMv3.3.1/) were considered. In the class of organic nitrates * , CH3NO3, 215 C2H5NO3, NC3H7NO3, IC3H7NO3, TC4H9NO3, NOA, ISOP34NO3, ISOPANO3, ISOPDNO3, ISOPCNO3, and ISOPBNO3 (MCM names) were considered. Considering that HNO3 is very sticky, we expect HNO3 was mostly absorbed by the filter and/or sampling tubes before the converter rather than being converted to NO by the converter. Therefore, HNO3 was generally not included in the family constraint and only considered for the uncertainty analysis. Figure 3 shows the model results of the relative contribution of each NO2 * species to NO2 * . At night with the absence of 220 photochemistry, the real NO2 dominated NO2 * components, with a contribution of >95%, suggesting a small interference on the NO2 measurement. However, the contribution of real NO2 was found to decrease during the daytime due to the increasing interference. For example, at 11:00, the real NO2 contributed 82% of the NOz * , which means the interference could be as high as +22% (calculated from 18%/82%). In particular, at 11:00, PANs caused the most interference by +21% (calculated from 17%/81%). 225 The variations of the simulated PANs and NO3 and their ratios to NO2 were similar to previous observations (Brown and Stutz, 2012;Roberts et al., 1998;Su et al., 2008;Villena et al., 2012;Xue et al., 2011), indicating that the uncertainty of the method is small. For the following model simulations and analysis, only the corrected NO2 was used. Besides, Figure S3 exhibits the parallel test results, in which HNO3 was included in the family constraint. It can be found that the interference became more significant; for instance, the interference could be as high as +75% (calculated from 43%/57%, at 11:00). This represents the 230 upper limits of the interference if the sampling tubes are heated so that HNO3 could reach the converter. Additionally, as shown in Figure S4, the simulated HNO4 showed 1) a different diurnal variation from, 2) generally 1 -2 235 orders of magnitude lower than, and 3) a very poor correlation (R 2 = 0.06) with the observed HONO, indicating its negligible https://doi.org/10.5194/acp-2021-531 Preprint. Discussion started: 30 July 2021 c Author(s) 2021. CC BY 4.0 License.
interference on the HONO measurement by the LOPAP technique (Legrand et al., 2014). It is worth noting that for the description of O3 formation in the polluted atmosphere, accurate measurements of VOCs and NOx are necessary.

Model Default Source (NO + OH) and Unknown Source Strength 240
The homogeneous reaction of NO and OH has been adopted as the default HONO source in atmospheric chemistry models, including MCM. Model results from Sce-1 that only contains the homogeneous source with the modeled OH from Sce-0 are shown in Figure 4. Apparently, the source of NO + OH is too small to explain the observed HONO as the simulated one is almost one order of magnitude lower than observations. Its contributions to the measured daytime or night-time HONO are 15% and 12%, respectively. 245
where the HONO loss rates through photolysis (L(HONO)pho) and reaction with OH (L(HONO)HONO+OH) and production rate from NO + OH were obtained from the base model scenario (Sce-0 with a constraint of the measured HONO). HONO mixing 255 ratio difference within a one-hour interval, Punknown rapidly increased in the morning and peaked nearly 3 ppbv h -1 at 11:00, followed by a decrease, revealing a photoenhanced source. Note that the profile of Punknown was asymmetric around 11:00, indicating the unknown source is not simply photolytic but also includes its precursors (e.g., NO2). The possible additional HONO sources that are responsible for Punknown 260 are discussed in the following section.

Direct Emission: ∆HONO/∆NOx Ratio
The ∆HONO/∆NOx ratio for the direct emission was determined from fresh plumes, which reached the following requirements: 1) at night when photolysis was absent, 2) rapid NO increase by >10 ppbv within 10 min. Only 17 cases were obtained 265 throughout the campaign due to the persistent high O3 and the fast NO-to-NO2 conversion, for which the inferred ∆HONO/∆NOx might be overestimated. In Table 4 the obtained ∆HONO/∆NOx was shown, varying from 0.18% to 1.86%, with an average of 0.98% and a median of 0.90%. The inferred value might be larger than the real one as NO2-to-HONO conversion leads to a positive interference, which is consistent with that the inferred HONO/NOx is generally higher in high RH conditions (in favor of NO2-to-HONO conversion) ( Figure 5). Also, we found that the observed HONO/NOx is convergent 270 as NO/NO2 increases ( Figure 5), which allows a further correction on ∆HONO/∆NOx. The reported NO/NO2 ratios from the combustion process vary from digits to hundreds, e.g., 6.7 in Wuppertal , ~18 in Denver (Wild et al., 2017), 5 -30 in London (Carslaw and Beevers, 2005), and 13 -43 from China IV/V vehicles (He et al., 2020). Furthermore, in the emission inventory, the NO/NO2 emission ratio in the NCP is about 9 ). However, the measured night-time NO/NO2 ratios were less than 3 ( Figure 5), much lower than that from on-road measurements, indicating the 275 obtained plumes were not fresh enough. By using a typical NO/NO2 ratio of 10 from car exhaust, the calculated HONO/NOx through the convergent function is 0.7%, similar to that obtained from laboratory or tunnel experiments (Kirchstetter et al., 1996;Kurtenbach et al., 2001;Liu et al., 2017).
Considering that HONO from direct emission (HONOemi) is likely significantly overestimated with a constant ∆HONO/∆NOx because of different lifetimes of HONO (τ(HONO)) and NOx (τ(NOx)) in the daytime (also see Section 3.1.1 where very poor 280 correlations of HONO with primary pollutants were presented and the minor role of direct emission in HONO formation was inferred). Then we calculated τ(HONO) and τ(NOx) (see method in Section 1 of the Supporting Information). As shown in Figure S6A, daytime τ(NOx) was typically one order of magnitude longer than τ(HONO) (Figure S6A), indicating the remarkable overestimation of HONOemi to the measured HONO when using a constant ∆HONO/∆NOx ( Figure S6B) In summary, direct emission contributed about 1 -26% of the measured HONO, with an average of 13%. Moreover, the new method developed here may have uncertainties but largely reduced the significant overestimation of HONOemi to the observations in the daytime compared to using only a constant ∆HONO/∆NOx ( Figure S6B). 290

NO2 Uptake on the Aerosol Surface
Parameterizations of HONO formation from the NO2 uptake on the aerosol surface without (P(HONO)a_dark) and with (P(HONO)a) photo-enhanced effects are described by (Eq-4) and (Eq-5), respectively. In (Eq-4) and (Eq-5), HONO yields of 50% and 100% were considered for the dark and the photo-enhanced NO2 conversion, respectively (Finlayson-Pitts et al., 2003;George et al., 2005). A relatively large NO2 uptake coefficient γa_dark of 1×10 -5 was used here to represent its upper limit. 300 Its overestimation should not cause significant uncertainties as P(HONO)a_dark was negligible to HONO formation (see the following discussion). NO2 uptake coefficient γa values of 1.3×10 -4 (overestimated one derived from the summit measurement) and 2×10 -5 (popularly used one derived from laboratory experiments) were used in (Eq-5) to constrain the upper limit and general one of P(HONO)a. where υ(NO2), Sa, [NO2], and J(NO2)measured denote the average NO2 molecular speed (m s -1 ), aerosol surface density (m -1 ), NO2 concentration (ppbv), and the measured NO2 photolysis frequency (s -1 ). As aerosol size distribution measurement was not available at the foot station, we estimated Sa based on the measured PM2.5 concentrations because they were highly correlated.
For instance, measurements at the summit station during this campaign and other sites in the NCP found high correlations 310 between PM2.5 and Sa (derived from particle size distribution measurement) with a Sa/PM2.5 ratio of about 810 -6 -1.310 -5 m 2 µg -1 (Wu et al., 2008;. Here a Sa/PM2.5 ratio of 1.010 -5 m 2 µg -1 was used, and its uncertainty will not cause significant changes in HONO simulation because of its small contribution (see the following discussion).
Diurnal variations of P(HONO)a_dark and P(HONO)a, in comparison with Punknown and P(HONO)NO+OH, are shown in Figure 6A.
Clearly, both P(HONO)a_dark and P(HONO)a (γa = 2×10 -5 ) were negligible compared to daytime Punknown. P(HONO)a increased 315 with γa, but even when using an extremely high γa = 1.3×10 -4 , it was still too small to be comparable to P(HONO)NO+OH and far from explaining Punknown, revealing minor impacts of P(HONO)a_dark and P(HONO)a in HONO formation, particularly during the daytime.

pNO3 Photolysis
Parameterization of HONO formation from particulate nitrate photolysis (P(HONO)n) is presented in (Eq-6). Recent studies found that EF values were generally lower than one magnitude, for instance, 7 from a field study (Romer et al. 2018) and ~1 from laboratory studies (Laufs and Kleffmann, 2016;Shi et al., 2021;Wang et al., 2021). Hence EF value of 7 was used in the 325 P(HONO)n calculation, and values of 1 and 15.6 (overestimated one derived from the summit measurement) were also used to test the sensitivities. where pNO3 and J(HNO3) represent the measured particulate nitrate (with unit converted from µg m -3 to ppbv) and the photolysis frequency of gas-phase HNO3 (s -1 ), respectively. 330 Diurnal variations of P(HONO)n with different EF values are shown in Figure 6A. With EF varying from 1 to 7, P(HONO)n was 1 -2 orders of magnitude lower than Punknown. Even using a high EF = 15.6, P(HONO)nitrate was still significantly less than half of P(HONO)NO+OH. Therefore, model results constrained by field measurements and recent kinetics suggested that the two aerosol-derived sources (NO2 conversion and nitrate photolysis) may not have significant impacts on daytime HONO formation, with their contributions significantly lower than half of P(HONO)NO+OH. 335

NO2 Uptake on the Ground Surface
Parameterizations of HONO production from the NO2 uptake on the ground surface without (P(HONO)g_dark) and with (P(HONO)g) photo-enhanced effects are demonstrated in (Eq-7) and (Eq-8), respectively. NO2 uptake coefficients of γg_dark and γg were set to 1.6×10 -6 and 2×10 -5 (Han et al., 2016;Stemmler et al., 2006Stemmler et al., , 2007, respectively. The photo-enhancement effect was reflected by It can be found that one of the most important parameters for calculating ground HONO formation in a box model is the mixing layer height (MLH) as it is part of the denominators in both (Eq-7) and (Eq-8). MLH for HONO should be significantly lower than the boundary layer height (BLH) due to its formation on the ground level and short lifetime, which could be confirmed 345 by the gradient measurements Meng et al., 2020;Vogel et al., 2003;Wong et al., 2012;Xing et al., 2021;Ye et al., 2018b). For instance, a recent gradient HONO measurement by the MAX-DOAS technique in southwest China found a very rapid HONO decrease as increasing altitude from 0 to 4 km (Xing et al., 2021). When considering their measurement at 17:00 (UTC+8) as an example, HONO levels rapidly decreased from 4.8 ppbv at the ground level (~4 m above the ground surface) to 1.6, 0.7, 0.3, 0.2, and 0.1 ppbv averaged in height ranges of 0 -100, 100 -200, 200 350 -300, 300 -400, and 400 -500 m above the ground level, respectively. In contrast, both NO2 and aerosol extinction remarkably increased from the ground level to about 200 m above the ground level and then decreased with altitude (>200 m), indicating that 1) ground-derived sources dominated daytime HONO formation; 2) the MLH for HONO was much less than 100 m, and 3) significant overestimation, i.e., by a factor of >3 in Xing et al. (2021), could be expected if using measurements on the ground surface to represent the average HONO within an MLH higher than 100 m. Therefore, 50 m were used to scale 355 the MLH with sensitivity tests on 35 and 100 m. Similar values (25 -100 m) were also used in previous box model studies (Lee et al., 2016;Xue et al., , 2021. It should be highlighted that a box model as used in the present study is not an ideal tool for studying a ground source when comparing with near ground surface measurements in the atmosphere. For future, gradient measurements are recommended, which should be compared with 1-D model simulations. Diurnal variations of P(HONO)g_dark and P(HONO)g, in comparison with Punknown and P(HONO)NO+OH, are shown in Figure  360 6B. P(HONO)g_dark was the largest HONO source during the night-time, while it was negligible during the daytime, which is consistent with many previous studies (Li et al., 2010;Liu et al., 2019;Vogel et al., 2003;Zhang et al., 2019bZhang et al., , 2019aZhang et al., , 2016. With the photo-enhanced effect, P(HONO)g showed a similar shape and a similar level to daytime Punknown, indicating the potential dominance of P(HONO)g in the daytime HONO formation. When changing MLH to 100 (or 35) m, the level of P(HONO)g became much lower (or higher) than Punknown, for which they were discussed here as sensitivity tests on 365 MLH but not used in Sce-2. Small differences in the shapes of measured and modeled results may be also caused by the variable MLH induced by variable vertical mixing in the atmosphere and the variable photolytic lifetime of HONO during the daytime.

HONO Budget
Along with the previous discussion, we conducted a model run (Sce-2) with all the discussed HONO sources. As shown in 370 HONO sources. In particular, the model exhibited very high performance in predicting noontime (10:00 -16:00) HONO as the modeled HONO was very close to the observed HONO ( Figure 7B). Moreover, in Sce-3 we reduced γg by a factor of 10 375 and enlarged γa from 2×10 -5 to 1.2×10 -3 or EF from 7 to 400. We found that the model could also generally predict the observed HONO levels (Figures S7A and S8A) but largely failed to reproduce the noontime observations in levels and variations ( Figures S7B and S8B), reinforcing the non-dominated roles of aerosol-derived sources in the daytime HONO formation. Figure 8 displays the relative contributions of different HONO sources at different hours. It clearly shows that dark NO2 uptake on the ground surface dominated (~70%) night-time HONO formation while photo-enhanced NO2 uptake on the ground surface dominated (~80%) daytime HONO formation. P(HONO)NO+OH played a moderate role throughout the whole day, with a contribution of 5 -15% except for a relatively larger contribution (~20%) in the early morning due to high NO levels. Direct 385 emissions made moderate contributions of 15 -25% at night but negligible ones during daytime. Contributions of P(HONO)a_dark, P(HONO)a, and P(HONO)n were always lower than 10%, and their contributions could be even smaller when using smaller kinetic parameters derived in recent studies. Therefore, aerosol-derived HONO sources may not significantly contribute to HONO formation at this site Neuman et al., 2016;Sarwar et al., 2008;Vogel et al., 2003;Wong et al., 2013;Zhang et al., 2016Zhang et al., , 2019b.

Other Potential Sources
As discussed before, the model (Sce-2) could generally well reproduce most observations except for the period from 25 th to 395 27 th June. A significant overestimation occurred from midday of 25 th to the morning of 26 th , which was caused by the enhanced wet/dry deposition due to the heavy rain (>100 mm, Figure S9) on the night of 24 th /25 th . In contrast, from midday of 26 th to the night of 27 th /28 th , a significant underestimation by the model was obtained. Besides, an elevation of HONO/NOx was found during this period ( Figure S9). This might be caused by 1) the enhanced HONO emission from urban soil or 2) the enhanced NO2 uptake on the ground surface. The former one may occur through biological processes observed in the laboratory 400 experiments or field measurements over the agricultural fields (Oswald et al., 2013;Scharko et al., 2015;Tang et al., 2019;Xue et al., 2019), while evidence for its occurrence on the urban soil after the rain was still not sufficient. At 13:00 on 26 th or 27 th June, the model predicted lower HONO by almost a factor of 2 -4 (observation: 0.45 or 0.45 ppbv; model: 0.13 or 0.21 ppbv), which needs an enhancement of at least 2 -4 in γg if using NO2 uptake on the ground surface to explain the underestimation. Current laboratory experiments have studied the enhancement effect of atmospheric RH (in the range of 10 405 -70%) on the NO2 uptake coefficient on the surface of target substances and the enhancement factor was less than 3 (Han et al., 2016;Stemmler et al., 2006Stemmler et al., , 2007. Campaign averages of the measured NO2 and RH at 13:00 were 7.4 ppbv and 35.5%, respectively. At 13:00 on 26 th (or 27 th ) June, the measured NO2 of 7.9 (or 4.3) ppbv was similar to (or lower than) the campaign average, but RH of 67.6% (or 53.1%) was significantly higher than the campaign average but still in the range (10 -70%) where RH showed an enhancement (less than 3) effect on γg. Hence, after rain, the enhanced NO2 uptake was likely to be 410 responsible for the underestimation. Meanwhile, soil HONO emission may co-exist but more evidence was needed. However, the impact of direct water addition to those substances (e.g., rainwater on the ground surface) was still not clear. i.e., it may https://doi.org/10.5194/acp-2021-531 Preprint. Discussion started: 30 July 2021 c Author(s) 2021. CC BY 4.0 License. enhance NO2 uptake and/or soil emission to produce HONO or deposition to consume HONO). Further studies may explore the impact of rain on urban soil surface processes, such as the soil HONO emission flux and NO2 uptake kinetics. Figure 9 shows the simulated radical concentrations in different model scenarios where their sensitivities to the constrained HONO were tested. It can be obtained that ROx radicals (OH, HO2, and RO2) were significantly affected by the constrained HONO, implying the vital role of HONO in the ROx budget. For instance, the peak OH concentration in the base case was 0.42 pptv (equivalent to 1.0×10 7 molecules cm -3 ). It decreased to 0.37 (or 0.32) pptv when HONO was reduced by 50% (or 420 100%) and increased to 0.46 (or 0.51) pptv when HONO was enlarged by 50% (or 100%). In contrast, modeled NO3

Role of HONO in Radical Concentrations
concentrations showed very small variations whether HONO was reduced or enlarged, which is because NO3 concentration is mainly governed by the levels of O3 + NO2 during night-time when HONO has no impact on radical levels caused by the missing photolysis. Nevertheless, the almost same radical concentrations in case NO + OH and case -100% indicate the minor role of NO + OH in the radical budget as this OH sink is exactly compensated by the OH production through (R-5). 425  Figure 10A and Figure 10B illustrate the production/loss rates of OH and NO3, respectively. The total production rates of these radicals were similar to their loss rates due to their short lifetimes and high reactivities. For OH ( Figure 10A), its largest source was the reaction of HO2 + NO, which is part of the propagation cycle and which is not a radical initiation source (Elshorbany et al., 2010). HONO photolysis was the second-largest OH source, and it is expected to be the largest primary OH source after https://doi.org/10.5194/acp-2021-531 Preprint. Discussion started: 30 July 2021 c Author(s) 2021. CC BY 4.0 License.

Radical Production/Loss Rates and Reactivity 430
subtracting OH loss through HONO + OH and NO + OH (see Section 3.3.4). Reactions with NO2, CO, and C5H8 acted as the 435 top three OH sinks but did not dominate OH loss due to high OH reactivity caused by various other reactions, particularly those with other VOCs (see below). Figure 10C and S10A show the OH reactivity with different classes of pollutants and their relative contributions, respectively.
Likewise, C5H8 alone contributed 4% of the OH reactivity in the early morning (0.85 s -1 ), and its contribution increased to 445 12% at noontime (2.1 s -1 ) as a result of high levels of C5H8 and OH at noontime. 450 Figure 10D and S10B show NO3 reactivity with different pollutant classes and their relative contributions, respectively.
Compared with the total OH reactivity, the total NO3 reactivity exhibited lower values and a different variation profile. It showed a minimum of 1 s -1 at noontime and increased to around 4 s -1 at 2:00. In addition to the N2O5 decomposition (R-17), NO2 + O3 (R-11) is the most important NO3 source, which is also, in fact, the most important net NO3 source, considering the same amount of NO3 loss during N2O5 production through (R-14). NO3 loss was dominated by photolysis and reactions with NO during the daytime and reactions with NO2 at night. More discussion on NO3 chemistry is presented in the following section.

NO3 Chemistry
As shown in Figure 9D, high NO3 levels (diurnal peak: 9.3 pptv, time-series peak: 22 pptv) were simulated by the model. High NO3 concentrations, as well as its high reactivity ( Figures 10D), generally appeared at night (18:00 to 4:00 in the next day) 460 when OH was very low and NO3 was not lost by photolysis, indicating that the NO3-initialized chemistry may play an important role in night-time chemistry at this site. To verify this implication, we compared the C5H8 oxidation and nitrate formation through NO3-induced reactions with other paths. Figure 11 shows the C5H8 loss rates (L(C5H8)) through different oxidation paths and their relative contributions. L(C5H8) 465 through O3 was generally in the range of 1.0 -3.2×10 -5 ppbv s -1 . L(C5H8) through OH showed high values in the daytime and low ones in the night-time. On the contrary to OH, low L(C5H8) through NO3 occurred in the daytime and high one occurred in the night-time. On average, L(C5H8) through OH, O3, and NO3 oxidation were 3.6×10 -4 , 2.0×10 -5 , and 4.5×10 -5 ppbv s -1 , with relative contributions of 84%, 5%, and 11%, respectively. During the daytime, L(C5H8) through OH oxidation was generally one order of magnitude higher than those through NO3 or O3 oxidation, leading to a dominated C5H8 loss contribution 470 of generally >90% through OH oxidation ( Figure 11B). However, at night, OH was much lower and NO3 was higher due to the absence of photochemistry, resulting in an increasing contribution of L(C5H8) through NO3 oxidation ( Figure 11B).

C5H8 Oxidation
Average L(C5H8) through night-time NO3 oxidation increased to 8.4×10 -5 ppbv s -1 , but L(C5H8) through OH oxidation decreased to 9.2×10 -5 ppbv s -1 , resulting in a relatively high contribution of NO3 oxidation (32 -57%). NO3 oxidation contributed to 44% of the night-time C5H8 loss, which is comparable to OH oxidation (48%) and much higher than O3 oxidation 475 (8%). Considering that C5H8 is an important common hemiterpene emitted from multitudinous vegetations and its oxidation plays a key role in secondary organic aerosol (SOA) formation, daytime OH-induced C5H8 oxidation was highlighted while NO3-induced oxidation of C5H8 may also significantly affect the SOA formation during the night-time (Brown and Stutz, 2012;Mellouki et al., 2021).

HNO3 Formation
As an important component of particulate matter, inorganic nitrate (pNO3) was produced through the partitioning of HNO3.
Hence, the production of HNO3, defined as P(HNO3) = P(HNO3)OH + P(HNO3)NO 3 , represents the upper limits of pNO3 production. P(HNO3)OH denotes the HNO3 production through (R-18) in the model (Sce-0). For P(HNO3)NO 3 calculation, both 485 HNO3 formation through N2O5 heterogeneous uptake on the aerosol surface (R-16) and other NO3-induced reactions were considered (the former was the dominated one). In the model, parameterization for the heterogeneous N2O5 uptake is presented in (Eq-9).
As shown in Figure 12, the overall P(HNO3) was high during the daytime and low during the night-time. During the daytime, P(HNO3)NO 3 was generally much lower than P(HNO3)OH, leading to high contributions of P(HNO3)OH (>90%). However, during the night-time, P(HNO3)OH remarkably decreased but P(HNO3)NO 3 showed an increase, which promotes the relative 495 contribution of P(HNO3)NO 3 to the sum P(HNO3). On average throughout all day, P(HNO3)NO 3 contributed 18%, significantly lower than P(HNO3)OH (82%). However, at night, P(HNO3)NO 3 contribution increased to 51%, slightly higher than P(HNO3)OH (49%). By far, very few NO3 measurements are available in China Suhail et al., 2019), while its high concentration and important role in chemical oxidation presented in this study shed light on the necessity of direct NO3 (as well as related species such as N2O5, ClNO2, etc.) measurements in the NCP, where summertime O3 level is substantially 500 increasing.
At night with the absence of photochemistry, ozonolysis was the major source for primary ROx and exhibited similar levels to itself during the daytime, leading to its important role in primary ROx production (39% for all day). Besides, T(ROx) was dominated by NO2 + OH → HNO3, NO2 + CH3COO2 → PAN, and HO2 + HO2 → H2O2 (Elshorbany et al., 2010(Elshorbany et al., , 2012Hofzumahaus et al., 2009;Liu et al., 2012;Stone et al., 2012).  Note that: 1) due to an integration problem, the top-20 net radical loss paths were summarized here and it could represent the majority of total T(ROx) as others (<1×10 -5 ppbv s -1 ) were at least 2 orders of magnitude lower than the sum of top-20, 2) night-time P(ROx)HONO_net was negative (a net sink for OH) so that its contribution was also negative at night 525 and 3) the same amounts of radical loss or production from equilibrium reactions (e.g., HO2 + NO2 ↔ HNO4; CH3COO2 + NO2 ↔ PAN) was excluded from radical initiation or termination.

Role of HONO in OH Production at the Foot and the Summit Stations
Although measurements at the foot and the summit stations were conducted during two consecutive periods rather than the same one in summer 2018, it still allows a reasonable comparison of HONO contribution to OH formation at the foot station 530 (lower boundary layer) and the summit station (upper boundary layer). Because of limited data available at the summit station, we only compared HONO with O3 in primary OH formation. As reported in the companion paper, rapid vertical transport maintains the high HONO level at the summit station, promising HONO an important role in integrated OH production with a contribution of 26% to the sum of OH production (P(OH)O 3 +HONO) considering only HONO and O3 photolysis. If OH loss through HONO + OH and NO + OH was subtracted from P(OH)HONO (then it becomes P(OH)HONO_net), its contribution 535 decreased to 18%, about one-quarter of P(OH)O 3 .
Then net OH production from HONO (P(OH)HONO_net) and O3 (P(OH)O 3 ) photolysis at the foot and summit stations were also summarized and compared. As shown in Figure 14, it is apparent that HONO photolysis initialized the daytime photochemistry at both the foot and the summit stations as P(OH)HONO_net dominated OH production in the early morning. Average P(OH)HONO_net and P(OH)O 3 at the foot station are 2.4×10 -4 and 1.4×10 -4 ppbv s -1 , respectively, both of which are significantly 540 higher than those (1.7×10 -5 and 7.7×10 -5 ppbv s -1 ) at the summit station as a result of relatively lower HONO and O3 concentrations and lower solar photolysis frequencies observed at the summit station. The latter was caused by frequent cloud formation neat the summit. Nevertheless, the considerable contributions of P(OH)HONO_net to P(OH)O 3 +HONO at the foot (64%) and the summit (18%) stations indicate the essential role of HONO in the atmospheric oxidizing capacity at both the ground (lower boundary layer) and the summit (upper boundary layer) levels in mountainous regions. The main conclusions are summarized as follows: 555 1. The default HONO source, NO + OH, significantly underestimated the observed HONO by 87%, revealing a strong unknown source (Punknown). The diurnal profile of Punknown rapidly increased in the morning and peaked nearly 3 ppbv h -1 at noon, suggesting additional photo-enhanced HONO formation processes.
2. A HONO/NOx ratio of 0.7% was derived for direct emission, and its contribution (15 -25% at night but negligible during the daytime) was furtherly quantified by a new method developed in this study. Based on the constraints on 560 the aerosol-derived HONO sources (NO2 uptake on the aerosol surface and nitrate photolysis) obtained from the summit measurement (see the companion paper) and from recent laboratory studies, we found that the aerosol-derived HONO sources may make moderate or small contributions to HONO formation at the summit level and the ground level, respectively, but their contributions were not higher than NO + OH. Heterogeneous NO2 conversion on the ground surface made the largest contribution to Punknown, but it was sensitive to the MLH used for its parameterization. 565 This addressed the importance of a reasonable MLH for exploring ground-level HONO formation in 0-D models and the necessity of vertical measurements.
3. HONO played an important role in ROx but a negligible role in NO3 concentrations. OH dominated the atmospheric oxidizing capacity in the daytime, while NO3 appeared to be significant at night. Peaks of NO3 time series and diurnal variation reached 22 and 9 pptv, respectively. NO3 induced reactions contribute 18% of nitrate formation potential and 11% of the C5H8 oxidation throughout the whole day. While at night, NO3 chemistry led to 51% or 44% of the nitrate formation potential or the C5H8 oxidation, respectively. NO3 chemistry may significantly affect night-time secondary organic and inorganic aerosol formation in this high O3 region. Hence, the direct measurement of NO3 (along with HOx, N2O5, ClNO2, etc.) in this region should be conducted. 4. A comparison of HONO contributions to primary OH at the summit and the ground levels was conducted and it was 575 confirmed that HONO photolysis initialized daytime photo-chemistry at both sites in the early morning. On average, HONO made contributions of 64% and 18% to P(OH)O 3 +HONO at the summit and the ground levels, respectively. As HONO observed at the summit level was mainly transported from the ground level, it addressed the role of HONO in the atmospheric oxidizing capacity in both the lower and the upper boundary layer over mountainous regions.