Our field observations were carried out ∼ 80 m above the ground at a supersite located on the top of the Administrative Building of the Institute of Urban Environment (IUE), Chinese Academy of Sciences (118∘04′13′′ E, 24∘ 36′52′′ N), in Xiamen, China, in August, October, and December 2018 and March 2019 (Fig. 1). The supersite was equipped with a complete set of measurement tools, including those for measuring gases and aerosol species composition, meteorology parameters, and photolysis rate constants, which provided a good chance to study the atmospheric chemistry of HONO in a coastal city of southeastern China. As shown in Fig. 1 (left), Xiamen is located at the southeastern coastal area of China and faces the Taiwan Strait in the east. It suffers from sea and land breeze throughout the year with spring and summer more frequently (Xun et al., 2017). The IUE supersite is surrounded by Xinglin Bay, several universities (or institutes), and several major roads with a large traffic fleet, such as Jimei Road, Shenhai Expressway (870 m), and Xiasha Expressway (2300 m) (Fig. 1 (right)). The area of Xiamen is 1700.61 km2 with a population of 4.11 million (http://tjj.xm.gov.cn/tjzl/, last access: 12 August 2019). The number of motor vehicles in 2018 was 1 572 088, which was 2.73 times as many as 10 years ago. The surrounding soil is used for landscape greening, not for agricultural production.

The atmospheric concentrations of both HONO and NO2 were determined using IBBCEAS, which has previously been widely applied to such measurements (Tang et al., 2019; Duan et al., 2018; Min et al., 2016). The IBBCEAS instrument was customized by the Anhui Institute of Optics and Fine Mechanics (AIOFM), Chinese Academy of Sciences (Duan et al., 2018). The resonant cavity is composed of a pair of highly reflective mirrors separated by 70 cm, and their reflectivity is approximately 0.99983 at 368.2 nm. The surface of the mirrors was purged by dry nitrogen at 0.1 standard liters per minute (SLM), and the air flow was controlled by a mass flow controller to prevent the surface of the mirror from being contaminated. Light was introduced into the resonant cavity and was emitted by a single light-emitting diode (LED) with full width at half maximum (FWHM) of 13 nm and a peak wavelength of 365 nm. Light transmitted through the cavity was received by a spectrometer (QE65000, Ocean Optics Inc., USA) through an optical fiber with 600 µm diameter and a 0.22 numerical aperture.

In order to avoid the drift of the center wavelength of the LED, the temperature of the LED was controlled to be approximately 25 ± 0.01 ∘C by using a thermoelectric cooler unit. In order to prevent particulate matter from entering the cavity and reducing the effect of particulate matter on the effective absorption path, a 1 µm polytetrafluoroethylene (PTFE) filter membrane (Tisch Scientific) was used in the front end of the sampling port. In order to ensure the quality of the data, the 1 µm PTFE filter membrane was usually replaced once every three days and the sampling tube was thoroughly cleaned with alcohol once a month. We increased the replacement frequency of the filter membrane and the cleaning frequency of the sampling tube in the event of heavy pollution to ensure that the filter membrane and sampling tube are in a clean state. The length of sampling tube with 6 mm outer diameter was approximately 3 m, the material was PFA with excellent chemical inertness, and the sampling flow rate was 6 SLM meaning that the residence time of the gas in the sampling tube was less than 0.5 s. Besides, the sampling loss was calibrated before the experiment. We assessed the measured spectrum every day to ensure the authenticity of the measurement results. Multiple reflections in the resonator cavity enhanced the length of the effective absorption path, thereby enhancing the detection sensitivity of the instrument. The 1σ detection limits for HONO and NO2 were about 60 and 100 ppt, respectively, and the time resolution was 1 min. The fitting wavelength range was selected as 359–387 nm. The measurement error of HONO of IBBCEAS was estimated to be about 9 %, considering both HONO secondary formation and sample loss. The sampling tube was heated to 35 ∘C and covered by insulation cotton materials to prevent the effect of condensation of the water vapor (Lee et al., 2013).

The inorganic composition of PM2.5 aerosols (SO42-, NO3-, Cl-, Na+, NH4+, K+, Ca2+, and Mg2+) and concentrations of gases (HONO, HNO3, HCl, SO2, NH3) were determined using a Monitor for AeRosols and Gases in ambient Air (MARGA, Model ADI 2080, Applikon Analytical B.V., the Netherlands). Ambient air was drawn into the sample box by a PM2.5 cyclone (Teflon coated, URG-2000-30ENB) at the flow rate of 1 m3 h-1. Air sample was drawn firstly through the wet rotating denuder (WRD) where gases diffused to the solution, and then particles were collected by a steam jet aerosol collector (SJAC). Absorption solutions were drawn from the SJAC and the WRD to syringes (25 mL). Samples were injected into Metrohm cation (500 µL loop) and anion (250 µL loop) chromatographs with the internal standard (LiBr) for 15 min after an hour when the syringes had been filled (Makkonen et al., 2012). Specific descriptions of the SJAC can be found in previous reports (Slanina et al., 2001; Wyers et al., 1993). Therefore, the times needed for the sampling period and the latter IC analysis on the MARGA system are a full hour and 15 min, respectively. The value measured in this hour is actually the concentration sampled in the previous hour, so the time corresponding to the sampling is matched with other instrument parameters (i.e., HONO, NOx, J values).

Photolysis frequencies were determined using a photolysis spectrometer (PFS-100, Focused Photonics Inc., Hangzhou, China). These were calculated by multiplying the actinic flux F, quantum yield φ(λ) and the known absorption cross section σ(φ). The measurements included the photolysis rate constants J (O1D), J (HCHO_M), J (HCHO_R), J(NO2), J (H2O2), J (HONO), J (NO3_M), and J (NO3_R), and the spectral band ranged from 270 to 790 nm. Hemispherical (2π sr) angular response deviations were within ±5 %. The photolysis rate constants with _R and _M represented a radical photolysis channel and molecular photolysis channel, respectively. Specifically, HCHO was removed by the Reactions (R1) and (R2), and NO3 was removed by the Reactions (R3) and (R4), respectively (Röckmann et al., 2010). R1HCHO+hv⟶CHO+HJ(HCHO_R)R2HCHO+hv⟶H2+COJ(HCHO_M)R3NO3+hv⟶NO2+O3PJ(NO3_R)R4NO3+hv⟶NO+O2J(NO3_M) The O3 concentration was determined by an ultraviolet photometric analyzer (model 49i, Thermo Environmental Instruments (TEI) Inc.), and the limit of the instrument is 1.0 ppb. The NO concentration was determined by a chemiluminescence analyzer (TEI model 42i) with a molybdenum converter. The detection limit and the uncertainty of the TEI model 42i were 0.5 ppb and 10 %, respectively. Although the TEI model 42i also measures the concentration of NO2, this value might actually include other active nitrogen components (Villena et al., 2012). As expected, the NO2 concentration measured by IBBCEAS had the same trend as the NO2 measured by TEI 42i, and NO2 concentration measured by IBBCEAS was always lower than that by TEI 42i (Supplement Fig. S1). Therefore, the NO2 concentration as measured by IBBCEAS was used in this study. An oscillating microbalance with a tapered element was applied to determine the PM2.5 concentration with uncertainty of 10 %–20 %. Black carbon (BC) was measured using an Aethalometer at 7 wavelengths (in using 880 nm wavelength). When the tape was < 10 %, aethalometer fiber tape was replaced. Meteorological parameters were determined by an ultrasonic atmospherium (150WX, Airmar, USA). The time resolution of all instruments was unified to 1 h to facilitate comparison. Ultraviolet radiation (UV) was determined by a UV radiometer (Kipp & Zonen, SUV5 Smart UV Radiometer).

Fig. 2 showed an overview of the determined HONO, NO, NO2, PM2.5, NO3-, BC, J(HONO), temperature (T), and relative humidity (RH) in this study. The entire campaign was characterized by a subtropical monsoon climate with high temperatures (9.82–34.42 ∘C) and high humidity (29.24 %–100 %). The mean values (± standard deviation) of temperature and relative humidity were 22.24 ± 5.41 ∘C and 78.35 ± 14.07 %, respectively. Elevated concentrations of NOx, i.e., up to 156.17 ppb of NO and 172.42 ppb of NO2, were observed, possibly due to dense vehicle emissions near this site. The photolysis rate constants J(O1D), J(HCHO_M), J(HCHO_R), J(NO2), J(H2O2), J(HONO), J(NO3_M), and J(NO3_R) had the same temporal variation (Fig. S2), although their orders of magnitude were different. The correlation coefficients between J(HONO) and other photolysis rate constants were above 0.965 (not shown). Both J(HONO) and UV peaked around noon, and the maximum of J(HONO) (2.02 × 10-3 s-1) and UV (55.62 W m-2) appeared at 13:00 LT on 11 March 2019, and 12:00 LT on 14 August 2018, respectively. This area was dominated by photochemical pollution, while particulate pollution was relatively light. No haze episodes occurred across four seasons with 111 d, because daily mass concentration of PM2.5 was lower than the National Ambient Air Quality Standard (Class II: 75 µg m-3). For O3, 10 episodes occurred with 8 h maximum concentrations of O3 exceeding the Class II: 160 µg m-3. Maximum mixing ratio of O3 was 113.81 ppb, occurring in the afternoon with strong ultraviolet radiation (42.72 W m-2) and a low NO concentration (0.75 ppb) of titrating O3. In general, the level of pollutants in this area was relatively low. Campaign-averaged levels of NO2, NO, NO3-, PM2.5, O3, and BC were 14.99 ± 8.93 ppb, 5.80 ± 11.98 ppb, 5.59 ± 6.26 µg m-3, 27.78 ± 17.95 µg m-3, 28.29 ± 21.14 ppb, and 1.67 ± 0.97 µg m-3, respectively. The maximum value of HONO (3.51 ppb) appeared at 08:00 LT on 4 December 2018. The high value of HONO was always accompanied by relative high values of NO and NO2 or PM2.5, BC, and NO3-. The average measured ambient HONO concentration at the measurement site for all measurement periods was 0.54 ± 0.47 ppb. The HONO concentration measured at this site was comparable to those measured at other suburban sites (Liu et al., 2019a; Xu et al., 2015; Nie et al., 2015; Park et al., 2004), was obviously lower than those measured at urban sites and industrial site (D. Li et al., 2018; Yu et al., 2009; Hou et al., 2016; Qin et al., 2009; Wang et al., 2013; Shi et al., 2020; Spataro et al., 2013; Huang et al., 2017; Wang et al., 2017), and was obviously higher than those measured at a marine background (Wen et al., 2019), marine boundary layer (Ye et al., 2016), and remote coastal region (Meusel et al., 2016), as shown in Table S1 in the Supplement.

As shown in Table 1, in the daytime (06:00–18:00 LT, including 06:00 LT), the highest concentration of HONO was found in spring and summer (0.72 ppb), followed by winter (0.61 ppb) and autumn (0.50 ppb). In short, the seasonal variation of HONO was well correlated with the seasonality of RH, with high RH in spring (84.21 %) and summer (84.12 %), followed by winter (78.13 %) and autumn (69.55 %). In conditions of low RH, the adsorption rate of NO2 is not as rapid as that of HONO, resulting in a reduction in the conversion rate of NO2 to HONO and thus a reduction in the concentration of HONO (Stutz et al., 2004). This seasonal variation in HONO concentration was different from those measured in Jinan (D. Li et al., 2018), Nanjing (Liu et al., 2019a), and Hong Kong (Xu et al., 2015). The elevated HONO concentrations in summer, when there is strong solar radiation, suggests the existence of strong sources of HONO and its important contribution to the production of OH radicals. Interestingly, the HONO concentration in the nighttime was lower than that in the daytime in all four seasons. Similar results were found in Hong Kong, which is also a coastal city (Xu et al., 2015). However, most previous studies have found that the HONO concentration at night is significantly higher than that during the day (Wang et al., 2015; Liu et al., 2019a; D. Li et al., 2018; Elshorbany et al., 2009; Acker et al., 2006; Yu et al., 2009). The higher HONO in the daytime is likely due to the higher NOx or nitrate photolysis as discussed in following section.

The ratio of HONO to NOx or the ratio of HONO to NO2 have been extensively applied to indicate heterogeneous conversion of NO2 to HONO (Li et al., 2012; Liu et al., 2019a; Zheng et al., 2020). Compared with the HONO / NO2 ratio, the HONO / NOx ratio can better avoid the influence of primary emissions (Liu et al., 2019a). In this study, the HONO / NOx ratios during the day were higher than those during the night, indicating that light promotes the conversion of NOx to HONO. The highest daytime HONO / NOx ratio was found in summer (0.072), followed in turn by autumn (0.048), spring (0.034), and winter (0.023). The elevated HONO / NOx ratio in summer indicates a greater net HONO production (Xu et al., 2015). The low HONO / NOx ratio in winter can probably be ascribed to heavy emissions and high concentrations of NO in winter (Table 1). The HONO / NOx ratios during every season in Xiamen were in general higher than those found in studies of other cities, which indicates greater net HONO production in Xiamen.

The diurnal patterns of HONO, NOx, HONO / NOx, and J(NO2) averaged for every hour in each season are shown in Fig. 3. As shown in Fig. 3a, the HONO concentration had similar diurnal variation patterns across the four seasons. The maximum values of the HONO concentration were 1.12 ppb in winter, 1.03 ppb in summer, 0.98 ppb in spring, and 0.65 ppb in autumn, and these occurred in the morning rush hour (07:00–08:00 LT), which indicates that direct vehicle emissions may be a significant source of HONO. The contribution of direct vehicle emissions to HONO will be quantified in Sect. 3.2. The HONO concentration reduced rapidly from the morning rush hour to sunset, and this was caused by rapid photolysis combined with increased height of the boundary layer. The minimum values of HONO concentration were 0.47 ppb in spring, 0.23 ppb in winter, 0.21 ppb in summer, and 0.14 ppb in autumn, and these appeared at sunset, between 16:00 and 18:00 LT. The HONO concentration increased gradually after sunset, which indicates that release from HONO sources exceeded its dry deposition (Wang et al., 2017). There was a slight difference in the diurnal variation of HONO between autumn and the other seasons. A rapid reduction of HONO after the morning rush hour was found in spring, summer, and winter. In comparison, the HONO in autumn had an almost constant concentration between 07:00 and 11:00 LT because NOx decreased slowly during this period.

As shown in Fig. 3b, NOx concentration followed an expected profile in the four seasons, with peaks of 45.58 ppb in winter, 40.47 ppb in spring, 32.47 ppb in summer, and 20.07 ppb in autumn, each occurring in the morning rush hour at 10:00, 09:00, 08:00, and 07:00 LT, respectively. After these peaks, NOx decreased during the day in each season, probably due to photochemical transformation and increasing boundary-layer depth. The NOx concentrations then began to rise from their minima of 8.20 ppb in summer, 8.85 ppb in autumn, 18.10 ppb in winter, and 23.09 ppb in spring after 14:00, 13:00, 15:00, and 16:00 LT, respectively, which was caused by a combination of weak photochemical transformation and reduction in the boundary-layer depth. The NOx concentrations during winter and spring were significantly higher than those during autumn and summer. Both the maxima and minima of NOx appeared later in spring and winter compared with summer and autumn.

It is possible to better describe the behavior of HONO using the HONO / NOx ratio. The higher HONO / NOx ratio found at noon in the different seasons, especially in summer and autumn (Fig. 3c), indicates an additional daytime HONO source (Liu et al., 2019a; Xu et al., 2015). It is worth noting that the maximum value of this ratio in summer (0.147) was significantly higher than the maximum in other seasons, especially in winter (0.034). Figure 3d shows that the value of the HONO / NOx ratio increased with the photolysis rate constant of NO2 in summer and autumn, suggesting that the additional HONO source is probably correlated with light (Xu et al., 2015; Wang et al., 2017; D. Li et al., 2018; Li et al., 2012). The increase in the HONO / NO2 ratio during the day can be seen more clearly in Fig. 4, and its high value indicates a high HONO production efficiency, which cannot be ascribed to NO2 conversion due to the weak correlation between HONO and NO2 in summer. Furthermore, high HONO / NO2 ratios were accompanied by high J(NO2) in summer, which indicates that HONO formation during the daytime is more likely to relate to light rather than Reaction (R5). R5NO2+NO2+H2O⟶surfHONO+HNO3 However, the observed maxima can also be ascribed to sources independent from NOx concentration, such as soil emissions (Su et al., 2011) and photolysis of particulate nitrate (Zhou et al., 2011; Ye et al., 2016), which are not influenced by the decrease in NOx concentration around noon. A more specific discussion of daytime HONO sources considering the photolysis of particulate nitrate will be given in Sect. 3.4.3. The HONO emissions from soil were estimated to be 2–5 ppb h-1 (Su et al., 2011). However, soil emission was a negligible source of HONO in this study since the surrounding soil is not used for agriculture, and this greatly reduces the amount of HONO released due to the lack of a fertilization process (Su et al., 2011).

The K+ levels were 0.26, 0.13, 0.14, and 0.24 µg m-3 for spring, summer, autumn, and winter, respectively. The K+ levels during the four seasons were lower than 2 µg m-3, which indicated that biomass burning has little effect on this site (Xu et al., 2019). Hence, only vehicle emissions were considered in this study. The consistent diurnal variations in HONO and NOx presented in Sect. 3.1 (Fig. 3) also indicate HONO emissions from local traffic. Five criteria were applied to choose cases that guaranteed the presence of fresh plumes (Xu et al., 2015; Liu et al., 2019a): (1) UV < 10 W m-2; (2) short-duration air masses (< 2 h); (3) HONO correlating well with NOx (R2 > 0.60, P < 0.05); (4) NOx > 20 ppb (highest 25 % of NOx value); and (5) ΔNO / ΔNOx > 0.85. A total of 23 cases met these strict criteria for estimation of the HONO vehicle emission ratios. The slopes of scatter plots of HONO vs. NOx were used as the emission factors.

A total of 23 vehicle emission plumes were summarized in Table 2, and these were used for estimation of the vehicle emission ratios. These plumes were considered to be truly fresh because the mean ΔNO / ΔNOx ratio (the linear slope of NO with NOx) of the selected air masses was 99 %. Vehicle plumes unavoidably mixing with other air masses resulted in the correlation coefficients (R2) between HONO and NOx varying among the cases, and these ranged from 0.64 to 0.92. The obtained ΔHONO / ΔNOx ratios (the linear slope of HONO with NOx) ranged from 0.24 % to 2.95 %, with an average value (±SD) of (1.45 ± 0.78) %. These ΔHONO / ΔNOx ratios have comparability to those obtained in Guangzhou (1.4 %, Qin et al., 2009; 1.8 %, Li et al., 2012) and Houston (1.7 %, Rappenglück et al., 2013) but are significantly higher than those measured in Jinan (0.53 %, D. Li et al., 2018) and Santiago (0.8 %, Elshorbany et al., 2009). The types of vehicle engine, the use of catalytic converters, and different fuels will affect the vehicle emission factors (Kurtenbacha et al., 2001). A potential reason for the relatively higher ΔHONO / ΔNOx values in our study is that heavy-duty diesel vehicles pass by on the surrounding highway (Rappenglück et al., 2013). It is necessary to examine the specific vehicle emission factors in target cities because of these differences in ΔHONO / ΔNOx ratios. Roughly assuming that NOx mainly arises from vehicle emissions, a mean ΔHONO / ΔNOx value of 1.45 % was used as the emission factor in this study, and this value was adopted to estimate the contribution of vehicle emissions Pemis to the HONO concentration using 1Pemis=NOx×0.0145. We can then obtain the corrected HONO concentration (HONOcorr) for further analysis from the equation 2HONOcorr=HONO-Pemis.

Through an empirical parameterized formula, we can explore an accurate parameterization method for HONO, discuss the main control factors for the HONO concentration and its chemical behavior, and quantify its main sources and key kinetic parameters. As mentioned in Sect. 3.1, the HONO / NOx ratio is better than HONO / NO2 as an indicator of HONO generation. In another study (Elshorbany et al., 2012), data were collected from 15 field observations all over the world to establish the correlation between the HONO / NOx ratio and the HONO concentration in global models. Therefore, we applied this method in this study to parameterize the HONO concentration. As shown in Fig. 10, the ΔHONO / ΔNOx ratios in the four seasons were close to the calculated value (0.02). However, there were seasonal variations in the slope, showing a maximum in summer (2.60 × 10-2), followed by autumn (2.06 × 10-2), and a minimum in winter (1.59 × 10-2). Except for in spring, HONO showed good correlation with NOx, with R2 values ranging from 0.8972 to 0.9621. Therefore, we used slopes of 2.60 × 10-2, 2.06 × 10-2, and 1.59 × 10-2 to parameterize the HONO concentrations in summer, autumn, and winter, respectively. As for spring, though only a weak correlation between HONO and NOx was found, the majority of the ΔHONO / ΔNOx ratios fluctuated round a slope of 0.02 because concentrations of NOx greater than 60 ppb only accounted for 8.83 % of the data. Therefore, a slope of 0.02 was applied in spring to parameterize the HONO concentration.

As can be seen from Fig. 11, the estimated values are very close to the observed values in the nighttime in autumn. After sunrise and before noon, the values observed were higher than the estimated values, and this difference gradually increases. After noon and before sunset, the values observed were still higher than the values estimated, but the difference gradually decreases. This phenomenon was also found in the daytime in spring and summer, but not in winter. Compared with the daytime, the estimated values during the nighttime were closer to the observed values in both trend and value in all four seasons, which further demonstrates that nighttime HONO is mainly produced from the direct vehicle emissions and heterogeneous reaction of NO2 on the ground or the surfaces of aerosols. Therefore, we should pay much more attention to simulation in the daytime. We distinguish two main sectors, nighttime and daytime, to analyze the factors affecting the HONO diurnal variation (Liu, 2017). Although J(HONO)× HONO also correlated well with J(NO2) × NO2 in all four seasons in this study and the linear fitting coefficients fluctuated around 0.01 in all four seasons (Fig. S5), bad simulation results during the daytime were found (Fig. S6) using 8HONO=k×[NO2]×J(NO2)/J(HONO), where k was the linear fitting coefficient between J(HONO)× HONO and J(NO2) × NO2. In contrast, excellent simulation results were found in a previous study using the same formula (Liu, 2017), which suggests that using the same simulation formula in different regions may obtain greatly varying results. Equation (8) can be regarded as a combination of [NO2] with J(NO2) / J(HONO). J(NO2) / J(HONO) stayed relatively constant (5.48–5.87) in the daytime in four seasons. Therefore, diurnal variation of [HONO] simulated by Eq. (8) depended on [NO2] (Fig. S7). Equation (8) is only suitable for regions where the diurnal variation of [NO2] is consistent with that of [HONO].

As discussed in Sect. 3.4.2, nitrate photolysis was perhaps the source of HONO in this study. Besides, the difference between the observed value and the simulated value kept increasing before noon, and the difference began to decrease after noon, which was consistent with nitrate photolysis. Therefore, we take the photolysis of nitrate into the HONO concentration simulation. The specific formulas for the simulation of spring, summer, autumn, and winter are shown as follows. 9HONOspring=2.00×10-2×NOx+[NO3-]×J(NO3_R)/410HONOsummer=2.60×10-2×NOx+[NO3-]×J(NO3_R)11HONOautumn=2.06×10-2×NOx+[NO3-]×J(NO3_R)12HONOwinter=1.59×10-2×NOx+[NO3-]×J(NO3_R)/4 In this way, the daytime simulation results are significantly improved (Fig. 11). This further demonstrates that the apportionment of HONO sources is credible. The parameterization described in this work was more reasonable and can be better used in the future in such coastal sites.

Comparing the OH radical production via photolysis of HONO and O3, the effect of the high HONO concentrations in the daytime on the tropospheric oxidation capacity was evaluated (Ryan et al., 2018). Nitrous acid is considered to be a crucial source of OH radicals (Lee et al., 2016). As shown in Eq. (12), OH production rates from O3 photolysis (POH(O3)) were calculated based on [O3], J(O1D), and [H2O] (Liu et al., 2019a). Only O(1D) atoms produced by the O3 photolysis at UV wavelengths less than 320 nm (Reaction R6) can combine with water to generate OH radicals (Reaction R7) in the atmosphere. The absolute water concentration was derived from temperature and RH. The Reaction (R8) rate for N2 is 3.1 × 10-11 cm3 molecules-1 s-1 and for O2 is 4.0 × 10-11 cm3 molecules-1 s-1 (Liu et al., 2019a). The net OH formation from HONO was estimated by Eq. (13) (Su et al., 2008a; Sörgel et al., 2011; D. Li et al., 2018; Atkinson et al., 2004). In addition to the two primary production modes of OH radicals mentioned above, there are the reaction of organic and hydro-peroxy radicals (RO2 and HO2) with NO, hydrogen peroxide photolysis, and the ozonolysis of alkenes (Hofzumahaus et al., 2009; Gligorovski et al., 2015; Wang et al., 2018). POHO3=2JO1DO3ϕOH,ϕOH12=k7[H2O]/(k7[H2O]+k8[M])R6O3+hv→O(1D)+O2(hv<320nm)R7O(1D)+H2O→2OHR8O(1D)+M→O(3P)+M(MisN2orO2)POH(HONO)=JHONO[HONO]-kOH+NO[NO][OH]13-kOH+HONO[HONO][OH] The diurnal patterns of P(OH) are shown in Fig. 12. The formation rates of OH from O3 photolysis peaked at midday at around 0.71, 5.80, 2.21, and 0.48 ppb h-1 for spring, summer, autumn, and winter, respectively. The variation of POH(O3) is consistent with J(O1D) (Fig. S8), peaking at midday and in summer on a diurnal and a seasonal timescale, respectively. For summer and autumn, POH(HONO) had a similar trend to POH(O3), peaking at around noon at the time of the highest J(HONO), but this was negligible at sunrise and sunset (Fig. S9). For spring and winter, however, POH(HONO) reached a maximum in the morning rush hour caused by the combined influences of high HONO concentration and high J(HONO). A similar result was also found in southwestern Spain from mid-November to mid-December 2008 (Sörgel et al., 2011).The HONO photolysis contributed significantly more OH than O3 photolysis during the whole daytime in spring, autumn, and winter. In summer, the HONO photolysis contributed to more OH in the early morning, and although the O3 photolysis produced more in the afternoon, HONO photolysis had a considerable effect on OH production. A similar result was also found in Nanjing in eastern China from November 2017 to November 2018 (Liu et al., 2019a). These results show that HONO contributes considerably to the atmospheric oxidizing capacity of the suburban atmosphere of Xiamen. Although HONO concentrations (average: 0.66 ppb) are much lower than O3 concentrations (average: 35.88 ppb) during 07:00–16:00 LT, daytime HONO photolysis forms significantly more OH than daytime photolysis of O3 in four seasons except for summer afternoons. Generally, the mean value of POH(HONO) from 07:00 to 16:00 LT was 1.89 ppb h-1, and the average POH(O3) was 1.14 ppb h-1. A similar result was found in Melbourne, where the peak OH production rate reached 2 ppb h-1 from 0.4 ppb HONO (Ryan et al., 2018). The important role of HONO in the production of OH promotes photochemical peroxyacetyl nitrate formation (Hu et al., 2020).