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Observation-Based Diagnostics of Reactive Nitrogen Recycling through HONO Heterogenous Production: Divergent Implications for Ozone Production and Emission Control
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Occurrence, Fate, and Transport of Contaminants in Indoor Air and Atmosphere

Observation-Based Diagnostics of Reactive Nitrogen Recycling through HONO Heterogenous Production: Divergent Implications for Ozone Production and Emission Control
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Environmental Science & Technology

Cite this: Environ. Sci. Technol. 2024, 58, 26, 11554–11567
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https://doi.org/10.1021/acs.est.3c07967
Published June 17, 2024

Copyright © 2024 The Authors. Published by American Chemical Society. This publication is licensed under

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Abstract

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Understanding of nitrous acid (HONO) production is crucial to photochemical studies, especially in polluted environments like eastern China. In-situ measurements of gaseous and particulate compositions were conducted at a rural coastal site during the 2018 spring Ozone Photochemistry and Export from China Experiment (OPECE). This data set was applied to investigate the recycling of reactive nitrogen through daytime heterogeneous HONO production. Although HONO levels increase during agricultural burning, analysis of the observation data does not indicate more efficient HONO production by agricultural burning aerosols than other anthropogenic aerosols. Box and 1-D modeling analyses reveal the intrinsic relationships between nitrogen dioxide (NO2), particulate nitrate (pNO3), and nitric acid (HNO3), resulting in comparable agreement between observed and simulated HONO concentrations with any one of the three heterogeneous HONO production mechanisms, photosensitized NO2 conversion on aerosols, photolysis of pNO3, and conversion from HNO3. This finding underscores the uncertainties in the mechanistic understanding and quantitative parametrizations of daytime heterogeneous HONO production pathways. Furthermore, the implications for reactive nitrogen recycling, ozone (O3) production, and O3 control strategies vary greatly depending on the HONO production mechanism. On a regional scale, the conversion of HONO from pNO3 can drastically enhance O3 production, while the conversion from NO2 can reduce O3 sensitivity to NOx changes in polluted eastern China.

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Synopsis

Analysis of an extensive observation dataset reveals observation ambiguity in the underlying heterogeneous HONO production mechanism and divergent implications for ozone production and emission control.

1. Introduction

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Nitrous acid (HONO) is a crucial component in boundary layer photochemistry, playing a significant role in the production of the hydroxyl radical (OH) via fast photolysis. (1−4) Understanding HONO formation mechanisms advances our knowledge of photochemical processes to better predict and regulate regional air pollution, such as the formation of ozone (O3) and secondary organic aerosols. (5−8)
HONO is produced through the gas-phase reaction of nitric oxide (NO) and OH (R1). (9) Direct emissions from combustion activities, such as vehicle exhaust and biomass burning (BB) can also contribute to HONO levels. (10−15) In recent years, observations in a variety of locations have found other daytime HONO sources to be important. (16,17) Soil emissions of HONO have been found to be potentially important for regions with livestock farming or after fertilization, highlighting the need of detailed quantification in future studies. (18) Moreover, various HONO production pathways have been proposed based on field measurements and laboratory experiments, with heterogeneous reactions highlighted as crucial contributors. (19−22) For example, studies have demonstrated the importance of heterogeneous nitrogen dioxide (NO2) conversion on ground surfaces (eq S3) as an important nocturnal HONO source. (23) However, this source and gas-phase production still cannot explain observed daytime HONO losses. (24−27) Aerosol reactions were recognized as a contributor to HONO productions as early as the 1990s. (28) Research by Ammann et al. detected HONO production from NO2 on suspended soot particles, and highlighted the important role of aerosols in HONO production. (29) Since then, numerous observations have reported HONO production from heterogeneous reactions on aerosols, including photosensitized conversion of NO2 (Supporting Information eqs S4–S5), (30−38) conversion from adsorbed nitric acid (HNO3) (Supporting Information eq S9), (39−41) and photolysis of particulate nitrate (pNO3) (Supporting Information eqs S6–S8). (42−45) More details regarding these HONO sources will be discussed in Section 2.2.
OH+NO+MHONO+M
(1)
Although the precise mechanism of aerosol-induced HONO production is still a subject of debate, (46−48) heterogeneous mechanisms have been included into atmospheric chemistry simulations and have shown promising agreement with observed levels of HONO. Various HONO budget analyses have been conducted, yet the prevailing mechanism responsible for daytime HONO production beyond R1 remains uncertain. (49−52) This is due in part to large uncertainties in key reaction parameters of heterogeneous daytime HONO production, such as the reactive uptake coefficient of NO2, the enhancement factor of pNO3 photolysis rate (jpNO3), and the HONO yield coefficient (YHONO) from HNO3 conversion, as well as variations in precursor concentrations observed in different campaigns.
Furthermore, environmental factors, such as BB (53−55) and aerosol acidity, (56,57) can impact HONO production. This provides additional challenges to characterize HONO sources. For example, Nie et al. (53) found that the conversion rate of NO2 to HONO in BB plumes is twice that in non-BB plumes, suggesting that BB aerosol composition can enhance NO2 conversion efficiencies of BB aerosols. Aerosol acidity can affect the volatility of aerosol components and gas-particle partitioning; and HONO heterogeneous production tends to increase with decreasing pH. (58−60)
In this study, we test these proposed HONO heterogeneous mechanisms, including photosensitized NO2 conversion on aerosols, photolysis of pNO3, and conversion from HNO3, using a comprehensive data set of gaseous and particulate compositions measured at a rural coastal site during the 2018 spring Ozone Photochemistry and Export from China Experiment (OPECE). (61) Observations at a remote location are ideal for diagnosing photochemical processes due in part to generally less-complex emission characteristics compared to urban regions. In this work we investigate the impact of BB and aerosol acidity on observed HONO concentrations. Focusing on the observations not affected by BB, we also analyze the intrinsic photochemical relationships between NO2, pNO3, and HNO3 in the remote boundary layer and how the relationships can obscure the diagnostics of HONO heterogeneous production mechanisms. Lagrangian box model simulations are used to demonstrate the utility of diagnosing O3 enhancements by the recycling of reactive nitrogen species through HONO production on aerosols. We also investigate the implications of the different HONO heterogeneous production mechanisms for O3 control strategy.

2. Materials and Methods

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2.1. Observations

The OPECE campaign was carried out at the Yellow River Delta Ecology Research Station of the Coastal Wetland in Dongying, Shandong, China. The rural coastal site (37.76°N, 118.98°E) is located in the Yellow River Delta region near the Shandong Yellow River Delta National Nature Reserve. (62) The closest urban area, Dongying, is located approximately 50 km southwest of the site. From 23 March to 22 April of 2018, surface concentrations of HONO were measured by a Long Path Absorption Photometer (LOPAP). (63−67) Additionally, measurements of NOx (NO+NO2), O3, acetonitrile (CH3CN), carbon monoxide (CO), volatile organic compounds (VOCs), aerosol size distribution, NO2 photolysis rate (jNO2), and meteorological parameters, including pressure, temperature, and relative humidity (RH) during this period were obtained. Detailed instrument information is provided in Supporting Information Table S1 and previous studies. (61)
Ambient aerosol surface area (SA) was derived using observation data and accounting for the hygroscopic effect, (68) using the observed RH to estimate the radius ratio of ambient to dry aerosols (Supporting Information Figure S1, eqs S1–S2). Aerosol chemical composition was measured by both an aerosol mass spectrometer (AMS) and PM2.5 filter samples. Comparisons of daily inorganic ion concentrations reported by the two instruments demonstrate reasonable agreement with p-values <0.05 and measurement ratios ranging from 0.83 to 1.1 (Supporting Information Figure S2). The AMS instrument samples particles with size <1 μm. Therefore, AMS data are generally lower than the filter measurements. In our study, we combined the two data sets to obtain pNO3 and other inorganic aerosol concentrations. Further details regarding data preparation, including the derivations of ambient aerosol surface area, pNO3 concentration, and aerosol pH and gas-particulate partitioning calculations, can be found in the Supporting Information Text S1.

2.2. Photochemical Models, Source Parameterizations, and Simulation Cases

In this study, we utilize the Regional chEmical trAnsport Model (REAM) in both one- and zero-dimensional configurations (69−71) to evaluate the recycling of reactive nitrogen species through heterogeneous HONO production and its impacts on O3 formation. The model incorporates 30 vertical layers in the troposphere, and the condensed chemistry mechanism from the GEOS-Chem model (v9–02) (72) for the Ox-NOx-VOCs photochemistry. For VOCs, ≥ 3C alkenes, ≥ 4C alkanes, and high reactivity aromatics are lumped while preserving the OH reactivities. Photolysis rates are calculated based on cloud fraction and optical depths simulated by the Weather Research and Forecasting (WRF) model and subsequently scaled using jNO2 observations. To ensure realistic meteorological conditions, observed data are used to constrain meteorological parameters such as temperature, RH, wind velocities and wind directions. Vertical mixing is included using eddy diffusion coefficient (Kzz) simulated by WRF. An averaged diurnal profile of mixing layer height diagnosed by Kzz (73) is shown in Supporting Information Figure S3. We constrain the model at a 1 min time step using surface observations of O3, NO2, NO, CO, and selected VOCs. Averaged midmorning to afternoon (10:00–15:00 LT) mixing ratios of VOCs are shown in Supporting Information Figure S4.
To investigate the relationship between HONO production and potential photoactive production pathways, we calculated the missing HONO source strength (pHONO) by
pHONO=d[HONO]+Lchem+LtransportPOH+NO
(2)
where, d[HONO] represents the net change of HONO calculated from observations for each 1 min time step, Lchem denotes the chemical loss of HONO through photolysis and gas-phase reactions, Ltransport denotes the loss of HONO through vertical transport, and POH+NO denotes the production of HONO through the gas-phase reaction of NO and OH. Primary emissions are not considered for this rural coastal site. Since we focus on using pHONO to diagnose daytime HONO sources, the impact of regional transport is not considered either.
Hourly pHONO data are calculated in the baseline 1-D model simulation by constraining surface HONO and other chemical concentrations to the observations (same chemical setup as case B in Table 1) using eq 2. To explain observation-based pHONO, five additional 1-D model simulations were conducted (Table 1). In case S0, HONO production from NO2 conversion on ground surface upon dry deposition of NO2 was parametrized with a yield coefficient of HONO production (f) of 0.24 (Supporting Information eq S3) to reproduce nighttime HONO observations following Liu et al. (24) As in the previous study, an uptake coefficient of 10–6 of NO2 uptake to aerosol surfaces under dark conditions was assigned. (74)
Table 1. Simulation Cases in This Study
CasesConfiguration
BNO + OH + M → HONO + M
S0B + NO2 conversions on ground (f = 0.24) and on aerosols (γ = 10–6) (Supporting Information eq. S3)
S1S0 + photosensitized NO2 conversions on aerosol γ = max (5 × 10–7×SWR, 10–6) (Supporting Information Eqs. S4–S5)
S2–1S0 + photolysis of pNO3, jpNO3= 80 × jHNO3 (Supporting Information eqs. S6–S7)
S2–2S0 + photolysis of pNO3, jpNO3= EF(pNO3, a = 3 × 104)×jHNO3 (Supporting Information eqs. S6–S8)
S3S0 + HONO from HNO3, YHONO = 0.45 (Supporting Information Eq. S9)
F0Free-running S0
F1F0 + photosensitized NO2 conversions on aerosol γ = max (5 × 10–7×SWR, 10–6)
F2–1F0 + photolysis of pNO3, jpNO3= EF × jHNO3, EF = 80
F2–2F0 + photolysis of pNO3, jpNO3= EF(pNO3, 3 × 104)×jHNO3
F3F0 + HONO from HNO3, YHONO = 0.45
To reproduce daytime HONO observations, three photoactive HONO production mechanisms were implemented in model simulations (Supporting Information eqs S4–S9) on top of S0. The parameters were chosen by considering the values and uncertainties from previous studies and minimizing the simulation errors (root-mean-square error, RMSE) of midmorning to afternoon (10:00–15:00 LT) HONO simulations (see Section 3.2). In S1, we considered a first-order enhancement on aerosol uptake of NO2 from short wave radiation (SWR), (24) in which γ = 5 × 10–7 × SWR, to simulate daytime enhancements of pHONO. In S2, photolysis of pNO3 (42) was implemented in two subsets. In S2–1, the pNO3 photolysis rate (jpNO3) was scaled from the photolysis rate of gas-phase HNO3 (jHNO3) by a constant enhancement factor (EF) of 80. (50) However, a recent study by Andersen et al. (44) indicated that EF depends on bulk pNO3 concentrations and can be parametrized as a Langmuir function of pNO3. In S2–2, EF was computed as a function of the concentration of pNO3 (Supporting Information eq S8), (75) ranging from 20 to 3600 with a median value of 106. Photolysis of nitrate in coarse particles has been found to contribute to HONO production at coastal sites. (76) In our study, this mechanism is not included due to a lack of coarse particle measurements. This omission may lead to a high bias in the estimated EF. A recent experimental study (77) has reported HONO production proportional to the production of HNO3 (P(HNO3)) from photooxidation of NO2, and proposed HONO production involving adsorbed HNO3. Utilizing this proportional relationship, in S3, we parametrized HONO production with a yield coefficient (YHONO, Supporting Information eq S9) of 0.45. More details of HONO source parametrizations are available in Supporting Information Text S2. The RMSEs of the simulated HONO compared to observations are 0.031, 0.024, 0.019, and 0.019 for S1, S2–1, S2–2, and S3.
Ground sources of HONO have been recognized as potentially important contributors. (25,78) The HONO isotope observations in China show significant influences of fertilizer applications and livestock farms. (18) Ground conversion from NO2 is found to be a minor HONO source in that study. In our study, the Dongying site is not affected by fertilizer applications or livestock farms. The ground HONO source from NO2 is estimated based on nighttime HONO observations. If we assume that the ground source of HONO (in S0) with f having a first-order dependence on SWR, (79) the best fit of the observed HONO gives f = min(1, 0.24 + 0.76*SWR/800) since f cannot be physically larger than 100%. Under this scenario, f reaches 100% during 10:30 to 14:30, but the simulated HONO concentrations still have a low bias compared to the observations (Supporting Information Figure S5). A conversion efficiency of 100% poses stringent requirements for ground surface properties. Equivalent maximum f values reported in prior literature (33,80) are 20% to 40%, much less than 100%; their daily average f values are less than or comparable to the nonphotosensitive f value used in this study. In terms of simulating observed HONO concentrations and the effect of heterogeneous NO2 to HONO conversion on O3, the conversion of NO2 to HONO on aerosols is equivalent to the conversion on ground surface. Therefore, in this study, the aerosol conversion of NO2 to HONO can be thought as representing the combined photosensitized conversions from NO2 to HONO on both aerosol and ground surfaces. Similarly, potential photoactive conversions of HONO from HNO3 or pNO3 deposited on ground surfaces are included in the respective aerosol conversion. During our study period, HNO3 predominantly partitions into the particulate phase (25) (Supporting Information Figure S6) and dry deposition of particulates is slower compared to that of NO2. Considering that the dry deposition of HNO3 is much faster, (81) we acknowledge the possible contribution of deposited HNO3 on ground to HONO. (82,83) Since the observations are insufficient to diagnose this contribution and it is implicitly included in the respective aerosol conversion pathway, the effect of this contribution on O3 is not separately analyzed in this study.
A major chemical impact of heterogeneous HONO production in S1–S3 is the recycling of reactive nitrogen. To understand the effect of this recycling on PO3, we conducted a set of Lagrangian free-running box-model simulations (F0 – F3) to investigate the evolution of plume aging. F0 represents the control case using the same chemical reactions as S0 (case Control). F1 incorporates photosensitized uptake of NO2 on aerosol surfaces as in S1. F2–1 considers the photolysis of pNO3 with a constant EF as in S2–1 (EF = 80), while F2–2 incorporates a changing EF as in S2–2 (changing EF). Lastly, case F3 includes the production of HONO from HNO3 as in S3. The models are initialized with average concentrations of observed O3, NO, NO2, HONO, CO, and VOCs and started at midnight.

3. Results and Discussion

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3.1. pHONO Dependence on Biomass Burning and Aerosol Acidity

Supporting Information Figure S7 shows the time series of observed O3, NOx, HONO, pNO3, and SA during the OPECE campaign. The average HONO concentration was 0.39 ± 0.27 parts per billion by volume (ppbv) with a maximum of 1.54 ppbv recorded at the site. These HONO levels have been reported in measurements from other rural coastal sites in China, (84−86) and some urban/suburban areas in Europe, (17,87) much higher than those observed in clean coastal regions. (88) The average concentrations of pNO3 and SA were 8 ± 16.8 μg/m3 and 527 ± 382 μm2/cm3, respectively. The diurnal profiles of HONO and other species were also examined (Supporting Information Figure S8). The diurnal profile of HONO illustrates a nighttime accumulation followed by a sharp decrease in the early morning due to rapid photolysis. A slower decrease was observed at noon, indicating the presence of a photoactive daytime HONO source. The average NOx concentration was 13.6 ± 10.4 ppbv, consistent with levels observed at a background site in the North China Plain (NCP) region. (89) However, notably higher O3 levels, exceeding 100 ppbv at times, were observed, with an average peak concentration of 70 ppbv. This elevated O3 level underscores the need to gain a better understanding of springtime photochemistry in China. In this study, we focus on investigating the impact of photoactive HONO sources on O3 enhancements, as explained in more detail in Section 3.3.
BB can significantly contribute to regional air pollution, including HONO, through both direct emissions and secondary production. The long lifetime and source-specific characteristics of CH3CN make it a widely used tracer for BB. (90−93) During the measurement campaign, NOx, SA, and organic aerosols showed concurrent enhancements with CH3CN (Supporting Information Figure S9), indicating the representativeness of CH3CN as an indicator of BB impact at this site. For observations with elevated levels of CH3CN, we investigated BB impacts on mixing ratios and production pathways of HONO. A previous study reported increased HONO/NO2 ratios (2.8% to 6.6%) in BB affected plumes, (53) suggesting efficient secondary production of HONO on BB aerosols. In our study, we examined the daytime HONO/NO2 and HONO/pNO3 ratios, which served as indicators for NO2 and pNO3 conversion efficiencies to HONO, respectively. We analyzed correlations of HONO, HONO/NO2, and HONO/pNO3 with CH3CN (Supporting Information Table S2). Despite the significant positive correlation between HONO and CH3CN, consistent with previous studies, (12,13,92,94,95) we did not find a positive correlation between either ratio with CH3CN (Figure 1(a)). To further characterize BB-impacted air masses, we employed a threshold of 0.1 ppbv CH3CN to define BB-impacted and clean airmasses. (96,97) We compared the daytime (8:00–17:00) averaged HONO, HONO/NO2, and HONO/pNO3 between these two groups. The average HONO concentrations increased from 0.25 to 0.33 ppbv (Figure 1(c)) due to BB. However, the ratios did not show significant differences (Figure 1(d), (e)). We also analyzed data from an agricultural burning event near the site on 31 March 2018 (61,62) and compared the ratios in BB and clean airmasses (Supporting Information Figure S10). While HONO was significantly enhanced during the burning event, the median ratios still showed little difference from those of clean airmasses, except for a slightly higher averaged HONO/NO2 ratio than that of the clean airmasses. To minimize the impact of rapid photolysis loss of HONO, nighttime data was also investigated (Supporting Information Figure S11 (a)). A similar pattern emerged, showing a significant positive correlation between HONO and CH3CN, insignificant correlations between either HONO/NO2 or HONO/pNO3 ratio and CH3CN. As these ratios may be affected by the enhancements of NOx and pNO3 during BB, (98) it is difficult to conclude whether the increased HONO levels can be attributed to direct emissions or secondary production. Normalized excess mixing ratio (NEMR) has been utilized to quantify fire emissions. Here, we calculated NEMR for HONO relative to NO2, i.e., ΔHONO/ΔNO2=(HONOBB-HONOnBB)/(NO2BB-NO2nBB), and compared that with previous studies focusing on direct HONO emissions from biomass burning. The NEMR measured in our study ranges from 1.8% for daytime hours (8:00–17:00) to 4.2% during nighttime (20:00–05:00), much lower than the 20% to 140% reported in previous studies. (95) This result suggests that the effect of direct agricultural burning emissions of HONO at the OPECE site is much less than those found for forest fires in previous studies.

Figure 1

Figure 1. (a) Scatter plots of HONO (blue), HONO/NO2 (red), and HONO/pNO3 (yellow) as functions of CH3CN. (b) Same as (a) but as functions of aerosol pH. (c) Box plots of HONO concentrations for clean (CH3CN < 0.1) and BB (CH3CN > 0.1) airmasses, where the mean value is denoted by the red dot. And the median is denoted by black line. (d) and (e) are same as (c) but for HONO/NO2 and HONO/pNO3, respectively.

In addition to BB, previous studies show that heterogeneous HONO production is affected by aerosol acidity through altering the partitioning between nitrite (NO2) and HONO. (99) Here, we investigated the correlation between pH and heterogeneous HONO production using non-BB data. No statistically significant correlations are found during daytime (Figure 1(b), Supporting Information Table S3). At night (Supporting Information Figure S11(b), Table S3), HONO shows a weak negative correlation with aerosol pH. The negative correlation between pH and SA reflects in part higher sulfate concentrations in large particles in China. (100) This contributes to the negative correlation of HONO/NO2 with pH, as higher SA leads to more conversion of NO2 to HONO. The positive correlation between HONO/pNO3 and pH reflects in part the degassing of pNO3 at higher acidity. (101)

3.2. Recycling of Reactive Nitrogen through Photoactive Heterogeneous HONO Production

Here we address the question to see if a comprehensive in situ observation data set can be applied to test the validity of the proposed heterogeneous mechanisms, including photosensitized NO2 conversion on aerosol, photolysis of pNO3 and HONO yield from adsorbed HNO3. A caveat in this analysis is that NO2, HNO3, and pNO3 are all part of the reactive nitrogen family and they have dependent relationships, which are reflected in the correlation analysis. To avoid the impact of BB, we focus on the observations not affected by BB, which comprises 55% of the total available data (Supporting Information Figure S12).
We assess each production pathway by examining the correlations of pHONO with the corresponding production terms associated with each pathway. A strong positive correlation lends observational support to the existence of the source, whereas a lack of correlation can be considered observational evidence for excluding the production pathway as a major contributor. Figure 2(a) shows a significant positive correlation between pHONO and pNO3, with a correlation coefficient r = 0.83. This correlation strengthens to r = 0.87 when considering the case of pNO3 × jHNO3 (Figure 2(a)). Additionally, although the parametrization for the production pathway via HNO3 is based on a smog experiment, which requires further validation and investigation, a strong correlation between pHONO and P(HNO3) is found with r = 0.9 in our study. We also find a significant correlation between pHONO and the production terms of photosensitized NO2 conversion on aerosols, with r increasing from 0.82 between pHONO and NO2 to 0.90 for that with NO2 × SA × SWR (Figure 2(b)). Correlation statistics can be found in Supporting Information Table S4. These strong correlations reflect the intrinsic relationships among the precursors of heterogeneous HONO production. (102,103)

Figure 2

Figure 2. (a) Scatter plot of pHONO as functions of pNO3 (blue), pNO3 × jHNO3 (red), and P(HNO3) (green). The corresponding correlation coefficients are shown at the bottom right corner of the plot. (b) Scatter plot of pHONO as functions of NO2 (blue) and NO2 × SA × SWR (red), and the corresponding correlation coefficients. (c) Observed diurnal HONO (black dots) with standard deviation (black vertical lines) and simulated HONO under the six cases (B, S0–S3) as described in Table 1. (d) Mean diurnal profiles of pNO3 (orange) and NO2 (blue) during the measurement period. All panels show the non-BB data.

To further assess the photoactive HONO production mechanisms, we conducted a set of constrained simulations (S0–S3, Table 1) incorporating additional HONO sources as described in Section 2. Consistent with previous studies, the observed HONO levels consistently exceed those predicted by R1 (Figure 2(c): B) throughout the day. Nocturnal HONO levels are explained by heterogeneous NO2 conversion on the ground surface with f = 0.24, while daytime HONO concentrations remain underestimated (Figure 2(c): S0).
After incorporating photoactive HONO production mechanisms (S1–S3), the model with any one of the mechanisms can reproduce the observed HONO levels within one standard deviation (Figure 2(c): S1–S3). A slightly higher bias of S1 in the morning hours (8:00–10:00) is seen, this is due to the fact that a constant scaling factor of 5 × 10–7 is used for γ, minimizing the 10:00–15:00 RMSE yields a higher bias in the morning when both NO2 and SA reach peak values. Importantly, the derived key parameters for HONO production, obtained by minimizing the simulation errors (i.e., scaling factor for γ (5 × 10–4), EF (80 or 106), and YHONO (0.45)), are comparable to those reported in previous experimental and modeling studies: [2 × 10–5, 1 × 10–3], [8, 700], and 0.53, respectively (Supporting Information Table S5). This agreement underscores the difficulty of assessing the underlying HONO heterogeneous production mechanism due to the intrinsic relationships among reactive nitrogen species. For example, higher HONO concentrations can promote pNO3 production, (25) and the photolysis of pNO3 can recycle reactive nitrogen from a reservoir species back into more reactive gaseous species, thereby sustaining atmospheric NO2 levels. (104−106) Moreover, as atmospheric HNO3 is produced through the reaction of OH and NO2, the production rate of HONO via this process is directly proportional to NO2. Given these intertwined relationships, it becomes challenging to establish a definitive causal relationship between HONO production and the proposed mechanisms solely through photochemical analysis of in situ observations.
However, these mechanisms have very different implications for the recycling of reactive nitrogen. The conversion from NO2 to HONO has the lowest impact since both species are short-lived. In springtime, the gas-particulate partitioning of nitrate strongly favors the formation of pNO3 from gaseous HNO3. (25) Therefore, the conversion of HNO3 to HONO reduces the lifetime of HNO3 and the formation of pNO3, effectively enhancing the recycling of reactive nitrogen. The photolysis of pNO3 significantly reduces the lifetime of the most long-lived reactive nitrogen reservoir and makes the entire inorganic reactive nitrogen family photochemically active, speeding up the recycling of reactive nitrogen the most.
We acknowledge that some reaction parameters derived in this study may appear larger than the values observed in laboratory experiments. For instance, the uptake coefficient for NO2 of 5 × 10–4 is greater than typical experimental results (Supporting Information Table S5). Two key points should be highlighted. First, the parameters were derived under the assumption that the unexplained daytime HONO is exclusively produced through the targeted mechanism. In the real atmosphere, multiple mechanisms may contribute to HONO simultaneously. Consequently, the parameters obtained in this study represent upper limits. Second, our primary focus is on understanding the implications for reactive nitrogen recycling and, consequently, O3 production. We are not aiming to establish the optimal kinetics parameters or identify the dominant mechanism, which cannot be accomplished through photochemical modeling of field observations as shown in our analysis. In the following section, we delve deeper into exploring the impact of HONO heterogeneous production on O3 production. It will become evident that, even if all these mechanisms yield the same amount of HONO, their effects on O3 production differ significantly due to varying efficiencies in reactive nitrogen recycling.

3.3. Sensitivity of O3 Production to the Heterogeneous Reactive Nitrogen Mechanism

The different implications of the heterogeneous HONO production mechanisms for the recycling of reactive nitrogen also strongly affect the production of O3. In this section, we investigate the changes of O3 chemistry by heterogeneous HONO production. The chemical analysis is isolated from meteorological impacts. As such, the box model simulations assume that the composition of an air mass is isolated from mixing and other meteorological processes. This analysis provides qualitative insights into the vastly different impacts of different heterogeneous HONO production pathways on O3 production.
We use Lagrangian box model simulations to analyze the chemical evolutions of isolated airmasses and investigate the consequences of the heterogeneous HONO production mechanisms (Table 1: F0 – F3). Figure 3(a) shows the divergent evolutions of O3, NOx, OH, and O3 production rate (PO3) among the HONO production mechanisms. Compared to the control case (F0) without HONO production from aerosols, the addition of photoactive conversion of NO2 (F1) or adsorbed HNO3 to HONO (F3) has a transient impact on reactive nitrogen, OH, and O3. In contrast, the impact from F2 cases via pNO3 photolysis is much larger and longer lasting. The difference lies in how the heterogeneous HONO production mechanism affects the recycling between NOx and pNO3. The conversion of NO2 to HONO on aerosols partitions reactive nitrogen into HONO, which effectively increases HONO/NO2 and HONO/pNO3 when compared to F0 (Figure 3(c), (d)), but does not significantly affect the ratio of NOx/pNO3 (Figure 3(e)). In the case of F3, the addition of HONO source reduces the production of HNO3 and hence the concentration of pNO3 by converting it to HONO and then to NOx, resulting in an increased NOx/pNO3 ratio (Figure 3(e)), (107) in addition to enhancing HONO/NO2 and HONO/pNO3 similar to F1. The photolysis of HONO adds a larger radical source and can speed up O3 production. (108) However, one O3 is lost for each conversion of NO2 to HONO. In contrast, the conversion from HNO3 to HONO does not incur this O3 loss and therefore produces more O3 than F1 (Figure 3(b)). These effects are most pronounced in the first day but diminish over time as NOx is converted to pNO3, which serves as a permanent sink for reactive nitrogen in these two cases.

Figure 3

Figure 3. (a) Simulated evolutions of O3, NOx, HONO, pNO3, OH, and PO3 in a Lagrangian box model for 7 days under cases F0 – F3. (b) O3 enhancement ratios, defined as the relative O3 increase from the initial O3 concentration for cases F0 – F3. (c), (d), and (e) are simulated HONO/NO2, HONO/pNO3 and NOx/pNO3 ratios. The dashed lines denote measured HONO/NO2, HONO/pNO3 and NOx/pNO3 values from Ye et al. (104)

In cases F2–1 and F2–2, however, the conversion of pNO3 to HONO allows a full recycling of inorganic reactive nitrogen species, (106,109−111) thus the highest NOx/pNO3 ratio (Figure 3(e)). In some previous modeling studies, in addition to HONO, NO2 is also included as a product of pNO3 photolysis, this could lead to direct recycling from pNO3 to NOx, thus increasing NOx/pNO3 ratio and subsequent O3 production even more. Daytime HONO and NOx concentrations are sustained at significant levels, leading to high OH concentrations and O3 production and a tripling of O3 concentrations of >100 ppbv after a week (Figure 3(a)). The increase of O3 is twice as much in F2–1 and F2–2 as that of F0, F1 or F3 (Figure 3(b)). The difference between F2–1 and F2–2 is smaller compared to their differences from F1 and F3. A recent study (112) implementing HONO conversion from NO2 and pNO3 into a chemistry-climate model found a reduction effect on O3 driven by HONO conversion from NO2 and a strong enhancement of NOx caused by HONO conversion from pNO3. A necessary condition to simulate an O3 reduction is that NO from HONO photolysis can only produce a fractional (<1) O3 despite the additional OH from HONO photolysis. This could occur when OH reacts with O3 due to a lack of reactive VOCs. Figure 3 shows that it is not the case in a polluted boundary layer.
Ye et al. (104) reported rapid cycling of pNO3 to HONO from aircraft measurements over the North Atlantic Ocean in the marine boundary layer, based on observed high HONO production with low-level NOx present. We reexamined their observation data using the simulation results from OPECE. Following the observation-model comparison procedure by Ye et al., we used model outputs at 2:30 pm on the second diurnal cycle, which represents a 1.5-day airmass transport from the coastal site in the marine boundary layer, to be compared to their observations. The observed HOHO/NO2, HONO/pNO3, and NOx/pNO3 ratios reported by Ye et al. are much higher than our simulation results (Figure 3 (c) - (e)), although our observations during OPECE are more comparable to the observed values reported by other ground-based studies (Supporting Information Table S6). Therefore, the marine observations by Ye et al. appear to suggest a chemical environment of extremely fast recycling of reactive nitrogen not seen over polluted land areas. (88,113)
Figure 3(b) shows that O3 production rate can be much higher from the pNO3 conversion mechanism (F2–1 and F2–2) than the two other mechanisms (F1 and F3) in comparison to the case of no heterogeneous HONO production (F0). However, the effects depend on the amount of reactive nitrogen. To understand how the relationship between O3 and NOx responds to these different mechanisms, (114,115) we conducted box model simulations for 3 h under noontime conditions with initial NOx mixing ratios in the range of 1–25 ppbv. Similar results are obtained when longer integration hours are used, but the differences of F2–1 and F2–2 from the other cases increase with time.
A few features emerge from the modeling analysis. Figure 4(a) shows the cumulative PO3 first increases with NOx and then starts to decrease with increasing NOx. The NOx concentration at which the cumulative PO3 maximizes is the transition from NOx-limited to VOC-limited O3 production. (71,108) The critical initial NOx concentration for this transition increases when heterogeneous HONO production is introduced (Figure 4). The largest increase occurs with the NO2 conversion mechanism (F1) because the OH production from HONO photolysis speeds up the conversion of NOx to its reservoir, HNO3. In F2 and F3 cases, HNO3 can be effectively recycled back to NOx but not in F1. Consequently, the reduction of NOx is fastest in F1 due to HONO production. In a polluted urban environment, where O3 production decreases as NOx increases, this faster NOx reduction and increased OH production tends to increase PO3. Previous modeling studies have also shown elevated O3 levels via F1 in high-NOx regions. (116) As the urban plume ages and is transported regionally to low-NOx areas, however, this mechanism could lead to significant NOx reductions. (112) It is therefore expected that pNO3 recycling (F2) will increase PO3 much more than NO2 recycling (F1) on a regional scale. Supporting Information Figure S13 shows the ozone production efficiency (OPE), defined as the cumulative PO3 in a three-hour period (ΔO3) per NOx consumed (ΔNOx) as a function of initial NOx concentration. The NO2 conversion case (F1) is slightly higher than the case without heterogeneous HONO production (F0) under high-NOx conditions. The pNO3 conversion case (F2–1 and F2–2) increases OPE by a factor of 2–4 compared to F0. The HNO3 conversion case (F3) is more similar to (and higher than) the NO2 conversion case (F1) under low NOx conditions and approaches the pNO3 conversion cases (F2–1 and F2–2) under high NOx conditions.

Figure 4

Figure 4. (a) Cumulative PO3 as a function of initial NOx for cases F0 - F3. The dashed lines denote NOx levels at which cumulative PO3 reaches its peak values. (b) Loss of NOx (ΔNOx) as a function of initial NOx.

3.4. Implications

Observed HONO levels are enhanced with increasing aerosol acidity and agricultural burning. However, analysis of the observation data does not indicate more efficient HONO production by agricultural burning aerosols. The NEMR of HONO over NO2 for agricultural burning in our study ranges from 1.8% in daytime to 4.2% at night, which is much lower than those reported for forest fires. Aerosol acidity is not found to enhance the non-BB aerosol HONO production efficiency in daytime data. The analysis of nighttime data is inconclusive since other factors may also lead to the observed correlations of aerosol acidity with HONO/NO2 and HONO/pNO3 ratios at night.
Modeling analysis of the OPECE gaseous and particulate measurements shows clear evidence for daytime heterogeneous production of HONO on aerosols in agreement with previous studies. By incorporating either photosensitized NO2 conversion on aerosols, photolysis of pNO3, or conversion from HNO3, the model can reproduce the observed HONO concentrations when other reactive nitrogen and chemical species are constrained by the observations. This modeling equivalency reflects the intrinsic relationships between NO2, pNO3, and HNO3 in the chemical system, indicating that inland in situ observations may not provide sufficient constraints to investigate the underlying mechanism of heterogeneous HONO production. This finding highlights the uncertainties in both the mechanistic understanding and quantitative parametrizations of daytime heterogeneous HONO production pathways. It appears necessary to conduct Lagrangian experiments away from sources such as in the marine boundary layer since the three mechanisms have drastically different predictions of HONO/NO2, HONO/pNO3, and NOx/pNO3 ratios (Figure 3(c)-(e)) after 1–2 days of chemical aging. Being able to follow the evolutions of these ratios can provide more information on the underlying mechanism.
The maritime observations by Ye et al. (104) are not as ideal as a Lagrangian experiment but can be carried out more easily. Their observations suggest a chemical environment with much faster reactive nitrogen recycling than the one found in OPECE or other inland data sets, indicating that additional processes may contribute to efficient reactive nitrogen recycling through HONO production over the ocean. However, it also implies much higher O3 production in offshore regions than in current model predictions. A survey of OPECE observations did not provide evidence for high O3 airmass transport from offshore regions, which may be related to prevailing westerly conditions during OPECE.
Quantifying the mechanism of heterogeneous HONO production on aerosols has profound implications for understanding O3 pollution and is important to formulating effective mitigation strategies on a regional scale. The conversion of HONO from pNO3 can drastically increase the lifetime of NOx and the cumulative O3 production during aging (Figures 3 and 4) compared to the other two mechanisms. It has the potential to substantially elevate O3 concentrations in regions located downwind from emission sources and to amplify O3 levels even in distant marine environments, potentially exerting a global influence.
On the other hand, the conversion of HONO from NO2 on aerosols can move the transition regime, when O3 production becomes insensitive to NOx, into higher NOx conditions and significantly flatten the PO3 decrease as NOx increases under high-NOx conditions (Figure 4(a)). The faster removal of NOx (Figure 4(b)) also implies a lower effect of background O3 production in downwind regions compared to the other cases since the OPEs of case F1 are among the lowest under low NOx conditions (Supporting Information Figure S13). The net effect is to reduce O3 sensitivity to NOx changes. It may help explain the observations that O3 concentrations in China have not changed much over the past decade (Supporting Information Figure S14) despite a ∼ 50% reduction in NOx (Supporting Information Figure S15) (117−120)
Despite the increasingly recognized significance of HONO over the years, the underlying production mechanisms of HONO are not understood and have considerable uncertainties. The kinetics parameters for heterogeneous HONO production have large uncertainties, and future laboratory studies are needed for improving the accuracy of these parameters. This study focuses on understanding their impacts on O3 production on a regional scale. Previous modeling studies have incorporated various HONO sources to address the underestimation of daytime HONO and to understand their corresponding impacts on photochemical pollutants. However, the simulated impacts on reactive nitrogen recycling and O3 are tied to the parametrizations of these sources in the models, which can introduce large uncertainties. Hence caution is warranted in the incoproration of HONO sources into models, as different production pathways can have vastly different implications, as found in this study.

Supporting Information

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.3c07967.

  • Details on the instrument used, preparation of observational data, and parametrizations of HONO sources; summary of the observed pollutant concentrations; detailed statistical test results for analyses on biomass burning impact, aerosol acidity impact, and HONO source evaluations; comparisons with previous studies on key kinetic parameters, and HONO/NO2/pNO3 measurements; O3 production efficiency with changing NO2 concentration; observed O3 and NO2 levels in eastern China from 2014 to 2022 (PDF)

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Author Information

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  • Corresponding Authors
  • Authors
    • Kezhen Chong - School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United StatesOrcidhttps://orcid.org/0000-0001-8799-3305
    • Mingming Zheng - School of Chemical and Environmental Engineering, Wuhan Polytechnic University, Wuhan 430024, China
    • Hang Qu - School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United StatesOrcidhttps://orcid.org/0000-0002-2924-2826
    • Ruixiong Zhang - School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
    • Young Ro Lee - School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
    • Yi Ji - School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
    • Lewis Gregory Huey - School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, United StatesOrcidhttps://orcid.org/0000-0002-0518-7690
    • Hua Fang - Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, ChinaPresent Address: (H. F.): School of Ecology and Environment, Anhui Normal University, Wuhu 241000, China
    • Wei Song - Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
    • Zheng Fang - Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, ChinaPresent Address: (Z. F.): Department of Earth and Planetary Sciences, Weizmann Institute of Science, Rehovot 76100, IsraelOrcidhttps://orcid.org/0000-0003-2355-5618
    • Cheng Liu - University of Science and Technology of China, Hefei 230026, ChinaOrcidhttps://orcid.org/0000-0002-3759-9219
    • Yang Gao - Key Laboratory of Marine Environment and Ecology, Ministry of Education of China, Ocean University of China, Qingdao 266100, ChinaOrcidhttps://orcid.org/0000-0001-6444-6544
    • Jianhui Tang - Yantai Institute of Coast Zone Research, CAS, Yantai 264003, ChinaOrcidhttps://orcid.org/0000-0002-9006-263X
  • Notes
    The authors declare no competing financial interest.

Acknowledgments

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This work is supported by the National Science Foundation Atmospheric Chemistry Program (grant 1743401). The authors thank the science teams of the OPECE 2018 campaign.

References

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  • Abstract

    Figure 1

    Figure 1. (a) Scatter plots of HONO (blue), HONO/NO2 (red), and HONO/pNO3 (yellow) as functions of CH3CN. (b) Same as (a) but as functions of aerosol pH. (c) Box plots of HONO concentrations for clean (CH3CN < 0.1) and BB (CH3CN > 0.1) airmasses, where the mean value is denoted by the red dot. And the median is denoted by black line. (d) and (e) are same as (c) but for HONO/NO2 and HONO/pNO3, respectively.

    Figure 2

    Figure 2. (a) Scatter plot of pHONO as functions of pNO3 (blue), pNO3 × jHNO3 (red), and P(HNO3) (green). The corresponding correlation coefficients are shown at the bottom right corner of the plot. (b) Scatter plot of pHONO as functions of NO2 (blue) and NO2 × SA × SWR (red), and the corresponding correlation coefficients. (c) Observed diurnal HONO (black dots) with standard deviation (black vertical lines) and simulated HONO under the six cases (B, S0–S3) as described in Table 1. (d) Mean diurnal profiles of pNO3 (orange) and NO2 (blue) during the measurement period. All panels show the non-BB data.

    Figure 3

    Figure 3. (a) Simulated evolutions of O3, NOx, HONO, pNO3, OH, and PO3 in a Lagrangian box model for 7 days under cases F0 – F3. (b) O3 enhancement ratios, defined as the relative O3 increase from the initial O3 concentration for cases F0 – F3. (c), (d), and (e) are simulated HONO/NO2, HONO/pNO3 and NOx/pNO3 ratios. The dashed lines denote measured HONO/NO2, HONO/pNO3 and NOx/pNO3 values from Ye et al. (104)

    Figure 4

    Figure 4. (a) Cumulative PO3 as a function of initial NOx for cases F0 - F3. The dashed lines denote NOx levels at which cumulative PO3 reaches its peak values. (b) Loss of NOx (ΔNOx) as a function of initial NOx.

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  • Supporting Information

    Supporting Information


    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.3c07967.

    • Details on the instrument used, preparation of observational data, and parametrizations of HONO sources; summary of the observed pollutant concentrations; detailed statistical test results for analyses on biomass burning impact, aerosol acidity impact, and HONO source evaluations; comparisons with previous studies on key kinetic parameters, and HONO/NO2/pNO3 measurements; O3 production efficiency with changing NO2 concentration; observed O3 and NO2 levels in eastern China from 2014 to 2022 (PDF)


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