References

ACP

Atmospheric Chemistry and Physics

ACP

1680-7324

Copernicus GmbH

Göttingen, Germany

10.5194/acp-12-6939-2012

Modeling nitrous acid and its impact on ozone and hydroxyl radical during the Texas Air Quality Study 2006

Czader

B. H.

¹ Rappenglück

¹ Percell

¹ Byun

D. W.

¹ ² ⁵ Ngan

¹ ³ Kim

¹ ⁴

Department of Earth and Atmospheric Sciences, University of Houston, Houston, TX, USA

Air Resources Laboratory, NOAA, Silver Spring, MD 20910, USA

present address: Air Resources Laboratory, NOAA, Silver Spring, MD 20910, USA

present address: Ajou University, Suwon, S. Korea

deceased

02 08 2012

12 15 6939 6951

This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Nitrous acid (HONO) mixing ratios for the Houston metropolitan area were simulated with the Community Multiscale Air Quality (CMAQ) Model for an episode during the Texas Air Quality Study (TexAQS) II in August/September 2006 and compared to in-situ MC/IC (mist-chamber/ion chromatograph) and long path DOAS (Differential Optical Absorption Spectroscopy) measurements at three different altitude ranges. Several HONO sources were accounted for in simulations, such as gas phase formation, direct emissions, nitrogen dioxide (NO2) hydrolysis, photo-induced formation from excited NO2 and photo-induced conversion of NO2 into HONO on surfaces covered with organic materials. Compared to the gas-phase HONO formation there was about a tenfold increase in HONO mixing ratios when additional HONO sources were taken into account, which improved the correlation between modeled and measured values. Concentrations of HONO simulated with only gas phase chemistry did not change with altitude, while measured HONO concentrations decrease with height. A trend of decreasing HONO concentration with altitude was well captured with CMAQ predicted concentrations when heterogeneous chemistry and photolytic sources of HONO were taken into account. Heterogeneous HONO production mainly accelerated morning ozone formation, albeit slightly. Also HONO formation from excited NO2 only slightly affected HONO and ozone (O3) concentrations. Photo-induced conversion of NO2 into HONO on surfaces covered with organic materials turned out to be a strong source of daytime HONO. Since HONO immediately photo-dissociates during daytime its ambient mixing ratios were only marginally altered (up to 0.5 ppbv), but significant increase in the hydroxyl radical (OH) and ozone concentration was obtained. In contrast to heterogeneous HONO formation that mainly accelerated morning ozone formation, inclusion of photo-induced surface chemistry influenced ozone throughout the day.

References 1

G – \textbfgas-phase HONO chemistry;

GEH – \textbfgas phase chemistry, HONO \textbfemissions, and HONO \textbfheterogeneous formation;

GEHP – same as GEH, but with addition of \textbfphoto-induced HONO production; \enditemize %f01 \beginfigure*[t] \includegraphics[width=13cm]acp-2012-40-f01.pdf \captionComparison of measured vs. simulated HONO time series at the UH Moody Tower for the time period 25~August–20~September 2006. Dots represent measured values, the solid lines represent CMAQ predicted concentration from G, GEH, and GEHP cases (explanation see text). Dashed vertical lines indicate midnight times. \textbf(a):~Comparison with data measured in-situ by a MC/IC system at the top of the Moody Tower, at 60 m a.g.l. \textbf(b–d):~Time series comparison of HONO measured from the Moody Tower by DOAS low light-path~\textbf(b), middle light path~\textbf(c), and upper path~\textbf(d). \endfigure* \sectionResults and discussion \subsectionEvaluation of HONO modeling Simulated HONO concentrations were compared with values measured in-situ by a mist-chamber/ion chromatograph (MC/IC) system at the top of the Moody Tower (60 m a.g.l.) on the University of Houston (UH) campus (Stutz et al., 2010) and are shown in Fig 1a for simulation cases G, GEH, and GEHP. The highest HONO mixing ratios up to 2 ppbv were measured during nighttimes and in the early mornings while daytime concentrations are much lower, but still appreciable. HONO values simulated with only gas-phase chemistry (case G) persistently show significant under prediction of HONO concentrations. HONO mixing ratios from GEH and GEHP cases are much closer to the observed values (e.g 31~August, 12 and 20~September). The advantage of including photochemical HONO sources can nicely be seen on 30~August, 7, 9, and 13~September (and others) when daytime HONO values from the GEHP case are higher and closer to measurements than HONO values from the GEH case. In some cases a mismatch between observed and simulated HONO values occurs (e.g 1 and 6~September). This is mostly related to mismatch in NO2 concentrations as discussed further below. In order to evaluate HONO modeling for different altitudes in the urban boundary layer~observational HONO data detected by Differential Optical Absorption Spectroscopy (DOAS) were utilized. These measurements were taken along different paths between the Moody Tower super site and Downtown Houston (Stutz et al., 2010). The low light-path detected mixing ratios between 20–70 m height which corresponds to the first and second CMAQ model layer, the middle light-path between 70–130 m corresponding to the second and third layer, and the upper light-path between 130–300 m, which falls into model layers three to five. Figures~1b–d shows comparisons of measured and simulated HONO values. While daytime measurements show only slight dependence on altitude, HONO mixing ratios at night and early morning decrease with altitude, with \mboxmaximum values reaching about 2 ppbv at the low level and only about 0.5 ppbv at the upper level. Contrary to the measured values, HONO mixing ratios from the G case do not show variation with height. HONO values obtained from GEH and GEHP cases correctly capture the trend towards lower nighttime and early morning mixing ratios at higher altitudes. In addition, including photolytic HONO sources in the GEHP case resulted in average 100 ppt higher daytime HONO concentrations at the low DOAS level and an average daytime increase of 50 and 30 ppt at the middle and upper DOAS levels, respectively. Since most of the photolytic HONO production occurs by NO2 reaction at the surface, stronger increase was obtained at the lower altitudes and changes in HONO mixing ratios at higher altitudes can be explained by upward transport of HONO (see discussion in Sect 3.2 and bottom graph in Fig 9). Figure~2 shows an average diurnal variation of HONO and NO2 based on the same data set (25~August–20~September 2006) for all simulated cases as well as MC/IC observed values. This presentation summarizes clearly the general differences in HONO model simulations. It can be seen that higher daytime values were obtained from the GEHP case, which includes photolytic HONO formation, in comparison with the GEH case, in which heterogeneous HONO production dominates HONO sources. The model tends to overpredict NO2 during nighttime and early morning which causes overprediction of simulated HONO at those times. %f02 \beginfigure[t] \includegraphics[width=8.3cm]acp-2012-40-f02.pdf \captionAverage diurnal variation of HONO (top) and NO2 (bottom) based on data for 25~August–20~September 2006 at the top of the Moody Tower, at 60 m a.g.l. Measured data obtained by MC/IC. \endfigure Figure~3 shows a time series comparison of NO2 measured by DOAS with the values simulated with the GEH case. Too high NO2 concentrations on 1 and 4~September resulted in over prediction of HONO concentrations at those times. In contrast, NO2 under prediction on 2, 7, and 8~September leads to under predictions of HONO. There may be several reasons for NO2 mismatches, such as uncertainties in emission inventory or mixing layer~height, in some cases these mismatches can be related to predictions of meteorological parameters. For example, on the night of 1~September the measurements indicate calm conditions, while the model predicts strong southerly winds, causing lower modeled concentrations at the location of measurements. On 6~September the model fails to predict precipitation correctly which in turn directly affects the concentration of pollutants. The correlation coefficient between HONO values measured at the DOAS low path and those simulated with GEH case is 0.68. However, when data points with wrong NO2 prediction were ignored and only NO2 values simulated within 70 % of measured value were considered the correlation coefficient for HONO increased to 0.82. %f03 \beginfigure*[t] \includegraphics[width=13cm]acp-2012-40-f03.pdf \captionTime series comparison of NO2 measured from the Moody Tower by DOAS low light-path (top graph), middle light path (middle graph), and upper path (bottom graph) with simulated mixing ratios for 25~August–20~September 2006. \endfigure* \subsectionImpact of HONO on HO$_\textx$ and ozone formation 31~August and 1~September were the days with the poorest air quality index for Houston in the entire year 2006 (see also Rappenglück et al., 2008): peak 8-h averages of up to 126 ppbv on 31~August and up to 129 ppbv on 1~September were measured in the Houston area. In particular for 31~August meteorological modeling (Ngan et al., 2012) has been extensively studied. Since it is of particular interest to analyze the potential impact of HONO on O3 formation for ozone exceedance days most of the analysis presented here is focusing on 31~August. %f04 \beginfigure*[t] \includegraphics[width=13cm]acp-2012-40-f04.pdf \captionTime series of observed and simulated OH mixing ratios at the top of the Moody Tower, at 60 m a.g.l., which corresponds to the second model layer. \endfigure* Figure~4 shows a comparison of observed and modeled OH radical for 31~August–2~September 2006. OH was measured with the Ground-based Tropospheric Hydrogen Oxides Sensor (GTHOS) at the top of the Moody Tower (Mao et al., 2010). Modeled concentration of OH from the GEH case is similar to the G case, while on average there is 35 % more OH from the GEHP case as compared to GEH case during morning hours (50 % more when looking only at 31~August values) and about 5 % more OH around noon. Therefore, HONO produced in a photochemical way has much more impact on OH than HONO formed in a heterogeneous process. A closer look at OH sources from particular reactions is presented in Fig 5. For this purpose the IRR analysis was employed. This analysis was based on data which were averaged in a box consisting of 25 horizontal cells with the middle cell corresponding to the location of the Moody Tower. The gray line in Fig 5 shows IRR results for the GEHP case for the sum of reactions HONO + $h\nu$ $\to$ OH + NO and NO2$^\ast$ + H2O $\to$ OH + HONO that can be interpreted as the amount of OH produced from these two reactions. For the GEH case the black line represents OH produced only from the first reaction which is photolysis of HONO. Therefore, the difference between these two cases is the amount of OH formed from HONO that was photo-chemically produced on surfaces. To further distinguish between the impact of NO2$^\ast$ on OH formation an additional simulation was performed in which photochemical HONO formation on surfaces covered with organic materials was not included; this simulation is indicated in the graph as "GEHP (no surface phot)". During morning hours OH production from the GEHP case was 2–3~times higher than production from the case without photochemical HONO formation (the GEH case) indicating that HONO produced in a photochemical way on surfaces is a significant source of OH in the morning. Reactions involving NO2$^\ast$ contributed only about 30 % to the increase in OH. %f05 \beginfigure[t] \includegraphics[width=8.3cm]acp-2012-40-f05.pdf \captionOH production from the reaction of HONO + $h\nu$ $\to$ OH + NO and NO2$^\ast$ + H2O $\to$ OH + HONO. \endfigure IRR analysis was also employed to assess HONO contribution to radical production relative to other radical sources. Due to the fast chemistry between OH and the hydroperoxyl radical (HO2) both radicals were considered in the analysis as HO$_\textx$ (HO$_\textx$=OH + HO2). Figure~6 illustrates the diurnal variations of contributions of O3, HCHO, HONO (from photolysis reaction), NO2$^\ast$, and alkenes to the HO$_\textx$ budget for 31~August 2006. The results from the G case show that the contribution of HONO to the HO$_\textx$ formation rates in the morning (06:00–09:00 a.m CST) is 45 %, which is low in comparison to other studies. For example, Mao et al (2010) demonstrated that in the Houston area HONO is the major contributor to HO$_\textx$ in the morning. In our model analysis the morning contribution of HONO to HO$_\textx$ formation rates in Houston became dominant (81 %) when HONO emissions and heterogeneous chemistry is taken into account (GEH case). In the GEHP case HONO contributes 83 % to HO$_\textx$ formation and NO2$^\ast$ contributes 7 % by directly forming OH radicals. The GEHP case also resulted in higher contributions throughout the day, especially between 09:00 a.m. and noon CST when HONO contribution to HO$_\textx$ is 52 % (20 % higher than contribution from GEH case at that time). %f06 \beginfigure[t] \includegraphics[width=8.5cm]acp-2012-40-f06.pdf \captionDiurnal variations of contributions of O3, HCHO, HONO (from photolysis reaction), NO$_2^\ast$, and alkenes to the HO$_\textx$ budget for the G case (top), GEH case (middle), and GEHP case (bottom) at the top of the Moody Tower, at 60 m a.g.l. \endfigure %f07 \beginfigure*[t] \includegraphics[width=16cm]acp-2012-40-f07.pdf \captionTime series comparison of O3 measured from the Moody Tower by DOAS low light-path with simulated mixing ratios for 25~August–20~ September 2006. The insert shows a blow-up for 31~August 2006, displaying all three model simulations vs. the observed O3 data on that day. \endfigure* Figure~7 shows comparison of observed and simulated ozone concentrations at the DOAS low level for simulated time period of 25~August–20~September 2006. As it is hard to distinguish differences in ozone concentrations among simulated cases the insert in Fig 7 shows details for 31~August. Compared to simulations with only gas phase HONO chemistry ozone concentration in the GEH case only slightly increases in the morning, but increases by about 7 ppbv and accelerates morning ozone formation for about 1–2 h when photo-induced HONO production is accounted for in the GEHP case. Figure~8 shows spatial differences in ozone between the GEH and G cases (left) and for the GEHP and G case (right) for 30~August, which is a day with low ozone values, compared to the 31~August 2006 case. More ozone was formed from HONO on 31~August as there were much higher NO2 mixing ratios and consequently higher HONO levels observed on that day as compared to 30 August. The first row of Fig 8 shows differences in ozone for 30~August at the time of the maximum HONO impact. While ozone changes due to HONO formed in a heterogeneous way are minimal (left graph), the difference in ozone between the GEHP and G cases reach 4.3 ppbv at noon (right graph). On 31~August ozone increase from the GEH case reaches 3 ppbv at 09:00 a.m CST while at the same time ozone differences for the GEHP case are around 8~ppbv (middle row). In the afternoon, at the time of the maximum impact of HONO on ozone mixing ratios, ozone changes in the GEH case reach only 2 ppbv and are confined to a smaller area, while in the GEHP case ozone increases up to 11 ppbv in comparison to the G case (bottom row). %f08 \beginfigure*[t] \includegraphics[width=13.3cm]acp-2012-40-f08.pdf \captionDifferences in surface ozone simulations between GEH-G case (left) and GEHP-G case (right) for 30~August at noon (top row), 31~August 2006 at 09:00 a.m CST (middle row) and 01:00 p.m CST (bottom row). \endfigure* %f09 \beginfigure[t] \includegraphics[width=8.5cm]acp-2012-40-f09.pdf \captionHONO mixing ratio (black line) in ppbv and contribution of different processes to changes in HONO mixing ratios (columns) for G case (top), GEH case (middle), and GEHP case (bottom) in the first model layer~(0–34 m~a.g.l.). Note that the scale is different in the graphs. GAS_PROD_HONO represents OH + NO reaction producing HONO, $h\nu$_NO2$^\ast$_HONO is HONO formed from excited NO2, $h\nu$_SF_HONO–is photochemical production of HONO on surfaces, HET_HONO represents change in HONO mixing ratio due to heterogeneous chemistry, VTRAN_HONO–vertical transport, HTRAN_HONO–horizontal transport, DDEP_HONO–dry deposition, CHEM_LOSS_HONO–oss of HONO by gas phase chemical reactions, EMIS_HONO–emissions. \endfigure %f10 \beginfigure[t] \includegraphics[width=8.5cm]acp-2012-40-f10.pdf \captionThe same as Fig 9 but for the model layer~2, which is between 34–85 m a.g.l. "VTRAN L2–L3" represents HONO transported upward from layer~2 to the next model layer. \endfigure Even though about a tenfold increase in HONO concentration related to its heterogeneous formation and emissions was simulated with the GEH case (see Fig 1) as compared to the G case, the impact of it on ozone was small. To get more insights into sources and losses of HONO the process analysis (PA) was utilized. The results of the process analysis for the G, GEH, and GEHP cases are presented in Fig 9 and can be interpreted as contributions of processes and chemical reactions to changes in HONO mixing ratios. Processes that contribute to an increase in HONO mixing ratios are plotted with positive values, and those contributing to a decrease in HONO mixing ratio are shown with negative values. Note that the rate of change at a given hour represents change in HONO mixing ratio between that hour and the previous hour. Since most of the HONO sources occur on the surface this analysis is confined to the first model layer~(0–34 m a.g.l.); horizontally data was averaged in 25 cells with the middle cell corresponding to the location of the Moody Tower. In the G case, the gas-phase chemistry (GAS_PROD_HONO) contributes the most to an increase in HONO mixing ratio; about 60 % of produced HONO is consumed by means of photolysis and reaction with OH during daytime (indicated in the graph as CHEM_LOSS_HONO), about 20 % is deposited to the ground, and 20 % removed by transport processes. In the GEH case, 71 % of HONO production is caused by heterogeneous surface chemistry (HET_HONO) during nighttime and early morning. The accumulation of HONO formed by heterogeneous chemistry and emitted during nighttime leads to the peak HONO concentration that occurs around 06:00 a.m CST (which is 3–4 h earlier in comparison to the G case). At that time direct HONO emissions contribute 27 %. During daytime contribution from emissions increases to 50 %, while heterogeneous and gas formation contributes 31 % and 19 %, respectively. The main removal of HONO from the surface layer~is through upward transport (VTRAN_HONO) that contributes 77 % to nighttime and 65 % to daytime reduction in HONO concentration. Dry deposition removes 23 % and 12 % of HONO during nighttime and daytime, respectively. 24 % of daytime HONO reacts to form OH. Although dry deposition and vertical transport are significant HONO removal processes during nighttime, the production of HONO is higher leading to a net increase of HONO concentration that result in the morning peak. After sunrise photochemical reactions add to the removal of HONO resulting in a decrease of HONO concentration. There are two additional pathways of photochemical HONO production in the GEHP case, these are photochemical formation on surfaces covered with organic material ($h\nu$_SF_HONO) and formation from excited NO2 ($h\nu$_NO2$^\ast$_HONO). Although we used the largest reaction coefficient for HONO formation from excited NO2 it resulted in small amount of HONO produced by this pathway, which is even less than that from gas phase chemistry, being negligible compared with other HONO production mechanisms. The photochemical formation on the surfaces has a major contribution of 61 % to HONO production during daytime. This production is overtaken mainly by vertical transport and chemical reactions leading to a net \mboxdecrease in daytime HONO mixing ratios. High removal by vertical transport can be explained by the fact that diffusion between a cell and a neighboring cell is proportional to the concentration gradient between those cells. Since the extra HONO in the GEHP case is added just in layer~1, then that increases vertical diffusion out of layer~1. HONO chemical loss (CHEM_LOSS_HONO) immediately after sunrise is more significant in the GEHP case than the GEH case. About twice as much HONO photo-dissociates in the GEHP case producing twice as much OH and NO as compared to the GEH case. As previously shown, this additional OH resulted in higher OH and O3 mixing ratios in the GEHP case (see Figs 5, 7, and 8). Figure~10 shows contributions of processes and chemical reactions to changes in HONO mixing ratios for the second model layer. Because the total vertical transport is a difference between HONO transported in from layer~1 and moved out to layer~3; therefore, it is much smaller than in layer~1. From the amount of HONO that was brought in from layer~1 and used in chemical reactions in layer~2 we estimated the amount of HONO that is moved upward to the next layer~(orange dashed line). This shows that a significant amount of HONO is moving upward as vertical diffusion continues to remove the extra HONO from layer~2 redistributing it to all the layers in the PBL. This removal of HONO from layer~2 makes the increase in concentration relatively small at that level as seen in Fig 1. \conclusions CMAQ simulations of HONO that included several sources of HONO were performed and compared with MC/IC measured values at the Moody Tower and DOAS measurements at three altitudes. In addition, source and losses for HONO as well as the impact of its different sources on HO$_\textx$ and O3 were examined. Accounting for additional HONO production (e.g heterogeneous HONO formation) as compared to gas-phase formation resulted in about a tenfold increase in the morning HONO concentrations causing improved correlation between modeled and measured values. Also, for the first time a 3-D chemistry transport model such as CMAQ could be to a large extent successfully validated against vertically resolved HONO measurements during day and nighttime, and was able to capture correctly a trend of decreasing HONO concentration with altitude. Since NO2 is a precursor of HONO the mismatches in NO2 modeling directly influence HONO predictions. The correlation between measured and simulated HONO values increased significantly when data points with wrong NO2 prediction were ignored and only NO2 values simulated within 70 % of the measured value were considered. Heterogeneous HONO production is a major source of HONO during nighttime leading to HONO accumulation and early morning peak concentration of up to 2 ppbv. Since HONO dissociation at that time is less important than deposition and vertical transport, heterogeneous HONO production only slightly increases concentrations of OH and O3 (up to 3 ppbv ozone increase). The implementation of additional photo-dependent HONO sources, in particular HONO formation from the photo-induced reaction of NO2 on surfaces covered with humic acid and similar organic materials, only resulted in an increase in HONO mixing ratios of at most 0.5 ppbv. However, process analysis shows that actually much more HONO was produced, but was quickly transported upward and dissociated, which resulted in doubled morning production of hydroxyl radical and an ozone increase of up to 11 ppbv. In contrast to heterogeneous HONO formation that mainly accelerates morning ozone formation, inclusion of HONO photochemical sources influences ozone throughout the day, affecting its peak concentration. Although daytime HONO formation mechanisms may not be understood in all details and the implementation of it to the model is based on many assumptions and simplifications, for example the estimation of urban surfaces or uncertainties in the uptake coefficient, this paper demonstrates that photochemical HONO formation can be a strong source of daytime HONO that directly impacts OH mixing ratios and peak ozone concentrations while nighttime and early morning HONO production by means of NO2 hydrolysis greatly affects the HONO morning peak concentration but only slightly increases hydroxyl radical and ozone concentrations. \beginacknowledgements The authors would like to thank the Houston Advanced Research Center (HARC) for support. Observational DOAS data provided by Jochen~Stutz, UCLA and MC/IC data provided by Jack~Dibb, UNH. OH data provided by Bill~Brune, Penn State University. Edited by: A Hofzumahaus \endacknowledgements \beginthebibliography

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