Introduction
Photolysis of nitrous acid (HONO) is an important source of hydroxyl radical
(OH). OH plays a crucial role in the oxidation of volatile organic compounds
(VOCs) leading to the formation of ozone and secondary organic particulate
matter. Main sources of OH are photolysis of ozone, formaldehyde, alkenes,
and nitrous acid (Elshorbany et al., 2009; Mao et al., 2010; Kim et al.,
2014). Photolysis of ozone and formaldehyde are the most important sources
of OH during midday and afternoon hours; however, the highest contribution
to radical production during early morning hours comes from photolysis of
HONO (e.g., Perner and Platt, 1979; Harris et al., 1982; Czader et al., 2012,
2013).
HONO can be either formed through chemical reactions or emitted to the
atmosphere from combustion processes. Among the most known chemical sources
of HONO is the gas-phase formation from the reaction between OH and nitric
oxide (NO) (Pagsberg et al., 1997) and the heterogeneous formation on
surfaces from the hydrolysis of nitrogen dioxide (NO2) (Kleffmann et
al., 1998; Finlayson-Pitts et al., 2003). Other chemical sources of HONO are
described elsewhere (Kleffmann, 2007; Kleffmann et al., 2005; George et al., 2005;
Stemmler et al., 2006, 2007; Crowley and Carl, 1997; Li et al., 2008, 2009;
Carr et al., 2009; Amedro et al., 2011). Emissions of HONO from traffic were
estimated by Kirchstetter et al. (1996) and Kurtenbach et al. (2001), who
performed tunnel studies and reported exhaust emission ratio of HONO to
NOx in a range of 0.003–0.008. The value of 0.008 is used in the
Community Multiscale Air Quality (CMAQ) model to calculate HONO emissions
from mobile sources (Foley et al., 2010) as well as in other models, for
example, in a box model employed to study HONO sources in Houston (Wong et
al., 2013). The relative contribution of HONO emissions from traffic to other
sources when using the HONO to NOx ratio of 0.008 is about 9 % based
on simulations for eastern US (Sarwar et al., 2008). For high NOx
areas in China Li et al. (2011) calculated as high as 26 % contribution of
HONO emissions to its total sources, but they could not reproduce the high
morning peak values of HONO associated with traffic emissions. Czader et al. (2012) studied HONO formation for Houston conditions and also applied the
0.008 HONO / NOx ratio to estimate HONO emissions. In addition to default
sources of HONO present in CMAQ, they implemented photolytic HONO formation;
however, on many occasions the peak morning values continued to be
underpredicted by the model. Recent measurements performed in Houston in
2009 show that the observed HONO / NOx emission ratio is 0.017
(Rappenglueck et al., 2013), which is about twice as high as previously
reported and implemented in CMAQ modeling system. The impact of using higher
HONO emissions in air quality modeling applications has not been evaluated.
Therefore, in this work HONO emissions from mobile sources will be doubled
to reflect the newly reported HONO / NOx emission ratio and the impact of
higher HONO traffic emissions on its mixing ratios will be estimated in the
WRF–CMAQ modeling system. The impact of increased HONO on the OH and O3
will also be investigated in this study.
In air quality applications HONO is derived from the total NOx
reported in an emission inventory, and chemical formation of HONO is directly
related to NO and NO2 mixing ratios; therefore, HONO predictions by air
quality models depend on how well the model captures emissions of NOx.
Czader et al. (2012) pointed out that the correlation between measured and
simulated HONO values increased significantly when data points with wrong
NO2 prediction were ignored and only data for which NO2 values
were simulated within 70 % of the measured value were considered.
Therefore, accurate estimation of NOx in air quality models is crucial
to properly simulate HONO mixing ratios. Previous studies used remote
sensing and in situ surface observations to analyze accuracy of NOx
emissions and indicated that the National Emission Inventory (NEI) has large
uncertainty in emissions in urban areas (Choi et al., 2012; Choi, 2014). Of
particular, Choi (2014) stated that both NEI2005 and NEI2008 have
significant NOx overestimates in Houston. Thus, in this study, before
proceeding with modifications of HONO emissions, NOx emissions will be
adjusted using the U.S. Environmental Protection Agency (EPA) annual trend
values and the absolute amounts of simulated surface NOx concentrations
will be evaluated.
Methodology
Meteorological parameters were derived with the Weather Research and
Forecasting (WRF) model version 3.5 (Skamarock and Klemp, 2008). NCEP North
American Regional Reanalysis (NARR) data provided by the NOAA/OAR/ESRL PSD
(available at http://www.esrl.noaa.gov/psd/) were utilized to
initialize WRF simulations. The 2008 National Emission Inventory (NEI2008)
generated by the Environmental Protection Agency (EPA) was processed with
the Sparse Matrix Operator Kernel Emissions (SMOKE) system to obtain
gridded, chemically and temporally resolved emission files ready to use in
an air quality model. The air quality simulations were performed with the
three-dimensional Community Multiscale Air Quality (CMAQ) model (Byun and
Schere, 2006) version 5.0.1 with the Carbon Bond 05 chemical mechanism and
aerosol 5 module (cb05tucl_ae5_aq).
Simulations were performed for a domain with 4 km grid resolution covering
southeast Texas, with 84 grid cells in east–west direction, 66 grid cells in
south–north direction, and 27 vertical layers. The boundary conditions were
obtained from the University of Houston air quality forecasting system
(http://spock.geosc.uh.edu) from a larger domain with 12 km
grid resolution, 150 grid cells in east–west direction and 134 grid cells in
south–north direction. Initial conditions were also obtained from the air
quality forecasting results from the nested south-east Texas domain.
Simulations were performed for the month of September 2013, during which the
DISCOVER-AQ campaign took place in Houston providing many different
meteorological and chemical measurements that could be utilized for model
evaluation.
Adjusting NO<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">x</mml:mi></mml:msub></mml:math></inline-formula> and HONO emissions
Previous studies used remote sensing and in situ surface observations to
analyze accuracy of NOx emissions and pointed to the fact that the
National Emission Inventory (NEI) has large uncertainties in emissions for
urban areas (Choi et al., 2012; Choi, 2014). Of particular, Choi (2014)
pointed out that both NEI2005 and NEI2008 might have significant overestimates of
NOx emissions in Houston even with the consideration of the
uncertainties caused from other chemical and physical processes. Adequate
estimation of NOx emissions is critical for properly predicting HONO
mixing ratios.
EPA emission trends for NOx (values reported in
thousands of tonnes.
NOx
2008
2009
2010
2011
2012
2013
Mobile
6941
6241
5734
5786
5398
5,010
Other
9872
9540
9144
8594
8114
7914
Total
16 813
15 781
14 878
14 380
13 512
12 924
Since our simulations employed NEI2008 there was a need to adjust
emissions to reflect conditions of 2013. In this study, instead of relying
on the remote-sensing-derived data or surface-measured data to adjust an
emission inventory (e.g., Kim et al., 2009; Kim et al., 2011; Choi et al.,
2012; Choi, 2014) we use the long-term trends of anthropogenic NOx
emission reported by U.S. EPA. Then the impact of the adjusted NOx
emissions on surface NOx concentrations is evaluated by comparing the
simulated and observed NOx concentrations. According to EPA, emissions
of nitrogen oxides from anthropogenic sources were reduced between 2008 and
2013. Table 1 shows emission values based on the EPA trends (available at
http://www.epa.gov/ttn/chief/trends/index.html) for
on-road mobile sources and other anthropogenic sources excluding wildfires.
Relatively to values for the year 2008 there was 28 % reduction in on-road
mobile NOx emissions on a nationwide scale and 20 % reduction in
other anthropogenic NOx emissions in year 2013. To follow the emissions
trends we created a sensitivity case in which on-road NOx emissions
were reduced by 30 % and anthropogenic point source emissions were reduced
by 20 %.
NEI provides emission rates for nitrogen oxides; during the processing with
SMOKE NOx emissions for mobile sources are separated into 90 % NO,
9.2 % NO2, and 0.8 % HONO. However, Rappenglueck et al. (2013)
report much higher HONO contribution from mobile sources in Houston; based
on all measurements HONO traffic emissions are 1.7 % of NOx
emissions,
which is about twice the previously estimated value of 0.8 % based on
tunnel measurements in 2001. The HONO / NOx ratio reported by Kurtenbach
et al. (2001) is based on measurements performed between 6 a.m. and 2 p.m., for
both weekdays and weekends where 22 200 ± 400 vehicles were passing on
weekdays and 13 300 ± 1 400 cars passing on weekends. The vehicle
fleet was composed of 6.0 % heavy-duty trucks, 6.0 % commercial vans,
12 % diesel and 75 % gasoline powered passenger cars, and 1.0 %
motorcycles. The ratio calculated by Rappenglueck et al. (2013) is based on
measurements performed during weekdays reflecting high-traffic, early
morning conditions (4–8 a.m.). The measurements were performed at highway
junction in Houston with very high traffic load (about 400 000 vehicles
passing daily), which is much larger than that in the tunnel study. The
vehicle fleet was represented by 93–95 % of gasoline fueled vehicles and
5–7 % by diesels during the morning hours. Another difference between
these two studies is in vehicle speed, with a typical speed of 50–90 km -1 in
the tunnel studies and much lower speed during the morning peak traffic
hours in Houston. To reflect the latest values of HONO emissions measured in
Houston in air quality modeling, an additional sensitivity case was created in
which contribution of HONO from mobile sources was doubled at the cost of
NO2. The following speciation was used for the sensitivity case: 90 %
NO, 8.4 % NO2, and 1.6 % HONO. It is worth noting that, since the
newly reported ratio reflects high-traffic conditions during morning rush
hours on weekdays, our model sensitivity study provides an estimate of the upper
bound of the impact of HONO emissions on pollutant levels in urban areas.
The following three simulations cases are performed and analyzed in this
study: (i) B – base case, with NOx emissions rates obtained from NEI2008
and HONO / NOx=0.008;
(ii) N – reduced emissions of NOx case: mobile sources × 0.7, point
sources × 0.8; HONO / NOx=0.008;
and (iii) NH – similar to N but with doubled HONO emissions from mobile
sources (this is HONO / NOx=0.016).
Summary of statistical parameters for the base case simulation (B)
and reduced NOx case (N).
Site
No. of points
Mean (ppb)
R
AME (ppb)
IOA
Obs.
Sim. B
Sim. N
Sim. B
Sim. N
Sim. B
Sim. N
Sim. B
Sim. N
1
700
15.60
18.95
12.41
0.44
0.45
10.52
8.18
0.62
0.58
2
695
6.34
9.13
5.42
0.49
0.54
5.39
3.62
0.54
0.70
8
699
9.93
11.89
8.24
0.73
0.76
5.45
4.53
0.83
0.84
9
699
5.50
10.02
6.54
0.60
0.59
5.66
3.74
0.66
0.74
15
668
10.48
12.98
7.92
0.42
0.44
8.20
6.26
0.61
0.56
26
697
5.61
12.58
9.58
0.52
0.56
7.96
5.45
0.47
0.61
35
649
6.63
10.33
6.95
0.67
0.64
5.87
3.93
0.72
0.79
45
699
3.83
4.87
3.45
0.60
0.52
2.94
2.42
0.72
0.70
53
684
7.69
11.56
8.80
0.76
0.77
5.74
4.28
0.82
0.87
64
690
4.01
2.51
1.91
0.44
0.54
2.72
2.57
0.61
0.57
78
617
3.29
10.45
7.56
0.54
0.55
7.66
5.01
0.41
0.54
84
533
4.34
9.08
6.88
0.69
0.70
5.57
3.82
0.68
0.78
114
708
13.94
20.87
13.79
0.48
0.50
11.44
7.54
0.62
0.68
311
635
4.58
6.75
4.92
0.52
0.58
3.74
2.70
0.66
0.75
403
696
14.87
27.20
20.08
0.40
0.42
16.40
11.83
0.54
0.61
408
703
15.17
12.01
8.90
0.55
0.59
7.08
7.74
0.67
0.61
411
692
16.57
22.24
15.81
0.59
0.60
10.59
7.87
0.69
0.76
416
702
13.95
28.35
19.43
0.71
0.71
16.39
9.29
0.69
0.81
617
705
6.20
7.42
4.93
0.50
0.48
4.50
3.61
0.64
0.68
618
697
2.90
3.15
1.80
0.58
0.61
1.62
1.42
0.71
0.71
619
559
3.04
2.38
1.67
0.38
0.48
2.34
2.01
0.58
0.58
620
399
7.05
7.66
4.86
0.37
0.36
6.74
5.67
0.57
0.49
640
675
2.14
1.57
1.10
0.26
0.30
1.50
1.36
0.47
0.47
643
671
6.30
1.82
1.35
0.21
0.24
4.97
5.15
0.46
0.44
1015
703
13.44
12.33
8.75
0.43
0.44
8.95
8.65
0.62
0.56
1016
608
2.25
5.18
3.73
0.40
0.40
3.55
2.50
0.48
0.57
1034
641
2.23
1.58
1.38
0.45
0.47
1.43
1.39
0.63
0.60
1035
692
4.45
7.91
5.12
0.53
0.55
4.49
2.96
0.64
0.74
1628
630
5.91
13.64
5.09
0.43
0.50
8.39
2.98
0.42
0.69
MT
703
9.93
20.53
14.59
0.64
0.64
12.46
8.02
0.63
0.74
ALL
19 849
7.76
11.11
7.59
0.58
0.59
6.76
4.94
0.71
0.75
Locations of stations performing NOx measurements in the
Houston–Galveston–Brazoria area during September 2013.
![]()
Measurements
Measured values from the Continuous Ambient Monitoring Stations (CAMS)
system, operated by the Texas Commission on Environmental Quality (TCEQ),
were utilized for evaluating NOx emission inventory. During the time
period of interest 30 stations inside our 4 km modeling domain reported
NOx measurements. Figure 1 shows locations of sites in the Houston–Galveston metropolitan area, where color of the symbol indicates the
measured mean NOx mixing ratios during the month of September 2013.
Several sites, such as 78, 84, 618, 619, and 1016, have low mean values;
those sites reflect regional and/or suburban conditions. A couple of sites, such
as 26 and 53, have medium-range NOx values reflecting urban air mixture
dominated by traffic emissions. Many sites close to highways or in downtown
Houston and east of downtown are exposed to heavy traffic as well as a
combination of traffic and industrial emissions. They have very high
NOx mean values; those are CAMS sites 1, 8, 114, 403, 408, 411 and the
Moody Tower (MT) site described below.
The Moody Tower, located east of downtown, was designated as a “super”
site during air quality study campaigns in Houston in years 2006 (Lefer and
Rappenglück, 2010) and 2009 (Olaguer et al., 2013) during which many
chemical and meteorological measurements were taken. During September 2013
measurements at the Moody Tower complemented the DISCOVER-AQ campaign. The
measurements were taken at 60 m a.g.l. In addition to NOx and ozone,
HONO was also measured on several days during the month of September 2013.
Time series comparing measured NOx against values simulated
with the base case and the reduced NOx case at CAMS sites 1 (top) and
411 (bottom).
![]()
Results
Evaluation of NO<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">x</mml:mi></mml:msub></mml:math></inline-formula> modeling
Table 2 shows summary of statistical parameters of modeled NOx mixing
ratios for the base case (B) and the reduced NOx case (N) as compared
to measured values at CAMS sites, where R is the Pearson coefficient, and AME
is the absolute mean error calculated as
AME=1n∑1nCm-Co,
where n is the number of data points, “m”
corresponds to modeled mixing ratios and “o” to observed ones;
IOA is the index of agreement, calculated according the following equation:
IOA=1-∑1nCm-Co2∑1nCo-Õ+Cm-Õ2,
where Õ corresponds to the observed mean value. Compared to a Pearson
coefficient the index of agreement is a more comprehensive measure of how
well the concentrations are predicted since it takes into account not only
scattering of data but also biases (Willmott, 1981).
NO and NO2 mixing ratio measured at the Moody Tower site and
modeled with the base case emissions as well as with reduced NOx
emissions.
![]()
Snapshot of differences in HONO emissions between a case with
emission ratio of HONO / NOx=0.016 (NH) and default emissions of
HONO / NOx=0.008 (N) at 7 a.m. LT on 12 September 2013.
![]()
Differences in HONO mixing ratios between a case with 0.016
HONO / NOx emission ratio (NH) and 0.008 HONO / NOx emissions (N) for
the surface (left) and the second model layer (right) at 7 a.m. LT on
12 September 2013.
![]()
Statistical parameters were calculated for all available data pairs from
CAMS sites inside the modeling domain. The measured mean value from all
sites is 7.76 ppbv; the simulated mean value dropped from 11.11 ppbv in the
base case to 7.59 ppbv in the reduced NOx case becoming closer to the
observed mean. Both R and IOA are improved in the reduced NOx case
(R=0.58, IOA = 0.71 in the base case; R=0.59, IOA = 0.75 in the
reduced NOx case) and AME is lowered from 6.76 to 4.94 ppbv.
Overall, the reduced NOx simulation case gives better NOx
prediction in comparison to the base case. When looking at individual
stations affected by emissions from different sources the improvement from
NOx reductions is beneficial for most of sites, but leads to
underpredictions at several sites. Many stations with medium-range NOx
mixing ratios, such as CAMS 35 and 53, show improvement from NOx
reduction. There are also cases when NOx continues to be too high even
after reduction of emissions. This is the case for CAMS sites 26 and 78 that
represent suburban conditions with low measured NOx mixing ratios
(usually below 10 ppb) and low mean values of 5.61 and 3.29, respectively.
The model represents them as urban sites with significant traffic signature
and therefore with much higher than measured mixing ratios. Even though in
our study we adjusted NOx emissions to reflect emission reduction
between the year 2008 and 2013 some overpredictions may occur since, as
pointed by Choi (2014), NOx rates in the base 2008 inventory might be
too high. Very high NOx mixing ratios are recorded in areas with heavy
traffic and close to industrial facilities in the eastern part of Houston –
such as at CAMS stations 1, 403, 411, and 416. NOx mixing ratios at
those stations were heavily overpredicted and consequently those stations
benefit the most from NOx reductions as presented in Fig. 2. Our
results are similar to the previous study by Choi (2014), who issued that
NOx mixing ratios at urban regions are overpredicted by air quality
models, but NOx at the rural regions is underpredicted.
The Moody Tower site served as a super site for a couple of measurement
campaigns in Houston, and many different chemical and meteorological
parameters were measured there, including NO, NO2, and HONO. It is
located in close proximity to downtown and major highways and is affected by
quite high NOx emissions. Figure 3 shows comparison of measured at the
Moody Tower and simulated mixing ratios of NO (top) and NO2 (bottom).
Again, two simulation cases are compared: the case with regular emissions as
included in NEI2008 (B) and the reduced NOx emissions case (N). It can
be seen that for both compounds the peak values were overpredicted by the
base case while reduced NOx case resulted in lower mixing ratios making
them closer to the observed values. In particular, NO mixing ratios are much
better predicted by the reduced NOx emission case. Both NO2 morning
peaks and low-range daytime and nighttime NO2 values, although lowered,
continue to be overpredicted most of the time.
HONO mixing ratios measured at the Moody Tower site and modeled
with and the regular HONO emissions (N) for which the HONO / NOx emission
ratio of 0.008 was used, and the increased HONO case (NH) for which the
HONO / NOx emission ratio of 0.016 was used.
![]()
HONO modeling
Since reduction of NOx emissions resulted in better prediction of
NOx mixing ratios at the Moody Tower and nearby areas, this case was
used as a base for modifying HONO emissions. Figure 4
shows changes in HONO emissions rates between the sensitivity case in which
HONO / NOx= 0.016 (indicated as NH) and the reduced NOx case that used
HONO / NOx= 0.008 (indicated as N). Doubling HONO emissions resulted in
up to 0.01 mole s-1 increase in emission rates from mobile sources along
highways. Figure 5 shows differences in simulated mixing ratios of HONO for
morning conditions at 7 a.m. LT that correspond to the time of the highest
HONO emissions from traffic and the highest HONO mixing ratios. The left
panel shows results for the surface layer. It can be seen that changes in
HONO mixing ratio at the surface occur along highways following the pattern
of emission changes presented in Fig. 4. Differences in HONO mixing ratios
at the second modeled layer, which corresponds to measurements taken at the
Moody Tower, are shown in the right panel of Fig. 5. At this level the air
is mixed and the spatial signature of mobile emissions diminishes.
Statistical parameters for modeling HONO mixing ratios for the
Moody Tower site.
Statistics
HONO
Number of points
200
Mean
Observed
0.69
Sim. (N)
0.30
Sim. (NH)
0.41
Max. value
Observed
3.15
Sim. (N)
2.62
Sim. (NH)
2.93
Correlation coefficient
Sim. (N)
0.58
Sim. (NH)
0.57
Mean bias
Sim. (N)
-0.39
Sim. (NH)
-0.28
Absolute mean error
Sim. (N)
0.46
Sim. (NH)
0.43
Index of agreement
Sim. (N)
0.63
Sim. (NH)
0.70
HONO is not routinely measured in Houston; in spite of that, during
September 2013 HONO was measured at the Moody Tower to complement
measurements during DISCOVER-AQ campaign. However, the measurements were not
continuous and the data are limited to several days. Figure 6 shows
time series of measured and simulated HONO mixing ratios at the Moody Tower.
The mixing ratios obtained from the reduced NOx simulation case (N),
for which the HONO / NOx emission ratio of 0.008 was used, are much lower
than observed HONO values. The values from the increased HONO case (NH),
with the HONO / NOx emission ratio of 0.016, are higher, especially the
morning peaks, and closer to the observations. The statistical parameters
for HONO modeling at the Moody Tower are presented in Table 3. The mean
value increased from 0.30 in the base case to 0.41 ppbv in the increased
HONO emissions case but continue to be lower than the observed mean of 0.69 ppbv. The index of agreement increased from 0.63 to 0.70 indicating benefits
of increased HONO emissions. Clearly, improvement in HONO peak values can be
seen on 12, 18, 23, 24, 25 and 30 September; especially on 12 September the
model with increased HONO emissions nicely follows HONO peak while the case
with low HONO / NOx emission rates resulted in underprediction of the
peak value. However, as pointed by Czader et al. (2012) HONO predictions
depend on how well the model captures NOx concentrations, especially
NO2, since heterogeneous HONO formation is directly related to NO2
concentrations and greatly influences morning HONO mixing ratios. It can be
seen that overprediction of NO and NO2 on 11, 19, and 24 September
leads to overprediction of HONO. We can conclude that to a large extent misprediction of
precursors is responsible for HONO misprediction and expect that if NOx
mixing ratios for those days are accurately simulated also HONO values would
be closer to observation. This is not the case on 18 September when, despite
the fact that NO is well predicted and NO2 overpredicted, HONO peak is
underpredicted. The reasoning for that is unknown, but it is probably due to
the uncertainties in other HONO sources. Also, variations of simulated HONO
mixing ratios from day to day are influenced not only by emissions but also
by other parameters, for example, the model capabilities to predict growth of
the mixing layer and wind fields as well as clouds that influence photolysis
rates. To more clearly present differences between the two simulated cases
(N and NH) and measured data we calculated the average diurnal profiles of
HONO and presented them in Figure 7. The modeled profiles follow the
measured one showing a high peak in the morning and low values during a
daytime. It can be seen that the NH scenario, in which higher emission ratio
was utilized, improves HONO morning peaks. Since only HONO emissions from
mobile sources were increased, it is expected to see the largest differences
in mixing ratios during early morning times when the traffic emissions are
high, the mixing layer height low allowing for accumulation of HONO,
and photochemistry not very active. It is worth noting that all available
measured data for HONO for September 2013 are from weekdays and the higher
HONO / NOx ratio measured in Houston was also calculated based on
measurements taken during weekdays. The model underprediction during daytime
can be explained by the fact that the default model version that we used in
this study does not account for the photochemical HONO sources. Also, too
low modeled average profile during daytime is caused by underpredictions of
HONO on 23–25 September which can be attributed to stronger modeled winds in
comparison to weak observed winds causing modeled HONO to be removed from
the observational site.
Average diurnal variation of HONO at the Moody Tower measurement
site.
![]()
OH mixing ratios (left) and differences in OH mixing ratios
(right) between the case with 0.008 HONO / NOx emission ratio (N) and
0.016 HONO / NOx emission ratio (NH) at noon local time on 12 September
2013.
![]()
The photolysis of HONO is a source of hydroxyl radical. Figure 8 shows a
snapshot of spatial pattern of OH mixing ratios (left) and differences in OH
mixing ratios (right) between simulations with increased HONO emissions (NH)
and regular emissions with 0.008 HONO / NOx emissions ratio (N) for
12 September, which is a day with nicely predicted HONO mixing ratios. An
increase in OH occurs along highways corresponding to increased HONO mobile
emissions. Based on the 1 month of simulated surface concentrations the
average increase in HONO due to doubling its emissions from mobile sources
is 36 % at the location of the Moody Tower and 10 % when averaged over
the urban area. The average increase in the morning OH (between 6 and 8 a.m.
LT) is 14 % at the location of the Moody Tower and 3 % when averaged
over the urban area. The ozone increase is below 1 % for both the Moody
Tower and the urban area. The average increase in OH during daytime (6 a.m.–8 p.m. LT) is 7 % for the Moody Tower and 1 % for the urban area. The
increase in ozone is again below 1 %. Since HONO emissions from mobile
sources that peak in the morning were modified, it is therefore
understandable that the impact of these additional HONO emissions on OH and
ozone is higher during morning time than afternoon hours.
To obtain more
insights on the fate of HONO we performed additional model simulations in
which we utilized the process analysis that provides information on chemical
and physical processes influencing pollutant mixing ratios. The analysis was
performed for the Moody Tower site for 10–13 September 2013. At the surface, at
the location of the Moody Tower the average contribution of vertical
transport to the loss of HONO is 77 %; horizontal transport contributes
8 %, chemical removal 11 % and dry deposition 4 %. HONO mixing ratios
along with process affecting changes in mixing ratios for the second model
layer, which corresponds to the altitude of measurements, are presented in
Fig. 9. It can be seen that transport (horizontal and vertical) continues
to be a dominant loss process at this altitude contributing on average
77 % to the total HONO loss while chemical loss contributes only 23 % to
the total loss. The chemical loss of HONO is dominant only during a couple of
morning hours. This explains the fact that, even though HONO mixing ratios
significantly increased upon additional emissions, HONO was removed mainly
by transport with only small portions taking part in chemical reactions
converting it to OH and furthermore to O3.
HONO mixing ratios (black line) and processes contributing to
changes in HONO mixing ratio at the Moody Tower site where the measurements
were taken, which corresponds to the second model layer, where VTRAN is
vertical transport, HTRAN is transport in horizontal direction, and
CHEM_HONO corresponds to changes due to chemical reactions.
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Summary
The WRF–SMOKE–CMAQ modeling system was used for evaluation and
adjustment of NOx emissions. In particular, HONO / NOx emission
ratio from mobile sources was increased and its impact on HONO mixing ratios
as well as on OH and O3 was evaluated.
First, NOx emissions were adjusted to reflect emission trends.
Simulations with adjusted NOx emissions resulted in overall better
NOx prediction as mixing ratios became closer to measured values. The
average NOx mean value from all analyzed sites dropped from 11.11
to 7.59 ppbv and is much closer to the observed mean of 7.76 ppbv. IOA is
improved in the reduced NOx case (0.71 vs. 0.75), and the AME is lowered
from 6.76 to 4.94. Therefore, the reduced NOx case was taken as a base
for adjusting HONO emissions according to values measured in Houston.
Doubling HONO emission from mobile sources and therefore making them closer
to the newly reported HONO / NOx ratio of 0.017 resulted in increased
HONO mixing ratios especially during morning peak values. Based on 1 month
of simulated data, a 36 % increase in HONO mixing ratio at the location of
the Moody Tower was obtained from the case with higher emission ratios
utilized in the simulation. The increase in HONO values averaged over the urban
area was 10 %. Simulated HONO mixing ratios were compared to values
measured at the Moody Tower. The mean value increased from 0.30 ppbv in the
base HONO emission case to 0.41 ppbv in the increased HONO emission case and
became closer to the observed mean of 0.69, but still low. The index of
agreement for simulation that used the 2001 HONO / NOx emission ratio of
0.008 is 0.63 while for the simulation with doubled HONO emissions IOA
increased to 0.70. Increased HONO emissions from mobile sources resulted in
a 14 % increase in OH during morning time at the location of the Moody Tower
and 3 % when averaged over the urban area. The increase calculated for daytime
was 7 and 1 % for the Moody Tower and the urban area, respectively.
The impact on ozone was found to be marginal (below 1 %).
This study results could shed light on the underestimated HONO in the
morning from global/regional chemical transport model with the typical
emission ratio of 0.8 % HONO emission out of the total NOx emissions.
In addition, since HONO is the major radical source in the morning (e.g.,
Perner and Platt, 1979; Harris et al., 1982; Czader et al., 2013),
underpredictions of HONO would lead to underprediction of OH radical.