Campaign observations and P(O3) time series
(a) Full-campaign 10 min temperature and relative humidity
in Golden, CO. The “warm” period is defined as days before 27 July 2014.
(b) Full-campaign 10 min O3 mixing ratios for 17 July to
10 August 2014. (c) P(O3) measured by the MOPS and modeled by RACM2 and MCMv331 for the same time period. Measured to modeled
comparisons are shown for days with available MOPS measurements and are
averaged over a 1 h time period.
Observed and modeled P(O3) were compared between 17 July and
10 August 2014 in Golden (Fig. ). From 17 to 27 July, the
campaign was characterized by a warmer, drier period followed by a relatively
cooler, wetter period until the end of the study. Daily O3 mixing ratios
typically peaked between 13:00 and 18:00 LT, with a median value of 59 ppbv.
Higher O3 levels exceeding 80 ppbv were observed on 22, 28, and 29 July
as well as 3 August. The highest O3 levels were observed on 22 July, with
a maximum mixing ratio of approximately 90 ppbv.
Full-campaign median hourly P(O3) measured by the MOPS and
modeled by RACM2 and MCMv331 for MOPS measurement days. Shaded areas
represent the variance in MOPS P(O3) due to the variation in the zero
correction. The RACM2 and MCMv331 relative error bars are shown at the
1σ confidence level.
Due to the terrain of the Front Range, the average diel wind direction during
the campaign period was westerly before 09:00 LT, easterly to northeasterly
from 09:00 to 14:00 LT, and then westerly again after 14:00 LT, with
diel-averaged speeds ranging between 2 and 3.5 m s-1. Thus, it is
possible for P(O3) in Golden to be influenced by pollutants advected
from nearby eastern source regions during the midmorning to early afternoon.
The corrected MOPS P(O3) maxima were routinely higher than
10 ppbv h-1 on most measurement days, with diurnal peaks between
09:00 and 11:00 LT. Observed P(O3) maxima on individual days range from
10 ppbv h-1 to almost 30 ppbv h-1 (Fig. ).
As mentioned earlier, MOPS P(O3) measurements were restricted to days
when the MOPS O3 analyzer relative humidity was less than 70 % when we
have confidence that the analyzer was not affected by significant baseline
drifting. This data filtering reduced the MOPS baseline variations to between
-5 and 5 ppbv h-1 at a 1 h time resolution.
Modeled P(O3) time series and comparisons to measurements
Full-campaign modeled P(O3) is also shown in Fig. for
both RACM2 and MCMv331. Modeled P(O3) for both mechanisms are a broad
peak with maxima that occurred between 09:00 and 12:00 LT, with values generally
10 ppb h-1 or lower. The modeled P(O3) behavior is essentially
identical on a day-to-day basis for both the RACM2 and MCMv331. On several
individual days, the MOPS P(O3) measurements exhibited maxima that were
a factor of 2 to 3 times higher than modeled P(O3) values during
the morning between 09:00 and 11:00 LT.
Median diel variations from MOPS and modeled P(O3) values are shown for MOPS
measurement days in Fig. . Median observed P(O3) began
to increase around 08:00 LT, peaked at greater than 10 ppbv h-1
around 10:00 LT, and decreased to 5 ppbv h-1 before falling off to
zero in the evening. Median modeled P(O3) also rose beginning at about
08:00 LT but peaked at around 5 ppbv h-1 between 11:00 and 12:00 LT
and was 3–4 ppbv h-1 in the afternoon. Median observed and modeled
P(O3) values are in good agreement in the afternoon as shown by overlapping
error bars, but median diel MOPS P(O3) is generally a factor of 2
higher than that modeled between 09:00 and 11:00 LT when NOx and VOC
levels were high due to abundant local or advected rush hour traffic
emissions. The shaded region in Fig. is the range of
possible measured P(O3) values obtained using the range of maximum to
minimum measured zero offset values. A midmorning difference between
measured and modeled diel-averaged P(O3) is apparent over this range of
zero corrections.
RACM2, MCMv331 (a), and MOPS (b) 30 min
P(O3) as a function of measured NO for all MOPS measurement days. Points are colored
by hour of day from 06:00 to 18:00 LT.
Figure indicates P(O3) as a function of NO levels and
time of day. Similar to , both measured and modeled diel
P(O3) increased between 06:00 and 08:00 LT during morning rush hour,
peaked before 12:00 LT, and then decreased later in the day with decreasing
NO and VOC radical abundances. Occasional secondary P(O3) peaks were
exhibited between 14:00 and 16:00 LT in both measured and modeled P(O3),
likely due to advection of O3 precursors from the Denver region or
increased local traffic emissions. The most striking difference is that the
measured P(O3) continues to rise as NO increases, while the modeled
P(O3) decreases for NO more than 1 ppbv. The missing modeled P(O3)
appears to increase monotonically with increasing NO for NO values greater
than roughly 1 ppbv (Fig. ). The difference between
measured and modeled P(O3) is near zero up to 1 ppbv NO and almost
20 ppbv h-1 at 5 ppbv NO. This unexpected increase in P(O3) with
increasing NO provides a clue as to what might be causing the difference
between measured and modeled P(O3).
Difference between measured and modeled P(O3) as a function of
measured NO. Individual points are averaged for 30 min, while the solid
line indicates the average P(O3) difference binned by NO.
Several reasons provide confidence in these P(O3) comparisons, which
result in higher P(O3) than that modeled during the morning hours.
First, median P(O3) values were used instead of the mean to compare MOPS
and modeled P(O3) so as not to bias diurnal P(O3) curves in the
event of P(O3) anomalies. Second, observed P(O3) peak values were
often much greater than the hourly MOPS 1σ uncertainty on individual
days as seen in Fig. , in which differences between the MOPS
and modeled P(O3) were typically between 10 and 20 ppbv h-1. Third,
when different relative humidity thresholds are used to correct the raw
P(O3) data, measured P(O3) consistently exhibits the same diurnal
behavior with a positive deviation from modeled P(O3) around 10:00 LT.
Fourth, deviations from the O3 differential baseline derived from
zeroing methods are observed between 09:00 and 11:00 LT even before correcting
the MOPS measurements. Thus, we have confidence in the positive MOPS
P(O3) signatures, which are greater than the modeled P(O3) during
the morning hours. All of these results provide confidence in the robustness
of the MOPS behavior relative to the models in Figs. and
and in the subsequent analyses.
Possible causes of the model–measurement P(O3) discrepancies
Higher morning P(O3) calculated from measured peroxy radicals has been
observed at high NO levels with a variety of measurement methods. The MOPS
observations, independent of these studies, yield similar results for the
dependence of P(O3) on NO, indicating that the MOPS and other measurement
methods both contain artifacts that act to increase P(O3) in a similar
manner or that the model–measurement disagreement occurs due to differences
in the chemistry between observational and computational methods used to
determine O3 production rates.
We explore several hypotheses for model–measurement disagreement during the
morning hours in the following sections. Possible explanations include MOPS
chamber artifacts, model input and parameter uncertainties, model peroxy
radical chemistry, modeled ambient HONO sources, and reactive chlorine
chemistry.
MOPS chamber artifacts
One hypothesis is that the MOPS P(O3) is positively biased due to
environmental chamber chemistry artifacts: that is, off-gassing of NO2,
nitrous acid (HONO), or other chemical species from the chamber walls. At
higher relative humidity, chemical species adsorption onto these
environmental chamber walls can be higher . It is possible
that subsequent desorption of NO2 or chemical species from the walls can
induce artificial chemistry in the MOPS chambers. However, as described
earlier, the MOPS chamber airflow isolates sampled air from the walls of the
MOPS chambers where surface reactions are most likely to occur. Chamber air
closest to the walls is exhausted, leaving mostly center flow to be sampled
by the MOPS O3 analyzer.
Additionally, adsorbed NO2 can result in heterogeneous formation of
HONO, and a HONO source within the chambers may result in excess P(O3)
from artificial OH production . For the Golden, CO, study,
NOx levels were a factor of 3 lower on average than in Houston, TX,
the relative humidity was 35 % lower on average, and the actinic flux was
similar. Although identifying MOPS chamber HONO production mechanisms will
require more intensive laboratory studies, we assume that the largest HONO
source within the MOPS chambers stems from NO2 adsorption on the chamber
walls. Thus, NOx levels in Golden are used to infer MOPS chamber HONO
levels in Golden. We have applied the observed chamber HONO : NOx
ratio in Houston, TX, to the Golden, CO, study because, under this assumption,
HONO production should linearly depend on NOx adhering to the walls. We
have calculated a maximum diurnal bias of +3 ppbv h-1 at 10:00 LT
(2σ) that decreases later in the day to less than 1 ppbv h-1 as
NOx decreases. However, this calculated P(O3) bias is rather
conservative; the chamber residence time of 130 s and the HONO photolysis
frequency for Golden, CO, can be used to determine the percentage of chamber
HONO that would be converted into O3-producing radicals. In doing so,
less than 15 % of chamber HONO is photolyzed. Consequently, the bias for
Golden, CO, would be less than 0.5 ppbv h-1 and would contribute
insignificantly to the observed P(O3) signal. In order to explain
observed and modeled P(O3) differences in Golden by chamber-induced HONO
production, HONO levels would need to be more than an order of magnitude
larger. Given the levels of MOPS chamber HONO measured in Houston and in
other areas , the likelihood of excess chamber HONO
production being a significant cause for the resultant differences between
modeled and measured P(O3) is small.
Model input and parameter uncertainty
A second hypothesis is that the uncertainties in the model P(O3) are
large enough that the differences in the measured and modeled P(O3) are
not statistically different. Model P(O3) uncertainty has been found to
be 2–5 % larger during the morning hours when differences between measured
and modeled P(O3) were observed. As described earlier, the RACM2 inputs
and parameters affecting model P(O3) uncertainty are determined based on
a RS-HDMR sensitivity analysis. Model uncertainty between 06:00 and 18:00 LT
is similar between both chemical mechanisms (Table S4); the average modeled
P(O3) uncertainty (1σ) from RACM2 and MCMv331 is about 30 % all
day. Due to similar model behavior and diurnal uncertainty estimates between
the RACM2 and the MCMv331, we expect that the influential inputs between the
two mechanisms – model constraints and parameters contributing largely to
calculated P(O3) uncertainty – will also be similar.
Model influential inputs are specific to both location and available
measurements. However, many constraints that contributed to P(O3)
uncertainty in Golden, CO, were found to be similar to prior sensitivity
analyses of chemical mechanisms conducted in much different environments
(, and references therein). For example, two
parameters consistently identified as having high importance for daytime
P(O3) uncertainty involve the reaction rates, kOH+NO2 and
kHO2+NO, which dictate HOx–NOx cycling and the
production and loss of HOx. These reaction-rate coefficients have large
contributions to the overall model uncertainty despite their relatively low
uncertainty factors of 1.3 and 1.15, respectively .
Other model constraints influential in dictating model P(O3) uncertainty
such as reaction rates, product yields, and mixing ratios of species were more
specific to time of day. Similar to overall results in ,
and in addition to HOx–NOx reaction rates, early morning P(O3)
uncertainty was attributed to reaction rates involving the oxidation of
reactive VOCs such as aldehydes and xylenes that initiate O3 formation
propagation and produce HOx. Additional Golden influential reaction
rates involved the decomposition and formation rates of PANs, a NOx reservoir. As O3 increases in the afternoon,
additional rates and product yields of reactions involving O3 loss also
become important, along with those between NO and other organic peroxy
species (RO2) that continue O3 formation.
As expected, model inputs and parameters involving the formation of RO2
or new NO2 outside of the NOx PSS that further propagate the
O3 formation cycle, along with inputs and parameters involving
production of HOx species, are all factors influencing model P(O3)
uncertainty. Although model uncertainty is not large enough to explain model
P(O3) behavior relative to the MOPS, greater emphasis should be placed
on quantifying the uncertainty in HOx–NOx cycling reaction rates to
reduce model P(O3) uncertainty and improve morning agreement between
observed and modeled P(O3) in Figs. and
.
Model peroxy radical chemistry
An explanation for the lower modeled P(O3) in the early morning is that
modeled HO2 is less than that measured at higher NO levels. Indeed, in previous
studies, measured HO2 often exceeds modeled HO2 for an NO value greater than
about 1 ppbv
.
Campaign median NO mixing ratios typically peaked between 09:00 and 11:00 LT at
about 2 ppbv, with maxima as high as 7 ppbv. The largest differences in
measured and modeled P(O3) occurred during this time period when NO was
greater than 1 ppbv. Thus, it is possible that the difference between
measured and modeled HO2 is related to the difference between measured
and modeled P(O3).
C-130 CIMS RO2 (a) and HO2 / OH ratio (b) and as a function of
aircraft NO (chemiluminescence, 20 pptv ± 10 %, 1σ
uncertainty) and modeled RO2 and HO2 / OH ratio versus
constrained NO measured continuously in Golden, CO. Aircraft measurements
used are limited to the first 1 km in the boundary layer and only for times
when the C-130 was within 20 km of Golden, CO. A well-mixed boundary layer
is assumed for all measurements.
Measurements of HO2, RO2 (35 % accuracy, 2σ), and OH
(45 % accuracy, 2σ) were made onboard the NSF/NCAR C-130 using
chemical ionization mass spectrometry (CIMS)
. Figure indicates that
the CIMS HO2 / OH ratio is approximately equal to the modeled
HO2 / OH ratio for NO values less than 1 ppbv but surpasses modeled
HO2 / OH for NO values greater than 1 ppbv, declining less rapidly than
models for increasing NO mixing ratios. In previous studies, the agreement
between measured and modeled OH has been independent of NO, so that the
deviation between the measured and modeled HO2 / OH ratio is due to
deviations between measured and modeled HO2
.
Modeled RO2 relative to the CIMS-observed RO2 is also
underestimated at high NO levels (Fig. ). Because the C-130
aircraft and ground-based inorganic and organic species mixing ratios in
Golden are within 30 % on average, a disagreement between measured and
modeled peroxy radicals at high NO levels observed on the aircraft is relevant to
understanding the MOPS measurements made at the Golden ground-based site.
One hypothesis is that a missing HO2 or RO2 source was not included
in the models and that this source may be proportional to NO. However,
42 total C2-C10 VOCs were measured using whole-air canister
samples representing a large suite of organic chemical species within the
models, including ones with high OH reactivities that are particularly
important for O3 formation. We have tested two hypotheses: first, that
an RO2 source that reacts with NO to form HO2 and NO2 is
missing in the models despite the suite of VOC measurements made in Golden,
and second, that an unknown peroxy radical source co-emitted with NO can
explain the missing modeled P(O3).
To test our first hypothesis, we have added a generic reaction involving
RO2 and NO to form additional HO2 + NO2 + RO in the
MCMv331, enhancing the HO2 produced from RO2 + NO reactions.
Since the reaction between the methylperoxy radical (CH3O2) and NO
is the dominant organic peroxy radical reacting with NO to form new O3
in both chemical mechanisms, this species' reaction rate coefficient was used
for this model test. By essentially doubling the CH3O2 rate
constant, this reaction only elevates modeled P(O3) throughout the day,
does not alter the diurnal P(O3) pattern (Fig. ),
and does not resolve the discrepancy between measured and modeled peroxy
radicals at high NO levels.
Model P(O3) scenarios using MCMv331 calculated for daytime
P(O3) hours between 06:00 and 18:00 LT. Median hourly P(O3) is
derived from the model case studies described in the main text and compared
to the MOPS median diel P(O3) and MCMv331 median diel base case.
To test our second hypothesis, additional RO2 was added to the model in
order to match the peak morning diel MOPS P(O3) in
Fig. and was scaled to NO as this unknown species is
likely co-emitted with NO. This addition improves model–measurement
P(O3) agreement in the morning and only slightly overestimates the
afternoon diel P(O3) relative to the MOPS, agreeing with measured
P(O3) within uncertainty levels. In magnitude, the model–measurement
RO2 agreement is improved with this case study. However, the RO2
continues to increase, while measured RO2 decreases at higher NO
(Fig. ). Thus, this model case study suggests that an
unknown missing RO2 source could possibly explain the differences
between measured and modeled P(O3) if this discrepancy between measured
and modeled RO2 source can be resolved and if the identity of the unknown
RO2 can be found.
Similarly, a VOC source that could explain prior model–measurement HOx
disagreement has not been identified in other literature studies in which
missing HO2 was of a magnitude similar to this study, even when proposed
VOCs were added to model base case scenarios
. discuss that,
if this missing HOx source is also a missing OH loss, then this loss
would be evidenced in measurements of OH reactivity at high NO levels, yet no such
OH loss was observed. Further, found that measured
peroxynitric acid (HO2NO2) is also elevated compared to models at
high NO or NOx levels. Peroxynitric acid thermally decomposes to form HO2
and NO2 and can also be weakly photolyzed to form HO2.
propose that increasing the thermal decomposition rate of
HO2NO2 could resolve model underestimation of HO2 at high NO levels,
but even when this decomposition rate was increased by a factor of 5, it
did not correct for higher measured than modeled P(O3) at high NO levels. Model
sensitivity runs for Golden, CO, using this increased decomposition rate for
HO2NO2 in MCMv331 corroborate this same result
(Fig. ).
Modeled ambient HONO sources
Another hypothesis is that ambient HONO is missing from the model. The
production and subsequent photolysis of nitrous acid (HONO) is an important
morning HOx source at high NO or NOx levels, often comparable to or larger
than other HOx sources such as peroxide and organic VOC photolysis or
O3 photolysis followed by the subsequent reaction between O(1D) and
water vapor to produce OH. In previous field studies, HONO photolysis was a
substantial contributor to daytime HOx production but can be largely
underpredicted by models, especially in urban environments, and may be a more
viable solution to the model–measurement discrepancy found in this study.
Nitrous acid was not measured during DISCOVER-AQ or FRAPPÉ but was
predicted by the gas-phase RACM2 and MCMv331 based on continuous,
ground-based NOx observations. Thus, model HONO sources in this study
only include those in the gas phase. Photolytic conversion of NO2 to
HONO on aerosol surfaces
adsorption of HNO3 on ground surfaces and subsequent photolysis
, and other photolytic heterogeneous sources are not
included. Therefore, model underprediction of HONO mixing ratios in the
morning can be one cause for modeled versus measured HO2 / OH
disagreement.
indicate that, even after additional gas-phase and
heterogeneous HONO sources were added to MCMv331, HONO was still
underestimated relative to models on average and that a missing HONO source
was correlated with JNO2, NO2, and the product of NO2
and OH reactivity for an urban area. Furthermore, only model results using
measured HONO were able to replicate observed OH levels. Field studies in
which HONO was continuously measured and used to constrain both
zero-dimensional and three-dimensional chemical models have been able to
replicate observed OH within uncertainty levels but still exhibit the same
behavior of higher measured HO2-to-OH ratios and P(O3) than modeled HO2-to-OH ratios and P(O3)
at high NO levels
.
A HONO source proportional to NOx was added to the MCMv331, resulting in
average HONO levels of 0.5–0.9 ppbv between 07:00 and 12:00 LT, with peak
HONO levels of 0.9 ppbv at 10:00 LT when MOPS P(O3) exhibits its diel
peak. This case study approximately replicates the observed morning
P(O3) (Fig. ) and observed OH within uncertainty
levels. However, while added HONO in the MCMv331 improves the agreement
between observed and modeled diel P(O3), midmorning HONO levels needed
to do so are over a factor of 2 higher than those observed in other areas
within Colorado and in environments with
much higher NOx levels . Thus, the
HO2 / OH ratio and the abnormally high HONO required to match the
observed P(O3) provide evidence that at most only a part of the observed
P(O3) can be explained by atmospheric HONO.
Reactive chlorine chemistry
Model underrepresentation of nitryl chloride (ClNO2) production is
another possible cause of the model underestimation of P(O3). Nitryl
chloride serves as a nocturnal NOx reservoir and, when photolyzed, can
produce additional reactive chlorine (Cl) and nitrogen dioxide (NO2).
Reactive chlorine, even at low mixing ratios, has been found to serve as a
major oxidant for VOCs, possibly increasing HO2 and O3 production
in the early morning hours by as much as 30 %
. The effects of
ClNO2 production on chlorine chemistry and VOC oxidation have been
provided in the literature as one possible explanation for the measured versus
model HO2 data mismatch at higher NO levels
.
Heterogeneous uptake of dinitrogen pentoxide (N2O5) on
chloride-containing aerosol particles can produce nitric acid (HNO3) and
ClNO2 in both marine and continental environments through the following
reaction:
N2O5→khetϕClNO2+(2-ϕ)HNO3,
in which khet is the heterogeneous reaction-rate coefficient
dependent upon the aerosol surface area density and the N2O5 uptake
coefficient on chloride-containing aerosols, and ϕ is the ClNO2
product yield.
To test this hypothesis, we constrained the MCMv331 with continuous, cavity
ring-down spectroscopy measurements of N2O5 from
a nearby measurement site (Boulder Atmospheric Observatory;
40.050∘ N, 105.010∘ W) and implemented a reduced chlorine
chemical mechanism in the MCMv331 provided by . We assumed
an N2O5 uptake coefficient of 0.02, which is within the range of
coefficients calculated in prior field studies
and laboratory experiments . To be consistent
with previous studies near Golden, the aerosol surface area density was
varied between 150 and 250 µm2 cm-3, and ϕ is
varied between 0.05 and 0.1 . It is important
to note that these assumptions vary largely with relative humidity and
aerosol surface area and composition
,
but modeling over a range of values can provide a qualitative prediction of
ClNO2 production effects on model P(O3) in this region. In each
model case, the MCMv331 runs including ClNO2 production, and Cl–VOC
chemistry resulted in average ClNO2 mixing ratios between 0.04 and
0.13 ppbv during the early morning hours (03:00–06:00 LT) and a slight
increase in diurnal P(O3) values of less than 5 %. Thus, although
chlorine chemistry can have a large effect on P(O3) during the winter
and in marine environments, these model runs indicate that Cl chemistry does
not play a large enough role in O3 photochemistry during this summer
campaign to explain the observed morning discrepancy between measured and
modeled O3 formation rates in Golden, CO.
Implications for O3 mitigation strategies
NOx–VOC sensitivity
The underestimation of model P(O3) relative to the MOPS at high NO or
NOx levels can have far-reaching implications for model assessment of the
dependency of P(O3) on NOx and VOCs. When examining model
sensitivity to NOx, levels were adjusted up or down by a factor of 2
and as a result, increasing NOx levels decreases P(O3) (as in a
VOC-sensitive regime), while lowering NOx levels acts to increase
P(O3) (Fig. ).
The fraction of free radicals removed by NOx, LN/Q, has been used
in the literature to assess NOx–VOC sensitivity in regions experiencing
high O3 . Here, LN is the
rate of total free radical removal by NOx, and Q is the total radical
production rate. When significantly above 0.5, the atmosphere is within a
VOC-sensitive regime, while when significantly below 0.5, the atmosphere is
within a NOx-sensitive regime . The median LN/Q
was calculated with the RACM2 using full-campaign observations, indicating
that the Golden, CO, modeled P(O3) is VOC-sensitive before 12:00 LT and
NOx-sensitive thereafter (Fig. S4). During DISCOVER-AQ and FRAPPÉ,
model sensitivity studies conducted for the Boulder Atmospheric Observatory
site just northeast of Golden also found maximum photochemical O3 to be
largely NOx-sensitive in the afternoon . If peroxy
radicals are underestimated by chemical mechanisms relative to observations
for NO levels greater than a few parts per billion volume, then the total radical production
rate, Q, may also be underestimated, thereby shifting LN/Q towards
NOx sensitivity in the early morning and prolonging this regime during
times of the day when O3 production is largest.
The largest O3 formation rates are measured between 09:00 and 11:00 LT
when NOx and VOC emissions are high and the mixing layer depth is
relatively developed at 600 to 1000 m, on average. Although a shallower
mixing layer could be one reason for high MOPS P(O3) before 11:00 LT,
we note that secondary diurnal MOPS P(O3) peaks are also evidenced on
individual days alongside increased NOx and VOCs during afternoon rush
hour in a fully developed mixing layer. Further, high P(O3) and the
shift from VOC to NOx sensitivity in the late morning could be
attributed to early morning entrainment of VOCs from the free troposphere in
the absence of NOx entrainment. However, these VOCs in the upper
troposphere are longer-lived and are less important in propagating O3
formation than other, higher-reactivity VOCs. Therefore, although entrainment
of species during the morning hours and the depth of the mixing layer
influence NOx–VOC sensitivity and these high morning P(O3) rates,
it is more likely that O3 precursor species at the surface level are the
predominant factors influencing P(O3) for this study.
Although longer-term analyses are generally required to suggest effective
O3 reduction strategies, if the P(O3) NOx–VOC sensitivity is
shifted more towards a NOx-sensitive regime in the morning as the MOPS
observations suggest, reducing NOx would be an effective strategy for
O3 mitigation in Golden, CO, and its immediate surroundings.
Ox advection
Ozone formation precursors can be transported westward to Golden because of
the Colorado Front Range terrain and its induced wind patterns. When air in
Golden is influenced by O3 precursor emissions from the east (e.g., the
Denver metropolitan and Commerce City regions), greater anthropogenic VOC and
NO mixing ratios are measured on average. Thus, we evaluate calculated
O3 advection using Eq. () in an attempt to evaluate the
impact of O3 advection derived from the MOPS and the models on observed
O3 patterns in Golden.
(a) Ox (O3 + NO2) and NO mixing ratios for
Denver plume (solid) versus all other days (dashed) from
17 July to 10 August 2014 in Golden, CO. (b) Median measured and
modeled P(O3) for Denver plume (solid) and non-Denver plume (dashed)
days between 06:00 and 18:00 LT.
Measured Ox maxima are 2–7 ppbv greater on these plume days than
when air is advected from elsewhere, and higher P(O3) is measured by
MOPS than is modeled by the RACM2 and MCMv331 (Fig. ). This
result is roughly consistent with the difference between measured and modeled
P(O3) as a function of NO shown in Fig. . When winds
are not easterly (non-plume days), lower levels of anthropogenic VOCs and
NO and lower Ox maxima are observed. Average measured diel P(O3)
is also 20 % lower than on plume days. The MOPS behavior stands in contrast
to the models, in which average diel RACM2 and MCMv331 P(O3) is
approximately 30 % higher on non-plume days than on plume days.
A simple advection analysis was performed to determine the factors in
Eq. () that most contribute to observed Ox levels in
Fig. for the campaign period. The transport rate of
Ox out of the mixing layer through deposition is calculated to be at
most 1 ppbv h-1 and is neglected here. The morning O3 entrainment
rate during DISCOVER-AQ and FRAPPÉ has been calculated for the Colorado
Front Range region to be 5 ppbv h-1 on average, with afternoon average
entrainment rates of approximately -1 ppbv h-1
(). Assuming an average entrainment rate of
5 ppbv h-1 for morning hours between 06:00 and 12:00 LT and an Ox
entrainment rate of -1 ppbv h-1 for times between 12:00 and 18:00 LT
and subtracting diel entrainment and observed P(Ox) from the local diel
Ox rate of change, the average Ox advection rate derived from MOPS
and models between 06:00 and 18:00 LT is -5.4 to -2.4 ppbv h-1 on
plume days and -1.7 to -3.5 ppbv h-1 for all other days,
respectively. This quick calculation suggests that advection contributes
weakly to observed Ox, while either entrainment or P(Ox) dominate
the Ox patterns observed in Golden and its surrounding areas. Because
these advection rates are derived quantities from the MOPS and the models,
and both methods for determining P(Ox) contain substantial uncertainty,
it is difficult to quantitatively assess Ox advection rates in Golden,
CO. Decreasing the uncertainty in P(Ox) is thus salient for accurately
calculating the terms in Eq. () contributing to observed Ox
levels in the Colorado Front Range.