Observations of total peroxy radical concentrations
([XO2] ≡ [RO2] + [HO2]) made
by the Ethane CHemical AMPlifier (ECHAMP) and concomitant observations of
additional trace gases made on board the Aerodyne Mobile Laboratory (AML)
during May 2017 were used to characterize ozone production at three sites in
the San Antonio, Texas, region. Median daytime [O3] was 48 ppbv at
the site downwind of central San Antonio. Higher concentrations of NO and
XO2 at the downwind site also led to median daytime ozone
production rates (P(O3)) of 4.2 ppbv h-1, a factor
of 2 higher than at the two upwind sites. The 95th percentile of
P(O3) at the upwind site was 15.1 ppbv h-1,
significantly lower than values observed in Houston. In situ observations,
as well as satellite retrievals of HCHO and NO2, suggest that the
region was predominantly NOx-limited. Only approximately
20 % of observations were in the VOC-limited regime, predominantly before
11:00 EST, when ozone production was low. Biogenic volatile organic
compounds (VOCs) comprised 55 % of total OH reactivity at the downwind
site, with alkanes and non-biogenic alkenes responsible for less than
10 % of total OH reactivity in the afternoon, when ozone production was
highest. To control ozone formation rates at the three study sites
effectively, policy efforts should be directed at reducing
NOx emissions. Observations in the urban center of San
Antonio are needed to determine whether this policy is true for the entire
region.
Introduction
Tropospheric ozone (O3) is a secondary air pollutant formed through
a series of reactions involving volatile organic compounds (VOCs) and
NOx ([NOx] ≡ [NO] + [NO2], where NO is nitric oxide and NO2 is
nitrogen dioxide). While tropospheric ozone exists naturally through
stratospheric transport (Holton et al., 1995) and in situ tropospheric
production, human activities have drastically perturbed these background
values (Lamarque et al., 2005). Exposure to ozone adversely impacts human
health, limiting lung and cardiac function, exacerbating chronic respiratory
illnesses, and precipitating early mortality (Bell et al., 2006; Park et al.,
2005; Jerrett et al., 2009; Silva et al., 2013). In response to these adverse
impacts, in 2015 the United States Environmental Protection Agency (EPA)
imposed an 8 h ozone standard of 70 ppbv, lowering the exposure limit from
the 75 ppbv standard set in 2008 (EPA, 2015). While ambient concentrations
of the ozone precursor NOx have declined significantly over
much of the US (Choi and Souri, 2015; He et al., 2013; Duncan et al., 2016;
Lamsal et al., 2015), reductions in ozone concentrations have been less
dramatic. Background ozone concentrations have actually increased in some
locations (Cooper et al., 2012; Choi and Souri, 2015); in other areas that
have seen decreases in ambient ozone concentrations, such as Texas and the
Mid-Atlantic region, ozone still periodically exceeds the EPA standard (e.g.,
He et al., 2013).
Ozone production is generally classified as either NOx- or
VOC-limited (Kleinman, 1994; Thornton, 2002). Net formation of ozone occurs
when NO is oxidized to NO2 by reaction with the hydroperoxyl radical
(HO2) or an organic peroxy radical (RO2). In the
NOx-limited regime, comparatively low concentrations of
NOx allow for the removal of ROx radicals
([ROx] ≡ [OH] + [HO2] + [RO2],
where OH is the hydroxyl radical) through self-reactions (e.g., Reactions R1–R3).
In the VOC-limited regime, ROx radicals are removed from the
atmosphere via reactions with NOx, producing less reactive
compounds such as nitric acid (HNO3) (Reactions R4–R6). In the
NOx-limited regime, reductions in NOx lead
to reductions in O3, while in the VOC-limited regime, reductions in
NOx without concomitant reductions in VOCs can actually
increase O3 production. One prominent example of this is the
weekday–weekend effect in the southern California South Coast Air Basin, where
O3 increases on weekends due to decreases in NOx
emissions from heavy-duty diesel trucks (Pollack et al., 2012). The effective
implementation of ozone reduction policies therefore requires a detailed
understanding of the ozone production regime of the target area.
HO2+OH→H2O+O2HO2+HO2+M→H2O2+O2+MHO2+RO2→ROOH+O2OH+NO2+M→HNO3+MNO+RO2+M→RONO2+MNO2+R(O)O2+M→R(O)O2NO2+M
Texas is the second most populous state in the US. With multiple large urban
centers and a mixture of urban and industrial emissions from petrochemical
processing facilities as well as from natural gas and oil extraction, the
state has complex pollution chemistry. This combination of a large population
and pollution makes understanding ozone production in this region
particularly important. Previous studies of ozone formation in Texas have
focused primarily on Houston and the surrounding region. Mazzuca et
al. (2016) used in situ observations of NOx and
O3 from the DISCOVER-AQ campaign in summer 2013 along with output
from the CMAQ model to find significant diurnal variability in ozone
production, with higher ozone production rates (P(O3)) in
the morning and a transition from the VOC- to NOx-limited
regime before the afternoon. Similar results were found during the TEXAQS2000,
TRAMP2006, and SHARP 2009 campaigns (Mao et al., 2010; Ren et al., 2013).
Multiple studies have found that anthropogenic alkenes, particularly ethylene
and propylene, are major contributors to OH reactivity and therefore
O3 production
(Mao et al., 2010; Kleinman et al., 2002; Ryerson et al., 2003) in the
region, leading to P(O3) greater than 50 ppbv h-1
(Mazzuca et al., 2016). OH reactivity is defined as the sum of the
products of the concentration of species X and the
reaction rate coefficient (kX+OH) of X with OH (Eq. 1).
kOH=∑ik(X+OH)[X]i
There have been comparatively few field campaigns, however, to study San
Antonio, Texas, the seventh most populous city in the US. In July 2018, the
EPA designated the San Antonio region as being in marginal non-attainment
with the new 70 ppbv standard, suggesting a need to understand the
O3 formation chemistry in the region. In addition, San Antonio has
a significantly different emissions profile than Houston. For example,
examination of long-term VOC monitoring in Floresville, Texas, a site
immediately upwind of San Antonio, suggests that OH reactivity is dominated
by alkanes (Schade and Roest, 2016) in contrast with the dominance of alkenes
in Houston. Figure 1 shows the trends in concentrations of ozone,
NOx, and Ox
(Ox≡O3+NO2)
at two Texas Commission on Environmental Quality (TCEQ) monitoring sites,
with one (Camp Bullis) located northwest of the urban center and the other
(Pecan Valley) in the downtown area (Fig. 2b). With the lowering of the 8 h
ozone standard from 75 ppbv (dashed purple line) to 70 ppbv (solid purple
line), the Camp Bullis site is much more likely to be in exceedance, while
the Pecan Valley site remains below both standards. Despite noticeable
decreases in maximum NOx at both sites over the 14-year
period shown here, there is little noticeable trend in ozone. This is in
agreement with Choi and Souri (2015), who found a 0.07×1015 cm-2 yr-1 decrease in tropospheric column NO2
over San Antonio between the years 2005 and 2014 while finding an increasing
trend of 0.64 ppbv yr-1 in the minimum value of surface ozone over the
same period. Further study is needed in the San Antonio region to understand
the driving factors behind ozone production.
Time series of maximum daily average 8 h (MDA8) O3,
NOx, and Ox at the Camp Bullis (a, c, e) and Pecan Valley (b, d, f) TCEQ sites for 2002–2017. Summer
months (May–September) are shown in red, and winter months
(December–February) are shown in blue. MDA8 is calculated by determining the
maximum value of a species from running 8 h averages throughout the day. The
purple dashed and solid red lines represent the 2008 (75 ppbv) and 2015
(70 ppbv) O3 standards, respectively. Data were downloaded from
https://www17.tceq.texas.gov/tamis/index.cfm?fuseaction=home.welcome
(last access: 27 January 2019).
In this paper, we present results from the San Antonio Field Study
(SAFS) conducted in the San Antonio, Texas, region in May 2017. We show
observations of total peroxy radical concentrations
([XO2] ≡ [RO2] + [HO2]) from
three sites in the San Antonio area, characterizing the
XO2 distribution in the region. We use these
XO2 measurements, along with observations of NO and other
trace gas species, to quantify ozone production in regions up- and downwind
of the urban core. Though there have been many prior determinations of
P(O3) using measurements of a subset of peroxy radicals
(i.e., using laser-induced fluorescence measurements of HO2 and a
fraction of RO2) (e.g., Ren et al., 2013), this is one of the few
determinations of ozone production using the direct observation of total
peroxy radicals (Sommariva et al., 2011). Combined with quantification of the
primary production of ROx radicals
(P(ROx)) and satellite retrievals of HCHO and
NO2, we determine the ozone production regime in San Antonio.
Finally, we explore the main contributors to OH reactivity in the region.
MethodologyCampaign description
The SAFS campaign was conducted from 11 to 31 May 2017 at several sites in
the greater San Antonio region. We describe measurements made on the
Aerodyne Mobile Laboratory (AML) at three sites: the University of Texas San
Antonio (UTSA) from 11 to 16 May and from 27 to 31 May, Floresville, Texas,
from 16 to 21 May, and Lake Corpus Christi (Corpus) from 21 to 26 May. The
sites were chosen to determine the impact of various emission sources on
ozone formation affecting San Antonio. During May in southeastern Texas, the
prevailing wind direction is southeasterly, coming off the Gulf of Mexico.
UTSA is located northwest (i.e. downwind) of downtown San Antonio (Fig. 2a),
while the Floresville and Corpus sites were both located upwind of the city.
This allows for the determination of background values of compounds through
observation at the Floresville and Corpus sites, while observations at UTSA
are more representative of air photochemically processed with urban
emissions. We define background here as values upwind of the UTSA site. The
AML was situated at all sites to minimize influence from local emissions. At
UTSA, the AML was located in a mostly vacant parking lot about 1 km south of
the nearest major roadway. In Floresville and Corpus, there were no nearby
major roadways, local traffic was at a minimum, and influence from local
point and mobile sources was limited. Potential influences from transient
local sources (e.g., lawn mowers and jet skis) were removed in the same
manner as interference from the generator emissions described below.
(a) The sampling locations for the AML are indicated: 1 – University of
Texas San Antonio, 2 – Floresville, and 3 – Lake Corpus Christi. The ratio of
total column HCHO to tropospheric column NO2 averaged over the months
of May through July 2017 is also shown for grid boxes with 10 or more
observations of both species over the indicated time period. The outlines of
the Eagle Ford Shale (grey) play and San Antonio city limits (purple) are
also shown for reference. (b) The major roadways and TCEQ monitoring stations
(6: Camp Bullis, 4: Pecan Valley, 5: Calaveras Lake) in the San Antonio
region used in this study are shown. The UTSA and Floresville SAFS sites are
also shown for reference.
The AML is outfitted to measure a suite of gas- and particle-phase
atmospheric species (Herndon et al., 2005). All instrument inlets were
mounted approximately 15 m above ground level on a retractable tower located
near the AML. At both the Floresville and UTSA sites, the AML was powered
through connection to the local electric utility, while at Corpus a diesel
generator was used. Although the generator was situated downwind of the
instrument inlets, some stagnation and recirculation did occur, allowing for
occasional sampling of generator exhaust. Air parcels affected by the
generator exhaust were removed through analysis of CO observations. A filter
for generator-influenced air was created by determining the minimum CO value
over a 100 s period every 5 min. Any air parcel with a CO mixing ratio
10 ppbv higher than this minimum was assumed to be impacted by a local
transient source, including the generator.
Trace gases measured during SAFS and used in this study are summarized here.
Unless otherwise indicated, data used in this study were reported as 1 min
averages and then averaged to the 2 min Ethane CHemical AMPlifier (ECHAMP)
time base, described in the following section. NO2 was measured at
1 Hz via Cavity Attenuated Phase Shift (CAPS) spectroscopy (Kebabian et al.,
2005, 2008). Nitric oxide (NO) was measured at 0.1 Hz through the same inlet
as NO2 and O3 using a Thermo Fisher 42i-TL
chemiluminescence analyzer, while O3 was measured with a 2B-Tech
model 205 ultraviolet (UV) absorption instrument. Uncertainties (2σ)
of the NO, NO2, and O3 observations on the ECHAMP
measurement timescale are below 5 %. The above instruments were zeroed
every 15 min with humidity-matched zero air. The zero air was generated by
passing ambient air through an Aadco ZA30 Catalyst system for VOC removal and
through Purafil Chemisorbant Media, a potassium-permanganate-based scrubber,
for NOx removal.
Quantum Cascade Tunable Infrared Laser Direct Absorption Spectrometer
(QC-TILDAS) instruments from Aerodyne Research Inc. (ARI) were used to measure CO and
H2O (2200 cm-1; measurement wave number), HCHO (1765 cm-1),
CH4 and C2H6 (2990 cm-1), H2O2 (1277 cm-1),
and C3H8 (2965 cm-1) (McManus et al., 2015). A
proton-transfer-reaction high-resolution time-of-flight (PTR-HR-ToF) mass spectrometer was used to
measure isoprene, acetaldehyde, acetone, benzene, methanol, the sum of
monoterpenes, the sum of methyl vinyl ketone (MVK) and methacrolein, and
toluene. Typical measurement uncertainties were on the order of 25 %.
Finally, a prototype of a commercially available gas chromatograph from ARI
with an electron-impact time-of-flight mass spectrometer (GC-EI-ToF-MS) was
used to measure a suite of VOCs, including isoprene, 1,2,3-trimethylbenzene,
ethylbenzene, cyclohexane, n-heptane, n-hexane, n-octane, n-pentane,
o-xylene, and the sum of m- and p-xylenes. The GC sampled with a
multicomponent adsorbent trap (Pollmann et al., 2006) for a
5 min integration period every 20 min. GC observations are
unavailable for 20–30 May. While toluene and m- and p-xylene measurement
uncertainty was on the order of 20 %, typical measurement uncertainties of
other observed species, except isoprene, were on the order of 10 %.
While there were two independent observations of isoprene, there were
limitations with both methods. It was determined that the actual isoprene
concentration in the calibration standard used in the field for the PTR had
degraded over time, resulting in erroneously high isoprene values. On the
other hand, the GC was not calibrated for isoprene during the campaign, and
observations are only available for half the time. As a result, we use the
PTR isoprene from the entire campaign scaled to the GC values, using a GC
isoprene sensitivity determined after the campaign. This method results in
an estimated isoprene uncertainty of ≈30 % (1σ). See the
Supplement for more information.
Temperature, wind speed, and wind direction were measured at the top of the
inlet tower with a 3-D R.M. Young (Model 81000RE) sonic anemometer. Atmospheric
pressure observations used in this study were taken from the National Weather
Service observations at the San Antonio International Airport for the UTSA
and Floresville sites and from the Corpus Christi International Airport for
the Corpus site. NO2 photolysis frequencies (JNO2) were measured
via a filter radiometer (MetCon, GmbH) located on top of the AML (Shetter
et al., 2003; Stark et al., 2007).
ECHAMP
Total peroxy radical concentrations ([XO2]) were measured
via chemical amplification by the ECHAMP instrument. A complete instrument
description can be found in Wood et al. (2017), and only the most relevant
details are summarized here, including a new sampling system that includes an
integrated, remotely controlled ROx calibration source.
Briefly, ECHAMP measures total XO2 concentration at a
2 min resolution by reacting peroxy radicals with excess NO and ethane
(C2H6). Through a series of chain reactions, each
XO2 radical produces approximately 20 NO2
molecules (depending on the relative humidity (RH)), which are then measured
with a commercially available NO2 monitor. Because this NO2
monitor also measures ambient O3 and NO2 (Ox), a
second channel and dedicated NO2 monitor are used to only measure the
sum of [O3] and [NO2]. The difference between the two
channels, divided by the “amplification factor” of ≈20, yields the
XO2 concentration.
The inlet box is a 39 cm × 44 cm × 16 cm fiberglass,
rainproof electrical enclosure. The box was mounted at the top of the
sampling tower and connected to the rest of the instrument via a bundle of
tubes and electrical cables. Ambient air was sampled at a flow rate of
6.5 L min-1
through 76 mm of 3.6 mm inner diameter glass into the inlet box (see
Supplement Fig. S1 for a schematic of the plumbing). The glass was internally
coated with halocarbon wax to minimize wall losses of XO2.
The flow was subsampled into two 1.9 cm3 reaction chambers at a flow
rate of 1.1 L min-1 each. Temperature and RH of the remaining 4.5 L min-1 of sampled
air were measured with a Vaisala probe (Model HMP60). Laboratory tests over a
range of flow rates and RH have demonstrated sampling losses of HO2
of less than 3 % and negligible losses of CH3O2 (Kundu et
al., 2019).
Reaction chambers cycled every minute between an amplification mode and a
background mode, for a total cycle time of 2 min. In both modes, 25 sccm of
39.3 ppmv NO in N2 (Praxair) was added at the beginning of the
reaction chamber, resulting in a final NO mixing ratio of 0.90 ppmv. In
amplification mode, 35 sccm of a 42.2 % ethane mixture in N2
(Praxair) was also added to the sampled air at the beginning of the reaction
chamber. The radical propagation scheme shown in Reactions (R7)–(R13), in
which Reactions (R9)–(R13) repeat numerous times, results in the formation of
NO2. The number of NO2 molecules formed per
XO2 molecule sampled is known as the amplification factor
(F) and varies with RH. During SAFS, F was 23 for dry air and decreased
to 12 at 58 % RH. The two calibration methods used to determine F are
described below and more fully in the Supplement. At 15.2 cm downstream of the
NO/C2H6 injection point, 35 sccm of N2 was added to
the flow. In the background chamber, the N2 and C2H6
flows were switched (N2 was added upstream, and C2H6
was added downstream), allowing XO2 radicals to react with
NO to form HONO or alkyl nitrates before 35 sccm of the 42.2 % ethane
mixture was added at the end of the reaction chamber. The resultant
NO2 from each chamber was then measured with separate, dedicated CAPS
instruments. Total XO2 was then determined by the
difference between the two NO2 measurements divided by F.
RO2+NO→RO+NO2RO→HO2+productsHO2+NO→OH+NO2OH+C2H6→H2O+C2H5C2H5+O2+M→C2H5O2+MC2H5O2+NO→C2H5O+NO2C2H5O+O2→CH3CHO+HO2
The CAPS instruments were calibrated for NO2 before, after, and once
during deployment via the quantitative reaction of known concentrations of
O3 generated with a 2B Technologies ozone generator (Model 306)
with excess NO. This ozone source agreed within 1 % with a separate Thermo
ozone generation source (Model 49C). All NO2 calibrations agreed
within 5 %. The amplification factor (F) was determined by producing known
amounts of peroxy radicals with two calibration methods: photolysis of
H2O and of CH3I. Both methods are described in more detail in the Supplement.
Briefly, the H2O photolysis method is similar to that used by most
HOx instruments, in which H2O was photolyzed at a wavelength of
184.9 nm to form an equimolar mixture of OH and HO2 (Mihele and
Hastie, 2000; Faloona et al., 2004). This mixture was then reacted with
H2 to convert the OH into HO2. Radical concentrations were
quantified using the relevant spectroscopic parameters and the measured
H2O and O3 concentrations in the calibration gas.
The second calibration method was based on 254 nm photolysis of CH3I in
humidified air, producing the CH3O2 radical. The radical
concentration is quantified by reaction of the CH3O2 with NO in
the absence of C2H6, producing 1.86 NO2 molecules per
CH3O2. The H2O photolysis method was performed six times, while
the CH3I method was performed once during the field campaign, on 31 May.
Both methods were repeated twice in the laboratory after the campaign.
Observations from ECHAMP agreed within 12 % with the H2O photolysis
calibration source operated by Indiana University during a comparison study
in 2015 (Kundu et al., 2019). For the
XO2 observations described in this paper, we use the
CH3I
calibration. While both methods agree within uncertainty, the H2O
photolysis method was only conducted for RH values of less than approximately
20 %, much lower than typical ambient RH. See the Supplement for further
information.
The total 2σ accuracy for XO2 during SAFS was
approximately 25 %. Calibrations were not performed at RH values greater
than 71 %. Therefore, we omit all observations with a sample RH greater
than 71 %. Approximately 85 % of these high RH points were observed
at nighttime, so we only consider daytime data (07:00–20:00 local time)
unless otherwise indicated.
Calculation of P(O3) and
P(ROx)
We use measurements of XO2 and NO to calculate the gross
rate of ozone production P(O3) using Eq. (2), in which
kNO+HO2 is the reaction constant for the reaction of NO with
HO2 and ki is the reaction constant for NO with an organic
peroxy radical [RO2]i. We note that this is more accurately
described as the rate of odd oxygen (Ox) production. Because
ECHAMP only measures the sum of peroxy radicals and not their speciation, we
assume a simplified form of this relationship (Eq. 3), where keff
is an effective rate constant taken as that of kNO+HO2. Box
modeling results for this site, which will be discussed more fully in a
forthcoming paper, show the dominant XO2 species are
HO2, CH3O2, and isoprene RO2. At 298 K,
kNO+HO2 is within 10 % of the k values for the reaction of
NO with CH3O2 and isoprene RO2 (Orlando and Tyndall,
2012), supporting our choice of keff. Further, while the reaction
of NO with acetyl peroxy radicals is approximately 2.5 times faster than with
other peroxy radicals at 298 K, box modeling results suggest that these
radicals comprise only 5 %–10 % of total XO2,
resulting in an average difference in P(O3) of 15 %
from the kNO+HO2 value used here. This uncertainty is comparable
to the total uncertainty of the kNO+HO2 rate constant, estimated
as 15 % (Sanders et al., 2011). As will be shown in Sect. 3.2, our
conclusions are insensitive to the value of keff chosen.
Uncertainty in gross P(O3) results from uncertainty in the
NO and XO2 measurements, 5 % and 25 %,
respectively, and keff, whose uncertainty we estimate at
23 %, determined by adding the uncertainty in the kNO+HO2
rate constant and the uncertainty in the choice of keff in
quadrature. This results in a total P(O3) uncertainty of
34 %.
P(O3)Gross=kNO+HO2NOHO2+NO∑iki[RO2]iP(O3)Gross=keffNOXO2LO3=kO1D+H2OH2OkO1D+H2OH2O+kO1D+N2N2+kO1D+O2O2JO1D+kOH+O3OH+kHO2+O3HO2+∑ikalkene-ialkeneiO3+kOH+NO2OHNO2M
The net formation rate of O3 is equal to
P(O3)Gross – L(O3). In
order to tie P(O3) completely to observations, we report
only gross P(O3), not net P(O3). That
is, we only calculate the production term (Eq. 2) and not the loss term
(Eq. 4) for net ozone production. Calculation of the loss term requires
knowledge of the concentration of OH and alkenes as well as the fraction of
total XO2 comprised of HO2. Of these quantities,
only a small subset of alkenes – isoprene and monoterpenes – were measured
during SAFS. Estimating the alkene loss term using concentrations from nearby
TCEQ monitoring sites suggests that O3 loss due to this pathway is
negligible for the data analyzed here, and we omit this from our calculation
of ozone loss. To estimate OH and the fraction of XO2
comprised of HO2 and to determine whether analyzing only gross
P(O3) affects our conclusions, we used the Framework for
0-Dimensional Atmospheric Modeling (F0AM) box model (Wolfe et al., 2016b) to
calculate OH and the fraction of RO2 comprised of HO2. A
description of the model setup can be found in the Supplement. For data
points that were not modeled due to missing model constraints, these values
were estimated from the interpolation of modeled values, if observations were
made within 2 h of a modeled data point, or from site-specific mean daily
profiles if no modeled points were available. Using these modeled-derived
values for OH and the HO2 fraction, median L(O3)
for daytime observations at all sites was determined to be
0.90 ppbv h-1, which is 16 % of the gross production rate.
We use Eq. (5) to calculate the primary ROx production rate.
Here, P(ROx) is the ROx production
rate, J indicates photolysis rate, and kO1D+H2O,
kO1D+O2, and kO1D+N2 are the reaction rate
constants for the reaction of O1D with the indicated
species. The Tropospheric Ultraviolet and Visible (TUV) model was used to
calculate photolysis rate constants (J values), which were then scaled to
the measured JNO2. HONO was not measured during SAFS. We
estimate HONO concentrations assuming an upper limit to the
[HONO]/[NOx] ratio of 0.04 as described in Lee et
al. (2013). This is an upper bound on the HONO concentration and thus on HONO
contribution to P(ROx). Alkene concentrations were
estimated from nearby TCEQ monitoring sites, as described in Sect. 3.3.
Alkene ozonolysis was calculated to have a negligible impact on
P(ROx) and is omitted from the analysis.
P(ROx)=2JO1DO3kO1D+H2OH2OkO1D+H2OH2O+kO1D+N2N2+kO1D+O2O2+2JHCHOHCHO+2JCH3CHOCH3CHO+2JAcetoneCH3COCH3+2JH2O2H2O2+JHONOHONO
Total P(ROx) peaks at midday at about 0.65 pptv s-1
on average and is dominated by the ozone and HCHO terms, terms 1 and 2 from
Eq. (5), respectively, with contributions from the other observed species
totaling less than 5 % on average. Contributions from HONO were generally
less than 0.1 pptv s-1, even assuming the upper bound in the HONO to
NOx ratio used here.
Satellite data
We use observations of NO2 and HCHO from the Ozone Monitoring
Instrument (OMI) to provide a remotely sensed estimate of the surface ozone
production regime in San Antonio (Duncan et al., 2010; Ring et al., 2018).
OMI has a local overpass time of about 13:30 and provides daily, global
coverage. The instrument measures backscattered solar radiation in the
UV–visible region, allowing for differential optical absorption spectroscopy
(DOAS)-type retrievals of multiple species, including NO2 and HCHO.
For NO2, we use the NASA Goddard Space Flight Center (GSFC) version 3
level 2 tropospheric column product (Bucsela et al., 2013; Krotkov et al.,
2017) gridded to 0.25∘ latitude ×0.25∘ longitude
resolution. For HCHO, we use the version 3 level 2 reference-sector-corrected
swath product from the Harvard Smithsonian Astrophysical Observatory (SAO)
retrieval (González Abad et al., 2015) also on a
0.25∘ latitude ×0.25∘ longitude grid. For both OMI
products, we only use pixels that satisfy quality and row anomaly flags, have
a cloud fraction less than 30 %, and have a solar zenith angle less than
70 ∘. Additionally, data from the two outer most pixels are removed
due to their large footprint (28 km × 150 km) compared to the
nadir view.
Time series of O3 (blue circles), XO2 (red
triangles), NO (black stars), JNO2 (blue triangles), and
P(O3) (magenta circles) measured at all sites. All data are
averaged over the XO2 sampling period.
We analyze the HCHO to NO2 ratio using OMI data from May to July
2017. While SAFS only lasted 1 month, missing data due to cloud cover, the
row anomaly, and other factors necessitate a longer time period for data
averaging. To calculate the ratio of HCHO to NO2, we first calculate
the standard deviations (σ) of the HCHO and NO2 data at each
grid point. When calculating the ratio, we only include days within 2σ of the average HCHO and NO2 observations and only include grid
boxes that have at least 10 days with coincident observations of both
species.
ResultsDistribution of ozone and its precursors
The highest ozone mixing ratios observed at UTSA were on 14 and 15 May,
reaching a maximum near 80 ppbv, while daytime values typically varied
between 40 and 60 ppbv during the remainder of the campaign (Fig. 3). Median
daytime [O3] at all three measurement sites was 37 ppbv (Fig. 4a).
Median ozone was 18 ppbv higher at UTSA than at the background site in
Floresville. Although the highest ozone values were seen at UTSA, there was
significant overlap in the ozone distribution between the UTSA and Corpus
sites. Consistent with the higher O3 abundance, concentrations of
the O3 precursors isoprene, NO, and XO2 were
also highest at the UTSA site. Median isoprene concentrations, one of the
largest contributors to OH reactivity, as will be shown later, were almost 2
orders of magnitude larger at UTSA (1.2 ppbv) than at the other sites (0.05
and 0.03 ppbv at Floresville and Corpus, respectively). While the difference
in median [NO] at the sites was not as extreme, a much larger range was seen
at UTSA, where the 95th percentile of observations was above 2 ppbv.
Similar results are seen for the [XO2] distribution
(Fig. 4c), with the highest XO2 mixing ratios (90 pptv)
coinciding with the maximum O3. Median [XO2] was
approximately 1.5 times higher at the UTSA site (37 pptv) than at
Floresville (26 pptv) and Corpus (25 pptv).
The distribution of O3(a), isoprene (b),
XO2(c), NO (d),
P(O3)(e), and
P(ROx)(f) for all observations during
SAFS taken between 07:00 and 20:00. The distribution for the entire campaign
(All) as well as at the individual sites is shown. Medians are indicated by
the black lines, and the 5th, 25th, 75th, and 95th percentiles are
shown by the edges of the boxes and whiskers.
XO2 concentrations showed a distinct diurnal profile
(Fig. 5). Overnight values were approximately constant with a median of
around 10 pptv, until a small decline after 03:00. A steady increase in
[XO2] began at 09:00, with a peak of 50 pptv at 15:00 and
then a decline to the overnight value by 20:00. The shape of this profile is
in agreement with other observations of peroxy radicals from a variety of
chemical environments (Sanchez et al., 2016; Mao et al., 2010; Whalley et
al., 2018). Noise in the nighttime data is a result of higher RH and thus
degraded precision of the ECHAMP measurement technique and is not an
indication of significant nighttime variability. Even though we have filtered
for data points with RH greater than 71 % as discussed in Sect. 2.2,
nighttime RH is higher than daytime values, on average, decreasing
measurement precision. Daytime variability resulted from changes in
insolation and biogenic VOC concentrations. The days that showed little or no
diurnal profile at UTSA and Corpus were overcast, as evidenced by low
JNO2 (Fig. 3). Concentrations of isoprene and the sum of methyl
vinyl ketone (MVK) and methacrolein, both isoprene degradation products, were
at a maximum when [XO2] peaked at 90 pptv.
The diurnal profile of all 2 min average XO2
observations made during SAFS. Observations made at UTSA are shown in blue,
Floresville in red, and Corpus in black. The median value for 15 min time
bins for observations at all sites is shown by the gold trace.
The higher O3 concentrations at UTSA are consistent with its
location downwind of the urban core of San Antonio. Figure S2 shows wind
roses colored by ozone and the ozone precursors described above. The wind
direction while at UTSA was predominantly southeasterly, in agreement with
the climatological average for the region. The highest ozone mixing ratios,
as well as the highest XO2 and isoprene, were seen when
air parcels originated from this direction, traveling over the city. The
highest [NO] (greater than 2.2 ppbv), however, was seen with northerly and
northeasterly winds. This is likely because of the proximity of a major
highway north of the UTSA site, which would provide a source of
recently emitted, less processed emissions than in air parcels that traveled
from downtown San Antonio. The CO distribution by wind direction (not shown)
is consistent with this explanation.
Ozone production
The highest P(O3) values (and highest [NO] and
[XO2]) were observed at UTSA. Median
P(O3) between 07:00 and 20:00 at UTSA was 4.1
ppbv h-1, compared to just over 1 ppbv h-1 at both Floresville
and Corpus. The 95th percentile, 12.6 ppbv h-1, is significantly
lower than rates found in Houston, which frequently topped 40 ppbv h-1
(Mazzuca et al., 2016; Mao et al., 2010). As with [O3] and
[XO2], the highest P(O3) rates occurred
when winds traveled over downtown San Antonio.
The variation of P(O3) with NO for all daytime
observations (07:00 to 20:00) made during SAFS (a). Observations are
colored by P(ROx). The same data as shown in
panel (a) but sorted by P(ROx) are shown
in panel (b). Observations with P(ROx)
greater than 0.4 pptv s-1 are shown in red, while observations with
P(ROx) less than 0.2 pptv s-1 are shown in
blue. Data are separated into NO bins with an equal number of observations
per bin. The mean value of each bin is shown, with the error bars showing 1 standard deviation. The subset of observations with
P(ROx)<0.2 pptv s-1 is further
separated into three categories: low VOC reactivity (VOCR<3 s-1; magenta), medium VOC reactivity
(3< VOCR <6 s-1; black), and high VOC
reactivity (6< VOCR <9 s-1;
green) (c). As in panel (b) data are separated into NO bins
with equal numbers of observations in each bin.
Figure 6a shows the variation in P(O3) with [NO], for
which the data points have been colored by P(ROx) for all
observations taken during SAFS. The relationship for the subset of
observations exclusively at UTSA is essentially identical. In general,
P(O3) increases with [NO], although a wide range of
P(O3) exists for a given value of NO. For a constant value
of [NO], P(O3) is consistently higher at higher
P(ROx). Figure 6b shows the same data as panel 6a
but binned both by NO mixing ratio and P(ROx). All
P(O3) observations have been separated into NO bins with
an equal number of observations, as well as into two bins of
P(ROx)<0.2 and
P(ROx)>0.4. The values of
P(ROx) were chosen to represent the low and high
ranges of P(ROx) observed during SAFS. The
conclusions drawn from the results are insensitive to the values chosen for
these bins.
Figure 6b demonstrates that the majority of observations made during SAFS
were in the NOx-limited regime. For the high
P(ROx) observations, there is a steady increase in
P(O3) up to the 500 pptv NO bin. Above this point,
P(O3) potentially plateaus, but there were insufficient
observations at higher NO to determine the location of the turnover point in
ozone production. Because the majority of NO observations at UTSA were less
than 500 pptv, we conclude that the site is predominantly
NOx-limited. Further observations at higher NO mixing ratios
are required to determine the turnover point for ozone production in this
region. The true turnover concentration for NO cannot be easily inferred by
inspection of a graph of P(O3) versus [NO], however,
because VOC concentrations are not constant for all points. To see if there
is any variation in this relationship with VOCs, we further separate the high
P(ROx) data by their VOC reactivity (Fig. S3). VOC
reactivity (VOCR) was calculated in the same manner as OH reactivity,
described in Sect. 3.3, but including only OH reactive VOCs. In addition,
VOCs exclusively observed by the GC instrument were not included in the
calculation as they were only available until 19 May. For data points with GC
observations available, VOC reactivity increased by only 2 % in the
afternoon and 12 % in the morning on average when including the GC
observations, suggesting that this omission does not significantly affect the
results. Data were then separated into low (VOCR <3 s-1),
medium (3 s-1< VOCR < 6 s-1), and high
(6 s-1< VOCR <9 s-1) VOC reactivity
bins. For the high P(ROx) case, the relationship is
similar for all VOC reactivities, showing a general increase in
P(O3) with NO, further suggesting the majority of
observations were NOx-limited for high
P(ROx). We note that for a constant
P(ROx) value, theoretically
P(O3) is expected to increase with [NO] at approximately
the same rate until the turnover point with little sensitivity to the VOC
reactivity. The 5th and 95th percentiles of
P(ROx) for the high
P(ROx) are 0.42 and 0.92 pptv s-1, more than
a factor of 2 different. This suggests that the differences in the rate of
change of P(O3) with NO for the different VOC reactivities
likely result from the wide range of P(ROx) values
analyzed.
When looking at all points for the low P(ROx) case
(Fig. 6b), there is a small peak in P(O3) at 200 pptv NO,
suggesting that in a low P(ROx) environment, UTSA
can be VOC-limited at higher NO mixing ratios. Separating these data points
by VOC reactivity shows more clearly the transition between the
NOx- and VOC-limited regimes. For the medium case,
P(O3) first increases with [NO], peaks at
5 ppbv h-1 at approximately 200 pptv [NO], and then declines to
2 ppbv h-1 at 400 pptv [NO]. This peak and decline suggests that, for
P(ROx)<0.2 pptv s-1, VOC
reactivities <6 s-1, and NO >200 pptv, the
region is VOC-limited. For NO >400 pptv, there is a slight
increase in P(O3) with [NO], although the spread of data
for a given [NO] also increases. For the low VOC reactivity scenario, the
range of P(O3) for a given [NO] is also large compared to
the mean P(O3), making it difficult to determine whether
these points obey a similar relationship. As with the high
P(ROx) scenario, each bin has a wide range of
P(ROx) and VOC reactivities, which could lead to
the large spread in data, suggesting the need for further observations.
Separating the data by location yields the same results, although VOC
reactivity at Floresville and Corpus was almost always below 3 s-1 due
to the lower isoprene concentration at these sites in comparison to UTSA.
Ozone production rates in a VOC-limited regime are typically below
5 ppbv h-1 and constitute only 20 % of the observations examined
here, suggesting that all three SAFS sites are predominantly
NOx-limited. The majority of the VOC-limited points here
(75 %) occur before 11:00 EDT, when NO concentrations are higher and
isoprene emissions and VOC reactivity are low. This is in agreement with the
LN/Q diurnal profile discussed below. For the
NOx-limited points, increases in VOC concentrations are
expected to have a small impact on P(O3); for the
VOC-limited points, increases in VOCs will lead to increased
P(O3).
Finally, the results presented here are insensitive to the value of
keff chosen. Figure S4 shows the relationship between
P(O3) and NO for four different values of
keff: kNO+HO2 (the keff used in this
analysis), kNO+CH3O2, kNO+IsopreneRO2, and
assuming kNO+acetylperoxy for 10 % of the value and
kNO+HO2 for the remainder. While the magnitude of
P(O3) does change with keff, the overall
relationship is the same. As mentioned previously, the uncertainty in
kNO+HO2 is larger than the uncertainty induced by the choice of
keff. Additional analysis further suggests that the majority of
the observations during SAFS were in the NOx-limited regime.
These results are consistent with the diurnal profile of the ozone production
regime as determined by the separate “LN/Q” metric, which is the ratio
of the ROx loss rate due to reactions with
NOx to the
total ROx loss rate (Q) (Kleinman, 2005). In general, when
more than half of the ROx loss is due to reaction with
NOx species (LN/Q>0.5) then
P(O3) is VOC-limited, whereas when the majority of
ROx loss is due to peroxy radical self-reactions
(LN/Q<0.5) P(O3) is
NOx-limited. The Framework for 0-Dimensional Atmospheric
Modeling (F0AM) photochemical box model (Wolfe et al., 2016b), constrained to
observations, was used to model the parameters needed to calculate LN/Q
at the SAFS sites. A full description of the model setup is in the Supplement. Using
the box model results and the method described in Kleinman (2005), we
calculated LN/Q for all box-modeled observations at UTSA (Fig. 7). A
clear diurnal pattern is evident with an early morning maximum and then a
quick decline to LN/Q<0.5 at 09:00, after which the ratio
remains below 0.1 for the remainder of the day. At 18:00, however, the ratio
does begin to increase, though it remains well in the
NOx-limited space. While LN/Q is highest in the
morning, P(O3) is at a minimum during this time period,
suggesting that there is little O3 production when
P(O3) is VOC-limited. Furthermore, time periods during
which ozone was found under VOC-limited conditions were likely confined to a
relatively small volume of air in the shallow, morning boundary layer. This
transition from a VOC- to NOx-limited regime between morning and
afternoon is consistent with other locations (Mazzuca et al., 2016; Mao et
al., 2010; Ren et al., 2013) and the high NO concentrations that build up in
the morning from local traffic and a low boundary layer.
The diurnal profiles of LN/Q calculated with the
F0AM box model (red), and the median P(O3) in 1 h time bins (blue). The median LN/Q value for half hour bins is shown by
the red line. Profiles are only for observations at UTSA. Points are
calculated by P(O3) calculated from observations. The
black line is approximately the separation between the NOx-
and VOC-limited regimes.
Finally, remotely sensed observations of NO2 and HCHO from the OMI
satellite corroborate the conclusion that ozone production in San Antonio is
NOx-limited. The ratio of column HCHO to tropospheric column
NO2 has been used as an indicator of the ozone production regime in
multiple regions (Duncan et al., 2010; Ring et al., 2018). According to
Duncan et al. (2010), a region is considered NOx-limited
when this ratio is greater than 2, VOC-limited for values less than 1, and in
a transition region for ratios between 1 and 2. Other studies dispute these
ranges, claiming that, in Houston, the NOx-limited regime
only begins for a ratio greater than 5 (Schroeder et al., 2017). Figure 2
shows the ratio averaged over the months May–July 2017 over Texas. In
agreement with the in situ observations and the above analysis, the
satellite data place all three locations in the NOx-limited
regime with ratios much greater than 5. Though they provide much higher
spatial coverage, polar orbiting satellite observations are limited in that
they provide coverage once daily and that data must be averaged over a long
period to gain meaningful statistics. Likewise, because of the satellite
footprint, any small regions in urban centers that may be VOC-limited might
not be evident here because of spatial averaging. Nevertheless, the
combination of satellite and in situ observations clearly demonstrates
that, at least at the three measurement sites, ozone production was
NOx-limited.
OH reactivity
In contrast with Houston, the OH reactivity, and thus ozone production, at
the UTSA measurement site was driven by biogenic species, particularly
isoprene. Figure 8 shows the OH reactivity for the UTSA and Floresville
sites. Observations after 19 May were excluded because of the lack of GC
observations. Concentrations of all observed OH reactive species were used to
calculate the total OH reactivity. These values were then divided into
several groups: biogenics (isoprene, MVK, methacrolein, and α-pinene), carbonyls (HCHO and acetaldehyde), alkanes (ethane, propane,
cyclohexane, octane, heptane, hexane, and pentane), NOx, CO,
CH4, O3, and other (benzene, 1,2,4-trimethylbenzene,
ethylbenzene, toluene, o-, p-, and m-xylene, methanol, and
C2H2).
The distribution of the various contributors to the overall OH
reactivity for the UTSA (13–16 May) and Floresville (17–19 May) sites is
shown for both the morning, for times between 07:00 and 11:00, and the afternoon,
for times between 13:00 and 20:00. The average OH reactivity (±1σ) is
also shown.
OH reactivity varied substantially at the two sites in both magnitude and
relative importance of the individual constituents. Overall, average
afternoon OH reactivity at UTSA and Floresville was 12 and 4.0 s-1,
respectively. While the main contributors to OH reactivity varied between
morning and afternoon at both sites, the total reactivity did not show
significant variation. The higher OH reactivity at UTSA is consistent with
the higher P(O3) rate and XO2
concentrations. At UTSA, the predominant contributors to OH reactivity were
NOx in the morning and biogenic VOCs in the afternoon,
comprising 46 % and 55 % of OH reactivity, respectively. Isoprene
dominated the biogenic contribution, with less than 10 % of total OH
reactivity resulting from monoterpenes, which have been assumed to be
100 % α-pinene. Although the contribution of biogenic VOCs was
lower at Floresville than at UTSA, they were still the largest component of
OH reactivity in the afternoon. The significant contribution to OH reactivity
from NOx during the morning is consistent with large on-road
emissions and a low boundary layer as well as with the VOC-limited nature of
O3 production in the morning. During these morning hours, when the
region is VOC-limited and P(ROx) is generally less
than 0.2 pptv s-1, NO can frequently exceed 500 pptv (Fig. 6c), as
compared to the campaign median of 225 pptv. CO and carbonyls were the other
major contributors to OH reactivity at all locations, with CO being the
dominant contributor at Floresville in the morning. Because one of the
dominant contributors to HCHO production is isoprene (Wolfe et al., 2016a),
it is likely that the biogenic contribution to OH reactivity is even higher
than indicated here. Contributions from alkanes were unimportant at the UTSA
site, 1 % or less during both morning and afternoon, and contributed only
4 %–5 % at Floresville.
The uncertainty in the isoprene measurements does not significantly alter the
conclusions presented here. To bound the effect of this uncertainty, we
adjusted the isoprene observations by ±32 % and recalculated the OH
reactivity. This results in a range of 10.5–13.4 and 3.8–4.3 s-1 in
total afternoon OH reactivity at UTSA and Floresville, respectively.
NOx remains the dominant contributor at UTSA in the morning.
For the lower bound, isoprene contributes 49 % of total OH reactivity at
UTSA, by far the largest contributor to afternoon OH reactivity, and 23 %
at Floresville, making it second in importance to CO (25 %).
Because of the large contribution of alkenes to OH reactivity at other Texas
sites (Mao et al., 2010), it is necessary to make an estimate of their
importance during SAFS. With the exception of isoprene and monoterpenes,
alkenes were not measured on board the AML and therefore have not been
included in the above analysis. To estimate the impact of anthropogenic
alkenes on OH reactivity, we include in our calculation of OH reactivity
observations of alkenes made at nearby TCEQ monitoring sites, Camp Bullis for
UTSA and a site in Floresville co-located with the AML. These sites provide
hourly observations of cis-2-butene, trans-2-butene, 1-pentene,
cis-2-pentene, trans-2-pentene, ethene, propene, 1,3-butadiene, and 1-butene.
Alkene concentrations at the SAFS monitoring sites were assumed to be
identical to those at the TCEQ monitoring sites and were interpolated to the
ECHAMP time base. This assumption is likely more accurate for the Floresville
site than for UTSA. A regression of hourly averaged n-pentane measured
on board the AML to that measured at the Camp Bullis TCEQ site has an r2
of 0.3, even after maximizing the correlation using a lead–lag analysis. In
addition, the maximum n-pentane concentrations at the Camp Bullis site are
almost a factor of 2 higher than those seen at UTSA. Regressions of
cyclohexane and benzene between the two sites show even lower r2 values.
On the other hand, a similar regression of n-pentane at the Floresville site
has an r2 of 0.83. Better agreement at Floresville is to be expected
since the AML and TCEQ monitor were co-located. Total OH reactivity was then
recalculated using the estimates of alkene concentrations. Alkenes contribute
less than 1 % of total reactivity at both UTSA and Floresville for
morning and afternoon times.
Discussion and conclusions
We have presented observations of O3, its precursors, and total
observations of XO2 at three sites in the San Antonio
region. We also presented determinations of P(O3)
calculated from measurements of total peroxy radicals. Median daytime
P(O3) at UTSA was 4.1 ppbv h-1, compared to just
over 1 ppbv h-1 at the other two SAFS sites. Ozone production rates at
UTSA were still far lower, however, than values observed during campaigns in
Houston. Mazzuca et al. (2016) found median near-surface gross
P(O3) of about 10 ppbv h-1 during the DISCOVER-AQ
campaign in the summer of 2013, with values up to 140 ppbv h-1 seen
over the Houston shipping channel. These values are consistent with previous
studies in the region (Sommariva et al., 2011). Higher concentrations of NO
and larger production rates of ROx were seen during
DISCOVER-AQ than during SAFS, both of which could lead to higher
P(O3).
During SAFS, ozone peaked at UTSA at 80 ppbv, with a median value of
47 ppbv, almost 20 ppbv higher than at the background site of Floresville,
upwind of San Antonio. Along with higher O3, the UTSA site also had
larger P(O3), isoprene, NO, and XO2
concentrations than upwind sites. Differences in [O3] between the
up- and downwind sites could be the result of the effects of urban emissions
on O3 production, or they could result from daily variability,
since simultaneous observations were not made at both sites and there are no
permanent O3 observations at Floresville. Figure S5 compares
O3 observations from the AML while at UTSA to those made by the
University of Houston, who measured O3 continuously at UTSA
during SAFS, and to observations from the TCEQ sites at Lake Calaveras,
located upwind of downtown San Antonio (Fig. 2b), and Pecan Valley, situated
in downtown San Antonio. Between 17 and 30 May, winds in the San Antonio
region were primarily southeasterly (i.e. they traveled in the general
direction from Lake Calaveras to UTSA, with downtown San Antonio in between).
During this period, there are both days when O3 is almost
identical at all sites and when O3 is 20 ppbv higher at UTSA than
at Lake Calaveras, suggesting significant O3 production in the air
as it traveled between the two sites. These results suggest that the
20 ppbv differences in median values between the UTSA and Floresville sites
could be either the result of day-to-day variability, in situ O3
production as the air traveled between the two sites, or a mixture of the
two. Further observations of O3 and its precursors in the region,
including in downtown San Antonio, are needed to fully characterize the
effects of the city on ozone production. In addition, future modeling studies
will investigate the evolution of ozone production during this campaign.
A variety of methods were used to show that with the exception of early
morning, when NO is high and XO2 concentrations are low
due to limited insolation, ozone production at the three SAFS sites is
NOx-limited. The relationship between
P(O3) and NO was consistent at the three sites, although
the lower P(ROx), NO, and VOC reactivity at
Floresville and Corpus Christi led to overall lower ozone production rates as
compared to UTSA. VOC-limited points comprised only 20 % of total daytime
observations and generally had P(O3) less than
5 ppbv h-1 at UTSA and less than 2 ppbv h-1 at the other two
sites. This diurnal cycle is in agreement with observations made in Houston
during the DISCOVER-AQ (Mazzuca et al., 2016) and SHARP (Ren et al., 2013)
campaigns. These results, however, are limited to the examined time period
and location, but comparison to O3 and NO levels at the Camp Bullis
site suggests the observations at UTSA are typical of an area downwind of
the San Antonio urban center. This is in contrast, however, to observations
at the TCEQ Pecan Valley site, which has not had an ozone exceedance day by
either EPA standard since 2015 but regularly has MDA8 NO greater than
50 ppbv, significantly larger than the maximum 2 min value of 4 ppbv seen
at the UTSA site. Mixing ratios of Ox at Pecan Valley and
Camp Bullis (Fig. 1) are essentially identical, suggesting that there is less
O3 titration downwind of central San Antonio than in the urban
core. Given the higher [NOx] in the urban core of San
Antonio, P(O3) could be significantly different than at
the UTSA site. Supporting this idea of variations in ozone production across
the San Antonio region is the time series of O3 at Pecan Valley,
UTSA, and Lake Calaveras during SAFS (Fig. S5). Ozone concentrations are
frequently lower at this site than at both UTSA and Lake Calaveras, despite
its location downwind of Lake Calaveras.
OH reactivity at UTSA was found to be 12 s-1, with the primary
contributor being isoprene. While the overall magnitude of the reactivity was
comparable to that observed and modeled during the TRAMP2006 campaign in
Houston (Mao et al., 2010), the contributors to OH reactivity were found to
be significantly different. Contributions from aromatics were negligible at
UTSA, while they were found to be 15 % during TRAMP2006. In Houston,
anthropogenic alkenes were found to be responsible for 20 %–30 % of
total reactivity, with biogenic VOCs making up less than 10 %. Here,
biogenic VOCs were responsible for 55 % of total daytime reactivity, with
alkenes making up less than 1 %, although alkene values were based on
estimates from a different site. We caution that this result cannot
necessarily be extrapolated to other areas in the San Antonio region.
Isoprene has a lifetime on the order of an hour, and the high biogenic
contribution to OH reactivity seen here could result from local influences.
While there are trees throughout the San Antonio region, the results at UTSA
cannot be extrapolated to areas with far less foliage without further
observations. Other VOCs could comprise a larger fraction of total OH
reactivity in less vegetated areas.
While the isoprene concentration at Floresville was significantly lower than
at UTSA, it was still the dominant contributor to OH reactivity during the
afternoon, although the total OH reactivity was a factor of 3 lower at this
site (4 s-1) than at UTSA. Schade and Roest (2016) found a
significantly different OH reactivity profile at Floresville than described
here, with alkanes accounting for approximately 70 % of total OH
reactivity and with biogenic VOCs contributing less than 5 %. Observed
isoprene at Floresville during SAFS was more than an order of magnitude
larger than that reported in Schade and Roest (2016), with alkane
concentrations consistent between the two studies. When the data used in
Schade and Roest (2016) only include a subset of observations at afternoon
times made in the months May through July, the contribution of isoprene to
VOC reactivity increases to a median value of 38 %, in agreement with the
results presented here (Gunnar W. Schade, personal communication, 2018). The
differences between the two studies do suggest that there could be
significant seasonal and diurnal variations in OH reactivity. Nevertheless,
these results suggest that policies designed to limit O3 production
at the SAFS sites discussed here should initially focus primarily on
NOx reductions as the region is NOx-limited
and the primary VOC contributor is biogenic. Further observations and
analysis are need to determine whether this holds true in the urban core of
downtown San Antonio.
Data availability
Data from SAFS are maintained on a private server but are
available upon request to the authors.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-2845-2019-supplement.
Author contributions
DCA and EW wrote the manuscript. All authors discussed the results and
commented on the manuscript. All authors also contributed to daily running of
the AML. SCH led the campaign. DCA, JP, and ECW measured
XO2. BML and WBK contributed to the measurement of organic
trace gases. JRR, TIY, and SCH led observations with TILDAS instruments as
well as measurements of NO, NO2, and O3.
Competing interests
The authors declare that they have no competing interests.
Acknowledgements
The authors acknowledge support from NSF grants AGS-1443842 and AGS-1719918.
In addition, this research was funded by a grant (project 17-032) from the
Texas Air Quality Research Program (AQRP) at the University of Texas Austin
through the Texas Emission Reduction Program (TERP) and the Texas Commission
on Environmental Quality (TCEQ). The findings, opinions, and conclusions are
the work of the authors and do not necessarily represent the findings,
opinions, or conclusions of the AQRP or the TCEQ. The authors thank
Ed Fortner, Paola Massoli, and Jordan Krechmer of ARI, Sam Hall and
Kirk Ullmann of NCAR, James Flynn of the University of Houston, Dave Sullivan
of the University of Texas at Austin, and Raj Nadkarni and Mark Estes of TCEQ
for their contributions to the SAFS campaign and this
paper. Edited by: Steven Brown
Reviewed by: two anonymous referees
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