Introduction
Secondary organic aerosol (SOA) particles are formed from condensation of
relatively low vapor pressure species in the atmosphere, generated through
oxidation of volatile, semi-volatile, or intermediate-volatility organic
compounds (VOCs, SVOCs, or IVOCs, respectively). Since both biogenic and
anthropogenic sources contribute to SOA precursors (Hallquist et al.,
2009), SOA particles are ubiquitous in the atmosphere and contribute to a
large fraction of the submicron non-refractory aerosol mass globally
(Zhang et al., 2007). Similar to other aerosol particles, SOA particles deteriorate air
quality and visibility and impact the climate directly through absorption
and scattering of radiation and indirectly through interactions with clouds
(Monks et al., 2009). Despite recent advances in the measurement and
modeling aspects of SOAs and their precursors (e.g., Donahue et al., 2006;
Ervens and Volkamer, 2010; Hodzic et al., 2010a; de Gouw et al., 2011; Hodzic
and Jimenez, 2011; Shrivastava et al., 2011; Ahmadov et al., 2012; Isaacman
et al., 2012; Yatavelli et al., 2012; Ehn et al., 2014; Ensberg et al.,
2014; Fast et al., 2014; Lopez-Hilfiker et al., 2014; Williams et al.,
2014), the full extent of SOA sources, formation processes, and therefore
their impact on air quality, human health, and climate are not fully
understood.
In recent decades, stricter regulations by the U.S. Environmental Protection
Agency and state agencies have resulted in lower emissions of black carbon,
hydrocarbons (including air toxics), and nitrogen oxides in many urban
environments (e.g., Parrish et al., 2002; Peischl et al., 2010; Sather
and Cavender, 2012; Warneke et al., 2012; Zhou et al., 2014; Kirchstetter et
al., 2017) while in other areas, both populated and remote, expansion or
emergence of new oil and natural gas (O&G) exploration and production
activities has led to higher emissions of air toxics, methane, and
non-methane hydrocarbons, e.g., C2-C8 and larger alkanes,
benzene, and larger aromatic species (e.g., Petron et al., 2012; Gilman et al.,
2013; Adgate et al., 2014; Helmig et al., 2014; Pekney et al., 2014; Warneke
et al., 2014; Field et al., 2015; Koss et al., 2015; Rutter et al., 2015;
Swarthout et al., 2015; Helmig et al., 2016; Prenni et al., 2016; Abeleira
et al., 2017; Koss et al., 2017). The impact of higher emissions of such
hydrocarbons from oil and gas fields of Utah and Wyoming on wintertime ozone
has been assessed through recent measurement and modeling studies (Carter
and Seinfeld, 2012; Edwards et al., 2014; Rappenglück et al., 2014;
Ahmadov et al., 2015).
The Wattenberg Field, located in the Denver–Julesburg basin (DJB) in the
Colorado Front Range and NE of Denver, is the largest oil- and natural-gas-producing
field in the state of Colorado and is one of the 20 largest O&G
fields in the United States (RockyMountainEnergyForum 2015). Gas composition in
this field is liquid-rich (containing more than 3.8e–4 m3 of
condensable hydrocarbons per 28 m3 of extracted gas) (Britannica
1998), making Colorado among the top five US states with high yields of
wet-gas production (USEDC, 2015). Since 2007, several studies in the
Front Range have been carried out in an effort to characterize emissions of
methane and light alkanes (up to C8) and aromatic species, including
benzene, toluene, C8- and C9- aromatics from the O&G activities
in the Front Range and their atmospheric impacts in the region (Petron et
al., 2012, 2014; Gilman et al., 2013; Swarthout et al., 2013;
Abeleira et al., 2017). In the measurement study by Gilman et al. (2013),
conducted during February–March 2011 at a site SW of the
Wattenberg Field, O&G emissions contributed to 70 % and 20–30 % of
emissions of light alkanes and aromatic species, respectively. Additionally,
a high fraction of OH reactivity (55±18 %) was attributed to the
light alkanes emitted from the O&G activities in the Wattenberg Field,
highlighting the significance of these emissions as ozone precursors. In
summer 2015, morning OH reactivity was dominated by O&G VOC emissions
while in the afternoon isoprene contributed to a higher OH reactivity
(Abeleira et al., 2017). Box model simulations corresponding
to observations made at Erie, CO (southwest corner of the Wattenberg Field), in
summer 2012 and 2014 estimated ∼ 80 % of gaseous organic
carbon had originated from O&G alkane emissions while contribution of
these species to local ozone production was estimated to be < 20 %
(McDuffie et al., 2016). On
the high-ozone days, O&G emissions have been estimated to contribute to
30–40 % of ozone in the northern Colorado Front Range – Denver metro area,
based on data synthesized from airborne measurements in the Front Range
during summer 2014 (Pfister et al., 2017). Despite these
recent studies, the contribution of O&G emissions to summertime organic
aerosol (OA) in the region has not been explored before.
During July–August 2014, the Colorado Department of Public Health and
Environment (CDPHE), National Science Foundation (NSF), and National
Aeronautics and Space Administration (NASA) cosponsored multiplatform field
projects in the Colorado Front Range to characterize emissions, processing,
and transport of various pollutants in the region. Here, analyses of the
airborne data obtained from the NSF/CDPHE-sponsored Front Range Air
Pollution and Photochemistry Éxperiment (FRAPPÉ) project, investigating
emissions of hydrocarbons, their impact on SOA formation, and OA chemical
characterization through positive matrix factorization (PMF), are presented. A
regional chemical transport model, the Weather Research and Forecasting model coupled
with Chemistry (WRF-Chem), is used with volatility basis set
parameterization and sensitivity runs to examine effects of primary OA (POA)
volatility, biogenic SOA aging schemes, and updated emissions of
hydrocarbons from the O&G sector on SOA formation in the Front Range.
Methods
Measurements
In situ measurements were made aboard the NSF/NCAR C-130 aircraft during
26 July–18 August 2014. The mountainous terrain of the Front Range leads to
terrain-induced air mass flow patterns in the region. Typically, during the
day, the thermally driven easterly flow transports pollutants towards and up the
foothills while at night the flow reverses. During 27–28 July, the region
was also under the influence of a mesoscale cyclonic flow, leading to
counterclockwise movement of air masses and transfer of pollutants from the
northern latitudes towards the Denver metro area (Vu et al., 2016). To
limit the current analysis to air masses influenced by emissions in the
boundary layer (BL) of the Front Range, analyses from samples collected over
the Denver metropolitan area and the eastern plains were limited to those at
altitudes typically below 1000 m above ground level (a.g.l.); over the
foothills and the continental divide, air masses under the influence of
easterly winds sampled at altitudes up to 2500 m a.g.l. were also considered.
Additionally, recirculated air masses, occasionally observed at altitudes up
to 1800 m a.g.l. over the metropolitan area, were also included in this
analysis. Overall, 91 % of the data presented here are from altitudes
lower than 1000 m a.g.l., and the contribution of recirculated air masses to
the data was minor (< 4 %). Average temperature in the plumes
presented in this work was 20.7±5.8 ∘C. The influence of
different emission sources on sampled air masses was determined based on the
measured trace gases as further explained in Sect. 2.2.
Non-refractory submicron aerosol composition, including organic aerosol,
was measured with 15 s frequency using a compact version (mAMS) of the
Aerodyne aerosol mass spectrometer equipped with a compact time-of-flight
(ToF) detector. Except for the shorter particle time-of-flight chamber and a
different pumping system, principles of operation of the mAMS are similar to
the full-size AMS instruments, described previously (Jayne et al., 2000;
Drewnick et al., 2005; Canagaratna et al., 2007). The mAMS sampled ambient
air through a forward-facing, diffusion-type NCAR High-performance
Instrumented Airborne Platform for Environmental Research (HIAPER) modular
inlet (HIMIL), mounted under the aircraft, and a pressure-controlled inlet
(Bahreini et al., 2008; Dingle et al., 2016; Vu et al., 2016). Residence
time in the inlet was estimated to be ∼ 0.5 s. Sensitivity
calibrations of the instrument were carried out routinely during the
project. Variability in the individual calibrations was observed to be less
than 10 % and thus an average calibration value was applied to the data
obtained from all flights (Vu et al., 2016).
Composition-dependent collection efficiency was applied to all the data
(Middlebrook et al., 2012a). The estimated uncertainty in the mass
concentration of OA was ∼ 30 %
(Bahreini et al., 2009) and the
detection limit was ∼ 0.4 µg m-3(15 s interval
measurements).
The auxiliary gas-phase data used in this analysis are carbon monoxide (CO) by
vacuum UV resonance fluorescence (Gerbig et al., 1999);
nitric oxide (NO) and nitrogen dioxide (NO2) by chemiluminescence
(Ridley et al., 2004); ethane (C2H6)
by infrared spectrometry (Richter et al., 2015); aromatic and
biogenic species by online proton-transfer-reaction mass spectrometry
(Lindinger et al., 1998; de Gouw and Warneke, 2007);
hydrogen cyanide (HCN); i-pentane and n-pentane by online cryogenic gas
chromatography–mass spectrometry (GC-MS) (Apel et al., 2015);
methylcyclohexane and n-octane by offline analysis of whole air canister
samples (WAS) by GC-MS (Colman et al., 2001); nitric
acid (HNO3) by chemical ionization mass spectrometry (CIMS) using
SF6- as the reagent ion (Huey et al., 1998);
peroxyacyl nitrates (PAN and PPN) by I- CIMS
(Zheng et al., 2011); alkyl nitrates by thermal
dissociation laser-induced fluorescence (Day et al.,
2002); and hydroxyl (OH), hydroperoxy (HO2), and alkyl peroxy
(RO2) radicals by CIMS (Mauldin et al., 1998; Hornbrook et al.,
2011; Ren et al., 2012). NOy was calculated by summing up the
individually measured nitrogen oxide species, namely NO, NO2,
HNO3, particulate nitrate, PAN, PPN, and alkyl nitrates.
Source characterization
To quantify the contribution of different types of OA factors to total OA,
positive matrix factorization was applied to the measured OA spectra
during 26 July–11 August. PMF is a multivariate factor analysis method by
which input data are categorized into constant profile factors (i.e., factor
mass spectra) with varying, positive contributions across time (i.e., factor
time series) while minimizing the residual matrix considering the errors
associated with each sample (Paatero and Tapper, 1994; Paatero, 1997).
The input mass spectra and error matrix of OA were generated by the ToF analysis
toolkit (v. 159) and used in the PMF Evaluation Toolkit (v. 2.08D).
Down-weighting of uncertain and weak fragments with a signal-to-noise ratio of
0.2–2 and fragments related to CO2+ (i.e., m/z 16, 17, 18, 28, and
44) was carried out following the procedures outlined in previous studies
(Ulbrich et al., 2009; Ng et al., 2010; Zhang et al., 2011). A total of
100 bootstrap iterations with the ideal number of factors (two, as discussed
further in Sect. 3.1) were also carried out to determine the robustness of the
resolved factors.
To compare OA production in plumes with an influence of pure urban-related vs. high-O&G-related
emissions, two air mass categories were defined using the
auxiliary gas-phase data of CO and C2H6 as tracers for urban and
O&G emissions, respectively. Urban-influenced air masses were defined as
air masses where CO enhancement over the background (105 ppbv, defined by
the mode in the frequency distribution of CO in the Front Range boundary
layer) was observed while C2H6/CO < 20 pptv ppbv-1
(Warneke et al., 2007; Borbon et al., 2013). Plumes with a high influence
of O&G emissions were defined by C2H6/CO > 80 pptv ppbv-1
and C2H6 mixing ratios greater than 10 ppbv
(Warneke et al., 2007; Borbon et al., 2013). Data from 11 to 12 August, when
influence from regional biomass burning emissions resulted in higher HCN
background values (540 vs. 300 pptv), were eliminated from analysis of
the ambient measurements, although the PMF input matrix included data from
11 August.
Settings and parameterizations used for the WRF-Chem simulations.
Category
Selected options and parameters
Land surface
Noah land surface model
PBL scheme
Mellor–Yamada–Nakanishi–Niino
Microphysics
WRF Single-moment, 5 class scheme
Cumulus
Grell–Freitas scheme (12 km domain only)
Short- and longwave radiation
RRTMG short- and longwave
Gas chemistry
RACM ESRL
Aerosol
MADE, VBS-based SOA parameterization
Photolysis
Madronich
Anthropogenic emissions
NEI 2011v1
Biogenic emissions
BEIS 3.14
Details on input parameters and assumptions used in the different
WRF-Chem simulation scenarios.
bVOC oxidation rate and
Case identifier
Emissions
POA volatility
subsequent SOA formation
BC-nOG
NEI, no O&G
Semi-volatile
kOH=1×10-12 cm3 molec-1 s-1
BC-tdOG
NEI, top-down O&G
Semi-volatile
kOH=1×10-12 cm3 molec-1 s-1
nvPOA-nOG
NEI, no O&G
Non-volatile
kOH=1×10-12 cm3 molec-1 s-1
nvPOA-tdOG
NEI, top-down O&G
Non-volatile
kOH=1×10-12 cm3 molec-1 s-1
nvPOA-tdOG-bVOCox
NEI, top-down O&G
Non-volatile
Limited formation of bSOA
WRF-Chem modeling
The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem)
(https://ruc.noaa.gov/wrf/wrf-chem/, last access: 14 January 2018)
is an online meteorology–chemistry
model, which is widely used in air quality and atmospheric chemistry
applications (Grell et al., 2005; Powers et al., 2017). Table 1 lists the
main configurations and parameterizations used to run WRF-Chem. The model
includes multiple gas and aerosol chemistry parameterizations with varying
levels of complexity, photolysis, and removal (dry and wet) mechanisms. The
model also contains the state-of-the-art SOA schemes based on a volatility
basis set approach. In this study, we used an SOA scheme mostly based on the
RACM_SOA_VBS mechanism described in Ahmadov et al. (2012).
In the model, five volatility bins
(10-1, 100, 101, 102, 103 µg m-3)
are assumed for organic aerosols. For the computational efficiency of the
model simulations, it is assumed that all the OA species in the first bin
(10-1 µg m-3) are in the particle phase. The major modification
to the SOA scheme here is the treatment of semi-volatile POA emissions. The
WRF-Chem model with the updated SOA code allows assigning different
volatility distributions for the POA emissions. Here, two scenarios for POA
volatility are presented. In the base case scenario, POA is emitted with a
volatility distribution similar to that of Tsimpidi et al. (2010), except that the distribution used to
partition the POA emissions in this study conserves total POA mass.
Specifically, we used the following coefficients to partition the POA
emissions across the five saturation bins: 0.09, 0.09, 0.14, 0.18, and 0.5.
In the other scenario, the POA is assumed to be non-volatile. Thus, in this
scenario all the emitted POA remains in the particle phase in the atmosphere
until it is removed by dry or wet deposition processes. Since there are
large uncertainties related to the missing SVOC emissions in inventories, we
did not scale up the POA emissions in this study. Therefore, total mass of
the emitted POA is the same in both modeling scenarios.
Another major update to the model is the addition of intermediate VOCs
(IVOCs). Unlike many other SOA modeling studies, we did not scale up the
IVOC emissions according to the POA emissions. Here, the unidentified VOC
emissions from the U.S. EPA NEI-2011v1 inventory were used as IVOCs. In
WRF-Chem the IVOCs are emitted and transported as other gaseous species.
They are oxidized by hydroxyl radical with the rate of 2.3×10-11 cm3
molecule-1 s-1, as hexadecane. A similar approach was
first applied in another WRF-Chem study in order to simulate SOA formation
from the Deepwater Horizon oil spill in the Gulf of Mexico (Middlebrook
et al., 2012b). As further discussed in Sect. 2.4, in the top-down
emission simulation scenarios, IVOC emissions from the O&G sector were
scaled using the top-down estimates of the alkane species (namely the HC8
species in the RACM mechanism). Lack of direct measurements of ambient IVOC
species makes it impossible to directly constrain their emissions using the
top-down approach. Table 2 highlights differences in emission estimates and
POA volatility assumptions used in the different simulation scenarios. In
the simulation case with limited biogenic SOA formation, the
first-generation semi-volatile organic condensable vapors are not oxidized
further, and therefore only first-generation
bVOC oxidation products
contributed to biogenic SOA production.
The WRF-Chem model, which includes the new SOA formation mechanisms, was
simulated on two domains, covering the contiguous United States (CONUS) and entire
Colorado, at 12 and 4 km resolutions, respectively. In addition to the
full gas and aerosol chemistry, a photolysis scheme, dry and wet removal
parameterizations for both gaseous and aerosol species were incorporated in
WRF-Chem. The anthropogenic and biogenic emissions were also included in the
simulations. First, all the model simulations were conducted on the CONUS
domain for the 24 July–14 August 2014 time period. Then, using a one-way
nesting approach, initial and boundary conditions for the inner domain
(Fig. S1 in the Supplement) were created to conduct various sensitivity simulations for
27 July–13 August. Simulations on the 4 km domain were conducted in 24 h
intervals. The model was initialized by using meteorological input from the
12 km domain, which in turn used North American mesoscale analysis fields
(www.emc.ncep.noaa.gov, last access: 21 July 2016)
as boundary and initial conditions. Simulated
chemical species were cycled between the 4 km domain runs to preserve the
fine-scale features captured by the inner domain. All the WRF-Chem modeling
results presented here are based on the 4 km domain simulations. To
determine model predictions along the flight track, the aircraft's flight track
was traced in the model domain and measured parameters along this track were
averaged over the model grid cells. The speed of the C-130 in the boundary
layer with a full payload is ∼ 100 m s-1; thus, with the AMS
averaging time of 15 s, 2–3 values from the AMS measurements were available
to average in a 4 km × 4 km grid cell to compare the model to.
There was no interpolation of the data in space or within the hourly
temporal resolution of the model. Note also that there was no drastic
variability within the hourly timescale of the modeled parameters.
Emissions
Since the focus of this paper is on quantification of SOA formation in the
Front Range, two emission scenarios are explored. Both emissions scenarios
are based on the U.S. EPA NEI-2011 emission inventory with the exception
that O&G activity emissions in the DJB are modified to allow direct
quantification of SOA formation from this sector. The NEI-2011 emissions
rely on the version 6.0 platform (https://www.epa.gov/air-emissions-modeling/2011-version-60-platform) and
are basically the same emissions used and documented in Ahmadov et al. (2015)
except for the chemical speciation profiles of the O&G sectors. In
some of the scenarios, all O&G-related activity emissions are removed
from the simulations. For other scenarios, VOC emissions from O&G
activity in the DJB are specified according to a top-down approach from
observations collected at the Boulder Atmospheric Observatory (BAO) tower,
on the western edge of the DJB. As previously mentioned, the “unknown” VOC
species within the NEI-2011 inventory is also included, representing direct
emissions of IVOCs. Summertime (July) conditions are assumed within the NEI
temporal allocation specifications, as are the diurnal profiles and spatial
allocations at 4 km horizontal resolution.
The top-down emissions from the DJB are derived using the same strategy as
in Ahmadov et al. (2015), whereby CH4 flux observations over a basin are
combined with basin-wide VOC to CH4 emission ratios. In this case, the
O&G activity sector CH4 flux estimate for May 2012 within the DJB of
Petron et al. (2014) (19.3±6.9 t h-1) is adopted.
VOC to CH4 emission ratios from O&G activity in the DJB for
individual compounds are derived from VOC measurements at the BAO tower
during the July–August 2012 SONNE (Summer Ozone Near Natural Gas Emissions)
field study
(https://www.esrl.noaa.gov/csd/groups/csd7/measurements/2012sonne/, last access: 21 July 2016).
Identical to the VOC analysis for the NACHTT-2011 field campaign, linear
regressions using two variables (propane and acetylene) are used to
distinguish O&G activity versus transportation-related sources
(Gilman et al., 2013). Table S1 summarizes the
correlation statistics of NOx and 43 VOCs with CH4,
C2H2, and C3H8 during SONNE. Derived emission ratios relative to propane
are nearly identical between the two field studies for the 18 VOCs measured
during NACHTT 2011 and are within 20 % of the emission ratios of five of
the VOCs from aircraft samples reported in Petron et al. (2014). All three DJB studies imply strong
correlations between propane, CH4, and other VOCs from O&G activity,
allowing high confidence in the regression slopes that define the emission
ratios used here. Spatial allocation and the area and point sector ratios of the
top-down inventory are taken from the O&G sector totals within NEI 2011,
and no diurnal variation is assumed. As discussed in Gilman et al. (2013),
NOx and CO emissions from the oil and gas sector are
indeterminable due to their overwhelming correlation with acetylene, so no
NOx or CO adjustments are possible in the top-down case. Likewise, no
changes to NEI-2011 aerosol emissions are considered.
NEI-2011 emissions (July, weekday) for the Denver–Julesburg basin box,
39.8–40.7∘ N, 104.25–105.4∘ W (9764 km2). Top-down
estimates for C2H6 and toluene point and area sources are
indicated. Units are kilo mole h-1 for gas-phase species
and short tons day-1 for
POC (primary organic carbon) and PNCOM (primary non-carbon organic matter).
(a)
Species
Total
Mobile on-road
Mobile non-road
Area
Point
NOx
144.36
53.50
20.56
14.28
56.00
CO
874.37
366.07
426.84
11.76
69.70
C2H6
232.34
0.56
0.57
221.93
9.28
Toluene
4.52
1.08
0.97
1.82
0.65
Unknown
1.87
0.15
0.00
1.35
0.36
POC
3.66
0.47
0.72
1.78
0.69
PNCOM
1.28
0.13
0.18
0.71
0.26
(b)
NOx
153.70
90.27
27.86
0.76
34.81
CO
1351.82
646.56
684.47
2.24
18.56
C2H6
3.76
0.94
0.81
1.50
0.52
Toluene
4.61
1.86
1.47
0.75
0.53
Unknown
0.68
0.26
0.01
0.06
0.36
POC
4.53
0.79
1.04
2.37
0.33
PNCOM
1.55
0.21
0.26
0.95
0.13
Table 3a and b provide emission totals from the NEI-2011 and the top-down VOC
inventories for areas covering the DJB and Denver metro region,
respectively. The DJB latitude and longitude limits in Table 3a are chosen to
capture sources contributing to the CH4 emission totals within Petron
et al. (2014), which also includes some
northern suburbs of Denver. O&G activity emissions are only included
within “area” and “point” emission sector categories and are
indeterminable within the mobile categories. The area category in particular
dominates the ethane and unknown VOC emissions. We note that
NEI 2011 does not specifically contain any POA emissions associated with
O&G activity. The largest area sources of POA in Table 3a are from
agricultural tilling, construction, and fugitives emissions from paved and
unpaved roads, though commercial cooking sources account for ∼ 43 %.
Emissions in the Denver metro region are dominated by mobile sources,
while the main source of POA (66 %) is commercial cooking.
Based on the BEIS 3.14 inventory, biogenic emission sources are mostly in the
south and west of Denver and over the mountains in western Colorado
(Fig. S2). Since the typical daytime flow of air masses during FRAPPÉ was from
east to west, it is apparent that transport of bVOCs into the Front Range
compared to local sources was not significant.
Scatter plot of OA against CO. The slopes are from weighted (by
30 % uncertainty in OA and 3 % uncertainty in CO) orthogonal distance regression (ODR) fits to the data
in relative fresh (NOx/NOy > 0.7) and aged
(NOx/NOy < 0.3) plumes. The estimated uncertainties in the
slope values represent 95 % confidence intervals.
Results and discussion
Ambient OA observations
Figure 1 highlights the general trends observed for OA vs. CO in the Front
Range BL. Data points appear to be bound by enhancement ratios of
ΔOA/ΔCO = 0.016–0.085 µg m-3 ppbv-1, with higher
values observed in air masses with NOx/NOy < 0.3, i.e.,
air masses with a higher degree of photochemical processing, compared to
fresher air masses with NOx/NOy > 0.7. Note that these
age categories best represent processing of plumes with NOx
emissions, while true aging of emissions in the absence of NOx is not captured.
Since daily flight patterns did not include regular upwind tracks, it was
not possible to determine daily background values to subtract from the
measured OA and CO. Therefore, the enhancement ratios were determined by
weighted, linear orthogonal distance regression (ODR) fits, with weights
representing the uncertainty in OA (30 %) and CO (3 %). Uncertainties in
the slopes represent 95 % confidence intervals. Almost a factor of 5.5
increase in ΔOA/ΔCO indicates a significant production of
SOA with photochemical aging in the Front Range. Another notable feature in
Fig. 1 is the higher ΔOA/ΔCO enhancement ratio observed in
the fresher plumes sampled in the Front Range compared to the typical
enhancement ratio of primary OA to CO (ΔPOA/ΔCO ∼ 0.010 ± 0.005 µg m-3 ppbv-1) observed
in fresh air masses over other urban environments (de Gouw et al., 2008).
This difference may arise from contributions of sources other than urban
vehicular exhaust to POA in this region, as is further discussed below.
Additionally, using the best estimates of the ODR slope and intercept values
of the regression lines to the data, the predicated OA at background levels
of CO (∼ 105 ppbv) was 1.82 µg m-3. This value,
which was very similar to the mode of the OA frequency distribution in the
BL at 1.85 µg m-3, is a substantial portion of total OA,
suggesting the presence of relatively high concentrations of non-combustion-related OA, likely of biogenic origin, in the region.
Enhancement ratios of OA with respect to CO in individual plumes
sampled in the Front Range BL. Points are sized with ΔOA/ΔCO
and color coded by the i-pentane / n-pentane ratio. Only cases where the
correlation coefficient (r) of OA vs. CO was greater than 0.5 and the
standard deviation of the ODR slopes was less than 50 % of the slope
itself are highlighted. Locations of O&G wells are shown with yellow
dots.
For a more detailed investigation of OA formation in different plumes,
correlations of OA vs. CO in ∼ 94 individual plumes in the
boundary layer on 26 July–11 August were investigated to determine the
corresponding ΔOA/ΔCO values by the slope of weighted ODR
fits. The spatial distribution of ΔOA/ΔCO values,
color-coded with the observed ratio of i-pentane / n-pentane, is summarized in
Fig. 2 for cases where the correlation coefficient (r) of OA vs. CO was
greater than 0.5 and where the standard deviation of the ODR slopes was less
than 50 % of the slope itself. Urban emissions of the pentane isomers
typically result in i-pentane / n-pentane values > 2 (Warneke et
al., 2007, 2013), while O&G emissions in DJB have shown
characteristic ratios ∼ 1 (Petron et al., 2012; Gilman et
al., 2013). Considering the location of the active O&G wells in the Front
Range (Fig. 2), the lower i-pentane / n-pentane values observed to the north
of the Denver metro area are a strong indicator for the influence of O&G emissions in
these plumes. There are several plumes with a high O&G emission influence
in this area that also display a large enhancement in OA with respect to CO.
The apparent difference in the enhancement ratios may be due to the lower CO
mixing ratios in the non-urban plumes or higher OA concentration in plumes
sampled to the north of the Denver metro area, either because of longer photochemical
age or higher concentrations of OA precursors in such plumes. We further
investigate these differences in the next sections.
Correlation coefficient of PMF factors with different species.
CO
Acetylene
C2H6
NO3-
SO42-
HOAa
OOAa
Factor 1 – OOA
0.68
0.71
0.46
0.64
0.69
0.50
0.95
Factor 2 – HOA
0.68
0.76
0.44
0.47
0.40
0.92
0.50
a HOA and OOA factors as identified in Ng et al. (2011).
PMF analysis of the OA spectra resolved two distinct profiles with spectra
shown in Fig. 3a. In the two-factor solution, the first factor had a higher
contribution of m/z 44 and is identified as the secondary and oxygenated factor
(OOA, oxygenated OA) as it correlated best with the OOA factor previously identified in
several field studies (Ng et al., 2011) as well as
secondary species such as sulfate and nitrate (Table 4). Increasing the
number of factors resulted in split factors and a minimal decrease in
Q/Qexpected. When examining correlation coefficients of two of the
factors (in a three-factor solution case) containing signal at m/z 44 with external
tracers, only the correlations with CO were significantly different
(r=0.03 vs. 0.28), while correlations with other anthropogenic and biogenic
tracers (e.g., acetylene, ethane, isoprene oxidation products – i.e., methyl
vinyl ketone and methacrolein – and monoterpenes) or aerosol nitrate and
sulfate were not. We therefore believe that the three-factor solution is unable
to determine a meaningful and independent third factor, and thus PMF is
unable to clearly isolate the contribution of biogenic vs. anthropogenic
sources to OOA in this environment. Statistically similar enhancement ratios
of OOA relative to CO or odd oxygen (Ox) in aged (i.e.,
NOx/NOy < 0.3) urban- and high-O&G-influenced plumes
were obtained (Fig. S3); however, median and mean OOA concentrations in
plumes with a large influence of O&G emissions were ∼ 25 %
higher than the values in urban-only plumes, under similar
non-cyclonic atmospheric conditions (Fig. 3c) (Sullivan et al., 2016; Vu
et al., 2016). The uncorrelated relationship between OOA and Ox under
cyclonic conditions in plumes with a high O&G influence is similar to an
observed large scatter in CO versus O3 (not shown). The influence of upwind
sources of CO and OOA that were not correlated with O3 formation
(e.g., biomass burning) cannot be ruled out under the cyclonic episodes sampled
here, resulting in mean and median OOA values in O&G-influenced plumes
during cyclonic flow that were outside the variability range of the values
observed during the non-cyclonic flow. More discussion on the role of
different emission sources on OA is presented in Sects. 3.2–3.3. Overall,
the OOA factor dominated the OA composition, contributing to 85 % of OA
mass. The second factor, referred to as HOA (hydrocarbon-like OA), had a pronounced fragmentation
pattern at delta patterns 0 and 2 (e.g., m/z 41, 43, 55, 57, 69, 71) that
are common for hydrocarbons (McLafferty and Turecek, 1993) and correlated
best with the HOA factor in previous field studies (Ng
et al., 2011) as well as primary combustion tracers such as acetylene and
CO; it therefore represents the fresh, hydrocarbon-like components of OA.
Mean HOA concentrations were ∼ 35 % higher (Fig. 3d) in
high-O&G-influenced plumes compared to urban plumes, under similar
non-cyclonic conditions, suggesting contribution of primary aerosol (in this
case, POA) emissions from equipment associated with O&G-related
activities (Field et al., 2014; Prenni et al., 2016). Averaged over all
plume types, the contribution of HOA to total OA mass was 15 %. Although
airborne measurements of aerosol optical extinction and HCN provided
evidence for long-range transport of biomass burning plumes during 11–12 August (Dingle et al.,
2016) to the Front Range, a factor with a significant contribution at
fragments associated with levoglucosan combustion (i.e., m/z 60 and 73) was not
identified. Therefore, either the contribution of wildfires to
non-refractory OA composition during the days of PMF analysis was negligible
or the photochemistry of the fire plumes during transport resulted in
chemical transformation of the biomass burning markers (Hennigan et al., 2010, 2011).
Mass spectra (a), fractional contribution (b), and mass
concentrations of OOA (c) and HOA (d) factors. Box and whisker plots depict
the 10th, 25th, 50th, 75th, and 90th percentiles. Mean
values of OOA and HOA in each plume type are shown in circles.
Statistical analysis of measured OA (a), various hydrocarbons (sum
of biogenic VOCs, bVOC, defined as isoprene + 2× (methyl vinyl
ketone + methacrolein) + monoterpenes (b); sum of aromatic VOCs defined
as benzene + toluene + C8 aromatics + C9 aromatics (c); sum of
methylcyclohexane and octane (d)), NO (e), and radicals (HO2 (f), OH
(g), and RO2 (h)) in urban- and high-O&G-influenced plumes. Box and
whisker plots depict the 10th, 25th, 50th, 75th, and 90th
percentiles. Mean values in each plume type are shown in circles.
Influence of urban and O&G emissions: measurements
To better understand the impact of urban vs. O&G emissions on SOA
formation in the Front Range, data on measured OA, known precursors of SOA,
and photochemical markers were examined in urban air masses and those with a
high influence of O&G emissions (Fig. 4). Mean and median values of OA
were ∼ 40–48 % higher in high-O&G-influenced plumes
compared to urban plumes. As discussed in Sect. 3.1 and Fig. 3, most of
the OA in the Front Range is oxygenated and secondary in nature. More
efficient SOA production in an air mass could be due to differences in
oxidation timescales, amounts of SOA precursors or oxidants, or oxidation
conditions, and thus SOA production yields. Statistical data in Fig. 4b–d
indicate that while the mixing ratio of biogenic species (sum of the
measured isoprene, monoterpene, and 2× (methyl vinyl ketone and
methacrolein)) in the two air mass types were similar within 20 %, the
median mixing ratio of the sum of aromatic species (i.e., benzene, toluene,
and C8 and C9 aromatics) and sum of methylcyclohexane and
n-octane, which are known SOA precursors (Odum et al., 1997a, b; Lim and Ziemann, 2005), were higher by factors of 2.4 and 4.7,
respectively, in high-O&G-influenced plumes relative to urban plumes.
Therefore, it is not surprising that higher OA and OOA concentrations were
measured in high-O&G-influenced plumes. Next, we examine photochemical
conditions that affect SOA production yields. Radical chemistry in different
NOx regimes leads to different SOA formation potentials, depending on
the branching ratio of RO2 radicals reacting with HO2 vs. NO
(Kroll et al., 2005; Ng et al., 2007a, b; Henze et al.,
2008). Median NO mixing ratios in urban and high-O&G plumes were at least
350 pptv (Fig. 4e), which is about a factor of 10 higher than the median
HO2 mixing ratios in these plumes (Fig. 4f), suggesting that the
oxidation conditions encountered in both urban- and high-O&G-influenced
air masses were NO-rich, and hence provide the conditions where RO2
radicals predominantly react with NO rather than HO2 radicals.
Furthermore, mean and median OH concentrations in both
urban- and high-O&G-influenced plumes were similar to within ∼ 15 %. The
dominance of NO over HO2 and lack of a significant difference in OH
concentrations in urban- and high-O&G-influenced air masses indicate
the presence of similar oxidation conditions in the two air mass types. Thus,
the higher OA values in high-O&G-influenced plumes compared to pure
urban plumes are hypothesized to be due to SOA formation from higher
concentrations of aromatics and larger alkanes. We further investigate the
contribution of O&G sources to SOA formation in simulation scenarios with
WRF-Chem modeling.
Comparison of the measured and WRF-Chem-predicted (no O&G and
top-down O&G emission scenarios) mixing ratios of ethane (a and b),
toluene (c and d), and CO (e and f) in urban- and high-O&G-influenced
plumes. Box and whisker plots depict the 10th, 25th, 50th, 75th,
and 90th percentiles. Mean values are shown in circles.
Influence of urban and O&G emissions: modeling
WRF-Chem simulations of gaseous species
We begin examining the results of WRF-Chem simulation runs by first
comparing predicted mixing ratios of various primary and secondary gases in
urban- and high-O&G-influenced air masses. This exercise was not performed
as a point and point comparison along the flight track since locations of
the simulated pollution plumes were sometimes shifted compared to the
measurements. An example of differences between the measured and modeled
distribution of plumes is shown for ethane in Fig. S4. Because of this,
flags similar to those used for characterizing plumes measured with urban
and high O&G emissions were defined, based on the modeled values of CO
and C2H6, and statistical analyses of data under each flag
type were carried out. To assess the impact of the emission scenarios, we
first compare measured and modeled values of C2H6, toluene, and CO
in urban- and high-O&G-influenced air masses. Figure 5a–b demonstrate
that there is a large influence of C2H6 from the O&G sector in
the Front Range and that neglecting those emissions significantly
underestimates C2H6 mixing ratios in both urban- and
high-O&G-influenced plumes. In urban plumes (Fig. 5c, e), the mean toluene
and CO mixing ratios were very similar under both emission scenarios and
overestimated compared to the measurements by a factor of 2 and 20 %,
respectively. In the high-O&G-influenced plumes (Fig. 5b, d, f),
neglecting the O&G emissions of VOCs resulted in underestimation of
C2H6 (by a factor > 10) and toluene (by 35 %) and
∼ 10 % overestimation of CO compared to the measurements.
When modifying the O&G emissions with the top-down approach, a reasonable
comparison for C2H6 was achieved in the high-O&G-influenced
plumes; additionally, the mean toluene mixing ratio was now within 12 % of the
measurements while the mean values for CO did not change. These comparisons
demonstrate that adjusting the O&G sector emissions by the top-down
approach was necessary to realistically capture the influence of such
emissions in the Front Range.
Comparison of the measured and WRF-Chem-predicted (top-down O&G
emission scenario) mixing ratios of isoprene (a), methyl vinyl ketone (only
available in measurements) and methacrolein (b), and monoterpenes (c) in the
Front Range boundary layer. Box and whisker plots depict the 10th, 25th,
50th, 75th, and 90th percentiles. Mean values are shown in
circles.
We next compare the mixing ratios of biogenic SOA precursors with the
modified NEI emissions. Since emissions of biogenic VOCs were not modified
in the top-down approach and because one goal of the current study is to
investigate the contribution of O&G emissions to OA formation, we focus
on the comparison between the measured values and only the modified,
top-down O&G emission scenario (Fig. 6). These comparisons suggest that
isoprene and its oxidation products are well represented in the model,
whereas the monoterpene mixing ratios are underestimated by as much as
50 %. The effect of this underestimation on total SOA formation however
may not be significant given the very low measured monoterpene mixing ratios
(average and median values of ∼ 40 pptv).
Comparison of the measured and WRF-Chem-predicted (no O&G and
top-down O&G emission scenarios) amounts of OH (a and b), HO2 (c and
d), and NO in urban- and high-O&G-influenced plumes. Box and
whisker plots depict the 10th, 25th, 50th, 75th, and 90th
percentiles. Mean values are shown in circles.
Overall, measured and predicted OH concentrations in urban- and
high-O&G-influenced plumes compared very well with the top-down estimates of
O&G emissions (Fig. 7a–b). Mean and median OH concentrations without
O&G emissions were overestimated in O&G-influenced plumes by
∼ 40 %. Mean and median values of HO2 were predicted
very well in the high-O&G-influenced plumes regardless of the emission
scenario, but with a lower degree of variability compared to the
measurements. Median and mean values of the measured urban HO2 were
about twice as much as the predicted values. However, given the measurement
uncertainty levels (up to 35 %), the comparison is still very good
(Fig. 7c–d). Predicted mean and median NO mixing ratios in urban plumes compared
well with the measurements, while the high NO values in plumes with a high
influence from O&G emissions were not predicted well, resulting in 60 %
lower mean NO values in these plumes (Fig. 7e–f). Since NO emissions from
the O&G sector remained the same in the different scenarios, comparisons
with only one scenario are shown here.
Comparison of the measured and WRF-Chem-predicted mixing ratios of
ozone in urban-influenced (a) and high-O&G-influenced (b) plumes. Box and
whisker plots depict the 10th, 25th, 50th, 75th, and 90th
percentiles. Mean values are shown in circles.
Measured and predicted values of O3 are compared in Fig. 8. Without
emissions from the O&G sector, mean predicted O3 values in urban- and
high-O&G-influenced plumes were ∼ 8.5 ppbv and ∼ 2 ppbv lower than measurements. The higher discrepancy observed in urban
plumes might be due to overestimation of primary urban emissions (e.g.,
toluene, CO, and NO) and subsequently higher O3 titration by NO, or
due to lower extent of mixing in the model. In simulations including the
O&G emissions, a minor (< 1 ppbv) increase in the mean urban
O3 was predicted while the increase in high-O&G-influenced plumes
was more significant, at ∼ 4.5 ppbv. It should be noted that
the uncertainties in meteorological simulations (e.g., wind speed and
direction) also contribute to the overall model–measurement
discrepancies of
the chemical species discussed here.
Cumulative distribution of HOA or POA (a) and OA (b) based on
measurements and various simulation scenarios.
WRF-Chem simulations of organic aerosol
In this section we examine simulated values of different OA types in the
different simulation runs and compare them with the factors resolved by PMF.
The cumulative distributions of PMF-derived HOA and simulated POA
concentrations in the Front Range boundary layer are shown in Fig. 9a. It is
apparent that the median value of POA in the base case and all the runs
using a similar volatility assumption of POA is significantly lower than the
HOA estimate derived from PMF. It is worth noting that cooking POA
contributions in NEI might be underestimated for the Front Range area, while
there could be some contribution of POA from cooking or sources other than
vehicular exhaust to the PMF-resolved HOA factor. For example, as shown in
Fig. 3d and discussed previously, there appears to be some contribution to
HOA from O&G-related activities. A higher POA emission factor from
O&G-related activities is not unexpected given typically high emissions
from diesel engines without after-treatment technology that might be working
at these sites (Ban-Weiss et al.,
2008; Jathar et al., 2017); however, as mentioned before, there were no
adjustments to POA emissions for the O&G sector in WRF-Chem when
modifying the top-down estimates of gaseous emissions. Despite this, it is
unlikely that NEI emission factors of POA from the urban areas are
underestimated by up to a factor of 8 (mean HOA ∼ 0.45 µg m-3
vs. mean POA ∼ 0.05 µg m-3). One possible
explanation for this discrepancy is the assumed volatility distribution of
the POA. Given the large uncertainties in volatility estimates of POA from
different sources (Hodzic et al., 2010b; May et al., 2013), to explore
the effect of POA volatility, simulations were repeated assuming
non-volatile POA. In these runs and regardless of O&G emission
treatments, the mean and median POA values increased by a factor of 5,
bringing the predicted POA values within a factor of 2 of the PMF-based HOA
concentrations. The non-volatile POA assumption may not be accurate, and
improved volatility distributions of POA from different combustion sources
would have to be considered to accurately account for the semi-volatility of POA
emissions in future air quality models. However, in the absence of better
estimates of POA emission ratios or volatility, the predicted POA values in
current simulations with non-volatile POA conditions are more comparable to
the PMF-based estimates of HOA in this environment.
Statistical comparisons of predicted OA, anthropogenic SOA (aSOA),
and biogenic SOA (bSOA) in urban-influenced (a, c, e) and high-O&G-influenced (b, d, e)
plumes in different model scenarios. Data from measured OA are also
included in (a–b). Box and whisker plots depict the 10th, 25th, 50th,
75th, and 90th percentiles.
Modeled total OA values in the Front Range BL are compared with the observed
values in Fig. 9b. The median values of most model scenarios, except when
biogenic aging was turned off, were ∼ 35 % higher than
measurements, which is an excellent agreement considering the uncertainties
in measurements, emissions (magnitude and speciation), meteorological
simulations, and other input parameters of the model. The extremely low and
high values of measured OA, however, were not predicted well with any of the
model runs, likely due to uncertainties in emissions of IVOCs from the urban and
O&G sector as well as uncertainties in the aging mechanisms of
hydrocarbons (e.g., extent of fragmentation vs. functionalization reactions
or aging of biogenic SVOC products). Measured and modeled total OA values in
urban- and O&G-influenced plumes are compared in Fig. 10a–b. Regardless of
model assumptions, predicted median values of OA were 0.6 to
1.8 µg m-3 (25 to 58 %) higher than the measured median values in urban
plumes. This overprediction may partly stem from higher-than-measured mixing
ratios of urban VOCs in the model (Fig. 5c). Comparisons in the
high-O&G-influenced plumes were better, with differences of only -0.2 to 0.8 µg m-3
(-6 to 25 %) between measured and predicted values.
Consistent with the observations in Fig. 9b, there was a bias towards higher
values in the modeled urban OA while the measured high values in
O&G-influenced plumes were underpredicted.
The effect of POA volatility was most apparent in predicted OA values in the
urban plumes. Considering results of pairs of runs with similar
consideration of O&G emissions, non-volatile POA runs resulted in
a ∼ 13 % (∼ 0.4 µg m-3) increase in total OA compared to scenarios where POA was assumed to be
semi-volatile (Fig. 10a). To determine how different components of OA were
affected by changes in POA volatility, anthropogenic and biogenic SOA values
(aSOA and bSOA, respectively) were considered separately. Assuming POA was
non-volatile actually reduced aSOA by < 5 % (∼ 0.05 µg m-3)
in urban plumes (Fig. 10c) while it increased bSOA by
2–4 % (0.04–0.08 µg m-3) (Fig. 10e). The reason for the
reduction in aSOA is that with the non-volatile assumption of POA its
semi-volatile components are not available for gas-phase oxidation, reducing
concentrations of anthropogenic oxidized species that are condensable and thus
leading to a decrease in aSOA. On the other hand, since POA concentration is
higher when assumed non-volatile, available aerosol mass for absorptive
partitioning is higher, resulting in increased partitioning of semi-volatile
bVOC oxidation products to the aerosol phase and thus an increase in bSOA
concentration. Therefore, it appears that most of the increase in urban
total OA in non-volatile POA scenarios is due to the contribution from POA.
Contributions from HOA, aSOA (non-O&G and O&G sources), and
bSOA to total OA as predicted by WRF-Chem in the case with non-volatile POA
and limited bSOA formation assumptions.
The effect of including top-down estimates of O&G emissions on predicted
OA was quantified from changes in predicted OA, under the same POA
volatility assumption, in the high-O&G-influenced plumes. Results
indicate at most a 4.7 % increase in OA from O&G emissions. Although
the net increase in OA due to O&G emissions was relatively small, there
was a ∼ 30–38 % (∼ 0.4 µg m-3)
increase in aSOA due to these emissions, depending on POA volatility. On the
other hand, median bSOA values decreased by 8–10 %
(< 0.2 µg m-3) after including the top-down estimates of O&G emissions, likely
due to reductions in OH with the additional VOC emissions in the
high-O&G-influenced plumes (Fig. 7b).
As apparent in the cumulative distribution of OA (Fig. 9b), the model cases
discussed so far do not capture ∼ 10 % of the data where
measured OA values are lower than 1 µg m-3, suggesting that the
background OA in these runs might be overpredicted. A final model run was
designed to investigate the role of successive biogenic VOC aging on the
predicted OA and its background values. Although the low-concentration OA
data points were still overpredicted in this model run (Fig. 10), the
overall comparisons with the observed OA values (Fig. 10a, b) were best when
consecutive formation of bSOA was turned off. Specifically, total predicted
OA values in these run were 0.8–1 µg m-3 lower than the scenarios
with similar POA volatility and O&G emissions while consecutive formation
of bSOA was active. This decrease was predominantly due to the decrease in
the bSOA portion of OA (Fig. 10e–f). It is worth highlighting that even with
these reduced bSOA values, the predicted contribution of bSOA to total OA in
the Front Range remained high, at ∼ 54 and 40 % in urban-
and O&G-influenced plumes, respectively (Fig. 11). This is qualitatively
consistent with the relatively high values of OA at background CO mixing
ratios as was shown in Fig. 1.
Correlation plots of predicted SOA against CO (a–b) and odd
oxygen, Ox, defined as O3 + NO2 (c–d) for model runs with
non-volatile POA and top-down estimates of O&G emissions when biogenic
SOA aging was turned on (a and c) and off (b and d). ODR slope values
indicated in parenthesis are obtained considering data when simulated
NOx/NOy < 0.3.
We further examine simulations of SOA formation in two scenarios with
non-volatile POA. With the standard treatment of bVOC oxidation and bSOA
formation, urban plumes with NOx/NOy n < 0.3 displayed a
50 % greater enhancement in SOA with respect to CO (ΔSOA/ΔCO) compared to plumes with a high O&G influence (Fig. 12a). On the
other hand, SOA enhancement with respect to Ox (ΔSOA/ΔOx) was 30 % higher in high-O&G-influenced plumes (Fig. 12c).
By turning off consecutive formation of bSOA, similar ΔSOA/ΔCO enhancement ratios were obtained in urban- and high-O&G-influenced plumes (Fig. 12b) while the difference in ΔSOA/ΔOx increased, with the ratio in high-O&G-influenced
plumes being ∼ 66 % higher than in urban plumes (Fig. 12d).
Both of these trends are consistent with reductions in bSOA in urban plumes.
Neither of the simulation scenarios resulted in ΔSOA/ΔCO
values similar to the observed ΔOOA/ΔCO in urban plumes,
although the predicted values in high-O&G-influenced plumes were
consistent with the lower values of the ODR fits to the observations
considering the 95 % confidence intervals (Fig. S3). It is worth noting
that not considering variable background levels of OOA and CO and the
uncertainties associated with PMF analysis might have also impacted the
comparisons discussed here. Simulated ΔSOA/ΔOx were
also significantly lower than observed ΔOOA/ΔOx in
urban plumes indicating that neither runs predicted an accurate relationship
for SOA and Ox formation in these plumes, despite predicting OA well.
Contrary to the measurements, predicted CO (Fig. 5e–f), NO (Fig. 7e–f), and
O3 (Fig. 8a–b) mixing ratios were different in urban- and
high-O&G-influenced plumes, therefore contributing to some of the differences
in predicted ΔSOA/ΔCO and ΔOOA/ΔOx in
urban-influenced vs. O&G-influenced plumes.
Conclusions and implications
Summertime OA in the Front Range displayed significant enhancement with
respect to CO in photochemically aged plumes. Substantial contributions of
OOA in plumes impacted by urban and O&G emissions were confirmed with PMF
analysis. In the absence of cyclonic flow and under similar atmospheric
conditions, differences in OOA and HOA concentrations in urban-influenced vs. high-O&G-influenced plumes were within the observed variabilities while mean
and median concentrations of OOA were significantly higher during the Denver
cyclone. Mixing ratios of aromatics, methyl cyclohexane, octane, and
RO2 radicals were significantly higher in high-O&G-influenced plumes
compared to urban plumes. Despite this, OH and HO2 mixing ratios were
highly similar.
To assess the role of O&G emissions on SOA production, WRF-Chem model
runs were carried out, with different considerations for POA volatility and
emission strengths from the O&G sector. Assuming a semi-volatile nature
for POA resulted in greater than factor of 10 lower mean and median POA
concentrations compared to the PMF-based HOA, while simulations with the
assumption of non-volatile POA resulted in only a factor of 2 lower POA
compared to HOA. Assuming non-volatile POA increased the predicted total OA
by ∼ 13 %, mainly through additional contribution of POA to
OA. Much improved comparisons between predicted mixing ratios of VOCs and
the measurements were achieved when using top-down modified emission
factors from the O&G sector in DJB. Overall, comparisons of the median
measured and predicted OA were satisfactory, with the best match obtained in
runs when consecutive aging of bVOCs and bSOA formation was turned off. The
extent of SOA formation due to emissions from the O&G sector was
estimated to be less than 5 % of total OA; however, the contribution of
O&G emissions to aSOA was more significant at ∼ 30–38 %.
Given the uncertainties in emissions of IVOCs from the O&G sector, more
simulations need to be carried out to better quantify the contribution of
O&G IVOC emissions to total OA. In addition, it is important to
characterize POA emissions associated with the O&G sector in future
emission inventories. A large fraction (∼ 40–54 %) of OA in
the Front Range was predicted to be from bSOA. Uncertainties in
photochemical processing and aging of bVOCs also warrant additional studies
to constrain the production of bSOA. It is worth noting that, in the wintertime
with lower boundary layer heights and lower temperatures,
higher aerosol mass and more favorable conditions for the partitioning of
semi-volatile species to the aerosol phase exist. Additionally, significantly lower
emissions of bVOCs are expected in wintertime; therefore, contributions of O&G emissions to SOA in the Front
Range could be more significant than what was observed during this
study.