Organic aerosol (OA) is one of the main components of the global particulate
burden and intimately links natural and anthropogenic emissions with air
quality and climate. It is challenging to accurately represent OA in global
models. Direct quantification of global OA abundance is not possible with
current remote sensing technology; however, it may be possible to exploit
correlations of OA with remotely observable quantities to infer OA
spatiotemporal distributions. In particular, formaldehyde (HCHO) and OA share
common sources via both primary emissions and secondary production from
oxidation of volatile organic compounds (VOCs). Here, we examine OA–HCHO
correlations using data from summertime airborne campaigns investigating
biogenic (NASA SEAC4RS and DC3), biomass burning (NASA SEAC4RS), and
anthropogenic conditions (NOAA CalNex and NASA KORUS-AQ). In situ OA
correlates well with HCHO (r=0.59–0.97), and the slope and intercept
of this relationship depend on the chemical regime. For biogenic and
anthropogenic regions, the OA–HCHO slopes are higher in low NOx
conditions, because HCHO yields are lower and aerosol yields are likely
higher. The OA–HCHO slope of wildfires is over 9 times higher than that for
biogenic and anthropogenic sources. The OA–HCHO slope is higher for highly
polluted anthropogenic sources (e.g., KORUS-AQ) than less polluted (e.g.,
CalNex) anthropogenic sources. Near-surface OAs over the continental US are
estimated by combining the observed in situ relationships with HCHO column
retrievals from NASA's Ozone Monitoring Instrument (OMI). HCHO vertical
profiles used in OA estimates are from climatology a priori profiles in the
OMI HCHO retrieval or output of specific period from a newer version of
GEOS-Chem. Our OA estimates compare well with US EPA IMPROVE data obtained
over summer months (e.g., slope =0.60–0.62, r=0.56 for August 2013),
with correlation performance comparable to intensively validated GEOS-Chem
(e.g., slope =0.57, r=0.56) with IMPROVE OA and superior to the
satellite-derived total aerosol extinction (r=0.41) with IMPROVE OA. This
indicates that OA estimates are not very sensitive to these HCHO vertical
profiles and that a priori profiles from OMI HCHO retrieval have a similar
performance to that of the newer model version in estimating OA. Improving
the detection limit of satellite HCHO and expanding in situ airborne HCHO and
OA coverage in future missions will improve the quality and spatiotemporal
coverage of our OA estimates, potentially enabling constraints on global OA
distribution.
Introduction
Aerosols are the largest source of uncertainty in climate
radiative forcing (IPCC, 2013; Carslaw et al., 2013) and decrease atmospheric
visibility and impact human health (Pope, 2002). Organic aerosols (OAs)
comprise a large portion (∼50 %) of submicron aerosols
(Jimenez et al., 2009; Murphy et al., 2006; Shrivastava et al., 2017), and
this fraction will grow with continued decline in SO2 emissions (Attwood
et al., 2014; Marais et al., 2017; Ridley et al., 2018). In addition, OAs
serve as cloud condensation nuclei (CCN) and affect cloud formation and
climate radiative forcing. OA components also have adverse health effects
(e.g., Walgraeve et al., 2010) and contribute significantly to regional
severe haze events (e.g., Hayes et al., 2013). Finally, because the response
of temperature to changes in climate forcing is non-linear (Taylor and
Penner, 1994) and the forcing by aerosols has strong regional character
(Kiehl and Briegleb, 1993), it is necessary to separate out different climate
forcing components to accurately forecast the climate response to changes in
forcing.
Despite their importance, it has been challenging to accurately represent OAs
in global models. Chemical transport models (CTMs) often underpredict OA
(e.g., more than a factor of 2 lower OA near the ground) compared to
observations, and model-to-model variability can exceed a factor of 100 in
the free troposphere (Tsigaridis et al., 2014; Heald et al., 2008, 2011). Fully explicit mechanisms have attempted to capture the full OA
chemical formation mechanisms (e.g., Lee-Taylor et al., 2015), but it is too
computationally expensive to apply these mechanisms to OA formation in
global CTMs at a useful resolution. For computational efficiency, 3-D models
such as GEOS-Chem include direct emissions of primary OA (POA) and represent
secondary OA (SOA) formation either by lumping SOA products according to
similar hydrocarbon classes (Kim et al., 2015) or based on the volatility of
the oxidation products (Pye et al., 2010). Marais et al. (2016) applied an
aqueous-phase mechanism for SOA formation from isoprene in GEOS-Chem to
reasonably simulate isoprene SOA in the southeastern (SE) US. Schroder et al. (2018) showed GEOS-Chem has a very large underprediction of SOA in the
northeastern US dominated by anthropogenic emissions. Accurate emission
inventories are also needed to correctly represent volatile organic
compounds (VOCs) and NOx (NOx=NO+NO2) inputs, and
these often have biases compared to observational constraints (Kaiser et
al., 2018; Travis et al., 2016; Anderson et al., 2014; McDonald et al.,
2018).
A quantitative measure of OA from space would be helpful for verifying
emissions and aerosol processes in models. However, direct measurements of
OA from space are currently unavailable. Aerosol optical depth (AOD)
measured by satellite sensors provides a coarse but global picture of total
aerosol distributions. The Multi-angle Imaging SpectroRadiometer (MISR) provides
aerosol property information such as size, shape, and absorbing properties,
which allows retrieving the AOD of a subset of aerosols (Kahn and Gaitley,
2015). Classification algorithms have been developed to speciate different
aerosol types (e.g., OA) based on AOD, extinction Ångström exponent, UV
aerosol index, and trace gas columns from satellite instruments (Penning de Vries et
al., 2015). Here, we aim to provide a quantitative estimation of OA mass
concentrations from satellite measurements.
Formaldehyde (HCHO) is one of the few VOCs that can be directly observed
from space. Sources emitting POA (e.g., biomass burning; BB) often
simultaneously release VOCs. HCHO and SOA are also both produced from
emitted VOCs. VOCs, as well as intermediate- and semi-volatile organic
compounds (I/SVOCs), are oxidized by hydroxyl radicals (OH) to form peroxy
radicals (RO2), which then react with NO, RO2, or
hydroperoxy radicals (HO2) or isomerize. These oxidation processes
produce HCHO and oxidized organic compounds with low volatility that
condense to form SOA (Robinson et al., 2007; Ziemann and Atkinson, 2012).
The yield of HCHO and SOA from hydrocarbon oxidation thus depends on the VOC
precursors, oxidants (OH, O3, and NO3), RO2 reaction pathway
(e.g., NO levels), and pre-existing aerosol abundance and properties (Wolfe
et al., 2016; Pye et al., 2010; Marais et al., 2016, 2017; Xu et al.,
2016). Moreover, although the lifetime of HCHO (1–3 h) is shorter than OA
(1 week), HCHO continues to form from slower-reacting VOCs, as well as from
the oxidation of later-generation products. Observations across megacities
around the world show that OA formation in polluted/urban areas happens over
about 1 day (e.g., DeCarlo et al., 2010; Hodzic and Jimenez, 2011; Hayes et
al., 2013, 2015), and HCHO is also significantly formed over this timescale
(Nault et al., 2018). In addition, Veefkind et al. (2011) found that
satellite AOD correlated with HCHO over the summertime SE US, BB regions,
and southeast Asian industrialized regions. This also suggests that OAs share
common emission sources and photochemical processes with HCHO and are a
major contributor to AOD in the regions above. Marais et al. (2016) further
used the relationship between aircraft OA and satellite HCHO to evaluate the
GEOS-Chem representation of SOA mass yields from biogenic isoprene in the SE
US.
We present an OA surface mass concentration estimate (OA estimate) derived
from a combination of satellite HCHO column observations and in situ OA–HCHO
relationships. Because the detection limit of satellite HCHO column
observations limits the quality of OA estimate, we focus our analyses on
summertime when HCHO levels are high. The OA estimate is evaluated against
OA measurements at ground sites. A 3-D model GEOS-Chem OA simulation is
shown for comparison.
MethodsIn situ airborne observations
Figure 1 shows flight tracks with altitudes < 1 km of the field
campaigns used in the current study. The Studies of Emissions, Atmospheric
Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS)
mission (Toon et al., 2016; SEAC4RS Science Team, 2013) covered the continental US with a focus on the
SE US in August–September 2013. The Deep Convective Clouds and Chemistry
Experiment (DC3) (Barth et al., 2015; DC3 Science Team, 2012) surveyed the central and SE US in
May–June 2012, targeting isolated deep convective thunderstorms and
mesoscale convective systems. The California Research at the Nexus of Air
Quality and Climate Change (CalNex) (Ryerson et al., 2013; CalNex Science Team, 2010) investigated the
California region in May–June 2010, targeting the Los Angeles (LA) Basin and
Central Valley. The Korea–United States Air Quality Study (KORUS-AQ) studied
South Korean air quality, sampling many large urban areas in South Korea and
continental Asian outflow over the West Sea, in May–June 2016 (KORUS-AQ Science Team, 2016). KORUS-AQ only includes data with longitude < 133∘ E to exclude the transit from the US because it targeted South
Korea and the nearby region. These field campaigns were selected as they had
recent high-quality in situ HCHO and OA data measured with state-of-the-art
instruments and studied summertime regional tropospheric chemical
composition.
Flight tracks of airborne field campaigns SEAC4RS (blue), DC3
(black), CalNex (red), and KORUS-AQ (green) with altitudes (< 1 km),
of which in situ OA and HCHO measurements were used.
In situ airborne HCHO observations were acquired by multiple instruments.
The DC3 NASA DC-8 payloads featured two HCHO measurements: the NASA In Situ
Airborne Formaldehyde (ISAF) (Cazorla et al., 2015) and the Difference
Frequency Generation Absorption Spectrometer (DFGAS) (Weibring et al.,
2006). The SEAC4RS NASA DC-8 payloads also featured two HCHO
measurements: the NASA ISAF and the Compact Atmospheric Multispecies
Spectrometer (CAMS) (Richter et al., 2015). HCHO measurements from ISAF were
found to be in good agreement with CAMS, with a correlation coefficient of
0.99 and a slope of 1.10 (Zhu et al., 2016). HCHO measurements from ISAF
also had a good agreement with DFGAS, with a correlation coefficient of 0.98
and a slope of 1.07. Because ISAF has higher data density, we used ISAF HCHO
data for DC3 and SEAC4RS. During KORUS-AQ, CAMS was the only HCHO
instrument aboard the DC-8. In CalNex, a proton transfer reaction mass
spectrometer (PTR-MS) (Warneke et al., 2011) was used to measure HCHO aboard the NOAA P3 aircraft.
In situ airborne OA from SEAC4RS, DC3, and KORUS-AQ was measured by the
University of Colorado high-resolution time-of-flight aerosol mass
spectrometer (HR-ToF-AMS; DeCarlo et al., 2006; Dunlea et al., 2009; Canagaratna et
al., 2007; Jimenez et al., 2016) and in situ airborne OA from CalNex was
measured by the NOAA compact time-of-flight aerosol mass spectrometer
(Drewnick et al., 2005; Canagaratna et al., 2007; Bahreini et al., 2012).
The OA measurements are from 1 min merge data and converted from
µg sm-3 (at 273 K and 1013 mbar) to µg m-3 under local T
and P
for each data point, to be consistent with HCHO concentrations in
µg m-3 or molec cm-3 at local T and P.
Although NO modulates the RO2 lifetime, and thus the production of
HCHO and SOA, NO cannot be directly observed via remote sensing. Instead,
NO2 can be directly observed in space by satellites, and because
NO2 represents typically ∼80 % (e.g., SEAC4RS and
KORUS-AQ) of the boundary layer NOx concentrations during the daytime,
NO2 can be used as a surrogate for daytime NO concentrations and
oxidative conditions around the globe. In situ airborne NO2 was
measured by the NOAA chemiluminescence NOyO3 instrument (Ryerson
et al., 2001) during SEAC4RS, DC3, and CalNex and by University of
Berkeley laser-induced fluorescence NO2 instrument (Day et al., 2002)
during KORUS-AQ. SEAC4RS isoprene measurements were from the
proton-transfer-reaction mass spectrometer (PTR-MS) (Wisthaler et al.,
2002).
Ground-based OA measurements
Ground-based OA measurements over the US were from the EPA Interagency
Monitoring of Protected Visual Environments (IMPROVE) (Malm et al., 1994;
Solomon et al., 2014; Hand et al., 2014, 2013; Malm et al.,
2017) and Southeastern Aerosol Research and Characterization (SEARCH)
(Edgerton et al., 2006) networks. In the IMPROVE network, aerosols were
collected on quartz fiber filters and analyzed in the lab by thermal optical
reflectance for organic and elemental carbon. The data were reported every
3 days from 1988 to 2014. Monthly averages were used for comparison in
this study. IMPROVE OA data over the SE US (east of 70∘ W)
in summertime were multiplied by a factor of 1.37 to correct for partial
evaporation during filter transport, following the recommendation of a
comparison study with SEARCH organic carbon (OC) measurements (Kim et al.,
2015; Hand et al., 2013). Although IMPROVE OA corrected for evaporation has
potential uncertainties with the constant scaling factor, the IMPROVE
measurements have high spatial coverage. SEARCH network (Edgerton et al.,
2006; Hidy et al., 2014) OC was determined by the difference between total
carbon (TC) detected by a tapered element oscillating microbalance (TEOM)
and black carbon (BC) measured by an in situ thermal–optical instrument.
This allowed real-time measurement of OC and prevented evaporation during
filter transport. Although the SEARCH network only has five sites available, we
used observations from this network due to their high accuracy. The IMPROVE
and SEARCH network OC measurements were converted to OA by multiplying by a
factor of 2.1 based on ground and aircraft observations (Pye et al., 2017;
Schroder et al., 2018).
Satellite measurements
Satellite HCHO column observations were derived from NASA's Ozone
Monitoring Instrument (OMI), a UV–visible nadir solar backscatter spectrometer
on the Aura satellite (Levelt et al., 2006). Aura passes over the Equator at
13:30 LT, daily. Here, we used the OMI HCHO version 2.0 (collection
3) gridded (0.25∘×0.25∘) retrieval data (Gonzalez
Abad et al., 2015) from the Smithsonian Astrophysical Observatory (SAO).
Satellite data for HCHO columns were subjected to data quality filters: (1) solar zenith angle lower than 70∘, (2) cloud fraction less than
40 %, and (3) main quality flag and the xtrackquality flag both equal to
zero (Harvard-Smithsonian Center for Astrophysics OMI HCHO data product
description, 2017). The monthly average HCHO columns were also weighted by the
column uncertainties of the pixels. The HCHO retrieval used a priori
profiles without aerosol information from the GEOS-Chem model (Gonzalez Abad
et al., 2015). Satellite NO2 column observations were also derived from
NASA's OMI level 3 data (Lamsal et al., 2014; Krotkov, 2013). Satellite NO2 observations were used to calculate
the NOx-related chemical-factor-dependent OA estimate (see Table 2). Satellite AOD
observations were acquired from the Moderate Resolution Imaging
Spectroradiometer (MODIS) aboard the Aqua satellite, using overpasses at
about 13:30 LT. Here, we used Collection 06 (Levy and Hsu, 2015), retrieved using the dark target (DT) and deep blue (DB) algorithms
(Levy et al., 2015), monthly average data.
GEOS-Chem
We used GEOS-Chem (v9-02) at 2∘×2.5∘ with 47
vertical layers to simulate HCHO and OA globally, the same as that in Marais
et al. (2016). GEOS-Chem was driven with meteorological fields from the NASA
Global Modeling and Assimilation Office (GMAO). The OA simulation included
POA from fires and anthropogenic activity and SOA from the volatility-based
reversible partitioning scheme (VBS) of Pye et al. (2010) for anthropogenic,
fire, and monoterpene sources, and an irreversible aqueous-phase reactive
uptake mechanism for isoprene. The aqueous-phase mechanism was coupled to
gas-phase isoprene chemistry and has been extensively validated using
surface and aircraft observations of isoprene SOA components in the SE US
(Marais et al., 2016). This model version used the fourth-generation Global
Fire Emissions Database (GFED4) (Giglio et al., 2013) as a BB emission
inventory. The model was driven with Goddard Earth Observing System –
Forward Processing (GEOS-FP) meteorology for 2013 and sampled along the
SEAC4RS (2013) and KORUS-AQ (2016) flight tracks. The model was also
run with a 10 % decrease in biomass burning, biogenic, or anthropogenic
emissions as a sensitivity test to evaluate the contributions of different
sources to the OA and HCHO budget. Model monthly mean surface layer OA and total
column formaldehyde were obtained around the OMI overpass time (12:00–15:00 LT)
for 2008–2013 using Modern-Era Retrospective analysis for
Research and Applications (MERRA) (Gelaro et al., 2017) meteorology, as
GEOS-FP was only available from 2012. This was compared to the OA estimate
derived from satellite HCHO.
Global isoprene emissions from the Model of Emissions of Gases and Aerosols
from Nature version 2.1 (MEGAN) (Guenther et al., 2006) and satellite
NO2 column data were used to calculate an isoprene- and
NO2-dependent OA estimate (see Table 2). Global isoprene emissions from MEGAN
were implemented in GEOS-Chem and driven with MERRA (MEGAN-MERRA).
Estimation of surface organic aerosol mass concentrations
An estimate for surface OA mass concentration was calculated based on a
simple linear transformation.
ε(i)=ΩHCHO(i)η(i)α(i)+β(i)
Here, ε(i) is the OA estimate for grid cell i (µg m-3), ΩHCHO(i) is the OMI HCHO column density
(molec cm-2) in each 0.25∘×0.25∘
grid cell (similar resolution to OMI HCHO nadir pixel data), η(i) is
the ratio of midday surface layer (∼60 m) HCHO concentrations
(molec cm-3) to column concentrations (molec cm-2) from GEOS-Chem,
and α(i) and β(i) are the slope and intercept of a linear
regression between OA and HCHO from low-altitude (< 1 km) airborne
in situ measurements. The in situ to column conversion factor η(i) was
similar to that used by Zhu et al. (2017) to convert HCHO columns into
surface concentrations. η(i) was derived from the HCHO a priori
profiles used in SAO OMI air mass factor (AMF) calculations (GEOS-Chem
v9-01-03 climatology) or from GEOS-Chem v9-02, which included an updated
isoprene scheme for OA and is the next version of the model (v9-01-03) for a
priori profiles used in SAO satellite HCHO retrievals. HCHO a priori
profiles were used to be consistent with satellite HCHO retrievals and also
to show that the OA estimate can be derived without running a global model
separately. The newer version of GEOS-Chem was used to test the sensitivity
of OA estimates to the updated version of η. The newer version of
GEOS-Chem also allows sampling through the flight tracks of a recent field
campaign (SEAC4RS) and examining the factors impacting η with
both modeled and measured HCHO profiles. The detailed information about the
impact of HCHO profiles on η is provided in Sect. 5.
Aerosol extinction from satellite measurements
Currently, remote sensing techniques observe aerosols by quantifying AOD. The
MISR satellite instrument can estimate a subset of AOD, using constraints on
size range, shape, and absorbing properties, but it cannot distinguish OA
from other submicron aerosol compounds such as sulfate and nitrate and also
requires AOD to be above 0.1. Because MISR estimates a subset of AOD, it is
discussed above to verify that we are not neglecting a satellite dataset
that has already captured OA AOD. Moreover, OAs account for a large and
relatively constant fraction of submicron aerosols in the SE US (Kim et al.,
2015; Wagner et al., 2015) and are one of the major submicron aerosol
components over the US (Jimenez et al., 2009). Therefore, AOD was converted
to extinction to represent OA for comparison:
Aext=AOD(i)δ(i),
where Aext is the calculated aerosol extinction (Mm-1),
AOD(i)
is aerosol optical depth from MODIS (see Sect. 2.3) in each
0.25∘×0.25∘ grid cell, and δ(i)
(m-1) is the ratio of surface layer OA concentrations (µg m-3, at ambient T and P) to column OA concentrations
(µg m-2) from GEOS-Chem multiplied by 106 m Mm-1.
The
shape of the average vertical profile of OA (OA fraction: 0.54–0.7) was
close to that of total aerosol mass over the SE US (Wagner et al., 2015), where a
large fraction of the enhanced non-BB aerosol concentrations in summertime
over the US are located. Data with BB plume interferences were excluded in
the following analysis. The potential contribution of dust and nitrate could
alter the shape of the vertical profiles and introduce uncertainties when
using OA vertical profiles for other parts of the US. However, the outliers
in the aerosol extinction compared to ground OA measurements (see Sect. 6.3)
were not located outside of the SE US. Similar vertical profile shapes of OA
and submicron particles were also observed in a campaign outside the US over
South Korea (Nault et al., 2018). Although OA accounted for ∼40 % of the total submicron particles, the shape of OA and total submicron
particles' vertical profiles were nearly identical.
In situ OA–HCHO relationship
Although OA and HCHO share common VOC emission sources and photochemical
processes, their production rates from different emission sources and
photochemical conditions vary, as do their loss rates. We found the main
factors that modulate OA–HCHO relationships from in situ measurements and
discussed them in the following section.
Linear regression parameters for OA vs. HCHO at low altitudes
(< 1 km).
USUSUSSouthWild-AgriculturalSEAC4RSSEAC4RS(SEAC4RS)(DC3)(CalNex)Koreafiresfireslow NO2high NO2(KORUS-AQ)(SEAC4RS)(SEAC4RS)andandisopreneisopreneIn situ measurements OA vs. HCHO Slopea1.93±0.071.30±0.101.34±0.022.75±0.0525.08±0.303.22±0.372.39±0.091.45±0.19Slopeb9.61±0.346.49±0.496.66±0.0913.71±0.25125.05±1.4916.04±1.8511.9±0437.25±0.96(×10-11)Interceptc0.34±0321.10±0.30-0.90±0.061.36±0.22-6.85±2.8010.41±5.82-1.14±0.371.14±1.22Correlation0.590.760.880.700.970.850.640.45coefficient rNumber of points150613417723425515321138226(1 min average)GEOS-Chem model sampled along the flight track OA vs. HCHO Slopea1.25±0.031.39±0.050.48±0.05Slopeb6.21±0.146.95±0.232.37±0.22(×10-11)Interceptc-1.32±0.111.88±0.070.12±0.03Correlation0.760.430.53coefficient r
a The unit of the slope is g g-1.
b The unit of the slope is pg molec-1.
c The unit of the intercept is µg m-3. The uncertainties are 1 standard
deviation.
Regional and source-driven variability
For all regions and/or sources investigated, near-surface in situ OA and
HCHO are well correlated. A scatter plot of in situ OA vs. HCHO at low
altitudes (< 1 km) from a number of field campaigns (SEAC4RS,
DC3, CalNex, and KORUS-AQ) is displayed in Fig. 2. The slopes, intercepts,
and correlation coefficients are summarized in Table 1. SEAC4RS, DC3,
and CalNex excluded BB data when acetonitrile was > 200 pptv (Hudson
et al., 2004). KORUS-AQ used a BB filter with higher acetonitrile
(> 500 pptv) because the air masses with moderate acetonitrile
enhancement (200–500 pptv) were actually from anthropogenic emissions. This
attribution is based on high levels of acetonitrile detected downwind of
Seoul and west coastal petrochemical facilities, the slope between
acetonitrile and CO being to urban emissions (Warneke et al., 2006), and the
concentrations of anthropogenic tracer CHCl3 being high (Warneke et
al., 2006). Similar to acetonitrile, another common BB tracer, hydrogen
cyanide (HCN), was also enhanced in these air masses. BB data (acetonitrile
> 200 pptv) for SEAC4RS were analyzed separately and are
in the inset in Fig. 2. Although all CalNex data had a tight correlation, we only
included the flight data near the LA Basin to target the area strongly
influenced by anthropogenic emissions. In general, the correlation
coefficients between in situ OA and HCHO were strong (r=0.59–0.97)
(Table 1).
The variety in OA–HCHO regression coefficients among different campaigns
reflects the regional and/or source-driven OA–HCHO variability. Considering
only the non-biomass burning (non-BB) air masses sampled, OA and HCHO had
the tightest correlation for CalNex, because CalNex focused on the LA area
(shown in Fig. 2) and Central Valley, while SEAC4RS and DC3 covered a
larger area with a potentially larger variety of sources and chemical
conditions. Although SEAC4RS and DC3 both sampled the continental US,
SEAC4RS had more spatial coverage and sampled more air masses at low
altitudes, while DC3 was designed to sample convective outflow air masses
and had more data at high altitudes. Although KORUS-AQ covered a much
smaller area compared to SEAC4RS, KORUS-AQ data also had a large
spread, which may be due to the complicated South Korean anthropogenic
sources mixed with transported air masses (e.g., from China) and maybe
biogenic sources. OA exhibits a tight correlation with HCHO for both
wildfires and agricultural fires during SEAC4RS. This is because the
production of HCHO and OA is much higher in BB air masses compared to
background. This may also suggest that the emissions of OA and HCHO in these
air masses are relatively constant. SEAC4RS data are chosen because the
campaign sampled fires and had state-of-the-art, high-quality measurements. More
intensive fire sampling is needed to probe the correlation between OA and
HCHO across fuel types and environmental conditions.
The different slopes of OA–HCHO among different campaigns also reflect the
regional or source-driven OA–HCHO variability. Among the BB, anthropogenic,
and biogenic sources, the slopes of OA vs. HCHO for BB air masses were the
highest. This is consistent with high POA emission in BB conditions (Heald
et al., 2008; Lamarque et al., 2010; Cubison et al., 2011), with low
addition of mass due to SOA formation (Cubison et al., 2011; Shrivastava et
al., 2017). The slope of OA to HCHO was higher for wildfires than
in agricultural fires during SEAC4RS though data were limited (see Table 1). This is consistent with more OA emitted in wildfires than agricultural
fires (Liu et al., 2017). The factors driving higher OA to HCHO with
wildfires are not clear and may be related to burning conditions and fuels.
For the non-BB sources, the slope of OA vs. HCHO was highest for South Korea
(KORUS-AQ), which is dominated by heavily polluted anthropogenic sources.
During KORUS-AQ, the high OA to HCHO air masses also had high acetonitrile.
By the time we sampled, most organic aerosols were secondary (Nault et al.,
2018). This indicates that the formation rates of OA and HCHO from different
emission sources contribute to the different slopes of OA–HCHO. This also
indicates that emission sources with enhanced acetonitrile tend to form more
OA relative to HCHO downwind. The slope of OA–HCHO for the LA Basin (California),
dominated by relatively clean anthropogenic emissions, was much lower than
that for South Korea. The potential difference in the anthropogenic emissions mix
could contribute to the different OA–HCHO slopes from the US LA region and South
Korean anthropogenic sources (Baker et al., 2008; Na et al., 2002, 2005). The slopes of OA vs. HCHO of SEAC4RS and DC3 dominated by
biogenic emissions in the SE US were in between heavily polluted (KORUS-AQ)
and clean anthropogenic sources (CalNex). As SEAC4RS had the largest
geographic coverage for low-altitude data over the US, the campaign average
slope of OA vs. HCHO was used to represent the US region in summer. CalNex
LA Basin data were used to represent large cities as case studies.
Scatter plots of in situ OA (µg m-3) vs. HCHO (µg m-3 or
molec cm-3) from SEAC4RS (excluding biomass burning) (blue), DC3
(dark grey), CalNex (pink), and KORUS-AQ (green) low-altitude (< 1 km) data. Inset shows wildfire (brown) and agricultural fire (grey)
SEAC4RS data. SEAC4RS biomass burning cases are defined as
acetonitrile > 200 pptv. The linear regression fits are shown as
the darker lines and correlation coefficients are provided.
Overall, the source-dependent OA–HCHO relationships (Fig. 2) showed higher
OA–HCHO slopes of BB and heavily polluted anthropogenic sources with
inefficient combustion (e.g., KORUS-AQ) compared to biogenic and relatively
clean anthropogenic sources. This indicated that inefficient combustions
contribute to the high slopes of OA–HCHO, probably due to both enhanced
primary OA and increased formation of SOA. Enhanced pre-existing aerosols
such as primary aerosols can provide more surfaces to increase VOC
condensation and SOA formation. VOCs co-emitted from heavily polluted
anthropogenic sources can also form more SOA. It is possible to extract the
factors that govern the different OA–HCHO relationships and potentially have
a universal application of the slopes as a function of the factors (e.g.,
sources and combustion efficiencies).
Dependence on NOx and VOC speciation
Biogenic and anthropogenic VOCs are oxidized by atmospheric oxidants (e.g.,
OH as the dominant oxidant) to form RO2. HCHO is produced from the
reactions of RO2 with HO2 or NO, with RO2+NO typically
producing more HCHO than RO2+HO2 (e.g., Wolfe et al.,
2016). RO2 can react with HO2 or NO, or isomerize to form oxidized
organic compounds with high molecular weight and low volatility, which
condense on existing particles to form SOA. The products of RO2+NO
tend to fragment instead of functionalize and often lead to higher
volatility compounds (e.g., HCHO) and thus less SOA formation compared to
the products of RO2+HO2 (Kroll et al., 2006; Worton et al.,
2013). Therefore, with the same VOC, we expect more HCHO and less OA formed
at high NO conditions, and vice versa. As mentioned before, NO2 instead of NO is easily measured from space and NO2 typically is
∼80 % of NOx in the boundary layer during the day.
Therefore, NO2 is used as a surrogate for the NO levels influencing OA
and HCHO production. The yields of HCHO and SOA also depend on VOC
speciation (e.g., Lee et al., 2006; Bianchi et al., 2016). Specifically,
isoprene has a higher yield of HCHO than most non-alkene VOCs (Dufour et
al., 2009).
(a) A scatter plot of OA vs. HCHO for SEAC4RS non-BB low-altitude data color coded with the product of NO2 and
isoprene in log scale. The red and blue lines are the linear regression fits
of high (> 0.5) and low (< 0.5) products of NO2
(ppbv) and isoprene (ppbv), respectively. (b) A scatter plot of OA vs. HCHO
for KORUS-AQ data color coded by log(NO2).
A scatter plot of OA vs. HCHO for SEAC4RS low-altitude data is shown in
Fig. 3a. The data are color coded by the product of in situ isoprene and
NO2, attempting to capture time periods strongly influenced by
oxidation products of isoprene at high NO conditions. No trends are evident
when the data are instead color coded by NO2 or isoprene only. This may
be because isoprene (biogenic source) and NO2 (anthropogenic sources)
are generally not co-located in the US (Yu et al., 2016) and isoprene is the
dominant source of HCHO compared to anthropogenic VOCs in the US (e.g.,
Millet et al., 2008). This plot shows that, at high NO2 and high
isoprene conditions, less OA was formed for each HCHO produced generally.
The correlation coefficient of 0.45 for high NO2 and isoprene
conditions during SEAC4RS is not very high but still shows significant
dependence of the OA–HCHO relationship on the product of NO2 and
isoprene, considering that these are ambient data and other factors (e.g.,
different specific sources) also play a role in determining OA–HCHO
relationships. This is consistent with high NO and isoprene conditions
promoting HCHO formation over SOA formation. We also looked at the dependence
on peroxy acetyl nitrate (PAN), as PAN is a product of the photooxidation
of VOCs, including isoprene, in the presence of NO2. The dependence on
PAN was not as clear as on the product of NO2 and isoprene.
KORUS-AQ OA vs. HCHO, color coded with NO2, is plotted in Fig. 3b.
The OA–HCHO ratio clearly decreased as NO2 levels increased during
KORUS-AQ, suggesting that high NO conditions accelerated HCHO formation more
than they did SOA production. OA–HCHO relationships do not have dependence
on local time of the day (not shown). This further confirms that NOx is
an important factor that affects the OA–HCHO relationship. Compared to
SEAC4RS, the KORUS-AQ OA–HCHO ratio does not depend on VOCs. This may
be consistent with the dominant VOCs being anthropogenic VOCs that are
co-located with NO sources. This may also suggest that the anthropogenic
VOCs generally have a lower HCHO yield than isoprene does. Because OA and
HCHO were tightly correlated during CalNex and DC3, we did not parse for
NOx. The NOx range during DC3 low-altitude data was smaller than
KORUS-AQ and SEAC4RS. DC3 OA–HCHO relationships only had a slight
dependence on NO2 (not shown here), largely due to the limited dataset.
The NOx range during CalNex low-altitude data was large. The OA and
HCHO correlation during CalNex was very tight and the slope of OA–HCHO did
not show clear dependence on NOx, which could be due to the combination
of different VOC sources and NOx levels.
Scatter plots of OA vs. HCHO for the US (SEAC4RS altitude
< 1 km non-BB), South Korea (KORUS-AQ altitude
< 1 km), and wildfires (SEAC4RS) from in situ measurements (a, b, c)
and GEOS-Chem outputs sampled along the flight tracks (d, e, f).
Comparison of OA–HCHO relationships: in situ vs. GEOS-Chem
In situ OA–HCHO relationships from SEAC4RS low-altitude non-BB (Fig. 4a), KORUS-AQ low-altitude (Fig. 4b), and SEAC4RS BB (Fig. 4c) air
masses were compared to GEOS-Chem model simulations (Fig. 4d–f) sampling
along the corresponding flight tracks. Similar to the in situ data,
GEOS-Chem model simulations also found correlations between OA and HCHO for
these three regions, especially for SEAC4RS non-BB. GEOS-Chem was
intensively validated with in situ measurements for the SE US (e.g., Marais et
al., 2016; Kim et al., 2015). The ratios of the slopes between OA and HCHO
for the US (SEAC4RS), South Korea (KORUS-AQ), and wildfire cases
(SEAC4RS) from GEOS-Chem were 1:1.1:0.4, which was different from the
in situ measurements of 1:1.4:13 (Table 1). GEOS-Chem could not capture any
wildfires in the US during SEAC4RS, which is probably due to poor
representation of the BB emission inventory for US wildfires and also the coarse
grid in GEOS-Chem. GEOS-Chem also significantly underpredicted the slope of
OA to HCHO for South Korea. We attribute this to a likely underprediction of
anthropogenic SOA, which was dominant in South Korea, in GEOS-Chem (Schroder
et al., 2018), as well as a different mix of OA and HCHO sources in the US
compared to South Korea and representation of these in GEOS-Chem. Although
GEOS-Chem contains isoprene chemistry with a focus on the SE US (Marais et
al., 2016), there is still room to improve the GEOS-Chem model especially for
anthropogenic and BB sources, as well as anthropogenic OA formation
mechanisms. For example, in the model, biogenic sources are more important
than anthropogenic sources for the OA and HCHO budgets in South Korea, which
is not the case from KORUS-AQ in situ measurements. In the model, a 10 %
decrease of emissions from biogenic, anthropogenic, and BB sources results in
6 %, 3 %, and 1 % decreases in OA and 2 %, 1 %, and 0 %
decreases in HCHO over South Korea in May 2016. However, the in situ airborne
field campaign KORUS-AQ found that OA and HCHO were higher near
anthropogenic emission sources compared to rural regions. The larger impact
of biogenic sources compared to anthropogenic sources on OA and HCHO in the
model can be due to both low-biased anthropogenic emission inventories and
low-biased anthropogenic SOA. Improving anthropogenic emissions inventories
in the models can bring model results closer to observations. Improving
anthropogenic SOA, such as implementation of the SIMPLE model, in GEOS-Chem
(Hodzic and Jimenez, 2011) can also improve the model results compared to
observations. Measurements or measurement-constrained estimation with
sufficient spatial and temporal coverage can help to narrow down the key
factors (e.g., emission inventories or chemical schemes) in GEOS-Chem to
better represent VOCs and OA globally. Furthermore, we did also find that
GEOS-Chem could not capture the observed higher slope of OA to HCHO at high
altitudes (not shown), which could be due to issues such as transport, OA
lifetime, and OA production.
Relating satellite HCHO column to surface HCHO concentrations
To utilize the derived in situ OA–HCHO relationship, the satellite HCHO
columns need to be converted to surface HCHO concentrations. We used a
vertical distribution factor η (cm-1) (Sect. 2.5), which is
defined as the ratio of surface HCHO concentrations (molec cm-3) to
HCHO column (molec cm-2), to estimate surface HCHO concentrations from
satellite column measurements. Zhu et al. (2017) used the same vertical
distribution factor for their study. The use of this factor is justified by
the fact that the derived surface HCHO retained the spatial pattern of the
satellite HCHO column and agreed with local surface measurements of HCHO for
a multi-year average (Zhu et al., 2017).
We also investigated the main factors affecting the variation of the
vertical distribution factor η. Because the factor is determined by
HCHO vertical distributions, we examined three typical normalized HCHO
vertical distribution profiles with the highest, median, and lowest η
values for the SEAC4RS field campaign (Fig. 5). Because the sensitivity
of OA estimates to η was investigated with η from different
GEOS-Chem versions (Sect. 6.2), we did not compare HCHO vertical profiles
from the model to the measurements from a comprehensive set of field
campaigns. We chose SEAC4RS to illustrate the main factors impacting
the η over the US because SEAC4RS had a larger spatial coverage than
DC3 and CalNex. GEOS-Chem can generally capture the vertical profiles of
measured HCHO. Boundary layer mixing height and surface emission strength
are the dominant factors in determining the fraction of HCHO near the
surface. Higher boundary layer mixing height results in lower η for SE
US profiles, where there are biogenic sources of HCHO from the surface and
HCHO has distinct concentration differences below and above the boundary
layer. However, there are exceptions, such as for the profiles over the
ocean and the coastal regions. Although the boundary layer is shallow in
these regions, a large portion of HCHO resides above the boundary layer,
resulting in low η. In these cases, surface emissions of HCHO or
precursors are very small, and therefore methane oxidation makes a large
contribution to the total HCHO column. High concentrations of HCHO (e.g., in
BB plumes) lofted by convection can also impact the vertical profile (Barth
et al., 2015), which is not further investigated because OA estimates with
BB influences over the US are excluded in current study. Overall, the source
intensities and boundary layer mixing height mostly determined the HCHO
vertical profiles.
Cases to estimate OA surface concentrations, based on the choice of
slope and intercept from a linear regression relationship between OA and
HCHO data found in Table 1.
LUMP-SUMaUsing non-BB SEAC4RS relationship to represent the entire continental USISOP-NOxbUsing NO2- and isoprene-dependent non-BB SEAC4RS relationship for the entire continental USURBANUsing the CalNex LA Basin relationship for large urban cites and the non-biomass-burning SEAC4RS relationship for other US regionsCOMBINEbUsing the CalNex LA Basin relationship for large urban cites and the NO2-and isoprene-dependent non-BB SEAC4RS relationship for other US regions
aSEAC4 RS was chosen to represent the entire continental US because it had
the largest horizontal and vertical coverage.
b In ISOP-NOx and COMBINE, when the product of NO2
column (Sect. 2.3) and surface isoprene emission rate (Sect. 2.4) was above
the threshold of 5×1027 molec cm-2 atom C cm-2 s-1, the slope and intercept from SEAC4RS high isoprene and
NO2 conditions were used. When the NO2 column isoprene
emission product was below that threshold, the slope and intercept from
SEAC4RS low isoprene and NO2 conditions were used. The threshold of
“isoprene ×NO2” was determined by its mean value over the SE
US (32–35∘ N, 83–96∘ W).
Large urban cities were categorized with high NO2 vertical columns
(> 4×1015 molec cm-2) (Tong et al., 2015)
based on the satellite NO2 levels over LA. Isoprene emissions instead
of concentrations were used because global models use the isoprene emission
inventory to simulate isoprene concentrations and the isoprene emission
inventory is easier to access. Since isoprene has a short lifetime of up to
a few hours (Guenther et al., 2006), the emissions have a similar
spatiotemporal distribution to the concentrations.
Three typical vertical profiles of the ratio of in situ HCHO
concentrations (molec cm-3) to integrated HCHO column from the SEAC4RS
flight track. These three profiles were located at the Kansas–Oklahoma border
(red), Arkansas–Tennessee border (black), and Gulf of Mexico (blue). Solid
curves were from GEOS-Chem results and the dashed ones were from ISAF
measurements. HCHO columns were integrated HCHO concentrations of these
vertical profiles extrapolated from 0 to 10 km, assuming the HCHO values below and
above the measured HCHO vertical profiles were the same as the HCHO at the
lowest and highest altitudes sampled, respectively. The boundary layer
heights (BLHs) of these three profiles are plotted by the shaded areas.
Construction of the OA estimateVariables to construct OA estimate
As mentioned in Sect. 2.5, the OA estimate value in each grid cell was
estimated from monthly average satellite HCHO column observation by the
linear Eq. (1). Satellite monthly average HCHO column data, ΩHCHO, were converted to surface HCHO concentrations by multiplying by
the η(i) factor either from climatology a priori profiles or monthly
average HCHO profiles. Surface OA was then estimated by multiplying the
derived surface HCHO concentrations by the slope αi and adding the intercept β(i). The slope αi and intercept β(i) were determined from the linear
regression of in situ OA and HCHO from aircraft field campaign data. The
relationship between OA and HCHO varies but previous sections demonstrated
that we can quantify the surface OA–HCHO relationship by their regions,
sources, and chemical conditions (e.g., NOx and isoprene levels). To
test the impact of the chosen OA–HCHO relationship on the calculated OA
estimate, the OA estimate in the US was calculated using four different
methods (see Table 2). The OA estimate was calculated on the monthly
timescale, largely because OA estimate is based on OMI HCHO observations, and
an uncertainty weighted average for a timescale of about 1 month (Gonzalo et
al., 2015; Zhu et al., 2016) is needed to reduce the noise in daily OMI HCHO
data. With improved satellite HCHO data from the Tropospheric Monitoring
Instrument (TROPOMI), higher time resolution
(e.g., weekly average) HCHO data could be useful to estimate OA in the
future.
(a) The maps of (a) surface OA estimate (LUMP-SUM) with η from
GEOS-Chem v9-02, (b) surface OA estimate (LUMP-SUM) with η from a priori
profiles, (c) surface aerosol extinction derived from MODIS AOD,
(d) GEOS-Chem simulated surface OA, and (e) IMPROVE (small dots) and SEARCH
(large dots) network ground sites color coded with OA concentrations for
August 2013. The scatter plots of the (f, g) surface OA estimate, (h) surface
aerosol extinction derived from MODIS AOD, and (i) surface GEOS-Chem OA vs.
IMPROVE network ground sites' OA. IMPROVE sites' OAs were corrected for
evaporation. (j) The scatter plots of the surface OA estimate and GEOS-Chem OA
vs. SEARCH network ground sites' OA for August 2013. GEOS-Chem OA and the OA
estimate did not have good correlations with SEARCH OA for other years (SI).
For the scatter plots, linear regressions are shown (blue and green lines)
and regression equations and correlation coefficients for the scatter plots
are listed. The dashed lines in the scatter plots indicate the 1:1 line.
Biomass burning data (UV aerosol index > 1.6) were excluded in
all panels.
OA estimate over the US
The monthly average surface OA estimates over the US in August 2013 using
SEAC4RS lump-sum slope and intercept (see Table 2) with different η are shown in Fig. 6a and b. Because BB regions in the US are not
covered by smoke continuously during a period of time and it is challenging
for satellite retrieval to separate thick BB plumes and clouds without
information on the time and location of the burning, thick BB events (OMI UV
aerosol index (UVAI) > 1.6) (Torres et al., 2007) were excluded
and shown as the blank (white) grid cells in Fig. 6a and b. The same filter
was also applied to aerosol extinction and GEOS-Chem OA abundance. To
evaluate the representative quality of the OA estimate, OA estimate data
were compared to the EPA IMPROVE ground sites' corrected-OA measurements over
the US and SEARCH ground sites' OA measurements in the SE US (Sect. 2.2). The
locations of IMPROVE and SEARCH sites are displayed in Fig. 6e as small and
large dots, respectively. The dot color represents the average OA mass
concentrations for August 2013.
Considering the uncertainties in satellite HCHO measurements, in using the
campaign lump-sum OA–HCHO relationship to represent spatial resolved OA, in
HCHO vertical profiles, and in ground IMPROVE network measurements, the
correlation (correlation coefficient r=0.56) between the OA estimate and
corrected IMPROVE network measurements (Fig. 6f and g) is reasonably good
and indicates that the OA estimate can generally capture the variation of OA
loading over the US. First, the correlation coefficient between HCHO SAO
retrievals and in situ measurements during SEAC4RS was not high (r=0.24), but this may be partly because they were not sampled at the same time.
The uncertainty in HCHO SAO data was likely less than 76 %. Second, the
uncertainty in applying a campaign lump-sum OA–HCHO relationship to
individual spatial resolved satellite HCHO data to estimate OA induced an
uncertainty of 41 % according to the correlation coefficient of OA–HCHO in
the field campaign. Third, η in the Fig. 6a OA estimate was from
GEOS-Chem v9-02 output for the specific month of August 2013. η in the
Fig. 6b OA estimate was from GEOS-Chem v9-01-03 climatology, the same as
satellite data a priori profiles. The good correlations of OA estimates with
IMPROVE OA indicate that OA estimates are not very sensitive to η from
different model versions. The largest difference between the two OA
estimates is their concentrations over east Texas. There are no IMPROVE OA
measurements in east Texas to evaluate which works better. Fourth, the
uncertainties in IMPROVE OA measurements, such as using a constant
correction factor to correct the partial evaporation across all SE US sites,
and the spatially dependent OA/OC ratio (Tsigaridis et al., 2014), may also
have contributed to the discrepancies between the OA estimate and EPA
IMPROVE sites' OA. Therefore, higher quality of satellite HCHO data and
refining OA–HCHO relationships will help improve our OA estimate products.
These combined with a spatially resolved IMPROVE OA correction factor and
OA/OC ratios will help improve the correlation coefficients between OA
estimates and IMPROVE OA.
The linear correlation between the OA estimate and IMPROVE OA measurements
yielded a slope of 0.62 or 0.60, indicating that the OA estimate
underestimated OA. First, the different data collection time for satellite
data, in situ measurements, and ground observations could contribute to the
bias. Satellite HCHO data were measured midday, in situ airborne OA and
HCHO were measured during the daytime, and IMPROVE network organic carbon was
collected day and night. Because ground OAs in the SE US were observed to
have little diurnal variation (Xu et al., 2015; Hu et al., 2015), the
different sampling time of ground and airborne OAs probably does not have a
significant impact on the comparison of OA estimate and IMPROVE OA. Surface
HCHO has evident diurnal profiles with the highest concentrations around
midday (Kaiser et al., 2016), which could add uncertainties to OA estimate
when using inconsistent time ranges of satellite HCHO data measured
midday and in situ airborne OA–HCHO relationships measured in the daytime.
The SEAC4RS HCHO concentrations were converted to 13:30 LT
concentrations according to the average HCHO diurnal profile from the
Southern Oxidant and Aerosol Study (SOAS) (Kaiser et al., 2016). The OA–HCHO
relationship with HCHO converted to 13:30 LT yielded a slope of 5 % lower
than the original OA–HCHO relationship. Second, the potential uncertainty
(±30 %) in the OA/OC ratio could also contribute to the systematic
difference because we used OA/OC of 2.1 and studies (e.g., Pye et al., 2017;
Canagaratna et al., 2015) showed that the OA/OC ratio can range from 1.4 to 2.8.
Third, the potential underestimation of HCHO from satellite retrieval (by
-37 %) (Zhu et al., 2016) compared to SEAC4RS may be one of the
most important reasons that cause the systematic difference (low slope)
between the OA estimate and IMPROVE OA according to Eq. (1). Satellite HCHO
data corrected by the low bias (by -37 %) (Zhu et al., 2016) will
increase our slopes of 0.60–0.62 to be close to the unity.
SEARCH OA data were also used to compare to the OA estimate. The correlation
was good for August 2013. Although the SEARCH network OA measurements have
better accuracy, the number of SEARCH sites is limited (five sites). The
correlation of OA estimate and SEARCH OA varied dramatically in 2008–2013 (Fig. S1 in the Supplement). GEOS-Chem OA did not correlate with SEARCH OA except for the year 2013
(Fig. S1). As the IMPROVE network has more sites and spatial coverage, we
used IMPROVE network data as ground OA measurements for comparison in the
remainder of the discussion.
Comparison to aerosol extinction from AOD
To further evaluate the method of using satellite HCHO to derive an OA
surface estimate, satellite aerosol measurements were used to approximate
surface OA extinction for comparison. Satellite measurements of AOD were
converted to surface extinction (see Sect. 2.6). Studies showed that OAs were
a dominant component of aerosol mass and extinction during SEAC4RS (Kim
et al., 2015; Wagner et al., 2015) and the fractions of OA were relatively
constant (interdecile 62 %–74 %) (Wagner et al., 2015). Therefore, AOD
variation is expected to generally reflect the OA variation during
SEAC4RS. Satellite measurements from MISR can provide more aerosol
property information to apportion total AOD to AOD of a subset of aerosols
with small to medium size and round shape, which can better capture OA, when
AOD is above 0.15 to 0.2 (Kahn and Gaitley, 2015; personal communication
with Ralph Kahn, 2018). Because MISR cannot distinguish OA and other submicron
aerosol components (e.g., sulfate and nitrate) and would cut off low AOD
data which accounted for near half of the data over the US, we used total AOD to
derive extinction for our comparison. The AOD-derived extinction map is
shown in Fig. 6c, and the scatter plot of AOD-derived extinction and
EPA-corrected OA is displayed in Fig. 6h. The same filter of high AI was also
applied to AOD-derived extinction to remove BB plumes. Generally, the
derived aerosol extinction had a correlation with IMPROVE OA, but the
correlation was not as good as for the OA estimate with IMPROVE OA. The high
surface aerosol extinctions (> 150 Mm-1) (outliers in the
scatter plot) were located in the SE US and therefore were not due to
potential contribution of dust and nitrate altering the shape of vertical
profiles outside of the SE US. This indicates that the OA estimate derived
from HCHO may be better than AOD at representing the concentrations of OA,
even for the regions where AOD is dominated by OA (Xu et al., 2015).
Comparison to GEOS-Chem OA
Surface OA over the US from a GEOS-Chem simulation for August 2013 is shown
in Fig. 6d, and the scatter plot of GEOS-Chem OA with IMPROVE OA is in
Fig. 6i. Although HCHO vertical profiles from GEOS-Chem were used in OA
estimate, the GEOS-Chem simulation had a coarser resolution than the OA
estimate. To be comparable to the OA estimate, the scatter plot in Fig. 6i
used GEOS-Chem results for the grid squares that overlapped with individual
IMPROVE sites. Compared to the OA estimate, GEOS-Chem OA had a similar
correlation coefficient with IMPROVE OA. Although the GEOS-Chem OA plot
appeared more scattered, there were many GEOS-Chem data points close to zero
when IMPROVE OA was low, making the overall correlation coefficient similar
to that for the OA estimate. GEOS-Chem underpredicted IMPROVE OA more with
a slope of 0.57 compared to the OA estimate. This is consistent with
underprediction of anthropogenic OA in Marais et al. (2016).
OA estimate with different OA–HCHO relationships
OAs were estimated with different OA–HCHO relationships for four cases (Table 2). LUMP-SUM was using the non-BB SEAC4RS campaign lump-sum
relationship, the same as shown in Fig. 6a; ISOP-NOx was using non-BB
SEAC4RS NO2- and isoprene-dependent relationship; URBAN was using
CalNex for large urban cities and SEAC4RS lump-sum for other US
regions; and COMBINE was using CalNex for large urban cities and
NO2- and isoprene-dependent non-BB SEAC4RS for other US regions. The OA
estimates from the four cases (Table 2) were compared to IMPROVE OA and the
correlation coefficients are shown in Fig. 7. In general, OA estimate
results from the four cases were similar.
The correlation coefficients of the linear regression between the
OA estimate from four cases (red, blue, grey, and yellow) vs. EPA-corrected OA
from 2008 to 2013 for June, July, and August. The monthly average ambient
temperature is in black.
The details about how to implement chemical-factor-dependent OA estimates
for the four cases are also provided in Table 2. Including the
NO2–isoprene-dependent OA–HCHO relationship (ISOP-NOx case) showed a
similar (or slightly worse) correlation between the OA estimate and IMPROVE
OA. OMI NO2 column observations were used to represent surface NO2
levels and surface isoprene emissions from MEGAN were used to represent
surface isoprene concentrations, assuming that NO2 column observations
reflect surface NO2 distributions and isoprene emissions reflect the
concentrations of isoprene due to its short lifetime (∼1 h).
The detailed implementation is provided in the notes in Table 2. As the in
situ data showed a moderate NO2–isoprene-dependent OA–HCHO
relationship, we attributed this to the locations of IMPROVE sites in rural
regions, the uncertainty in IMPROVE network measurements, the uncertainty in
isoprene emissions from MEGAN, or factors (e.g., source-dependent OA–HCHO)
that also need to be taken into account when determining the specific
OA–HCHO relationship. Satellite OMI NO2 data (at 13:30 LT) were used
to represent NO2 levels, big cities were defined as NO2
> 4×1015 molec cm-2, and the CalNex in situ
OA–HCHO relationship was applied for big cities. It turned out that only one
IMPROVE site (San Gabriel, SAGA1) near LA was affected by high NO2 and
led to the insignificant change in URBAN compared to LUMP-SUM. This is not
unexpected because IMPROVE sites are in rural regions. The OA estimate in
SAGA1 decreased from 1.88 g m-3 from LUMP-SUM to 0.17 g m-3 in
URBAN, while the measured OA in IMPROVE SAGA1 was 1.52 g m-3. This may
infer that CalNex is not very consistent with SEAC4RS due to different
sampling instruments, strategies and seasons. Lowering the NO2 threshold
when defining big cities did not help improve the agreement either.
Because separating large urban areas and other regions and applying a simple
chemical-regime-dependent in situ OA–HCHO relationship did not improve the
agreement between the OA estimate and IMPROVE OA, we used the lump-sum
OA–HCHO relationship to derive the OA estimate (shown in Fig. 6).
SEAC4RS and DC3 only had a few low-altitude data in the midwest and did
not cover the northeast US. The measured OA–HCHO relationship in the midwest
did not show significant difference from the SE US. The scatter plots (Fig. 6f and g)
of OA estimates and IMPROVE OA do not show outliers for the
northeast and midwest. This indicates that using the SEAC4RS lump-sum
OA–HCHO relationship can reasonably capture regions outside of the SE US.
Temporal variation of the agreement between OA estimate and IMPROVE
OA
Besides August 2013 (see Fig. 6), the correlations between the OA estimate
and IMPROVE OA for the summer months (June–July–August 2008–2013) were also
examined and shown in Fig. 7. Generally, the correlation coefficients
between the OA estimate and IMPROVE OA were > 0.5 for summer
months of the years investigated. The correlation coefficients were
generally higher in June compared to July and August. The lower average
temperature in June might be related to the higher correlation coefficients.
IMPROVE network aerosol samples were transported at ambient temperature in a
truck and more organic vapors likely evaporated at higher temperature. The
different temperatures and distances from IMPROVE sites to the laboratory
may lead to inhomogeneous evaporation among the samples and result in lower
correlation coefficients. Although higher temperatures in July and August
may also lead to more BB, the average aerosol index over the US was not higher
in July (mean: 0.35) and August (mean: 0.36) compared to June (mean: 0.39)
for these years. The underlying cause for the lowest correlation
coefficients in July and August 2012 is not clear and may be related to the
severe drought in 2012 (Seco et al., 2015). The correlation coefficients
were also low for the linear regressions (not shown) of IMPROVE OA with both
GEOS-Chem OA and AOD-derived extinction. Because the lowest correlation
coefficients were consistently observed for multiple OA-related products and
not just the OA estimate, we attributed this to uncertainties in the IMPROVE
OA measurements or some unknown bias shared by the satellite HCHO, GEOS-Chem
OA, and satellite AOD.
South Korea OA estimate
We attempted to estimate an OA estimate for South Korea using airborne in
situ measurements of OA and HCHO from the KORUS-AQ field campaign (KORUS-AQ Science Team, 2016) and SAO OMI HCHO measurements. The National Institute of
Environmental Research (NIER) ground sites' OC measurements during KORUS-AQ
over South Korea could be used to validate the OA estimate. However, OMI
HCHO measurements were below the detection limit (Zhu et al., 2016) in May 2016.
Also, there were no OMI data available in June 2016 when airborne
measurements and ground sites' OC measurements were available during
KORUS-AQ. Because an OA estimate for South Korea could not be well retrieved
and validated, it was not presented in this study. Although an OA estimate
for South Korea could not be retrieved in the current study, the consistency
in the dependence of OA–HCHO relationships on chemical factors (e.g.,
emission sources, NOx, and altitudes) provides important information
for potential application of chemical-factor-dependent OA–HCHO
relationships to the geographical domain beyond the continental US,
especially with improved satellite HCHO data from TROPOMI.
Limitations of the OA estimate and future work
Because the OA estimate is based on satellite HCHO data, the detection limit
of satellite HCHO data affects the quality of the OA estimate. Currently,
due to the limited sensitivity of OMI for HCHO, the OA estimate is valid
only when high levels of HCHO are present, such as during summertime and
near large HCHO sources. With the new TROPOMI satellite instrument and
future missions (TEMPO and GEMS), satellite HCHO measurements will have higher
spatial and temporal resolutions and lower detection limits. These higher-quality
satellite HCHO measurements will improve the quality and spatial and
temporal coverage of our OA estimate.
Because the OA estimate uses the relationship of in situ HCHO and OA
measurements, the coverage of in situ aircraft field campaigns will impact
the OA estimate quality. Currently, in situ airborne measurements of OA and
HCHO focus on the continental US. Extending measurements to regions such as
African BB, South America, and east Asia, where HCHO and OA have high
concentrations, will increase the spatial coverage of the OA estimate
product. Ground site measurements of OA with consistent quality control in
those regions will also be important for validating the OA estimate.
Improvement of satellite HCHO retrieval during the BB cases will also
improve OA estimate quality. BB cases with high UV aerosol index over the US
were excluded in the current OA estimate. With improvement in the satellite
retrieval of HCHO, we may be able to estimate OA during BB cases over the
US. Upcoming field campaigns such as the Fire Influence on Regional and
Global Environments Experiment – Air Quality (FIREX-AQ) will provide
opportunities to improve the OA estimate in BB cases in the US.
This OA estimate method has limitations in remote regions far away from HCHO
sources. Because the lifetimes of HCHO (1–3 h) and OA (1 week) are
different, the slopes and intercepts between HCHO and OA are expected to
change when air masses are aged (e.g., in remote regions). HCHO is close to
being in steady state with production rates roughly equal to loss rates
while OA is not in steady state with a lifetime of a week. Therefore, OA can
be accumulated relative to HCHO when air masses are aged. OA vs. HCHO from
SEAC4RS and KORUS-AQ field campaigns, color coded with altitude as an
indicator of air mass age, are plotted in Fig. S2a and b, respectively.
A relative depletion of HCHO at high altitudes was observed due to its
shorter lifetime. This also suggests that, at remote regions far away from
the sources, the ratios of OA and HCHO could be much higher and the
relationship between OA and HCHO derived near the sources may no longer
apply. On the other hand, the lifetime of 1–3 h for HCHO does not imply
that the OA estimate only works within this timescale. HCHO is formed from
oxidation of transported gas-phase VOCs, including the oxidation products of
the primary emitted VOCs, as well as of the slower-reacting VOCs (e.g.,
ethane and benzene). Most gas-to-particle oxidation processes that might
produce HCHO can last up to 1–2 days (Palm et al., 2018). Figure S3 shows the
ratios of OA and HCHO did not change significantly downwind for the Rim Fire
plume for about 1 day of aging, which was determined by the distance from
the source and the wind speed. A lower photolysis rate of HCHO in the plume
can also contribute to this. However, we do not expect the relationship of
OA and HCHO to remain past one to two boundary layer ventilation cycles (Palm et
al., 2018). Although OA–HCHO relationships depend on air mass age, it does
not largely affect our study for monthly average surface OA over the continental
US because our OA estimates showed reasonably good agreement with ground
sites IMPROVE OA measurements. This also indicates that SOAs are enhanced
near the source regions statistically. Nault et al. (2018) also showed the
production of HCHO and SOA are similar and plateau around 0.5–1
photochemical days. So, in the near field of emissions and chemistry, the
production of these two species is similar; however, outside the near field
of emissions and rapid chemistry, the long lifetime of OA vs. the steady
state of HCHO would start controlling the slopes and correlations.
Summary
We have developed a satellite-based estimate of the surface OA concentration
(“OA estimate”) based on in situ observations. This estimate is based on
the empirical relationships of in situ OA and HCHO for several regions. OA
and HCHO share VOC sources with different yields and lifetimes. Using
surface OA and HCHO linear regression slopes and intercepts, we can relate
surface HCHO to OA. To estimate the surface HCHO concentration from the
satellite HCHO column, we used a vertical distribution factor η from
either climatology satellite data a priori profiles or an updated model run for
a specific period, which is largely determined by boundary layer height and
surface emissions and found to reasonably retrieve surface HCHO from column
HCHO.
The OA estimate over the continental US generally correlated well with EPA
IMPROVE network OA measurements corrected for partial evaporation, with a
biased low slope of 0.62 or 0.60, mostly due to underestimation of HCHO
concentrations from the OMI HCHO retrieval. The good correlations are not
only for the time during SEAC4RS but also for most summer months over
the several years (2008–2013) investigated. Compared to aerosol extinction
derived from AOD, the OA estimate had slightly higher correlation
coefficients with IMPROVE OA. GEOS-Chem can predict OA with a similar
correlation coefficient with IMPROVE OA compared to the OA estimate when
GEOS-Chem was intensively validated with in situ measurements for the SE US.
Better satellite HCHO data from TROPOMI and future TEMPO and GEMS and
extending spatiotemporal coverage of in situ measurements will improve the
quality and coverage of the OA estimate.
Data availability
The OA estimate products, the GEOS-Chem outputs, and satellite HCHO data in
this study can be obtained by contacting the corresponding author, Jin Liao (jin.liao@nasa.gov).
In situ SEAC4RS data are available at
10.5067/Aircraft/SEAC4RS/Aerosol-TraceGas-Cloud (SEAC4RS Science Team, 2013). DC3 data are available
at 10.5067/Aircraft/DC3/DC8/Aerosol-TraceGas (DC3 Science Team, 2012). KORUS-AQ data are available at
10.567/Suborbital/KORUSAQ/DATA01 (KORUS-AQ Science Team, 2016). CalNex data are available at
https://www.esrl.noaa.gov/csd/groups/csd7/measurements/2010calnex/ (CalNex Science Team, 2010). Satellite NO2 data are available at
10.5067/Aura/OMI/DATA2017 (Krotkov, 2013). Satellite MODIS AOD data are available at https://ladsweb.nascom.nasa.gov/ (Levy and Hsu, 2015).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-2765-2019-supplement.
Author contributions
JL performed the analysis and wrote the paper. TFH directed the research
topic and discussed the analysis with JL. TFH, GMW, JSC, AF, and CW provided
in situ HCHO measurements. JLJ, PCJ, and BAN provided in situ OA
measurements. EAM provided GEOS-Chem model results. GGA and KC provided
satellite HCHO data. HTJ provided MODIS AOD data. TBR provided in situ
NO2 measurements. AW provided in situ isoprene and acetonitrile
measurements. GMW, TFH, JSC, JLJ, BAN, PCJ, EAM, and GGA provided
constructive comments to help improve the paper. All authors have reviewed and
edited the paper.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
Jin Liao, Thomas F. Hanisco, Glenn M. Wolfe, and Jason St. Clair were supported by NASA grants NNH15ZDA001N and
NNH10ZDA001N. Benjamin A. Nault, Pedro Campuzano-Jost,
and Jose L. Jimenez were supported by NASA grants NNX15AT96G and
80NSSC18K0630. Armin Wisthaler and PTR-MS measurements during DC3, SEAC4RS, and
KORUS-AQ were supported by the Austrian Federal Ministry for Transport,
Innovation and Technology (bmvit) through the Austrian Space Applications
Programme (ASAP) of the Austrian Research Promotion Agency (FFG). The PTR-MS
instrument team (Philipp Eichler, Lisa Kaiser, Tomas Mikoviny, and Markus Müller) is
acknowledged for their field support. We thank Eric Edgerton for providing the
SEARCH network data.
Edited by: Sally E. Pusede
Reviewed by: two anonymous referees
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