Anthropogenic secondary organic aerosol (ASOA), formed from anthropogenic
emissions of organic compounds, constitutes a substantial fraction of the
mass of submicron aerosol in populated areas around the world and
contributes to poor air quality and premature mortality. However, the
precursor sources of ASOA are poorly understood, and there are large
uncertainties in the health benefits that might accrue from reducing
anthropogenic organic emissions. We show that the production of ASOA in 11
urban areas on three continents is strongly correlated with the reactivity
of specific anthropogenic volatile organic compounds. The differences in
ASOA production across different cities can be explained by differences in
the emissions of aromatics and intermediate- and semi-volatile organic
compounds, indicating the importance of controlling these ASOA precursors.
With an improved model representation of ASOA driven by the observations,
we attribute 340 000 PM2.5-related premature deaths per year to ASOA, which is
over an order of magnitude higher than prior studies. A sensitivity case
with a more recently proposed model for attributing mortality to PM2.5
(the Global Exposure Mortality Model) results in up to 900 000 deaths. A
limitation of this study is the extrapolation from cities with detailed
studies and regions where detailed emission inventories are available to
other regions where uncertainties in emissions are larger. In addition to
further development of institutional air quality management infrastructure,
comprehensive air quality campaigns in the countries in South and Central
America, Africa, South Asia, and the Middle East are needed for further
progress in this area.
Introduction
Poor air quality is one of the leading causes of premature mortality
worldwide (Cohen et al., 2017; Landrigan et al., 2018). Roughly 95 % of
the world's population live in areas where PM2.5 (fine particulate
matter with a diameter smaller than 2.5 µm) exceeds the World Health
Organization's 10 µg m-3 annual average guideline
(Shaddick et al., 2018). This is especially true for urban areas, where high
population density is co-located with increased emissions of PM2.5 and
its gas-phase precursors from human activities. It is estimated that
PM2.5 leads to 3 to 4 million premature deaths per year, higher than
the deaths associated with other air pollutants (Cohen et al., 2017). More
recent analysis using concentration–response relationships derived from
studies of populations' exposure to high levels of ambient PM2.5 suggest that
the global premature death burden could be up to twice this value (Burnett
et al., 2018).
The main method to estimate premature mortality with PM2.5 is to use
measured PM2.5 from ground observations along with derived PM2.5
from satellites to fill in missing ground-based observations (van Donkelaar
et al., 2015, 2016). To go from total PM2.5 to species-dependent and
even sector-dependent associated premature mortality from PM2.5,
chemical transport models (CTMs) are used to predict the fractional
contribution of species and/or sector (e.g., Lelieveld et al., 2015; van
Donkelaar et al., 2015, 2016; Silva et al., 2016). However, although CTMs may
get total PM2.5 or even total species (e.g., organic aerosol – OA),
correct, the model may be getting the values right for the wrong reason
(e.g., de Gouw and Jimenez, 2009; Woody et al., 2016; Murphy et al., 2017;
Baker et al., 2018; Hodzic et al., 2020). This is especially important for
OA in urban areas, where models have a long-standing issue with underpredicting
secondary OA (SOA) with some instances of overpredicting primary OA (POA)
(de Gouw and Jimenez, 2009; Dzepina et al., 2009; Hodzic et al., 2010b;
Woody et al., 2016; B. Zhao et al., 2016; Janssen et al., 2017; Jathar et al.,
2017). Further, this bias has even been observed for highly aged aerosols in
remote regions (Hodzic et al., 2020). As has been found in prior studies for
urban areas (e.g., Zhang et al., 2007; Kondo et al., 2008; Jimenez et al.,
2009; DeCarlo et al., 2010; Hayes et al., 2013; Freney et al., 2014; Hu et
al., 2016; Nault et al., 2018; Schroder et al., 2018) and highlighted here
(Fig. 1), a substantial fraction of the observed submicron PM is OA, and a
substantial fraction of the OA is composed of SOA (approximately a factor of
2 to 3 higher than POA). Thus, to better understand the sources and
apportionment of PM2.5 that contributes to premature mortality, CTMs
must improve their prediction of SOA versus POA, as the sources of SOA
precursors and POA can be different.
Non-refractory submicron aerosol composition measured in urban and
urban outflow regions from field campaigns used in this study (all in units
of µg m-3) at standard temperature (273 K) and pressure (1013 hPa) (sm-3). See Sect. S3 and Table 1 for further information on
measurements, studies, and apportionment of SOA into ASOA and BSOA.
However, understanding the gas-phase precursors of photochemically produced
anthropogenic SOA – ASOA, defined as the photochemically produced SOA formed
from the photooxidation of anthropogenic volatile organic compounds (AVOCs)
(de Gouw et al., 2005; DeCarlo et al., 2010) – quantitatively is challenging
(Hallquist et al., 2009). Note, for the rest of the paper, unless explicitly
stated otherwise, ASOA refers to SOA produced from the photooxidation of
AVOCs, as there are potentially other relevant paths for the production of
SOA in urban environments (e.g., Petit et al., 2014; Kodros et al., 2018,
2020; Stavroulas et al., 2019). Although the enhancement of ASOA is largest in
large cities, these precursors and the production of ASOA should be important in
any location impacted by anthropogenic emissions (e.g., Fig. 1). ASOA
comprises a wide range of condensable products generated by numerous
chemical reactions involving AVOC precursors (Hallquist et al., 2009; Hayes
et al., 2015; Shrivastava et al., 2017). The number of AVOC precursors, as
well as the role of “nontraditional” AVOC precursors, along with the
condensable products and chemical reactions, compound to lead to differences
in the observed versus predicted ASOA for various urban environments (e.g.,
de Gouw and Jimenez, 2009; Dzepina et al., 2009; Hodzic et al., 2010b; Woody
et al., 2016; Janssen et al., 2017; Jathar et al., 2017; McDonald et al.,
2018). One solution to improve the prediction in CTMs is to use a simplified
model, where lumped ASOA precursors react, non-reversibly, at a given rate
constant, to produce ASOA (Hodzic and Jimenez, 2011; Hayes et al., 2015; Pai
et al., 2020). This simplified model has been found to reproduce the
observed ASOA from some urban areas (Hodzic and Jimenez, 2011; Hayes et al.,
2015) but has issues in other urban areas (Pai et al., 2020). This may stem from
the simplified model being parameterized to two urban areas (Hodzic and
Jimenez, 2011; Hayes et al., 2015). These inconsistencies impact the model-predicted fractional contribution of ASOA to total PM2.5 and, thus, the
ability to understand the source attribution to PM2.5 and premature
deaths.
The main categories of gas-phase precursors that dominate ASOA have been the
subject of intensive research. The debate on what dominates can, in turn,
impact the understanding of what precursors to regulate in order to reduce ASOA, to
improve air quality, and to reduce premature mortality associated with ASOA.
Transportation-related emissions (e.g., tailpipe, evaporation, refueling)
were assumed to be the major precursors of ASOA, which was supported by
field studies (Parrish et al., 2009; Gentner et al., 2012; Warneke et al.,
2012; Pollack et al., 2013). However, budget closure of observed ASOA mass
concentrations could not be achieved with transportation-related VOCs
(Ensberg et al., 2014). The contribution of urban-emitted biogenic
precursors to SOA in urban areas is typically small. Biogenic SOA (BSOA) in
these regions typically results from advection of regional background
concentrations rather than processing of locally emitted biogenic VOCs
(e.g., Hodzic et al., 2009, 2010a; Hayes et al., 2013; Janssen et al.,
2017). BSOA is thought to dominate globally (Hallquist et al., 2009), but as
shown in Fig. 1, the contribution of BSOA (1 % to 20 %) to urban
concentrations, while often substantial, is typically smaller than that of
ASOA (17 % to 39 %) (see Sect. S3.1).
Many of these prior studies generally investigated AVOC with high
volatility, where volatility here is defined as the saturation
concentration, C* (in µg m-3) (de Gouw et al., 2005; Volkamer et
al., 2006; Dzepina et al., 2009; Freney et al., 2014; Woody et al., 2016).
More recent studies have identified lower-volatility compounds in
transportation-related emissions (e.g., Y. Zhao et al., 2014, 2016; Lu et al.,
2018). These compounds have been broadly identified as intermediate-volatility
organic compounds (IVOCs) and semi-volatile organic compounds (SVOCs). IVOCs
generally have a C* of 103 to 106µg m-3, whereas SVOCs generally
have a C* of 1 to 102µg m-3. Due to their lower
volatility and functional groups, these classes of compounds generally form
ASOA more efficiently than traditional, higher-volatility AVOCs; however,
S/IVOCs (SVOCs and IVOCs) have also been more difficult to measure (e.g., Zhao et al., 2014;
Pagonis et al., 2017; Deming et al., 2019). IVOCs have generally been the
more difficult of the two classes to measure and identify, as these compounds
cannot be collected onto filters to be sampled off-line (Lu et al., 2018)
and generally show up as an unresolved complex mixture for in situ measurements
using gas chromatography (GC) (Zhao et al., 2014). SVOCs, on the other hand,
can be more readily collected onto filters and sampled off-line due to their
lower volatility (Lu et al., 2018). Another potential issue has been an
underestimation of the S/IVOC aerosol production as well as an
underestimation in the contribution of photochemically produced S/IVOC from
photooxidized “traditional” VOCs, due to partitioning of these low-volatility compounds to chamber walls and tubing (Krechmer et al., 2016; Ye et
al., 2016; Liu et al., 2019). Accounting for this underestimation increases
the predicted ASOA (Ma et al., 2017). The inclusion of these classes of
compounds has led to improvement in some urban SOA budget closure; however,
many more recent studies have still indicated a general shortfall in the ASOA budget, even
when including these compounds from transportation-related emissions
(Dzepina et al., 2009; Tsimpidi et al., 2010; Hayes et al., 2015; Cappa et
al., 2016; Ma et al., 2017; McDonald et al., 2018).
Recent studies have indicated that emissions from volatile chemical products
(VCPs), defined as pesticides, coatings, inks, adhesives, personal care
products, and cleaning agents (McDonald et al., 2018), as well as cooking
emissions (Hayes et al., 2015), asphalt emissions (Khare et al., 2020), and
solid-fuel emissions from residential wood burning and/or cookstoves (e.g.,
Hu et al., 2013, 2020; Schroder et al., 2018), are important. While the total
amounts of ASOA precursors released in cities have dramatically declined
(largely due to three-way catalytic converters in cars; Warneke et al.,
2012; Pollack et al., 2013; Zhao et al., 2017; Khare and Gentner, 2018),
VCPs have not declined as quickly (Khare and Gentner, 2018; McDonald et al.,
2018). Besides a few cities in the USA (Coggon et al., 2018; Khare and
Gentner, 2018; McDonald et al., 2018), extensive VCP emission quantification
has not yet been published.
Due to the uncertainty on the emissions of ASOA precursors and on the amount
of ASOA formed from them, the number of premature deaths associated with
urban organic emissions is largely unknown. Since numerous studies have
shown the importance of VCPs and other nontraditional VOC emission sources,
efforts have been made to try to improve the representation and emissions of
VCPs (Seltzer et al., 2021), which can reduce the uncertainty in ASOA
precursors and the associated premature death estimations. Currently, most
studies have not treated ASOA explicitly (e.g., Lelieveld et al., 2015;
Silva et al., 2016; Ridley et al., 2018) in source apportionment
calculations of the premature deaths associated with long-term exposure of
PM2.5. Most models represented total OA as non-volatile POA and
“traditional” ASOA precursors (transportation-based VOCs), which largely
underpredict ASOA (Ensberg et al., 2014; Hayes et al., 2015; Nault et al.,
2018; Schroder et al., 2018) while overpredicting POA (e.g., Hodzic et al.,
2010b; B. Zhao et al., 2016; Jathar et al., 2017). This does not reflect the
current understanding that POA is volatile and contributes to ASOA mass
concentration (e.g., Grieshop et al., 2009; Lu et al., 2018). Although the
models are estimating total OA correctly (Ridley et al., 2018; Hodzic et
al., 2020; Pai et al., 2020), the attribution of premature deaths to POA
instead of SOA formed from traditional and nontraditional sources,
including IVOCs from both sources, could lead to regulations that may not
target the emissions that would reduce OA in urban areas. As PM1 and
SOA mass are highest in urban areas (Fig. 1), also shown in Jimenez et al. (2009), it is necessary to quantify the amount and identify the sources of
ASOA to target future emission standards that will optimally improve air
quality and the associated health impacts. As these emissions are from human
activities, they will contribute to SOA mass outside urban regions and to
potential health impacts outside urban regions as well. Although there are
potentially other important exposure pathways to PM that may increase
premature mortality, such as exposure to solid-fuel emissions indoors (e.g.,
Kodros et al., 2018), the focus of this paper is on exposure to outdoor ASOA
and its associated impacts on premature mortality.
Here, we investigate the factors that control ASOA using 11 major urban
(including megacities) field studies (Fig. 1 and Table 1). The empirical
relationships and numerical models are then used to quantify the attribution
of premature mortality to ASOA around the world, using the observations to
improve the modeled representation of ASOA. The results provide insight into
the importance of ASOA to global premature mortality due to PM2.5 and
further understanding of the precursors and sources of ASOA in urban
regions.
Methods
Here, we introduce the ambient observations from various campaigns used to
constrain ASOA production (Sect. 2.1), a description of the simplified model
used in CTMs to better predict ASOA (Sect. 2.2), and a description of how
premature mortality was estimated for this study (Sect. 2.3). In the Supplement, the
following can be found: a description of the emissions used to calculate the
ASOA budget for five different locations (Sect. S1), a description of how the
ASOA budget was calculated for the five different locations (Sect. S2),
a description of the CTM (GEOS-Chem, or the Goddard Earth Observing System Chemistry model) used in this study (Sects. S3–S4), and
an error analysis for the observations (Sect. S5).
Ambient observations
For values not previously reported in the literature (Table S4),
observations taken between 11:00 and 16:00 LT (local time) were used to determine
the slopes of SOA versus formaldehyde (HCHO) (Fig. S1), peroxy acetyl
nitrate (PAN) (Fig. S2), and Ox (Ox= O3+ NO2)
(Fig. S3). For the California Research at the Nexus of Air Quality and Climate Change (CalNex) campaign, there was an approximate 48 % difference between
the two HCHO measurements (Fig. S4). Therefore, the average between the two
measurements was used in this study, similar to what has been done in other
studies for other gas-phase species (Bertram et al., 2007). All linear fits,
unless otherwise noted, use the orthogonal distance regression (ODR) fitting
method.
For values in Table S4 through Table S8 not previously reported in the
literature, the following procedure was applied to determine the emissions
ratios, similar to the methods of Nault et al. (2018). An OH exposure
(OHexp= [OH] ×Δt), which is also the photochemical
age (PA), was estimated by using the ratio of NOx/ NOy (Eq. 1) or
the ratio of m+p-xylene / ethylbenzene (Eq. 2). For the
m+p-xylene/ethylbenzene, the emission ratio (Table S5) was established by
determining the average ratio during minimal photochemistry, similar to
prior studies (de Gouw et al., 2017). This was done for only one study,
Texas Air Quality Study 2000 (TexAQS 2000). This method could be applied in that case as it was a ground
campaign that operated both day and night; therefore, a ratio at night could
be determined when there was minimal loss of both VOCs. The average emission
ratio for the other VOCs was determined using Eq. (3) after the OHexp was
calculated in Eq. (1) or Eq. (2). The rate constants used for determining
OHexp and emission ratios are found in Table S12.
1OHexp=OH×t=lnNOxNOykOH+NO22OHexp=OH×t=-1km+p-xylene-kethylbenzene×ln(m+p-xylenetethylbenzenet-m+p-xyelene0ethylbenzene0)3VOCiCO0=-VOCiCOt×1-1exp-ki×[OH]exp×t×ki+VOCiCOt×ki
List of campaigns used here, and the values previously reported for
these campaigns. “W” denotes winter, “Sp” denotes spring,
and “Su” denotes summer.
LocationField CampaignCoordinates Time PeriodSeasonPrevious publication/Campaign overviewLong. (∘)Lat. (∘)Houston, TX, USA (2000)TexAQS 2000-95.429.815 Aug 2000–15 Sep 2000SuJimenez et al. (2009)a,Wood et al. (2010)bNortheast USA (2002)NEAQS 2002-78.1 to -70.532.8 to 43.126 Jul 2002; 29 Jul 2002–10 Aug 2002SuJimenez et al. (2009)a,de Gouw and Jimenez (2009)c,Kleinman et al. (2007)cMexico City, Mexico (2003)MCMA-2003-99.219.531 Mar 2003–04 May 2003SpMolina et al. (2007),Herndon et al. (2008)bTokyo, Japan (2004)139.735.724 Jul 2004–14 Aug 2004SuKondo et al. (2008)a,Miyakawa et al. (2008)a,Morino et al. (2014)bMexico City, Mexico (2006)MILAGRO-99.4 to -98.619.0 to 19.804 Mar 2006–29 Mar 2006SpMolina et al. (2010),DeCarlo et al. (2008)a,Wood et al. (2010)b,DeCarlo et al. (2010)cParis, France (2009)MEGAPOLI48.92.413 Jul 2009–29 Jul 2009SuFreney et al. (2014)a,Zhang et al. (2015)bPasadena, CA, USA (2010)CalNex-118.134.115 May 2010–16 Jun 2010SpRyerson et al. (2013),Hayes et al. (2013)a,b,cChangdao Island, China (2011)CAPTAIN120.738.021 Mar 2011–24 Apr 2011SpHu et al. (2013)a,cBeijing, China (2011)CAREBeijing 2011116.439.903 Aug 2011–15 Sep 2011SuHu et al. (2016)a,b,cLondon, UK (2012)ClearfLo0.151.522 Jul 2012–18 Aug 2012SuBohnenstengel et al. (2015)Houston, TX, USA (2013)SEAC4RS-96.0 to -94.029.2 to 30.301 Aug 2013–23 Sep 2013SuToon et al. (2016)New York City, NY, USA (2015)WINTER-74.0 to -69.039.5 to 42.507 Feb 2015WSchroder et al. (2018)a,cSeoul, South Korea (2016)KORUS-AQ124.6 to 128.036.8 to 37.601 May 2016–10 Jun 2016SpNault et al. (2018)a,b,c,d
a Reference used for PM1 composition. b Reference used for
SOA / Ox slope. c Reference used for ΔOA /ΔCO value.
d Reference used for SOA/HCHO and SOA/PAN slopes.
Updates to the SIMPLE model
With the combination of the new dataset, which expands across urban areas on
three continents, the SIMPLE parameterization for ASOA (Hodzic and Jimenez,
2011) is updated in the standard GEOS-Chem model to reproduce observed ASOA
in Fig. 2a. The parameterization operates as represented by Eq. (4).
Emissions→SOAP⟶k×[OH]ASOA
SOAP represents the lumped precursors of ASOA, k is the reaction rate
coefficient with OH (1.25 × 10-11 cm3 molec.-1 s-1), and [OH] is the OH concentration (in molec. cm-3). This
rate constant is also consistent with observed ASOA formation timescale of
∼ 1 d that has been observed across numerous studies (e.g.,
de Gouw et al., 2005; DeCarlo et al., 2010; Hayes et al., 2013; Nault et
al., 2018; Schroder et al., 2018).
SOAP emissions were calculated based on the relationship between ΔSOA /ΔCO and Raromatics/ΔCO in Fig. 2a. First, we
calculated Raromatics/ΔCO (Eq. 5) for each grid cell and time
step as follows:
RaromaticsΔCO=EB×kB+ET×kT+EX×kXECO,
where E and k stand for the emission rate and reaction rate coefficient with
OH, respectively, for benzene (B), toluene (T), and xylenes (X, here all three isomers).
Ethylbenzene was not included in this calculation because its emission was
not available in version 2 of the Hemispheric Transport of Air Pollution (HTAPv2) emission inventory. However, ethylbenzene
contributed a minor fraction of the mixing ratio (∼ 7 %,
Table S5) and reactivity (∼ 6 %) of the total BTEX across
the campaigns. Reaction rate constants used in this study were
1.22 × 10-12, 5.63 × 10-12, and 1.72 × 10-11 cm3 molec.-1 s-1 for benzene, toluene, and xylenes,
respectively (Atkinson and Arey, 2003; Atkinson et al., 2006). Raromatics/ΔCO allows a dynamic calculation of the E(VOC) /E(CO) = SOA /ΔCO. Hodzic and Jimenez (2011) and Hayes et al. (2015) used
a constant value of 0.069 g g-1, which worked well for the two cities
investigated but not for the expanded dataset studied here. Thus, both the
aromatic emissions and CO emissions are used in this study to better
represent the variable emissions of ASOA precursors (Fig. S5).
Second, ESOAP/ECO can be obtained from the result of Eq. (6), using the
slope and intercept in Fig. 2a, with a correction factor (F) to consider
additional SOA production after 0.5 PA equivalent days, as Fig. 2a shows
the comparison at 0.5 PA equivalent days.
ESOAPECO=slope×RAromaticsΔCO+intercept×F,
where the slope is 24.8 and the intercept is -1.7 from Fig. 2a. F (Eq. 7) can be
calculated as follows:
F=ASOAt=∞ASOAt=0.5=SOAPt=0SOAPt=0×1-exp-k×Δt×[OH],Δt=43200s.F was calculated as 1.8 by using [OH] = 1.5 × 106 molec. cm-3, which was used in the definition of 0.5 PA equivalent days for
Fig. 2a.
Finally, ESOAP can be computed by multiplying CO emissions (ECO)
for every grid point and time step in GEOS-Chem by the ESOAP/ECO
ratio.
(a) Scatterplot of background and dilution-corrected ASOA
concentrations (ΔSOA/ΔCO at PA = 0.5 equivalent days)
versus BTEX (benzene, toluene, ethylbenzene, and xylenes) emission reactivity ratio (RBTEX=∑i[VOCCO]i) for multiple major field campaigns on three continents. Comparison of ASOA
versus (b) Ox, (c) PAN, and (d) HCHO slopes versus the ratio of the
BTEX / total emission reactivity, where total is the OH reactivity for the
emissions of BTEX + C2-3 alkenes + C2-6 alkanes (Tables S5–S7), for the campaigns studied here. For all figures, red shading is
the ± 1σ uncertainty of the slope, and the bars are ± 1σ uncertainty of the data (see Sect. S5).
Estimation of premature mortality attribution
Premature deaths were calculated for five disease categories: ischemic heart
disease (IHD), stroke, chronic obstructive pulmonary disease (COPD), acute
lower respiratory illness (ALRI), and lung cancer (LC). We calculated
premature mortality for the population aged more than 30 years, using Eq. (8).
Premature death=Pop×y0×RR-1RR
Mortality rate, y0, varies according to the particular disease category
and geographic region, which is available from the Global Burden of Disease
Study 2015 database – GBD 2015 (IHME, 2016). Population (Pop) was obtained from the
Columbia University Center for International Earth Science Information
Network (CIESIN) for 2010 (CIESIN, 2017). Relative risk, RR, can be
calculated as shown in Eq. (9).
RR=1+α×(1-exp(β×PM2.5-PM2.5,Thresholdρ))α, β, and ρ values depend on disease category and are
calculated from Burnett et al. (2014) (see Table S14 and the associated file).
If the PM2.5 concentrations are below the PM2.5 threshold value
(Table S14), premature deaths were computed as zero. However, there could be
some health impacts at concentrations below the PM2.5 threshold values
(Krewski et al., 2009); following the methods of the GBD studies, these can
be viewed as lower bounds on estimates of premature deaths.
We performed an additional sensitivity analysis using the Global Exposure
Mortality Model (GEMM) (Burnett et al., 2018). For the GEMM analysis, we
also used age stratified population data from GPWv3. Premature death is
calculated the same as shown in Eq. (8); however, the relative risk differs.
For the GEMM model, the relative risk can be calculated as shown in Eq. (10).
RR=expθ×λwithλ=log1+zα1+expμ^-zπ
Here z= max (0, PM2.5–PM2.5,Threshold); θ, π, μ^, α, and PM2.5,Threshold depend on disease category and are
from Burnett et al. (2018). Similar to the Eq. (9), if the concentrations are
below the threshold (2.4 µg m-3; Burnett et al., 2018),
premature deaths are computed as zero; however, the GEMM has a lower
threshold than the GBD method.
For GBD, we do not consider age-specific mortality rates or risks. For GEMM,
we calculate age-specific health impacts with age-specific parameters in the
exposure response function (Table S15). We combine the age-specific results
of the exposure-response function with age-distributed population data from
GPW (Gridded Population of the World; CIESIN, 2017) and a national mortality rate across all ages to assess
age-specific mortality.
We calculated total premature deaths using annual average total PM2.5
concentrations derived from satellite-based estimates at the resolution of
0.1∘× 0.1∘ from van Donkelaar et al. (2016).
Application of the remote-sensing-based PM2.5 at the 0.1∘× 0.1∘ resolution rather than direct use of the GEOS-Chem
model concentrations at the 2∘× 2.5∘
resolution helps reduce uncertainties in the quantification of PM2.5
exposure inherent in coarser estimates (Punger and West, 2013). We also
calculated deaths by subtracting the total annual average
ASOA concentrations derived from GEOS-Chem from this amount (Fig. S11). To reduce
uncertainties related to spatial gradients and total concentration
magnitudes in our GEOS-Chem simulations of PM2.5, our modeled ASOA was
calculated as the fraction of ASOA to total PM2.5 in GEOS-Chem,
multiplied by the satellite-based PM2.5 concentrations (Eq. 11).
ASOAsat=ASOAmod/PM2.5,mod×PM2.5,sat
Finally, this process for estimating PM2.5 health impacts considers
only PM2.5 mass concentration and does not distinguish toxicity by
composition, consistent with the current US EPA position expressed in Sacks
et al. (2019).
Observations of ASOA production across three continentsObservational constraints of ASOA production across three continents
Measurements during intensive field campaigns in large urban areas better
constrain the concentrations and atmospheric formation of ASOA because the scale
of ASOA enhancement is large compared with SOA from a regional background.
Generally, ASOA increased with the amount of urban precursor VOCs and with
atmospheric PA (de Gouw et al., 2005; de Gouw and Jimenez, 2009; DeCarlo et
al., 2010; Hayes et al., 2013; Nault et al., 2018; Schroder et al., 2018;
Shah et al., 2018). In addition, ASOA correlates strongly with gas-phase
secondary photochemical species, including Ox, HCHO, and PAN (Herndon
et al., 2008; Wood et al., 2010; Hayes et al., 2013; Zhang et al., 2015;
Nault et al., 2018; Liao et al., 2019) (Table S4; Figs. S1–S3), which
are indicators of photochemical processing of emissions.
However, as initially discussed by Nault et al. (2018) and shown in Fig. 3,
there is large variability in these various metrics across the urban areas
evaluated here. To the best of the authors' knowledge, this variability has
not been explored and its physical meaning has not been interpreted. However, as
shown in Fig. 3, the trends in ΔSOA /ΔCO are similar
to the trends in the slopes of SOA versus Ox, PAN, or HCHO. For
example, Seoul is the highest for nearly all metrics and is approximately a
factor of 6 higher than the urban area, Houston, that generally showed the
lowest photochemical metrics. This suggests that the variability is related
to a physical factor, including emissions and chemistry.
(a) A comparison of the ΔSOA /ΔCO for the urban
campaigns on three continents. Comparison of (b) SOA / Ox, (c) SOA / HCHO, and
(d) SOA / PAN slopes for the urban areas (Table S4). For panels (b) through (d),
cities marked with * have no HCHO, PAN, or hydrocarbon data.
The VOC concentration, together with how quickly the emitted VOCs react
(Σki× [VOC]i, i.e., the hydroxyl radical, or OH,
reactivity of VOCs), where k is the OH rate coefficient for each VOC, are a
determining parameter for ASOA formation over urban spatial scales (Eq. 12).
ASOA formation is normalized here to the excess CO mixing ratio (ΔCO) to account for the effects of meteorology, dilution, and nonurban
background levels as well as allow for easier comparison between different
studies:
ΔASOAΔCO∝OH×Δt×∑iki×VOCCOi×Yi,
where Y is the aerosol yield for each compound (mass of SOA formed per unit
mass of precursor reacted), and [OH] ×Δt is the PA.
BTEX are one group of known ASOA precursors (Gentner et al., 2012; Hayes et
al., 2013), and their emission ratio (to CO) was determined for all
campaigns (Table S5). Thus, BTEX can provide insight into ASOA production.
Fig. 2a shows that the variation in ASOA (at PA = 0.5 equivalent days) is
highly correlated with the emission reactivity ratio of BTEX (RBTEX, ∑i[VOCCO]i) across all of the studies. However, BTEX alone cannot account for much of the
ASOA formation (see budget closure discussion below), and instead, BTEX may
be better thought of as both partial contributors and also as indicators for
the co-emission of other (unmeasured) organic precursors that are also
efficient at forming ASOA.
Ox, PAN, and HCHO are produced from the oxidation of a much wider set
of VOC precursors (including small alkenes, which do not appreciably produce
SOA when oxidized). These alkenes have similar reaction rate constants with
OH as the most reactive BTEX compounds (Table S12); however, their emissions
and concentration can be higher than BTEX (Table S7). Thus, alkenes would
dominate RTotal, leading to Ox, HCHO, and PAN being produced more
rapidly than ASOA (Fig. 2b–d). When RBTEX becomes more important for
RTotal, the emitted VOCs are more efficient in producing ASOA. Thus,
the ratio of ASOA to gas-phase photochemical products shows a strong
correlation with RBTEX/RTotal (Fig. 2b–d).
An important aspect of this study is that most of these observations
occurred during spring and summer, when solid-fuel emissions are expected to
be lower (e.g., Chafe et al., 2015; Lam et al., 2017; Hu et al., 2020).
Further, the most important observations used here are during the afternoon,
specifically investigating the photochemically produced ASOA. These results
might partially miss any ASOA produced through nighttime aqueous
chemistry or oxidation by nitrate radical (Kodros et al., 2020). However,
two of the studies included in our analysis, Chinese outflow (Campaign of Air Pollution at Typical Coastal Areas IN Eastern China, CAPTAIN 2011; Hu et al., 2013)
and New York City (Wintertime INvestigation of Transport, Emissions, and Reactivity, WINTER 2015; Schroder et al.,
2018), occurred in late winter/early spring, when
solid-fuel emissions were important. We find that these observations lie within the uncertainty in the
slope between ASOA and RBTEX (Fig. 2a). Their photochemically produced
ASOA observed under strong impact from solid-fuel emissions shows similar
behavior to the ASOA observed during spring and summer time. Thus, given the
limited datasets currently available, photochemically produced ASOA is
expected to follow the relationship shown in Fig. 2a and is also expected to
follow this relationship for regions impacted by solid-fuel burning. Future
comprehensive studies in regions strongly impacted by solid-fuel burning are
needed to further investigate photochemical ASOA production under those
conditions.
Budget closure of ASOA for four urban areas on three continents indicates
reasonable understanding of ASOA sources
To investigate the correlation between ASOA and RBTEX, a box model
using the emission ratios from BTEX (Table S5), other aromatics (Table S8),
IVOCs (Sect. S1), and SVOCs (Sect. S1) was run for five urban areas: New
York City, 2002; Los Angeles; Beijing; London; and New York City, 2015 (see
Sects. S1 and S3 for more information). The differences in the results shown
in Fig. 4 are due to differences in the emissions for each city. We show
that BTEX alone cannot explain the observed ASOA budget for urban areas
around the world. Figure 4a shows that approximately 25 ± 6 % of the
observed ASOA originates from the photooxidation of BTEX. The fact that BTEX only
explains 25 % of the observed ASOA is similar to prior studies that have
undertaken budget analysis of precursor gases and observed SOA (e.g., Dzepina et
al., 2009; Ensberg et al., 2014; Hayes et al., 2015; Ma et al., 2017; Nault
et al., 2018). Therefore, other precursors must account for most of the ASOA
produced.
(a) Budget analysis for the contribution of the observed ΔSOA /RBTEX (Fig. 2) for cities with known emissions inventories for
different volatility classes (see Fig. 5 and Fig. S6 in the Supplement). Panel (b) is the same as
panel (a) but for sources of emissions. For panels (a) and (b), SVOC is the contribution
from both vehicle and other (cooking, etc.) sources. See the Supplement for information
about the emissions, ASOA precursor contribution, error analysis, and a
discussion about the sensitivity of emission inventory IVOC/BTEX ratios for
different cities and years in the USA.
Because alkanes, alkenes, and oxygenated compounds with carbon numbers less
than six are not significant ASOA precursors, we focus on emissions and
sources of BTEX, other mono-aromatics, IVOCs, and SVOCs. These three classes
of VOCs, aromatics, IVOCs, and SVOCs, have been suggested to be significant
ASOA precursors in urban atmospheres (Robinson et al., 2007; Hayes et al.,
2015; Ma et al., 2017; McDonald et al., 2018; Nault et al., 2018; Schroder
et al., 2018; Shah et al., 2018), originating from both fossil fuel and VCP
emissions.
Using the best available emission inventories from cities on three
continents (EMEP/EEA, 2016; McDonald et al., 2018; Li et al., 2019) and
observations, we quantify the emissions of BTEX, other mono-aromatics,
IVOCs, and SVOCs for both fossil fuel (e.g., gasoline, diesel, and kerosene), VCPs (e.g., coatings, inks, adhesives, personal care products, and
cleaning agents), and cooking sources (Fig. 5). This builds off the work of
McDonald et al. (2018) for urban regions on three different continents.
Note that the emissions investigated here ignore any oxygenated VOC emissions
not associated with IVOCs and SVOCs due to the challenge in estimating the
emission ratios for these compounds (de Gouw et al., 2018). Further, SVOC
emission ratios are estimated from the average POA observed by the Aerodyne aerosol mass spectrometer (AMS)
during the specific campaign and scaled by profiles from the literature for a
given average temperature and average OA (Robinson et al., 2007; Worton et
al., 2014; Lu et al., 2018). As most of the campaigns had an average OA
between 1 and 10 µg m-3 and a temperature of ∼ 298 K, this led to the majority of the estimated emitted SVOC gases being in the
highest SVOC bin. However, as discussed later, this does not lead to SVOCs
dominating the predicted ASOA due to the fact the fragmentation
and overall yield from the photooxidation of SVOC to ASOA are taken into account.
Combining these inventories and observations for the various locations
provides the following insights about the potential ASOA precursors not
easily measured or quantified in urban environments (e.g., Zhao et al.,
2014; Lu et al., 2018):
Aromatics from fossil fuel account for
14 %–40 % (mean 22 %) of the total BTEX and IVOC emissions for the five
urban areas investigated in-depth (Fig. 5), agreeing with prior studies that
have shown that the observed ASOA cannot be reconciled by the observations
or emission inventory of aromatics from fossil fuels (e.g., Ensberg et al.,
2014; Hayes et al., 2015).
BTEX from both fossil fuels and VCPs account
for 25 %–95 % (mean 43 %) of BTEX and IVOC emissions (Fig. 5). China has
the lowest contribution of IVOCs, potentially due to differences in chemical
make-up of the solvents used daily (Li et al., 2019), but more research is
needed to investigate the differences in IVOCs / BTEX from Beijing versus the USA
and UK emission inventories. Nonetheless, this shows the importance of IVOCs
for both emissions and ASOA precursors.
IVOCs are generally equal to, if
not greater than, the emissions of BTEX in four of the five urban areas
investigated here (Fig. 5).
Overall, VCPs account for a large fraction
of the BTEX and IVOC emissions for all five cities.
Finally, SVOCs
account for 27 %–88 % (mean 53 %) of VOCs generally considered ASOA
precursors (VOCs with volatility saturation concentrations ≤ 107µg m-3) (Fig. S6). Beijing has the highest contribution of SVOCs
to ASOA precursors due to the use of solid fuels and cooking emissions (Hu
et al., 2016). Also, this indicates the large contribution of a class of
VOCs that are difficult to measure (Robinson et al., 2007) and are an important ASOA
precursor (e.g., Hayes et al., 2015), showing that further emphasis should be
placed in quantifying the emissions of this class of compounds.
These results provide the ability to further investigate the mass balance of
predicted and observed ASOA for these urban locations (Fig. 4). The
inclusion of IVOCs, other aromatics not including BTEX, and SVOCs leads to
the ability to explain, on average, 85 ± 12 % of the observed ASOA
for these urban locations around the world (Fig. 4a). Further, the VCP
contribution to ASOA is important for all of these urban locations, accounting
for, on average, 37 ± 3 % of the observed ASOA (Fig. 4b).
This bottom-up mass budget analysis provides important insights to further
explain the correlation observed in Fig. 2. First, IVOCs are generally
co-emitted from similar sources to those for BTEX for the urban areas investigated
in-depth (Fig. 5). The oxidation of these co-emitted species leads to the
ASOA production observed across the urban areas around the world. Second,
S/IVOCs generally have similar rate constants to toluene and xylenes (≥1 × 10-11 cm3 molec.-1 s-1) (Zhao et al., 2014,
2017), the compounds that contribute the most to RBTEX, explaining the
rapid ASOA production that has been observed in various studies (de Gouw and
Jimenez, 2009; DeCarlo et al., 2010; Hayes et al., 2013; Hu et al., 2013,
2016; Nault et al., 2018; Schroder et al., 2018) and the correlation (Fig. 2).
Finally, the contribution of VCPs and fossil fuel sources to ASOA is similar
across the cities, expanding upon and further supporting the conclusion of
McDonald et al. (2018) in the importance of identifying and understanding
VCP emissions in order to explain ASOA.
This investigation shows that the bottom-up calculated ASOA agrees with
observed top-down ASOA within 15 %. As highlighted above, this ratio is
explained by the co-emissions of IVOCs with BTEX from traditional sources
(diesel, gasoline, and other fossil fuel emissions) and VCPs (Fig. 5) along
with similar rate constants for these ASOA precursors (Table S12). Thus, the
ASOA /RBTEX ratio obtained from Fig. 2 results in accurate predictions
of ASOA for the urban areas evaluated here, and this value can be used to
better estimate ASOA with chemical transport models (Sect. 4).
Comparison of BTEX and IVOC sources for (a) Beijing (see the Supplement
section about the Beijing emission inventory), (b) London (see the Supplement section about the
London/UK emission inventory), and (c) Los Angeles, (d) Northeast USA, and (e) New York City (see the Supplement section about the USA for panels c–e). For panel (a), BTEX is on the left axis and IVOC is on the right axis, due
to the small emissions per day for IVOC.
Improved urban SIMPLE model using multi-cities to constrain
The SIMPLE (SIMPLifiEd parameterization of combustion SOA) model was originally designed and tested against the observations
collected around Mexico City (Hodzic and Jimenez, 2011). It was then tested
against observations collected in Los Angeles (Hayes et al., 2015; Ma et
al., 2017). As both datasets have nearly identical ΔSOA /ΔCO
and RBTEX (Figs. 2, 3), it is not surprising that the SIMPLE
model did well in predicting the observed ΔSOA /ΔCO for these
two urban regions with consistent parameters. Although the SIMPLE model
generally performed better than more explicit models, it generally had lower
skill in predicting the observed ASOA in urban regions outside of Mexico
City and Los Angeles (Shah et al., 2019; Pai et al., 2020).
This may stem from the original SIMPLE model, with constant parameters,
missing the ability to change the amount and reactivity of the emissions,
which are different for the various urban regions, versus the ASOA
precursors being emitted proportionally to only CO (Hodzic and Jimenez,
2011; Hayes et al., 2015). For example, in the HTAP emissions inventory, the
CO emissions for Seoul, Los Angeles, and Mexico City are all similar
(Fig. S8); thus, the original SIMPLE model would suggest similar ΔSOA /ΔCO for all three urban locations. However, as shown in Figs. 2
and 3, the ΔSOA /ΔCO is different by nearly a factor of 2. The inclusion of the emissions and reactivity, where RBTEX for Seoul
is approximately a factor of 2.5 higher than Los Angeles and Seoul, into the
“improved” SIMPLE model better accounts for the variability in SOA production,
as shown in Fig. 2. Thus, the inclusion and use of this improved SIMPLE
model refines the simplified representation of ASOA in chemical transport
models and/or box models.
The improved SIMPLE model shows higher ASOA compared with the default volatility basis set (VBS) GEOS-Chem (Fig. 6a, b). In areas strongly impacted by urban emissions, e.g.,
Europe, East Asia, India, the east and west coast of the USA, and regions impacted by
Santiago (Chile), Buenos Aires (Argentina), Sāo Paulo (Brazil), Durban and Cape
Town (South Africa), and Melbourne and Sydney (Australia), the improved
SIMPLE model predicts up to 14 µg m-3 more ASOA, or
∼ 30 to 60 times more ASOA than the default scheme (Fig. 6c, d). As shown in Fig. 1, during intensive measurements, the ASOA composed
17 %–39 % of PM1, with an average contribution of ∼ 25 %. The default ASOA scheme in GEOS-Chem greatly underestimates the
fractional contribution of ASOA to total PM2.5 (<2 %; Fig. 6e). The improved SIMPLE model greatly improves the predicted fractional
contribution, showing that ASOA in the urban regions ranges from 15 % to 30 %,
with an average of ∼ 15 % for the grid cells corresponding
to the urban areas investigated here (Fig. 6f). Thus, the improved
SIMPLE model predicts the fractional contribution of ASOA to total PM2.5 far
more realistically, compared with observations. As discussed in Sect. 2.3 and
Eq. (11), having the model accurately predict the fractional contribution of
ASOA to the total PM is very important, as the total PM2.5 is derived
from satellite-based estimates (van Donkelaar et al., 2015), and the model
fractions are then applied to those total PM2.5 estimates. The ability
of the improved SIMPLE model to better represent the ASOA composition
provides confidence attributing the ASOA contribution to premature
mortality.
(a) Annual average modeled ASOA using the default VBS. (b) Annual
average modeled ASOA using the updated SIMPLE model. (c) Difference between the
annual average modeled updated SIMPLE and default VBS. Note that
values less than 0.05 µg m-3 are white in panels (a) and (b), and values less
than 0.02 µg m-3 are white in panel (c). (d) The ratio between the annual average
modeled updated SIMPLE (b) and default VBS (a). (e) The percent contribution of
annual average modeled ASOA using default VBS to total modeled PM2.5.
(f) The percent contribution of the annual average modeled ASOA using updated SIMPLE
to total modeled PM2.5.
Preliminary evaluation of worldwide premature deaths due to ASOA with the
updated SIMPLE parameterization
The improved SIMPLE parameterization is used along with GEOS-Chem to provide
an accurate estimation of ASOA formation in urban areas worldwide and
provide the ability to obtain realistic simulations of ASOA based on
measurement data. We use this model to quantify the attribution of
PM2.5 ASOA to premature deaths. Analysis up to this point has been for
PM1; however, both the chemical transport model and epidemiological
studies utilize PM2.5. For ASOA, this will not impact the discussion
or results here because the mass of OA (typically 80 %–90 %) is dominated
by PM1 (e.g., Bae et al., 2006; Seinfeld and Pandis, 2006), and ASOA is
formed mostly through condensation of oxidized species, which favors
partitioning onto smaller particles (Seinfeld and Pandis, 2006).
The procedure for this analysis is described in Fig. 7 and Sects. 2.3 and S3.
Briefly, we combine high-resolution satellite-based PM2.5 estimates
(for exposure) and a chemical transport model (GEOS-Chem, for fractional
composition) to estimate ASOA concentrations and various sensitivity
analysis (van Donkelaar et al., 2015). We calculated that ∼ 3.3 million premature deaths (using the integrated exposure-response, IER,
function) are due to long-term exposure to ambient PM2.5 (Fig. S9,
Table S16), consistent with recent literature (Cohen et al., 2017).
Flowchart describing how observed ASOA production was used to
calculate ASOA in GEOS-Chem, and how the satellite-based PM2.5
estimates and GEOS-Chem PM2.5 speciation were used to estimate the
premature mortality and the attribution of premature mortality by ASOA. See
Sect. 2 and the Supplement for further information about the details in the figure.
SIMPLE is described in Eq. (4) and by Hodzic and Jimenez (2011) and Hayes et
al. (2015). The one of two methods mentioned include either the integrated
exposure-response (IER) (Burnett et al., 2014) with Global Burden of Disease
(GBD) dataset (IHME, 2016) or the new Global Exposure Mortality Model (GEMM)
(Burnett et al., 2018) methods. For both IER and GEMM, the marginal method
(Silva et al., 2016) or the attributable fraction method (Anenberg et al., 2019)
are used.
The attribution of ASOA PM2.5 premature deaths can be calculated in one of
two ways: (a) the marginal method (Silva et al., 2016) or (b) the attributable
fraction method (Anenberg et al., 2019). For method (a), it is assumed that
a fraction of the ASOA is removed, keeping the rest of the PM2.5
components approximately constant, and the change in deaths is calculated
from the deaths associated with the total concentration minus the deaths
calculated using the reduced total PM2.5 concentrations. For method (b), the health impact is attributed to each PM2.5 component by
multiplying the total deaths by the fractional contribution of each
component to total PM2.5. For method (a), the deaths attributed to ASOA
are ∼ 340 000 people per year (Fig. 8); whereas, for method (b), the deaths are ∼ 370 000 people per year. Both of these
are based on the IER response function (Cohen et al., 2017).
The 5-year average (a) estimated reduction in PM2.5-related
premature deaths, by country, upon removing ASOA from total PM2.5, and
(b) the fractional reduction (reduction in PM2.5 premature deaths/total
PM2.5 premature deaths) in PM2.5-related premature deaths, by
country, upon removing ASOA from GEOS-Chem. The IER methods are used here.
See Figs. S9 and S12 for results using GEMM. See Fig. S10 for the
10 km × 10 km area results in comparison with country-level
results.
Attribution of premature mortality to ASOA with (a) IER or (b) GEMM, using the non-volatile primary OA and traditional SOA precursors
method from prior studies (e.g., Ridley et al., 2018). The increase in
the attribution of premature mortality to ASOA for the SIMPLE model (Fig. 8)
versus the non-volatile primary OA and traditional SOA precursor method
(default), for (c) IER and (d) GEMM.
Additional recent work (Burnett et al., 2018) has suggested less reduction
in the premature deaths versus PM2.5 concentration relationship at
higher PM2.5 concentrations, and lower concentration limits for the
threshold below which this relationship is negligible, both of which lead to
much higher estimates of PM2.5-related premature deaths. This is
generally termed the Global Exposure Mortality Model (GEMM). Using the two
attribution methods described above (a and b), the ASOA PM2.5-related premature
deaths are estimated to be ∼ 640 000 (method a) and
∼ 900 000 (method b) (Figs. S9, S12; Table S17).
Compared with prior studies using chemical transport models to estimate
premature deaths associated with ASOA (e.g., Silva et al., 2016; Ridley et
al., 2018), which assumed non-volatile POA and traditional ASOA
precursors, the attribution of premature mortality due to ASOA is over an
order of magnitude higher in this study (Fig. 9). This occurs using either
the IER or the GEMM approach for estimating premature mortality (Fig. 9). For
regions with larger populations and more PM2.5 pollution, the
attribution is between a factor of 40 and 80 higher. This stems from the
non-volatile POA and traditional ASOA precursors overestimating POA and
underestimating ASOA compared with observations (Schroder et al., 2018).
These offsetting errors will lead to model-predicted total OA values similar to
observations (Ridley et al., 2018; Schroder et al., 2018), although different
conclusions on whether POA versus SOA is more important for reducing
PM2.5-related premature mortality. Using a model constrained to
daytime atmospheric observations (Figs. 2, 4; see Sect. 4) leads to a more accurate estimation than earlier estimation of the contribution of photochemically produced ASOA to PM2.5-related premature mortality
than those available in prior studies. We note that ozone
concentrations change little as we change the ASOA simulation (see Sect. S4
and Fig. S14).
A limitation in this study is the lack of sufficient measurements in South
and Southeast Asia, eastern Europe, Africa, and South America (Fig. 1),
although these areas account for 44 % of the predicted reduction in
premature mortality for the world (Table S16). However, as highlighted in
Table S18, these regions likely still consume both transportation fuels and
VCPs, although in lower per capita amounts than more industrialized
countries. This consumption is expected to lead to the same types of
emissions as for the cities studied here, although more field measurements are
needed to validate global inventories of VOCs and the resulting oxidation
products in the developing world. Transportation emissions of VOCs are
expected to be more dominant in the developing world due to higher VOC
emission factors associated with inefficient combustion engines, such as
two-stroke scooters (Platt et al., 2014) and auto rickshaws (e.g., Goel and
Guttikunda, 2015).
Solid fuels are used for residential heating and cooking, which impact the
outdoor air quality as well (Hu et al., 2013, 2016; Lacey et al., 2017;
Stewart et al., 2021), and also lead to SOA (Heringa et al., 2011). As
discussed in Sect. 3.1, although the majority of the studies evaluated here
occurred in spring to summer time, when solid-fuel emissions are decreased,
two studies occurred during the winter/early spring time, during which time solid-fuel
emissions are important (Hu et al., 2013; Schroder et al., 2018). These
studies still follow the same relationship between ASOA and RBTEX as
the studies that focused on spring/summer photochemistry. Thus, the
limited datasets available indicate that photochemically produced ASOA from
solid fuels follow a similar relationship to that from other ASOA sources.
Also, solid-fuel sources are included in the inventories used in our
modeling. For the HTAP emission inventory used here (Janssens-Maenhout et
al., 2015), small-scale combustion, which includes heating and cooking
(e.g., solid-fuel use), is included in the residential emission sector. Both
CO and BTEX are included in this source and can account for a large
fraction of the total emissions where solid fuel use may be important (Fig. S15). Thus, as CO and BTEX are used in the updated SIMPLE model, and
campaigns that observed solid-fuel emissions fall within the trend for all
urban areas, the solid-fuel contribution to photochemically produced ASOA is
accounted for (as accurately as allowed by current datasets) in the
estimation of ASOA with respect to the attribution to premature mortality.
Note that recent work has observed potential nighttime aqueous chemistry
and/or oxidation by nitrate radicals from solid-fuel emissions to produce
ASOA (Kodros et al., 2020). Thus, missing this source of ASOA may lead to an
underestimation of total ASOA versus the photochemically produced ASOA that we
discuss here, leading to a potential underestimation in the attribution of
ASOA to premature mortality. From the studies that investigated “nighttime
aging” of solid-fuel emissions to form SOA, we predict that the total ASOA
may be underestimated by 1 to 3 µg m-3 (Kodros et al., 2020).
However, this potential underestimation is less than the current
underestimation in ASOA in GEOS-Chem (default versus updated SIMPLE).
Recently, emission factors from Abidjan, Côte d'Ivoire, a developing
urban area, showed the dominance of emissions from transportation and solid-fuel burning, with BTEX being an important fraction of the total emissions,
and that all the emissions were efficient with respect to producing ASOA (Dominutti et
al., 2019). Further, investigation of emissions in the New Delhi region of India
demonstrated the importance of both transportation and solid-fuel emissions
(Stewart et al., 2021; Wang et al., 2020), whereas model comparisons with
observations show an underestimation of OA compared with observations due to a
combination of emissions and OA representation (Jena et al., 2020). Despite
emission source differences, SOA is still an important component of
PM2.5 (e.g., Singh et al., 2019) and, thus, will impact air quality and
premature mortality in developing regions. Admittedly, though, our estimates
will be less accurate for these regions.
Conclusions
In summary, ASOA is an important – although inadequately constrained – component
of air pollution in megacities and urban areas around the world. This stems
from the complexity associated with the numerous precursor emission sources,
chemical reactions, and oxidation products that lead to observed ASOA
concentrations. We have shown here that the variability in observed ASOA
across urban areas is correlated with RBTEX, a marker for the
co-emissions of IVOC from both transportation and VCP emissions. Global
simulations indicate that ASOA contributes to a substantial fraction of the
premature mortality associated with PM2.5. Reductions in the ASOA
precursors will decrease the premature deaths associated with PM2.5,
indicating the importance of identifying and reducing exposure to sources of
ASOA. These sources include both traditional emissions
(transportation) and nontraditional emissions of emerging importance
(VCPs) to ambient PM2.5 concentrations in cities around the world.
Further investigation of speciated IVOCs and SVOCs for urban areas around
the world along with SOA mass concentration and other photochemical products
(e.g., Ox, PAN, and HCHO) for other urban areas, especially in South
Asia, throughout Africa, and throughout South America, would provide further
constraints to improve the SIMPLE model and our understanding of the
emission sources and chemistry that leads to the observed SOA and its impact
on premature mortality.
Data availability
TexAQS measurements are available at https://esrl.noaa.gov/csl/groups/csl7/measurements/2000TexAQS/LaPorte/DataDownload/ (TexAQS 2000 Science Team, 2000)
and upon request. NEAQS measurements are available at https://www.esrl.noaa.gov/csl/groups/csl7/measurements/2002NEAQS/ (NEAQS 2002 Science Team, 2002). MILAGRO
measurements are available at http://doi.org/10.5067/Aircraft/INTEXB/Aerosol-TraceGas (MILAGRO Science Team, 2006). CalNex
measurements are available at https://esrl.noaa.gov/csl/groups/csl7/measurements/2010calnex/Ground/DataDownload/ (CalNex Science Team, 2010).
ClearfLo measurements are available at https://catalogue.ceda.ac.uk/uuid/6a5f9eedd68f43348692b3bace3eba45 (ClearfLo Science Team, 2012).
SEAC4RS measurements are available at http://doi.org/10.5067/Aircraft/SEAC4RS/Aerosol-TraceGas-Cloud (SEAC4RS Science Team, 2013). WINTER
measurements are available at https://data.eol.ucar.edu/master_lists/generated/winter/ (WINTER Science Team, 2015).
KORUS-AQ measurements are available at http://doi.org/10.5067/Suborbital/KORUSAQ/DATA01 (KORUS-AQ Science Team, 2015). Data from Chinese
campaigns are available upon request, and the rest of the data used are located in the
papers cited in the text. GEOS-Chem data are available upon request. Figures will be made
available at https://cires1.colorado.edu/jimenez/group_pubs.html (Jimenez, 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-11201-2021-supplement.
Author contributions
BAN, DSJ, BCM, JAdG, and JLJ designed the experiment and wrote
the paper. BAN, PC-J, DAD, WH, JCS, JA, DRB, MRC, HC,
MMC, PFD, GSD, RD, FF, AF, JBG, GG, JFH, TFH, PLH,
JH, MH, LGH, BTJ, WCK, JL, IBP, JP, BR, CER, DR,
JMR, TBR, MS, JW, CW, PW, GMW, DEY, BY, JAdG, and
JLJ collected and analyzed the data. DSJ and AH ran the GEOS-Chem
model, and BAN, DSJ, and JLJ analyzed the model output. BAN,
PLH, JMS, and JLJ ran and analyzed the 0-D model used for the ASOA
budget analysis of ambient observations. BCM, AL, ML, and QZ
analyzed and provided the emission inventories used for the 0-D box model.
DSJ, DKH, and MON conducted the ASOA attribution to mortality
calculation, and BAN, DSJ, DKH, MON, JAdG, and JLJ analyzed
the results. All authors reviewed the paper.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
This article has not been formally reviewed by EPA. The views expressed in
this document are solely those of the authors and do not necessarily reflect
those of the Agency. EPA does not endorse any products or commercial
services mentioned in this publication.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We thank Katherine Travis for useful
discussions. We acknowledge Brian J. Bandy, James Lee, Graham P. Mills, Denise D. Montzka, Jochen Stutz, Andrew J. Weinheimer, Eric J. Williams, Ezra C. Wood, Douglas R. Worsnop for
use of their data.
Financial support
This research has been supported by the National Aeronautics and Space Administration (grant nos. NNX15AT96G and NNX16AQ26G), the Alfred P. Sloan Foundation (grant no. 2016-7173), the National Science Foundation (grant no. AGS-1822664), the U.S. Environmental Protection Agency (grant no. STAR 83587701-0), the Natural Environment Research Council (grant nos. NE/H003510/1, NE/H003177/1, and NE/H003223/1), the National Oceanic and Atmospheric Administration (grant no. NA17OAR4320101), the National Centre for Atmospheric Science (grant no. R8/H12/83/037), the Natural Sciences and Engineering Research Council of Canada (grant no. RGPIN/05002-2014), and the Fonds de recherche du Québec – Nature et technologies (grant no. 2016-PR-192364).
Review statement
This paper was edited by Maria Kanakidou and reviewed by three anonymous referees.
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