Secondary organic aerosols from anthropogenic volatile organic compounds contribute substantially to air pollution mortality
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Atmos. Chem. Phys., 21, 11201–11224, 2021 https://doi.org/10.5194/acp-21-11201-2021 © Author(s) 2021. This work is distributed under the Creative Commons Attribution 4.0 License. Secondary organic aerosols from anthropogenic volatile organic compounds contribute substantially to air pollution mortality Benjamin A. Nault1,2,a , Duseong S. Jo1,2 , Brian C. McDonald2,3 , Pedro Campuzano-Jost1,2 , Douglas A. Day1,2 , Weiwei Hu1,2,b , Jason C. Schroder1,2,c , James Allan4,5 , Donald R. Blake6 , Manjula R. Canagaratna7 , Hugh Coe5 , Matthew M. Coggon2,3 , Peter F. DeCarlo8 , Glenn S. Diskin9 , Rachel Dunmore10 , Frank Flocke11 , Alan Fried12 , Jessica B. Gilman3 , Georgios Gkatzelis2,3,d , Jacqui F. Hamilton10 , Thomas F. Hanisco13 , Patrick L. Hayes14 , Daven K. Henze15 , Alma Hodzic11,16 , James Hopkins10,17 , Min Hu18 , L. Greggory Huey19 , B. Thomas Jobson20 , William C. Kuster3,29, , Alastair Lewis10,17 , Meng Li2,3 , Jin Liao13,21 , M. Omar Nawaz15 , Ilana B. Pollack22 , Jeffrey Peischl2,3 , Bernhard Rappenglück23 , Claire E. Reeves24 , Dirk Richter12 , James M. Roberts3 , Thomas B. Ryerson3,e , Min Shao25 , Jacob M. Sommers14,26 , James Walega12 , Carsten Warneke2,3 , Petter Weibring12 , Glenn M. Wolfe13,27 , Dominique E. Young5,f , Bin Yuan25 , Qiang Zhang28 , Joost A. de Gouw1,2 , and Jose L. Jimenez1,2 1 Department of Chemistry, University of Colorado Boulder, Boulder, CO, USA 2 Cooperative Institute for Research in Environmental Sciences, Boulder, CO, USA 3 National Oceanic and Atmospheric Administration Chemical Sciences Laboratory, Boulder, CO, USA 4 National Centre for Atmospheric Sciences, School of Earth and Environmental Sciences, The University of Manchester, Manchester, UK 5 Centre of Atmospheric Science, School of Earth and Environmental Sciences, The University of Manchester, Manchester, UK 6 Department of Chemistry, University of California, Irvine, Irvine, CA, USA 7 Center for Aerosol and Cloud Chemistry, Aerodyne Research Inc., Billerica, MA, USA 8 Department of Environmental Health Engineering, Johns Hopkins University, Baltimore, MD, USA 9 NASA Langley Research Center, Hampton, VA, USA 10 Wolfson Atmospheric Chemistry Laboratories, Department of Chemistry, University of York, York, UK 11 Atmospheric Chemistry Observations and Modeling Laboratory, National Center for Atmospheric Research, Boulder, CO, USA 12 Institute of Arctic and Alpine Research, University of Colorado Boulder, Boulder, CO, USA 13 Atmospheric Chemistry and Dynamic Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA 14 Department of Chemistry, Université de Montréal, Montréal, QC, Canada 15 Paul M. Rady Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, USA 16 Laboratoires d’Aréologie, Université de Toulouse, CNRS, UPS, Toulouse, France 17 Department of Chemistry, National Centre for Atmospheric Sciences, University of York, York, UK 18 State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, China 19 School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA 20 Department of Civil and Environmental Engineering, Laboratory for Atmospheric Research, Washington State University, Pullman, WA, USA 21 Universities Space Research Association, GESTAR, Columbia, MD, USA 22 Department of Atmospheric Science, Colorado State University, Fort Collins, CO, USA 23 Department of Earth and Atmospheric Science, University of Houston, Houston, TX, USA 24 Centre for Ocean and Atmospheric Sciences, School of Environmental Sciences, University of East Anglia, Norwich, UK 25 Institute for Environmental and Climate Research, Jinan University, Guangzhou, China 26 Air Quality Research Division, Environment and Climate Change Canada, Toronto, Ontario, Canada 27 Joint Center for Earth Systems Technology, University of Maryland, Baltimore, MD, USA Published by Copernicus Publications on behalf of the European Geosciences Union.
11202 B. A. Nault et al.: Secondary organic aerosols from anthropogenic volatile organic compounds
28 Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science,
Tsinghua University, Beijing, China
29 independent researcher
a now at: Center for Aerosol and Cloud Chemistry, Aerodyne Research Inc., Billerica, MA, USA
b now at: State Key Laboratory at Organic Geochemistry, Guangzhou Institute of Geochemistry,
Chinese Academy of Sciences, Guangzhou, China
c now at: Colorado Department of Public Health and Environment, Denver, CO, USA
d now at: Forschungszentrum Jülich GmbH, Jülich, Germany
e now at: Scientific Aviation, Boulder, CO, USA
f now at: Air Quality Research Center, University of California, Davis, CA, USA
retired
Correspondence: Benjamin A. Nault (bnault@aerodyne.com) and Jose L. Jimenez (jose.jimenez@colorado.edu)
Received: 30 August 2020 – Discussion started: 11 November 2020
Revised: 16 June 2021 – Accepted: 24 June 2021 – Published: 27 July 2021
Abstract. Anthropogenic secondary organic aerosol 2018). Roughly 95 % of the world’s population live in ar-
(ASOA), formed from anthropogenic emissions of organic eas where PM2.5 (fine particulate matter with a diameter
compounds, constitutes a substantial fraction of the mass smaller than 2.5 µm) exceeds the World Health Organiza-
of submicron aerosol in populated areas around the world tion’s 10 µg m−3 annual average guideline (Shaddick et al.,
and contributes to poor air quality and premature mortal- 2018). This is especially true for urban areas, where high
ity. However, the precursor sources of ASOA are poorly population density is co-located with increased emissions of
understood, and there are large uncertainties in the health PM2.5 and its gas-phase precursors from human activities.
benefits that might accrue from reducing anthropogenic It is estimated that PM2.5 leads to 3 to 4 million premature
organic emissions. We show that the production of ASOA deaths per year, higher than the deaths associated with other
in 11 urban areas on three continents is strongly correlated air pollutants (Cohen et al., 2017). More recent analysis using
with the reactivity of specific anthropogenic volatile organic concentration–response relationships derived from studies of
compounds. The differences in ASOA production across populations’ exposure to high levels of ambient PM2.5 sug-
different cities can be explained by differences in the gest that the global premature death burden could be up to
emissions of aromatics and intermediate- and semi-volatile twice this value (Burnett et al., 2018).
organic compounds, indicating the importance of controlling The main method to estimate premature mortality with
these ASOA precursors. With an improved model repre- PM2.5 is to use measured PM2.5 from ground observations
sentation of ASOA driven by the observations, we attribute along with derived PM2.5 from satellites to fill in miss-
340 000 PM2.5 -related premature deaths per year to ASOA, ing ground-based observations (van Donkelaar et al., 2015,
which is over an order of magnitude higher than prior stud- 2016). To go from total PM2.5 to species-dependent and
ies. A sensitivity case with a more recently proposed model even sector-dependent associated premature mortality from
for attributing mortality to PM2.5 (the Global Exposure Mor- PM2.5 , chemical transport models (CTMs) are used to pre-
tality Model) results in up to 900 000 deaths. A limitation dict the fractional contribution of species and/or sector (e.g.,
of this study is the extrapolation from cities with detailed Lelieveld et al., 2015; van Donkelaar et al., 2015, 2016; Silva
studies and regions where detailed emission inventories are et al., 2016). However, although CTMs may get total PM2.5
available to other regions where uncertainties in emissions or even total species (e.g., organic aerosol – OA), correct,
are larger. In addition to further development of institutional the model may be getting the values right for the wrong rea-
air quality management infrastructure, comprehensive air son (e.g., de Gouw and Jimenez, 2009; Woody et al., 2016;
quality campaigns in the countries in South and Central Murphy et al., 2017; Baker et al., 2018; Hodzic et al., 2020).
America, Africa, South Asia, and the Middle East are needed This is especially important for OA in urban areas, where
for further progress in this area. models have a long-standing issue with underpredicting sec-
ondary OA (SOA) with some instances of overpredicting pri-
mary OA (POA) (de Gouw and Jimenez, 2009; Dzepina et
al., 2009; Hodzic et al., 2010b; Woody et al., 2016; B. Zhao
1 Introduction
et al., 2016; Janssen et al., 2017; Jathar et al., 2017). Further,
Poor air quality is one of the leading causes of premature this bias has even been observed for highly aged aerosols in
mortality worldwide (Cohen et al., 2017; Landrigan et al., remote regions (Hodzic et al., 2020). As has been found in
Atmos. Chem. Phys., 21, 11201–11224, 2021 https://doi.org/10.5194/acp-21-11201-2021
B. A. Nault et al.: Secondary organic aerosols from anthropogenic volatile organic compounds 11203 prior studies for urban areas (e.g., Zhang et al., 2007; Kondo tailpipe, evaporation, refueling) were assumed to be the ma- et al., 2008; Jimenez et al., 2009; DeCarlo et al., 2010; Hayes jor precursors of ASOA, which was supported by field stud- et al., 2013; Freney et al., 2014; Hu et al., 2016; Nault et al., ies (Parrish et al., 2009; Gentner et al., 2012; Warneke et 2018; Schroder et al., 2018) and highlighted here (Fig. 1), al., 2012; Pollack et al., 2013). However, budget closure of a substantial fraction of the observed submicron PM is OA, observed ASOA mass concentrations could not be achieved and a substantial fraction of the OA is composed of SOA with transportation-related VOCs (Ensberg et al., 2014). The (approximately a factor of 2 to 3 higher than POA). Thus, contribution of urban-emitted biogenic precursors to SOA to better understand the sources and apportionment of PM2.5 in urban areas is typically small. Biogenic SOA (BSOA) that contributes to premature mortality, CTMs must improve in these regions typically results from advection of regional their prediction of SOA versus POA, as the sources of SOA background concentrations rather than processing of locally precursors and POA can be different. emitted biogenic VOCs (e.g., Hodzic et al., 2009, 2010a; However, understanding the gas-phase precursors of pho- Hayes et al., 2013; Janssen et al., 2017). BSOA is thought tochemically produced anthropogenic SOA – ASOA, de- to dominate globally (Hallquist et al., 2009), but as shown in fined as the photochemically produced SOA formed from Fig. 1, the contribution of BSOA (1 % to 20 %) to urban con- the photooxidation of anthropogenic volatile organic com- centrations, while often substantial, is typically smaller than pounds (AVOCs) (de Gouw et al., 2005; DeCarlo et al., 2010) that of ASOA (17 % to 39 %) (see Sect. S3.1). – quantitatively is challenging (Hallquist et al., 2009). Note, Many of these prior studies generally investigated AVOC for the rest of the paper, unless explicitly stated otherwise, with high volatility, where volatility here is defined as the ASOA refers to SOA produced from the photooxidation of saturation concentration, C ∗ (in µg m−3 ) (de Gouw et al., AVOCs, as there are potentially other relevant paths for the 2005; Volkamer et al., 2006; Dzepina et al., 2009; Freney production of SOA in urban environments (e.g., Petit et al., et al., 2014; Woody et al., 2016). More recent studies 2014; Kodros et al., 2018, 2020; Stavroulas et al., 2019). Al- have identified lower-volatility compounds in transportation- though the enhancement of ASOA is largest in large cities, related emissions (e.g., Y. Zhao et al., 2014, 2016; Lu et these precursors and the production of ASOA should be im- al., 2018). These compounds have been broadly identified portant in any location impacted by anthropogenic emissions as intermediate-volatility organic compounds (IVOCs) and (e.g., Fig. 1). ASOA comprises a wide range of condensable semi-volatile organic compounds (SVOCs). IVOCs generally products generated by numerous chemical reactions involv- have a C ∗ of 103 to 106 µg m−3 , whereas SVOCs generally ing AVOC precursors (Hallquist et al., 2009; Hayes et al., have a C ∗ of 1 to 102 µg m−3 . Due to their lower volatil- 2015; Shrivastava et al., 2017). The number of AVOC pre- ity and functional groups, these classes of compounds gen- cursors, as well as the role of “nontraditional” AVOC pre- erally form ASOA more efficiently than traditional, higher- cursors, along with the condensable products and chemical volatility AVOCs; however, S/IVOCs (SVOCs and IVOCs) reactions, compound to lead to differences in the observed have also been more difficult to measure (e.g., Zhao et al., versus predicted ASOA for various urban environments (e.g., 2014; Pagonis et al., 2017; Deming et al., 2019). IVOCs de Gouw and Jimenez, 2009; Dzepina et al., 2009; Hodzic et have generally been the more difficult of the two classes to al., 2010b; Woody et al., 2016; Janssen et al., 2017; Jathar et measure and identify, as these compounds cannot be col- al., 2017; McDonald et al., 2018). One solution to improve lected onto filters to be sampled off-line (Lu et al., 2018) the prediction in CTMs is to use a simplified model, where and generally show up as an unresolved complex mixture for lumped ASOA precursors react, non-reversibly, at a given in situ measurements using gas chromatography (GC) (Zhao rate constant, to produce ASOA (Hodzic and Jimenez, 2011; et al., 2014). SVOCs, on the other hand, can be more read- Hayes et al., 2015; Pai et al., 2020). This simplified model ily collected onto filters and sampled off-line due to their has been found to reproduce the observed ASOA from some lower volatility (Lu et al., 2018). Another potential issue has urban areas (Hodzic and Jimenez, 2011; Hayes et al., 2015) been an underestimation of the S/IVOC aerosol production but has issues in other urban areas (Pai et al., 2020). This may as well as an underestimation in the contribution of pho- stem from the simplified model being parameterized to two tochemically produced S/IVOC from photooxidized “tradi- urban areas (Hodzic and Jimenez, 2011; Hayes et al., 2015). tional” VOCs, due to partitioning of these low-volatility com- These inconsistencies impact the model-predicted fractional pounds to chamber walls and tubing (Krechmer et al., 2016; contribution of ASOA to total PM2.5 and, thus, the ability Ye et al., 2016; Liu et al., 2019). Accounting for this underes- to understand the source attribution to PM2.5 and premature timation increases the predicted ASOA (Ma et al., 2017). The deaths. inclusion of these classes of compounds has led to improve- The main categories of gas-phase precursors that dominate ment in some urban SOA budget closure; however, many ASOA have been the subject of intensive research. The de- more recent studies have still indicated a general shortfall bate on what dominates can, in turn, impact the understand- in the ASOA budget, even when including these compounds ing of what precursors to regulate in order to reduce ASOA, from transportation-related emissions (Dzepina et al., 2009; to improve air quality, and to reduce premature mortality as- Tsimpidi et al., 2010; Hayes et al., 2015; Cappa et al., 2016; sociated with ASOA. Transportation-related emissions (e.g., Ma et al., 2017; McDonald et al., 2018). https://doi.org/10.5194/acp-21-11201-2021 Atmos. Chem. Phys., 21, 11201–11224, 2021
11204 B. A. Nault et al.: Secondary organic aerosols from anthropogenic volatile organic compounds Figure 1. 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 informa- tion on measurements, studies, and apportionment of SOA into ASOA and BSOA. Recent studies have indicated that emissions from volatile ganic emissions is largely unknown. Since numerous stud- chemical products (VCPs), defined as pesticides, coatings, ies have shown the importance of VCPs and other nontra- inks, adhesives, personal care products, and cleaning agents ditional VOC emission sources, efforts have been made to (McDonald et al., 2018), as well as cooking emissions try to improve the representation and emissions of VCPs (Hayes et al., 2015), asphalt emissions (Khare et al., 2020), (Seltzer et al., 2021), which can reduce the uncertainty in and solid-fuel emissions from residential wood burning ASOA precursors and the associated premature death esti- and/or cookstoves (e.g., Hu et al., 2013, 2020; Schroder et al., mations. Currently, most studies have not treated ASOA ex- 2018), are important. While the total amounts of ASOA pre- plicitly (e.g., Lelieveld et al., 2015; Silva et al., 2016; Ridley cursors released in cities have dramatically declined (largely et al., 2018) in source apportionment calculations of the pre- due to three-way catalytic converters in cars; Warneke et al., mature deaths associated with long-term exposure of PM2.5 . 2012; Pollack et al., 2013; Zhao et al., 2017; Khare and Gen- Most models represented total OA as non-volatile POA and tner, 2018), VCPs have not declined as quickly (Khare and “traditional” ASOA precursors (transportation-based VOCs), Gentner, 2018; McDonald et al., 2018). Besides a few cities which largely underpredict ASOA (Ensberg et al., 2014; in the USA (Coggon et al., 2018; Khare and Gentner, 2018; Hayes et al., 2015; Nault et al., 2018; Schroder et al., McDonald et al., 2018), extensive VCP emission quantifica- 2018) while overpredicting POA (e.g., Hodzic et al., 2010b; tion has not yet been published. B. Zhao et al., 2016; Jathar et al., 2017). This does not re- Due to the uncertainty on the emissions of ASOA pre- flect the current understanding that POA is volatile and con- cursors and on the amount of ASOA formed from them, tributes to ASOA mass concentration (e.g., Grieshop et al., the number of premature deaths associated with urban or- 2009; Lu et al., 2018). Although the models are estimating Atmos. Chem. Phys., 21, 11201–11224, 2021 https://doi.org/10.5194/acp-21-11201-2021
B. A. Nault et al.: Secondary organic aerosols from anthropogenic volatile organic compounds 11205
total OA correctly (Ridley et al., 2018; Hodzic et al., 2020; Therefore, the average between the two measurements was
Pai et al., 2020), the attribution of premature deaths to POA used in this study, similar to what has been done in other
instead of SOA formed from traditional and nontraditional studies for other gas-phase species (Bertram et al., 2007).
sources, including IVOCs from both sources, could lead to All linear fits, unless otherwise noted, use the orthogonal dis-
regulations that may not target the emissions that would re- tance regression (ODR) fitting method.
duce OA in urban areas. As PM1 and SOA mass are highest For values in Table S4 through Table S8 not previously re-
in urban areas (Fig. 1), also shown in Jimenez et al. (2009), it ported in the literature, the following procedure was applied
is necessary to quantify the amount and identify the sources to determine the emissions ratios, similar to the methods of
of ASOA to target future emission standards that will opti- Nault et al. (2018). An OH exposure (OHexp = [OH] × 1t),
mally improve air quality and the associated health impacts. which is also the photochemical age (PA), was estimated
As these emissions are from human activities, they will con- by using the ratio of NOx / NOy (Eq. 1) or the ratio
tribute to SOA mass outside urban regions and to potential of m + p-xylene / ethylbenzene (Eq. 2). For the m + p-
health impacts outside urban regions as well. Although there xylene/ethylbenzene, the emission ratio (Table S5) was es-
are potentially other important exposure pathways to PM that tablished by determining the average ratio during minimal
may increase premature mortality, such as exposure to solid- photochemistry, similar to prior studies (de Gouw et al.,
fuel emissions indoors (e.g., Kodros et al., 2018), the focus of 2017). This was done for only one study, Texas Air Qual-
this paper is on exposure to outdoor ASOA and its associated ity Study 2000 (TexAQS 2000). This method could be ap-
impacts on premature mortality. plied in that case as it was a ground campaign that operated
Here, we investigate the factors that control ASOA using both day and night; therefore, a ratio at night could be de-
11 major urban (including megacities) field studies (Fig. 1 termined when there was minimal loss of both VOCs. The
and Table 1). The empirical relationships and numerical average emission ratio for the other VOCs was determined
models are then used to quantify the attribution of premature using Eq. (3) after the OHexp was calculated in Eq. (1) or
mortality to ASOA around the world, using the observations Eq. (2). The rate constants used for determining OHexp and
to improve the modeled representation of ASOA. The results emission ratios are found in Table S12.
provide insight into the importance of ASOA to global pre- [NO ]
x
mature mortality due to PM2.5 and further understanding of [NOy ]
the precursors and sources of ASOA in urban regions. OHexp = [OH] × t = ln (1)
kOH+NO2
1
OHexp = [OH] × t = −
2 Methods km+p−xylene − kethylbenzene
m + p − xylene t
Here, we introduce the ambient observations from various × ln
campaigns used to constrain ASOA production (Sect. 2.1), ethylbenzene t
a description of the simplified model used in CTMs to bet- m + p − xyelene 0
ter predict ASOA (Sect. 2.2), and a description of how pre- − (2)
ethylbenzene 0
mature mortality was estimated for this study (Sect. 2.3).
[VOCi ] [VOCi ]
In the Supplement, the following can be found: a descrip- (0) = − (t)
tion of the emissions used to calculate the ASOA budget for [CO] [CO]
!
five different locations (Sect. S1), a description of how the 1
ASOA budget was calculated for the five different locations × 1−
exp −ki × [OH]exp × t
(Sect. S2), a description of the CTM (GEOS-Chem, or the
Goddard Earth Observing System Chemistry model) used in [VOCi ]
× ki + (t) × ki (3)
this study (Sects. S3–S4), and an error analysis for the obser- [CO]
vations (Sect. S5).
2.1 Ambient observations 2.2 Updates to the SIMPLE model
For values not previously reported in the literature (Ta- With the combination of the new dataset, which expands
ble S4), observations taken between 11:00 and 16:00 LT (lo- across urban areas on three continents, the SIMPLE parame-
cal time) were used to determine the slopes of SOA ver- terization for ASOA (Hodzic and Jimenez, 2011) is updated
sus formaldehyde (HCHO) (Fig. S1), peroxy acetyl nitrate in the standard GEOS-Chem model to reproduce observed
(PAN) (Fig. S2), and Ox (Ox = O3 + NO2 ) (Fig. S3). For the ASOA in Fig. 2a. The parameterization operates as repre-
California Research at the Nexus of Air Quality and Climate sented by Eq. (4).
Change (CalNex) campaign, there was an approximate 48 %
k×[OH]
difference between the two HCHO measurements (Fig. S4). Emissions → SOAP −−−−→ ASOA (4)
https://doi.org/10.5194/acp-21-11201-2021 Atmos. Chem. Phys., 21, 11201–11224, 2021
11206 B. A. Nault et al.: Secondary organic aerosols from anthropogenic volatile organic compounds
Table 1. List of campaigns used here, and the values previously reported for these campaigns. “W” denotes winter, “Sp” denotes spring, and
“Su” denotes summer.
Location Field Campaign Coordinates Time Period Season Previous publication/
Campaign overview
Long. (◦ ) Lat. (◦ )
Houston, TX, USA (2000) TexAQS 2000 −95.4 29.8 15 Aug 2000– Su Jimenez et al. (2009)a ,
15 Sep 2000 Wood et al. (2010)b
Northeast USA (2002) NEAQS 2002 −78.1 to −70.5 32.8 to 43.1 26 Jul 2002; Su Jimenez et al. (2009)a ,
29 Jul 2002– de Gouw and Jimenez (2009)c ,
10 Aug 2002 Kleinman et al. (2007)c
Mexico City, Mexico (2003) MCMA-2003 −99.2 19.5 31 Mar 2003– Sp Molina et al. (2007),
04 May 2003 Herndon et al. (2008)b
Tokyo, Japan (2004) 139.7 35.7 24 Jul 2004– Su Kondo et al. (2008)a ,
14 Aug 2004 Miyakawa et al. (2008)a ,
Morino et al. (2014)b
Mexico City, Mexico (2006) MILAGRO −99.4 to −98.6 19.0 to 19.8 04 Mar 2006– Sp Molina et al. (2010),
29 Mar 2006 DeCarlo et al. (2008)a ,
Wood et al. (2010)b ,
DeCarlo et al. (2010)c
Paris, France (2009) MEGAPOLI 48.9 2.4 13 Jul 2009– Su Freney et al. (2014)a ,
29 Jul 2009 Zhang et al. (2015)b
Pasadena, CA, USA (2010) CalNex −118.1 34.1 15 May 2010– Sp Ryerson et al. (2013),
16 Jun 2010 Hayes et al. (2013)a,b,c
Changdao Island, China (2011) CAPTAIN 120.7 38.0 21 Mar 2011– Sp Hu et al. (2013)a,c
24 Apr 2011
Beijing, China (2011) CAREBeijing 2011 116.4 39.9 03 Aug 2011– Su Hu et al. (2016)a,b,c
15 Sep 2011
London, UK (2012) ClearfLo 0.1 51.5 22 Jul 2012– Su Bohnenstengel et al. (2015)
18 Aug 2012
Houston, TX, USA (2013) SEAC4 RS −96.0 to −94.0 29.2 to 30.3 01 Aug 2013– Su Toon et al. (2016)
23 Sep 2013
New York City, NY, USA (2015) WINTER −74.0 to −69.0 39.5 to 42.5 07 Feb 2015 W Schroder et al. (2018)a,c
Seoul, South Korea (2016) KORUS-AQ 124.6 to 128.0 36.8 to 37.6 01 May 2016– Sp Nault et al. (2018)a,b,c,d
10 Jun 2016
a Reference used for PM composition. b Reference used for SOA / O slope. c Reference used for 1OA / 1CO value. d Reference used for SOA/HCHO and SOA/PAN slopes.
1 x
SOAP represents the lumped precursors of where E and k stand for the emission rate and reaction rate
ASOA, k is the reaction rate coefficient with OH coefficient with OH, respectively, for benzene (B), toluene
(1.25 × 10−11 cm3 molec.−1 s−1 ), and [OH] is the OH (T), and xylenes (X, here all three isomers). Ethylben-
concentration (in molec. cm−3 ). This rate constant is also zene was not included in this calculation because its emis-
consistent with observed ASOA formation timescale of sion was not available in version 2 of the Hemispheric
∼ 1 d that has been observed across numerous studies (e.g., Transport of Air Pollution (HTAPv2) emission inventory.
de Gouw et al., 2005; DeCarlo et al., 2010; Hayes et al., However, ethylbenzene contributed a minor fraction of the
2013; Nault et al., 2018; Schroder et al., 2018). mixing ratio (∼ 7 %, Table S5) and reactivity (∼ 6 %) of
SOAP emissions were calculated based on the relationship the total BTEX across the campaigns. Reaction rate con-
between 1SOA / 1CO and Raromatics /1CO in Fig. 2a. First, stants used in this study were 1.22 × 10−12 , 5.63 × 10−12 ,
we calculated Raromatics /1CO (Eq. 5) for each grid cell and and 1.72 × 10−11 cm3 molec.−1 s−1 for benzene, toluene,
time step as follows: and xylenes, respectively (Atkinson and Arey, 2003; Atkin-
son et al., 2006). Raromatics /1CO allows a dynamic calcu-
lation of the E(VOC) / E(CO) = SOA / 1CO. Hodzic and
Jimenez (2011) and Hayes et al. (2015) used a constant value
Raromatics EB × kB + ET × kT + EX × kX of 0.069 g g−1 , which worked well for the two cities investi-
= , (5)
1CO ECO
Atmos. Chem. Phys., 21, 11201–11224, 2021 https://doi.org/10.5194/acp-21-11201-2021B. A. Nault et al.: Secondary organic aerosols from anthropogenic volatile organic compounds 11207
gated but not for the expanded dataset studied here. Thus, at concentrations below the PM2.5 threshold values (Krewski
both the aromatic emissions and CO emissions are used in et al., 2009); following the methods of the GBD studies, these
this study to better represent the variable emissions of ASOA can be viewed as lower bounds on estimates of premature
precursors (Fig. S5). deaths.
Second, ESOAP /ECO can be obtained from the result of We performed an additional sensitivity analysis using the
Eq. (6), using the slope and intercept in Fig. 2a, with a cor- Global Exposure Mortality Model (GEMM) (Burnett et al.,
rection factor (F ) to consider additional SOA production af- 2018). For the GEMM analysis, we also used age stratified
ter 0.5 PA equivalent days, as Fig. 2a shows the comparison population data from GPWv3. Premature death is calculated
at 0.5 PA equivalent days. the same as shown in Eq. (8); however, the relative risk dif-
fers. For the GEMM model, the relative risk can be calculated
ESOAP RAromatics as shown in Eq. (10).
= slope × + intercept × F, (6)
ECO 1CO
where the slope is 24.8 and the intercept is −1.7 from Fig. 2a. RR = exp (θ × λ) with λ
log 1 + αz
F (Eq. 7) can be calculated as follows:
= (10)
1 + exp ( )
µ̂−z
ASOAt=∞ π
F=
ASOAt=0.5
SOAPt=0 Here z = max (0, PM2.5 –PM2.5,Threshold ); θ, π, µ̂, α, and
= ,
SOAPt=0 × (1 − exp (−k × 1t × [OH])) PM2.5,Threshold depend on disease category and are from Bur-
1t = 43 200 s. (7) nett et al. (2018). Similar to the Eq. (9), if the concentrations
are below the threshold (2.4 µg m−3 ; Burnett et al., 2018),
F was calculated as 1.8 by using premature deaths are computed as zero; however, the GEMM
[OH] = 1.5 × 106 molec. cm−3 , which was used in the has a lower threshold than the GBD method.
definition of 0.5 PA equivalent days for Fig. 2a. For GBD, we do not consider age-specific mortality rates
Finally, ESOAP can be computed by multiplying CO emis- or risks. For GEMM, we calculate age-specific health im-
sions (ECO ) for every grid point and time step in GEOS- pacts with age-specific parameters in the exposure response
Chem by the ESOAP /ECO ratio. function (Table S15). We combine the age-specific results
of the exposure-response function with age-distributed pop-
2.3 Estimation of premature mortality attribution ulation data from GPW (Gridded Population of the World;
CIESIN, 2017) and a national mortality rate across all ages
Premature deaths were calculated for five disease categories: to assess age-specific mortality.
ischemic heart disease (IHD), stroke, chronic obstructive We calculated total premature deaths using annual average
pulmonary disease (COPD), acute lower respiratory illness total PM2.5 concentrations derived from satellite-based esti-
(ALRI), and lung cancer (LC). We calculated premature mor- mates at the resolution of 0.1◦ × 0.1◦ from van Donkelaar et
tality for the population aged more than 30 years, using al. (2016). Application of the remote-sensing-based PM2.5 at
Eq. (8). the 0.1◦ × 0.1◦ resolution rather than direct use of the GEOS-
RR − 1 Chem model concentrations at the 2◦ × 2.5◦ resolution helps
Premature death = Pop × y0 × (8) reduce uncertainties in the quantification of PM2.5 exposure
RR
inherent in coarser estimates (Punger and West, 2013). We
Mortality rate, y0 , varies according to the particular dis- also calculated deaths by subtracting the total annual aver-
ease category and geographic region, which is available from age ASOA concentrations derived from GEOS-Chem from
the Global Burden of Disease Study 2015 database – GBD this amount (Fig. S11). To reduce uncertainties related to
2015 (IHME, 2016). Population (Pop) was obtained from the spatial gradients and total concentration magnitudes in our
Columbia University Center for International Earth Science GEOS-Chem simulations of PM2.5 , our modeled ASOA was
Information Network (CIESIN) for 2010 (CIESIN, 2017). calculated as the fraction of ASOA to total PM2.5 in GEOS-
Relative risk, RR, can be calculated as shown in Eq. (9). Chem, multiplied by the satellite-based PM2.5 concentrations
(Eq. 11).
ρ
RR = 1 + α × 1 − exp β × PM2.5 − PM2.5,Threshold (9)
ASOAsat = ASOAmod /PM2.5,mod × PM2.5,sat (11)
α, β, and ρ values depend on disease category and are calcu-
lated from Burnett et al. (2014) (see Table S14 and the asso- Finally, this process for estimating PM2.5 health impacts
ciated file). If the PM2.5 concentrations are below the PM2.5 considers only PM2.5 mass concentration and does not dis-
threshold value (Table S14), premature deaths were com- tinguish toxicity by composition, consistent with the current
puted as zero. However, there could be some health impacts US EPA position expressed in Sacks et al. (2019).
https://doi.org/10.5194/acp-21-11201-2021 Atmos. Chem. Phys., 21, 11201–11224, 202111208 B. A. Nault et al.: Secondary organic aerosols from anthropogenic volatile organic compounds
Figure 2. (a) Scatterplot of background and dilution-corrected ASOA concentrations (1SOA/1CO at PA = 0.5 equivalent days) versus
BTEX (benzene, toluene, ethylbenzene, and xylenes) emission reactivity ratio (RBTEX = i [ VOC
P
CO ] 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).
3 Observations of ASOA production across three (Table S4; Figs. S1–S3), which are indicators of photochem-
continents ical processing of emissions.
However, as initially discussed by Nault et al. (2018) and
shown in Fig. 3, there is large variability in these various met-
3.1 Observational constraints of ASOA production rics across the urban areas evaluated here. To the best of the
across three continents 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 1SOA / 1CO are similar to
Measurements during intensive field campaigns in large ur- the trends in the slopes of SOA versus Ox , PAN, or HCHO.
ban areas better constrain the concentrations and atmospheric For example, Seoul is the highest for nearly all metrics and is
formation of ASOA because the scale of ASOA enhancement approximately a factor of 6 higher than the urban area, Hous-
is large compared with SOA from a regional background. ton, that generally showed the lowest photochemical metrics.
Generally, ASOA increased with the amount of urban precur- This suggests that the variability is related to a physical fac-
sor VOCs and with atmospheric PA (de Gouw et al., 2005; de tor, including emissions and chemistry.
Gouw and Jimenez, 2009; DeCarlo et al., 2010; Hayes et al., The VOC concentration, together with how quickly the
2013; Nault et al., 2018; Schroder et al., 2018; Shah et al., emitted VOCs react (6ki × [VOC]i , i.e., the hydroxyl rad-
2018). In addition, ASOA correlates strongly with gas-phase ical, or OH, reactivity of VOCs), where k is the OH rate
secondary photochemical species, including Ox , HCHO, and coefficient for each VOC, are a determining parameter for
PAN (Herndon et al., 2008; Wood et al., 2010; Hayes et al., ASOA formation over urban spatial scales (Eq. 12). ASOA
2013; Zhang et al., 2015; Nault et al., 2018; Liao et al., 2019)
Atmos. Chem. Phys., 21, 11201–11224, 2021 https://doi.org/10.5194/acp-21-11201-2021B. A. Nault et al.: Secondary organic aerosols from anthropogenic volatile organic compounds 11209
Figure 3. (a) A comparison of the 1SOA / 1CO 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.
formation is normalized here to the excess CO mixing ratio sured) organic precursors that are also efficient at forming
(1CO) to account for the effects of meteorology, dilution, ASOA.
and nonurban background levels as well as allow for easier Ox , PAN, and HCHO are produced from the oxidation of a
comparison between different studies: much wider set of VOC precursors (including small alkenes,
which do not appreciably produce SOA when oxidized).
1ASOA
X
VOC
These alkenes have similar reaction rate constants with OH
∝ [OH] × 1t × k ×
i i
× Yi , (12) as the most reactive BTEX compounds (Table S12); however,
1CO CO i
their emissions and concentration can be higher than BTEX
(Table S7). Thus, alkenes would dominate RTotal , leading
where Y is the aerosol yield for each compound (mass to Ox , HCHO, and PAN being produced more rapidly than
of SOA formed per unit mass of precursor reacted), and ASOA (Fig. 2b–d). When RBTEX becomes more important
[OH] × 1t is the PA. for RTotal , the emitted VOCs are more efficient in producing
BTEX are one group of known ASOA precursors (Gen- ASOA. Thus, the ratio of ASOA to gas-phase photochemi-
tner et al., 2012; Hayes et al., 2013), and their emission ratio cal products shows a strong correlation with RBTEX /RTotal
(to CO) was determined for all campaigns (Table S5). Thus, (Fig. 2b–d).
BTEX can provide insight into ASOA production. Fig. 2a An important aspect of this study is that most of these ob-
shows that the variation in ASOA (at PA = 0.5 equivalent servations occurred during spring and summer, when solid-
days) is highly correlated with the emission reactivity ratio fuel emissions are expected to be lower (e.g., Chafe et
of BTEX (RBTEX , i [ VOC
P
CO ]i ) across all of the studies. How- al., 2015; Lam et al., 2017; Hu et al., 2020). Further, the
ever, BTEX alone cannot account for much of the ASOA for- most important observations used here are during the af-
mation (see budget closure discussion below), and instead, ternoon, specifically investigating the photochemically pro-
BTEX may be better thought of as both partial contributors duced ASOA. These results might partially miss any ASOA
and also as indicators for the co-emission of other (unmea-
https://doi.org/10.5194/acp-21-11201-2021 Atmos. Chem. Phys., 21, 11201–11224, 202111210 B. A. Nault et al.: Secondary organic aerosols from anthropogenic volatile organic compounds
produced through nighttime aqueous chemistry or oxidation sions of BTEX, other mono-aromatics, IVOCs, and SVOCs
by nitrate radical (Kodros et al., 2020). However, two of the for both fossil fuel (e.g., gasoline, diesel, and kerosene),
studies included in our analysis, Chinese outflow (Campaign VCPs (e.g., coatings, inks, adhesives, personal care prod-
of Air Pollution at Typical Coastal Areas IN Eastern China, ucts, and cleaning agents), and cooking sources (Fig. 5). This
CAPTAIN 2011; Hu et al., 2013) and New York City (Win- builds off the work of McDonald et al. (2018) for urban re-
tertime INvestigation of Transport, Emissions, and Reactiv- gions on three different continents.
ity, WINTER 2015; Schroder et al., 2018), occurred in late Note that the emissions investigated here ignore any oxy-
winter/early spring, when solid-fuel emissions were impor- genated VOC emissions not associated with IVOCs and
tant. We find that these observations lie within the uncer- SVOCs due to the challenge in estimating the emission ra-
tainty in the slope between ASOA and RBTEX (Fig. 2a). Their tios for these compounds (de Gouw et al., 2018). Further,
photochemically produced ASOA observed under strong im- SVOC emission ratios are estimated from the average POA
pact from solid-fuel emissions shows similar behavior to the observed by the Aerodyne aerosol mass spectrometer (AMS)
ASOA observed during spring and summer time. Thus, given during the specific campaign and scaled by profiles from the
the limited datasets currently available, photochemically pro- literature for a given average temperature and average OA
duced ASOA is expected to follow the relationship shown in (Robinson et al., 2007; Worton et al., 2014; Lu et al., 2018).
Fig. 2a and is also expected to follow this relationship for re- As most of the campaigns had an average OA between 1 and
gions impacted by solid-fuel burning. Future comprehensive 10 µg m−3 and a temperature of ∼ 298 K, this led to the ma-
studies in regions strongly impacted by solid-fuel burning are jority of the estimated emitted SVOC gases being in the high-
needed to further investigate photochemical ASOA produc- est SVOC bin. However, as discussed later, this does not lead
tion under those conditions. to SVOCs dominating the predicted ASOA due to the fact
the fragmentation and overall yield from the photooxidation
3.2 Budget closure of ASOA for four urban areas of SVOC to ASOA are taken into account.
on three continents indicates reasonable Combining these inventories and observations for the var-
understanding of ASOA sources ious locations provides the following insights about the po-
tential ASOA precursors not easily measured or quantified in
To investigate the correlation between ASOA and RBTEX , a urban environments (e.g., Zhao et al., 2014; Lu et al., 2018):
box model using the emission ratios from BTEX (Table S5),
1. Aromatics from fossil fuel account for 14 %–40 %
other aromatics (Table S8), IVOCs (Sect. S1), and SVOCs
(mean 22 %) of the total BTEX and IVOC emissions
(Sect. S1) was run for five urban areas: New York City, 2002;
for the five urban areas investigated in-depth (Fig. 5),
Los Angeles; Beijing; London; and New York City, 2015
agreeing with prior studies that have shown that the
(see Sects. S1 and S3 for more information). The differences
observed ASOA cannot be reconciled by the observa-
in the results shown in Fig. 4 are due to differences in the
tions or emission inventory of aromatics from fossil fu-
emissions for each city. We show that BTEX alone cannot
els (e.g., Ensberg et al., 2014; Hayes et al., 2015).
explain the observed ASOA budget for urban areas around
the world. Figure 4a shows that approximately 25 ± 6 % of 2. BTEX from both fossil fuels and VCPs account for
the observed ASOA originates from the photooxidation of 25 %–95 % (mean 43 %) of BTEX and IVOC emis-
BTEX. The fact that BTEX only explains 25 % of the ob- sions (Fig. 5). China has the lowest contribution of
served ASOA is similar to prior studies that have undertaken IVOCs, potentially due to differences in chemical make-
budget analysis of precursor gases and observed SOA (e.g., up of the solvents used daily (Li et al., 2019), but
Dzepina et al., 2009; Ensberg et al., 2014; Hayes et al., 2015; more research is needed to investigate the differences
Ma et al., 2017; Nault et al., 2018). Therefore, other precur- in IVOCs / BTEX from Beijing versus the USA and UK
sors must account for most of the ASOA produced. emission inventories. Nonetheless, this shows the im-
Because alkanes, alkenes, and oxygenated compounds portance of IVOCs for both emissions and ASOA pre-
with carbon numbers less than six are not significant ASOA cursors.
precursors, we focus on emissions and sources of BTEX,
other mono-aromatics, IVOCs, and SVOCs. These three 3. IVOCs are generally equal to, if not greater than, the
classes of VOCs, aromatics, IVOCs, and SVOCs, have been emissions of BTEX in four of the five urban areas in-
suggested to be significant ASOA precursors in urban atmo- vestigated here (Fig. 5).
spheres (Robinson et al., 2007; Hayes et al., 2015; Ma et al., 4. Overall, VCPs account for a large fraction of the BTEX
2017; McDonald et al., 2018; Nault et al., 2018; Schroder et and IVOC emissions for all five cities.
al., 2018; Shah et al., 2018), originating from both fossil fuel
and VCP emissions. 5. Finally, SVOCs account for 27 %–88 % (mean 53 %) of
Using the best available emission inventories from cities VOCs generally considered ASOA precursors (VOCs
on three continents (EMEP/EEA, 2016; McDonald et al., with volatility saturation concentrations ≤ 107 µg m−3 )
2018; Li et al., 2019) and observations, we quantify the emis- (Fig. S6). Beijing has the highest contribution of SVOCs
Atmos. Chem. Phys., 21, 11201–11224, 2021 https://doi.org/10.5194/acp-21-11201-2021B. A. Nault et al.: Secondary organic aerosols from anthropogenic volatile organic compounds 11211
Figure 4. (a) Budget analysis for the contribution of the observed 1SOA / 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.
to ASOA precursors due to the use of solid fuels and the rapid ASOA production that has been observed in various
cooking emissions (Hu et al., 2016). Also, this indi- studies (de Gouw and Jimenez, 2009; DeCarlo et al., 2010;
cates the large contribution of a class of VOCs that are Hayes et al., 2013; Hu et al., 2013, 2016; Nault et al., 2018;
difficult to measure (Robinson et al., 2007) and are an Schroder et al., 2018) and the correlation (Fig. 2). Finally, the
important ASOA precursor (e.g., Hayes et al., 2015), contribution of VCPs and fossil fuel sources to ASOA is sim-
showing that further emphasis should be placed in quan- ilar across the cities, expanding upon and further supporting
tifying the emissions of this class of compounds. the conclusion of McDonald et al. (2018) in the importance
of identifying and understanding VCP emissions in order to
These results provide the ability to further investigate the explain ASOA.
mass balance of predicted and observed ASOA for these ur- This investigation shows that the bottom-up calculated
ban locations (Fig. 4). The inclusion of IVOCs, other aro- ASOA agrees with observed top-down ASOA within 15 %.
matics not including BTEX, and SVOCs leads to the ability As highlighted above, this ratio is explained by the co-
to explain, on average, 85 ± 12 % of the observed ASOA for emissions of IVOCs with BTEX from traditional sources
these urban locations around the world (Fig. 4a). Further, the (diesel, gasoline, and other fossil fuel emissions) and VCPs
VCP contribution to ASOA is important for all of these ur- (Fig. 5) along with similar rate constants for these ASOA pre-
ban locations, accounting for, on average, 37 ± 3 % of the cursors (Table S12). Thus, the ASOA / RBTEX ratio obtained
observed ASOA (Fig. 4b). from Fig. 2 results in accurate predictions of ASOA for the
This bottom-up mass budget analysis provides impor- urban areas evaluated here, and this value can be used to bet-
tant insights to further explain the correlation observed in ter estimate ASOA with chemical transport models (Sect. 4).
Fig. 2. First, IVOCs are generally co-emitted from sim-
ilar sources to those for BTEX for the urban areas in-
vestigated in-depth (Fig. 5). The oxidation of these co- 4 Improved urban SIMPLE model using multi-cities to
emitted species leads to the ASOA production observed constrain
across the urban areas around the world. Second, S/IVOCs
generally have similar rate constants to toluene and xylenes The SIMPLE (SIMPLifiEd parameterization of combustion
(≥1 × 10−11 cm3 molec.−1 s−1 ) (Zhao et al., 2014, 2017), SOA) model was originally designed and tested against
the compounds that contribute the most to RBTEX , explaining the observations collected around Mexico City (Hodzic and
https://doi.org/10.5194/acp-21-11201-2021 Atmos. Chem. Phys., 21, 11201–11224, 202111212 B. A. Nault et al.: Secondary organic aerosols from anthropogenic volatile organic compounds Figure 5. 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. Jimenez, 2011). It was then tested against observations col- SOA production, as shown in Fig. 2. Thus, the inclusion and lected in Los Angeles (Hayes et al., 2015; Ma et al., 2017). use of this improved SIMPLE model refines the simplified As both datasets have nearly identical 1SOA / 1CO and representation of ASOA in chemical transport models and/or RBTEX (Figs. 2, 3), it is not surprising that the SIMPLE box models. model did well in predicting the observed 1SOA / 1CO The improved SIMPLE model shows higher ASOA com- for these two urban regions with consistent parameters. Al- pared with the default volatility basis set (VBS) GEOS- though the SIMPLE model generally performed better than Chem (Fig. 6a, b). In areas strongly impacted by urban more explicit models, it generally had lower skill in predict- emissions, e.g., Europe, East Asia, India, the east and west ing the observed ASOA in urban regions outside of Mexico coast of the USA, and regions impacted by Santiago (Chile), City and Los Angeles (Shah et al., 2019; Pai et al., 2020). Buenos Aires (Argentina), Sāo Paulo (Brazil), Durban and This may stem from the original SIMPLE model, with Cape Town (South Africa), and Melbourne and Sydney constant parameters, missing the ability to change the (Australia), the improved SIMPLE model predicts up to amount and reactivity of the emissions, which are differ- 14 µg m−3 more ASOA, or ∼ 30 to 60 times more ASOA ent for the various urban regions, versus the ASOA pre- than the default scheme (Fig. 6c, d). As shown in Fig. 1, cursors being emitted proportionally to only CO (Hodzic during intensive measurements, the ASOA composed 17 %– and Jimenez, 2011; Hayes et al., 2015). For example, in 39 % of PM1 , with an average contribution of ∼ 25 %. The the HTAP emissions inventory, the CO emissions for Seoul, default ASOA scheme in GEOS-Chem greatly underesti- Los Angeles, and Mexico City are all similar (Fig. S8); mates the fractional contribution of ASOA to total PM2.5 thus, the original SIMPLE model would suggest similar (
B. A. Nault et al.: Secondary organic aerosols from anthropogenic volatile organic compounds 11213
vations. As discussed in Sect. 2.3 and Eq. (11), having the Additional recent work (Burnett et al., 2018) has suggested
model accurately predict the fractional contribution of ASOA less reduction in the premature deaths versus PM2.5 con-
to the total PM is very important, as the total PM2.5 is derived centration relationship at higher PM2.5 concentrations, and
from satellite-based estimates (van Donkelaar et al., 2015), lower concentration limits for the threshold below which
and the model fractions are then applied to those total PM2.5 this relationship is negligible, both of which lead to much
estimates. The ability of the improved SIMPLE model to bet- higher estimates of PM2.5 -related premature deaths. This
ter represent the ASOA composition provides confidence at- is generally termed the Global Exposure Mortality Model
tributing the ASOA contribution to premature mortality. (GEMM). Using the two attribution methods described above
(a and b), the ASOA PM2.5 -related premature deaths are esti-
mated to be ∼ 640 000 (method a) and ∼ 900 000 (method b)
(Figs. S9, S12; Table S17).
5 Preliminary evaluation of worldwide premature Compared with prior studies using chemical transport
deaths due to ASOA with the updated SIMPLE models to estimate premature deaths associated with ASOA
parameterization (e.g., Silva et al., 2016; Ridley et al., 2018), which assumed
non-volatile POA and traditional ASOA precursors, the at-
The improved SIMPLE parameterization is used along with tribution of premature mortality due to ASOA is over an
GEOS-Chem to provide an accurate estimation of ASOA for- order of magnitude higher in this study (Fig. 9). This oc-
mation in urban areas worldwide and provide the ability to curs using either the IER or the GEMM approach for esti-
obtain realistic simulations of ASOA based on measurement mating premature mortality (Fig. 9). For regions with larger
data. We use this model to quantify the attribution of PM2.5 populations and more PM2.5 pollution, the attribution is be-
ASOA to premature deaths. Analysis up to this point has tween a factor of 40 and 80 higher. This stems from the non-
been for PM1 ; however, both the chemical transport model volatile POA and traditional ASOA precursors overestimat-
and epidemiological studies utilize PM2.5 . For ASOA, this ing POA and underestimating ASOA compared with obser-
will not impact the discussion or results here because the vations (Schroder et al., 2018). These offsetting errors will
mass of OA (typically 80 %–90 %) is dominated by PM1 lead to model-predicted total OA values similar to observa-
(e.g., Bae et al., 2006; Seinfeld and Pandis, 2006), and ASOA tions (Ridley et al., 2018; Schroder et al., 2018), although
is formed mostly through condensation of oxidized species, different conclusions on whether POA versus SOA is more
which favors partitioning onto smaller particles (Seinfeld and important for reducing PM2.5 -related premature mortality.
Pandis, 2006). Using a model constrained to daytime atmospheric observa-
The procedure for this analysis is described in Fig. 7 tions (Figs. 2, 4; see Sect. 4) leads to a more accurate estima-
and Sects. 2.3 and S3. Briefly, we combine high-resolution tion than earlier estimation of the contribution of photochem-
satellite-based PM2.5 estimates (for exposure) and a chem- ically produced ASOA to PM2.5 -related premature mortality
ical transport model (GEOS-Chem, for fractional composi- than those available in prior studies. We note that ozone con-
tion) to estimate ASOA concentrations and various sensi- centrations change little as we change the ASOA simulation
tivity analysis (van Donkelaar et al., 2015). We calculated (see Sect. S4 and Fig. S14).
that ∼ 3.3 million premature deaths (using the integrated A limitation in this study is the lack of sufficient measure-
exposure-response, IER, function) are due to long-term ex- ments in South and Southeast Asia, eastern Europe, Africa,
posure to ambient PM2.5 (Fig. S9, Table S16), consistent with and South America (Fig. 1), although these areas account
recent literature (Cohen et al., 2017). for 44 % of the predicted reduction in premature mortality
The attribution of ASOA PM2.5 premature deaths can be for the world (Table S16). However, as highlighted in Ta-
calculated in one of two ways: (a) the marginal method (Silva ble S18, these regions likely still consume both transporta-
et al., 2016) or (b) the attributable fraction method (Anenberg tion fuels and VCPs, although in lower per capita amounts
et al., 2019). For method (a), it is assumed that a fraction of than more industrialized countries. This consumption is ex-
the ASOA is removed, keeping the rest of the PM2.5 com- pected to lead to the same types of emissions as for the
ponents approximately constant, and the change in deaths cities studied here, although more field measurements are
is calculated from the deaths associated with the total con- needed to validate global inventories of VOCs and the result-
centration minus the deaths calculated using the reduced to- ing oxidation products in the developing world. Transporta-
tal PM2.5 concentrations. For method (b), the health impact tion emissions of VOCs are expected to be more dominant
is attributed to each PM2.5 component by multiplying the in the developing world due to higher VOC emission factors
total deaths by the fractional contribution of each compo- associated with inefficient combustion engines, such as two-
nent to total PM2.5 . For method (a), the deaths attributed to stroke scooters (Platt et al., 2014) and auto rickshaws (e.g.,
ASOA are ∼ 340 000 people per year (Fig. 8); whereas, for Goel and Guttikunda, 2015).
method (b), the deaths are ∼ 370 000 people per year. Both Solid fuels are used for residential heating and cooking,
of these are based on the IER response function (Cohen et which impact the outdoor air quality as well (Hu et al., 2013,
al., 2017). 2016; Lacey et al., 2017; Stewart et al., 2021), and also lead
https://doi.org/10.5194/acp-21-11201-2021 Atmos. Chem. Phys., 21, 11201–11224, 202111214 B. A. Nault et al.: Secondary organic aerosols from anthropogenic volatile organic compounds Figure 6. (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 . to SOA (Heringa et al., 2011). As discussed in Sect. 3.1, al- use), is included in the residential emission sector. Both CO though the majority of the studies evaluated here occurred and BTEX are included in this source and can account for a in spring to summer time, when solid-fuel emissions are de- large fraction of the total emissions where solid fuel use may creased, two studies occurred during the winter/early spring be important (Fig. S15). Thus, as CO and BTEX are used in time, during which time solid-fuel emissions are important the updated SIMPLE model, and campaigns that observed (Hu et al., 2013; Schroder et al., 2018). These studies still solid-fuel emissions fall within the trend for all urban ar- follow the same relationship between ASOA and RBTEX as eas, the solid-fuel contribution to photochemically produced the studies that focused on spring/summer photochemistry. ASOA is accounted for (as accurately as allowed by current Thus, the limited datasets available indicate that photochem- datasets) in the estimation of ASOA with respect to the attri- ically produced ASOA from solid fuels follow a similar rela- bution to premature mortality. tionship to that from other ASOA sources. Note that recent work has observed potential nighttime Also, solid-fuel sources are included in the inventories aqueous chemistry and/or oxidation by nitrate radicals from used in our modeling. For the HTAP emission inventory used solid-fuel emissions to produce ASOA (Kodros et al., 2020). here (Janssens-Maenhout et al., 2015), small-scale combus- Thus, missing this source of ASOA may lead to an under- tion, which includes heating and cooking (e.g., solid-fuel estimation of total ASOA versus the photochemically pro- Atmos. Chem. Phys., 21, 11201–11224, 2021 https://doi.org/10.5194/acp-21-11201-2021
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