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PM2.5 air quality study
Atmospheric Environment 44 (2010) 1331–1340
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Source apportionment of wintertime secondary organic aerosol during the California regional PM10/PM2.5 air quality study Jianjun Chen a, Qi Ying b, Michael J. Kleeman a, * a b
Department of Civil and Environmental Engineering, University of California, Davis, CA 95616, USA Zachry Department of Civil Engineering, Texas A&M University, College Station, TX 77843, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 April 2009 Received in revised form 4 July 2009 Accepted 6 July 2009
The UCD/CIT air quality model with the Caltech Atmospheric Chemistry Mechanism (CACM) was used to predict source contributions to secondary organic aerosol (SOA) formation in the San Joaquin Valley (SJV) from December 15, 2000 to January 7, 2001. The predicted 24-day average SOA concentration had a maximum value of 4.26 mg m3 50 km southwest of Fresno. Predicted SOA concentrations at Fresno, Angiola, and Bakersfield were 2.46 mg m3, 1.68 mg m3, and 2.28 mg m3, respectively, accounting for 6%, 37%, and 4% of the total predicted organic aerosol. The average SOA concentration across the entire SJV was 1.35 mg m3, which accounts for approximately 20% of the total predicted organic aerosol. Averaged over the entire SJV, the major SOA sources were solvent use (28% of SOA), catalyst gasoline engines (25% of SOA), wood smoke (16% of SOA), non-catalyst gasoline engines (13% of SOA), and other anthropogenic sources (11% of SOA). Diesel engines were predicted to only account for approximately 2% of the total SOA formation in the SJV because they emit a small amount of volatile organic compounds relative to other sources. In terms of SOA precursors within the SJV, long-chain alkanes were predicted to be the largest SOA contributor, followed by aromatic compounds. The current study identifies the major known contributors to the SOA burden during a winter pollution episode in the SJV, with further enhancements possible as additional formation pathways are discovered. Ó 2009 Elsevier Ltd. All rights reserved.
Keywords: CACM CRPAQS Secondary organic aerosol Source apportionment UCD/CIT air quality model
1. Introduction The United States (US) Environmental Protection Agency has established air quality standards for 24-h and annual average PM2.5 concentrations (particulate matter (PM) of aerodynamic diameter smaller than 2.5 mm) to protect human health. The San Joaquin Valley (SJV) in the southern portion of California’s Central Valley is one of the largest PM2.5 non-attainment areas in the US (Chow et al., 2006). Organic aerosol (OA) is one of the most important constituents of PM2.5 in the SJV (Chow et al., 2006). OA consists of primary organic aerosol (POA) and secondary organic aerosol (SOA). POA is directly emitted to the atmosphere in condensed particle form (e.g., Schauer et al., 2001, 2002a) after which it may partially evaporate depending on ambient conditions (Robinson et al., 2007). SOA is formed in the atmosphere from the oxidation of volatile or semi-volatile organic compounds (VOCs) (Seinfeld and Pankow, 2003). The importance of SOA has been recognized and modeled extensively during photochemical events (Schell et al., 2001; Griffin et al., 2002b; Pun et al., 2003; Johnson et al., 2006;
* Corresponding author. Tel.: þ1 530 752 8386; fax: þ1 530 752 7872. E-mail address: [email protected] (M.J. Kleeman). 1352-2310/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2009.07.010
Kleeman et al., 2007). However, there are only a limited number of studies that have evaluated the formation of SOA under wintertime conditions, presumably because SOA has been assumed to be minor due to low photochemical activity in winter. On the other hand, meteorological conditions in winter (e.g., low temperature, high humidity, and stagnant air) favor the accumulation of condensable water-soluble SOA. Strader et al. (1999) studied the SOA formation in the SJV during the winter of 1995–1996 and concluded that while most of OA was of primary origin, as much as 15–20 mg C m3 of SOA could be produced under certain conditions. Lanz et al. (2008) applied advanced factor analysis to the aerosol mass spectra collected at an urban site in Zurich, Switzerland in January 2006 and found that approximately half of winter OA was oxygenated OA (OOA) which they assumed was SOA, even though production of OOA is lower in winter compared to summer (Shrivastava et al., 2008). Given the limited number of studies concerning the wintertime SOA formation, further modeling studies of the quantity and source origin of SOA in the SJV are clearly needed. Recently, a mechanistic technique has been developed for the source apportionment of SOA (Kleeman et al., 2007). SOA precursor emissions from different sources are tracked individually in an air quality model. Chemical reaction products leading to SOA formation are labeled with the source information of the reactants. This
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approach allows accurate tracking of SOA source contributions through known chemical pathways at receptor sites. Similar approaches have been developed and applied to study source contributions to secondary inorganic PM components (Mysliwiec and Kleeman, 2002; Ying and Kleeman, 2006). The purpose of this work is to study SOA formation and source contributions in the SJV between December 15, 2000 and January 7, 2001. This episode is part of the California Regional Particulate Air Quality Study (CRPAQS), which was designed to improve our understanding of the causes of excessive PM levels in central California (Chow et al., 2006). The UCD/CIT air quality model is used in this study with the Caltech Atmospheric Chemistry Mechanism (CACM) that has been expanded to perform source apportionment of SOA (Kleeman et al., 2007). 2. Model description The UCD/CIT air quality model is based on the CIT airshed model (Harley et al., 1993) that has been expanded to treat particles as a source-oriented external mixture (Kleeman and Cass, 2001). The version of the UCD/CIT model used in this study was coupled with the Caltech Atmospheric Chemistry Mechanism (CACM) (Griffin et al., 2002a, 2005) as described in detail by Ying et al. (2007) and Kleeman et al. (2007). Semi-volatile organic compounds (SVOCs)
predicted from CACM are lumped into 10 surrogate species. These surrogates are then allowed to partition to both the aqueous and organic phases of particles to form SOA. The partitioning of SVOCs is calculated as a dynamic process using equations described by Kleeman et al. (1997). The equilibrium vapor pressure for the surrogate species above particles is calculated according to the Raoult’s law corrected by activity coefficients (Kleeman et al., 2007). In order to perform source apportionment of SOA, the CACM mechanism was expanded to track the reactions of SOA precursors emitted from various sources. As an example, a simple reaction involving a reactant A and an oxidant to form B is given.
A þ oxidant/B
(1)
If there are two sources for the reactant A, A_X1 and A_X2 are used to represent A from the two different sources. Reaction (1) is then expanded to two reactions.
A X1 þ oxidant/B X1
(2)
A X2 þ oxidant/B X2
(3)
Thus, the products B_X1 and B_X2 contain the source information of A_X1 and A_X2, respectively. Further details describing the SOA source apportionment technique are provided by Kleeman et al.
Fig. 1. Comparisons of predicted (open squares) and observed OA (solid squares) during the IOPs for a) Fresno, b) Angiola, and c) Bakersfield.
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(2007). Several updates were also made to the model framework in the current study including the calculation of the minimum vertical diffusion coefficient and composition-dependent accommodation coefficients for N2O5 hydrolysis on wet particles as described by Ying et al. (2008). 3. Model application The UCD/CIT/CACM model was applied to simulate the quantity and source origin of SOA formation in the SJV between December 15, 2000 and January 7, 2001. The modeling domain covers the Central Valley of California and is horizontally divided into 8 km 8 km cells, with 95 grid cells in each direction. Vertically there are 10 layers extending from ground level to 5000 m above the surface (Ying et al., 2008). Meteorological fields were generated using the objective analysis method of Goodin et al. (1979, 1980) and are described in detail by Ying et al. (2008). The boundary and initial conditions used in the study were derived from measurements (Ying et al., 2008). Hourly emissions used in the study are based on the emission inventory of 4 km spatial resolution generated by the California Air Resources Board (CARB) that were aggregated spatially to be consistent with the 8 km modeling grid. In addition, volatile organic compounds (VOCs) in the original emission inventory were converted into CACM model species using measured VOCs profiles (Harley et al., 1992; Schauer et al., 1999a,b, 2001, 2002a,b). In order to perform SOA source apportionment, VOC emissions were grouped into nine source categories: solvent use, wood smoke, diesel engines, catalyst gasoline engines, noncatalyst gasoline engines, gasoline disposal and storage, high sulfur fuel combustion, other anthropogenic sources, and the biogenic source. These source categories account for the majority of the VOC emissions in the study domain. PM emissions were also chemically speciated into nine primary inorganic model species and nine primary carbonaceous model species that were then assigned into 15 size sections based on source emission measurements (Kleeman et al., 2007). 4. Results 4.1. Organic aerosol predictions The basecase CRPAQS episode has been studied by Ying et al. (2008) applying the UCD/CIT model with an updated form of the SAPRC90 gas-phase chemistry mechanism. The present study builds on this previous work by implementing the CACM gas-phase chemistry mechanism and SOA source apportionment module. The performance of the UCD/CIT/CACM and UCD/CIT/SAPRC90 models was similar for gases (e.g., ozone, nitrogen oxides (NOx)) and PM constituents except SOA and the total OA. In the following sections the predictions of SOA and OA concentrations made by the UCD/ CIT/CACM model are discussed in greater detail. Fig. 1 compares model predictions of OA to measured values. Filter based OA measurements were made at Fresno (FSF), Angiola (ANGI), and Bakersfield (BAC) during three winter Intensive Operating Periods (IOPs) (December 15–18, 2000, December 26–28, 2000, and January 4–7, 2001). On each day of the IOPs, filters were collected during five intervals (at 0000–0500, 0500–1000, 1000– 1300, 1300–1600, 1600–2400 PST). Predicted OA generally agrees with the observations at Fresno, with a slight under-prediction of only 13% on average. At the rural Angiola location measured OA concentrations are much lower than at the urban sites, but OA is under predicted by approximately 64%. Modeled OA showed much weaker temporal variations than the observed values. Ying et al. (2008) found that elemental carbon, carbon monoxide, and NOx were also under predicted at Angiola and suggested that sources
Fig. 2. Fractional bias for OA predictions at Fresno (FSF), Angiola (ANGI), and Bakersfield (BAC) during three IOPs.
may be missing in the emission inventory surrounding that location. In contrast to the under-predictions at Fresno and Angiola, OA is over-predicted at Bakersfield, especially during the night. On average, the predicted OA is approximately 34% higher than the observed values. As suggested by Ying et al. (2008), sharp spatial gradients exist for predicted OA concentrations at urban sites during the evening hours, and these gradients may be a major cause for the bias in predicted OA at Bakersfield. Fig. 2 summarizes the fractional bias (FB) for OA predictions at Fresno, Angiola, and Bakersfield. The average FB is 0.11 for Fresno, 0.78 for Angiola, and 0.13 for Bakersfield. The performance of OA predictions is comparable to other similar studies (e.g., Tesche et al., 2006). Organic aerosol measurements cannot definitively quantify the SOA fraction of total OA. Various methods have been developed to estimate the SOA concentration such as using the ratio of OC/EC measured by thermal–optical techniques (e.g., Strader et al., 1999), using the ratio of oxygen/carbon measured by mass spectrometry (e.g., Lanz et al., 2008), using tracers for major SOA categories (Kleindienst et al., 2007), and using the fraction of OA that can’t be explained by tracers for major POA categories (e.g., Schauer and Cass, 2000). The EC/OC technique can only be applied over very long averaging times due to the potential variation in EC/OC ratios as primary emissions change over time. Oxygen/carbon ratios and SOA tracers were not measured during the current study, and so SOA concentrations will be estimated using the difference between the measured total OA and the POA identified using source apportionment techniques. Fig. 3 compares SOA concentrations predicted by the UCD/ CIT/CACM model to the concentration of ‘‘unknown OA’’
Fig. 3. Comparison of the average predicted SOA with the average unknown OA (Kleeman et al., 2009) at Sacramento (SAC), Modesto (MOD), and Bakersfield (BAC) for daytime hours (Day, 1000–1800 PST) and nighttime hours (Night, 2000–0800 PST) during December 15, 2000–January 7, 2001. (Uncertainty for unknown OA is also from Kleeman et al., 2009.)
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Fig. 4. Ratios of the average predicted SOA to the average predicted OA during December 25, 2000 and January 7, 2001.
(¼OA – identified POA) described by Kleeman et al. (2009). Based on the availability of measurement data, comparisons are made at the urban locations of Sacramento, Modesto, and Bakersfield spanning a north–south transect of the SJV for both daytime (10–18 PST) and nighttime (20–8 PST) hours. Both the predicted SOA concentrations and the calculated ‘‘unknown’’ OA concentrations are less than 5 mg m3 at all sites and times except during the evening hours at Bakersfield when calculated ‘‘unknown’’ OA concentrations were w10 mg m3. These concentrations correspond to 0–29% of the total measured OA at these sites (41% of measured OA during the evening hours at Bakersfield). Error bars for ‘‘unknown’’ OA concentrations reflect the uncertainty in the molecular marker source apportionment technique. There appears to be substantial variations in ‘‘unknown’’ OA among the three sites but much of this variation falls within the uncertainty range of the measurement technique except for significantly higher concentrations of ‘‘unknown’’ OA during nighttime at Bakersfield. SOA concentrations predicted by the UCD/CIT/CACM model agree with measured concentrations of ‘‘unknown’’ OA at Sacramento, Modesto and Bakersfield (during daytime hours) within the uncertainty range of the measurements. The predicted SOA concentration is lower than the calculated ‘‘unknown’’ OA during nighttime hours at Bakersfield, which may indicate an under-prediction of SOA concentrations at this time and location. Our current understanding of SOA formation is far from complete (Kroll and Seinfeld, 2008), and SOA concentrations are often under predicted from three-dimensional air quality models (e.g., Morris et al., 2006; Ying et al., 2007). Potentially important pathways for SOA formation that are missing or only partially included in the current SOA module may include oligomerization (e.g., Kalberer et al., 2004) and aqueous-phase chemistry (e.g., Volkamer et al., 2008). Complete
mechanisms describing these pathways will be added once quantitative descriptions of these processes become available. The SOA identified in the current study is likely a correct but incomplete picture of total SOA formation in the SJV. Fig. 4 shows the ratios of the predicted SOA to the predicted OA averaged over the period from December 25, 2000 to January 7, 2001. The final two weeks of the model episode were selected for this analysis to minimize the effect of initial conditions on the calculations. While predicted SOA only accounts for approximately 5% of total OA at Fresno and Bakersfield, predicted SOA can constitute 37% of predicted OA at Angiola. In addition, fractions of predicted SOA/OA are up to 50% at rural locations within the SJV and along the coastlines north of San Francisco. The predicted SOA accounts for approximately 20% of the predicted OA averaged across the entire SJV, indicating the importance of considering the best available estimates of wintertime SOA in regional model calculations. 4.2. Source apportionment of SOA Fig. 5 shows the total SOA concentrations and SOA source contributions predicted by the UCD/CIT/CACM model for the entire modeling domain averaged between December 25, 2000 and January 7, 2001. SOA concentrations are normalized by the domain-wide maximum value in each sub-panel of Fig. 5. SOA formation is prevalent within the Central Valley and generally exceeds 2.0 mg m3 along California State Route 99 that connects Sacramento and Bakersfield. The maximum predicted SOA concentration of 4.26 mg m3 is located approximately 50 km southwest of Fresno. SOA concentrations greater than 1.5 mg m3 also are predicted at the coastal areas in Mendocino County
J. Chen et al. / Atmospheric Environment 44 (2010) 1331–1340
(north of San Francisco) and further south between San Francisco and Monterey. The dominant regional sources of SOA are predicted to be solvent use, catalyst gasoline engines, wood smoke, and noncatalyst gasoline engines. The maximum SOA concentrations from
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these four sources are 1.22 mg m3, 1.04 mg m3, 0.60 mg m3, and 0.51 mg m3, respectively. The spatial patterns of SOA concentrations from these four sources are quite similar to the spatial pattern of the total SOA formation except at the coastal areas of Mendocino County. SOA formation in the coastal areas of Mendocino County is
Fig. 5. Predicted SOA concentrations and SOA source contributions averaged between December 25, 2000 and January 7, 2001. Each sub-panel illustrates concentrations normalized by the domain-wide maximum (shown below the title in mg m3).
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Fig. 6. Time series of SOA concentrations and SOA source contributions during December 25, 2000 and January 7, 2001 for a) Fresno, b) Angiola, c) Bakersfield, and d) average of the SJV.
dominated by biogenic emissions, producing ambient concentrations at ground level of approximately 1.81 mg m3. The maximum SOA contributions from high sulfur fuel combustion and other anthropogenic sources are 0.33 mg m3 and 1.6 mg m3, respectively, over very limited regions. The maximum predicted diesel engine SOA concentration is 0.07 mg m3, which is much smaller than that of gasoline engines (catalyst plus non-catalyst). This reflects the fact that total VOC emissions from diesel engines were much lower than those from gasoline engines in the current study. The maximum SOA contributions from gasoline disposal þ storage and initial þ boundary conditions are 0.07 mg m3 and 0.02 mg m3, respectively, which are both small compared to contributions from other sources. Fig. 6 shows the time series of hourly-average source contributions to SOA concentrations at (a) Fresno, (b) Angiola, (c) Bakersfield, and (d) averaged across the entire SJV. At Fresno, Angiola, and Bakersfield, the maximum predicted hourly-average SOA concentration is approximately 5.0–6.0 mg m3, and the maximum predicted daily-average SOA concentration is approximately 3.0–4.5 mg m3. The maximum daily-average SOA formation across the entire SJV is approximately 2.0 mg m3, which is smaller
than the corresponding values at Fresno, Angiola, and Bakersfield. Time series of SOA source apportionment for Fresno, Angiola, Bakersfield, and the SJV indicates that the source contribution diurnal patterns are fairly consistent. Table 1 shows the SOA source
Table 1 Source apportionment of SOA at selected locations in Central California between December 25, 2000 and January 7, 2001a Sources
FRES
ANGI
BAC
SJV
Initial/boundary conditions Solvent use Wood smoke Diesel engines Non-catalyst gasoline engines Catalyst gasoline engines Gasoline storage and disposal High sulfur fuel combustion Other anthropogenic sources Biogenic source Total SOA
0.014 0.776 0.401 0.030 0.312 0.611 0.045 0.010 0.212 0.026 2.463
0.009 0.664 0.322 0.043 0.303 0.610 0.042 0.032 0.242 0.016 2.284
0.008 0.469 0.244 0.027 0.204 0.432 0.032 0.011 0.236 0.018 1.680
0.009 0.376 0.212 0.022 0.173 0.332 0.023 0.027 0.147 0.030 1.351
a Unit in mg m3; FRES: Fresno; ANGI: Angiola; BAC: Bakersfield; SJV: average of the entire SJV.
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apportionment at these locations averaged from December 25, 2000 to January 2001. Solvent use, catalyst gasoline engines, wood smoke, non-catalyst gasoline engines, and other anthropogenic sources are major SOA contributors at these locations, accounting for w28–32%, w25–27%, w14–16%, w12–13%, and w9–14% of SOA formation, respectively. Contributions from the remaining sources are minor, accounting for only w5–8% of the SOA. Fig. 7 shows the diurnal pattern of predicted SOA concentrations and source apportionment at (a) Fresno, (b) Angiola, and (c) Bakersfield. Fig. 7d shows the predicted SOA diurnal pattern averaged across all grid cells in the SJV. SOA contributions from the five main sources have similar diurnal patterns. At the rural Angiola site (Fig. 7b) and across the entire SJV (Fig. 7d), SOA concentrations remain high at night and fall to the lowest values in the afternoon. These trends are partly driven by the diurnal variations of temperature and humidity: low temperature and high relative humidity at night favors the partitioning of SVOCs to the particle phase. At urban locations such as Fresno (Fig. 7a) and Bakersfield (Fig. 7c), SOA concentrations peak first in the late morning, drop to a minimum in the afternoon, and then build up again and remain relatively high throughout the night. This late morning peak in SOA concentrations is driven by increased local traffic emissions during
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the morning rush hours which increase the concentration of both absorbing POA and semi-volatile SOA precursor gases. Fig. 8 shows the contributions from selected chemical surrogate species to the SOA concentrations averaged between December 25, 2000 and January 7, 2001. SOA concentrations are normalized by the domain-wide maximum value in each sub-panel of Fig. 8. Only surrogates with maximum concentrations greater than 0.01 mg m3 are shown. Surrogate species S9 dominates SOA formation within the modeling domain except at the coastal region north of San Francisco. The maximum SOA concentration produced from S9 is 3.85 mg m3 south of Fresno between CA Highway 99 and Interstate 5. S9 is produced from anthropogenic emissions of ALKH (highSOA-yield alkanes or long-chain alkanes) in the CACM mechanism (Griffin et al., 2002a). The importance of SOA formation from longchain alkanes has recently been demonstrated in laboratory experiments. For example, Lim and Ziemann (2005) found that SOA mass yield from alkanes of carbon number 13 was approximately 50%, which is nearly an order of magnitude greater than typical SOA yields from aromatic compounds. The maximum predicted SOA concentrations produced by surrogate species S2, S6, and S7 add up to 0.33 mg m3 with a similar spatial pattern to S9. S2, S6, and S7 are formed from AROH (aromatics with high SOA yield), AROL
Fig. 7. Diurnal variation of SOA concentrations and SOA source contributions averaged between December 25, 2000 and January 7, 2001 for a) Fresno, b) Angiola, c) Bakersfield and d) average of the SJV.
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Fig. 8. SOA contributions from selected chemical surrogate species in the SOA module averaged between December 25, 2000 and January 7, 2001. Contributions from S3 and S8 are not shown because they are negligible. Each sub-panel illustrates concentrations normalized by the domain-wide maximum (shown below the title in mg m3).
(aromatics with low SOA yield), ARAL (aromatic monoaldehydes), and AROO (phenolic species) in CACM (Griffin et al., 2002a). The total maximum concentration from surrogate species S4, S5, and S10 is 1.82 mg m3 at coastal and foothill locations where biogenic emissions are largest. These compounds are generated from BIOH (monoterpenes of high SOA yield) and BIOL (monoterpenes of low SOA yield) in CACM (Griffin et al., 2002a). The maximum contribution from the surrogate species S1 is 0.02 mg m3. S1 represents reaction products from both anthropogenic and biogenic species. SOA formation from S1 is relatively unimportant compared to SOA contributions from high-SOA-yield alkanes, biogenic monoterpenes, and anthropogenic aromatic compounds. Table 2 summarizes daily emissions of parent compounds that form SOA within the modeling domain. Total ALKH emissions are 968 kmol day1. Solvent use, catalyst gasoline engines, wood smoke, non-catalyst gasoline engines, and other anthropogenic sources are the main sources of ALKH, contributing approximately 31%, 25%, 14%, 14%, and 10% of total emissions, respectively. Because ALKH is the dominant SOA precursor within the SJV, it is not surprising that the pattern of source contributions to ALKH emissions (Table 2) generally matches source contributions to ambient
SOA concentrations (Table 1). Total aromatic compound emissions, including those of AROH, AROL, ARAL, and AROO, amount to 1944 kmol day1 with 92% of these emissions coming from catalyst gasoline engines, non-catalyst gasoline engines, solvent use, and
Table 2 Emissions of SOA precursors from each source category on a typical week daya Sources
ALKH
AROH
AROL
ARAL
AROO
BIOL/BIOH
Solvent use Wood smoke Diesel engines Non-catalyst gasoline engines Catalyst gasoline engines Gasoline storage and disposal High sulfur fuel combustion Other anthropogenic sources Biogenic source Total emissions
308 135 12 135 244 27 10 97 0 968
132 12 23 234 329 16 3 78 0 827
219 20 42 261 350 20 7 168 0 1087
0 0 6 14 5 0 1 0 0 26
0 0 0 0 0 0 1 3 0 4
0 0 0 0 0 0 0 0 864 864
a Unit in kmol day1; ALKH: long-chain alkanes; AROH: aromatics of high SOA yield; AROL: aromatics of low SOA yield; ARAL: aromatic monoaldehydes; AROO: phenolic species; BIOL/BIOH: monoterpenes of low/high SOA yield.
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other anthropogenic sources. Diesel engine emissions account for only 1–3% of ALKH and aromatic compounds, resulting in minor SOA formation from this source in the current study. Total monoterpene emissions from biogenic sources (BIOH and BIOL) are 864 kmol day1. 5. Conclusions The quantity and origin of SOA formation in the SJV between December 15, 2000 and January 7, 2001 was predicted using a state of the science SOA formation mechanism. While the majority of predicted OA in the SJV during the winter episode was of primary origin, SOA concentrations were not negligible. Low mixing depths and stagnant winds promoted the buildup of pollutants near the surface while low temperatures þ high relative humidity favored the partitioning of SVOCs to the particle phase despite the low photochemical activity in the winter episode. Averaged across the entire SJV, predicted SOA accounted for approximately 20% of the total OA at ground level. Averaged over the period of December 25, 2000 and January 7, 2001, maximum predicted SOA concentrations were 4.26 mg m3 in the region between Fresno and Bakersfield. Source apportionment calculations identified five major SOA sources in the SJV based on the current understanding of atmospheric chemistry. Averaged over December 25, 2000–January 7, 2001, the dominant sources of SOA in the SJV were solvent use (28%), catalyst gasoline engines (25%), wood smoke (16%), noncatalyst gasoline engines (13%), and other anthropogenic sources (11%). The contribution from diesel engines to SOA formation in the SJV was much smaller, while biogenic sources accounted for large amounts of SOA formation outside of the SJV in locations with high biogenic precursor emissions. Studies focused on VOC emissions and SOA formation potentials from major sources should be carried out in order to improve SOA predictions in the SJV during winter stagnation events. Given the significant impacts of SOA on human health and climate change, detailed knowledge about the formation, properties, and sources of SOA is required. This study quantifies the known formation rate and sources of SOA in a populated and highly polluted region during a wintertime episode. Such information is useful for local policy makers who must design effective measures to protect public health from the effects of atmospheric SOA/OA. Acknowledgements We would like to thank Robert Griffin for helpful comments. This research was supported by the United States Environmental Protection Agency Science To Achieve Results (STAR) program under grant number RD831082. Although the research described in this article has been funded by the United States Environmental Protection Agency, it has not been subjected to the Agency’s required peer and policy review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred. Further support for this project was provided by the California Air Resources Board and the San Joaquin Valleywide Air Pollution Study Agency under contract 2000-05PM. The statements, opinions, findings, and conclusions of this paper are those of the authors and do not necessarily represent the views of the California Air Resources Board. References Chow, J.C., Chen, L.W.A., Watson, J.G., Lowenthal, D.H., Magliano, K.A., Turkiewicz, K., Lehrman, D.E., 2006. PM2.5 chemical composition and spatiotemporal variability during the California Regional PM10/PM2.5 Air Quality Study (CRPAQS). Journal of Geophysical Research 111, D10S04. doi:10.1029/ 2005JD006457.
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Robinson, A.L., Donahue, N.M., Shrivastava, M.K., Weitkamp, E.A., Sage, A.M., Grieshop, A.P., Lane, T.E., Pierce, J.R., Pandis, S.N., 2007. Rethinking organic aerosols: semivolatile emissions and photochemical aging. Science 315, 1259–1262. Schauer, J.J., Cass, G.R., 2000. Source apportionment of wintertime gas-phase and particle-phase air pollutants using organic compounds as tracers. Environmental Science & Technology 34, 1821–1832. Schauer, J.J., Kleeman, M.J., Cass, G.R., Simoneit, B.R.T., 1999a. Measurement of emissions from air pollution sources. 1. C-1 through C-29 organic compounds from meat charbroiling. Environmental Science & Technology 33, 1566–1577. Schauer, J.J., Kleeman, M.J., Cass, G.R., Simoneit, B.R.T., 1999b. Measurement of emissions from air pollution sources. 2. C-1 through C-30 organic compounds from medium duty diesel trucks. Environmental Science & Technology 33, 1578–1587.
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Contents lists available at ScienceDirect
Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Source apportionment of wintertime secondary organic aerosol during the California regional PM10/PM2.5 air quality study Jianjun Chen a, Qi Ying b, Michael J. Kleeman a, * a b
Department of Civil and Environmental Engineering, University of California, Davis, CA 95616, USA Zachry Department of Civil Engineering, Texas A&M University, College Station, TX 77843, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 April 2009 Received in revised form 4 July 2009 Accepted 6 July 2009
The UCD/CIT air quality model with the Caltech Atmospheric Chemistry Mechanism (CACM) was used to predict source contributions to secondary organic aerosol (SOA) formation in the San Joaquin Valley (SJV) from December 15, 2000 to January 7, 2001. The predicted 24-day average SOA concentration had a maximum value of 4.26 mg m3 50 km southwest of Fresno. Predicted SOA concentrations at Fresno, Angiola, and Bakersfield were 2.46 mg m3, 1.68 mg m3, and 2.28 mg m3, respectively, accounting for 6%, 37%, and 4% of the total predicted organic aerosol. The average SOA concentration across the entire SJV was 1.35 mg m3, which accounts for approximately 20% of the total predicted organic aerosol. Averaged over the entire SJV, the major SOA sources were solvent use (28% of SOA), catalyst gasoline engines (25% of SOA), wood smoke (16% of SOA), non-catalyst gasoline engines (13% of SOA), and other anthropogenic sources (11% of SOA). Diesel engines were predicted to only account for approximately 2% of the total SOA formation in the SJV because they emit a small amount of volatile organic compounds relative to other sources. In terms of SOA precursors within the SJV, long-chain alkanes were predicted to be the largest SOA contributor, followed by aromatic compounds. The current study identifies the major known contributors to the SOA burden during a winter pollution episode in the SJV, with further enhancements possible as additional formation pathways are discovered. Ó 2009 Elsevier Ltd. All rights reserved.
Keywords: CACM CRPAQS Secondary organic aerosol Source apportionment UCD/CIT air quality model
1. Introduction The United States (US) Environmental Protection Agency has established air quality standards for 24-h and annual average PM2.5 concentrations (particulate matter (PM) of aerodynamic diameter smaller than 2.5 mm) to protect human health. The San Joaquin Valley (SJV) in the southern portion of California’s Central Valley is one of the largest PM2.5 non-attainment areas in the US (Chow et al., 2006). Organic aerosol (OA) is one of the most important constituents of PM2.5 in the SJV (Chow et al., 2006). OA consists of primary organic aerosol (POA) and secondary organic aerosol (SOA). POA is directly emitted to the atmosphere in condensed particle form (e.g., Schauer et al., 2001, 2002a) after which it may partially evaporate depending on ambient conditions (Robinson et al., 2007). SOA is formed in the atmosphere from the oxidation of volatile or semi-volatile organic compounds (VOCs) (Seinfeld and Pankow, 2003). The importance of SOA has been recognized and modeled extensively during photochemical events (Schell et al., 2001; Griffin et al., 2002b; Pun et al., 2003; Johnson et al., 2006;
* Corresponding author. Tel.: þ1 530 752 8386; fax: þ1 530 752 7872. E-mail address: [email protected] (M.J. Kleeman). 1352-2310/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2009.07.010
Kleeman et al., 2007). However, there are only a limited number of studies that have evaluated the formation of SOA under wintertime conditions, presumably because SOA has been assumed to be minor due to low photochemical activity in winter. On the other hand, meteorological conditions in winter (e.g., low temperature, high humidity, and stagnant air) favor the accumulation of condensable water-soluble SOA. Strader et al. (1999) studied the SOA formation in the SJV during the winter of 1995–1996 and concluded that while most of OA was of primary origin, as much as 15–20 mg C m3 of SOA could be produced under certain conditions. Lanz et al. (2008) applied advanced factor analysis to the aerosol mass spectra collected at an urban site in Zurich, Switzerland in January 2006 and found that approximately half of winter OA was oxygenated OA (OOA) which they assumed was SOA, even though production of OOA is lower in winter compared to summer (Shrivastava et al., 2008). Given the limited number of studies concerning the wintertime SOA formation, further modeling studies of the quantity and source origin of SOA in the SJV are clearly needed. Recently, a mechanistic technique has been developed for the source apportionment of SOA (Kleeman et al., 2007). SOA precursor emissions from different sources are tracked individually in an air quality model. Chemical reaction products leading to SOA formation are labeled with the source information of the reactants. This
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approach allows accurate tracking of SOA source contributions through known chemical pathways at receptor sites. Similar approaches have been developed and applied to study source contributions to secondary inorganic PM components (Mysliwiec and Kleeman, 2002; Ying and Kleeman, 2006). The purpose of this work is to study SOA formation and source contributions in the SJV between December 15, 2000 and January 7, 2001. This episode is part of the California Regional Particulate Air Quality Study (CRPAQS), which was designed to improve our understanding of the causes of excessive PM levels in central California (Chow et al., 2006). The UCD/CIT air quality model is used in this study with the Caltech Atmospheric Chemistry Mechanism (CACM) that has been expanded to perform source apportionment of SOA (Kleeman et al., 2007). 2. Model description The UCD/CIT air quality model is based on the CIT airshed model (Harley et al., 1993) that has been expanded to treat particles as a source-oriented external mixture (Kleeman and Cass, 2001). The version of the UCD/CIT model used in this study was coupled with the Caltech Atmospheric Chemistry Mechanism (CACM) (Griffin et al., 2002a, 2005) as described in detail by Ying et al. (2007) and Kleeman et al. (2007). Semi-volatile organic compounds (SVOCs)
predicted from CACM are lumped into 10 surrogate species. These surrogates are then allowed to partition to both the aqueous and organic phases of particles to form SOA. The partitioning of SVOCs is calculated as a dynamic process using equations described by Kleeman et al. (1997). The equilibrium vapor pressure for the surrogate species above particles is calculated according to the Raoult’s law corrected by activity coefficients (Kleeman et al., 2007). In order to perform source apportionment of SOA, the CACM mechanism was expanded to track the reactions of SOA precursors emitted from various sources. As an example, a simple reaction involving a reactant A and an oxidant to form B is given.
A þ oxidant/B
(1)
If there are two sources for the reactant A, A_X1 and A_X2 are used to represent A from the two different sources. Reaction (1) is then expanded to two reactions.
A X1 þ oxidant/B X1
(2)
A X2 þ oxidant/B X2
(3)
Thus, the products B_X1 and B_X2 contain the source information of A_X1 and A_X2, respectively. Further details describing the SOA source apportionment technique are provided by Kleeman et al.
Fig. 1. Comparisons of predicted (open squares) and observed OA (solid squares) during the IOPs for a) Fresno, b) Angiola, and c) Bakersfield.
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(2007). Several updates were also made to the model framework in the current study including the calculation of the minimum vertical diffusion coefficient and composition-dependent accommodation coefficients for N2O5 hydrolysis on wet particles as described by Ying et al. (2008). 3. Model application The UCD/CIT/CACM model was applied to simulate the quantity and source origin of SOA formation in the SJV between December 15, 2000 and January 7, 2001. The modeling domain covers the Central Valley of California and is horizontally divided into 8 km 8 km cells, with 95 grid cells in each direction. Vertically there are 10 layers extending from ground level to 5000 m above the surface (Ying et al., 2008). Meteorological fields were generated using the objective analysis method of Goodin et al. (1979, 1980) and are described in detail by Ying et al. (2008). The boundary and initial conditions used in the study were derived from measurements (Ying et al., 2008). Hourly emissions used in the study are based on the emission inventory of 4 km spatial resolution generated by the California Air Resources Board (CARB) that were aggregated spatially to be consistent with the 8 km modeling grid. In addition, volatile organic compounds (VOCs) in the original emission inventory were converted into CACM model species using measured VOCs profiles (Harley et al., 1992; Schauer et al., 1999a,b, 2001, 2002a,b). In order to perform SOA source apportionment, VOC emissions were grouped into nine source categories: solvent use, wood smoke, diesel engines, catalyst gasoline engines, noncatalyst gasoline engines, gasoline disposal and storage, high sulfur fuel combustion, other anthropogenic sources, and the biogenic source. These source categories account for the majority of the VOC emissions in the study domain. PM emissions were also chemically speciated into nine primary inorganic model species and nine primary carbonaceous model species that were then assigned into 15 size sections based on source emission measurements (Kleeman et al., 2007). 4. Results 4.1. Organic aerosol predictions The basecase CRPAQS episode has been studied by Ying et al. (2008) applying the UCD/CIT model with an updated form of the SAPRC90 gas-phase chemistry mechanism. The present study builds on this previous work by implementing the CACM gas-phase chemistry mechanism and SOA source apportionment module. The performance of the UCD/CIT/CACM and UCD/CIT/SAPRC90 models was similar for gases (e.g., ozone, nitrogen oxides (NOx)) and PM constituents except SOA and the total OA. In the following sections the predictions of SOA and OA concentrations made by the UCD/ CIT/CACM model are discussed in greater detail. Fig. 1 compares model predictions of OA to measured values. Filter based OA measurements were made at Fresno (FSF), Angiola (ANGI), and Bakersfield (BAC) during three winter Intensive Operating Periods (IOPs) (December 15–18, 2000, December 26–28, 2000, and January 4–7, 2001). On each day of the IOPs, filters were collected during five intervals (at 0000–0500, 0500–1000, 1000– 1300, 1300–1600, 1600–2400 PST). Predicted OA generally agrees with the observations at Fresno, with a slight under-prediction of only 13% on average. At the rural Angiola location measured OA concentrations are much lower than at the urban sites, but OA is under predicted by approximately 64%. Modeled OA showed much weaker temporal variations than the observed values. Ying et al. (2008) found that elemental carbon, carbon monoxide, and NOx were also under predicted at Angiola and suggested that sources
Fig. 2. Fractional bias for OA predictions at Fresno (FSF), Angiola (ANGI), and Bakersfield (BAC) during three IOPs.
may be missing in the emission inventory surrounding that location. In contrast to the under-predictions at Fresno and Angiola, OA is over-predicted at Bakersfield, especially during the night. On average, the predicted OA is approximately 34% higher than the observed values. As suggested by Ying et al. (2008), sharp spatial gradients exist for predicted OA concentrations at urban sites during the evening hours, and these gradients may be a major cause for the bias in predicted OA at Bakersfield. Fig. 2 summarizes the fractional bias (FB) for OA predictions at Fresno, Angiola, and Bakersfield. The average FB is 0.11 for Fresno, 0.78 for Angiola, and 0.13 for Bakersfield. The performance of OA predictions is comparable to other similar studies (e.g., Tesche et al., 2006). Organic aerosol measurements cannot definitively quantify the SOA fraction of total OA. Various methods have been developed to estimate the SOA concentration such as using the ratio of OC/EC measured by thermal–optical techniques (e.g., Strader et al., 1999), using the ratio of oxygen/carbon measured by mass spectrometry (e.g., Lanz et al., 2008), using tracers for major SOA categories (Kleindienst et al., 2007), and using the fraction of OA that can’t be explained by tracers for major POA categories (e.g., Schauer and Cass, 2000). The EC/OC technique can only be applied over very long averaging times due to the potential variation in EC/OC ratios as primary emissions change over time. Oxygen/carbon ratios and SOA tracers were not measured during the current study, and so SOA concentrations will be estimated using the difference between the measured total OA and the POA identified using source apportionment techniques. Fig. 3 compares SOA concentrations predicted by the UCD/ CIT/CACM model to the concentration of ‘‘unknown OA’’
Fig. 3. Comparison of the average predicted SOA with the average unknown OA (Kleeman et al., 2009) at Sacramento (SAC), Modesto (MOD), and Bakersfield (BAC) for daytime hours (Day, 1000–1800 PST) and nighttime hours (Night, 2000–0800 PST) during December 15, 2000–January 7, 2001. (Uncertainty for unknown OA is also from Kleeman et al., 2009.)
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Fig. 4. Ratios of the average predicted SOA to the average predicted OA during December 25, 2000 and January 7, 2001.
(¼OA – identified POA) described by Kleeman et al. (2009). Based on the availability of measurement data, comparisons are made at the urban locations of Sacramento, Modesto, and Bakersfield spanning a north–south transect of the SJV for both daytime (10–18 PST) and nighttime (20–8 PST) hours. Both the predicted SOA concentrations and the calculated ‘‘unknown’’ OA concentrations are less than 5 mg m3 at all sites and times except during the evening hours at Bakersfield when calculated ‘‘unknown’’ OA concentrations were w10 mg m3. These concentrations correspond to 0–29% of the total measured OA at these sites (41% of measured OA during the evening hours at Bakersfield). Error bars for ‘‘unknown’’ OA concentrations reflect the uncertainty in the molecular marker source apportionment technique. There appears to be substantial variations in ‘‘unknown’’ OA among the three sites but much of this variation falls within the uncertainty range of the measurement technique except for significantly higher concentrations of ‘‘unknown’’ OA during nighttime at Bakersfield. SOA concentrations predicted by the UCD/CIT/CACM model agree with measured concentrations of ‘‘unknown’’ OA at Sacramento, Modesto and Bakersfield (during daytime hours) within the uncertainty range of the measurements. The predicted SOA concentration is lower than the calculated ‘‘unknown’’ OA during nighttime hours at Bakersfield, which may indicate an under-prediction of SOA concentrations at this time and location. Our current understanding of SOA formation is far from complete (Kroll and Seinfeld, 2008), and SOA concentrations are often under predicted from three-dimensional air quality models (e.g., Morris et al., 2006; Ying et al., 2007). Potentially important pathways for SOA formation that are missing or only partially included in the current SOA module may include oligomerization (e.g., Kalberer et al., 2004) and aqueous-phase chemistry (e.g., Volkamer et al., 2008). Complete
mechanisms describing these pathways will be added once quantitative descriptions of these processes become available. The SOA identified in the current study is likely a correct but incomplete picture of total SOA formation in the SJV. Fig. 4 shows the ratios of the predicted SOA to the predicted OA averaged over the period from December 25, 2000 to January 7, 2001. The final two weeks of the model episode were selected for this analysis to minimize the effect of initial conditions on the calculations. While predicted SOA only accounts for approximately 5% of total OA at Fresno and Bakersfield, predicted SOA can constitute 37% of predicted OA at Angiola. In addition, fractions of predicted SOA/OA are up to 50% at rural locations within the SJV and along the coastlines north of San Francisco. The predicted SOA accounts for approximately 20% of the predicted OA averaged across the entire SJV, indicating the importance of considering the best available estimates of wintertime SOA in regional model calculations. 4.2. Source apportionment of SOA Fig. 5 shows the total SOA concentrations and SOA source contributions predicted by the UCD/CIT/CACM model for the entire modeling domain averaged between December 25, 2000 and January 7, 2001. SOA concentrations are normalized by the domain-wide maximum value in each sub-panel of Fig. 5. SOA formation is prevalent within the Central Valley and generally exceeds 2.0 mg m3 along California State Route 99 that connects Sacramento and Bakersfield. The maximum predicted SOA concentration of 4.26 mg m3 is located approximately 50 km southwest of Fresno. SOA concentrations greater than 1.5 mg m3 also are predicted at the coastal areas in Mendocino County
J. Chen et al. / Atmospheric Environment 44 (2010) 1331–1340
(north of San Francisco) and further south between San Francisco and Monterey. The dominant regional sources of SOA are predicted to be solvent use, catalyst gasoline engines, wood smoke, and noncatalyst gasoline engines. The maximum SOA concentrations from
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these four sources are 1.22 mg m3, 1.04 mg m3, 0.60 mg m3, and 0.51 mg m3, respectively. The spatial patterns of SOA concentrations from these four sources are quite similar to the spatial pattern of the total SOA formation except at the coastal areas of Mendocino County. SOA formation in the coastal areas of Mendocino County is
Fig. 5. Predicted SOA concentrations and SOA source contributions averaged between December 25, 2000 and January 7, 2001. Each sub-panel illustrates concentrations normalized by the domain-wide maximum (shown below the title in mg m3).
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Fig. 6. Time series of SOA concentrations and SOA source contributions during December 25, 2000 and January 7, 2001 for a) Fresno, b) Angiola, c) Bakersfield, and d) average of the SJV.
dominated by biogenic emissions, producing ambient concentrations at ground level of approximately 1.81 mg m3. The maximum SOA contributions from high sulfur fuel combustion and other anthropogenic sources are 0.33 mg m3 and 1.6 mg m3, respectively, over very limited regions. The maximum predicted diesel engine SOA concentration is 0.07 mg m3, which is much smaller than that of gasoline engines (catalyst plus non-catalyst). This reflects the fact that total VOC emissions from diesel engines were much lower than those from gasoline engines in the current study. The maximum SOA contributions from gasoline disposal þ storage and initial þ boundary conditions are 0.07 mg m3 and 0.02 mg m3, respectively, which are both small compared to contributions from other sources. Fig. 6 shows the time series of hourly-average source contributions to SOA concentrations at (a) Fresno, (b) Angiola, (c) Bakersfield, and (d) averaged across the entire SJV. At Fresno, Angiola, and Bakersfield, the maximum predicted hourly-average SOA concentration is approximately 5.0–6.0 mg m3, and the maximum predicted daily-average SOA concentration is approximately 3.0–4.5 mg m3. The maximum daily-average SOA formation across the entire SJV is approximately 2.0 mg m3, which is smaller
than the corresponding values at Fresno, Angiola, and Bakersfield. Time series of SOA source apportionment for Fresno, Angiola, Bakersfield, and the SJV indicates that the source contribution diurnal patterns are fairly consistent. Table 1 shows the SOA source
Table 1 Source apportionment of SOA at selected locations in Central California between December 25, 2000 and January 7, 2001a Sources
FRES
ANGI
BAC
SJV
Initial/boundary conditions Solvent use Wood smoke Diesel engines Non-catalyst gasoline engines Catalyst gasoline engines Gasoline storage and disposal High sulfur fuel combustion Other anthropogenic sources Biogenic source Total SOA
0.014 0.776 0.401 0.030 0.312 0.611 0.045 0.010 0.212 0.026 2.463
0.009 0.664 0.322 0.043 0.303 0.610 0.042 0.032 0.242 0.016 2.284
0.008 0.469 0.244 0.027 0.204 0.432 0.032 0.011 0.236 0.018 1.680
0.009 0.376 0.212 0.022 0.173 0.332 0.023 0.027 0.147 0.030 1.351
a Unit in mg m3; FRES: Fresno; ANGI: Angiola; BAC: Bakersfield; SJV: average of the entire SJV.
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apportionment at these locations averaged from December 25, 2000 to January 2001. Solvent use, catalyst gasoline engines, wood smoke, non-catalyst gasoline engines, and other anthropogenic sources are major SOA contributors at these locations, accounting for w28–32%, w25–27%, w14–16%, w12–13%, and w9–14% of SOA formation, respectively. Contributions from the remaining sources are minor, accounting for only w5–8% of the SOA. Fig. 7 shows the diurnal pattern of predicted SOA concentrations and source apportionment at (a) Fresno, (b) Angiola, and (c) Bakersfield. Fig. 7d shows the predicted SOA diurnal pattern averaged across all grid cells in the SJV. SOA contributions from the five main sources have similar diurnal patterns. At the rural Angiola site (Fig. 7b) and across the entire SJV (Fig. 7d), SOA concentrations remain high at night and fall to the lowest values in the afternoon. These trends are partly driven by the diurnal variations of temperature and humidity: low temperature and high relative humidity at night favors the partitioning of SVOCs to the particle phase. At urban locations such as Fresno (Fig. 7a) and Bakersfield (Fig. 7c), SOA concentrations peak first in the late morning, drop to a minimum in the afternoon, and then build up again and remain relatively high throughout the night. This late morning peak in SOA concentrations is driven by increased local traffic emissions during
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the morning rush hours which increase the concentration of both absorbing POA and semi-volatile SOA precursor gases. Fig. 8 shows the contributions from selected chemical surrogate species to the SOA concentrations averaged between December 25, 2000 and January 7, 2001. SOA concentrations are normalized by the domain-wide maximum value in each sub-panel of Fig. 8. Only surrogates with maximum concentrations greater than 0.01 mg m3 are shown. Surrogate species S9 dominates SOA formation within the modeling domain except at the coastal region north of San Francisco. The maximum SOA concentration produced from S9 is 3.85 mg m3 south of Fresno between CA Highway 99 and Interstate 5. S9 is produced from anthropogenic emissions of ALKH (highSOA-yield alkanes or long-chain alkanes) in the CACM mechanism (Griffin et al., 2002a). The importance of SOA formation from longchain alkanes has recently been demonstrated in laboratory experiments. For example, Lim and Ziemann (2005) found that SOA mass yield from alkanes of carbon number 13 was approximately 50%, which is nearly an order of magnitude greater than typical SOA yields from aromatic compounds. The maximum predicted SOA concentrations produced by surrogate species S2, S6, and S7 add up to 0.33 mg m3 with a similar spatial pattern to S9. S2, S6, and S7 are formed from AROH (aromatics with high SOA yield), AROL
Fig. 7. Diurnal variation of SOA concentrations and SOA source contributions averaged between December 25, 2000 and January 7, 2001 for a) Fresno, b) Angiola, c) Bakersfield and d) average of the SJV.
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Fig. 8. SOA contributions from selected chemical surrogate species in the SOA module averaged between December 25, 2000 and January 7, 2001. Contributions from S3 and S8 are not shown because they are negligible. Each sub-panel illustrates concentrations normalized by the domain-wide maximum (shown below the title in mg m3).
(aromatics with low SOA yield), ARAL (aromatic monoaldehydes), and AROO (phenolic species) in CACM (Griffin et al., 2002a). The total maximum concentration from surrogate species S4, S5, and S10 is 1.82 mg m3 at coastal and foothill locations where biogenic emissions are largest. These compounds are generated from BIOH (monoterpenes of high SOA yield) and BIOL (monoterpenes of low SOA yield) in CACM (Griffin et al., 2002a). The maximum contribution from the surrogate species S1 is 0.02 mg m3. S1 represents reaction products from both anthropogenic and biogenic species. SOA formation from S1 is relatively unimportant compared to SOA contributions from high-SOA-yield alkanes, biogenic monoterpenes, and anthropogenic aromatic compounds. Table 2 summarizes daily emissions of parent compounds that form SOA within the modeling domain. Total ALKH emissions are 968 kmol day1. Solvent use, catalyst gasoline engines, wood smoke, non-catalyst gasoline engines, and other anthropogenic sources are the main sources of ALKH, contributing approximately 31%, 25%, 14%, 14%, and 10% of total emissions, respectively. Because ALKH is the dominant SOA precursor within the SJV, it is not surprising that the pattern of source contributions to ALKH emissions (Table 2) generally matches source contributions to ambient
SOA concentrations (Table 1). Total aromatic compound emissions, including those of AROH, AROL, ARAL, and AROO, amount to 1944 kmol day1 with 92% of these emissions coming from catalyst gasoline engines, non-catalyst gasoline engines, solvent use, and
Table 2 Emissions of SOA precursors from each source category on a typical week daya Sources
ALKH
AROH
AROL
ARAL
AROO
BIOL/BIOH
Solvent use Wood smoke Diesel engines Non-catalyst gasoline engines Catalyst gasoline engines Gasoline storage and disposal High sulfur fuel combustion Other anthropogenic sources Biogenic source Total emissions
308 135 12 135 244 27 10 97 0 968
132 12 23 234 329 16 3 78 0 827
219 20 42 261 350 20 7 168 0 1087
0 0 6 14 5 0 1 0 0 26
0 0 0 0 0 0 1 3 0 4
0 0 0 0 0 0 0 0 864 864
a Unit in kmol day1; ALKH: long-chain alkanes; AROH: aromatics of high SOA yield; AROL: aromatics of low SOA yield; ARAL: aromatic monoaldehydes; AROO: phenolic species; BIOL/BIOH: monoterpenes of low/high SOA yield.
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other anthropogenic sources. Diesel engine emissions account for only 1–3% of ALKH and aromatic compounds, resulting in minor SOA formation from this source in the current study. Total monoterpene emissions from biogenic sources (BIOH and BIOL) are 864 kmol day1. 5. Conclusions The quantity and origin of SOA formation in the SJV between December 15, 2000 and January 7, 2001 was predicted using a state of the science SOA formation mechanism. While the majority of predicted OA in the SJV during the winter episode was of primary origin, SOA concentrations were not negligible. Low mixing depths and stagnant winds promoted the buildup of pollutants near the surface while low temperatures þ high relative humidity favored the partitioning of SVOCs to the particle phase despite the low photochemical activity in the winter episode. Averaged across the entire SJV, predicted SOA accounted for approximately 20% of the total OA at ground level. Averaged over the period of December 25, 2000 and January 7, 2001, maximum predicted SOA concentrations were 4.26 mg m3 in the region between Fresno and Bakersfield. Source apportionment calculations identified five major SOA sources in the SJV based on the current understanding of atmospheric chemistry. Averaged over December 25, 2000–January 7, 2001, the dominant sources of SOA in the SJV were solvent use (28%), catalyst gasoline engines (25%), wood smoke (16%), noncatalyst gasoline engines (13%), and other anthropogenic sources (11%). The contribution from diesel engines to SOA formation in the SJV was much smaller, while biogenic sources accounted for large amounts of SOA formation outside of the SJV in locations with high biogenic precursor emissions. Studies focused on VOC emissions and SOA formation potentials from major sources should be carried out in order to improve SOA predictions in the SJV during winter stagnation events. Given the significant impacts of SOA on human health and climate change, detailed knowledge about the formation, properties, and sources of SOA is required. This study quantifies the known formation rate and sources of SOA in a populated and highly polluted region during a wintertime episode. Such information is useful for local policy makers who must design effective measures to protect public health from the effects of atmospheric SOA/OA. Acknowledgements We would like to thank Robert Griffin for helpful comments. This research was supported by the United States Environmental Protection Agency Science To Achieve Results (STAR) program under grant number RD831082. Although the research described in this article has been funded by the United States Environmental Protection Agency, it has not been subjected to the Agency’s required peer and policy review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred. Further support for this project was provided by the California Air Resources Board and the San Joaquin Valleywide Air Pollution Study Agency under contract 2000-05PM. The statements, opinions, findings, and conclusions of this paper are those of the authors and do not necessarily represent the views of the California Air Resources Board. References Chow, J.C., Chen, L.W.A., Watson, J.G., Lowenthal, D.H., Magliano, K.A., Turkiewicz, K., Lehrman, D.E., 2006. PM2.5 chemical composition and spatiotemporal variability during the California Regional PM10/PM2.5 Air Quality Study (CRPAQS). Journal of Geophysical Research 111, D10S04. doi:10.1029/ 2005JD006457.
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