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Source apportionment of secondary organic aerosol during a severe photochemical smog episode
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Atmospheric Environment 41 (2007) 576–591 www.elsevier.com/locate/atmosenv
Source apportionment of secondary organic aerosol during a severe photochemical smog episode Michael J. Kleemana,, Qi Yinga, Jin Lua, Mitchel J. Mysliwieca, Robert J. Griffinb, Jianjun Chenb, Simon Cleggc a
Department of Civil and Environmental Engineering, University of California, 1 Shields Avenue, Davis, CA 95616, USA Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, 39 College Road, Durham, NH 03824, USA c School of Environmental Sciences, University of East Anglia, Norwhich England NR4 7TJ, USA
b
Received 9 February 2006; received in revised form 17 August 2006; accepted 17 August 2006
Abstract The UCD/CIT air quality model was modified to predict source contributions to secondary organic aerosol (SOA) by expanding the Caltech Atmospheric Chemistry Mechanism to separately track source apportionment information through the chemical reaction system as precursor species react to form condensable products. The model was used to predict source contributions to SOA in Los Angeles from catalyst-equipped gasoline vehicles, non-catalyst equipped gasoline vehicles, diesel vehicles, combustion of high sulfur fuel, other anthropogenic sources, biogenic sources, and initial/ boundary conditions during the severe photochemical smog episode that occurred on 9 September 1993. Gasoline engines (catalyst+non-catalyst equipped) were found to be the single-largest anthropogenic source of SOA averaged over the entire model domain. The region-wide 24-h average concentration of SOA produced by gasoline engines was predicted to be 0.34 mg m3 with a maximum 24-h average concentration of 1.81 mg m3 downwind of central Los Angeles. The regionwide 24-h average concentration of SOA produced by diesel engines was predicted to be 0.02 mg m3, with a maximum 24-h average concentration of 0.12 mg m3 downwind of central Los Angeles. Biogenic sources are predicted to produce a region-wide 24-h average SOA value of 0.16 mg m3, with a maximum 24-h average concentration of 1.37 mg m3 in the less-heavily populated regions at the northern and southern edges of the air basin (close to the biogenic emissions sources). SOA concentrations associated with anthropogenic sources were weakly diurnal, with slightly lower concentrations during the day as mixing depth increased. SOA concentrations associated with biogenic sources were strongly diurnal, with higher concentrations of aqueous biogenic SOA at night when relative humidity (RH) peaked and little biogenic SOA formation during the day when RH decreased. r 2006 Elsevier Ltd. All rights reserved. Keywords: SOA; Source apportionment; CACM; UCD/CIT air quality model
1. Introduction
Corresponding author.
E-mail address: [email protected] (M.J. Kleeman). 1352-2310/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2006.08.042
Organic compounds are ubiquitous constituents of atmospheric particulate matter (PM) in some of the most heavily polluted airsheds within the United States (Gray et al., 1984; Hughes et al., 1999; Kim
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et al., 2000; Chow et al., 1994; Fraser et al., 1996; Seinfeld and Pankow, 2003). Primary organic aerosol is released directly to the atmosphere in the particle phase (see e.g. Rogge et al., 1997; Hildemann et al., 1991b; Schauer et al., 2002a) while secondary organic aerosol (SOA) forms in the atmosphere through the chemical reaction of precursor gases that produce condensable products. It is currently estimated that SOA accounts for 10–35% of the organic aerosol found in typical urban atmospheres (Schauer et al., 1996, 2002b). The quantity and source origin of SOA must be identified before effective emissions control programs can be implemented for this pollutant. Chamber experiments have identified the first few generations of reaction products from different classes of parent VOC compounds (see e.g. Dalton et al., 2005; Jaoui et al., 2004; Jang and Kamens, 1999; Keywood et al., 2004; Cocker et al., 2001; Kalberer et al., 2000; Yu et al., 1999; Jungkamp et al., 1997). These studies have revealed that the reaction pathways for organic species are complex with numerous products. Statistical source apportionment methods for primary organic aerosol (Schauer et al., 1996, 2002b) are not able to determine the source-origin of SOA because chemical ‘‘fingerprints’’ for individual SOA species produced by different sources are not available. Recently, a mechanistic technique has been developed for the source apportionment of secondary PM (Mysliwiec and Kleeman, 2002; Ying and Kleeman, 2006). Precursor emissions from different sources are tracked separately through an air quality model as they are transformed by chemical reactions leading to the formation of low-volatility products that can partition to the particle phase. Each product is labeled with the source-identity of the reactant so that source attribution information is preserved. The new technique has been used for the regional source apportionment of particulate nitrate, sulfate, and ammonium ion in two of the
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most heavily polluted airsheds in the United States: the South Coast Air Basin (SoCAB) and the San Joaquin Valley (SJV) (Ying and Kleeman, 2006). The purpose of the current study is to describe the application of the new apportionment technique for secondary PM to reveal the sources of SOA formation in the SoCAB during a severe particulate air quality episode that occurred on 7–9 September 1993. The UCD/CIT air quality model is combined with a version of the Caltech Atmospheric Chemistry Model (CACM) that is expanded to predict SOA source contributions from diesel engines, catalyst-equipped gasoline engines, non-catalyst equipped gasoline engines, combustion of fuel with high sulfur content, biogenic sources, and other sources with a resolution of 5 km across the entire SoCAB. 2. Background The basecase version of the CACM implemented in this project contains approximately 430 reactions involving 210 species describing the current state-ofthe-science understanding of SOA formation in urban atmospheres (Griffin et al., 2002a, 2003, 2005). The formation of SOA is initiated by the reaction of a parent VOC compound with a suitable oxidant (OH, O3, or NO3) leading to the formation of further reactive species. As an example, Table 1 illustrates reactions that produce SOA from the CACM parent species high yield alkanes(ALKH). The condensable products from this reaction sequence are AP11, AP12, and UR20. Table 2 illustrates the same reaction sequence that has been modified to retain information for two sources denoted with the suffix ‘‘_X1’’ and ‘‘_X2’’, respectively. These reaction mechanisms can be converted into a set of ordinary differential equations describing the evolution of species concentrations with time. The total predicted concentration of each SOA species is identical for both mechanisms (e.g.
Table 1 Chemical reactions leading to the formation of SOA species from high-SOA yield alkanes ALKH+OHQH2O+RO232+RO2T RO232+NOQCF(33) AP11+CF(34) NO2+CF(34) RO241+CF(34) RO2T RO232+RO2TQRO241+2 RO2T+O2 RO232+HO2QOH+RO241+RO2T RO241+NOQCF(35) AP12+CF(36) NO2+CF(36) HO2+CF(36) UR20 RO241+RO2TQHO2+UR20+RO2T+O2 RO241+HO2QHO2+OH+UR20 Variables starting with the characters ‘‘CF’’ are adjustable coefficients in CACM.
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Table 2 Source-resolved chemical reactions leading to the formation of SOA species from high-SOA yield alkanes ALKH_X1+OHQH2O+RO232_X1+RO2T ALKH_X2+OHQH2O+RO232_X2+RO2T RO232_X1+NOQCF(33) AP11+CF(34) NO2+CF(34) RO241_X1+CF(34) RO2T RO232_X2+NOQCF(33) AP11+CF(34) NO2+CF(34) RO241_X2+CF(34) RO2T RO232_X1+RO2TQRO241_X1+2RO2T+O2 RO232_X2+RO2TQRO241_X2+2 RO2T+O2 RO232_X1+HO2QOH+RO241_X1+RO2T RO232_X2+HO2QOH+RO241_X2+RO2T RO241_X1+NOQCF(35) AP12_X1+CF(36) NO2+CF(36) HO2+CF(36) UR20_X1 RO241_X2+NOQCF(35) AP12_X2+CF(36) NO2+CF(36) HO2+CF(36) UR20_X2 RO241_X1+RO2TQHO2+UR20_X1+RO2T+O2 RO241_X2+RO2TQHO2+UR20_X2+RO2T+O2 RO241_X1+HO2QHO2+OH+UR20_X1 RO241_X2+HO2QHO2+OH+UR20_X2 Two sources are used in the current example, denoted by the suffix _X1 and _X2. See Table 1 for the original reaction sequence.
AP11 ¼ AP11_X1+AP11_X2), but the mechanism shown in Table 2 retains the source information of the compounds. In the current study, eight sources of SOA were tracked through the chemical mechanism, leading to an additional 1616 reactions and 440 species. This increases the computational time required to integrate this equation set, but the time needed for gasphase chemistry is still small compared to the gasto-particle conversion calculations. The mechanistic source apportionment method therefore has the same computational burden as other simulations carried out with the basecase version of CACM. 3. Model description The UCD/CIT photochemical air quality model is based on the CIT airshed model (McRae et al., 1982; Harley et al., 1993) with additions to track airborne particles as a source-oriented external mixture (Kleeman and Cass, 2001). Particles emitted from different sources are described with 15 discrete particle sizes equally spaced on a logarithmic scale between 0.01 and 10.0 mm particle diameter. The model includes a full description of transport, deposition, gas-phase chemical reaction, gas-to-particle conversion, and aqueous-phase chemical reaction. Previous studies (Mysliwiec and Kleeman, 2002; Ying and Kleeman, 2006; Kleeman and Cass, 2001, Held et al., 2004) have described the UCD/CIT source-oriented air quality model, and so only those aspects that were changed during the current project are discussed here. The CACM (Griffin et al., 2002a, 2005) and the thermodynamic
data describing the partitioning of semi-volatile reaction products between the gas phase, condensed organic phase, and condensed aqueous phase (Griffin et al., 2005; Pun et al., 2002) were adapted to work with the UCD/CIT air quality model. Ethane was removed from the lumped model species ALKL and tracked as an individual species so that a more appropriate rate constant could be specified for reaction with OH. A single model subroutine was created that simultaneously predicted the vapor pressure of semi-volatile inorganic and organic species above each particle by calculating the thermodynamic equilibrium concentration for each species between a solid (inorganic species only), organic (organic species only) and aqueous (inorganic and organic species) phase within each particle. Activity coefficients for inorganic species were calculated using the method described by Kusik and Meissner (1978). Activity coefficients for organic species were calculated using UNIFAC (Fredenslund et al., 1977). Experimental data describing interactions between organic and inorganic species are sparse (Clegg et al., 2001, 2003) and so these interactions were neglected in the current study. Likewise, heterogeneous acid-catalyzed reactions leading to the formation of lowvolatility organic compounds have been observed in several studies (Czoschke et al., 2003; Jang et al., 2003; Kalberer et al., 2004), but a comprehensive representation of these reactions suitable for use in an air quality model has not yet been formulated. In the current study, the general effect of acidcatalyzed reactions was simulated by adjusting the vapor pressures of surrogate compounds used to
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represent the semi-volatile organic species as discussed by (Griffin et al. (2005). This procedure was uniformly applied to all particles. To calculate the thermodynamic equilibrium state of each particle, the concentration of hydrogen ion [H+] in the aqueous phase was first calculated based on the distribution of inorganic ions between the solid and aqueous phases using the method described by Wexler and Seinfeld (1991) as modified by Kleeman et al. (1997). The partial vapor pressure of semi-volatile inorganic species HNO3, HCl, and NH3 was then calculated. In this algorithm, sulfuric acid has zero vapor pressure independent of pH. The distribution of organic species between the aqueous and organic phases was considered next. Several organic species are acids that undergo partial dissociation in the aqueous phase. The fraction of each semi-volatile organic species in the condensed organic phase on each particle was calculated with an iteration scheme: C iorg ¼
TOTi . , 1 þ Rðgiorg TOTaq giaq TOTorg Þ
(1)
where Ciorg is the concentration (mmol) of chemical species i in that particle’s organic phase, TOTi is total concentration (mmol) of chemical species i in the organic and aqueous phase of that particle, TOTaq is the total concentration (mmol) of all species in the aqueous phase, TOTorg is the total concentration (mmol) of all species in the organic phase, giorg is the activity coefficient of the chemical species in the organic phase, and giaq is the activity coefficient of the chemical species in the aqueous phase. The factor R accounts for the effects of acid dissociation: R¼1þ
K ia;1 K ia;1 K ia;2 , þ ½Hþ ½Hþ 2
(2)
where Kia,1 is the first acid dissociation constant (mol kg1 water) for chemical species i, Kia,2 is the second acid dissociation constant (mol kg1 water) for chemical species i, and [H+] is the aqueous concentration of hydrogen ion (mol kg1 water). The value of [H+] is re-calculated after each iteration based on charge balance and the concentration of aqueous inorganic electrolytes+dissociated organic acids. In this method, the partial vapor pressures of the organic acids fully sense the effect of the inorganic ions in the aqueous phase, but the partial vapor pressures of HNO3, HCl, and
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NH3 do not directly sense the effect of semi-volatile organic acids (recall that H2SO4 has an effective vapor pressure of zero under all conditions). This semi-coupled approach is computationally efficient and suitably accurate given that the concentration of ammonium nitrate aerosol is usually far greater than the concentration of SOA and given that other factors such as the omission of organic–inorganic interactions due to lack of thermodynamic data lead to greater uncertainties in the calculation anyway. Both inorganic and organic species in the aqueous phase are used to calculate the partial vapor pressure of water above the particle surface. The exchange of all semi-volatile species (including water) between the gas phase and particles is calculated as a dynamic process using equations described by Kleeman et al. (1997). At low RH, when aerosol water content becomes so small that some particles become solid, the volume available for aqueous organic species to partition into becomes negligible, reducing aqueous organic concentrations to zero. Under these conditions, the only semi-volatile species contained on particles are solid inorganic species and SOA in the condensed organic phase. Dry particles can spontaneously form a new aqueous phase when RH increases. Fig. 1 shows the different pathways leading to the formation of SOA products in the CACM. Each pathway begins with a parent hydrocarbon denoted by a clear box surrounded by a solid border. Volatile intermediate products are denoted by a clear box with a dashed border, and semi-volatile SOA products are denoted with a shaded box. The species names shown in Fig. 1 correspond to the description provided by Griffin et al. (2002a, 2005) and Pun et al. (2002). A summary of the general lumped categories that each semi-volatile products fall into is shown in Table 3. Figs. 1(a)–(c) show the SOA formation pathways for biogenic parent hydrocarbons BIOL (low-yield biogenics), BIOH (high yield biogenics), and ISOP (isoprene), respectively. BIOL is a lumped model species designed to represent low SOA yield biogenic compounds that is chiefly composed of a-pinene in the current simulation. BIOH is a lumped model species designed to represent high SOA yield biogenic compounds such as b-pinene, myrcene, b-phellandrene, d-limonene, terpinene, and 3-carene. ISOP represents isoprene emissions in model simulations. The results of the current study indicate that SOA derived from isoprene by CACM is negligible
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(a)
(b)
(c)
(d)
(e)
(f) Fig. 1. Formation pathways from parent hydrocarbons (clear boxes with solid lines) through intermediate products (clear boxes with dashed lines) to semi-volatile products (shaded boxes). Names refer to CACM species described by Griffin et al. (2002). Table 3 General categorization of semi-volatile organic aerosol species used in model calculations Category 1 2 3 4 5 6 7 8 9 10
Description
CACM semi-volatile species
Low carbon number High carbon number, anthropogenic, aromatic fragments, dissociative High carbon number, anthropogenic, aromatic fragments, non-dissociative High carbon number, biogenic, dissociative High carbon number, biogenic, non-dissociative High carbon number, anthropogenic, aromatic, low volatility High carbon number, anthropogenic, aromatic, high volatility High carbon number, anthropogenic, polyaromatic High carbon number, anthropogenic, alkane-derived High carbon number, biogenic, ring-retaining
UR21, UR28 RP13, RP17, RP18, UR26, UR29, UR30
during the period 9 September 1993. While this result may reflect missing formation pathways, it is beyond the scope of the current study to
RPR9, RP12 UR3, UR8, UR23 UR7, UR17 AP1, AP6, UR31 ADAC, RPR7, RP14, RP19, UR2, UR14, UR27, ARAC AP10, UR11, UR15, UR19 AP11, AP12, UR20 AP7, AP8, UR5, UR6
expand the reaction pathways in CACM, and so isoprene formation products will not be discussed further.
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Figs. 1(d)–(f) show the SOA formation pathways for anthropogenic parent hydrocarbons ALKH, AROH (high-yield aromatics), AROL (low yield aromatics), and PAH (polycyclic aromatic hydrocarbons), respectively. ALKH is a lumped model species that represents long-chain alkanes (4C12). AROH is a lumped model species that represents high SOA yield aromatic compounds such as ethyltoluene, butyl-benzene, etc. AROL is a corresponding lumped model species that represents low SOA yield aromatic compounds such as trimethyl-benzene, xylenes, etc. Both AROH and AROL can react to form phenolic compounds represented with the lumped model species PHENOL. Phenolic compounds can also be emitted directly from combustion sources. Likewise, AROL can react to form benzaldehyde (model species BALD) which can also be directly emitted from combustion sources. PAH is a lumped model species used to represent polycyclic aromatic hydrocarbons such as naphthalenes, phenanthrene, etc. The partitioning of the 36 semi-volatile products denoted by the shaded boxes in Fig. 1 between the gas phase, condensed organic phase, and condensed aqueous phase is determined by their vapor pressures and activity coefficients. To increase the computational efficiency of the calculation, each of the 36 semi-volatile species is represented by one of the 10 surrogate species shown in Fig. 1 of Griffin et al. (2003) with similar chemical structure. A more detailed discussion of the structure and chemical properties of the surrogate species is contained in several recent publications (Griffin et al., 2002a, b, 2003, 2005). 4. Model application The preparation of meteorological and emissions input data needed for photochemical modeling of this episode has been described previously (Griffin et al., 2002b; Fraser et al., 2000) and so only a brief summary is provided here. Surface and elevated meteorological parameters were measured at +50 sites and two sites, respectively. The meteorological measurements were interpolated using a diagnostic meteorology model (Goodin et al., 1979) to produce continuous meteorological fields with hourly resolution. Wind fields and mixing depth fields have been updated in the current publication to correct the errors associated with data extraction and to improve precision relative to previous work (Griffin et al., 2002b; Fraser et al., 2000). Background
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concentrations on the western edge of the model domain were specified based on measurements made at San Nicolas Island off the California coast during the study period. Background concentrations on the northern edge of the model domain were set to very low values. Model calculations were initialized at hour 0 on 7 September 1993 and allowed to proceed through hour 24 on 9 September 1993 (2 day spinup time). Results from the final day of the simulation will be discussed in the following sections, along with an analysis showing that boundary and initial conditions have little effect on the predicted concentrations. Emissions inventories created by the South Coast Air Quality Management District (SCAQMD) as part of the 1997 Air Quality Management Plan were adapted for 7–9 September 1993 by adjusting evaporative emissions based on temperature. The emissions from the Los Angeles International Airport (LAX) were updated based on the inventory developed for the summer 1997 Southern California Ozone Study (SCOS97). Biogenic emissions developed for the late August 1987 episode of the Southern California Air Quality Study (SCAQS) were adjusted for the temperature conditions experienced on 7–9 September 1993. Particle-phase emissions were speciated using measured emissions profiles (Cooper et al., 1989; Taback et al., 1979; Schauer et al., 1999a, b, 2001, 2002a, c) and then combined into nine inorganic model species and nine lumped carbonaceous model species (normal alkanes, PAH, oxygenated PAH, diacids, aliphatic acids, substituted monoaromatic compounds, cyclic petroleum biomarkers, ‘‘other organic compounds’’, and elemental carbon). The size distribution of PM emissions from different sources was specified in 15 sections spanning 0.01–10 mm particle diameter based on source emission measurements (Hildemann et al., 1991a; Kleeman et al., 1999, 2000). Gas-phase organic emissions were speciated using measured emissions profiles (Schauer et al., 1999a, b, 2001, 2002a, c; Harley et al., 1992) and then combined into 24 lumped model organic species (Griffin et al., 2002a) based on chemical structure, reactivity, and experimentally determined SOA formation potential. Nine of the lumped parent VOC species can react to form SOA (see Fig. 1). Fig. 2 shows the 24-h average emission rate for each of these nine lumped parent VOCs in the SoCAB on 9 September 1993. Figs. 2(a) and (b) show that the regions with the largest emission rates
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Fig. 2. Average emission rates for parent hydrocarbons on 9 September 1993. Units are ppm m day1. The theoretical maximum change in atmospheric concentration can be calculated in units of ppm day1 by dividing each value by the height of the first model layer (38.5 m). Atmospheric transport, mixing, deposition, and chemical reactions reduce the atmospheric concentrations.
of high and low SOA yield biogenic species are located around the edges of the SoCAB where natural vegetation is abundant. Typical emission rates for high-SOA yield biogenic species (BIOH) were 1 ppm m day1, with peak values of 1.76 ppm m day1 to the south of central Los Angeles. Typical emissions rates for low SOA yield biogenic species (BIOL) were also 1 ppm m day1, with peak values of 4.09 ppm m day1. Figs. 2(c)–(e) show that anthropogenic emissions of parent VOCs are highest in the center of the SoCAB and along the transportation corridors that lead to this region. Maximum emissions rates of high SOA yield alkanes (ALKH) and PAH are 0.5 and 0.99 ppm m day1, respectively. Maximum emissions of high- and low-yield aromatic compounds
AROH+AROL are an order of magnitude higher at 42.54 ppm m day1. The AROH and AROL emission rates are shown together because they lead to the formation of the same reaction products (see Fig. 1). 5. Source apportionment results A detailed comparison has been carried out between the predicted and measured concentration of gas- and particle-phase pollutants on 8–9 September 1993 in a separate study (Ying and Kleeman, submitted). The performance of the UCD/CIT/CACM model was found to be satisfactory during this episode. Predicted ozone concentrations had a normalized gross error of 0.49, and
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both NOx and VOC concentrations showed reasonable agreement with observations. Predicted 24h average PM2.5 concentrations also showed good agreement with measurements, but concentrations of elemental carbon (EC) and organic carbon (OC) in the PM2.5 size fraction were under-predicted during morning rush hour at multiple locations, likely because of problems with the emissions inventory. The concentration of OC in the PM2.5 size fraction was also under-predicted during the afternoon hours with the highest photochemical activity. This may be related to unknown sources of POA and/or SOA in the SoCAB. The field measurements available for the current study were not specific enough to support an analysis of primary vs. secondary organic aerosol concentrations with time resolution shorter than 24 h. Measured concentrations of gas-phase species
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within the AROL and AROH lumped model species agreed reasonably well with predictions. Measured concentrations of gas-phase species within the PHEN and BALD lumped model species were generally lower than the predicted values, possibly because some of the species within these classes were not measured. The results shown in the remainder of the current study identify the origin of the SOA that forms from known sources through chemical pathways represented in the CACM. Fig. 3 shows the predicted 24-h average concentration of SOA in the SoCAB on 9 September 1993, which forms from each of the parent hydrocarbons shown in Fig. 1. The regions with the maximum concentration for each SOA product generally occur downwind of the highest emissions rate for the parent VOCs. Figs. 3(a) and (b) show that the
Fig. 3. Predicted 24-h average concentration of SOA derived from different parent hydrocarbons on 9 September 1993. Units are mg m3.
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predicted concentration of biogenic SOA products is greatest around the edges of the SoCAB. The maximum predicted concentrations of SOA derived from high and low yield biogenic species is 0.96 and 0.41 mg m3, respectively. Figs. 3(c)–(e) show that the predicted concentrations of anthropogenic SOA are highest in the region east (downwind) of central
Los Angeles. The maximum predicted 24-h average concentration of SOA products derived from alkanes and PAH are 0.1 and 0.19 mg m3, respectively. The predicted concentration of SOA products derived from aromatic VOCs are considerably higher, with maximum 24-h average concentrations of 2.04 mg m3.
Fig. 4. Total predicted SOA ( a) and source contributions to predicted SOA concentrations( b–h) on 9 September 1993. Units are mg m3.
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Fig. 4a shows that the predicted 24-h average SOA concentration from all sources on 9 September 1993 has a peak value of 3.49 mg m3 in the region southeast of Claremont. This predicted SOA concentration is lower than the values shown in previous studies (Griffin et al., 2002b; Held et al., 2005) because recent updates to the surrogate species used in CACM (Griffin et al., 2005) corrected an upward bias in the predicted SOA concentration. Additional corrections to the meteorological fields and the use of on-line calculations for UV photolysis rates (Ying and Kleeman, 2003) result in lower predictions of SOA concentrations compared to other recent studies (Vutukuru et al., 2006). Figs. 4(b)–(d) show that catalyst-equipped gasoline engines, non-catalyst-equipped gasoline engines, and diesel engines contribute 1.2, 0.61, and 0.12 mg m3, respectively, to the predicted total 24-h average SOA concentrations. High sulfur fuel
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combustion is predicted to form relatively small amounts of SOA (o0.03 mg m3) mainly in the plume downwind of the LAX. Fig. 4(f) shows that the amount of SOA formed from ‘‘other anthropogenic sources’’ has a peak concentration of 0.42 mg m3 in the region southeast of Claremont. Sources within this category include fuel combustion other than diesel engines, gasoline engines, or high sulfur fuel. Fig. 4(g) shows that biogenic sources contribute up to 1.37 mg m3 to the predicted 24-h average SOA concentration primarily at the northern and southern end of the SoCAB. Finally, Fig. 4(h) shows that initial and boundary conditions contribute 0.03 mg m3 to predicted SOA concentrations at the far downwind end of the air basin. This SOA is produced by measured concentrations of precursor gases in the interior of the model domain at the start of the simulation or at the edges of the model domain during the simulation.
Fig. 5. Source apportionment of SOA derived from alkanes averaged over each hour of the day on 9 September 1993. Units are mg m3.
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Fig. 6. Source apportionment of SOA derived from aromatic compounds averaged over each hour of the day on 9 September 1993. Units are mg m3.
Figs. 5–7 illustrate how the different sources shown in Fig. 4 contribute to the SOA concentrations shown in Fig. 3. Figs. 5(a)–(c) show that the spatial distribution of SOA produced from alkanes released from diesel engines and gasoline engines is similar, with the highest concentrations occurring southeast of Azusa. The predicted maximum 24-h average SOA concentration produced by alkanes released from diesel engines (0.082 mg m3) is larger than the contribution from gasoline engines (o0.003 mg m3) but still small in absolute terms. Figs. 5(d) and (e) show that the combustion of fuel with high sulfur content and other sources also contribute a negligible amount of predicted alkaneSOA during the current study. Figs. 6(a)–(c) show that the predicted regional distribution of 24-h average aromatic-SOA pro-
duced by diesel engines and gasoline engines is similar, with the highest concentrations occurring in the region southeast of Claremont. The maximum predicted 24-h average aromatic-SOA concentrations associated with diesel engines, noncatalyst-equipped gasoline engines, and catalystequipped gasoline engines are 0.01, 0.50, and 0.94 mg m3, respectively. Fig. 6(d) shows that combustion of fuel with high sulfur contributes only 0.01 mg m3 of aromatic-SOA in the region downwind of the LAX. Fig. 6(e) shows that other emissions sources produce approximately 0.36 mg m3 of aromatic-SOA in the region southeast of Claremont. Initial and boundary conditions of aromatic compounds produce 0.025 mg m3 of aromatic-SOA at the far eastern edge of the model region.
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Fig. 7. Source apportionment of SOA derived from PAH averaged over each hour of the day on 9 September 1993. Units are mg m3.
Fig. 7 shows predicted regional source contributions to SOA produced by PAHs in the SoCAB averaged over each hour of the day on 9 September 1993. Fig. 7c shows that catalyst-equipped gasoline engines dominate the predicted PAH-SOA concentrations with maximum 24-h average concentrations of 0.12 mg m3 in the region southeast of Claremont. This trend reflects the fact that approximately 94% of the vehicle miles traveled in the SoCAB during the current study period are associated with gasoline-powered vehicles. Fig. 8 illustrates the diurnal pattern of predicted SOA concentrations on 9 September 1993 averaged over the inland portion of the active model region (no ocean cells included). Fig. 8a shows SOA concentrations associated with different parent VOCs (see Fig. 1). The maximum predicted 1-haverage SOA concentration for the inland portion
of the model domain is 0.96 mg m3 at 24:00 on 9 September 1993. In general, predicted SOA concentrations are larger during the morning and evening hours because increased RH promotes the formation of aqueous-phase SOA. Fig. 8a shows that SOA produced from aromatic and biogenic compounds contribute to the predicted regionalaverage SOA concentrations during the night hours, but biogenic SOA vanishes during the day as temperature increases, RH falls, and the water content of particles decreases. Predicted concentrations of SOA produced from aromatic, alkane, and PAH compounds have less diurnal variation because these species favor partitioning to the organic phase. Fig. 8b shows SOA concentrations associated with emissions from gasoline engines, diesel engines, high-sulfur fuel combustion, biogenic sources, other sources, and initial/boundary
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Fig. 8. Diurnal variation of SOA concentrations averaged across the SoCAB shown by speciation (a) and source origin (b). SOA concentrations are greater in the morning and during the evening partly because of larger aerosol water content promoting the partitioning of SOA to the aqueous phase.
Table 4 Spatially and temporally averaged SOA mass derived from low-yield biogenics (BIOL), high-yield biogenics (BIOH), high-yield alkanes (ALKH), aromatics (ARO), and polycyclic aromatic hydrocarbons (PAH) in the SoCAB on 9 September 1993 Source
SOA Species Family
Total
BIOL
BIOH
ALKH
ARO
PAH
Diesel engines Non-catalyst gasoline engines Catalyst gasoline engines High sulfur fuel combustion Other anthropogenic sources Biogenic sources Initial/boundary conditions
0.000 0.000 0.000 0.000 0.000 0.073 0.000
0.000 0.000 0.000 0.000 0.000 0.084 0.000
0.013 0.000 0.000 0.001 0.001 0.000 0.000
0.003 0.115 0.209 0.002 0.071 0.000 0.003
0.005 0.005 0.016 0.000 0.000 0.000 0.000
0.021 0.120 0.225 0.003 0.072 0.157 0.003
Total
0.073
0.084
0.015
0.402
0.027
0.601
3
Units are mg m .
conditions. Predicted SOA concentrations derived from anthropogenic sources are relatively constant throughout the day. The single largest anthropogenic source of regional-average SOA is predicted to be catalyst-equipped gasoline engines, followed by non-catalyst-equipped gasoline engines and diesel engines. Table 4 shows the predicted 24-h average SOA concentration for the inland portion of the modeling region on September 9, 1993 sorted according to the source-origin of the SOA and the class of SOA product. Total 24-h average SOA concentrations for the entire inland portion of the model region are predicted to be 0.6 mg m3. Approximately 67% of this SOA is produced by aromatic compounds, 26%
from biogenic compounds, and the remainder from alkanes and PAHs. Diesel engines, non-catalystequipped gasoline engines, and catalyst equipped gasoline engines account for 3%, 20%, and 38% of the predicted regional-average SOA concentration, respectively. Other primary sources contributed 12% and initial/boundary conditions contributed 0.5% to predicted SOA concentrations. 6. Conclusions The predicted composition and source-origin of SOA concentrations in the SoCAB is a strong function of location. Predicted SOA concentrations downwind of central Los Angeles are dominated by
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aromatic compounds originally released from gasoline engines, with smaller contributions from diesel engine alkanes and gasoline engine PAHs. The maximum 24-h average predicted SOA concentration on 9 September 1993 is 3.49 mg m3 in the region southeast of Claremont. Gasoline engines (catalyst+non-catalyst-equipped) contribute 1.81 mg m3 to this peak concentration, with additional contributions from diesel engines (0.12 mg m3). The maximum SOA concentrations associated with other sources occur at different locations. The maximum predicted SOA concentration associated with high sulfur fuel combustion is 0.03 mg m3 in the region adjacent to the LAX. The maximum predicted SOA concentration associated with biogenic sources is 1.37 mg m3 in the northern and southern regions of the air basin near the coast. SOA concentrations associated with anthropogenic sources are predicted to be weakly diurnal, with slightly lower concentrations during the day as mixing depth increased. SOA concentrations associated with biogenic sources are predicted to be strongly diurnal, with large concentrations of aqueous biogenic SOA at night when coastal RH is larger and smaller biogenic SOA concentrations during the day when aerosol water content is reduced. While the current study represent a stateof-the-science prediction for particulate organic carbon concentrations in the atmosphere, the model results still under-predict observed carbon concentrations by approximately 50% (Ying and Kleeman, submitted). This finding is consistent with previous photochemical modeling studies (Kleeman and Cass, 2001) and with receptor-oriented statistical studies (Schauer et al., 2002d) for the SoCAB. The combined results of these previous studies and the current analysis suggests that either an unknown source of primary organic aerosol or an unknown formation pathway for secondary organic aerosol remains to be discovered for the SoCAB.
Acknowledgements This research was supported by the Environmental Protection Agency Science To Achieve Results (STAR) program under Grant no. 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
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the Agency and no official endorsement should be inferred.
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Atmospheric Environment 41 (2007) 576–591 www.elsevier.com/locate/atmosenv
Source apportionment of secondary organic aerosol during a severe photochemical smog episode Michael J. Kleemana,, Qi Yinga, Jin Lua, Mitchel J. Mysliwieca, Robert J. Griffinb, Jianjun Chenb, Simon Cleggc a
Department of Civil and Environmental Engineering, University of California, 1 Shields Avenue, Davis, CA 95616, USA Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, 39 College Road, Durham, NH 03824, USA c School of Environmental Sciences, University of East Anglia, Norwhich England NR4 7TJ, USA
b
Received 9 February 2006; received in revised form 17 August 2006; accepted 17 August 2006
Abstract The UCD/CIT air quality model was modified to predict source contributions to secondary organic aerosol (SOA) by expanding the Caltech Atmospheric Chemistry Mechanism to separately track source apportionment information through the chemical reaction system as precursor species react to form condensable products. The model was used to predict source contributions to SOA in Los Angeles from catalyst-equipped gasoline vehicles, non-catalyst equipped gasoline vehicles, diesel vehicles, combustion of high sulfur fuel, other anthropogenic sources, biogenic sources, and initial/ boundary conditions during the severe photochemical smog episode that occurred on 9 September 1993. Gasoline engines (catalyst+non-catalyst equipped) were found to be the single-largest anthropogenic source of SOA averaged over the entire model domain. The region-wide 24-h average concentration of SOA produced by gasoline engines was predicted to be 0.34 mg m3 with a maximum 24-h average concentration of 1.81 mg m3 downwind of central Los Angeles. The regionwide 24-h average concentration of SOA produced by diesel engines was predicted to be 0.02 mg m3, with a maximum 24-h average concentration of 0.12 mg m3 downwind of central Los Angeles. Biogenic sources are predicted to produce a region-wide 24-h average SOA value of 0.16 mg m3, with a maximum 24-h average concentration of 1.37 mg m3 in the less-heavily populated regions at the northern and southern edges of the air basin (close to the biogenic emissions sources). SOA concentrations associated with anthropogenic sources were weakly diurnal, with slightly lower concentrations during the day as mixing depth increased. SOA concentrations associated with biogenic sources were strongly diurnal, with higher concentrations of aqueous biogenic SOA at night when relative humidity (RH) peaked and little biogenic SOA formation during the day when RH decreased. r 2006 Elsevier Ltd. All rights reserved. Keywords: SOA; Source apportionment; CACM; UCD/CIT air quality model
1. Introduction
Corresponding author.
E-mail address: [email protected] (M.J. Kleeman). 1352-2310/$ - see front matter r 2006 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2006.08.042
Organic compounds are ubiquitous constituents of atmospheric particulate matter (PM) in some of the most heavily polluted airsheds within the United States (Gray et al., 1984; Hughes et al., 1999; Kim
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et al., 2000; Chow et al., 1994; Fraser et al., 1996; Seinfeld and Pankow, 2003). Primary organic aerosol is released directly to the atmosphere in the particle phase (see e.g. Rogge et al., 1997; Hildemann et al., 1991b; Schauer et al., 2002a) while secondary organic aerosol (SOA) forms in the atmosphere through the chemical reaction of precursor gases that produce condensable products. It is currently estimated that SOA accounts for 10–35% of the organic aerosol found in typical urban atmospheres (Schauer et al., 1996, 2002b). The quantity and source origin of SOA must be identified before effective emissions control programs can be implemented for this pollutant. Chamber experiments have identified the first few generations of reaction products from different classes of parent VOC compounds (see e.g. Dalton et al., 2005; Jaoui et al., 2004; Jang and Kamens, 1999; Keywood et al., 2004; Cocker et al., 2001; Kalberer et al., 2000; Yu et al., 1999; Jungkamp et al., 1997). These studies have revealed that the reaction pathways for organic species are complex with numerous products. Statistical source apportionment methods for primary organic aerosol (Schauer et al., 1996, 2002b) are not able to determine the source-origin of SOA because chemical ‘‘fingerprints’’ for individual SOA species produced by different sources are not available. Recently, a mechanistic technique has been developed for the source apportionment of secondary PM (Mysliwiec and Kleeman, 2002; Ying and Kleeman, 2006). Precursor emissions from different sources are tracked separately through an air quality model as they are transformed by chemical reactions leading to the formation of low-volatility products that can partition to the particle phase. Each product is labeled with the source-identity of the reactant so that source attribution information is preserved. The new technique has been used for the regional source apportionment of particulate nitrate, sulfate, and ammonium ion in two of the
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most heavily polluted airsheds in the United States: the South Coast Air Basin (SoCAB) and the San Joaquin Valley (SJV) (Ying and Kleeman, 2006). The purpose of the current study is to describe the application of the new apportionment technique for secondary PM to reveal the sources of SOA formation in the SoCAB during a severe particulate air quality episode that occurred on 7–9 September 1993. The UCD/CIT air quality model is combined with a version of the Caltech Atmospheric Chemistry Model (CACM) that is expanded to predict SOA source contributions from diesel engines, catalyst-equipped gasoline engines, non-catalyst equipped gasoline engines, combustion of fuel with high sulfur content, biogenic sources, and other sources with a resolution of 5 km across the entire SoCAB. 2. Background The basecase version of the CACM implemented in this project contains approximately 430 reactions involving 210 species describing the current state-ofthe-science understanding of SOA formation in urban atmospheres (Griffin et al., 2002a, 2003, 2005). The formation of SOA is initiated by the reaction of a parent VOC compound with a suitable oxidant (OH, O3, or NO3) leading to the formation of further reactive species. As an example, Table 1 illustrates reactions that produce SOA from the CACM parent species high yield alkanes(ALKH). The condensable products from this reaction sequence are AP11, AP12, and UR20. Table 2 illustrates the same reaction sequence that has been modified to retain information for two sources denoted with the suffix ‘‘_X1’’ and ‘‘_X2’’, respectively. These reaction mechanisms can be converted into a set of ordinary differential equations describing the evolution of species concentrations with time. The total predicted concentration of each SOA species is identical for both mechanisms (e.g.
Table 1 Chemical reactions leading to the formation of SOA species from high-SOA yield alkanes ALKH+OHQH2O+RO232+RO2T RO232+NOQCF(33) AP11+CF(34) NO2+CF(34) RO241+CF(34) RO2T RO232+RO2TQRO241+2 RO2T+O2 RO232+HO2QOH+RO241+RO2T RO241+NOQCF(35) AP12+CF(36) NO2+CF(36) HO2+CF(36) UR20 RO241+RO2TQHO2+UR20+RO2T+O2 RO241+HO2QHO2+OH+UR20 Variables starting with the characters ‘‘CF’’ are adjustable coefficients in CACM.
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Table 2 Source-resolved chemical reactions leading to the formation of SOA species from high-SOA yield alkanes ALKH_X1+OHQH2O+RO232_X1+RO2T ALKH_X2+OHQH2O+RO232_X2+RO2T RO232_X1+NOQCF(33) AP11+CF(34) NO2+CF(34) RO241_X1+CF(34) RO2T RO232_X2+NOQCF(33) AP11+CF(34) NO2+CF(34) RO241_X2+CF(34) RO2T RO232_X1+RO2TQRO241_X1+2RO2T+O2 RO232_X2+RO2TQRO241_X2+2 RO2T+O2 RO232_X1+HO2QOH+RO241_X1+RO2T RO232_X2+HO2QOH+RO241_X2+RO2T RO241_X1+NOQCF(35) AP12_X1+CF(36) NO2+CF(36) HO2+CF(36) UR20_X1 RO241_X2+NOQCF(35) AP12_X2+CF(36) NO2+CF(36) HO2+CF(36) UR20_X2 RO241_X1+RO2TQHO2+UR20_X1+RO2T+O2 RO241_X2+RO2TQHO2+UR20_X2+RO2T+O2 RO241_X1+HO2QHO2+OH+UR20_X1 RO241_X2+HO2QHO2+OH+UR20_X2 Two sources are used in the current example, denoted by the suffix _X1 and _X2. See Table 1 for the original reaction sequence.
AP11 ¼ AP11_X1+AP11_X2), but the mechanism shown in Table 2 retains the source information of the compounds. In the current study, eight sources of SOA were tracked through the chemical mechanism, leading to an additional 1616 reactions and 440 species. This increases the computational time required to integrate this equation set, but the time needed for gasphase chemistry is still small compared to the gasto-particle conversion calculations. The mechanistic source apportionment method therefore has the same computational burden as other simulations carried out with the basecase version of CACM. 3. Model description The UCD/CIT photochemical air quality model is based on the CIT airshed model (McRae et al., 1982; Harley et al., 1993) with additions to track airborne particles as a source-oriented external mixture (Kleeman and Cass, 2001). Particles emitted from different sources are described with 15 discrete particle sizes equally spaced on a logarithmic scale between 0.01 and 10.0 mm particle diameter. The model includes a full description of transport, deposition, gas-phase chemical reaction, gas-to-particle conversion, and aqueous-phase chemical reaction. Previous studies (Mysliwiec and Kleeman, 2002; Ying and Kleeman, 2006; Kleeman and Cass, 2001, Held et al., 2004) have described the UCD/CIT source-oriented air quality model, and so only those aspects that were changed during the current project are discussed here. The CACM (Griffin et al., 2002a, 2005) and the thermodynamic
data describing the partitioning of semi-volatile reaction products between the gas phase, condensed organic phase, and condensed aqueous phase (Griffin et al., 2005; Pun et al., 2002) were adapted to work with the UCD/CIT air quality model. Ethane was removed from the lumped model species ALKL and tracked as an individual species so that a more appropriate rate constant could be specified for reaction with OH. A single model subroutine was created that simultaneously predicted the vapor pressure of semi-volatile inorganic and organic species above each particle by calculating the thermodynamic equilibrium concentration for each species between a solid (inorganic species only), organic (organic species only) and aqueous (inorganic and organic species) phase within each particle. Activity coefficients for inorganic species were calculated using the method described by Kusik and Meissner (1978). Activity coefficients for organic species were calculated using UNIFAC (Fredenslund et al., 1977). Experimental data describing interactions between organic and inorganic species are sparse (Clegg et al., 2001, 2003) and so these interactions were neglected in the current study. Likewise, heterogeneous acid-catalyzed reactions leading to the formation of lowvolatility organic compounds have been observed in several studies (Czoschke et al., 2003; Jang et al., 2003; Kalberer et al., 2004), but a comprehensive representation of these reactions suitable for use in an air quality model has not yet been formulated. In the current study, the general effect of acidcatalyzed reactions was simulated by adjusting the vapor pressures of surrogate compounds used to
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represent the semi-volatile organic species as discussed by (Griffin et al. (2005). This procedure was uniformly applied to all particles. To calculate the thermodynamic equilibrium state of each particle, the concentration of hydrogen ion [H+] in the aqueous phase was first calculated based on the distribution of inorganic ions between the solid and aqueous phases using the method described by Wexler and Seinfeld (1991) as modified by Kleeman et al. (1997). The partial vapor pressure of semi-volatile inorganic species HNO3, HCl, and NH3 was then calculated. In this algorithm, sulfuric acid has zero vapor pressure independent of pH. The distribution of organic species between the aqueous and organic phases was considered next. Several organic species are acids that undergo partial dissociation in the aqueous phase. The fraction of each semi-volatile organic species in the condensed organic phase on each particle was calculated with an iteration scheme: C iorg ¼
TOTi . , 1 þ Rðgiorg TOTaq giaq TOTorg Þ
(1)
where Ciorg is the concentration (mmol) of chemical species i in that particle’s organic phase, TOTi is total concentration (mmol) of chemical species i in the organic and aqueous phase of that particle, TOTaq is the total concentration (mmol) of all species in the aqueous phase, TOTorg is the total concentration (mmol) of all species in the organic phase, giorg is the activity coefficient of the chemical species in the organic phase, and giaq is the activity coefficient of the chemical species in the aqueous phase. The factor R accounts for the effects of acid dissociation: R¼1þ
K ia;1 K ia;1 K ia;2 , þ ½Hþ ½Hþ 2
(2)
where Kia,1 is the first acid dissociation constant (mol kg1 water) for chemical species i, Kia,2 is the second acid dissociation constant (mol kg1 water) for chemical species i, and [H+] is the aqueous concentration of hydrogen ion (mol kg1 water). The value of [H+] is re-calculated after each iteration based on charge balance and the concentration of aqueous inorganic electrolytes+dissociated organic acids. In this method, the partial vapor pressures of the organic acids fully sense the effect of the inorganic ions in the aqueous phase, but the partial vapor pressures of HNO3, HCl, and
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NH3 do not directly sense the effect of semi-volatile organic acids (recall that H2SO4 has an effective vapor pressure of zero under all conditions). This semi-coupled approach is computationally efficient and suitably accurate given that the concentration of ammonium nitrate aerosol is usually far greater than the concentration of SOA and given that other factors such as the omission of organic–inorganic interactions due to lack of thermodynamic data lead to greater uncertainties in the calculation anyway. Both inorganic and organic species in the aqueous phase are used to calculate the partial vapor pressure of water above the particle surface. The exchange of all semi-volatile species (including water) between the gas phase and particles is calculated as a dynamic process using equations described by Kleeman et al. (1997). At low RH, when aerosol water content becomes so small that some particles become solid, the volume available for aqueous organic species to partition into becomes negligible, reducing aqueous organic concentrations to zero. Under these conditions, the only semi-volatile species contained on particles are solid inorganic species and SOA in the condensed organic phase. Dry particles can spontaneously form a new aqueous phase when RH increases. Fig. 1 shows the different pathways leading to the formation of SOA products in the CACM. Each pathway begins with a parent hydrocarbon denoted by a clear box surrounded by a solid border. Volatile intermediate products are denoted by a clear box with a dashed border, and semi-volatile SOA products are denoted with a shaded box. The species names shown in Fig. 1 correspond to the description provided by Griffin et al. (2002a, 2005) and Pun et al. (2002). A summary of the general lumped categories that each semi-volatile products fall into is shown in Table 3. Figs. 1(a)–(c) show the SOA formation pathways for biogenic parent hydrocarbons BIOL (low-yield biogenics), BIOH (high yield biogenics), and ISOP (isoprene), respectively. BIOL is a lumped model species designed to represent low SOA yield biogenic compounds that is chiefly composed of a-pinene in the current simulation. BIOH is a lumped model species designed to represent high SOA yield biogenic compounds such as b-pinene, myrcene, b-phellandrene, d-limonene, terpinene, and 3-carene. ISOP represents isoprene emissions in model simulations. The results of the current study indicate that SOA derived from isoprene by CACM is negligible
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(a)
(b)
(c)
(d)
(e)
(f) Fig. 1. Formation pathways from parent hydrocarbons (clear boxes with solid lines) through intermediate products (clear boxes with dashed lines) to semi-volatile products (shaded boxes). Names refer to CACM species described by Griffin et al. (2002). Table 3 General categorization of semi-volatile organic aerosol species used in model calculations Category 1 2 3 4 5 6 7 8 9 10
Description
CACM semi-volatile species
Low carbon number High carbon number, anthropogenic, aromatic fragments, dissociative High carbon number, anthropogenic, aromatic fragments, non-dissociative High carbon number, biogenic, dissociative High carbon number, biogenic, non-dissociative High carbon number, anthropogenic, aromatic, low volatility High carbon number, anthropogenic, aromatic, high volatility High carbon number, anthropogenic, polyaromatic High carbon number, anthropogenic, alkane-derived High carbon number, biogenic, ring-retaining
UR21, UR28 RP13, RP17, RP18, UR26, UR29, UR30
during the period 9 September 1993. While this result may reflect missing formation pathways, it is beyond the scope of the current study to
RPR9, RP12 UR3, UR8, UR23 UR7, UR17 AP1, AP6, UR31 ADAC, RPR7, RP14, RP19, UR2, UR14, UR27, ARAC AP10, UR11, UR15, UR19 AP11, AP12, UR20 AP7, AP8, UR5, UR6
expand the reaction pathways in CACM, and so isoprene formation products will not be discussed further.
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Figs. 1(d)–(f) show the SOA formation pathways for anthropogenic parent hydrocarbons ALKH, AROH (high-yield aromatics), AROL (low yield aromatics), and PAH (polycyclic aromatic hydrocarbons), respectively. ALKH is a lumped model species that represents long-chain alkanes (4C12). AROH is a lumped model species that represents high SOA yield aromatic compounds such as ethyltoluene, butyl-benzene, etc. AROL is a corresponding lumped model species that represents low SOA yield aromatic compounds such as trimethyl-benzene, xylenes, etc. Both AROH and AROL can react to form phenolic compounds represented with the lumped model species PHENOL. Phenolic compounds can also be emitted directly from combustion sources. Likewise, AROL can react to form benzaldehyde (model species BALD) which can also be directly emitted from combustion sources. PAH is a lumped model species used to represent polycyclic aromatic hydrocarbons such as naphthalenes, phenanthrene, etc. The partitioning of the 36 semi-volatile products denoted by the shaded boxes in Fig. 1 between the gas phase, condensed organic phase, and condensed aqueous phase is determined by their vapor pressures and activity coefficients. To increase the computational efficiency of the calculation, each of the 36 semi-volatile species is represented by one of the 10 surrogate species shown in Fig. 1 of Griffin et al. (2003) with similar chemical structure. A more detailed discussion of the structure and chemical properties of the surrogate species is contained in several recent publications (Griffin et al., 2002a, b, 2003, 2005). 4. Model application The preparation of meteorological and emissions input data needed for photochemical modeling of this episode has been described previously (Griffin et al., 2002b; Fraser et al., 2000) and so only a brief summary is provided here. Surface and elevated meteorological parameters were measured at +50 sites and two sites, respectively. The meteorological measurements were interpolated using a diagnostic meteorology model (Goodin et al., 1979) to produce continuous meteorological fields with hourly resolution. Wind fields and mixing depth fields have been updated in the current publication to correct the errors associated with data extraction and to improve precision relative to previous work (Griffin et al., 2002b; Fraser et al., 2000). Background
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concentrations on the western edge of the model domain were specified based on measurements made at San Nicolas Island off the California coast during the study period. Background concentrations on the northern edge of the model domain were set to very low values. Model calculations were initialized at hour 0 on 7 September 1993 and allowed to proceed through hour 24 on 9 September 1993 (2 day spinup time). Results from the final day of the simulation will be discussed in the following sections, along with an analysis showing that boundary and initial conditions have little effect on the predicted concentrations. Emissions inventories created by the South Coast Air Quality Management District (SCAQMD) as part of the 1997 Air Quality Management Plan were adapted for 7–9 September 1993 by adjusting evaporative emissions based on temperature. The emissions from the Los Angeles International Airport (LAX) were updated based on the inventory developed for the summer 1997 Southern California Ozone Study (SCOS97). Biogenic emissions developed for the late August 1987 episode of the Southern California Air Quality Study (SCAQS) were adjusted for the temperature conditions experienced on 7–9 September 1993. Particle-phase emissions were speciated using measured emissions profiles (Cooper et al., 1989; Taback et al., 1979; Schauer et al., 1999a, b, 2001, 2002a, c) and then combined into nine inorganic model species and nine lumped carbonaceous model species (normal alkanes, PAH, oxygenated PAH, diacids, aliphatic acids, substituted monoaromatic compounds, cyclic petroleum biomarkers, ‘‘other organic compounds’’, and elemental carbon). The size distribution of PM emissions from different sources was specified in 15 sections spanning 0.01–10 mm particle diameter based on source emission measurements (Hildemann et al., 1991a; Kleeman et al., 1999, 2000). Gas-phase organic emissions were speciated using measured emissions profiles (Schauer et al., 1999a, b, 2001, 2002a, c; Harley et al., 1992) and then combined into 24 lumped model organic species (Griffin et al., 2002a) based on chemical structure, reactivity, and experimentally determined SOA formation potential. Nine of the lumped parent VOC species can react to form SOA (see Fig. 1). Fig. 2 shows the 24-h average emission rate for each of these nine lumped parent VOCs in the SoCAB on 9 September 1993. Figs. 2(a) and (b) show that the regions with the largest emission rates
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Fig. 2. Average emission rates for parent hydrocarbons on 9 September 1993. Units are ppm m day1. The theoretical maximum change in atmospheric concentration can be calculated in units of ppm day1 by dividing each value by the height of the first model layer (38.5 m). Atmospheric transport, mixing, deposition, and chemical reactions reduce the atmospheric concentrations.
of high and low SOA yield biogenic species are located around the edges of the SoCAB where natural vegetation is abundant. Typical emission rates for high-SOA yield biogenic species (BIOH) were 1 ppm m day1, with peak values of 1.76 ppm m day1 to the south of central Los Angeles. Typical emissions rates for low SOA yield biogenic species (BIOL) were also 1 ppm m day1, with peak values of 4.09 ppm m day1. Figs. 2(c)–(e) show that anthropogenic emissions of parent VOCs are highest in the center of the SoCAB and along the transportation corridors that lead to this region. Maximum emissions rates of high SOA yield alkanes (ALKH) and PAH are 0.5 and 0.99 ppm m day1, respectively. Maximum emissions of high- and low-yield aromatic compounds
AROH+AROL are an order of magnitude higher at 42.54 ppm m day1. The AROH and AROL emission rates are shown together because they lead to the formation of the same reaction products (see Fig. 1). 5. Source apportionment results A detailed comparison has been carried out between the predicted and measured concentration of gas- and particle-phase pollutants on 8–9 September 1993 in a separate study (Ying and Kleeman, submitted). The performance of the UCD/CIT/CACM model was found to be satisfactory during this episode. Predicted ozone concentrations had a normalized gross error of 0.49, and
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both NOx and VOC concentrations showed reasonable agreement with observations. Predicted 24h average PM2.5 concentrations also showed good agreement with measurements, but concentrations of elemental carbon (EC) and organic carbon (OC) in the PM2.5 size fraction were under-predicted during morning rush hour at multiple locations, likely because of problems with the emissions inventory. The concentration of OC in the PM2.5 size fraction was also under-predicted during the afternoon hours with the highest photochemical activity. This may be related to unknown sources of POA and/or SOA in the SoCAB. The field measurements available for the current study were not specific enough to support an analysis of primary vs. secondary organic aerosol concentrations with time resolution shorter than 24 h. Measured concentrations of gas-phase species
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within the AROL and AROH lumped model species agreed reasonably well with predictions. Measured concentrations of gas-phase species within the PHEN and BALD lumped model species were generally lower than the predicted values, possibly because some of the species within these classes were not measured. The results shown in the remainder of the current study identify the origin of the SOA that forms from known sources through chemical pathways represented in the CACM. Fig. 3 shows the predicted 24-h average concentration of SOA in the SoCAB on 9 September 1993, which forms from each of the parent hydrocarbons shown in Fig. 1. The regions with the maximum concentration for each SOA product generally occur downwind of the highest emissions rate for the parent VOCs. Figs. 3(a) and (b) show that the
Fig. 3. Predicted 24-h average concentration of SOA derived from different parent hydrocarbons on 9 September 1993. Units are mg m3.
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predicted concentration of biogenic SOA products is greatest around the edges of the SoCAB. The maximum predicted concentrations of SOA derived from high and low yield biogenic species is 0.96 and 0.41 mg m3, respectively. Figs. 3(c)–(e) show that the predicted concentrations of anthropogenic SOA are highest in the region east (downwind) of central
Los Angeles. The maximum predicted 24-h average concentration of SOA products derived from alkanes and PAH are 0.1 and 0.19 mg m3, respectively. The predicted concentration of SOA products derived from aromatic VOCs are considerably higher, with maximum 24-h average concentrations of 2.04 mg m3.
Fig. 4. Total predicted SOA ( a) and source contributions to predicted SOA concentrations( b–h) on 9 September 1993. Units are mg m3.
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Fig. 4a shows that the predicted 24-h average SOA concentration from all sources on 9 September 1993 has a peak value of 3.49 mg m3 in the region southeast of Claremont. This predicted SOA concentration is lower than the values shown in previous studies (Griffin et al., 2002b; Held et al., 2005) because recent updates to the surrogate species used in CACM (Griffin et al., 2005) corrected an upward bias in the predicted SOA concentration. Additional corrections to the meteorological fields and the use of on-line calculations for UV photolysis rates (Ying and Kleeman, 2003) result in lower predictions of SOA concentrations compared to other recent studies (Vutukuru et al., 2006). Figs. 4(b)–(d) show that catalyst-equipped gasoline engines, non-catalyst-equipped gasoline engines, and diesel engines contribute 1.2, 0.61, and 0.12 mg m3, respectively, to the predicted total 24-h average SOA concentrations. High sulfur fuel
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combustion is predicted to form relatively small amounts of SOA (o0.03 mg m3) mainly in the plume downwind of the LAX. Fig. 4(f) shows that the amount of SOA formed from ‘‘other anthropogenic sources’’ has a peak concentration of 0.42 mg m3 in the region southeast of Claremont. Sources within this category include fuel combustion other than diesel engines, gasoline engines, or high sulfur fuel. Fig. 4(g) shows that biogenic sources contribute up to 1.37 mg m3 to the predicted 24-h average SOA concentration primarily at the northern and southern end of the SoCAB. Finally, Fig. 4(h) shows that initial and boundary conditions contribute 0.03 mg m3 to predicted SOA concentrations at the far downwind end of the air basin. This SOA is produced by measured concentrations of precursor gases in the interior of the model domain at the start of the simulation or at the edges of the model domain during the simulation.
Fig. 5. Source apportionment of SOA derived from alkanes averaged over each hour of the day on 9 September 1993. Units are mg m3.
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Fig. 6. Source apportionment of SOA derived from aromatic compounds averaged over each hour of the day on 9 September 1993. Units are mg m3.
Figs. 5–7 illustrate how the different sources shown in Fig. 4 contribute to the SOA concentrations shown in Fig. 3. Figs. 5(a)–(c) show that the spatial distribution of SOA produced from alkanes released from diesel engines and gasoline engines is similar, with the highest concentrations occurring southeast of Azusa. The predicted maximum 24-h average SOA concentration produced by alkanes released from diesel engines (0.082 mg m3) is larger than the contribution from gasoline engines (o0.003 mg m3) but still small in absolute terms. Figs. 5(d) and (e) show that the combustion of fuel with high sulfur content and other sources also contribute a negligible amount of predicted alkaneSOA during the current study. Figs. 6(a)–(c) show that the predicted regional distribution of 24-h average aromatic-SOA pro-
duced by diesel engines and gasoline engines is similar, with the highest concentrations occurring in the region southeast of Claremont. The maximum predicted 24-h average aromatic-SOA concentrations associated with diesel engines, noncatalyst-equipped gasoline engines, and catalystequipped gasoline engines are 0.01, 0.50, and 0.94 mg m3, respectively. Fig. 6(d) shows that combustion of fuel with high sulfur contributes only 0.01 mg m3 of aromatic-SOA in the region downwind of the LAX. Fig. 6(e) shows that other emissions sources produce approximately 0.36 mg m3 of aromatic-SOA in the region southeast of Claremont. Initial and boundary conditions of aromatic compounds produce 0.025 mg m3 of aromatic-SOA at the far eastern edge of the model region.
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Fig. 7. Source apportionment of SOA derived from PAH averaged over each hour of the day on 9 September 1993. Units are mg m3.
Fig. 7 shows predicted regional source contributions to SOA produced by PAHs in the SoCAB averaged over each hour of the day on 9 September 1993. Fig. 7c shows that catalyst-equipped gasoline engines dominate the predicted PAH-SOA concentrations with maximum 24-h average concentrations of 0.12 mg m3 in the region southeast of Claremont. This trend reflects the fact that approximately 94% of the vehicle miles traveled in the SoCAB during the current study period are associated with gasoline-powered vehicles. Fig. 8 illustrates the diurnal pattern of predicted SOA concentrations on 9 September 1993 averaged over the inland portion of the active model region (no ocean cells included). Fig. 8a shows SOA concentrations associated with different parent VOCs (see Fig. 1). The maximum predicted 1-haverage SOA concentration for the inland portion
of the model domain is 0.96 mg m3 at 24:00 on 9 September 1993. In general, predicted SOA concentrations are larger during the morning and evening hours because increased RH promotes the formation of aqueous-phase SOA. Fig. 8a shows that SOA produced from aromatic and biogenic compounds contribute to the predicted regionalaverage SOA concentrations during the night hours, but biogenic SOA vanishes during the day as temperature increases, RH falls, and the water content of particles decreases. Predicted concentrations of SOA produced from aromatic, alkane, and PAH compounds have less diurnal variation because these species favor partitioning to the organic phase. Fig. 8b shows SOA concentrations associated with emissions from gasoline engines, diesel engines, high-sulfur fuel combustion, biogenic sources, other sources, and initial/boundary
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Fig. 8. Diurnal variation of SOA concentrations averaged across the SoCAB shown by speciation (a) and source origin (b). SOA concentrations are greater in the morning and during the evening partly because of larger aerosol water content promoting the partitioning of SOA to the aqueous phase.
Table 4 Spatially and temporally averaged SOA mass derived from low-yield biogenics (BIOL), high-yield biogenics (BIOH), high-yield alkanes (ALKH), aromatics (ARO), and polycyclic aromatic hydrocarbons (PAH) in the SoCAB on 9 September 1993 Source
SOA Species Family
Total
BIOL
BIOH
ALKH
ARO
PAH
Diesel engines Non-catalyst gasoline engines Catalyst gasoline engines High sulfur fuel combustion Other anthropogenic sources Biogenic sources Initial/boundary conditions
0.000 0.000 0.000 0.000 0.000 0.073 0.000
0.000 0.000 0.000 0.000 0.000 0.084 0.000
0.013 0.000 0.000 0.001 0.001 0.000 0.000
0.003 0.115 0.209 0.002 0.071 0.000 0.003
0.005 0.005 0.016 0.000 0.000 0.000 0.000
0.021 0.120 0.225 0.003 0.072 0.157 0.003
Total
0.073
0.084
0.015
0.402
0.027
0.601
3
Units are mg m .
conditions. Predicted SOA concentrations derived from anthropogenic sources are relatively constant throughout the day. The single largest anthropogenic source of regional-average SOA is predicted to be catalyst-equipped gasoline engines, followed by non-catalyst-equipped gasoline engines and diesel engines. Table 4 shows the predicted 24-h average SOA concentration for the inland portion of the modeling region on September 9, 1993 sorted according to the source-origin of the SOA and the class of SOA product. Total 24-h average SOA concentrations for the entire inland portion of the model region are predicted to be 0.6 mg m3. Approximately 67% of this SOA is produced by aromatic compounds, 26%
from biogenic compounds, and the remainder from alkanes and PAHs. Diesel engines, non-catalystequipped gasoline engines, and catalyst equipped gasoline engines account for 3%, 20%, and 38% of the predicted regional-average SOA concentration, respectively. Other primary sources contributed 12% and initial/boundary conditions contributed 0.5% to predicted SOA concentrations. 6. Conclusions The predicted composition and source-origin of SOA concentrations in the SoCAB is a strong function of location. Predicted SOA concentrations downwind of central Los Angeles are dominated by
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aromatic compounds originally released from gasoline engines, with smaller contributions from diesel engine alkanes and gasoline engine PAHs. The maximum 24-h average predicted SOA concentration on 9 September 1993 is 3.49 mg m3 in the region southeast of Claremont. Gasoline engines (catalyst+non-catalyst-equipped) contribute 1.81 mg m3 to this peak concentration, with additional contributions from diesel engines (0.12 mg m3). The maximum SOA concentrations associated with other sources occur at different locations. The maximum predicted SOA concentration associated with high sulfur fuel combustion is 0.03 mg m3 in the region adjacent to the LAX. The maximum predicted SOA concentration associated with biogenic sources is 1.37 mg m3 in the northern and southern regions of the air basin near the coast. SOA concentrations associated with anthropogenic sources are predicted to be weakly diurnal, with slightly lower concentrations during the day as mixing depth increased. SOA concentrations associated with biogenic sources are predicted to be strongly diurnal, with large concentrations of aqueous biogenic SOA at night when coastal RH is larger and smaller biogenic SOA concentrations during the day when aerosol water content is reduced. While the current study represent a stateof-the-science prediction for particulate organic carbon concentrations in the atmosphere, the model results still under-predict observed carbon concentrations by approximately 50% (Ying and Kleeman, submitted). This finding is consistent with previous photochemical modeling studies (Kleeman and Cass, 2001) and with receptor-oriented statistical studies (Schauer et al., 2002d) for the SoCAB. The combined results of these previous studies and the current analysis suggests that either an unknown source of primary organic aerosol or an unknown formation pathway for secondary organic aerosol remains to be discovered for the SoCAB.
Acknowledgements This research was supported by the Environmental Protection Agency Science To Achieve Results (STAR) program under Grant no. 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
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the Agency and no official endorsement should be inferred.
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