Validation of the Chemical Mass Balance Receptor Model Applied to

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Environ. Sci. Technol. 1994, 28, 1633-1649

Validation of the Chemical Mass Balance Receptor Model Applied to Hydrocarbon Source Apportionment in the Southern California Air Quality Study Erlc M. Fujita,’ John G. Watson, Judith C. Chow, and Zhiqiang Lu Energy and Environmental Engineering Center of the Desert Research Institute, P.O. Box 60220, Reno, Nevada 89506

The non-methane hydrocarbon (NMHC) data base acquired during the Southern California Air Quality Study (SCAQS) was used to assess the performance of the chemical mass balance (CMB) receptor model following the CMB applications and validation protocol. As a prelude to the actual CMB effective variance runs, initial source contribution estimates were made to determine the optimal combination of source profiles and fitting species. Several different source compositionprofiles were selected for major source types to determine the effect of alternative profiles on the source contribution estimates and on overall model performance. The ambient NMHC data were also examined by less complex tracer and bivariate regression methods to gain additional insights about probable source contributions, spatial and temporal patterns of emission sources, and photochemical reactions of various hydrocarbon species. NMHC was apportioned to motor vehicle exhaust, liquid fuel, gasoline vapor, gas leaks, architectural and industrial coatings, and biogenic emissions. Attribution of source contributions among the motor vehicle source categories is highly sensitive to the abundance of ethyne and light olefins to NMHC in the exhaust composition profile, which varies with emission control technologyand vehicle maintenance and operation.

Introduction The Southern California Air Quality Study (SCAQS) (1)was conducted in the summer and fall of 1987 to better understand the causes of excessive pollution concentrations in California’s South Coast Air Basin (SoCAB).The SCAQSfield study consisted of 11intensive sampling days in the summer (June 19, 24, and 25, July 13-15, August 27-29, and September 2 and 3) and six intensive sampling days in the fall (November 11-13 and December 3,10, and 11). Speciated hydrocarbons were measured for up to five periods per day at nine sites during the summer and at six sites during the fall. This paper applies the chemical mass balance (CMB) receptor model to these data to estimate spatial and diurnal changes in source contributions and evaluates the applicability of the CMB receptor model to the SCAQS non-methane hydrocarbon (NMHC) ambient data and source profiles following the steps outlined in the applications and validation protocol for PMlo source apportionment (2). Model calculations were made using the US.EPA/DRI Version 7.0 of the CMB model (3). The CMB model consists of a least-squares solution to a set of linear equations which expresses each receptor concentration of a chemical species as a linear sum of products of source profile species and source contributions. The sourceprofile species (the fractional amount of the species in the NMHC

* Corresponding author. 0013-936X/94/0928-1633$04.50/0

0 1994 American Chemical Society

emissions from each source type) and the receptor concentrations, each with realistic uncertainty estimates, serve as input data to the CMB model. The output consists of the contributions for each source type to the total ambient NMHC as well as to individual hydrocarbon concentrations. The model calculates values for contributions from each source and the uncertainties of those values. Input data uncertainties are used both to weigh the relative importance of the input data to the model solution and to estimate uncertainties of the source contributions. The CMB model (4) has been widely applied to the apportionment of suspended particles to their sources (5, 6). Many multivariate data sets have been assembled for ambient particulate chemical concentrations and source profiles (7, 8). Few valid data sets for NMHC ambient concentrations and source profiles exist to which the CMB can be applied (e.g., refs9 and 10). While many validation studies have been conducted to assess the performance of the CMB applied to suspended particles (e.g., refs 11and 12), systematic validation studies applicable to the apportionment of NMHCs to sources have not been performed. Though the CMB applications and validation protocol was originally developed for PMlo source apportionment, it contains many general features which are applicable to the source apportionment of other pollutants. This is the first time that the CMB applications and validation protocol has been applied to NMHC receptor modeling. As part of this process, modifications and amplifications are identified that will allow this process to be generalizedto the source apportionment of pollutants other than suspended particles.

Ambient Measurements The surface sampling sites, shown in Figure 1, were located in Anaheim, Azusa, Burbank, Claremont, Hawthorne, Long Beach, Los Angeles, and Rubidoux. The Azusa and Claremont sites were operated during the summer study only. One-hour integrated ambient air samples were collected by AeroVironment in 6-L electropolished stainless steel canisters at all SCAQS sites during the summer and fall beginning at 0700,1200, and 1600 (PDT in summer, PST in winter) (13). Additional samples were collected at 0500, 0900, and 1400 at Claremont and Long Beach during the summer and at Los Angeles and Long Beach during the fall. A total of 473 surface samples were collected during the study. The samples were analyzed for CZ-C~hydrocarbons at the Oregon Graduate Institute (OGI) (14) and for C2-Cl2 hydrocarbons by the Heterogeneous Chemistry and Aerosol Research Branch of the US.Environmental Protection Agency’s Atmospheric Research and Exposure Assessment Laboratory (15). Results of a laboratory comparison study involving the two laboratories performing the SCAQS analyses and two other laboratories showed that the Environ. Scl. Technol., Vol. 28, No. 9, 1994

1635

Flgure 1. Sampling network for the 1987 Southern California Air Quality Study.

coefficients of variation (CV) among the four laboratories were generally within f10 92 for hydrocarbon concentrations above 5 ppb (16). The reported minimum detection limit (MDL) is 0.2 ppb of C for both U.S. EPA and OGI analyses. In this paper, NMHC refers to C ~ - C Ihydrocarbons ~ that were collected in stainless steel canisters and measured by gas chromatography with flame ionization detection (GC-FID). This is an operational definition which reflects the selectivity and sensitivity of the analytical method that was applied. Total NMHC includes all FID chromatographic peaks except peaks verified to be oxygenated compounds. The SCAQS NMHC data base also includes several halogenated hydrocarbons which have lower response to FID depending upon the amount of substitution. Since contributions of sources of halogenated hydrocarbons to total NMHC were consistently small (1-32,) in initial CMB runs, adjustments for lower responses to FID for halogenated hydrocarbons were not applied in the ambient data base. The calculated source contribution estimates for sources emitting these compounds should be considered lower limits. The CMB model was applied in this study to the SCAQS NMHC data set only, and the weight fractions for individual species in the source composition profiles were normalized to total NMHC. The SCAQS NMHC data base was validated by Lurmann and Main who also describe the spatial and temporal patterns of NMHC concentrations and composition. The validated NMHC data base includes OGI’s C+23 hydrocarbons and the U S . EPA’s C4-C12 hydro-

(In,

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Environ. Sci. Technol., Vol. 28, No. 9, 1994

carbons. The EPA’s c2-c3 data were included in the SCAQS data base only for cases where OGI’s CZ-C~data were missing (71 out of 399 valid surface samples). In these cases, only the total CZ’Swere included in the data base as EPA analytical conditions did not allow complete resolution of the CZcompounds. Because ethene (CZ)and ethyne (CZ)are key species in motor vehicle exhaust, the 71 samples with unresolved C2 data were removed from the final data base used in this study. Additionally, since the first day of the study was considered a “shake-down” day, data for June 19,1987,were not considered in these analyses. CMB source apportionments were individually performed on 305 SCAQS ambient NMHC samples. For this study, an uncertainty was estimated for each concentration value from the following formula a(c) = 1.42

x M D L ) ~+ (cv x C i 2

where a(c) is the root mean square error for the concentration value (c), MDL is the minimum detection limit for the GC-FID method (0.2 ppb of C), and CV is the coefficient of variation (*lo%) reported by Fujita and Collins (16) for the SCAQS laboratory comparison study. Ambient data near the detection limit are automatically given less weight in the CMB calculations because this formula assigns higher uncertainties to these values. The ambient data (originally reported in units of ppb of C) were converted to micrograms per cubic meter prior to calculating the weight percentages using species-specific conversion factors. These uncertainties are explicit inputs to the CMB receptor model. They are propagated by the

Table 1. Source Profiles Applied in SCAQS NMHC Receptor Modeling source type vehicle exhaust vehicle exhaust vehicle exhauste vehicle exhaust vehicle exhaust vehicle exhaust vehicle exhaust gasoline evaporation gasoline evaporation gasoline evaporation liquid gasoline liquid gasoline gasoline vapor gasoline vapor solvent solvent solvent solvent biogenic gas gas gas unidentified

data base identifier Exh801a Exh80la ExhCTb ExhCS564” E~hSt565~ ExhHS566” AOCompd AODiurnd AOHSoakd AORunLsd LGS709a LGW729a VGS710a VGW73oa AC0atl96~ IC0at783~ Degreasea DryClean” Biogenic CNGC GNGc LPGC Unid

description of source profile FTP composite from ARB MEDS 801 (EPA 46-car study) Exh8Ol with more appropriate splits for unresolved species mean composition of 13 samples from the Caldecott Tunnel mean incremental cold start, Auto/Oil Program current MEDS 564 mean incremental stabilized, Auto/Oil Program current MEDS 565 mean incremental hot start, Auto/Oil Program current MEDS 566 FTP composite from Auto/Oil Program older fleet diurnal evaporative emissions, Auto/Oil Program older fleet hot soak evaporative emissions, Auto/Oil Program older fleet running loss evaporative emissions, Auto/Oil Program older fleet summer liquid gasoline, MEDS 709 with updated sales weighting winter liquid gasoline, MEDS 729 with updated sales weighting summer gasoline headspace, MEDS 710 with updated sales weighting winter gasoline headspace, MEDS 730 with updated sales weighting architectural coatings, composite solvent, MEDS 196 industrial coatings, solvent based, MEDS 783 industrial degreasers, MEDS 515 dry cleaing solvents, MEDS 516 isoprene emission from biogenic sources commercial natural gas geogenic natural gas liquefied petroleum gas sum of unidentified species

*

e

Ref 18. The number corresponds to the profile number in ARB’SModeling Emissions Data System (MEDS). Ref 23. Ref 31. d Ref 29. Contains running and resting losses as well as exhaust.

model mathematics to calculate the standard errors of source contribution estimates and to give lower influence to imprecise values in the least square estimation process. Investigators are encouraged to quantify and report measurement precision with their ambient data so that they will be more applicable to receptor models.

Source Profiles The most applicable, though not ideal, NMHC profiles for SCAQS data are those compiled by the California Air ResourcesBoard‘s (ARB)ModelingEmission Data System (MEDS) (18) and from measurements from the recent AutoIOil Program (19). Several of the MEDS profiles have been used to speciate the SCAQS reactive organic gas (ROG) emissions inventory for photochemical modeling (20). An added benefit of using these profiles for receptor modeling of SCAQS data is to see how well they reproduce the ambient measurements. If these profiles cannot perform adequately in CMB source apportionment of ambient measurements, they are unlikely to supply accurate emission input to photochemical models. Several of the MEDS source profiles have been adjusted with updated weighting factors, and weight percentages for unresolved species or groups have been allocated to individual species. NMHC compounds other than the 60 NMHCs in the ambient data base or those which are reported as “unidentified” were grouped into a category named “others”. While this is often deemed adequate for source-oriented modeling since “others” do not usually constitute more than a few percent of reactive organic gases, it is undesirable for receptor models because the detection of individual species, even at low levels, can minimize collinearity and more precisely resolve one source contribution from another. Table 1lists the source types, a short identifier for each specific profile, and a brief description of the 23 source profiles used for source apportionment of the SCAQS NMHC measurements. The actual source profiles are

presented in Table 2, sections A-C. The profiles are expressed as weight percentages of total NMHC. As in the ambient data base, source profile data reported in units of ppb of C were converted to micrograms per cubic meter prior to calculating the weight percentages using species-specific conversion factors. Vehicle Exhaust. Table 2, section A, presents several NMHC profiles for motor vehicle exhaust. Exh801 was derived from the Federal Test Procedure (FTP) tests of Sigsby et al. (21) which involved 46 in-use passenger vehicles for 1975-1982 model years. Profile Exh8Ol was recalculated by the ARB from the Sigsbyet al. (21)original measurements to provide a more complete chemical breakdown. Propanelpropene, benzenelcyclohexane,and toluene/2,3-dimethylhexanewere not separately reported by Sigsby et al. (211, so ratios of 3:1, 1:1, and 9:l were assumed by the ARB for these pairs of species, respectively (22). However, motor vehicle exhaust profiles measured in the San Francisco Bay area’s Caldecott Tunnel by Zielinska and Fung (23) and in FTP dynamometer tests by Stump et al. (24,251,Hoekman (261,Burns et al. (27), and Chock and Winkler (28) are inconsistent with the abundances in Exh801. Propanelpropene, benzene/ cyclohexane, and toluene/2,3-dimethylhexaneratios of 3:22,19:1, and 1:0,consistent with those found by Zielinska and Fung (23), were applied to obtain profile Exh80la from the data of Sigsby et al. (21). The ExhCT profile in Table 2, section A, is an average of the 13 samples collected from the Caldecott Tunnel in June 1991(23). The NMHC profile in this highway tunnel represents hot stabilized emissions since most of the traffic consisted of commuters between Oakland and the residential areas of Contra Costa and Alameda Counties. Local traffic was negligible in comparison. This profile also includes running and resting loss emissions. Profiles ExhCS564, ExhSt565, and ExhHS566 (18)are averages for incremental cold start, stabilized, and hot start emissionsderived from the AutoIOilProgram exhaust Envlron. Sci. Technot., Vol. 28,

No. 9, 1994 1635

Table 2 Section A Light-Duty Vehicle Exhaust Emissions Profiles (wt 7% of NMHC)" Exh8Ol Exh8Ola ExhCT ExhCS564 ExhSt565 ExhHS566

species *ethane ethene *ethyne propene *propane *isobutane butene 1,3-butadiene *n-butane trans-2-butene cis-2-butene 3-methyl-1-butene *isopentane I-pentene 2-methylbutene *n-pentane *isoprene trans-2-pentene cis-2-pentene 2,2-dimethylbutane *cyclopentane *2,3-dimethylbutane *2-methylpentane *3-methylpentane *n-hexane 2-methyl-2-pentene *methylcyclopentane *2,4-dimethylpentane *benzene 3,3-dimethylpentane *cyclohexane *2-methylhexane *2,3-dimethylpentane *3-methylhexane 2,2,4-trimethylpentane * n-heptane *methylcyclohexane 2,3,4-trimethylpentane *toluene 2-methylheptane 3-methylheptane 2,2,5-trimethylhexane cycloheptane *n-octane 2,5-dimethylheptane ethylbenzene m,p-xylene 4-methyloctane 3-methyloctane o-xylene 1-nonene n-nonane n-propylbenzene m-ethyltoluene p-ethyltoluene o-ethyltoluene 1,2,4-trimethylbenzene n-decane *l,l,l-trichloroethane *Freon 113 others

2.87 9.90 2.78 0.89 2.67 0.92 0.86 0.00 6.44 0.22 0.59 0.27 4.93 0.31 0.63 2.21 0.00 0.43 0.27 0.44 0.21 0.81 1.91 1.37 0.92 0.27 0.92 0.82 2.06 0.00 2.06 0.76 0.76 1.38 2.91 0.78 0.64 0.17 6.39 0.33 0.55 0.76 0.00 0.44 0.14 0.79 3.32 0.00 0.00 2.22 0.00 0.31 0.69 1.63 0.00 0.28 3.33 0.17 0.00 0.00 22.27

species

AODiurn

*ethane ethene *ethyne propene *propane *isobutane butene 1,3-butadiene *n-butane trans-2-butene

0.01 0.00 0.00

1636

2.87 9.90 2.78 3.13 0.43 0.92 0.86 0.00 6.44 0.22 0.59 0.27 4.93 0.31 0.63 2.21 0.00 0.43 0.27 0.44 0.21 0.81 1.91 1.37 0.92 0.27 0.92 0.82 3.91 0.00 0.22 0.76 0.76 1.38 2.91 0.78 0.64 0.17 7.10 0.33 0.55 0.76 0.00 0.44 0.14 0.79 3.32 0.00 0.00 2.22 0.00 0.31 0.69 1.63 0.00 0.28 3.33 0.17 0.00 0.00 21.56

0.87 6.43 2.12 2.75 0.36 0.70 1.71 0.57 2.84 0.32 0.26 0.00 7.79 0.29 0.44 2.73 0.00 0.48 0.27 0.35 0.44 0.89 3.17 1.85 1.72 0.00 1.77 0.00 5.42 0.00 0.29 1.20 0.46 1.25 1.59 0.93 0.51 0.00 8.27 0.47 0.00 0.33 0.00 0.32 0.00 1.50 5.95 0.00 0.00 2.46 0.00 0.00

0.36 1.64 0.85 0.00 2.27 0.00 0.00 0.00 22.81

2.50 9.06 4.08 4.55 0.00 0.05 0.42 0.73 3.75 0.43 0.20 0.00 3.23 0.10 0.73 2.54 0.00 0.43 0.09 1.01 0.00 1.24 3.47 1.82 0.77 0.00 0.77 0.83 4.98 0.00

0.02 1.06 1.20 1.19 2.79 0.00 0.23 1.55 7.77 0.58 0.95 0.32 0.00 0.58 0.00 3.20 7.78 0.00 0.00 2.71 0.00 0.06 0.51 0.00 0.00 0.76 2.59 0.06 0.00 0.00 16.37

12.53 2.06 0.00 0.00 0.00 0.00 0.00 0.00 19.57 0.00 0.00 0.00 13.17 0.00 0.00 3.62 0.00 0.00 0.00 0.00 0.00 0.00 3.69 1.27 1.72 0.00 0.00 0.00 12.11 0.00 0.00 0.00 0.00 0.00 7.11 0.00 0.00 0.00 9.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.92 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 11.99

Section B: Gasoline Emissions Profiles (wt % of NMHC) AOHSoak A0RunLs LGS709 LGW729

0.00

0.95 1.76 0.00 0.00 37.69 0.00

Environ. Sci. Technol., Vol. 28, No. 9, 1994

0.13 0.38 0.09 0.00 0.56 0.60

0.00 0.00 0.00 0.00

0.00 0.00

0.00 0.00

0.00 0.00

11.27 0.00

54.97 0.00

3.18 0.10

0.16 1.69

0.00 0.00 0.00 0.00 0.00

0.75

6.55 10.25 0.00 2.18 0.00 0.03 0.00 0.00 7.59 0.00 0.50 0.00 6.34 0.00 0.01 5.49 0.00 0.00 0.00 0.85 0.00

1.68 6.40 3.05 2.94 0.00 0.83 0.90 7.53 0.00 0.00 0.81 1.55 1.45 4.84 0.94 0.00 1.37 9.03 0.30 0.63 0.18 0.00 0.25 0.00 2.86 5.69 0.00 0.00 1.97 0.00 0.00 0.00 0.00 0.00 0.20 1.13 0.00 0.00 0.00 3.67

AOComp 5.14 8.23 4.36 3.01 0.28 0.21 0.36 0.45 7.80 0.32 0.18 0.09 5.68 0.13 0.22 4.09 0.23 0.31 0.14 1.73 0.34 1.91 4.94 2.56 2.37 0.13 0.86 0.88 4.08 0.11 0.17 1.08 1.53 1.32 3.54 0.77 0.35 1.49 5.14 0.61 0.83 0.32 0.00 0.42 0.00 1.92 4.49 0.28 0.20 1.52 0.14 0.11 0.46 1.38 0.55 0.37 1.19 0.09 0.00 0.00

8.61

VGS710

VGW730

0.05

0.19

0.10

0.00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00

0.07 2.10 0.00 0.00 5.60 0.24

2.19 11.51 0.00

2.11 17.40

0.00

0.00 0.00

30.22 1.69

30.48 1.64

Table 2 (Continued) species

Section B: Gasoline Emissions Profiles (wt % of NMHC) LGW729 AODiurn AOHSoak AORunLs LGS709 0.00 0.00 6.35 0.31 0.62 5.25 0.00 1.05 0.60 1.14 0.44 1.61 5.36 2.59 2.79 0.39 1.14 0.84 3.72 0.00 0.42 1.20 1.31 1.30 2.32 1.06 0.68 1.38 12.29 0.63 0.80 0.41 0.00 0.54 0.00 3.82 8.96 0.60 0.62 2.96 0.00 0.23 0.67 1.98 0.85 0.59 2.40 0.00 0.00 0.00 4.73

0.00 0.00 13.29 0.57 0.89 8.11 0.00 1.39 0.85 1.40 0.47 1.54 4.99 2.00 1.85 0.30 0.69 0.53 1.45 0.00 0.47 0.70 0.61 0.64 1.11 0.45 0.32 0.61 4.31 0.33 0.36 0.00 0.00 0.30 0.00 1.20 2.68 0.00 0.47 0.93 0.00 0.00 0.27 0.88 0.41 0.24 0.73 0.00 0.00 0.00 1.23

cis-2-butene 3-methyl-1-butene lisopentane 1-pentene 2-methylbutene *n-pentane *isoprene trans-2-pentene cis-2-pentene 2,2-dimethylbutane *cyclopentane *2,3-dimethylbutane *2-methylpentane *3-methylpentane *n-hexane 2-methyl-2-pentene *methylcyclopentane *2,4-dimethylpentane *benzene 3,3-dimethylpentane *cyclohexane *2-methylhexane *2,3-dimethylpentane *3-methylhexane 2,2,4-trimethylpentane *n-heptane *methylcyclohexane 2,3,4-trimethylpentane *toluene 2-methylheptane 3-methylheptane 2,2,5-trimethylhexane cycloheptane *n-octane 2,5-dimethylheptane ethylbenzene m,p-xylene 4-methyloctane 3-methyloctane o-xylene 1-nonene n-nonane n-propylbenzene m-ethyltoluene p-ethyltoluene o-ethyltoluene 1,2,4-trimethylbenzene n-decane * l,l,l-trichloroethane *Freon 113 others species

Acoatl96

*ethane ethene *ethyne propene *propane *isobutane butene 1,3-butadiene *n-butane trans-2-butene cis-2-butene 3-methyl-1-butene *isopentane 1-pentene 2-methyl-butene *n-pentane *isoprene trans-2-pentene cis-2-pentene 2,2-dimethylbutane

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.05 16.63 0.43 0.84 7.94 0.02 1.20 0.67 1.33 0.14 1.13 3.34 1.30 1.03 0.06 0.20 0.12 0.10 0.00 0.04 0.19 0.12 0.17 0.28 0.12 0.00 0.11 0.16 0.05 0.07 0.04 0.00 0.02 0.00 0.03 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 5.19

0.13 0.10 6.60 0.34 0.39 3.63 0.00 0.69 0.34 1.05 0.46 0.80 2.59 2.03 1.94 0.00 2.44 1.09 1.98 0.69 0.57 2.26 1.79 1.90 0.00 1.96 0.97 0.00 10.67 0.00 0.00 0.00 0.00 0.95 0.00 1.95 8.66 0.00 0.00 3.25 0.00 0.56 0.55 2.73 0.00 1.14 3.32 0.19 0.00 0.00 25.24

0.22 0.08 6.19 0.31 0.38 2.56 0.00 0.59 0.32 0.91 0.49 0.71 2.32 1.77 1.69 0.00 2.19 0.98 1.88 0.52 0.50 1.68 1.34 1.51 0.00 1.58 0.86 0.00 9.64 0.00 0.00 0.00 0.00 0.90 0.00 2.02 8.63 0.00 0.00 3.42 0.00 0.58 0.65 3.12 0.00 1.21 3.87 0.25 0.00 0.00 26.08

Section C: Solvent and Other Emissions Profiles (wt % of NMHC) ICoat783 Degrease DryClean Biogenic CNG 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

69.40 0.00 0.00 0.00 21.30 2.10 0.00 0.00 3.10 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00

0.70 0.00 0.00 0.70 100.00

0.00 0.00 0.00 0.00

VGS710

VGW730

1.35 0.42 22.40 1.01 1.24 6.32 0.00 1.68 0.85 1.06 0.51 0.72 2.33 1.56 1.14 0.00 1.08 0.40 0.59 0.13 0.33 0.41 0.33 0.33 0.00 0.22 0.09 0.00 0.57 0.00 0.00 0.00 0.00 0.02 0.00 0.03 0.11 0.00 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.03 0.31 0.00 0.00 0.00 6.58

1.19 0.26 14.28 0.70 0.90 4.40 0.00 1.09 0.56 0.89 0.53 0.63 2.03 1.49 1.19 0.00 1.49 0.56 1.20 0.27 0.27 0.87 0.69 0.62 0.00 0.53 0.26 0.00 1.54 0.00 0.00 0.00 0.00 0.13 0.00 0.15 0.51 0.00 0.00 0.23 0.00 0.01 0.00 0.05 0.00 0.01 0.03 0.00 0.00 0.00 8.71

GNG

LPG

15.80 0.00 0.00 0.00 25.20 5.90 0.00 0.00 14.60

4.10 0.00 0.00 5.10 90.40 0.20 0.00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

6.20 0.00 0.00 6.20 0.00 0.00 0.00 0.00

Unid

Envlron. Scl. Technol., Vol. 28, No. 9, 1994

1637

Table 2 (Continued) species *cyclopentane *2,3-dimethylbutane *2-methylpentane *3-methylpentane *n-hexane 2-methyl-2-pentene *methylcyclopentane *2,4-dimethylpentane *benzene 3,3-dimethylpentane *cyclohexane *2-methylhexane *2,3-dimethylpentane *3-methylhexane 2,2,4-trimethylpentane *n-heptane *methylcyclohexane 2,3,4-trimethylpentane *toluene 2-methylheptane 3-methylheptane 2,2$-trimethylhexane cycloheptane *n-octane 2,5-dimethylheptane ethylbenzene m,p-xylene 4-methyloctane 3-methyloctane o-xylene 1-nonene n-nonane n-propylbenzene m-ethyltoluene p-ethyltoluene o-ethyltoluene 1,2,4-trimethylbenzene n-decane *l,l,1-trichloroethane *Freon 113 others a

Section C: Solvent and Other Emissions Profiles (wt % of NMHC) Acoatl96’ ICoat783 Degrease DryClean Biogenic CNG 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00

38.71 0.00 0.00 0.00

0.00 0.00 0.00 2.32 0.00 0.00 0.68 0.00 0.00 0.00 0.00 3.96 4.91 0.00 51.57 0.00 0.00 0.00

0.00 0.00

38.71 0.00 0.00 0.00 0.00 0.00 0.00 0.00

9.67 0.00 0.00 0.00 0.00 0.30 0.00 8.04 4.86 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.30 0.10 0.40 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.20 0.30 0.20 0.10 0.30 0.20 0.10 0.00 0.00 0.40 0.00 0.00 0.00

0.00 0.00 0.00

30.00 0.68 0.00 0.00 6.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

29.88

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

20.00 30.00 60.00

40.00 60.00

0.00

0.00 0.00 0.50

GNG

LPG

Unid

0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

100.00

2.90 1.50 1.80 0.00

2.60 0.00 0.00 0.00 0.00 0.00 0.00 4.50 0.90 1.20 2.20 0.00 0.00 3.30 0.00 0.00 0.00 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 4.40

An asterisk (*) indicates a fitting species.

experiment for the “current” (1989) vehicle fleet using industry average gasoline (fuel A, based on 1988 Motor Vehicle Manufacturers Association [MVMAI summer nationwide fuel survey). AOComp is the FTP composite profile for the Auto/Oil Program (27, 28) “older” fleet (1983-1985) using fuel A (29). The exhaust profiles for the 1989 model year vehicles from the Auto/Oil Program were used to test the CMB model’s sensitivity to alternative profiles and were not considered valid options for apportionment of the 1987 SCAQS ambient data. Evaporative Emissions of Gasoline. Table 2, section B, shows seven profiles for gasoline vapor. Profiles AODiurn, AOHSoak, and AORunLs are average diurnal, hot soak, and running loss emissions, respectively, for the Auto/Oil Programolder fleet (29). The four MEDS profiles for liquid gasoline and headspace vapor are LGS709, LGW709, LGW729, VGS710, and VGW730, based on data reported by Oliver and Peoples (30) for gasoline sold in the Los Angeles area in 1984. Oliver and Peoples (30) sampled leaded and unleaded fuels, regular and premium grade, summertime and wintertime, and analyzed the whole gasoline and headspace hydrocarbon concentrations. They used the gasoline sales distributions for 1979 to calculate composite liquid gasoline and gasoline vapor profiles for summer and winter. Harley et al. (20) 1638 Environ. Scl. Technol., Vol. 28, No. 9, 1994

recalculated these composite profiles to reflect the sales distribution for 1987. The liquid gasoline and headspace vapor profiles in Table 2B reflect the 1987 sales distribution. Additionally, the weight percentages in composite liquid gasolines that were reported by Oliver and Peoples (30) as isomers of hexane and isomers of heptane were assigned to individual isomers according to the average ratios in liquid gasoline reported by Marysohn et al. (31)and Sigsby et al. (21). The ratios are 1:3 for 2,3-dimethylbutane and 2-methylpentane and 1:3:3:1 for 3,3-dimethylpentane7 2-methylhexane, 2,3-dimethylpentane, and 3-ethylpentane. Other Sources. NMHC profiles for solvent, biogenic, natural, and liquefied petroleum gases, and unidentified NMHC are shown in Table 2, section C. MEDS profiles 196 and 783 were used for industrial and architectural coatings, respectively. A large variety of formulations are used in surface coatings, and it is unlikely that one or two profiles adequately represent the emissions from all surface coatings. Furthermore, coatings contain a variety of oxygenated compounds, which in many cases account for a majority of the solvent content. The hydrocarbon fraction of the coating solvents consists of complex

mixtures of higher molecular weight hydrocarbons that have not been adequately characterized. Isoprene is taken to constitute 100% of NMHC in the biogenic emissions profile, mainly because other biogenic species such as pinenes were not detected in SCAQS ambient samples and because detailed profiles are not available. Biogenic NMHC emissions are highly reactive in the atmosphere, and biogenic source contributions derived from CMB modeling will supply only a lower limit to the actual contributions from biogenic emissions. The commercial natural gas (CNG) profile is based on samples taken in the summer of 1972 at Los Angeles, CA, and in the summer of 1973at El Monte, CA. The geogenic natural gas (GNG) profile is based upon samples taken in the spring of 1972 in Newhall, CA, and at a well head in Redondo Beach, CA, in the fall of 1973. The composition of the samples of both types of natural gas did not vary despite the differences in time and location of sample collection. Weight fractions for compounds in the emissionsprofiles that were not identified in the ambient samples or reported as unidentifigd were combined into “others”. A single component source profile named “unidentified was used to account for total NMHC that could not be assigned to other sources. The species involved in “others” do not correspond exactly to total unidentified NMHC in the ambient data base. The ambient data consist of three categories of species: 60 uniquely identified compounds, 17 compounds identified as generic isomers (e.g., Cg aromatic), and the sum of unidentified compounds. The sum of isomeric species should properly be combined with total unidentified and renamed “others” in order to relate directly to the source profiles. Since the sum of the isomeric species in the ambient samples was typically less than 1or 2 % of the total mass, this was not applied here. The consequenceis a slight, but negligible, underprediction in the contribution of the “Unid” source type listed in Table 2, section C. Source Profile Uncertainties. The quality of the measurements used in the NMHC source profiles is as important as the ambient data quality. Because less variable species abundances provide a larger influence on the source contribution estimates, source profile values must not only be accurately and precisely measured, their uncertainties must also represent the range of variability expected from a number of individual emitters in the same source type. The published profiles do not adequately estimate uncertainties, even though the profile values result from averaging a number of tests and standard deviations could be calculated. Where uncertainty estimates are provided, they apply to a limited sample of emitters and to conditions which may not reflect the actual range of variability for the source type. To a first approximation, the NMHC profiles can be no more precise than the uncertainties of the analytical methods applied to obtain them. The analytical uncertainties for hydrocarbons should be similar for ambient and sourcemeasurements since the same analytical method is used. This uncertainty is typically in the range of *IO15% for values exeeding five times the lower quantifiable limit. A default coefficient of variation of f20 % is applied for all species in the profiles with a minimum detectable weight percentage of 0.1 % or larger. For values less than 0.1 % , the uncertainty is estimated as the square root of the sum of the square of the lower quantifiable limit and

the square of the product of the relative standard error and weight percentage. This is analogous to the error propagation applied to ambient NMHC measurements to estimate their precision. These default uncertainty estimates do not necessarily account for the variability among individual emitters in a source type or range of operating conditions. Past experiments show that, in many cases, this type of variability can far exceed the variability due to measurement errors. For example, Sigsby et al. (21) report the average NMHC weight fraction of acetylene between 5.2 % for 1975 model-year vehicles and 0.5% for 1982 modelyear vehicles with a relative standard error of 108% for the 46-car data set. For comparison, the NMHC weight fraction of acetylene was 2.1 % in the Caldecott Tunnel (ExhCT) (23) and 4.4% (AOComp) for the composite vehicle exhaust profile for the Auto/Oil Program older fleet (29). While the variability in the acetylene/NMHC ratio among the different vehicle exhaust profiles is quite large, the variability in the corresponding ambient ratio is much smaller. The acetylene/NMHC ratios from the SCAQS ambient data base averaged 2.8 f 1.1%for all sampling sites, periods, and seasons and 3.0 f 1.0% for the 07000800 PST commute period. These comparisons demonstrate that variability among available sourceprofiles taken from small numbers of sources (relative to the total number contributing in a given airshed) can be substantiallygreater than the actual variability for a source type at the time and location of the ambient measurements. This is expected considering that source profiles were collected in different areas over a period spanning many years and with many different sampling and analysis methods. Moreover, thevehicle exhaust profiles were developedfrom a limited sample, while the ambient data reflect the average contributions from a large population of on-road vehicles. Lacking profiles that were specifically acquired for receptor modeling, this study assumes uncertainties which are slightly higher than the analytical uncertainty. The effect of this decision is that all species have nearly the same influence on the CMB fit to ambient data. This sacrifices one of the main advantages of the effective variance solution to the CMB (32), which gives less influence to those species with higher uncertainty and greater influence to those species that are more precisely determined. The assigned source profile uncertainties are still propagated along with the ambient measurement uncertainties so that a realistic standard error is associated with source contribution estimates. A subset of the profiles in Table 2, sections A-C, was selected for inclusion in the individual CMB analyses of each SCAQS NMHC sample based upon sensitivity tests which examined the ability of these profiles to explain the SCAQS data. The U.S. EPA/DRI Version 7.0 CMB software (3) provides several performance measures that can be used to evaluate the validity of a profile, its uncertainties, and the species which should be used in a CMB fit. Selection of Fitting Species. A prerequisite for using receptor models is that the relative proportions of chemical species change little between source and receptor. For the majority of organic compounds emitted from anthropogenic and biogenic sources, reaction with the hydroxyl radical is the sole chemical sink process, and typical atmospheric residence times can be estimated from the Environ. Scl. Technol., Vol. 28. No. 9, 1994

1639

rate constants [summarized by Atkinson (33)] for the reaction of the hydroxyl radical with various organic compounds at typical hydroxyl radical concentrations. Summertime average lifetimes for highly reactive atmospheres are 5 days for acetylene, 5 h to 4 days for alkanes, 4 days for benzene, and 9 h for toluene. These lifetimes are comparable to or exceed the typical summer residence time of air masses during SCAQS [ ,- 12 h according to SCAQS trajectories calculated by Douglas et al. (3411. These species were used as fitting species in chemical mass balance (CMB) receptor model calculations. Species with short lifetimes, such as 2-4 h for xylenes and less than 2 h for most of the alkenes, are appropriate fitting species when emissions are fresh (i.e., sampled within a few hours after release) but will have substantially changed in proportion to the other species after the air mass containing them has aged for a few hours. Since atmospheric concentrations of the hydroxyl radical are substantially lower in the fall and winter as compared to the summer, the lifetimes are expected to be 3-4 times as long, and the ratios of species concentrations should be relatively constant for typical aging times between source emissions and receptor measurement. However, ventilation of the SoCAB may take several days during the fall, and longer emission aging times may still result in profile changes for these reactive species. The default fitting species are designated by asterisks in Table 2. These species are major constituents in all samples and have atmospheric lifetimes equal to or greater than toluene (with the exception of isoprene). More reactive species were not used to calculate source contribution estimates but were retained in the CMB calculations as “floating species”. The comparison of calculated and measured values for floating species is part of the model validation process (2).

CMB Application and Validation The CMB applications and validation protocol was developed because there is no single parameter or indicator that can assess the model’s validity (2). The protocol consists of seven steps: (1) determination of model applicability, (2) initial source contribution estimates, (3) examination of model outputs and performance measures, (4) identification of deviations from model assumptions, (5) identification and correction of model input errors, (6) verification of the consistency and stability of source contribution estimates, and (7) evaluation of the results of the CMB analysis with respect to other source assessment methods. The activities carried out for each of these steps are described in this section. CMB Model Applicability. NMHC samples were taken by well-characterized methods during the SCAQS summer and fall studies. Extra care was taken to obtain a full range of chemical speciation. The importance of the CZspecies was noted earlier, and SCAQS data which did not attain this resolution were eliminated for CMB analysis. All major types of hydrocarbon sources in the SoCAB have been identified (35),and profiles for these sources were assembled from published data. The potentially contributing source types are (1)motor vehicle exhaust, (2) evaporated gasoline, (3) liquid gasoline which evaporates in air, (4) industrial and domestic solvents and degreasers, (5) architectural and industrial coatings, (6) 1640

Environ. Sci. Technot., Vol. 28, No. 9. 1994

natural gas, (7) liquefied petroleum gas, (8) biogenic emissions, and (9) geogenic emissions. Refinery profiles are similar to those of evaporated gasoline, and graphic arts profiles are similar to those of solvents, so separate categories of these sources are not included. Annualized reactive organic gas emissions estimates are 2.2 t/day from refineries and 8.8 t/day from graphic arts out of a total of 1300 tlday. These sources are not expected to be significant contributors in the SoCAB. Though the source profile data base is limited, several reasonable profiles have been identified, and rational uncertainties have been assigned to them. To evaluate the effects of some of these source profile limitations, several different NMHC profiles have been selected for major source types (e.g., vehicle exhaust profile based on dynamometer studies of different fleets, data from a tunnel study) to determine the effects of alternative source profiles on the source contribution estimates and on overall model performance. These sensitivity tests were the basis for selecting the default set of source profiles that were used in the initial CMB model run for all valid ambient samples. Reasonable uncertainty estimates were assigned to the profiles. The typical lifetimes of different organic compounds in the profiles were examined to determine which compounds might retain their relative abundances between source and receptor and which ones would not. Given these precautions, the CMB receptor model is applicable to source apportionment of the SCAQS NMHC data base. Initial Source Contribution Estimates (SCE). Initial CMB fits, using a variety of source profile and fitting species combinations, were performed to determine the effects of alternative source profiles on the source contribution estimates and overall model performance. Though many tests were performed on samples from different sites a t different times of the day, the sample from the Anaheim site on September 2,1987, from 0600 to 0700 PST is examined here to illustrate the general conclusions drawn from all of the tests. The CMB software and input data files are available from the authors to those who wish to examine specific cases that are not reported here. The results of the sensitivity tests using different combinations of source profiles and fitting species are summarized in Tables 3-5. The default fitting species are designated by asterisks in Table 2. Source contribution estimates were found to vary considerably with different motor vehicle exhaust profiles. Table 3 shows that exhaust profiles with higher ethyne (acetylene) abundances (relative to the base case) yield lower source contributions for exhaust and higher source contributions for liquid gasoline. The differences are not as much as they would be, however, if a single “tracer” species, such as ethyne, were used instead of all of the fitting species. The performance measures indicate that Exh80la and ExhCT (Caldecott Tunnel) motor vehicle exhaust profiles provide the best fits to the ambient data. Exh80la was selected as the default profile for SCAQS NMHC modeling because the bore in which the ExhCT tunnel samples were collected has a 4 % upgrade. The Exh80la profile best reflects the NMHC speciation in the SCAQS emissions inventory. The source contribution estimates from both the Exh80la and ExhCT profiles are similar for most samples however.

Table 3. Sensitivity of C M B Results to Exhaust Profiles (Anaheim, 9/2/87 at 0600 PST) source profile Exh801 Exh8Ola ExhCT ExhCS564 ExhSt565 AOComp AODiurn AOHSoak LGS709 VGS710 ACoatl96 Degrease Biogenic CNG GNG LPG R2 X2

*

% massc

base

2

1

3

4

5

6

429.1 f 84.6 31.7 f 50.1

452.3 f 74.6

624.4 f 113.7 755.2 f 125.1 711.2 f 97.1 417.0 f 67.2

342.5 f 82.9 187.1 f 40.7 5.8 f 3.2 66.0 f 11.0 2.2 f 1.0 32.6 f 17.7 81.4 f 53.8 84.8 f 22.1 0.95 2.40 102.5

455.6 f 78.1 197.0 f 40.7 -9.2 f 6.4 65.9 f 10.9 2.2 f 1.0 42.2 f 17.7 66.1 f 53.8 71.8 f 21.3 0.93 3.62 99.8

342.1 f 71.2 189.4 f 38.6 3.0 f 3.1 66.0 f 10.9 2.2 f 1.0 56.6 f 17.0 71.9 f 51.3 82.6 f 21.5 0.96 2.12 100.4

471.8 f 70.0 185.3 f 38.6 4.5 f 3.0 70.4 f 11.3 1.3 f 0.8 28.3 f 17.7 102.2 f 51.1 82.5 f 21.6 0.94 2.96 89.7

502.0 f 104 -54.5 f 53.9 407.6 f 117 476.8 f 70.6 209.4 f 42.6 7.1 & 3.2 68.2 f 11.1 2.2 f 0.9 33.0 f 18.6 125.0 f 51.7 75.6 f 21.4 0.94 3.11 96.0

470.6 f 70.2 222.8 f 39.4 7.2 f 3.2 68.1 f 11.1 2.2 f 0.9 38.4 f 17.0 123.2 f 51.8 74.6 f 21.3 0.94 2.90 96

7.6 f 3.1 83.6 f 12.8 1.1f 0.9 -23.0 f 21.8 331.4 f 46.6 33.0 f 22.3 0.88 6.01 84.8

a R2 measures the variance in the receptor concentratrions, which is explained by the calculated species concentrations. x 2 is the weighted sum of the squared of differences between calculated and measured species divided by the effective variance and degrees of freedom. Percent of mass accounted for is the ratio of the sum of the source contributions to the measured mass.

Table 4. Sensitivity of C M B Results to Gasoline and Other Vapor Profiles (Anaheim, 9/2/87 at 0600 PST). source profile Exh80la AODiurn AOHSoak LGS709 VGS710 ACoatl96 ICoast783 Degrease Biogenic CNG GNG LPG

R2 X2 %O

a

mass

base

1

2

755.2 f 125.1 891.0 f 135.3 730.1 f 124.0 -6.4 f 55.1 285.2 f 105.6 378.8 f 82.0 342.5 f 82.9 196.8 k 42.1 187.1 f 40.7 7.2 f 3.4 5.8 f 3.2 3.5 f 25.8 65.9 f 11.0 67.8 f 11.3 66.0 f 11.0 2.2 f 1.1 2.2 f 1.0 2.2 f 1.0 -4.8 f 18.3 36.9 f 17.7 32.6 f 17.7 66.0 f 53.6 81.4 f 53.8 241.3 f 50.5 51.4 f 21.6 88.0 f 22.3 84.8 f 22.1 0.93 0.95 0.95 3.45 2.56 2.40 102.5 101.0 103.2

3

4

6

5

7

753.0 f 126.3 767.5 f 127.6 721.6 f 123.5 980.8 f 131.2 749.3 f 118.2 341.2 f 41.9 188.2 f 41.9 5.8 f 3.2 2.4 f 25.5 66.1 f 11.0 2.2 f 1.0 32.7 f 17.8 81.2 f 53.8 84.8 f 22.1 0.95 2.54 102.5

377.6 f 81.4 217.7 f 38.5 5.1 f 3.2

388.5 f 79.8 238.9 f 39.7 4.9 f 3.2

272.7 f 82.3 206.0 f 39.5 5.8 f 3.5

271.9 f 73.3 127.9 f 35.7 7.0 f 3.2

65.7 f 11.0 2.2 f 1.0 50.0 f 14.4

65.9 f 11.0 2.2 f 1.0 100.8 f 18.1

65.4 f 11.1 2.2 f 1.2

66.6 f 11.0 2.2 f 1.0 244.5 f 34.0

102.6 f 20.4 0.95 2.26 104.5

121.7 f 22.0 0.88 6.01 84.8

0.89 4.89 100.2

0.94 3.08 96.7

See footnckes to Table 3.

Table 5. Sensitivity of C M B Results to Fitting Species (Anaheim, 9/2/87 at 0600 PST). source profile

base, -ethene +ethyne

base, +ethene

base, +ethene -ethyne

base, -ethene -ethyne

base, -toluene

Exh8Ola LGS709 VGS710 ACoatl96 Degrease Biogenic CNG GNG LPG R2

755.2 f 125.1 342.5 f 82.9 187.1 f 40.7 5.8 f 3.2 66.0 f 11.0 2.2 f 1.0 32.6 f 17.7 81.4 f 53.8 84.8 22.1 0.95 2.40 102.5

587.3 f 82.9 434.9 f 68.2 201.8 f 39.7 5.0 f 3.1 66.4 f 10.9 2.2 f 0.9 40.3 f 17.1 77.3 f 52.3 84.6 f 21.8 0.94 2.75 98.7

402.2 f 75.5 548.7 f 68.8 217.1 f 40.3 4.1 f 3.1 66.7 f 10.9 2.2 f 0.9 49.1 f 17.3 71.0 f 53.3 84.8 f 22.0 0.96 2.15 95.2

517.8 f 150.6 475.0 f 98.5 207.9 f 41.4 4.7 f 3.1 66.5 f 10.9 2.2 f 0.9 43.4 f 17.9 75.8 f 52.3 84.5 21.8 0.96 2.29 97.3

752.0 f 125.7 339.7 f 83.2 187.6 f 40.7 5.8 f 3.2 66.1 f 11.0 2.2 f 1.0 32.2 f 17.7 83.8 f 54.7 84.3 f 22.2 0.95 2.54 102.2

X2

% mass a

*

*

See footnotes to Table 3.

When the AOHSoak (Auto/Oil Program hot soak) and AOdiurn (Auto/OilProgram diurnal evaporation) profiles are substituted for liquid gasoline and gasoline vapor, nearly half of the mass originally attributed to the two gasoline source profiles is apportioned in approximately equal portions to motor vehicle exhaust and geogenic natural gas as shown in Table 4. These results demonstrate

that there is significant collinearity among the profiles for vehicle exhaust, evaporative emissions, and geogenic natural gas. The source contributionsfor architectural and industrial coatings were detectable, but negligible. Most of the ambient toluene, which is the major constituent in these profiles, is derived from motor vehicle exhaust, which is Envlron. Scl. Technol., Vol. 28, No. 9, 1994

1641

consistent with the strong correlation in the SCAQS data between toluene and carbon monoxide. Using commercial natural gas, geogenic natural gas, and liquefied petroleum gas together provides better model performance than any one of the profiles alone. The individual source contributions from the three gas profiles should be combined since they all contain ethane and propane as their main nonmethane components, and these profiles are often collinear with each other. The combined source can be interpreted as an ethane- and propane-enriched source, which is probably some mixture of different types of gas leaks. Table 5 summarizes tests which examine the effects of adding to or subtracting species from the CMB fit. Since ethene (ethylene) is a combustion product, adding it to the fit has the same effect as using vehicle profiles with higher fractions of ethyne: the contribution of vehicle exhaust is reduced and reassigned to liquid gasoline. The ambient ethene/NMHC ratios (3-4.5%) are about half the corresponding ratios in the vehicle exhaust profiles while the ambient ethyne/NMHC ratios (2.5-3.5%) are consistent with the exhaust profiles. Because ethene is overrepresented in the profile relative to ethyne, retaining ethene in the fit and removing ethyne shifts the source apportionmentto liquid gasoline. However, the combined contributions of vehicle exhaust and liquid gasoline remain relatively constant. Removing toluene from the fit has no effect on the source contributions. As a result of the sensitivity tests, the following default set of source profiles was applied to the first CMB model application to each ambient sample: (1)Exh80la, vehicle exhaust using the adjusted ARB MEDS profile number 801; (2) LGS709 and LGW729, liquid gasoline using the updated ARB MEDS profile number 709 for the summer samples and profile number 729 for the fall samples; (3) VGS710 and VGW730, gasoline vapor using the updated ARB MEDS profile number 710 for the summer samples and number 730 for the fall samples; (4) Acoat 196, architectural coating using ARB MEDS profile number 196; (5) Degrease, degreasing solvents using ARB MEDS profile number 515; (6) DryClean, dry cleaning solvents using ARB MEDS profile number 516; (7) CNG, commercial natural gas; (8) GNG, geogenic natural gas; (9) LPG, liquefied petroleum gas; and (10) Unid, unidentified. Model Outputs and Performance Measures. Summer and fall average source contributions by site and sampling period are summarized in Tables 6 and 7 , respectively. Sampling sites are listed in the tables according to increasing distance from the coast along prevailing transport trajectories. The uncertainties associated with these averages are the root mean squared (RMS) errors of the source contribution estimates for individual samples rather than the standard error of the averages. During SCAQS, the major contributors to NMHC at all sites were as follows: (1)vehicle exhaust, (2) liquidgasoline, (3) gasoline vapor, and (4) natural gas and propane gas. Motor vehicle exhaust is the main source of NMHC a t all sites and time periods, ranging from 30% to 70% of the total NMHC. The relative contributions from vehicle exhatust were larger during the fall period, particularly for the morning samples (66-71% in the fall versus 42-65% in the summer). This seasonal difference is consistent with the emission inventory, which shows higher relative contribution from vehicle exhaust during the fall than during the summer. 1642

Envlron. Scl. Technol., Vol. 20. No. 9, 1994

In general, the contributions of vehicle exhaust were uniform throughout the basin with slightly higher contributions during the morning and afternoon commute periods. The Hawthorne site is an exception to this pattern, having substantially lower contributions of exhaust during the midday and afternoon periods. In addition, carbon monoxide concentrations a t the Hawthorne site were significantly lower during the midday and afternoon periods than during the morning or a t any other sampling site. The lower pollutant concentrations in the afternoon at the Hawthorne site are likely due to the close proximity of the site to the coastline and the increasing strength of the sea breeze. Although the Hawthorne site is adjacent to the San Diego Freeway, the impact of the freeway is minimized by the sea breeze, which transports the freeway plume away from the sampling site. Accordingly, the composition of NMHCs at the Hawthorne site during the afternoon resembles a background rather than a traffic-dominated site. Neglecting anomalous results for afternoon samples at the Hawthorne site, the contribution of liquid gasoline was greater in the morning samples at most sites during the summer, higher in the central part of the basin (15-23%), and lower in the afternoon in the eastern part of the basin (2-5 70 1. The relative contribution of liquid gasoline was more uniform, spatially and temporally, during fall. Contributions from gasoline vapor were higher in the western part of the basin during the morning and midday periods (15-24%), lowest in the central basin (2-12%), and higher in the afternoon in the eastern basin (10-1570 )~ The combined contributions of commercial natural gas, geogenic natural gas, and liquefied petroleum gas had the same pattern as gasoline vapor. Contributions from architectural and industrial coatings were both negligible, which is consistent with the strong correlation in the ambient data between toluene and carbon monoxide. This analysis did not include oxygenated organic compounds, which in some cases account for the majority of the solvent content of surface coatings and domestic solvents. The contribution of this source type to volatile organic compounds cannot be ascertained until composition profiles are developed for these sources. The CMB model provides values for several performance measures that are used to evaluate the solution. These measures are defined in the footnotes for Table 3. The model performance measures were individually examined for each CMB fit to the ambient data. The model performed reasonably well for most samples with R2 ranging from 0.90 to 0.95 and x2 ranging from 2.0 to 4.0. In particular, the mass percent of total NMHC mass was monitored closely to determine how well the modelcalculated concentrations reproduced the ambient concentrations. The calculated NMHC concentrations are slightly higher on average than measured values at most sites but are generally within 10% of the measured concentrations, indicating that all major source categories were included in the calculations. Species-by-species comparisons of calculated versus measured mass show that the reactive species account for most of the excess predicted mass, which is consistent with the tendency for overprediction to increase from morning to afternoon and from western (source area) to eastern part (receptor area) of the basin. The overprediction of mass increases with increasing photochemical

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Environ. Scl. Technol., Vol. 28, No. 9, 1984 1648

1644 Envlron. Scl. Technol., Vol. 28, No. 9, 1994

I

700

e

1200

0

IO

20

30

40

so

0

Calculated Ethyne (ug/m3)

20

40

60

100

80

I20

Calculated Toluene (ug/m3)

700

700

1200

1200

e

e

lg

fy

0

”

Y

0

20

40

60

80

100

Calculated Ethene (ug/m3)

0

20

40

60

80

Calculated m&p-Xylene (ug/m3) .

Flgure 2. Scatterplots of calculated versus measured non-methane hydrocarbon mass concentrations.

aging because predicted concentrations for reactive species are calculated from source contribution estimates obtained by fitting only the less reactive species. Reactive species were retained in the CMB calculations as floating species and have no influence on the apportionment. Excess predicted mass for reactive species also may be related to errors in the source profiles. Scatterplots for all summer samples of calculated versus measured mass concentrations of ethyne, ethene, toluene, and m+p-xylene are shown in Figure 2 for three time periods (0700,1200, and 1600 PDT). The scatterplots show that relative abundances of toluene and m+p-xylene in the source profiles agreed well with ambient measurements. However, calculated ethyne concentrations are much lower than measured values while calculated ethene concentrations are conversely much higher. Mean ratios of calculated to measured values for ethyne and ethene are 0.7 and 2.5, respectively. As mentioned before, lower ethyne mass fraction in the source profile cause the CMB model to give higher SCEs for vehicle exhaust and lower contributions for liquid gasoline. Higher SCEs for vehicle exhaust in turn cause predicted concentrations of ethene and other combustion-related olefins to exceed measured values. Thus, the excess predicted mass is most likely caused by an underestimation of the fractional abundance of ethyne in profile Exh80la. Deviations from CMB Model Assumptions. The basic assumptions of the CMB model are as follows: (1)

composition of source emissions are constant over the period of ambient and sourcesampling; (2) chemicalspecies do not react with one another (i.e., they add linearly); (3) all sources which may significantly contribute to the receptor have been identified and their emissions characterized; (4) the number of source categories is less than or equal to the number of chemical species; ( 5 ) the source profiles are linearly independent; and (6) measurement uncertainties are random, uncorrelated, and normally distributed. The extent that CMB model assumptions are met for VOC ambient data and source profiles is discussed here. With respect to the consistency of source profiles, the previous analyses have shown that profiles that do not represent emissionsin the SoCAB are clearly evident when performance measures deviate substantially from their target values. Tables 3-5 show that the sourcecontribution estimates for vehicle exhaust and evaporative emissions vary with different profiles which represent the same source but that the sum of SCEs for vehicle exhaust and liquid gasoline are relatively constant. With respect to assumption 2 concerning the reactions of different species with each other, only those species with lifetimes comparable to air mass residence times are used as fitting species. With respect to assumption 3 involvingthe inclusion of all source types, it appears from the percent mass performance measures that all of the significant contribuEnviron. Sci. Technol., Vol. 28, No. 9, 1994 1645

Table 8. Relative Emissions of Ethene and Ethyne in Vehicle Exhaust from Dynamometer and Tunnel Measurements FTP Composite ethene/ ethynel NMHC ethene/ model no.of ethene ethyne ethyne NMHC ( % ) (g/mi) NMHC ( % ) ref years cars (mglmi) (mglmi) study 1.77 5.60 4.250 9.91 421.36 238.16 3 1975 21 Sigsby et al., 1987 1.32 11.36 3.619 15.03 411.06 544.05 1 1963 47 Black et al., 1980 1.17 7.24 3.055 8.44 221.10 4 257.95 1977 21 Sigsby et al., 1987 1.07 7.50 2.797 7.99 209.69 4 223.37 1970-78 26 Hockman, 1992 3.74 2.94 2.739 10.99 80.46 300.98 5 1978 21 Sigsby et al., 1987 3.86 2.26 2.023 8.73 45.78 176.58 5 1979 21 Sigsby et al., 1987 4.86 2.32 1.932 11.25 44.73 4 217.26 1976 21 Sigsby et al., 1987 3.30 2.76 1.501 9.11 41.40 136.80 1975-82 46 21 Sigsby et al., 1987 4.90 2.31 1.159 11.33 26.80 7 131.32 21 1980 Sigsby et al., 1987 4.54 1.62 0.618 7.35 10.01 45.43 12 21 1981 Sigsby et al., 1987 4.45 1.39 0.498 6.16 6.90 30.68 6 1986-90 49 Stump et al., 1992 3.10 3.42 0.450 10.58 15.37 47.62 5 1976-82 26 Hoekman, 1992 7.20 0.66 0.434 4.72 2.85 20.52 6 21 1982 Sigsby et al., 1987 1.89 4.36 0.392 8.23 17.12 32.29 14 29 1983-85 Gorse, 1992 3.95 1.53 0.352 6.05 5.38 21.28 9 50 1987-89 Stump et al., 1992b 2.35 2.70 0.342 6.34 9.24 21.67 11 25 1985-87 Stump et al., 1990 3.01 2.26 0.327 6.80 5 7.38 22.24 1983-90 26 Hoekman, 1992 6.87 0.94 0.302 6.47 2.84 19.51 5 1986-89 26 Hoekman, 1992 1.54 3.49 0.279 5.37 9.74 14.98 9 24 1984-87 Stump et al., 1989 2.91 2.89 0.155 8.41 4.48 20 13.03 29 1989 Gorse, 1992

Lonneman et al., 1986 Lonneman et al., 1986 Zielinska et al., 1992

ref

tunnel

51 51 23

Lincoln Lincoln Caldecott

Tunnel Measurements PPb PPb year ofC ofC 1970 1982 1991

1375 409 154

tors have been included in the CMBs. The “Unid”fraction accounts for those parts of the NMHC in the ambient sample which are labeled as unidentified. This accounting is carried through the model so that the frequencies and magnitudes of the unidentified NMHC can be examined relative to the other source contribution estimates. It is impossiblefor the CMB model to apportion this unidentified fraction until it is further resolved into specific compounds or compound groupings by chemical analysis. Contributions of Unid were generally lower during the morning period, when motor vehicles are the dominant source of emissionsand photochemical activity is minimal, and higher during the afternoon. With respect to assumption 4 concerning the number of species and the number of sources, 27 NMHCs and up to nine source profiles were used in each calculation. The number of chemical species always exceeded the number of source types. With respect to assumption 5 concerning collinearity, the initial source contribution estimates show the potential for collinearity among the GNG, CNG, and LPG profiles. The uncertainty/similarity (U/S) clusters defined by Watson et al. (36)and based on the methods of Henry (37, 38) often appeared during the analyses which grouped together two or more of the Exh80la, LGS709, and VGS710 profiles. The U/S clusters do not necessarily mean that profiles are collinear-they really mean that the standard error assigned to a category representing the profiles in the clusters might be lower than the standard errors assigned to the individual source contribution estimates associated with each profile. Though the standard errors for these source types often approach 30% of the source contribution estimate, indicating collinearity uncertainty in addition to propagated analytical uncertainty, all three vehicle profiles were usually retained so that temporal and spatial variations in their contributions could be examined. 1646 Envlron. Scl. Technol., Vol. 28, No. 9, 1994

1033 161 56

PPm ofC

ethene/ NMHC(%)

ethynel NMHC (% )

ethene/ ethyne

16.5 4.2 2.5

8.31 9.67 6.16

6.24 3.80 2.24

1.33 2.54 2.75

As is the case for suspended particulate matter, the effects of deviations from assumption 6 on the randomness and normality of measured errors remain to be studied. For this study, all of the CMB assumptions are met. Identification and Correction of Model Input Errors. During SCAQS, the major contributors to NMHC at all sites were as follows: (1)vehicle exhaust, (2) liquid gasoline, (3) gasoline vapor, and (4) natural gas and propane gas. What these profiles actually represent is open to some interpretation. Exhaust emissions are actually a mixture of hydrocarbons produced during combustion along with unburned gasoline resulting from incomplete combustion. Siegl et al. (39)showed that unburned fuel represents most (>50%)of the hydrocarbon emissionsfrom a spark-ignited single-cylinder engine. In the CMB calculations, liquid gasoline represents the additional unburned gasoline (due to misfiring and other engine malfunctions) that is not included in the exhaust profile plus evaporative emissions from gasoline spillage and hot soaks. As seen from the sensitivity tests, the contributions calculated for these categories depend on the ratios of ethyne and light olefins to NMHC in the exhaust composition profile, which vary with emission control technology, vehicle condition, and operating mode. Table 8 shows the relative emissions of ethene and ethyne in vehicle exhaust from various dynamometer and tunnel measurements made over the past three decades. Since the introduction of emission controls, emissions of ethene and ethyne have both decreased as a fraction of total NMHC. However, the decrease for ethyne has been greater because it is removed more efficiently by the catalyst. Well-maintained catalyst-equipped vehicles have ethene/ethyne ratios of 3 or greater based upon FTP emission tests (21,26,29)while noncatalyst vehicles have ethene/ethyne ratios near 1(26,40). Fuel-rich conditions (39, 4 1 ) due to engine misfire or “open-loop” operation during high acceleration and load can also produce lower

ethenetethyne ratios relative to normal driving conditions. Excess emissionsdue to malfunctioning catalysts and fuelrich driving conditions may also explain the lower ambient ethene/ethyne ratios during the morning commute period during the SCAQS (average of 1.5) as compared to corresponding ratios from FTP dynamometer tests (about 3). In a separate study, Harley et al. (20)applied the CMB model to the SCAQSnon-methane organic compound data base and reported higher average contribution (all sites and time periods during the summer study) from liquid gasoline than was obtained by our calculations (37 % versus 14%). Their higher estimates for contributions of liquid gasoline are attributed to their composite vehicle exhaust profile, which contains twice the ethyne abundance of profile Exh80la. Their profile includes noncatalyst vehicles (equally weighted with catalyst-equipped vehicles), which contain higher abundances of ethyne than catalyst vehicles. As noted earlier, the use of profile Exh80la causes underpredictions for the abundance of ethyne, which suggests that some weighting for high emitters is probably necessary to obtain more representative vehicle exhaust profiles. Alternatively, on-road measurements (e.g., highway tunnel) or statistical analyses of the ambient data base (e.g., ref 10) might be used to derive more representative and site-specificvehicle exhaust profiles. While the source attributions between exhaust and liquid gasoline may vary with different exhaust profile, sensitivity runs show that the sum of the two source contributions are less variable. This is demonstrated by the reasonably good agreement for the average combined vehicle exhaust and liquid gasoline contributions from the two studies (65% by Harley et al. and 72% in this present study). Consistency and Stability of Source Contribution Estimates. Over 350 separate CMBs were performed on the SCAQS NMHC samples. The source contribution estimates and the statistics and diagnostic information were reviewed to determine the validity of the initial model results. The analysis was repeated by dropping source profiles that gave negative source contribution estimates or standard errors which exceed the source contribution estimates. Motor vehicles accounted for the majority of NMHC emissions throughout the basin at all sampling periods, which is consistent with the high correlation between total NMHC with carbon monoxide, ethyne, and NO,, which are primarily emitted by motor vehicle in the SoCAB, and the relatively small spatial, temporal, and day-to-day variations in the ambient NMHC composition (17). Reconciliation with Other Source Apportionment Methods. One of the objectives of this study was to compare SCEs obtained by CMB modeling with corresponding emission inventory estimates. Table 9 shows the mean SCEs for all sites combined by season and sampling period for the three motor vehicle source categories versus all other source categories in comparison to the corresponding SCAQS basinwide day-specific emission inventory data for August 28,1987, and December 10,1987. The fraction of total NMHC emissionsattributed by CMB to motor vehicle exhaust and evaporative emissions during the 7-8 A.M. sampling period is higher than the corresponding summer and fall emission inventory estimates by about a factor of 2 and 3, respectively. These discrepancies are substantially higher during the

Table 9. CMB Versus Emission Inventory Source Contribution Estimatesa period

vehicle exhaust

liquid gasoline

gasoline vapor

nonMV

Summer Study CMB 0700-0800 1200-1300 1600-1700

50.5 53.7 48.8

16.6 14.0 11.4

10.9 11.2 10.3

22.1 21.0 29.5

49.5 21.9 30.4 28.3

9.1 5.5 7.3 6.3

5.6 3.4 8.0 4.5

35.9 69.2 54.3 59.0

emission inventory 0600-0800 1100-1300 1500-1700

daily total

Fall Study CMB 0700-0800 1200-1300 1600-1700

67.9 53.7 56.2

14.5 14.0 14.7

6.8 11.2 9.5

10.8 21.0 19.6

62.1 26.4 38.1 37.4

8.8 6.0 7.2 6.8

2.9 3.1 6.4 3.5

26.2 64.5 48.3 52.3

emission inventory 0600-0800 1100-1300 1500-1700

daily total

a Mean SCEsfor all sites combined by season and samplingperiods versus source contributions from SCAQS basinwide emission inventory for August 28, 1987, and December 10, 1987.

midday periods, indicating that either nonmotor vehicle sources of hydrocarbons are too widely dispersed to consistently affect the sampling sites with their emissions or that these emissions have been overestimated in the inventory relative to on-road motor vehicle emissions. The larger calculated contributions of vehicle exhaust and evaporative emissions are consistent with recent studies that suggest that the motor vehicle hydrocarbon emission inventories for motor vehicles have been substantially underestimated (42-46). According to a study which compared emission model estimates against emissions measured in a highway tunnel during SCAQS, measured NMHC rates were a factor of 3.8 higher than model predictions, while NO, emission rates agreed reasonably well with model predictions (39). Fujita et al. (45) also found that the 7-8 A.M. NMHC/NO, ratios measured during SCAQS were 2-2.5 times higher than corresponding emission inventory estimates. If the discrepancy is attributed entirely to the hot running exhaust component of the motor vehicle emission inventory, the adjustment factor is about 4, which is similar to the tunnel study results (cold start and evaporative emissions were assumed to be minimal in the tunnel). Furthermore, the three groups (California Air Resources Board, South Coast Air Quality Management District, and Carnegie Mellon/ California Institute of Technology) (46-48) that have used the SCAQSdata base for photochemical modelverification studies obtained predicted ozone values that were substantially lower than observed. Model performance was greatly improved when the base on-road motor vehicle organic gas emissions were increased by substantial margins (ARB and SCAQMD increased total on-road motor vehicle emissions by a factor of 2.5, and CMU/CIT increased hot exhaust emissions by a factor of 3).

Conclusions The CMB application and validation protocol developed for PMlo source apportionment is applicable to the validation of CMB for NMHC source apportionment. This Envlron. Scl. Technol., Vol. 28, No. 9. 1994

1647

validation reveals that the limited availability of NMHC source profiles specifically designed for receptor modeling needs to be remedied in future studies. In particular, the attribution of source contributions among the motor vehicle source categories was found to be highly sensitive to the choice of fitting species and to the relative abundances of combustion byproducts in the exhaust profile, which vary with emission control technology, level of vehicle maintenance, and operating mode. Profiles that approximate the fleet-averaged exhaust composition should be derived from a composite of exhaust profiles for noncatalyst or high-emitting vehicles and catalystequipped vehicles with site-specific weighting factors, if available. Alternatively, on-road measurements or statistical analyses of the ambient data may be used to derive a more representative and site-specific vehicle exhaust profile. Measurements also need to be made on a representative sample of liquid gasolines, gasoline vapors, surface coatings, waste containers, gas leaks (natural gas, liquefied petroleum gas, and geogenicgas), and other major sources in the emission inventory. The species measured need to be complete and consistent with those in the ambient data base. Sufficient samples are needed so that uncertainties as well as abundances of individual species in NMHC can be estimated. Acknowledgments

We thank Rei Rasmussen at the Oregon Graduate Institute of Science and Technology and Len Stockburger and Ken Knapp of the U.S. Environmental Protection Agency for kindly providing data obtained during the Southern California Air Quality Study. We also acknowledge the assistance of Paul Allen and Bart Croes of the California Air Resources Board for providing emission source profile data. Literature Cited (1) Lawson, D. R. J . Air Waste Manage. Assoc. 1990, 40 (2), 156-165. (2) Pace, T. G.; Watson, J. G. Protocol for Applying and Validating the CMB Model; EPA 45014-87-010; U.S. Environmental Protection Agency: Research Triangle Park, NC, 1987. (3) Watson, J. G.; Robinson, N. F.; Chow, J. C.; Henry, R. C.; Kim, B. M.; Pace, T. G.; Meyer, E. L.; Nguyen, Q. Enuiron. Software 1990, 5 (l),38-49. (4) Friedlander, S. K. Environ. Sci. Technol. 1973, 7,235-240. (5) Hopke, P. K., Ed. Receptor Modeling in Environmental Chemistry; John Wiley & Sons: New York, 1985. (6) Hopke, P. K., Ed. Receptor Modeling for Air Quality

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for Air Quality Management, Data Handling in Science and Technology; Hopke, P. K., Ed.; Elsevier Science Publishers: New York, 1991; Vol. 7, pp 83-116. (37) Henry, R. C. In Receptor Models Applied to Contemporary Air Pollution Problems; Dattner, S. L., Hopke, P. K., Eds.; Air Pollution Control Association: Pittsburgh, PA, 1982; pp 141-154. (38) Henry, R. C. Atmos. Environ. 1992,26A, 5933-938. (39) Siegl, W. 0.;McCabe, R. W.; Chun, W.; Kaiser, E. W.; Perry, J.; Henig, Y. I.;Trinker, F. H.; Anderson, R. W. J.Air Waste Manage. Assoc. 1992, 42, 912-920. (40) Black, F. M.; High, L. E.; Lang, J. M. J.Air Pollut. Control ASSOC.1980, 30, 1216-1221. (41) McCabe, R. W.; Siegl, W. 0.;Chun, W.; Perry, J., Jr. J.Air. Waste Manage. Assoc. 1992,42, 1071-1077. (42) Ingalls, M. N.; Smith, L. R.; Kirksey, R. E. Measurement

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(43) Pierson, W. R.; Gertler, A. W.; Bradow, R. L. J. Air Waste Manage. Assoc. 1990,40, 1495-1504. (44) NationalResearch Council. Rethinking the OzoneProblem in Urban and Regional Air Pollution; National Academy Press: Washington, DC, 1991. (45) Fujita, E. M.; Croes, B. E.; Bennett, C. L.; Lawson, D. R.; Lurmann, F. W.; Main, H. H. J.Air Waste Manage. Assoc. 1992, 42, 264-276. (46) Harley, R. A.; Armistead, A. E.; McRae, G. J.; Cass G. R.; Seinfeld, J. H. Environ. Sei. Technol. 1993, 27, 278-288. (47) Wagner, K. K.; Wheeler, N. J. M.; McNerny, D. L. The Effect of Emission Inventory Uncertainty on Urban Airshed Model Sensitivity to Emission Reductions. Presented a t the Air and Waste Management Association International Conference on Tropospheric Ozone: Nonattainment and Design Value Issues, Boston, MA, 1992. (48) Chico, T.; Bassett, M.; Cassmassi, J.; Mitsutomi, S.; Zhang, X.; Hogo, H. Application of the Urban Airshed Model for a SCAQS Episode in the South Coast Air Basin. Presented a t the A&WMA SCAQS Data Analysis Conference, Los Angeles, CA July 21-23, 1992. Southern California Air

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Received for review March 1, 1994. Accepted May 19, 1994.' @

Abstract published in Advance ACS Abstracts, July 1, 1994.

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