Improvement of receptor model use in analytical aspect

In this study, four certified particle standards including NIST SRM 1648 urban particulate matter, BCR Reference Material No. 176 city waste incineration ash, NIST SRM 2709 San Joaquin soil, and NIST SRM 1633b coal fly ash were used to simulate ambient particulate matter. Twenty-five samples were prepared with the four certified particulate standards. A total of 23 elements were analyzed per sample, 19 by ICP-AES and ICP-MS, three by IC, and one element, Si, by spectrophotometer. Results showed that combining the three IC-analyzed ionic species with the 19 ICP-AES/MS analyzed elements into the CMB model did not improve the source identification significantly. In addition, when all 23 analyzed chemical species per sample were used in the CMB model, they were still not good enough to effectively make the parameters of the CMB model fit the statistical criteria. Some of high variation and low recovery chemical species, i.e. Cd, V, Sb, etc., may have caused poor CMB model simulation. Omitting some poor quality analyzed species (such as relative analysis error >20%) could improve the CMB model simulation. Therefore, high quality chemical species data are important for the CMB model. In addition, co-linearity of source profiles also affects the CMB model; combining the co-linear sources could enhance the solubility of the CMB model. In this study, a two-step procedure was developed for CMB model simulation to improve source identification.     

Receptor Model Evaluation of the Southeast Michigan Ozone Study Ambient NMOC Measurements

Abstract

Large-scale studies like the Southeast Michigan Ozone Study (SEMOS) have focused attention on quantifying and spedating inventories for volatile organic compounds (VOCs). One approach for evaluating the accuracy of a VOC emission inventory is the development of a chemical mass balance (CMB) receptor model for ambient non-methane organic compound (NMOC) measurements. CMB evaluations of ambient hydrocarbon data provide a sample-specific allocation of emissions to individual source categories. This study summarizes the results of an application of the CMB model to the NMOC data from the SEMOS study. Comparison of CMB results with emission inventory values for the Detroit area show that vehicle emissions are well represented by the inventory, as are architectural coatings and coke ovens. Estimated emissions from petroleum refineries and graphic arts industries are much lower in the inventory than determined from the receptor allocation. Under-reporting of fugitive VOC emissions from petroleum refineries is an ongoing problem. Emissions from graphic arts industries are underestimated in the inventory partly because of the broad characterization of the emission factor (i.e., mass emitted/capita), which may be less useful when specific locations and days are under consideration. This study also demonstrates the effectiveness of the CMB approach when used prospectively to track the implementation of emission control strategies. While vehicle emission concentrations were unchanged from 1988 to 1993, measurement-based CMB results suggest a decrease in evaporative emissions during this time period resulting from Reid vapor pressure (RVP) reductions (from 11.0 psi in 1988 to 8.6 psi in 1993) and fleet turnover. Changes in emissions from coke plants and petroleum refineries were also seen in the CMB allocations for these sources.     

Contribution of motor vehicle emissions to organic carbon and fine particle mass in Pittsburgh, Pennsylvania: Effects of varying source profiles and seasonal trends in ambient marker concentrations

We present estimates of the vehicular contribution to ambient organic carbon (OC) and fine particle mass (PM) in Pittsburgh, PA using the chemical mass balance (CMB) model and a large dataset of ambient molecular marker concentrations. Source profiles for CMB analysis are selected using a method of comparing the ambient ratios of marker species with published profiles for gasoline and diesel vehicle emissions. The ambient wintertime data cluster on a hopanes/EC ratio–ratio plot, and therefore can be explained by a large number of different source profile combinations. In contrast, the widely varying summer ambient ratios can be explained by a more limited number of source profile combinations. We present results for a number of different CMB scenarios, all of which perform well on the different statistical tests used to establish the quality of a CMB solution. The results illustrate how CMB estimates depend critically on the marker-to-OC and marker-to-PM ratios of the source profiles. The vehicular contribution in the winter is bounded between 13% and 20% of the ambient OC (274±56–416±72 ng-C m⁻³). However, variability in the diesel profiles creates uncertainty in the gasoline–diesel split. On an OC basis, one set of scenarios suggests gasoline dominance, while a second set indicates a more even split. On a PM basis, all solutions indicate a diesel-dominated split. The summer CMB solutions do not present a consistent picture given the seasonal shift and wide variation in the ambient hopanes-to-EC ratios relative to the source profiles. If one set of source profiles is applied to the entire dataset, gasoline vehicles dominate vehicular OC in the winter but diesel dominates in the summer. The seasonal pattern in the ambient hopanes-to-EC ratios may be caused by photochemical decay of hopanes in the summer or by seasonal changes in vehicle emission profiles.     

Combined source apportionment,using positive matrix factorization–chemical mass balance and principal component analysis/multiple linear regression–chemical mass balance models

The methods of positive matrix factorization–chemical mass balance and principal component analysis/multiple linear regression–chemical mass balance were studied in this paper, for combined source apportionment. Due to the high similarity among the source profiles, several problems would raised when only one receptor model was applied. For example, the collinearity problem would result in the negative contributions when applying CMB model; certain sources would not to be separated out when applying PCA or PMF model. In this study, PCA/MLR–CMB model and PMF–CMB were attempted to resolve the problem, where the combined models were applied to study the synthetic and ambient datasets. In synthetic dataset, there were seven sources (six actual sources from real world, and one unknown source). The results obtained by the combined models show that the combined source apportionment technique is feasible. In addition, an ambient dataset from a northern city in China was analyzed by PCA/MLR–CMB model and PMF–CMB model, and these two models got the similar results. The results show that coal combustion contributed the largest fraction to the total mass.     

Source contributions to fine particulate matter in an urban atmosphere

This paper proposes a practical method for estimating source attribution by using a three-step methodology. The main objective of this study is to explore the use of the three-step methodology for quantifying the source impacts of 24-h PM2.5 particles at an urban site in Seoul, Korea. 12-h PM2.5 samples were collected and analyzed for their elemental composition by ICP-AES/ICP-MS/AAS to generate the source composition profiles. In order to assess the daily average PM2.5 source impacts, 24-h PM2.5 and polycyclic aromatic hydrocarbons (PAH) ambient samples were simultaneously collected at the same site. The PM2.5 particle samples were then analyzed for trace elements. Ionic and carbonaceous species concentrations were measured by ICP-AES/ICP-MS/AAS, IC, and a selective thermal MnO2 oxidation method. The 12-h PM2.5 chemical data was used to estimate possible source signatures using the principal component analysis (PCA) and the absolute principal component scores method followed by the multiple linear regression analysis. The 24-h PM2.5 source categories were extracted with a combination of PM2.5 and some PAH chemical data using the PCA, and their quantitative source contributions were estimated by chemical mass balance (CMB) receptor model using the estimated source profiles and those in the literature. The results of PM2.5 source apportionment using the 12-h derived source composition profiles show that the CMB performance indices; chi2, R2, and percent of mass accounted for are 2.3%, 0.97%, and 100.7%, which are within the target range specified. According to the average PM2.5 source contribution estimate results, motor vehicle exhaust was the major contributor at the sampling site, contributing 26% on average of measured PM2.5 mass (41.8 microg m-3), followed by secondary sulfate (23%) and nitrate (16%), refuse incineration (15%), soil dust (13%), field burning (4%), oil combustion (2.7%), and marine aerosol (1.3%). It can be concluded that quantitative source attribution to PM2.5 in an urban area where source profiles have not been developed can be estimated using the proposed three-step methodology approach.     

Sensitivity of Source Apportionment of Urban Particulate Matter to Uncertainty in Motor Vehicle Emissions Profiles

Abstract

A sensitivity analysis was conducted to characterize sources of uncertainty in results of a molecular marker source apportionment model of ambient particulate matter using mobile source emissions profiles obtained as part of the Gasoline/Diesel PM Split Study. A chemical mass balance (CMB) model was used to determine source contributions to samples of fine particulate matter (PM_2.5) collected over 3 weeks at two sites in the Los Angeles area in July 2001. The ambient samples were composited for organic compound analysis by the day of the week to investigate weekly trends in source contributions. The sensitivity analysis specifically examined the impact of the uncertainty in mobile source emissions profiles on the CMB model results. The key parameter impacting model sensitivity was the source profile for gasoline smoker vehicles. High-emitting gasoline smoker vehicles with visible plumes were seen to be a significant source of PM in the area, but use of different measured profiles for smoker vehicles in the model gave very different results for apportionment of gasoline, diesel, and smoker vehicle tailpipe emissions. In addition, the contributions of gasoline and diesel emissions to total ambient PM varied as a function of the site and the day of the week.     

Comparison and Evaluation of Chemically Speciated Mobile Source PM2.5 Particulate Matter Profiles

ABSTRACT

Mobile sources are significant contributors to ambient PM_{2 5}, accounting for 50% or more of the total observed levels in some locations. One of the important methods for resolving the mobile source contribution is through chemical mass balance (CMB) receptor modeling. CMB requires chemically speciated source profiles with known uncertainty to ensure accurate source contribution estimates. Mobile source PM profiles are available from various sources and are generally in the form of weight fraction by chemical species. The weight fraction format is commonly used, since it is required for input into the CMB receptor model. This paper examines the similarities and differences in mobile source PM_2.5 profiles that contain data for elements, ions, elemental carbon (EC) and organic carbon (OC), and in some cases speciated organics (e.g., polycyclic aromatic hydrocarbons [PAHs]), drawn from four different sources.

Notable characteristics of the mass fraction data include variability (relative contributions of elements and ions) among supposedly similar sources and a wide range of average EC:OC ratios (0.60 ± 0.53 to 1.42 ± 2.99) for light-duty gasoline vehicles (LDGVs), indicating significant EC emissions from LDGVs in some cases. For diesel vehicles, average EC:OC ratios range from 1.09 ± 2.66 to 3.54 ± 3.07. That different populations of the same class of emitters can show considerable variability suggests caution should be exercised when selecting and using profiles in source apportionment studies.     

Insights into the primary–secondary and regional–local contributions to organic aerosol and PM2.5 mass in Pittsburgh,Pennsylvania

This paper presents chemical mass balance (CMB) analysis of organic molecular marker data to investigate the sources of organic aerosol and PM_2.5 mass in Pittsburgh, Pennsylvania. The model accounts for emissions from eight primary source classes, including major anthropogenic sources such as motor vehicles, cooking, and biomass combustion as well as some primary biogenic emissions (leaf abrasion products). We consider uncertainty associated with selection of source profiles, selection of fitting species, sampling artifacts, photochemical aging, and unknown sources. In the context of the overall organic carbon (OC) mass balance, the contributions of diesel, wood-smoke, vegetative detritus, road dust, and coke-oven emissions are all small and well constrained; however, estimates for the contributions of gasoline-vehicle and cooking emissions can vary by an order of magnitude. A best-estimate solution is presented that represents the vast majority of our CMB results; it indicates that primary OC only contributes 27±8% and 50±14% (average±standard deviation of daily estimates) of the ambient OC in the summer and winter, respectively. Approximately two-thirds of the primary OC is transported into Pittsburgh as part of the regional air mass. The ambient OC that is not apportioned by the CMB model is well correlated with secondary organic aerosol (SOA) estimates based on the EC-tracer method and ambient concentrations of organic species associated with SOA. Therefore, SOA appears to be the major component of OC, not only in summer, but potentially in all seasons. Primary OC dominates the OC mass balance on a small number of nonsummer days with high OC concentrations; these events are associated with specific meteorological conditions such as local inversions. Primary particulate emissions only contribute a small fraction of the ambient fine-particle mass, especially in the summer.     

Second Generation Chemical Mass Balance Source Apportionment of Sulfur Oxides and Sulfate at the Grand Canyon during the Project MOHAVE Summer Intensive

ABSTRACT

Receptor-based chemical mass balance (CMB) analysis techniques are designed to apportion species that are conserved during pollutant transport using conserved source profiles. The techniques will fail if non-conservative species (or profiles) are not properly accounted for in the CMB model. The straightforward application of the CMB model developed for Project MOHAVE using regional profiles resulted in a significant under-prediction of total sulfate oxides (SO_x, SO₂ plus fine particulate sulfate) for many samples at Meadview, AZ. In addition, for these samples the concentration of the inert tracer emitted from the MOHAVE Power Project (MPP), ocPDCH, was also under-predicted. A second-generation model has been developed which assumes that separation of particles and SO₂ can occur in the MPP plume during nighttime stable plume conditions. This second-generation CMB model accounts for all SO_x present at the various receptor sites. In addition, the concentrations of ocPDCH and the presence of other inert tracers of emission from regional sources are accurately predicted. The major source of SO_x at Meadview was the MPP, but the major source of sulfate at this site was the Las Vegas urban area. At Hopi Point in the Grand Canyon, the Baja California region (Imperial Valley and northwestern Mexico) was the major source of both SO_x and sulfate.     

Fine particulate source apportionment using data from the USEPA speciation trends network in Chicago,Illinois: Comparison of two source apportionment models

Data from two of the United States Environmental Protection Agency's speciation trends network fine particulate matter sites within Chicago, Illinois were analyzed using the chemical mass balance (CMB) and positive matrix factorization (PMF) models to determine source contributions to the ambient fine particulate concentrations. The results from the two models were compared to determine the similarities and differences in the source contributions. This included examining the differences in the magnitude of the individual source contributions as well as the correlation between the contribution values from the two methods. The results showed that both models predicted sulfates, nitrates and motor vehicles as the three highest fine particle contributors for the two sites accounting for approximately 80% of the total. The PMF model attributed a slightly greater amount of fine particulate to the road salt, steel and soil sources while vegetative burning contributed more in the CMB results. Correlations between the contribution results from the two models were high for sulfates, nitrates and road salt with very good correlations existing for motor vehicles and petroleum refineries. The predicted PMF profiles agreed well with measured source profiles for the major species associated with each source.     

Chemical mass balance receptor model applied to ambient C2–C9 VOC concentration in Seoul,Korea: Effect of chemical reaction losses

A chemical mass balance (CMB) receptor model was used for estimating the diurnal contributions of VOC emission sources to the ambient C₂–C₉ VOC concentration in Seoul, Korea. For this purpose, the VOC concentrations were measured in the morning, the afternoon, and the evening. The samples were collected using a 2-h integrated SUMMA canister. The source profiles were developed for the CMB calculation in the Seoul area. To investigate the effect of the chemical reaction loss of VOCs on the CMB calculation, the modified model employing a decay factor and the standard model that considers no loss were compared. The modified model estimated that the vehicle exhaust (52%) was the largest leading source of VOCs in the Seoul atmosphere, followed by the use of solvents (26%), gasoline evaporation (15%), the use of liquefied petroleum gas (LPG) (5%), and the use of liquefied natural gas (LNG) (2%). Relative source contribution for vehicle exhaust showed a clear diurnal variation with a high in the morning and evening and a low in the afternoon, while the contribution of evaporative emissions (gasoline evaporation and solvent usage) showed a different diurnal pattern from that of the vehicle exhaust, exhibiting a high in the afternoon and evening and a low in the morning. It was found that the difference between the total source contribution (μg m⁻³) estimated from these two models was not statistically significant. However, when the paired-sample t-test is applied to the individual sources, a significant difference was found for the vehicle exhaust and the solvent use. In addition, the modified model brought forth a better performance with high R² and low χ² as compared to those obtained from the standard model in the CMB calculation. The vehicle exhaust and solvent use were estimated to be the largest and the second largest contributors to ambient benzene as well as ozone formation potential (OFP), respectively. Based on above results we believe that incorporating the reaction loss in the CMB calculations helps to better fit the source profile to the ambient VOC concentrations. However, the reaction loss does not significantly affect the estimation of source contributions.     

Estimating uncertainties and uncertainty contributors of CMB PM2.5 source apportionment results

The chemical mass balance (CMB) model was applied for source apportionment of PM_2.5 in Atlanta in order to explore levels and causes of uncertainties in source contributions. Monte Carlo analysis with Latin hypercube sampling (MC-LHS) was performed to evaluate the source impact uncertainties and quantify how uncertainties in ambient measurement and source profile data affect results. In general, uncertainties in the source profile data contribute more to the final uncertainties in source apportionment results than do those in ambient measurement data. Uncertainty contribution estimates suggest that non-linear interactions among source profiles also affect the final uncertainties although their influence is typically less than uncertainties in source profile data.     

Determination of Microgram Quantities of Inorganic Sulfate in Atmospheric Particulates

Abstract

Before a community-wide woodstove changeout program, a chemical mass balance (CMB) source apportionment study was conducted in Libby, MT, during the winter of 2003–2004 to identify the sources of fine particulate matter (PM_2.5) within the valley. Results from this study showed that residential woodstoves were the major source, contributing approximately 80% of the ambient PM_2.5 throughout the winter months. In an effort to lower the ambient PM_2.5, a large woodstove changeout program was conducted in Libby from 2005 to 2007 in which nearly 1200 old woodstoves were changed out with cleaner burning models. During the winter of 2007–2008, a follow-up CMB source apportionment study was conducted to evaluate the effectiveness of the changeout. Results from this study showed that average winter PM_2.5 mass was reduced by 20%, and woodsmoke-related PM_2.5 (as identified by the CMB model) was reduced by 28% when compared with the pre-changeout winter of 2003– 2004. These results suggest that a woodstove changeout can be an effective tool in reducing ambient levels of PM_2.5 in woodstove-impacted communities.     

Source apportionment of PM10 in six cities of northern China

Ambient PM₁₀ was sampled in six northern China cities (Urumqi, Yinchuan, Taiyuan, Anyang, Tianjin and Jinan) from December 1999 to July 2002, and analyzed for 16 chemical elements, two water-soluble ions, total carbon, and organic carbon. In addition, chemical source profiles consisting of the same particulate components were obtained from a number of naturally occurring geological sources (soil dust from exposed lands) and sources of atmospheric particulates resulting from human activities (resuspended dust, cement, coal combustion fly ash, vehicle exhaust, and secondary particles). Ambient and source data were used in a chemical mass balance (CMB) receptor model to determine the major source of PM₁₀ in these six cities. Results of CMB modeling showed that the major source of ambient PM₁₀ in all the cities was resuspended dust. Significant contributions from coal fly ash were also found in all six cities.     

Determination of Source Contributions to Ambient PM2.5 in Kaohsiung,Taiwan, Using a Receptor Model

ABSTRACT

Ambient particulates of PM_2.5 were sampled at three sites in Kaohsiung, Taiwan, during February and March 1999. In addition, resuspended PM_2.5 collected from traffic tunnels, paved roads, fly ash of a municipal solid waste (MSW) incinerator, and seawater was obtained. All the samples were analyzed for twenty constituents, including water-soluble ions, organic carbon (OC), elemental carbon (EC), and metallic elements. In conjunction with local source profiles and the source profiles in the model library SPECIATE EPA, the receptor model based on chemical mass balance (CMB) was then applied to determine the source contributions to ambient PM_2.5.

The mean concentration of ambient PM_2.5 was 42.6953.68 μj.g/m³ for the sampling period. The abundant species in ambient PM_2.5 in the mass fraction for three sites were OC (12.7-14.2%), SO₄ ^2- (12.8-15.1%), NO₃ ^- (8.110.3%), NH₄+ (6.7-7.5%), and EC (5.3-8.5%). Results of CMB modeling show that major pollution sources for ambient PM_2.5 are traffic exhaust (18-54%), secondary aerosols (30-41% from SO₄ ^2- and NO₃ ^-), and outdoor burning of agriculture wastes (13-17%).     

Source apportionment of fine particles at urban background and rural sites in the UK atmosphere

Airborne fine particulate matter (PM_2.5) has been collected at two sites in the West Midlands conurbation, UK, representing urban background and rural locations. Chemical analyses have been carried out for major anions, trace metals, total OC and EC, and for individual organic marker species including n-alkanes, hopanes, PAHs, organic acids and sterols. Source apportionment has been conducted using both a pragmatic mass closure model and the US EPA chemical mass balance (CMB) model. The pragmatic mass closure model is well able to account for the measured PM_2.5 mass in terms of chemical/source components, and the chemical mass balance model has been used to apportion the carbonaceous component of the aerosol. The dominant components of PM_2.5 at both sites are secondary inorganic (sulphate and nitrate) and carbonaceous particles. The CMB model shows the latter to arise mainly from road traffic sources, with smaller contributions from vegetative detritus, wood smoke, natural gas, coal, and dust/soil. The CMB model also identifies an important component of the organic aerosol not associated with these primary sources, which correlates very strongly with secondary organic aerosol estimated from the OC/EC ratio. The split between different automotive source types does not relate well to UK emission inventories, and may indicate that CMB source profiles from North American studies and different carbon analysis protocols may lead to erroneous conclusions.     

Source contribution of atmospheric aerosols in Bangkok metropolitan area by CMB8 receptor model

The CMB₈ model was applied for source apportionment of particulate matter in Bangkok area. The 24 h of ambient data were collected and analysed for elemental composition during December 1996 to January 1997 by high volume air samplers at a station in Bangkok, Thailand. Seven source profiles and the average mass concentrations of 42 ambient data were used to run the chemical mass balance (CMB₈) model. The source apportionment by CMB₈ gave similar results comparing with factor analysis – multiple regression (FA–MR) model of the same data. The results revealed that major sources of particulate matter in Bangkok were: soil (33%), road dust (33%) and automobile (15%). The minor source contributions were: sea salt (4.34%), refuse incineration and biomass burning (0.84%), steel mill (0.62%) and fuel oil combustion (0.35%). The lack of source profile for biomass or open burning in Bangkok resulted in much lower predicted contribution of this source when compared to that from FA–MR. When apply this CMB₈ model with daily ambient data, the result revealed that one fourth of daily CMB₈ source apportionment had high value of ² (>4). These exceedance values of Chi² also point out that one of the selected sources (biomass burning) may not be the true contributing sources. Presumably, accurate biomass burning source profile is needed to improve the CMB calculation of source contributions for particulate matter in Bangkok metropolitan area.     

A Chemical Mass Balance for Volatile Organics in Chicago

Increasing attention to the presence of atmospheric volatile organic compounds has focused interest on the sources and fate of organics in ambient air. The purpose of this study was to develop a chemical mass balance receptor model (CMB) to determine the contributions of major organic pollution source types to ambient pollution levels. Twenty mid-day ambient air samples were analyzed for the presence of volatile hydrocarbons by gas chromatographlc procedures. Based on these measurements, contributions from vehicles, gasoline vapor emissions, and petroleum refineries to ambient organic concentrations were estimated. For the receptor site studied, vehicles were the dominant source type and accounted for 60.8 percent of the organics evaluated. Contributions from refineries, gasoline vapor, and all other sources were 10.1, 11.1, and 17.9 percent, respectively. Validation of the predictions showed that the model is sensitive to the effect of overall upwind emissions. The CMB model was shown to produce reasonable predictive results for vehicles, gasoline vapor, and refinery contributions to ambient non-methane organic concentrations.     

Determination of source contributions to ambient PM2.5 in Kaohsiung, Taiwan, using a receptor model

Ambient particulates of PM2.5 were sampled at three sites in Kaohsiung, Taiwan, during February and March 1999. In addition, resuspended PM2.5 collected from traffic tunnels, paved roads, fly ash of a municipal solid waste (MSW) incinerator, and seawater was obtained. All the samples were analyzed for twenty constituents, including water-soluble ions, organic carbon (OC), elemental carbon (EC), and metallic elements. In conjunction with local source profiles and the source profiles in the model library SPECIATE EPA, the receptor model based on chemical mass balance (CMB) was then applied to determine the source contributions to ambient PM2.5. The mean concentration of ambient PM2.5 was 42.69-53.68 micrograms/m3 for the sampling period. The abundant species in ambient PM2.5 in the mass fraction for three sites were OC (12.7-14.2%), SO4(2-) (12.8-15.1%), NO3- (8.1-10.3%), NH4+ (6.7-7.5%), and EC (5.3-8.5%). Results of CMB modeling show that major pollution sources for ambient PM2.5 are traffic exhaust (18-54%), secondary aerosols (30-41% from SO4(2-) and NO3-), and outdoor burning of agriculture wastes (13-17%).     

Sensitivity of source apportionment of urban particulate matter to uncertainty in motor vehicle emissions profiles

A sensitivity analysis was conducted to characterize sources of uncertainty in results of a molecular marker source apportionment model of ambient particulate matter using mobile source emissions profiles obtained as part of the Gasoline/Diesel PM Split Study. A chemical mass balance (CMB) model was used to determine source contributions to samples of fine particulate matter (PM2.5) collected over 3 weeks at two sites in the Los Angeles area in July 2001. The ambient samples were composited for organic compound analysis by the day of the week to investigate weekly trends in source contributions. The sensitivity analysis specifically examined the impact of the uncertainty in mobile source emissions profiles on the CMB model results. The key parameter impacting model sensitivity was the source profile for gasoline smoker vehicles. High-emitting gasoline smoker vehicles with visible plumes were seen to be a significant source of PM in the area, but use of different measured profiles for smoker vehicles in the model gave very different results for apportionment of gasoline, diesel, and smoker vehicle tailpipe emissions. In addition, the contributions of gasoline and diesel emissions to total ambient PM varied as a function of the site and the day of the week.