Tracer-based source apportionment of polycyclic aromatic hydrocarbons in PM2.5 in Guangzhou, southern China, using positive matrix factorization (PMF)

From 28 November to 23 December 2009, 24-h?PM_2.5 samples were collected simultaneously at six sites in Guangzhou. Concentrations of 18 polycyclic aromatic hydrocarbons (PAHs) together with certain molecular tracers for vehicular emissions (i.e., hopanes and elemental carbon), coal combustion (i.e., picene), and biomass burning (i.e., levoglucosan) were determined. Positive matrix factorization (PMF) receptor model combined with tracer data was applied to explore the source contributions to PAHs. Three sources were identified by both inspecting the dominant tracer(s) in each factor and comparing source profiles derived from PMF with determined profiles in Guangzhou or in the Pearl River Delta region. The three sources identified were vehicular emissions (VE), biomass burning (BB), and coal combustion (CC), accounting for 11?±?2 %, 31?±?4 %, and 58?±?4 % of the total PAHs, respectively. CC replaced VE to become the most important source of PAHs in Guangzhou, reflecting the effective control of VE in recent years. The three sources had different contributions to PAHs with different ring sizes, with higher BB contributions (75?±?3 %) to four-ring PAHs such as pyrene and higher CC contributions (57?±?4 %) to six-ring PAHs such as benzo[ghi]perylene. Temporal variations of VE and CC contributions were probably caused by the change of weather conditions, while temporal variations of BB contributions were additionally influenced by the fluctuation of BB emissions. Source contributions also showed some spatial variations, probably due to the source emission variations near the sampling sites.     

Examining intra-urban variation in fine particle mass constituents using GIS and constrained factor analysis

Recent studies have used land use regression (LUR) techniques to explain spatial variability in exposures to PM_2.5 and traffic-related pollutants. Factor analysis has been used to determine source contributions to measured concentrations. Few studies have combined these methods, however, to construct and explain latent source effects. In this study, we derive latent source factors using confirmatory factor analysis constrained to non-negative loadings, and develop LUR models to predict the influence of outdoor sources on latent source factors using GIS-based measures of traffic and other local sources, central site monitoring data, and meteorology. We collected 3–4 day samples of nitrogen dioxide (NO₂) and PM_2.5 outside of 44 homes in summer and winter, from 2003 to 2005 in and around Boston, Massachusetts. Reflectance analysis, X-ray fluorescence spectroscopy (XRF), and high-resolution inductively-coupled plasma mass spectrometry (ICP-MS) were performed on particle filters to estimate elemental carbon (EC), trace element, and water-soluble metals concentrations. Within our constrained factor analysis, a five-factor model was optimal, balancing statistical robustness and physical interpretability. This model produced loadings indicating long-range transport, brake wear/traffic exhaust, diesel exhaust, fuel oil combustion, and resuspended road dust. LUR models largely corroborated factor interpretations through covariate significance. For example, ‘long-range transport’ was predicted by central site PM_2.5 and season; ‘brake wear/traffic exhaust’ and ‘resuspended road dust’ by traffic and residential density; ‘diesel exhaust’ by percent diesel traffic on nearest major road; and ‘fuel oil combustion’ by population density. Results suggest that outdoor residential PM_2.5 source contributions can be partially predicted using GIS-based terms, and that LUR techniques can support factor interpretation for source apportionment. Together, LUR and factor analysis facilitate source identification, assessment of spatial and temporal variability, and more refined source exposure assignment for evaluation of source contributions to health outcomes in epidemiological studies.     

Levels of total suspended particulate matter and major trace elements in Kosovo: a source identification and apportionment study

Concentration levels of total suspended particles (TSP) and 27 major, minor and trace elemental components were determined at four sites in Kosovo through a 1-year survey (January-December 2002). Ambient concentrations were evaluated in comparison to limit values. The origin of elemental TSP constituents was investigated by calculating enrichment factors and diagnostic ratios. Multivariate statistics, such as hierarchical cluster analysis and factor analysis, were also employed to identify emission sources. A multivariate statistical receptor model (Absolute Principal Component Analysis, APCA) was applied to quantify source contributions. Soil dust, cement production, vehicular emissions, brake wear, and fuel combustion were identified as major sources with variable contributions at the four sampling sites.     

Comparison and evaluation of chemically speciated mobile source PM2.5 particulate matter profiles

Mobile sources are significant contributors to ambient PM2.5, 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 PM2.5 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.     

Receptor modelling using Positive Matrix Factorisation,back trajectories and Radon-222

PM_2.5 aerosols were sampled and atmospheric ²²²Rn (radon) was measured, at Hong Kong, China, over 3 years 2001–2003. The aerosol samples were analysed using accelerator-based Ion Beam Analysis (IBA) techniques to provide quantitative information on 21 of their major and minor elemental contributions. The radon concentration on aerosol sampling days was then used to classify the degree of land contact (high or low) experienced by air masses en route to the receptor site. It was found that elements known to originate from anthropogenic sources (e.g. Zn, K, Br, Pb and Black Carbon) were positively correlated with observed radon concentration. An eight-factor Positive Matrix Factorisation (PMF) analysis was performed on the data set, which resulted in elemental profiles (“fingerprints”) for eight potential sources and we identified source factors that were correlated with radon. The Potential Source Contribution Function technique was then used to identify the geographic regions most likely to have significantly contributed to the aerosol samples collected at the receptor site.     

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.     

Optimized variable source-profile approach for source apportionment

An expanded chemical mass balance (CMB) approach for PM_2.5 source apportionment is presented in which both the local source compositions and corresponding contributions are determined from ambient measurements and initial estimates of source compositions using a global-optimization mechanism. Such an approach can serve as an alternative to using predetermined (measured) source profiles, as traditionally used in CMB applications, which are not always representative of the region and/or time period of interest. Constraints based on ranges of typical source profiles are used to ensure that the compositions identified are representative of sources and are less ambiguous than the factors/sources identified by typical factor analysis (FA) techniques. Gas-phase data (SO₂, CO and NO_y) are also used, as these data can assist in identifying sources. Impacts of identified sources are then quantified by minimizing the weighted-error between apportioned and measured levels of the fitting species. This technique was applied to a dataset of PM_2.5 measurements at the former Atlanta Supersite (Jefferson Street site), to apportion PM_2.5 mass into nine source categories. Good agreement is found when these source impacts are compared with those derived based on measured source profiles as well as those derived using a current FA technique, Positive Matrix Factorization. The proposed method can be used to assess the representativeness of measured source-profiles and to help identify those profiles that may be in significant error, as well as to quantify uncertainties in source-impact estimates, due in part to uncertainties in source compositions.     

Isotopic and molecular fractionation in combustion; three routes to molecular marker validation,including direct molecular ‘dating’ (GC/AMS)

The identification of unique isotopic, elemental, and molecular markers for sources of combustion aerosol has growing practical importance because of the potential effects of fine particle aerosol on health, visibility and global climate. It is urgent, therefore, that substantial efforts be directed toward the validation of assumptions involving the use of such tracers for source apportionment. We describe here three independent routes toward carbonaceous aerosol molecular marker identification and validation: (1) tracer regression and multivariate statistical techniques applied to field measurements of mixed source, carbonaceous aerosols; (2) a new development in aerosol ¹⁴C metrology: direct, pure compound accelerator mass spectrometry (AMS) by off-line GC/AMS (‘molecular dating’); and (3) direct observation of isotopic and molecular source emissions during controlled laboratory combustion of specific fuels. Findings from the combined studies include: independent support for benzo(ghi)perylene as a motor vehicle tracer from the first (statistical) and second (direct ‘dating’) studies; a new indication, from the third (controlled combustion) study, of a relation between ¹³C isotopic fractionation and PAH molecular fractionation, also linked with fuel and stage of combustion; and quantitative data showing the influence of both fuel type and combustion conditions on the yields of such species as elemental carbon and PAH, reinforcing the importance of exercising caution when applying presumed conservative elemental or organic tracers to fossil or biomass burning field data as in the first study.     

Fine particulate matter source apportionment for the Chemical speciation Trends Network site at Birmingham, Alabama, using Positive Matrix Factorization

The Positive Matrix Factorization (PMF) receptor model version 1.1 was used with data from the fine particulate matter (PM2.5) Chemical Speciation Trends Network (STN) to estimate source contributions to ambient PM2.5 in a highly industrialized urban setting in the southeastern United States. Model results consistently resolved 10 factors that are interpreted as two secondary, five industrial, one motor vehicle, one road dust, and one biomass burning sources. The STN dataset is generally not corrected for field blank levels, which are significant in the case of organic carbon (OC). Estimation of primary OC using the elemental carbon (EC) tracer method applied on a seasonal basis significantly improved the model's performance. Uniform increase of input data uncertainty and exclusion of a few outlier samples (associated with high potassium) further improved the model results. However, it was found that most PMF factors did not cleanly represent single source types and instead are "contaminated" by other sources, a situation that might be improved by controlling rotational ambiguity within the model. Secondary particulate matter formed by atmospheric processes, such as sulfate and secondary OC, contribute the majority of ambient PM2.5 and exhibit strong seasonality (37 +/- 10% winter vs. 55 +/- 16% summer average). Motor vehicle emissions constitute the biggest primary PM2.5 mass contribution with almost 25 +/- 2% long-term average and winter maximum of 29 +/- 11%. PM2.5 contributions from the five identified industrial sources vary little with season and average 14 +/- 1.3%. In summary, this study demonstrates the utility of the EC tracer method to effectively blank-correct the OC concentrations in the STN dataset. In addition, examination of the effect of input uncertainty estimates on model results indicates that the estimated uncertainties currently being provided with the STN data may be somewhat lower than the levels needed for optimum modeling results.     

Sensitivity of molecular marker-based CMB models to biomass burning source profiles

To assess the contribution of sources to fine particulate organic carbon (OC) at four sites in North Carolina, USA, a molecular marker chemical mass balance model (MM-CMB) was used to quantify seasonal contributions for 2 years. The biomass burning contribution at these sites was found to be 30–50% of the annual OC concentration. In order to provide a better understanding of the uncertainty in MM-CMB model results, a biomass burning profile sensitivity test was performed on the 18 seasonal composites. The results using reconstructed emission profiles based on published profiles compared well, while model results using a single source test profile resulted in biomass burning contributions that were more variable. The biomass burning contribution calculated using an average regional profile of fireplace emissions from five southeastern tree species also compared well with an average profile of open burning of pine-dominated forest from Georgia. The standard deviation of the results using different source profiles was a little over 30% of the annual average biomass contributions. Because the biomass burning contribution accounted for 30–50% of the OC at these sites, the choice of profile also impacted the motor vehicle source attribution due to the common emission of elemental carbon and polycyclic aromatic hydrocarbons. The total mobile organic carbon contribution was less effected by the biomass burning profile than the relative contributions from gasoline and diesel engines.     

Current factor analysis receptor models are ill-posed

Current factor models lack sufficient physical constraints to guarantee a unique, physically valid solution; in this sense they are ill-posed. Any realistic factor model must obey certain natural physical constraints, for example, the predicted source contributions and elemental compositions must be non-negative. Five such constraints are given in the paper. As shown by a simple example with only two sources and three elements, these natural constraints are insufficient to define a unique factor model. The same is shown to be true for a more complex example with seven sources and 10 elements. Since the examples use simulated data without observational or other errors, they prove that current factor models are, in general, biased in the statistical sense. The examples also show that the bias, or systematic error, can be very large. Thus, while factor analysis continues to be a valuable screening tool for unexpected sources, in the hands of the inexperienced it could lead to serious errors in source apportionment and derived source compositions.     

Temporal variations of source impacts at the receptor,as derived from air particulate monitoring data in Ho Chi Minh City,Vietnam

Varimax rotation factor analysis was applied to monthly concentrations of elements in total suspended air particulate (TSP) matter in Ho Chi Minh City collected from December 1992 to November 1996, covering four dry/rainy seasons. Six pollution source types were revealed. Resuspended soil/road dust accounts for 74% of the TSP mass loading. Motor vehicles and a source which emits particulates containing arsenic account for 10% and 9%, respectively. There are three minor sources, namely, cement dust from the nearby construction site, road dust of local traffic origin and burning emissions. The contributions from these source were estimated with high uncertainties. The interpretation of sources was corroborated by studying source profiles and temporal variations of source contributions. The monthly variations of source contributions at the receptor were modelled by using source apportionment techniques. From the variation patterns, emission scenarios for burning, construction and motor vehicle sources were reproduced. Source contributions also exhibit seasonal variability induced by changes of meteorological conditions. No seasonal change was found for the As-containing particulates, suggesting a speculation on their origin as coal fly ash emitting from any local coal burning source.     

A quantitative assessment of source contributions to inhalable particulate matter pollution in metropolitan Boston

In this paper, source apportionment techniques are employed to identify and quantify the major particle pollution source classes affecting a monitoring site in metropolitan Boston, MA. A Principal Component Analysis (PCA) of paniculate elemental data allows the estimation of mass contributions for five fine mass panicle source classes (soil, motor vehicle, coal related, oil and salt aerosols), and six coarse panicle source classes (soil, motor vehicle, refuse incineration, residual oil, salt and sulfate aerosols). Also derived are the elemental characteristics of those source aerosols and their contributions to the total recorded elemental concentrations (i.e. an elemental mass balance). These are estimated by applying a new approach to apportioning mass among various PCA source components: the calculation of Absolute Principal Component Scores, and the subsequent regression of daily mass and elemental concentrations on these scores.One advantage of the PCA source apportionment approach developed is that it allows the estimation of mass and source particle characteristics for an unconventional source category: transported (coal combustion related) aerosols. This particle class is estimated to represent a major portion of the aerosol mass, averaging roughly 40 per cent of the fine mass and 25 per cent of the inhalable particle mass at the Watertown, MA site. About 45 per cent of the fine particle sulfur is ascribed to this one component, with only 20 per cent assigned to pollution from local sources. The composition of the coal related aerosol at this site is found to be quite different from particles measured in the stacks of coal-fired power plants. Sulfates were estimated to comprise a much larger percentage of the ambient coal related aerosol than has been measured in stacks, while crustal element percentages were much reduced. This is thought to be due to primary panicle deposition and secondary aerosol accretion experienced during transport. Overall, the results indicate that the application of further emission controls to local point sources of particles would have less influence on fine aerosol and sulfate concentrations than would the control of more distant emissions causing aerosols transported into the Boston vicinity.     

Bayesian treatment of a chemical mass balance receptor model with multiplicative error structure

The chemical mass balance (CMB) receptor model is commonly used in source apportionment studies as a means for attributing measured airborne particulate matter (PM) to its constituent emission sources. Traditionally, error terms (e.g., measurement and source profile uncertainty) associated with the model have been treated in an additive sense. In this work, however, arguments are made for the assumption of multiplicative errors, and the effects of this assumption are realized in a Bayesian probabilistic formulation which incorporates a ‘modified’ receptor model. One practical, beneficial effect of the multiplicative error assumption is that it automatically precludes the possibility of negative source contributions, without requiring additional constraints on the problem. The present Bayesian treatment further differs from traditional approaches in that the source profiles are inferred alongside the source contributions. Existing knowledge regarding the source profiles is incorporated as prior information to be updated through the Bayesian inferential scheme. Hundreds of parameters are therefore present in the expression for the joint probability of the source contributions and profiles (the posterior probability density function, or PDF), whose domain is explored efficiently using the Hamiltonian Markov chain Monte Carlo method. The overall methodology is evaluated and results compared to the US Environmental Protection Agency's standard CMB model using a test case based on PM data from Fresno, California.     

Fine Particle Receptor Modeling in the Atmosphere of Mexico City

Abstract

Source apportionment analyses were carried out by means of receptor modeling techniques to determine the contribution of major fine particulate matter (PM_2.5) sources found at six sites in Mexico City. Thirty-six source profiles were determined within Mexico City to establish the fingerprints of particulate matter sources. Additionally, the profiles under the same source category were averaged using cluster analysis and the fingerprints of 10 sources were included. Before application of the chemical mass balance (CMB), several tests were carried out to determine the best combination of source profiles and species used for the fitting. CMB results showed significant spatial variations in source contributions among the six sites that are influenced by local soil types and land use. On average, 24-hr PM_2.5 concentrations were dominated by mobile source emissions (45%), followed by secondary inorganic aerosols (16%) and geological material (17%). Industrial emissions representing oil combustion and incineration contributed less than 5%, and their contribution was higher at the industrial areas of Tlalnepantla (11%) and Xalostoc (8%). Other sources such as cooking, biomass burning, and oil fuel combustion were identified at lower levels. A second receptor model (principal component analysis, [PCA]) was subsequently applied to three of the monitoring sites for comparison purposes. Although differences were obtained between source contributions, results evidence the advantages of the combined use of different receptor modeling techniques for source apportionment, given the complementary nature of their results. Further research is needed in this direction to reach a better agreement between the estimated source contributions to the particulate matter mass.     

Locations and preferred pathways of possible sources of Arctic aerosol

The chemical composition of particles collected at Alert, Northwest Territories, Canada, show strong, persistent seasonal variations. In a previous study, a 2-way/3-way mixed factor model was performed on the weekly average concentrations of 24 aerosol components measured over the period from 1980 to 1991. The Multilinear Engine (ME), a new mathematical technique, was used to obtain the solution. The two modes of the 2-way model consist of the source composition profiles and mass contributions over the 11 yr, while for the three modes of the 3-way model, source profiles, mass contributions variations over the weeks within a year, and the year-to-year variation over the 11 yr within the measurement period. Five 2-way and two 3-way factors were found to provide a good fit to the data and were easily interpreted. In this investigation, potential source contribution function (PSCF) analysis was applied to the source contributions derived from the ME analysis by incorporating meteorological information in the form of 5-d air parcel back trajectories. The potential locations and/or the preferred pathways of these possible sources were then determined by the PSCF analysis.     

Source apportionment of exposures to volatile organic compounds. I. Evaluation of receptor models using simulated exposure data

Four receptor-oriented source apportionment models were evaluated by applying them to simulated personal exposure data for select volatile organic compounds (VOCs) that were generated by Monte Carlo sampling from known source contributions and profiles. The exposure sources modeled are environmental tobacco smoke, paint emissions, cleaning and/or pesticide products, gasoline vapors, automobile exhaust, and wastewater treatment plant emissions. The receptor models analyzed are chemical mass balance, principal component analysis/absolute principal component scores, positive matrix factorization (PMF), and graphical ratio analysis for composition estimates/source apportionment by factors with explicit restriction, incorporated in the UNMIX model. All models identified only the major contributors to total exposure concentrations. PMF extracted factor profiles that most closely represented the major sources used to generate the simulated data. None of the models were able to distinguish between sources with similar chemical profiles. Sources that contributed <5% to the average total VOC exposure were not identified.     

Source apportionment of PAHs and n-alkanes in respirable particles in Tehran,Iran by wind sector and vertical profile

The vertical concentration profiles and source contributions of polycyclic aromatic hydrocarbons (PAHs) and n-alkanes in respirable particle samples (PM₄) collected at 10, 100, 200 and 300-m altitude from the Milad Tower of Tehran, Iran during fall and winter were investigated. The average concentrations of total PAHs and total n-alkanes were 16.7 and 591 ng/m³, respectively. The positive matrix factorization (PMF) model was applied to the chemical composition and wind data to apportion the contributing sources. The five PAH source factors identified were: ‘diesel’ (56.3 % of total PAHs on average), ‘gasoline’ (15.5 %), ‘wood combustion, and incineration’ (13 %), ‘industry’ (9.2 %), and ‘road soil particle’ (6.0 %). The four n-alkane source factors identified were: ‘petrogenic’ (65 % of total n-alkanes on average), ‘mixture of petrogenic and biomass burning’ (15 %), ‘mixture of biogenic and fossil fuel’ (11.5 %), and ‘biogenic’ (8.5 %). Source contributions by wind sector were also estimated based on the wind sector factor loadings from PMF analysis. Directional dependence of sources was investigated using the conditional probability function (CPF) and directional relative strength (DRS) methods. The calm wind period was found to contribute to 4.4 % of total PAHs and 5.0 % of total n-alkanes on average. Highest average concentrations of PAHs and n-alkanes were found in the 10 and 100 m samples, reflecting the importance of contributions from local sources. Higher average concentrations in the 300 m samples compared to those in the 200 m samples may indicate contributions from long-range transport. The vertical profiles of source factors indicate the gasoline and road soil particle-associated PAHs, and the mixture from biogenic and fossil fuel source-associated n-alkanes were mostly from local emissions. The smaller average contribution of diesel-associated PAHs in the lower altitude samples also indicates that the restriction of diesel-fueled vehicle use in the central area of Tehran has been effective in reducing the PAHs concentration.     

Identification of sources of Phoenix aerosol by positive matrix factorization

Chemical composition data for fine and coarse particles collected in Phoenix, AZ, were analyzed using positive matrix factorization (PMF). The objective was to identify the possible aerosol sources at the sampling site. PMF uses estimates of the error in the data to provide optimum data point scaling and permits a better treatment of missing and below-detection-limit values. It also applies nonnegativity constraints to the factors. Two sets of fine particle samples were collected by different samplers. Each of the resulting fine particle data sets was analyzed separately. For each fine particle data set, eight factors were obtained, identified as (1) biomass burning characterized by high concentrations of organic carbon (OC), elemental carbon (EC), and K; (2) wood burning with high concentrations of Na, K, OC, and EC; (3) motor vehicles with high concentrations of OC and EC; (4) nonferrous smelting process characterized by Cu, Zn, As, and Pb; (5) heavy-duty diesel characterized by high EC, OC, and Mn; (6) sea-salt factor dominated by Na and Cl; (7) soil with high values for Al, Si, Ca, Ti, and Fe; and (8) secondary aerosol with SO4(-2) and OC that may represent coal-fired power plant emissions. For the coarse particle samples, a five-factor model gave source profiles that are attributed to be (1) sea salt, (2) soil, (3) Fe source/motor vehicle, (4) construction (high Ca), and (5) coal-fired power plant. Regression of the PM mass against the factor scores was performed to estimate the mass contributions of the resolved sources. The major sources for the fine particles were motor vehicles, vegetation burning factors (biomass and wood burning), and coal-fired power plants. These sources contributed most of the fine aerosol mass by emitting carbonaceous particles, and they have higher contributions in winter. For the coarse particles, the major source contributions were soil and construction (high Ca). These sources also peaked in winter.     

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.