Aerosol laser ablation mass spectrometry of suspended powders from PM sources and its implications to receptor modeling

Primary sources of particulate matter (PM) were analyzed by suspending powdered samples into an aerosol laser ablation mass spectrometer (LAMS). PM sources studied included vehicle exhaust particulates, dust from a non-ferrous smelter, cement powder, incinerator fly ash, two coal fly ash samples, and two soils. Marker peaks signified certain PM source sectors: construction particles could be distinguished by abundant Ca and Ca compounds, fuel combustion was marked by elemental carbon clusters, and nonferrous industrial particles showed inorganic As, Cu, Pb, Zn, and SOx. In addition to the distinction between particles from these different source sectors, mass spectral results also showed that for a single source, different particle types existed, and among different sources within a sector, similar spectra were present. The aerosol LAMS results show the difficulty in differentiating among separate fly ash sources as well as among different soil samples. A particle class balance receptor model that measures the amount of specific particle types rather than the amount of a chemical component is suggested as a means of source apportionment when particle spectra with overlapping source possibilities occur. The assumptions and limitations of receptor modeling aerosol LAMS data are also described. In particular, methods need to be developed to account for the contribution of secondary sources.     

Source apportionment of fine particles in Washington, DC, utilizing temperature-resolved carbon fractions

Integrated ambient particulate matter < or =2.5 microm in aerodynamic diameter (PM2.5) samples were collected at a centrally located urban monitoring site in Washington, DC, on Wednesdays and Saturdays using Interagency Monitoring of Protected Visual Environments samplers. Particulate carbon was analyzed using the thermal optical reflectance method that divides carbon into four organic carbon fractions, pyrolyzed organic carbon, and three elemental carbon fractions. A total of 35 variables measured in 718 samples collected between August 1988 and December 1997 were analyzed. The data were analyzed using Positive Matrix Factorization and 10 sources were identified: sulfate (SO4(2-))-rich secondary aerosol I (43%), gasoline vehicle (21%), SO4(2-)-rich secondary aerosol II (11%), nitrate-rich secondary aerosol (9%), SO4(2-)-rich secondary aerosol III (6%), incinerator (4%), aged sea salt (2%), airborne soil (2%), diesel emissions (2%), and oil combustion (2%). In contrast to a previous study that included only total organic carbon and elemental carbon fractions, motor vehicles were separated into fractions identified as gasoline vehicle and diesel emissions containing carbon fractions whose abundances were different between the two sources. This study indicates that the temperature-resolved carbon fraction data can be utilized to enhance source apportionment, especially with respect to the separation of diesel emissions from gasoline vehicle sources. Conditional probability functions using surface wind data and deduced source contributions aid in the identifications of local sources.     

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.     

Evaluations of the chemical mass balance method for determining contributions of gasoline and diesel exhaust to ambient carbonaceous aerosols

The US. Department of Energy Gasoline/Diesel PM Split Study was conducted to assess the sources of uncertainties in using an organic compound-based chemical mass balance receptor model to quantify the relative contributions of emissions from gasoline (or spark ignition [SI]) and diesel (or compression ignition [CI]) engines to ambient concentrations of fine particulate matter (PM2.5) in California's South Coast Air Basin (SOCAB). In this study, several groups worked cooperatively on source and ambient sample collection and quality assurance aspects of the study but worked independently to perform chemical analysis and source apportionment. Ambient sampling included daily 24-hr PM2.5 samples at two air quality-monitoring stations, several regional urban locations, and along freeway routes and surface streets with varying proportions of automobile and truck traffic. Diesel exhaust was the dominant source of total carbon (TC) and elemental carbon (EC) at the Azusa and downtown Los Angeles, CA, monitoring sites, but samples from the central part of the air basin showed nearly equal apportionments of CI and SI. CI apportionments to TC were mainly dependent on EC, which was sensitive to the analytical method used. Weekday contributions of CI exhaust were higher for Interagency Monitoring of Protected Visual Environments (IMPROVE; 41+/-3.7%) than Speciation Trends Network (32+/-2.4%). EC had little effect on SI apportionment. SI apportionments were most sensitive to higher molecular weight polycyclic aromatic hydrocarbons (indeno[123-cd]pyrene, benzo(ghi)perylene, and coronene) and several steranes and hopanes, which were associated mainly with high emitters. Apportionments were also sensitive to choice of source profiles. CI contributions varied from 30% to 60% of TC when using individual source profiles rather than the composites used in the final apportionments. The apportionment of SI vehicles varied from 1% to 12% of TC depending on the specific profile that was used. Up to 70% of organic carbon (OC) in the ambient samples collected at the two fixed monitoring sites could not be apportioned to directly emitted PM emissions.     

Identification of sources contributing to Mid-Atlantic regional aerosol

Source types or source regions contributing to the concentration of atmospheric fine particles measured at Brigantine National Wildlife Refuge, NJ, were identified using a factor analysis model called Positive Matrix Factorization (PMF). Cluster analysis of backward air trajectories on days of high- and low-factor concentrations was used to link factors to potential source regions. Brigantine is a Class I visibility area with few local sources in the center of the eastern urban corridor and is therefore a good location to study Mid-Atlantic regional aerosol. Sulfate (expressed as ammonium sulfate) was the most abundant species, accounting for 49% of annual average fine mass. Organic compounds (22%; expressed as 1.4 x organic carbon) and ammonium nitrate (10%) were the next abundant species. Some evidence herein suggests that secondary organic aerosol formation is an important contributor to summertime regional aerosol. Nine factors were identified that contributed to PM2.5 mass concentrations: coal combustion factors (66%, summer and winter), sea salt factors (9%, fresh and aged), motor vehicle/mixed combustion (8%), diesel/Zn-Pb (6%), incinerator/industrial (5%), oil combustion (4%), and soil (2%). The aged sea salt concentrations were highest in springtime, when the land breeze-sea breeze cycle is strongest. Comparison of backward air trajectories of high- and low-concentration days suggests that Brigantine is surrounded by sources of oil combustion, motor vehicle/mixed combustion, and waste incinerator/industrial emissions that together account for 17% of PM2.5 mass. The diesel/Zn-Pb factor was associated with sources north and west of Brigantine. Coal combustion factors were associated with coal-fired power plants west and southwest of the site. Particulate carbon was associated not only with oil combustion, motor vehicle/mixed combustion, waste incinerator/industrial, and diesel/Pb-Zn, but also with the coal combustion factors, perhaps through common transport.     

The Denver Aerosol Sources and Health (DASH) Study: Overview and Early Findings

Improved understanding of the sources of air pollution that are most harmful could aid in developing more effective measures for protecting human health. The Denver Aerosol Sources and Health (DASH) study was designed to identify the sources of ambient fine particulate matter (PM(2.5)) that are most responsible for the adverse health effects of short-term exposure to PM (2.5). Daily 24-hour PM(2.5) sampling began in July 2002 at a residential monitoring site in Denver, Colorado, using both Teflon and quartz filter samplers. Sampling is planned to continue through 2008. Chemical speciation is being carried out for mass, inorganic ionic compounds (sulfate, nitrate and ammonium), and carbonaceous components, including elemental carbon, organic carbon, temperature-resolved organic carbon fractions and a large array of organic compounds. In addition, water soluble metals were measured daily for 12 months in 2003. A receptor-based source apportionment approach utilizing positive matrix factorization (PMF) will be used to identify PM (2.5) source contributions for each 24-hour period. Based on a preliminary assessment using synthetic data, the proposed source apportionment should be able to identify many important sources on a daily basis, including secondary ammonium nitrate and ammonium sulfate, diesel vehicle exhaust, road dust, wood combustion and vegetative debris. Meat cooking, gasoline vehicle exhaust and natural gas combustion were more challenging for PMF to accurately identify due to high detection limits for certain organic molecular marker compounds. Measurements of these compounds are being improved and supplemented with additional organic molecular marker compounds. The health study will investigate associations between daily source contributions and an array of health endpoints, including daily mortality and hospitalizations and measures of asthma control in asthmatic children. Findings from the DASH study, in addition to being of interest to policymakers, by identifying harmful PM(2.5) sources may provide insights into mechanisms of PM effect.     

Sensitivity of a molecular marker based positive matrix factorization model to the number of receptor observations

To investigate the impact of the number of observations on molecular marker-based positive matrix factorization (MM-PMF) source apportionment models, daily PM_2.5 samples were collected in East St. Louis, IL, from April 2002 through May 2003. The samples were analyzed for daily 24-h average concentrations of elemental and organic carbon, trace elements, and speciated particle-phase organic compounds. A total of 273 sets of observations were used in the model and consisted of all valid sets of observations from the year long data set minus one sixth of the measurements, which were collected every 6th day and were analyzed by different chemical analysis techniques. In addition to the base case of 273 samples, systematic subsets of the data set were analyzed by PMF. These subsets of data included 50% of the observations (135–138 days), 33% of the observations (90–92 days) and 20% of the observations (52–56 days). In addition, model runs were also examined that used 48-h, 72-h, 6-day, and weekly average concentrations as model inputs. All MM-PMF model runs were processed following the same procedures to explore the stability of the source attribution results. Consistent with previous MM-PMF results for East St. Louis, the main sources of organic aerosol were found to be mobile sources, secondary organic aerosols (SOAs), resuspended soil and biomass combustions, as well as an n-alkane dominated point source and other combustion sources. The MM-PMF model was reasonably stable when the number of observations in the input was reduced to ninety, or approximately 33% of observations present in the base case. In these cases, the key factors including resuspended soil, mobile and secondary factors, which accounted for more than 70% of the measured OC concentrations, were stable as defined by a relative standard deviation (RSD) of less than 30%. Similar results were obtained from the smaller data subsets, but resulted in larger uncertainties, with several of these factors yielding RSD of greater than 30%. The three factors with the largest OC contributions were more stable than the other minor factors, even when the number of observations was nominally 50 days. Secondary organic aerosol (SOA) was the most stable factor observed in the model runs. Since it is unclear if these results can be broadly applied to all MM-PMF models, additional studies of this nature are needed to assess the broader applicability of these conclusions. Until such studies are implemented, this paper provides a foundation to design future studies in sampling strategies for source apportionment using MM-PMF.     

Assessment of source apportionment by Positive Matrix Factorization analysis on fine and coarse urban aerosol size fractions

This study was conducted in order to investigate the differences observed in source profiles in the urban environment, when chemical composition parameters from different aerosol size fractions are subjected to factor analysis. Source apportionment was performed in an urban area where representative types of emission sources are present. PM₁₀ and PM₂ samples were collected within the Athens Metropolitan area and analysed for trace elements, inorganic ions and black carbon. Analysis by two-way and three-way Positive Matrix Factorization was performed, in order to resolve sources from data obtained for the fine and coarse aerosol fractions. A difference was observed: seven factors describe the best solution in PMF3 while six factors in PMF2. Six factors derived from PMF3 analysis correspond to those described by the PMF2 solution for the fine and coarse particles separately. These sources were attributed to road dust, marine aerosol, soil, motor vehicles, biomass burning, and oil combustion. The additional source resolved by PMF3 was attributed to a different type of road dust. Combustion sources (oil combustion and biomass burning) were correctly attributed by PMF3 solely to the fine fraction and the soil source to the coarse fraction. However, a motor vehicle's contribution to the coarse fraction was found only by three-way PMF. When PMF2 was employed in PM₁₀ concentrations the optimum solution included six factors. Four source profiles corresponded to the previously identified as vehicles, road dust, biomass burning and marine aerosol, while two could not be clearly identified. Source apportionment by PMF2 analysis based solely on PM₁₀ aerosol composition data, yielded unclear results, compared to results from PMF2 and PMF3 analyses on fine and coarse aerosol composition data.     

Source apportionment of particulate matter in a large city of southeastern Po Valley (Bologna, Italy)

This study reports the results of an experimental research project carried out in Bologna, a midsize town in central Po valley, with the aim at characterizing local aerosol chemistry and tracking the main source emissions of airborne particulate matter. Chemical speciation based upon ions, trace elements, and carbonaceous matter is discussed on the basis of seasonal variation and enrichment factors. For the first time, source apportionment was achieved at this location using two widely used receptor models (principal component analysis/multi-linear regression analysis (PCA/MLRA) and positive matrix factorization (PMF)). Four main aerosol sources were identified by PCA/MLRA and interpreted as: resuspended particulate and a pseudo-marine factor (winter street management), both related to the coarse fraction, plus mixed combustions and secondary aerosol largely associated to traffic and long-lived species typical of the fine fraction. The PMF model resolved six main aerosol sources, interpreted as: mineral dust, road dust, traffic, secondary aerosol, biomass burning and again a pseudo-marine factor. Source apportionment results from both models are in good agreement providing a 30 and a 33 % by weight respectively for PCA-MLRA and PMF for the coarse fraction and 70 % (PCA-MLRA) and 67 % (PMF) for the fine fraction. The episodic influence of Saharan dust transport on PM₁₀ exceedances in Bologna was identified and discussed in term of meteorological framework, composition, and quantitative contribution.     

Determination of single particle mass spectral signatures from heavy-duty diesel vehicle emissions for PM2.5 source apportionment

The size and chemical composition of individual diesel exhaust particles were measured in order to determine unique mass spectral signatures that can be used to identify particle sources in future ambient studies. The exhaust emissions from seven in-use heavy-duty diesel vehicles (HDDVs) operating on a chassis dynamometer were passed through a dilution tunnel and residence chamber and analyzed in real time by aerosol time-of-flight mass spectrometry (ATOFMS). Seven distinct particle types describe the majority of particles emitted by HDDVs and were emitted by all seven vehicles. The dominant chemical types originated from unburned lubricant oil, and the contributions of the various types varied with particle size and driving conditions. A comparison of light-duty vehicle (LDV) exhaust particles with the HDDV signatures provide insight into the challenges associated with developing an accurate source apportionment technique and possible ways of how they may be overcome.     

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.     

Identification of Sources Contributing to Mid-Atlantic Regional Aerosol

Abstract

Source types or source regions contributing to the concentration of atmospheric fine particles measured at Brigantine National Wildlife Refuge, NJ, were identified using a factor analysis model called Positive Matrix Factorization (PMF). Cluster analysis of backward air trajectories on days of high- and low-factor concentrations was used to link factors to potential source regions. Brigantine is a Class I visibility area with few local sources in the center of the eastern urban corridor and is therefore a good location to study Mid-Atlantic regional aerosol. Sulfate (expressed as ammonium sulfate) was the most abundant species, accounting for 49% of annual average fine mass. Organic compounds (22%; expressed as 1.4 × organic carbon) and ammonium nitrate (10%) were the next abundant species. Some evidence herein suggests that secondary organic aerosol formation is an important contributor to summertime regional aerosol.

Nine factors were identified that contributed to PM_2.5 mass concentrations: coal combustion factors (66%, summer and winter), sea salt factors (9%, fresh and aged), motor vehicle/mixed combustion (8%), diesel/Zn-Pb (6%), incinerator/industrial (5%), oil combustion (4%), and soil (2%). The aged sea salt concentrations were highest in springtime, when the land breeze-sea breeze cycle is strongest. Comparison of backward air trajectories of high- and low-concentration days suggests that Brigantine is surrounded by sources of oil combustion, motor vehicle/mixed combustion, and waste incinerator/industrial emissions that together account for 17% of PM_2.5 mass. The diesel/Zn-Pb factor was associated with sources north and west of Brigantine. Coal combustion factors were associated with coal-fired power plants west and southwest of the site. Particulate carbon was associated not only with oil combustion, motor vehicle/mixed combustion, waste incinerator/industrial, and diesel/Pb-Zn, but also with the coal combustion factors, perhaps through common transport.     

Wintertime and summertime São Paulo aerosol source apportionment study

A detailed aerosol source apportionment study was performed with two sampling campaigns, during wintertime and summertime in the heavily polluted metropolitan area of São Paulo, Brazil. In addition to 12 h fine and coarse mode filter sampling, several real time aerosol and trace gas monitors were used. PM₁₀ was sampled using stacked filter units that collects fine (d<2.5 μm) and coarse (2.5<d<10 μm) particulate matter, providing mass, black carbon (BC) and elemental concentration for each aerosol mode. The concentration of about 20 elements was determined using the particle induce X-ray emission technique. Real time aerosol monitors provided PM₁₀ aerosol mass (TEOM), organic and elemental carbon (Carbon Monitor 5400, R&P) and BC concentration (Aethalometer). A complex system of sources and meteorological conditions modulates the heavy air pollution of the urban area of São Paulo. The boundary layer height and the primary emissions by motor vehicles controls the strong pattern of diurnal cycles obtained for PM₁₀, BC, CO, NO_x, and SO₂. Absolute principal factor analysis results showed a very similar source pattern between winter and summer field campaigns, despite the different locations of the sampling sites of both campaigns, pointing that there are no significant change in the main air pollution sources. The source identified as motor vehicle represented 28% and 24% of the PM_2.5 for winter and summer, respectively. Resuspended soil dust accounted for 25% and 30%. The oil combustion source represented 18% and 21%. Sulfates accounts for 23% and 17% and finally industrial emissions contributed with 5% and 6% of PM_2.5, for winter and summer, respectively. The resuspended soil dust accounted for a large fraction (75–78%) of the coarse mode aerosol mass. Certainly automobile traffic and soil dust are the main air pollution sources in São Paulo. The sampling and analytical procedures applied in this study showed that it is possible to perform a quantitative aerosol source apportionment in a complex urban area such as São Paulo.     

A combined approach for the evaluation of a volatile organic compound emissions inventory

Emissions inventories significantly affect photochemical air quality model performance and the development of effective control strategies. However, there have been very few studies to evaluate their accuracy. Here, to evaluate a volatile organic compound (VOC) emissions inventory, we implemented a combined approach: comparing the ratios of carbon bond (CB)-IV VOC groups to nitrogen oxides (NOx) or carbon monoxide (CO) using an emission preprocessing model, comparing the ratios of VOC source contributions from a source apportionment technique to NOx or CO, and comparing ratios of CB-IV VOC groups to NOx or CO and the absolute concentrations of CB-IV VOC groups using an air quality model, with the corresponding ratios and concentrations observed at three sites (Maryland, Washington, DC, and New Jersey). The comparisons of the ethene/NOx ratio, the xylene group (XYL)/NOx ratio, and ethene and XYL concentrations between estimates and measurements showed some differences, depending on the comparison approach, at the Maryland and Washington, DC sites. On the other hand, consistent results at the New Jersey site were observed, implying a possible overestimation of vehicle exhaust. However, in the case of the toluene group (TOL), which is emitted mainly from surface coating and printing sources in the solvent utilization category, the ratios of TOL/ NOx or CO, as well as the absolute concentrations revealed an overestimate of these solvent sources by a factor of 1.5 to 3 at all three sites. In addition, the overestimate of these solvent sources agreed with the comparisons of surface coating and printing source contributions relative to NOx from a source apportionment technique to the corresponding value of estimates at the Maryland site. Other studies have also suggested an overestimate of solvent sources, implying a possibility of inaccurate emission factors in estimating VOC emissions from surface coating and printing sources. We tested the impact of these overestimates with a chemical transport model and found little change in ozone but substantial changes in calculated secondary organic aerosol concentrations.     

Estimation of Organic Carbon Blank Values and Error Structures of the Speciation Trends Network Data for Source Apportionment

Abstract

Because the particulate organic carbon (OC) concentrations reported in U.S. Environment Protection Agency Speciation Trends Network (STN) data were not blank corrected, the OC blank concentrations were estimated using the intercept in particulate matter ≤2.5 µm in aerodynamic diameter (PM_2.5) regression against OC concentrations. The estimated OC blank concentrations ranged from 1 to 2.4 μg/m³ showing higher values in urban areas for the 13 monitoring sites in the northeastern United States. In the STN data, several different samplers and analyzers are used, and various instruments show different method detection limit (MDL) values, as well as errors. A comprehensive set of error structures that would be used for numerous source apportionment studies of STN data was estimated by comparing a limited set of measured concentrations and their associated uncertainties. To examine the estimated error structures and investigate the appropriate MDL values, PM_2.5 samples collected at a STN site in Burlington, VT, were analyzed through the application of the positive matrix factorization. A total of 323 samples that were collected between December 2000 and December 2003 and 49 species based on several variable selection criteria were used, and eight sources were successfully identi?ed in this study with the estimated error structures and min values among different MDL values from the ?ve instruments: secondary sulfate aerosol (41%), secondary nitrate aerosol (20%), airborne soil (15%), gasoline vehicle emissions (7%), diesel emissions (7%), aged sea salt (4%), copper smelting (3%), and ferrous smelting (2%). Time series plots of contributions from airborne soil indicate that the highly elevated impacts from this source were likely caused primarily by dust storms.     

Development of a modified factor analysis/multiple regression model to apportion suspended particulate matter in a complex urban airshed

A modified factor analysis/multiple regression (FA/MR) receptor-oriented source apportionment model has been developed which permits application of FA/MR statistical methods when some of the tracers are not unique to an individual source type. The new method uses factor and regression analyses to apportion non-unique tracer ambient concentrations in situations where there are unique tracers for all sources contributing to the non-unique tracer except one, and ascribes the residual concentration to that source. This value is then used as the source tracer in the final FA/MR apportionment model for ambient paniculate matter. In addition, factor analyses results are complemented with examination of regression residuals in order to optimize the number of identifiable sources.The new method has been applied to identify and apportion the sources of inhalable particulate matter (IPM; D₅₀15 μm), Pb and Fe at a site in Newark, NJ. The model indicated that sulfate/secondary aerosol contributed an average of 25.8 μ⁻³ (48%) to IPM concentrations, followed by soil resuspension (8.2 μ⁻³ or 15%), paint spraying/paint pigment (6.7/gmm⁻³or 13%), fuel oil burning/space heating (4.3 μ⁻³ or 8 %), industrial emissions (3.6 μm⁻³ or 7 %) and motor vehicle exhaust (2.7 μ⁻³ or 15 %). Contributions to ambient Pb concentrations were: motor vehicle exhaust (0.16μm⁻³or 36%), soil resuspension (0.10μm⁻³ or 24%), fuel oil burning/space heating (0.08μm⁻³or 18%), industrial emissions (0.07 μ⁻³ or 17 %), paint spraying/paint pigment (0.036 μm⁻³or 9 %) and zinc related sources (0.022 μ⁻³ or 5 %). Contributions to ambient Fe concentrations were: soil resuspension (0.43μ⁻³or 51%), paint spraying/paint pigment (0.28 μm⁻³or 33 %) and industrial emissions (0.15 μ⁻³or 18 %). The models were validated by comparing partial source profiles calculated from modeling results with the corresponding published source emissions composition.     

“Environmental Quality — Energy Quantity”

Source resolution of the organic component of the fine fraction of the ambient aerosol (dp < 3.5 fim) has been carried out by combining source information from the inorganic component with thermal analysis and local emission inventories. The primary and secondary carbon containing components were identified using a source fingerprint thermogram. The primary carbon emitted from automobiles was calculated using the ambient lead concentration and a recent estimate of the lead to carbon ratio for this source. The remaining primary carbon was apportioned to the other sources according to the local emission inventory. Source apportionment of the secondary component was performed by scaling the secondary carbon to a recent emission inventory for reactive organic gases (ROG) neglecting biogenic contributions and assuming equal potential for aerosol formation for the various ROG. The method was applied to data collected at Lennox and Duarte on 10/23/80. The major source at both sites was automobiles which were responsible for 68% and 36% of the carbon containing component of the fine aerosol at Lennox and Duarte respectively. Industrial sources made a relatively higher contribution at Duarte, 26%, than at Lennox 12%. Good correlation was found between ozone and secondary carbon. However, a weaker correlation was found between sulfates and secondary carbon. Contributions to the visibility budget by various chemical species were calculated at Lennox and Duarte on 10/23/80 using an appropriate regression model. It was found that the carbon containing component was responsible for 27% and 44% of the incident light extinction at Lennox and Duarte, respectively.     

Source Apportionment Using Positive Matrix Factorization on Daily Measurements of Inorganic and Organic Speciated PM(2.5)

Particulate matter less than 2.5 microns in diameter (PM(2.5)) has been linked with a wide range of adverse health effects. Determination of the sources of PM(2.5) most responsible for these health effects could lead to improved understanding of the mechanisms of such effects and more targeted regulation. This has provided the impetus for the Denver Aerosol Sources and Health (DASH) study, a multi-year source apportionment and health effects study relying on detailed inorganic and organic PM(2.5) speciation measurements.In this study, PM(2.5) source apportionment is performed by coupling positive matrix factorization (PMF) with daily speciated PM(2.5) measurements including inorganic ions, elemental carbon (EC) and organic carbon (OC), and organic molecular markers. A qualitative comparison is made between two models, PMF2 and ME2, commonly used for solving the PMF problem. Many previous studies have incorporated chemical mass balance (CMB) for organic molecular marker source apportionment on limited data sets, but the DASH data set is large enough to use multivariate factor analysis techniques such as PMF.Sensitivity of the PMF2 and ME2 models to the selection of speciated PM(2.5) components and model input parameters was investigated in depth. A combination of diagnostics was used to select an optimum, 7-factor model using one complete year of daily data with pointwise measurement uncertainties. The factors included 1) a wintertime/methoxyphenol factor, 2) an EC/sterane factor, 3) a nitrate/polycyclic aromatic hydrocarbon (PAH) factor, 4) a summertime/selective aliphatic factor, 5) an n-alkane factor, 6) a middle oxygenated PAH/alkanoic acid factor and 7) an inorganic ion factor. These seven factors were qualitatively linked with known PM(2.5) emission sources with varying degrees of confidence. Mass apportionment using the 7-factor model revealed the contribution of each factor to the mass of OC, EC, nitrate and sulfate. On an annual basis, the majority of OC and EC mass was associated with the summertime/selective aliphatic factor and the EC/sterane factor, respectively, while nitrate and sulfate mass were both dominated by the inorganic ion factor. This apportionment was found to vary substantially by season. Several of the factors identified in this study agree well with similar assessments conducted in St. Louis, MO and Pittsburgh, PA using PMF and organic molecular markers.     

Source apportionment and source/sink relationships of PAHs in the coastal atmosphere of Chicago and Lake Michigan

Multivariate statistical techniques were used to investigate source apportionment and source/sink relationships for polycyclic aromatic hydrocarbons (PAHs) in the urban and adjacent coastal atmosphere of Chicago/Lake Michigan in 1994–1995. The PAH signatures for the atmospheric particle phase, surface water particle phase and sediments indicate that atmospheric deposition is the major source of PAHs to the sediments and water column particulate phase of Lake Michigan. The PAH signature for the atmospheric gas phase and water dissolved phase indicate an intimate linkage between the lake and its overlying atmosphere. A modified factor analysis-multiple regression model was successfully applied to the source apportionment of atmospheric PAHs (gas+particle). Coal combustion accounted for 48±5% of the ΣPAH concentration in both the urban and adjacent coastal atmosphere, natural gas combustion accounted for 26±2%, coke ovens accounted for 14±3%, and vehicle emissions (gas+diesel) accounted for 9±4%. Each is an identified source category for the region. These results are consistent with the mix of fossil fuel combustion sources and ratios of indicator PAHs.     

The distribution of particle-phase organic compounds in the atmosphere and their use for source apportionment during the Southern California Children''s Health Study

Atmospheric particulate matter (PM) samples from 12 sites in southern California, collected as part of the Southern California Children's Health Study (SCCHS), were analyzed using gas chromatography/mass spectrometry (GC/MS) techniques. Ninety-four organic compounds were quantified in these samples, including n-alkanes, fatty acids, polycyclic aromatic hydrocarbons (PAH), hopanes, steranes, aromatic diacids, aliphatic diacids, resin acids, methoxyphenols, and levoglucosan. Annual average concentrations of all detected compounds, as well as average concentrations for three seasonal periods, were determined at all 12 sites for the calendar year of 1995. These measurements provide important information about the seasonal and spatial distribution of particle-phase organic compounds in southern California. Also, co-located samples from one site were analyzed to assess precision of measurement. Excellent agreement was observed between annual average concentrations for the broad range of organic compounds measured in this study. Measured concentrations from the 12 sampling sites were used in a previously developed molecular-marker source apportionment model to quantify the primary source contributions to the PM10 organic carbon and mass concentrations at these 12 sites. Source contributions to atmospheric PM from six important air pollution sources were quantified: gasoline-powered motor vehicle exhaust, diesel vehicle exhaust, wood smoke, vegetative detritus, tire wear, and natural gas combustion. Important trends in the seasonal and spatial patterns of the impact of these six sources were observed. In addition, contributions from meat smoke were detected in selected samples.