Source apportionment of VOCs in the Los Angeles area using positive matrix factorization

Eight 3-h speciated hydrocarbon measurements were collected daily by the South Coast Air Quality Management District (SCAQMD) as part of the Photochemical Assessment Monitoring Stations (PAMS) program during the summers of 2001–03 at two sites in the Los Angeles air basin, Azusa and Hawthorne. Over 30 hydrocarbons from over 500 samples at Azusa and 600 samples at Hawthorne were subsequently analyzed using the multivariate receptor model positive matrix factorization (PMF). At Azusa and Hawthorne, five and six factors were identified, respectively, with a good comparison between predicted and measured mass. At Azusa, evaporative emissions (a median of 31% of the total mass), motor vehicle exhaust (22%), liquid/unburned gasoline (27%), coatings (17%), and biogenic emissions (3%) factors were identified. Factors identified at Hawthorne were evaporative emissions (a median of 34% of the total mass), motor vehicle exhaust (24%), industrial process losses (15%), natural gas (13%), liquid/unburned gasoline (13%), and biogenic emissions (1%). Together, the median contribution from mobile source-related factors (exhaust, evaporative emissions, and liquid/unburned gasoline) was 80% and 71% at Azusa and Hawthorne, respectively, similar to previous source apportionment results using the chemical mass balance (CMB) model. There is a difference in the distribution among mobile source factors compared to the CMB work, with an increase in the contribution from evaporative emissions, though the cause (changes in emissions or differences between models) is unknown.     

Source apportionment of airborne fine particulate matter in an underground mine

The chemical mass balance source apportionment technique was applied to an underground gold mine to assess the contribution of diesel exhaust, rock dust, oil mists, and cigarette smoke to airborne fine (<2.5 microm) particulate matter (PM). Apportionments were conducted in two locations in the mine, one near the mining operations and one near the exit of the mine where the ventilated mine air was exhausted. Results showed that diesel exhaust contributed 78-98% of the fine particulate mass and greater than 90% of the fine particle carbon, with rock dust making up the remainder. Oil mists and cigarette smoke contributions were below detection limits for this study. The diesel exhaust fraction of the total fine PM was higher than the recently implemented mine air quality standards based on total carbon at both sample locations in the mine.     

Source apportionment of fine particulate matter in the southeastern United States

Particulate matter (PM) less than 2.5 microm in size (PM2.5) source apportionment by chemical mass balance receptor modeling was performed to enhance regional characterization of source impacts in the southeastern United States. Secondary particles, such as NH4HSO4, (NH4)2SO4, NH4NO3, and secondary organic carbon (OC) (SOC), formed by atmospheric photochemical reactions, contribute the majority (>50%) of ambient PM2.5 with strong seasonality. Source apportionment results indicate that motor vehicle and biomass burning are the two main primary sources in the southeast, showing relatively more motor vehicle source impacts rather than biomass burning source impacts in populated urban areas and vice versa in less urbanized areas. Spatial distributions of primary source impacts show that each primary source has distinctively different spatial source impacts. Results also find impacts from shipping activities along the coast. Spatiotemporal correlations indicate that secondary particles are more regionally distributed, as are biomass burning and dust, whereas impacts of other primary sources are more local.     

Application of positive matrix factorization in source apportionment of particulate pollutants in Hong Kong

An advanced algorithm called positive matrix factorization (PMF) in receptor modeling was used to identify the sources of respirable suspended particulates (RSP) in Hong Kong. The compositional data obtained from the Hong Kong Environmental Protection Department from 1992 to 1994 were analyzed. The species analyzed in this study are Al, Ca, Mg, Pb, Na⁺, V, Cl⁻, NH₄⁺, SO₄²⁻, Br⁻, Mn, Fe, Ni, Zn, Cd, K⁺, Ba, Cu, and As. Unlike the conventional receptor modeling algorithm, factor analysis PMF only generates non-negative source profiles. To eliminate sulfate from such factors where it is not physically plausible, special penalty terms were included in the model so that sulfate concentrations could be selectively decreased in specified factors. A 9-factor model containing non-zero sulfate concentrations in three factors gives the most satisfactory source profiles. Ammonium sulfate, chloride depleted marine aerosols and crustal aerosols are the three non-zero sulfate sources. Other factors are marine aerosols, non-ferrous smelters, particulate copper, fuel oil burning, vehicular emission and bromide/road dust. The last two sources can be combined as a single source of vehicle/road dust. The compositional profiles of these factors were also developed. The mass profiles obtained can be improved by further refinement of distribution of sulfate in the sources.     

Source identifications of airborne fine particles using positive matrix factorization and U.S. Environmental Protection Agency positive matrix factorization

The widely used source apportionment model, positive matrix factorization (PMF2), has been applied to various air pollution data. Recently, U.S. Environmental Protection Agency (EPA) developed EPA positive matrix factorization (PMF), a version of PMF that will be freely distributed by EPA. The objectives of this study were to conduct source apportionment studies for particulate matter less than 2.5 microm in aerodynamic diameter (PM(2.5)) speciation data using PMF2 and EPA PMF (version 1.1) and to compare identified sources between the two models. In the present study, ambient PM(2.5) compositional datasets of 24-hr integrated samples collected at EPA Speciation Trends Network monitoring sites in Chicago, IL, and Portland, OR, were analyzed. Both PMF2 and EPA PMF extracted eight sources for the Chicago data and 10 sources for the Portland data. The model-resolved source profiles were similar between two models for both datasets. However, in several sources, the average contributions did not agree well and the time series contributions were not highly correlated. The differences between PMF2 and EPA PMF solutions were caused by the different least-square algorithm and the different nonnegativity constraints. Most of the average source contributions resolved by both models were within 5-95% uncertainty provided by EPA PMF, indicating that the sources resolved by both models were reproducible.     

Compilation and assessment of recent positive matrix factorization and UNMIX receptor model studies on fine particulate matter source apportionment for the eastern United States

In 1997, the U.S. Environmental Protection Agency (EPA) revised its particulate matter standards to include an annual standard for fine particulate matter (PM2.5; 15 microg/m3) and a 24-hr standard (65 microg/m3). The 24-hr standard was lowered to 35 microg/m3 in 2006 in an effort to further reduce overall ambient PM2.5 concentrations. Identifying and quantifying sources of particulate matter affecting a particular location through source apportionment methods is now an important component of the information available to decision makers when evaluating the new standards. This literature compilation summarizes a subset of the source apportionment research and general findings on fine particulate matter in the eastern half of the United States using Positive Matrix Factorization. The results between studies are generally comparable when comparable datasets are used; however, methodologies vary considerably. Commonly identified source categories include: secondary sulfate/coal burning (sometimes over 50% of total mass), secondary organic carbon/mobile sources, crustal sources, biomass burning, nitrate, various industrial processes, and sea salt. The source apportionment tools and methodologies have passed the proof-of-concept stage and are now being used to understand the ambient composition of particulate matter for sites across the United States and the spatial relationship of sources to the receptor. Recommendations are made for further and standardized method development for source apportionment studies, and specific research areas of interest for the eastern United States are proposed.     

Source apportionment of indoor, outdoor, and personal PM2.5 in Seattle, Washington, using positive matrix factorization

As part of a large exposure assessment and health-effects panel study, 33 trace elements and light-absorbing carbon were measured on 24-hr fixed-site filter samples for particulate matter with an aerodynamic diameter <2.5 microm (PM2.5) collected between September 26, 2000, and May 25, 2001, at a central outdoor site, immediately outside each subject's residence, inside each residence, and on each subject (personal sample). Both two-way (PMF2) and three-way (PMF3) positive matrix factorization were used to deduce the sources contributing to PM2.5. Five sources contributing to the indoor and outdoor samples were identified: vegetative burning, mobile emissions, secondary sulfate, a source rich in chlorine, and a source of crustal-derived material. Vegetative burning contributed more PM2.5 mass on average than any other source in all microenvironments, with average values estimated by PMF2 and PMF3, respectively, of 7.6 and 8.7 microg/m3 for the outdoor samples, 4 and 5.3 microg/m3 for the indoor samples, and 3.8 and 3.4 microg/m3 for the personal samples. Personal exposure to the combustion-related particles was correlated with outdoor sources, whereas exposure to the crustal and chlorine-rich particles was not. Personal exposures to crustal sources were strongly associated with personal activities, especially time spent at school among the child subjects.     

Source apportionment of airborne particulate matter using organic compounds as tracers

A chemical mass balance receptor model based on organic compounds has been developed that relates source contributions to airborne fine particle mass concentrations. Source contributions to the concentrations of specific organic compounds are revealed as well. The model is applied to four air quality monitoring sites in southern California using atmospheric organic compound concentration data and source test data collected specifically for the purpose of testing this model. The contributions of up to nine primary particle source types can be separately identified in ambient samples based on this method, and approximately 85% of the organic fine aerosol is assigned to primary sources on an annual average basis. The model provides information on source contributions to fine mass concentrations, fine organic aerosol concentrations and individual organic compound concentrations. The largest primary source contributors to fine particle mass concentrations in Los Angeles are found to include diesel engine exhaust, paved road dust, gasoline-powered vehicle exhaust, plus emissions from food cooking and wood smoke, with smaller contribution from tire dust, plant fragments, natural gas combustion aerosol, and cigarette smoke. Once these primary aerosol source contributions are added to the secondary sulfates, nitrates and organics present, virtually all of the annual average fine particle mass at Los Angeles area monitoring sites can be assigned to its source.     

Receptor modeling of ambient particulate matter data using positive matrix factorization: review of existing methods

Methods for apportioning sources of ambient particulate matter (PM) using the positive matrix factorization (PMF) algorithm are reviewed. Numerous procedural decisions must be made and algorithmic parameters selected when analyzing PM data with PMF. However, few publications document enough of these details for readers to evaluate, reproduce, or compare results between different studies. For example, few studies document why some species were used and others not used in the modeling, how the number of factors was selected, or how much uncertainty exists in the solutions. More thorough documentation will aid the development of standard protocols for analyzing PM data with PMF and will reveal more clearly where research is needed to help future analysts select from the various possible procedures and parameters available in PMF. For example, research likely is needed to determine optimal approaches for handling data below detection limits, ways to apportion PM mass among sources identified by PMF, and ways to estimate uncertainties in the solution. The review closes with recommendations for documenting the methodological details of future PMF analyses.     

南昌市大气PM2.5中多环芳烃的来源解析

在南昌市布设5个采样点,分别代表工业区、居住区、交通干线区、商业区以及郊区,于2007年7～8月进行大气PM2.5的采样.根据5个采样点测得的数据,通过因子分析法判断南昌市大气PM2.5中多环芳烃的主要来源,再利用多元线性回归法确定各主要来源对多环芳烃的贡献率.结果表明,南昌市多环芳烃的主要来源为车辆排放源、高温加热源、燃煤污染源,对多环芳烃的贡献率分别为37.9%、28.2%、22.0%.     

Source apportionment of primary and secondary organic aerosols using positive matrix factorization (PMF) of molecular markers

Monthly average ambient concentrations of more than eighty particle-phase organic compounds, as well as total organic carbon (OC) and elemental carbon (EC), were measured from March 2004 through February 2005 in five cities in the Midwestern United States. A multi-variant source apportionment receptor model, positive matrix factorization (PMF), was applied to explore the average source contributions to the five sampling sites using molecular markers for primary and secondary organic aerosols (POA, SOA). Using the molecular makers in the model, POA and SOA were estimated for each month at each site. Three POA factors were derived, which were dominated by primary molecular markers such as EC, hopanes, steranes, and polycyclic aromatic hydrocarbons (PAHs), and which represented the following POA sources: urban primary sources, mobile sources, and other combustion sources. The three POA sources accounted for 57% of total average ambient OC. Three factors, characterized by the presence of reaction products of isoprene, α-pinene and β-caryophyllene, and displaying distinct seasonal trends, were consistent with the characteristics of SOA. The SOA factors made up 43% of the total average measured OC. The PMF-derived results are in good agreement with estimated SOA concentrations obtained from SOA to tracer yield estimates obtained from smog chamber experiments. A linear regression comparing the smog chamber yield estimates and the PMF SOA contributions had a regression slope of 1.01 ± 0.07 and an intercept of 0.19 ± 0.10 μg OC m^?3 (adjusted R² of 0.763, n = 58).     

Apportionment of ambient primary and secondary fine particulate matter at the Pittsburgh National Energy Laboratory particulate matter characterization site using positive matrix factorization and a potential source contributions function analysis

Fine particulate matter (PM2.5) concentrations associated with 202 24-hr samples collected at the National Energy Technology Laboratory (NETL) particulate matter (PM) characterization site in south Pittsburgh from October 1999 through September 2001 were used to apportion PM2.5 into primary and secondary contributions using Positive Matrix Factorization (PMF2). Input included the concentrations of PM2.5 mass determined with a Federal Reference Method (FRM) sampler, semi-volatile PM2.5 organic material, elemental carbon (EC), and trace element components of PM2.5. A total of 11 factors were identified. The results of potential source contributions function (PSCF) analysis using PMF2 factors and HYSPLIT-calculated back-trajectories were used to identify those factors associated with specific meteorological transport conditions. The 11 factors were identified as being associated with emissions from various specific regions and facilities including crustal material, gasoline combustion, diesel combustion, and three nearby sources high in trace metals. Three sources associated with transport from coal-fired power plants to the southeast, a combination of point sources to the northwest, and a steel mill and associated sources to the west were identified. In addition, two secondary-material-dominated sources were identified, one was associated with secondary products of local emissions and one was dominated by secondary ammonium sulfate transported to the NETL site from the west and southwest. Of these 11 factors, the four largest contributors to PM2.5 were the secondary transported material (dominated by ammonium sulfate) (47%), local secondary material (19%), diesel combustion emissions (10%), and gasoline combustion emissions (8%). The other seven factors accounted for the remaining 16% of the PM2.5 mass. The findings are consistent with the major source of PM2.5 in the Pittsburgh area being dominated by ammonium sulfate from distant transport and so decoupled from local activity emitting organic pollutants in the metropolitan area. In contrast, the major local secondary sources are dominated by organic material.     

Source apportionment of particulate matter at urban mixed site in Indonesia using PMF

A receptor model of positive matrix factorization (PMF) was used to identify the emission sources of fine and coarse particulates in Bandung, a city located at about 150 km south-east of Jakarta. Total of 367 samples were collected at urban mixed site, Tegalega area, in Bandung City during wet and dry season in the period of 2001–2007. The samples of fine and coarse particulate matter were collected simultaneously using dichotomous samplers and mini-volume samplers. The Samples from dichotomous Samplers were analyzed for black carbon and elements while samples from mini-volume samplers were analyzed for ions. The species analyzed in this study were Na, Mg, Al, Si, K, Ca, Ti, Cr, Mn, Fe, Ni, Cu, Pb, Cl^?, NO₃^?, SO₄^2?, and NH₄⁺. The data were then analyzed using PMF to determine the source factors. Different numbers of source factors were found during dry and wet season. During dry season, the main source factors for fine particles were secondary aerosol (NH₄)₂SO₄, electroplating industry, vehicle emission, and biomass burning, while for coarse particles, the dominant source factors were electroplating industry, followed by aged sea salt, volcanic dust, soil dust, and lime dust. During the wet season, the main source factors for fine particulate matter were vehicle emission and secondary aerosol. Other sources detected were biomass burning, lime dust, soil and volcanic dust. While for coarse particulate matter, the main source factors were sulphate-rich industry, followed by lime dust, soil dust, industrial emission and construction dust.     

Source apportionment of daily fine particulate matter at Jefferson Street, Atlanta, GA, during summer and winter

The primary emission source contributions to fine organic carbon (OC) and fine particulate matter (PM2.5) mass concentrations on a daily basis in Atlanta, GA, are quantified for a summer (July 3 to August 4, 2001) and a winter (January 2-31, 2002) month. Thirty-one organic compounds in PM2.5 were identified and quantified by gas chromatography/mass spectrometry. These organic tracers, along with elemental carbon, aluminum, and silicon, were used in a chemical mass balance (CMB) receptor model. CMB source apportionment results revealed that major contributors to identified fine OC concentrations include meat cooking (7-68%; average: 36%), gasoline exhaust (7-45%; average: 21%), and diesel exhaust (6-41%; average: 20%) for the summer month, and wood combustion (0-77%; average: 50%); gasoline exhaust (14-69%; average: 33%), meat cooking (1-14%; average: 5%), and diesel exhaust (0-13%; average: 4%) for the winter month. Primary sources, as well as secondary ions, including sulfate, nitrate, and ammonium, accounted for 86 +/- 13% and 112 +/- 15% of the measured PM2.5 mass in summer and winter, respectively.     

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.     

Source apportionment of fine particulate matter (PM2.5) at a rural Ohio River Valley site

Twenty four-hour averaged concentrations of fine particulate matter were collected at Athens, OH between March 2004 and November 2005 in an effort to characterize the nature of PM_2.5 and apportion its sources. PM_2.5 samples were chemically analyzed and positive matrix factorization was applied to this speciation data to identify the probable sources. PMF arrived at a 7-factor model to most accurately apportion sources of the PM_2.5 observed at Athens. Conditional probability function (CPF) and potential source contribution function (PSCF) were applied to the identified sources to investigate the geographical location of these sources. Secondary sulfate source dominated the contributions with a total contribution of 62.6% with the primary and secondary organic source following second with 19.9%. Secondary nitrate contributed a total of 6.5% with the steel production source and Pb- and Zn-source coming in at 3.1% and 2.9%, respectively. Crustal and mobile sources were small contributors (2.5% each) of PM_2.5 to the Athens region. The secondary sulfate, secondary organic and nitrate portrayed a clear seasonal nature with the sulfate and secondary organic peaking in the warm months and the nitrate reaching a high in the cold months. The high percentage of secondary sulfate observed at a rural site like Athens suggests the involvement of regional transport mechanisms.     

Source identification of personal exposure to fine particulate matter using organic tracers

Personal exposure to fine particulate matter (PM2.5) is due to both indoor and outdoor sources. Contributions of sources to personal exposure can be quite different from those observed at ambient sampling locations. The primary goal of this study was to investigate the effectiveness of using trace organic speciation data to help identify sources influencing PM2.5 exposure concentrations. Sixty-four 24-h PM2.5 samples were obtained on seven different subjects in and around Boulder, CO. The exposure samples were analyzed for PM2.5 mass, elemental and organic carbon, organic tracer compounds, water-soluble metals, ammonia, and nitrate. This study is the first to measure a broad distribution of organic tracer compounds in PM2.5 personal samples. PM2.5 mass exposure concentrations averaged 8.4 μg m^?3. Organic carbon was the dominant constituent of the PM2.5 mass. Forty-four organic species and 19 water-soluble metals were quantifiable in more than half of the samples. Fifty-four organic species and 16 water-soluble metals had measurement signal-to-noise ratios larger than two after blank subtraction.The dataset was analyzed by Principal Component Analysis (PCA) to determine the factors that account for the greatest variance. Eight significant factors were identified; each factor was matched to its likely source based primarily on the marker species that loaded the factor. The results were consistent with the expectation that multiple marker species for the same source loaded the same factor. Meat cooking was an important source of variability. The factor that represents meat cooking was highly correlated with organic carbon concentrations (r = 0.84). The correlation between ambient PM2.5 and PM2.5 exposure was relatively weak (r = 0.15). Time participants spent performing various activities was generally not well correlated with PCA factor scores, likely because activity duration does not measure emissions intensity. The PCA results demonstrate that organic tracers can aid in identifying factors that influence personal exposures to PM2.5.     

Source apportionment of suspended particulate matter at two traffic junctions in Mumbai,India

Very high concentration of suspended particulate matter (SPM) is observed at traffic junctions in India. Factor analysis-multiple regression (FA-MR), a receptor modelling technique has been used for quantitative apportionment of the sources contributing to the SPM at two traffic junctions (Sakinaka and Gandhinagar) in Mumbai, India. Varimax rotated factor analysis identified (qualitative) five possible sources; road dust, vehicular emissions, marine aerosols, metal industries and coal combustion. A quantitative estimation by FA-MR model indicated that road dust contributed to 41%, vehicular emissions to 15%, marine aerosols to 15%, metal industries to 6% and coal combustion to 6% of the SPM observed at Sakinaka traffic junction. The corresponding figures for Gandhinagar traffic junction are 33%, 18%, 15%, 8% and 11%, respectively. Due to limitation in source marker elements analysed about 16% of the remaining SPM at these two traffic junctions could not be apportioned to any possible sources by this technique. Of the observed lead in the SPM, FA-MR apportioned 62% to vehicular emissions, 17% to road dust, 11% to metal industries, 7% to coal combustion and 3% to marine aerosols at Gandhinagar traffic junction and about a similar apportionment for lead in SPM at Sakinaka traffic junction.     

Chemical characterization and source apportionment of fine and coarse particulate matter in Lahore,Pakistan

Lahore, Pakistan is an emerging megacity that is heavily polluted with high levels of particle air pollution. In this study, respirable particulate matter (PM_2.5 and PM₁₀) were collected every sixth day in Lahore from 12 January 2007 to 19 January 2008. Ambient aerosol was characterized using well-established chemical methods for mass, organic carbon (OC), elemental carbon (EC), ionic species (sulfate, nitrate, chloride, ammonium, sodium, calcium, and potassium), and organic species. The annual average concentration (±one standard deviation) of PM_2.5 was 194 ± 94 μg m^?3 and PM₁₀ was 336 ± 135 μg m^?3. Coarse aerosol (PM_10?2.5) was dominated by crustal sources like dust (74 ± 16%, annual average ± one standard deviation), whereas fine particles were dominated by carbonaceous aerosol (organic matter and elemental carbon, 61 ± 17%). Organic tracer species were used to identify sources of PM_2.5 OC and chemical mass balance (CMB) modeling was used to estimate relative source contributions. On an annual basis, non-catalyzed motor vehicles accounted for more than half of primary OC (53 ± 19%). Lesser sources included biomass burning (10 ± 5%) and the combined source of diesel engines and residual fuel oil combustion (6 ± 2%). Secondary organic aerosol (SOA) was an important contributor to ambient OC, particularly during the winter when secondary processing of aerosol species during fog episodes was expected. Coal combustion alone contributed a small percentage of organic aerosol (1.9 ± 0.3%), but showed strong linear correlation with unidentified sources of OC that contributed more significantly (27 ± 16%). Brick kilns, where coal and other low quality fuels are burned together, are suggested as the most probable origins of unapportioned OC. The chemical profiling of emissions from brick kilns and other sources unique to Lahore would contribute to a better understanding of OC sources in this megacity.     

Source identification of atlanta aerosol by positive matrix factorization

Data characterizing daily integrated particulate matter (PM) samples collected at the Jefferson Street monitoring site in Atlanta, GA, were analyzed through the application of a bilinear positive matrix factorization (PMF) model. A total of 662 samples and 26 variables were used for fine particle (particles < or = 2.5 microm in aerodynamic diameter) samples (PM2.5), and 685 samples and 15 variables were used for coarse particle (particles between 2.5 and 10 microm in aerodynamic diameter) samples (PM10-2.5). Measured PM mass concentrations and compositional data were used as independent variables. To obtain the quantitative contributions for each source, the factors were normalized using PMF-apportioned mass concentrations. For fine particle data, eight sources were identified: SO4(2-) -rich secondary aerosol (56%), motor vehicle (22%), wood smoke (11%), NO(3-) -rich secondary aerosol (7%), mixed source of cement kiln and organic carbon (OC) (2%), airborne soil (1%), metal recycling facility (0.5%), and mixed source of bus station and metal processing (0.3%). The SO4(2-) -rich and NO(3-) -rich secondary aerosols were associated with NH(4+). The SO4(2-) -rich secondary aerosols also included OC. For the coarse particle data, five sources contributed to the observed mass: airborne soil (60%), NO(3-)-rich secondary aerosol (16%), SO4(2-) -rich secondary aerosol (12%), cement kiln (11%), and metal recycling facility (1%). Conditional probability functions were computed using surface wind data and identified mass contributions from each source. The results of this analysis agreed well with the locations of known local point sources.