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.     

A new methodological approach: The combined use of two-stage streaker samplers and optical particle counters for the characterization of airborne particulate matter

We describe a new experimental methodology based on the contemporary use of two-stage continuous streaker samplers and optical particle counters. This is a complementary approach to size-segregated particulate matter (PM) sampling, and it is able to give information on the elemental size distribution and to assess the contribution of major PM source to size bins. PM samples in the fine and coarse fraction of PM10 have been collected by a two-stage streaker sampler and analyzed by particle-induced X-ray emission (PIXE) to obtain elemental concentration time series with hourly resolution. PM sources and profiles were singled out by positive matrix factorization (PMF). A multi-linear regression of size-segregated number of particles versus the sources, resolved by PMF, made possible the apportionment of size-segregated particles number in a fast and direct way. Results obtained in three sampling sites, located in different urban districts are discussed.     

Particulate matter source apportionment in a village situated in industrial region of Central Europe

The bilinear receptor model positive matrix factorization (PMF) was used to apportion particulate matter with an aerodynamic diameter of 1–10 μm (PM_1–10) sources in a village, B?ezno, situated in an industrial region of northern Bohemia in Central Europe. The receptor model analyzed the data sets of 90- and 60-min integrations of PM_1–10 mass concentrations and elemental composition for 27 elements. The 14-day sampling campaigns were conducted in the village in summer 2008 and winter 2010. Also, to ensure seasonal and regional representativeness of the data sets recorded in the village, the spatial-temporal variability of the 24-hr PM₁₀ and PM_1–10 within 2008–2010 in winter and summer across the multiple sites was evaluated. There were statistically significant interseasonal differences of the 24-hr PM data, but not intrasummer or intrawinter differences of the 24-hr PM_1–10 data across the multiple sites. PMF resolved seven sources of PM_1–10. They were high-temperature coal combustion; combustion in local heating boilers; marine aerosol; mineral dust; primary biological/wood burning; road dust, car brakes; and gypsum. The main summer factors were assigned to mineral dust (38.2%) and primary biological/wood burning (33.1%). In winter, combustion factors dominated (80%) contribution to PM_1–10. The conditional probability function (CPF) helped to identified local sources of PM_1–10. The source of marine aerosol from the North Sea and English Channel was indicated by the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT).

Implications: This is the first application of PMF to highly time/size resolved PM data in Czech Republic. The coarse aerosol fraction, PM_1–10, was chosen with regard to industrial character of the region, sampling site near the coal strip mine and coal power stations. Contrary to expectation, source apportionment did not show dominance of emissions from the coal strip mine. The results will enable local authorities and state bodies responsible for air quality assessment to focus on sources most responsible for air pollution in this industrial region.

Supplemental Materials:?Supplemental materials are available for this paper. Go to the publisher's online edition of the Journal of the Air & Waste Management Association for (1) details of measurement campaigns; (2) CPF for each of the sources contributing to PM_1–10; (3) factors contribution to PM_1–10 resolved by PMF; (4) diurnal pattern of road dust, car brake factor in summer and winter; (5) trajectories during the marine aerosol episode in winter 2010; and (6) temporal temperature, concentration, and wind speed relationships during the summer 2008 campaign and winter 2010 campaign.     

Source apportionment of the carcinogenic potential of polycyclic aromatic hydrocarbons (PAH) associated to airborne PM10 by a PMF model

In order to perform a study of the carcinogenic potential of polycyclic aromatic hydrocarbons (PAH), benzo(a)pyrene equivalent (BaP-eq) concentration was calculated and modelled by a receptor model based on positive matrix factorization (PMF). Nineteen PAH associated to airborne PM10 of Zaragoza, Spain, were quantified during the sampling period 2001–2009 and used as potential variables by the PMF model. Afterwards, multiple linear regression analysis was used to quantify the potential sources of BaP-eq. Five sources were obtained as the optimal solution and vehicular emission was identified as the main carcinogenic source (35 %) followed by heavy-duty vehicles (28 %), light-oil combustion (18 %), natural gas (10 %) and coal combustion (9 %). Two of the most prevailing directions contributing to this carcinogenic character were the NE and N directions associated with a highway, industrial parks and a paper factory. The lifetime lung cancer risk exceeded the unit risk of 8.7?×?10^?5 per ng/m³ BaP in both winter and autumn seasons and the most contributing source was the vehicular emission factor becoming an important issue in control strategies.     

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 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.     

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.     

Identification and chemical characterization of industrial particulate matter sources in southwest Spain

A detailed physical and chemical characterization of coarse particulate matter (PM10) and fine particulate matter (PM2.5) in the city of Huelva (in Southwestern Spain) was carried out during 2001 and 2002. To identify the major emission sources with a significant influence on PM10 and PM2.5, a methodology was developed based on the combination of: (1) real-time measurements of levels of PM10, PM2.5, and very fine particulate matter (PM1); (2) chemical characterization and source apportionment analysis of PM10 and PM2.5; and (3) intensive measurements in field campaigns to characterize the emission plumes of several point sources. Annual means of 37, 19, and 16 microg/m3 were obtained for the study period for PM10, PM2.5, and PM1, respectively. High PM episodes, characterized by a very fine grain size distribution, are frequently detected in Huelva mainly in the winter as the result of the impact of the industrial emission plumes on the city. Chemical analysis showed that PM at Huelva is characterized by high PO4(3-) and As levels, as expected from the industrial activities. Source apportionment analyses identified a crustal source (36% of PM10 and 31% of PM2.5); a traffic-related source (33% of PM10 and 29% of PM2.5), and a marine aerosol contribution (only in PM10, 4%). In addition, two industrial emission sources were identified in PM10 and PM2.5: (1) a petrochemical source, 13% in PM10 and 8% in PM2.5; and (2) a mixed metallurgical-phosphate source, which accounts for 11-12% of PM10 and PM2.5. In PM2.5 a secondary source has been also identified, which contributed to 17% of the mass. A complete characterization of industrial emission plumes during their impact on the ground allowed for the identification of tracer species for specific point sources, such as petrochemical, metallurgic, and fertilizer and phosphate production industries.     

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.     

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.     

Improving source apportionment of fine particles in the eastern United States utilizing temperature-resolved carbon fractions

The objectives of this study were to examine the use of carbon fractions to identify particulate matter (PM) sources, especially traffic-related carbonaceous particle sources, and to estimate their contributions to the particle mass concentrations. In recent studies, positive matrix factorization (PMF) was applied to ambient fine PM (PM2.5) compositional data sets of 24-hr integrated samples including eight individual carbon fractions collected at three monitoring sites in the eastern United States: Atlanta, GA, Washington, DC, and Brigantine, NJ. Particulate carbon was analyzed using the Interagency Monitoring of Protected Visual Environments/Thermal Optical Reflectance method that divides carbon into four organic carbons (OC): pyrolized OC and three elemental carbon (EC) fractions. In contrast to earlier PMF studies that included only the total OC and EC concentrations, gasoline emissions could be distinguished from diesel emissions based on the differences in the abundances of the carbon fractions between the two sources. The compositional profiles for these two major source types show similarities among the three sites. Temperature-resolved carbon fractions also enhanced separations of carbon-rich secondary sulfate aerosols. Potential source contribution function analyses show the potential source areas and pathways of sulfate-rich secondary aerosols, especially the regional influences of the biogenic, as well as anthropogenic secondary aerosol. This study indicates that temperature-resolved carbon fractions can be used to enhance the source apportionment of ambient PM2.5.     

Methods of characterizing the distribution of exhaust emissions from light-duty, gasoline-powered motor vehicles in the U.S. fleet

Mobile sources significantly contribute to ambient concentrations of airborne particulate matter (PM). Source apportionment studies for PM10 (PM < or = 10 microm in aerodynamic diameter) and PM2.5 (PM < or = 2.5 microm in aerodynamic diameter) indicate that mobile sources can be responsible for over half of the ambient PM measured in an urban area. Recent source apportionment studies attempted to differentiate between contributions from gasoline and diesel motor vehicle combustion. Several source apportionment studies conducted in the United States suggested that gasoline combustion from mobile sources contributed more to ambient PM than diesel combustion. However, existing emission inventories for the United States indicated that diesels contribute more than gasoline vehicles to ambient PM concentrations. A comprehensive testing program was initiated in the Kansas City metropolitan area to measure PM emissions in the light-duty, gasoline-powered, on-road mobile source fleet to provide data for PM inventory and emissions modeling. The vehicle recruitment design produced a sample that could represent the regional fleet, and by extension, the national fleet. All vehicles were recruited from a stratified sample on the basis of vehicle class (car, truck) and model-year group. The pool of available vehicles was drawn primarily from a sample of vehicle owners designed to represent the selected demographic and geographic characteristics of the Kansas City population. Emissions testing utilized a portable, light-duty chassis dynamometer with vehicles tested using the LA-92 driving cycle, on-board emissions measurement systems, and remote sensing devices. Particulate mass emissions were the focus of the study, with continuous and integrated samples collected. In addition, sample analyses included criteria gases (carbon monoxide, carbon dioxide, nitric oxide/nitrogen dioxide, hydrocarbons), air toxics (speciated volatile organic compounds), and PM constituents (elemental/organic carbon, metals, semi-volatile organic compounds). Results indicated that PM emissions from the in-use fleet varied by up to 3 orders of magnitude, with emissions generally increasing for older model-year vehicles. The study also identified a strong influence of ambient temperature on vehicle PM mass emissions, with rates increasing with decreasing temperatures.     

Source contributions to fine particulate matter in an urban atmosphere

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

Daytime resolved analysis of polycyclic aromatic hydrocarbons in urban aerosol samples - impact of sources and meteorological conditions

Urban aerosol was collected in a summer and a winter campaign for 7 and 3 days, respectively. Low volume samples were taken with a time resolution of 160 min using a filter/sorption cartridge system extended by an ozone scrubber. Concentrations of mainly particle associated polycyclic aromatic hydrocarbons (PAH) and oxidised PAH (O-PAH) were determined by gas chromatography/high resolution mass spectrometry. The sampling site was located in the city centre of Augsburg, Germany, near major roads with high traffic volume. The daily concentrations and profiles were mainly governed by local emissions from traffic and domestic heating, as well as by the meteorological conditions. During the winter campaign, concentrations were more than 10 fold higher than during the summer campaign. Highest concentrations were found concurrent with low boundary layer heights and low wind speeds. Significant diurnal variation of the PAH profiles was observed. Enhanced influences of traffic related PAH on the PAH profiles were evident during daytime in summer, whereas emissions from hot water generation and domestic heating were obvious during the night time of both seasons. A general idea about the global meteorological situation was acquired using back trajectory calculations (NOAA ARL HYSPLIT4). Due to high local emissions in combination with low air exchange during the two sampling campaigns, effects of mesoscale transport were not clearly observable.     

Comparison of receptor models for source apportionment of volatile organic compounds in Beijing, China

Identifying the sources of volatile organic compounds (VOCs) is key to reducing ground-level ozone and secondary organic aerosols (SOAs). Several receptor models have been developed to apportion sources, but an intercomparison of these models had not been performed for VOCs in China. In the present study, we compared VOC sources based on chemical mass balance (CMB), UNMIX, and positive matrix factorization (PMF) models. Gasoline-related sources, petrochemical production, and liquefied petroleum gas (LPG) were identified by all three models as the major contributors, with UNMIX and PMF producing quite similar results. The contributions of gasoline-related sources and LPG estimated by the CMB model were higher, and petrochemical emissions were lower than in the UNMIX and PMF results, possibly because the VOC profiles used in the CMB model were for fresh emissions and the profiles extracted from ambient measurements by the two-factor analysis models were "aged".     

Pedestrian exposure to size-resolved particles in Milan

Measurement campaigns for airborne particles along a pedestrian route in the city center of Milan were performed by means of a portable instrument consisting of an optical particle counter and a global positioning system (GPS) signal receiver. Based on the size-resolved particle number concentration data and on proper density factors experimentally determined for Milan urban area, the mass concentrations were calculated in terms of particulate matter with aerodynamic diameters < or =10 microm (PM10), < or =2.5 pm (PM2.5), and < or =1 microm (PM1). Besides directly measuring the personal exposure to PM throughout the route, the measurement campaigns pointed out small spatial and temporal variations of the concentration ranges in the different urban microenvironments visited along the route as well as very peculiar features of the particles levels in the underground subway. These findings suggested that the personal exposure of pedestrians in the city center could be estimated by simply taking into account the exposure at the open air and in the subway. The comparison between measured and calculated exposures according to the microenvironment-based estimation results in reasonable accordance, even though the estimations tend to slightly underestimate (12%) the actual measured exposure.     

Investigation of sources of atmospheric aerosol at a hot spot area in Dhaka, Bangladesh

Samples of fine and coarse fractions of airborne particulate matter were collected at the Farm Gate area in Dhaka from July 2001 to March 2002. Dhaka is a hot spot area with very high pollutant concentrations because of the proximity of major roadways. The samples were collected using a "Gent" stacked filter unit in two fractions of 0- to 2.2-microm and 2.2- to 10-microm sizes. The samples were analyzed for elemental concentrations by particle-induced X-ray excitation (PIXE) and for black carbon by reflectivity methods, respectively. The data were analyzed by positive matrix factorization (PMF) to identify the possible sources of atmospheric aerosols in this area. Six sources were found for both the coarse and fine PM fractions. The data sets were also analyzed by an expanded model to explore additional sources. Seven and six factors were obtained for coarse and fine PM fractions, respectively, in these analyses. The identified sources are motor vehicle, soil dust, emissions from construction activities, sea salt, biomass burning/brick kiln, resuspended/fugitive Pb, and two-stroke engines. From the expanded modeling, approximately 50% of the total PM2.2 mass can be attributed to motor vehicles, including two-stroke engine vehicle in this hot spot in Dhaka, whereas the PMF modeling indicates that 45% of the total PM2.2 mass is from motor vehicles. The PMF2 and expanded models could resolve approximately 4% and 3% of the total PM2.2 mass as resuspended/fugitive Pb, respectively. Although, Pb has been eliminated from gasoline in Bangladesh since July 1999, there still may be substantial amounts of accumulated lead in the dust near roadways as well as fugitive Pb emissions from battery reclaimation and other industries. Soil dust is the largest component of the coarse particle fraction (PM2.2-10) accounting for approximately 71% of the total PM2.2-10 mass in the expanded model, whereas from the PMF modeling, the dust (undifferentiated) contribution is approximately 49%.     

The impact of infield biomass burning on PM levels and its chemical composition

In the South of Italy, it is common for farmers to burn pruning waste from olive trees in spring. In order to evaluate the impact of the biomass burning source on the physical and chemical characteristics of the particulate matter (PM) emitted by these fires, a PM monitoring campaign was carried out in an olive grove. Daily PM10 samples were collected for 1 week, when there were no open fires, and when biomass was being burned, and at two different distances from the fires. Moreover, an optical particle counter and a polycyclic aromatic hydrocarbon (PAH) analyzer were used to measure the high time-resolved dimensional distribution of particles emitted and total PAHs concentrations, respectively. Chemical analysis of PM10 samples identified organic and inorganic components such as PAHs, ions, elements, and carbonaceous fractions (OC, EC). Analysis of the collected data showed the usefulness of organic and inorganic tracer species and of PAH diagnostic ratios for interpreting the impact of biomass fires on PM levels and on its chemical composition. Finally, high time-resolved monitoring of particle numbers and PAH concentrations was performed before, during, and after biomass burning, and these concentrations were seen to be very dependent on factors such as weather conditions, combustion efficiency, and temperature (smoldering versus flaming conditions), and moisture content of the wood burned.     

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.     

Particle size distributions and elemental composition of atmospheric particulate matter in southern Italy

Three 2-wk seasonal field campaigns were performed in 2003 and 2004 at a sampling site on the southern Tyrrhenian coast of Italy with the aim to investigate the dynamics and characteristics of particle-bound pollutants in the Mediterranean area. Fine (PM(2.5)) and coarse particulate matter (PM(10-2.5)) size fractions were collected by a manual dichotomous sampler on 37-mm Teflon filters over a 24-hr sampling period. On average, 70% of the total PM(10) (PM(2.5) + PM(10-2.5)) mass was associated with the coarse fraction and 30% with the fine fraction during the three campaigns. The ambient concentrations of Pb, Ni, Cr, Zn, Mn, V, Cd, Fe, Cu, Ca, and Mg associated with both size fractions were determined by atomic absorption spectrometry. Ambient concentrations showed differences in their absolute value, ranging from few ng x m(-3) to microg x m(-3), as well as in their variability within the PM(2.5) and PM(10-2.5) size fractions. PM(10) levels were well below the European Union (EU) limit value during the study period with the exception of three events during the first campaign (fall) and five events during the third campaign (spring). Two main sources were identified as the major contributors including mineral dust, transported from North Africa, and sea spray from the Tyrrhenian Sea. Comparing the results with backward trajectories, calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) and Total Ozone Mapping Spectrometer-National Aeronautics and Space Administration (TOMS-NASA) maps, it was observed that in central and eastern Europe, the Tyrrhenian Sea and North Africa were the major emission source regions that affected the temporal variations and daily averages of PM(2.5) and PM(10-2.5) concentrations.