An alternative way to determine the size distribution of airborne particulate matter

We developed and tested a methodology to extract both the size-segregated source apportionment of atmospheric aerosol and the size distribution of each detected element. The experiment is based on the parallel use of a standard low-volume sampler to collect Particulate Matter (PM) and an Optical Particle Counter (OPC). The approach is complementary to size-segregated PM sampling, and it was tested versus a 12-stage cascade impactor. Samples were collected inside the urban area of Genoa (Italy) and their elemental composition was measured by Energy Dispersive-X Ray Fluorescence (ED-XRF). Positive Matrix Factorization (PMF) was applied to time series of elemental concentrations to identify major PM sources, and both PM mass concentration and size-segregated particle number concentration were apportioned. Source profiles and temporal trends extracted by PMF were analyzed together with the OPC data to obtain the size distribution for several elements. The new methodology proved to be reliable for the PM apportionment as well as in providing the elemental concentrations in PM10, PM2.5, and PM1 (PM with aerodynamic diameter, D_ae < 10, 2.5, and 1 μm, respectively). The elemental size distributions are in good agreement with those obtained by the cascade impactor for several elements but some discrepancies, in particular for traffic emissions, are stressed and discussed in the text. The new methodology has two main advantages: it only requires standard semi-automatic sampling equipment and compositional analysis and it provides size-segregated information averaged over quite long periods (typically several months). This is particularly important since campaigns with cascade impactors are generally laborious and thus limited to short periods.     

Synopsis of the temporal variation of particulate matter composition and size

A synopsis of the detailed temporal variation of the size and number distribution of particulate matter (PM) and its chemical composition on the basis of measurements performed by several regional research consortia funded by the U.S. Environmental Protection Agency (EPA) PM Supersite Program is presented. This program deployed and evaluated a variety of research and emerging commercial measurement technologies to investigate the physical and chemical properties of atmospheric aerosols at a level of detail never before achieved. Most notably these studies demonstrated that systematic size-segregated measurements of mass, number, and associated chemical composition of the fine (PM2.5) and ultrafine (PM0.1) fraction of ambient aerosol with a time resolution down to minutes and less is achievable. A wealth of new information on the temporal variation of aerosol has been added to the existing knowledge pool that can be mined to resolve outstanding research and policy-related questions. This paper explores the nature of temporal variations (on time scales from several minutes to hours) in the chemical and physical properties of PM and its implications in the identification of PM formation processes, and source attribution (primary versus secondary), the contribution of local versus transported PM and the development of effective PM control strategies. The PM Supersite results summarized indicate that location, time of day, and season significantly influence not only the mass and chemical composition but also the size-resolved chemical/elemental composition of PM. Ambient measurements also show that ultrafine particles have different compositions and make up only a small portion of the PM mass concentration compared with inhalable coarse and fine particles, but their number concentration is significantly larger than their coarse or fine counterparts. PM size classes show differences in the relative amounts of nitrates, sulfates, crustal materials, and most especially carbon as well as variations in seasonal and diurnal patterns.     

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.     

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 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.     

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.     

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.     

Characteristics of inhalable particulate matter concentration and size distribution from power plants in China

In this investigation, the collection efficiency of particulate emission control devices (PECDs), particulate matter (PM) emissions, and PM size distribution were determined experimentally at the inlet and outlet of PECDs at five coal-fired power plants. Different boilers, coals, and PECDs are used in these power plants. Measurement in situ was performed by an electrical low-pressure impactor with a sampling system, which consisted of an isokinetic sampler probe, precut cyclone, and two-stage dilution system with a sample line to the instruments. The size distribution was measured over a range from 0.03 to 10 microm. Before and after all of the PECDs, the particle number size distributions display a bimodal distribution. The PM2.5 fraction emitted to atmosphere includes a significant amount of the mass from the coarse particle mode. The controlled and uncontrolled emission factors of total PM, inhalable PM (PM10), and fine PM P(M2.5) were obtained. Electrostatic precipitator (ESP) and baghouse total collection efficiencies are 96.38-99.89% and 99.94%, respectively. The minimum collection efficiency of the ESP and the baghouse both appear in the particle size range of 0.1-1 microm. In this size range, ESP and baghouse collection efficiencies are 85.79-98.6% and 99.54%. Real-time measurement shows that the mass and number concentration of PM10 will be greatly affected by the operating conditions of the PECDs. The number of emitted particles increases with increasing boiler load level because of higher combustion temperature. During test run periods, the data reproducibility is satisfactory.     

Identification of Sources of Phoenix Aerosol by Positive Matrix Factorization

ABSTRACT

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 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.     

Time-resolved measurements of aerosol elemental concentrations in indoor working environments

We have measured the elemental concentrations in aerosols with a 2-h time resolution in two different types of working environment: a chemistry laboratory dealing with the processing of advanced nanoparticulate materials and a medium-sized machine workshop. Non-stop 10-day and 12-day samplings were performed at each location in order to determine the concentration trends during the non-working/working and weekday/weekend periods. Supplementary measurements of PM10 aerosols with a 2-day sample collection time were performed with a standard Gent PM10 sampler to compare the elemental concentrations with the time-averaged concentrations detected by the 2D step-sampler. The concentrations were determined a posteriori by analyzing the x-ray spectra of aerosol samples emitted after 3-MeV proton bombardment. The PM10 samples collected in the chemistry laboratory were additionally inspected by scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) to determine the chemical compositions of the individual particles. In the workshop, a total PM10 mass sampling was performed simultaneously with a minute resolution to compare the signal with typical outdoor PM10 concentration levels. A factor analysis of the time-resolved dataset points to six and eight factors in the chemistry laboratory and the machine workshop, respectively. These factors describe most of the data variance, and their composition in terms of different elements can be related to specific indoor activities and conditions. We were able to demonstrate that the elemental concentration sampling with hourly resolution is an excellent tool for studying the indoor air pollution. While sampling the total PM10 mass concentration with a minute resolution may lack the potential to identify the emission sources in a “noisy” environment, the time averaging on a day time scale is too coarse to cope with the working dynamics, even if elemental sensitivity is an option.     

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.     

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.     

Continuous and semicontinuous monitoring techniques for particulate matter mass and chemical components: a synthesis of findings from EPA's Particulate Matter Supersites Program and related studies

The U.S. Environmental Protection Agency (EPA) established the Particulate Matter (PM) Supersites Program to provide key stakeholders (government and private sector) with significantly improved information needed to develop effective and efficient strategies for reducing PM on urban and regional scales. All Supersites projects developed and evaluated methods and instruments, and significant advances have been made and applied within these programs to yield new insights to our understanding of PM accumulation in air as well as improved source-receptor relationships. The tested methods include a variety of continuous and semicontinuous instruments typically with a time resolution of an hour or less. These methods often overcome many of the limitations associated with measuring atmospheric PM mass concentrations by daily filter-based methods (e.g., potential positive or negative sampling artifacts). Semicontinuous coarse and ultrafine mass measurement methods also were developed and evaluated. Other semicontinuous monitors tested measured the major components of PM such as nitrate, sulfate, ammonium, organic and elemental carbon, trace elements, and water content of the aerosol as well as methods for other physical properties of PM, such as number concentration, size distribution, and particle density. Particle mass spectrometers, although unlikely to be used in national routine monitoring networks in the foreseeable future because of their complex technical requirements and cost, are mentioned here because of the wealth of new information they provide on the size-resolved chemical composition of atmospheric particles on a near continuous basis. Particle mass spectrometers likely represent the greatest advancement in PM measurement technology during the last decade. The improvements in time resolution achieved by the reported semicontinuous methods have proven to be especially useful in characterizing ambient PM, and are becoming essential in allowing scientists to investigate sources of particulate pollution and to probe into the dynamics and mechanisms of aerosol formation in the atmosphere.     

Chemical and morphological characterization of aerosol particles at Mt. Krvavec, Slovenia, during the Eyjafjallajökull Icelandic volcanic eruption

Objective

In this work, continuous and size-segregated aerosol measurements at Mt. Krvavec, Slovenia, during the Eyjafjallajökull volcanic eruption were performed. Based on chemical and morphological characteristics of size-segregated particles, the presence of the volcanic aerosols after long-range transport to Slovenia was to be confirmed.

Results and conclusions

Continuous measurements with the aethalometer and SMPS indicated the suspected volcanic ash plume passing over the sampling site. The aerosols collected by discrete sampling showed a chemical signature similar to the known elemental signature of the Icelandic volcanic ash. Coarse particles showed a composition typical for silicates rich in metals; in many cases also S was present. Morphological analysis showed particles with features indicative of an explosive volcanic eruption, e.g., pumice and pumice shards, glass shards, minerals, evidence of steam condensation, etc. The high sulfate concentration associated with the fine particles resulted in sulfate crystallization within the cascade impactor leading to the formation of large structures resembling a “fern”. Mass size distributions for Fe, Ti, Mn, Ca, Na, and Mg showed one primary peak (for Fe, Mn, and Ti at 2.8 μm; for Ca, Na, and Mg at ca. 4 μm), which supports the fact that most of the particles in the coarse sizes were silicates rich in metals. The size distribution of the water-soluble SO ₄ ^2? showed a maximum peak at 0.75 μm, which also confirms the high sulfate concentration in the fine particles. Chemical and morphological characterization of aerosols collected at Mt. Krvavec indeed confirmed that volcanic ash plume passed over Slovenia.     

A Federal Tax on Energy

Abstract

In this investigation, the collection efficiency of particulate emission control devices (PECDs), particulate matter (PM) emissions, and PM size distribution were determined experimentally at the inlet and outlet of PECDs at five coal-fired power plants. Different boilers, coals, and PECDs are used in these power plants. Measurement in situ was performed by an electrical low-pressure impactor with a sampling system, which consisted of an isokinetic sampler probe, precut cyclone, and two-stage dilution system with a sample line to the instruments. The size distribution was measured over a range from 0.03 to 10 µm. Before and after all of the PECDs, the particle number size distributions display a bimodal distribution. The PM_2.5 fraction emitted to atmosphere includes a significant amount of the mass from the coarse particle mode. The controlled and uncontrolled emission factors of total PM, inhalable PM (PM₁₀), and fine PM P(M_2.5) were obtained. Electrostatic precipitator (ESP) and baghouse total collection efficiencies are 96.38–99.89% and 99.94%, respectively. The minimum collection efficiency of the ESP and the baghouse both appear in the particle size range of 0.1–1 µm. In this size range, ESP and baghouse collection efficiencies are 85.79–98.6% and 99.54%. Real-time measurement shows that the mass and number concentration of PM₁₀ will be greatly affected by the operating conditions of the PECDs. The number of emitted particles increases with increasing boiler load level because of higher combustion temperature. During test run periods, the data reproducibility is satisfactory.     

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.     

Studies of potential sources that contributed to atmospheric mercury in Toronto,Canada

This study identified sources of mercury (Hg) in downtown Toronto, Canada by analyzing gaseous elemental mercury (GEM), mercury associated with particles with sizes less than 2.5 microns (PHg < 2.5), and gaseous oxidized inorganic mercury (GOIM), commonly referred to as reactive gaseous mercury (RGM), and air pollutants (CO, NO_x, O₃, PM_2.5, SO₂) concentrations between Dec 2003 and Nov 2004. The data were analyzed using Positive Matrix Factorization (PMF) model, Principal Components Analysis (PCA), ratio analysis, back trajectories, and correlation analyses. The analyses suggest industrial sources (chemical production, metal production, sewage treatment), rather than coal combustion, were the major contributors to measured Hg levels. Overlap in source profiles for the Hg sources listed in the Canadian National Pollutant Release Inventory (NPRI) and lack of source profiles for urban sources were the major limitations to positively identifying sources from the PMF and PCA factors. Correlation analyses revealed direct emissions were the sources of GOIM in spring, summer, and fall, and the occurrence of GEM oxidation by ozone in the summer. Elevated Hg events are attributed to emissions from urban sources near the sampling site, regional point sources, and photochemical processes involving ozone.     

Source profiles of particulate matter emissions from a pilot-scale boiler burning North American coal blends.

Recent awareness of suspected adverse health effects from ambient particulate matter (PM) emission has prompted publication of new standards for fine PM with aerodynamic diameter less than 2.5 microm (PM2.5). However, scientific data on fine PM emissions from various point sources and their characteristics are very limited. Source apportionment methods are applied to identify contributions of individual regional sources to tropospheric particulate concentrations. The existing industrial database developed using traditional source measurement techniques provides total emission rates only, with no details on chemical nature or size characteristics of particulates. This database is inadequate, in current form, to address source-receptor relationships. A source dilution system was developed for sampling and characterization of total PM, PM2.5, and PM10 (i.e., PM with aerodynamic diameter less than 10 pm) from residual oil and coal combustion. This new system has automatic control capabilities for key parameters, such as relative humidity (RH), temperature, and sample dilution. During optimization of the prototype equipment, three North American coal blends were burned using a 0.7-megawatt thermal (MWt) pulverized coal-fired, pilot-scale boiler. Characteristic emission profiles, including PM2.5 and total PM soluble acids, and elemental and carbon concentrations for three coal blends are presented. Preliminary results indicate that volatile trace elements such as Pb, Zn, Ti, and Se are preferentially enriched in PM2.5. PM2.5 is also more concentrated in soluble sulfates relative to total PM. Coal fly ash collected at the outlet of the electrostatic precipitator (ESP) contains about 85-90% PM10 and 30-50% PM2.5. Particles contain the highest elemental concentrations of Si and Al while Ca, Fe, Na, Ba, and K also exist as major elements. Approximately 4-12% of the materials exists as soluble sulfates in fly ash generated by coal blends containing 0.2-0.8% sulfur by mass. Source profile data for an eastern U.S. coal show good agreement with those reported from a similar study done in the United States. Based on the inadequacies identified in the initial sampling equipment, a new, plume-simulating fine PM measurement system with modular components for field use is being developed for determining coal combustion PM source profiles from utility boiler stacks.     

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

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