Chemical mass balance source apportionment for combined PM2.5 measurements from U.S. non-urban and urban long-term networks

The Minnesota Particulate Matter 2.5 (PM_2.5) Source Apportionment Study was undertaken to explore the utility of PM_2.5 mass, element, ion, and carbon measurements from long-term speciation networks for pollution source attribution. Ambient monitoring data at eight sites across the state were retrieved from the archives of the Interagency Monitoring of Protected Visual Environments (IMPROVE) and the Speciation Trends Network (STN; part of the Chemical Speciation Network [CSN]) and analyzed by an Effective Variance – Chemical Mass Balance (EV-CMB) receptor model with region-specific geological source profiles developed in this study. PM_2.5 was apportioned into contributions of fugitive soil dust, calcium-rich dust, taconite (low grade iron ore) dust, road salt, motor vehicle exhaust, biomass burning, coal-fired utility, and secondary aerosol. Secondary sulfate and nitrate contributed strongly (49–71% of PM_2.5) across all sites and was dominant (≥60%) at IMPROVE sites. Vehicle exhausts accounted for 20–70% of the primary PM_2.5 contribution, largely exceeding the proportion in the primary PM_2.5 emission inventory. The diesel exhaust contribution was separable from the gasoline engine exhaust contribution at the STN sites. Higher detection limits for several marker elements in the STN resulted in non-detectable coal-fired boiler contributions which were detected in the IMPROVE data. Despite the different measured variables, analytical methods, and detection limits, EV-CMB results from a nearby IMPROVE-STN non-urban/urban sites showed similar contributions from regional sources – including fugitive dust and secondary aerosol. Seasonal variations of source contributions were examined and extreme PM_2.5 episodes were explained by both local and regional pollution events.     

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.     

Source apportionment of airborne particulate matter in Southeast Texas using a source-oriented 3D air quality model

A nested version of the source-oriented externally mixed UCD/CIT model was developed to study the source contributions to airborne particulate matter (PM) during a two-week long air quality episode during the Texas 2000 Air Quality Study (TexAQS 2000). Contributions to primary PM and secondary ammonium sulfate in the Houston–Galveston Bay (HGB) and Beaumont–Port Arthur (BPA) areas were determined.The predicted 24-h elemental carbon (EC), organic compounds (OC), sulfate, ammonium ion and primary PM_2.5 mass are in good agreement with filter-based observations. Predicted concentrations of hourly sulfate, ammonium ion, and primary OC from diesel and gasoline engines and biomass burning organic aerosol (BBOA) at La Porte, Texas agree well with measurements from an Aerodyne Aerosol Mass Spectrometer (AMS).The UCD/CIT model predicts that EC is mainly from diesel engines and majority of the primary OC is from internal combustion engines and industrial sources. Open burning contributes large fractions of EC, OC and primary PM_2.5 mass. Road dust, internal combustion engines and industries are the major sources of primary PM_2.5. Wildfire dominates the contributions to all primary PM components in areas near the fires. The predicted source contributions to primary PM are in general agreement with results from a chemical mass balance (CMB) model. Discrepancy between the two models suggests that further investigations on the industrial PM emissions are necessary.Secondary ammonium sulfate accounts for the majority of the secondary inorganic PM. Over 80% of the secondary sulfate in the 4 km domain is produced in upwind areas. Coal combustion is the largest source of sulfate. Ammonium ion is mainly from agriculture sources and contributions from gasoline vehicles are significant in urban areas.     

Source apportionment of Phoenix PM2.5 aerosol with the Unmix receptor model

The multivariate receptor model Unmix has been used to analyze a 3-yr PM2.5 ambient aerosol data set collected in Phoenix, AZ, beginning in 1995. The analysis generated source profiles and overall average percentage source contribution estimates (SCEs) for five source categories:gasoline engines (33 +/- 4%), diesel engines (16 +/- 2%), secondary SO4(2-) (19 +/- 2%), crustal/soil (22 +/- 2%), and vegetative burning (10 +/- 2%). The Unmix analysis was supplemented with scanning electron microscopy (SEM) of a limited number of filter samples for information on possible additional low-strength sources. Except for the diesel engine source category, the Unmix SCEs were generally consistent with an earlier multivariate receptor analysis of essentially the same data using the Positive Matrix Factorization (PMF) model. This article provides the first demonstration for an urban area of the capability of the Unmix receptor model.     

Analysis and apportionment of organic carbon and fine particulate matter sources at multiple sites in the midwestern United States

Speciated fine particulate matter (PM2.5) data collected as part of the Speciation Trends Network at four sites in the Midwest (Detroit, MI; Cincinnati, OH; Indianapolis, IN; and Northbrook, IL) and as part of the Interagency Monitoring of Protected Visual Environments program at the rural Bondville, IL, site were analyzed to understand sources contributing to organic carbon (OC) and PM2.5 mass. Positive matrix factorization (PMF) was applied to available data collected from January 2002 through March 2005, and seven to nine factors were identified at each site. Common factors at all of the sites included mobile (gasoline)/secondary organic aerosols with high OC, diesel with a high elemental carbon/OC ratio (only at the urban sites), secondary sulfate, secondary nitrate, soil, and biomass burning. Identified industrial factors included copper smelting (Northbrook, Indianapolis, and Bondville), steel/manufacturing with iron (Northbrook), industrial zinc (Northbrook, Cincinnati, Indianapolis, and Detroit), metal plating with chromium and nickel (Detroit, Indianapolis, and Bondville), mixed industrial with copper and iron (Cincinnati), and limestone with calcium and iron (Bondville). PMF results, on average, accounted for 96% of the measured PM2.5 mass at each site; residuals were consistently within tolerance (+/-3), and goodness-of-fit (Q) was acceptable. Potential source contribution function analysis helped identify regional and local impacts of the identified source types. Secondary sulfate and soil factors showed regional characteristics at each site, whereas industrial sources typically appeared to be locally influenced. These regional factors contributed approximately one third of the total PM2.5 mass, on average, whereas local mobile and industrial sources contributed to the remaining mass. Mobile sources were a major contributor (55-76% at the urban sites) to OC mass, generally with at least twice as much mass from nondiesel sources as from diesel. Regional OC associated with secondary sulfate and soil was generally low.     

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.     

Sources of fine urban particulate matter in Detroit, MI

Data from the speciation trends network (STN) was used to evaluate the amount and temporal patterns of particulate matter originating from local industrial sources and long-range transport at two sites in Detroit, MI: Allen Park, MI, southwest of both Detroit and the areas of heavy industrial activity; Dearborn, MI, located on the south side of Detroit near the most heavily industrialized region. Using positive matrix factorization (PMF) and comparing source contributions at Allen Park to those in Dearborn, contributions made by local industrial sources (power plants, coke refineries, iron smelting, waste incineration), local area sources (automobile and diesel truck) and long range sources of PM(2.5) can be distinguished in greater Detroit. Overall, the mean mass concentration measured at Dearborn was 19% higher than that measured at Allen Park. The mass at Allen Park was apportioned as: secondary sulfate 31%, secondary nitrate 28%, soil 8%, mixed aged sea and road salts 4%, gasoline 15%, diesel 4%, and biomass burning 3%. At Dearborn the mass was apportioned as: secondary sulfate 25%, secondary nitrate 20%, soil 12%, mixed aged sea and road salts 4%, gasoline 20%, diesel 8%, iron and steel, 5%, and mixed industrial 7%. The impact of the iron and steel, soil, and mixed aged sea and road salt was much higher at the Dearborn site than at the Allen Park site, suggesting that close proximity to a local industrial complex has a direct negative impact on local air quality.     

Estimation of organic carbon blank values and error structures of the speciation trends network data for source apportionment

Because the particulate organic carbon (OC) concentrations reported in U.S. Environment Protection Agency Speciation Trends Network (STN) data were not blank corrected, the OC blank concentrations were estimated using the intercept in particulate matter < or = 2.5 microm in aerodynamic diameter (PM2.5) regression against OC concentrations. The estimated OC blank concentrations ranged from 1 to 2.4 microg/m3 showing higher values in urban areas for the 13 monitoring sites in the northeastern United States. In the STN data, several different samplers and analyzers are used, and various instruments show different method detection limit (MDL) values, as well as errors. A comprehensive set of error structures that would be used for numerous source apportionment studies of STN data was estimated by comparing a limited set of measured concentrations and their associated uncertainties. To examine the estimated error structures and investigate the appropriate MDL values, PM2.5 samples collected at a STN site in Burlington, VT, were analyzed through the application of the positive matrix factorization. A total of 323 samples that were collected between December 2000 and December 2003 and 49 species based on several variable selection criteria were used, and eight sources were successfully identified in this study with the estimated error structures and min values among different MDL values from the five instruments: secondary sulfate aerosol (41%), secondary nitrate aerosol (20%), airborne soil (15%), gasoline vehicle emissions (7%), diesel emissions (7%), aged sea salt (4%), copper smelting (3%), and ferrous smelting (2%). Time series plots of contributions from airborne soil indicate that the highly elevated impacts from this source were likely caused primarily by dust storms.     

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.     

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.     

Estimation of source apportionment and potential source locations of PM2.5 at a west coastal IMPROVE site

Particle composition data for PM_2.5 samples collected at Kalmiopsis Interagency Monitoring of Protected Visual Environments (IMPROVE) site in southwestern Oregon from March 2000 to May 2004 were analyzed to provide source identification and apportionment. A total of 493 samples were collected and 32 species were analyzed by particle induced X-ray emission, proton elastic scattering analysis, photon-induced X-ray fluorescence, ion chromatography, and thermal optical reflectance methods. Positive matrix factorization (PMF) was used to estimate the source profiles and their mass contributions. The PMF modeling identified nine sources. In the Kalmiopsis site, the average mass was apportioned to wood/field burning (38.4%), secondary sulfate (26.9%), airborne soil including Asian dust (8.6 %), secondary nitrate (7.6%), fresh sea salt (5.8%), OP-rich sulfate (4.9%), aged sea salt (4.5 %), gasoline vehicle (1.9%), and diesel emission (1.4%). The potential source contribution function (PSCF) was then used to help identify likely locations of the regional sources of pollution. The PSCF map for wood/field burning indicates there is a major potential source area in the Siskiyou County and eastern Oregon. The potential source locations for secondary sulfate are found in western Washington, northwestern Oregon, and the near shore Pacific Ocean where there are extensive shipping lanes. It was not possible to extract a profile directly attributable to ship emissions, but indications of their influence are seen in the secondary sulfate and aged sea salt compositions.     

Source Apportionment of Phoenix PM2.5 Aerosol with the Unmix Receptor Model

Abstract

The multivariate receptor model Unmix has been used to analyze a 3-yr PM_2.5 ambient aerosol data set collected in Phoenix, AZ, beginning in 1995. The analysis generated source profiles and overall average percentage source contribution estimates (SCEs) for five source categories: gasoline engines (33 ± 4%), diesel engines (16 ± 2%), secondary SO₄ ^2? (19 ± 2%), crustal/soil (22 ± 2%), and vegetative burning (10 ± 2%). The Unmix analysis was supplemented with scanning electron microscopy (SEM) of a limited number of filter samples for information on possible additional low-strength sources. Except for the diesel engine source category, the Unmix SCEs were generally consistent with an earlier multivariate receptor analysis of essentially the same data using the Positive Matrix Factorization (PMF) model. This article provides the first demonstration for an urban area of the capability of the Unmix receptor model.     

Sources of fine particles in the South Coast area,California

PM_2.5 (particulate matter less than 2.5 μm in aerodynamic diameter) speciation data collected between 2003 and 2005 at two United State Environmental Protection Agency (US EPA) Speciation Trends Network monitoring sites in the South Coast area, California were analyzed to identify major PM_2.5 sources as a part of the State Implementation Plan development. Eight and nine major PM_2.5 sources were identified in LA and Rubidoux, respectively, through PMF2 analyses. Similar to a previous study analyzing earlier data (Kim and Hopke, 2007a), secondary particles contributed the most to the PM_2.5 concentrations: 53% in LA and 59% in Rubidoux. The next highest contributors were diesel emissions (11%) in LA and Gasoline vehicle emissions (10%) in Rubidoux. Most of the source contributions were lower than those from the earlier study. However, the average source contributions from airborne soil, sea salt, and aged sea salt in LA and biomass smoke in Rubidoux increased.To validate the apportioned sources in this study, PMF2 results were compared with those obtained from EPA PMF (US EPA, 2005). Both models identified the same number of major sources and the resolved source profiles and contributions were similar at the two monitoring sites. The minor differences in the results caused by the differences in the least square algorithm and non-negativity constraints between two models did not affect the source identifications.     

PM2.5 source apportionment with organic markers in the Southeastern Aerosol Research and Characterization (SEARCH) study

Positive matrix factorization (PMF) and effective variance (EV) solutions to the chemical mass balance (CMB) were applied to PM_2.5 (particulate matter with an aerodynamic diameter <2.5 μm) mass and chemically speciated measurements for samples taken from 2008 to 2010 at the Atlanta, Georgia, and Birmingham, Alabama, sites. Commonly measured PM_2.5 mass, elemental, ionic, and thermal carbon fraction concentrations were supplemented with detailed nonpolar organic speciation by thermal desorption-gas chromatography/mass spectrometry (TD-GC/MS). Source contribution estimates were calculated for motor vehicle exhaust, biomass burning, cooking, coal-fired power plants, road dust, vegetative detritus, and secondary sulfates and nitrates for Atlanta. Similar sources were found for Birmingham, with the addition of an industrial source and the separation of biomass burning into open burning and residential wood combustion. EV-CMB results based on conventional species were qualitatively similar to those estimated by PMF-CMB. Secondary ammonium sulfate was the largest contributor, accounting for 27–38% of PM_2.5, followed by biomass burning (21–24%) and motor vehicle exhaust (9–24%) at both sites, with 4–6% of PM_2.5 attributed to coal-fired power plants by EV-CMB. Including organic compounds in the EV-CMB reduced the motor vehicle exhaust and biomass burning contributions at both sites, with a 13–23% deficit for PM_2.5 mass. The PMF-CMB solution showed mixing of sources within the derived factors, both with and without the addition of speciated organics, as is often the case with complex source mixtures such as those at these urban-scale sites. The nonpolar TD-GC/MS compounds can be obtained from existing filter samples and are a useful complement to the elements, ions, and carbon fractions. However, they should be supplemented with other methods, such as TD-GC/MS on derivitized samples, to obtain a wider range of polar compounds such as sterols, sugars, and organic acids. The PMF and EV solutions to the CMB equations are complementary to, rather than replacements for, each other, as comparisons of their results reveal uncertainties that are not otherwise evident.

Implications:?Organic markers can be measured on currently acquired PM_2.5 filter samples by thermal methods. These markers can complement element, ion, and carbon fraction measurements from long-term speciation networks. Applying the positive matrix factorization and effective variance solutions for the chemical mass balance equations provides useful information on the accuracy of the source contribution estimates. Nonpolar compounds need to be complemented with polar compounds to better apportion cooking and secondary organic aerosol contributors.     

Chemical composition and source apportionment of PM2.5 particles in the Sihwa area, Korea

To investigate the chemical characteristics of fine particles in the Sihwa area, Korea, atmospheric aerosol samples were collected using a dichotomous PM10 sampler and two URG PM2.5 cyclone samplers during five intensive sampling periods between February 1998 and February 1999. The Inductively Coupled Plasma (ICP)-Atomic Emission Spectrometry (AES)/ICP-Mass Spectrometry (MS), ion chromatograph (IC), and thermal manganese dioxide oxidation (TMO) methods were used to analyze the trace elements, ionic species, and carbonaceous species, respectively. Backward trajectory analysis, factor analysis, and a chemical mass balance (CMB) model were used to estimate quantitatively source contributions to PM2.5 particles collected in the Sihwa area. The results of PM2.5 source apportionment using the CMB7 receptor model showed that (NH4)2SO4 was, on average, the major contributor to PM2.5 particles, followed by nontraffic organic carbon (OC) emission, NH4NO3, agricultural waste burning, motor vehicle emission, road dust, waste incineration, marine aerosol, and others. Here, the nontraffic OC sources include primary anthropogenic OC emitted from the industrial complex zone, secondary OC, and organic species from distant sources. The source impact of waste incineration emission became significant when the dominant wind directions were from southwest and west sectors during the sampling periods. It was found that PM2.5 particles in the Sihwa area were influenced mainly by both anthropogenic local sources and long-range transport and transformation of air pollutants.     

Source characterization of ambient fine particles at multiple sites in the Seattle area

To identify major PM_2.5 (particulate matter ≤2.5 μm in aerodynamic diameter) sources with a particular emphasis on the ship engine emissions from a major port, integrated 24 h PM_2.5 speciation data collected between 2000 and 2005 at five United State Environmental Protection Agency's Speciation Trends Network monitoring sites in Seattle, WA were analyzed. Seven to ten PM_2.5 sources were identified through the application of positive matrix factorization (PMF). Secondary particles (12–26% for secondary nitrate; 17–20% for secondary sulfate) and gasoline vehicle emissions (13–31%) made the largest contributions to the PM_2.5 mass concentrations at all of the monitoring sites except for the residential Lake Forest site, where wood smoke contributed the most PM_2.5 mass (31%). Other identified sources include diesel vehicle emissions, airborne soil, residual oil combustion, sea salt, aged sea salt, metal processing, and cement kiln. Residual oil combustion sources identified at multiple monitoring sites point clearly to the Port of Seattle suggesting ship emissions as the source of oil combustion particles. In addition, the relationship between sulfate concentrations and the oil combustion emissions indicated contributions of ship emissions to the local sulfate concentrations. The analysis of spatial variability of PM_2.5 sources shows that the spatial distributions of several PM_2.5 sources were heterogeneous within a given air shed.     

Utilizing the Chemical Mass Balance and Positive Matrix Factorization models to determine influential species and examine possible rotations in receptor modeling results

Data from two of the United States Environmental Protection Agency's Speciation Trends Network fine particulate matter sites within Chicago, Illinois were used to examine the influence that the results and profiles of the Chemical Mass Balance (CMB) receptor model have on the source contributions and profiles of the Positive Matrix Factorization (PMF) model. This was accomplished using the target shape technique, which utilizes a priori information from the CMB source profiles inputted into the PMF model. The target shape methodology involves inputting specific information for the source profiles into the PMF model as it is resolving source profile and contribution matrices. The target shape results demonstrated it is possible to determine in both the CMB and PMF source profiles those species, which do not influence the solutions of either model.A second method utilizing information from the CMB results was used to impose a condition where the Motor Vehicles source never had a zero contribution as was applied to the CMB model. This involved utilizing an edge rotation to rotate the PMF results to yield a different solution without worsening the fit of the original results. The purpose of this work is to achieve a rotation, which produced a PMF solution where all of the Motor Vehicles contributions were greater than zero. Comparing the rotated Motor Vehicles and Sulfates source contributions in PMF to those obtained from CMB showed a better correlation between the PMF Motor Vehicles contributions to the original CMB results than those prior to rotation.     

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

Abstract

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

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.     

Calibration and Certification of Audit Devices for Transmissometers

Abstract

Speciated fine particulate matter (PM_2.5) data collected as part of the Speciation Trends Network at four sites in the Midwest (Detroit, MI; Cincinnati, OH; Indianapolis, IN; and Northbrook, IL) and as part of the Interagency Monitoring of Protected Visual Environments program at the rural Bondville, IL, site were analyzed to understand sources contributing to organic carbon (OC) and PM_2.5 mass. Positive matrix factorization (PMF) was applied to available data collected from January 2002 through March 2005, and seven to nine factors were identified at each site. Common factors at all of the sites included mobile (gasoline)/secondary organic aerosols with high OC, diesel with a high elemental carbon/OC ratio (only at the urban sites), secondary sulfate, secondary nitrate, soil, and biomass burning. Identified industrial factors included copper smelting (North–brook, Indianapolis, and Bondville), steel/manufacturing with iron (Northbrook), industrial zinc (North–brook, Cincinnati, Indianapolis, and Detroit), metal plating with chromium and nickel (Detroit, Indianapolis, and Bondville), mixed industrial with copper and iron (Cincinnati), and limestone with calcium and iron (Bondville). PMF results, on average, accounted for 96% of the measured PM_2.5 mass at each site; residuals were consistently within tolerance (±3), and goodness–of–fit (Q) was acceptable. Potential source contribution function analysis helped identify regional and local impacts of the identified source types. Secondary sulfate and soil factors showed regional characteristics at each site, whereas industrial sources typically appeared to be locally influenced. These regional factors contributed approximately one third of the total PM_2.5 mass, on average, whereas local mobile and industrial sources contributed to the remaining mass. Mobile sources were a major contributor (55–76% at the urban sites) to OC mass, generally with at least twice as much mass from nondiesel sources as from diesel. Regional OC associated with secondary sulfate and soil was generally low.