Improving Source Apportionment of Fine Particles in the Eastern United States Utilizing Temperature-Resolved Carbon Fractions

Abstract

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 (PM_2.5) 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 PM_2.5.     

Atmospheric aerosol over two urban–rural pairs in the southeastern United States: Chemical composition and possible sources

Positive matrix factorization (PMF) was used to infer the sources of PM_2.5 observed at four sites in Georgia and Alabama. One pair of urban and rural sites in each state is used to examine the regional and urban influence on PM_2.5 concentrations in the Southeast. Eight factors were resolved for the two urban sites and seven factors were resolved for the two rural sites. Spatial correlations of factors were investigated using the square of correlation coefficient (R²) calculated from the resolved G factors. Fourier transform was used to define the temporal characteristics of PM_2.5 factors at these sites. Factors were normalized by using aerosol fine mass concentration data through multiple linear regression to obtain the quantitative factor contributions for each resolved factor. Common factors include: (1) secondary sulfate dominated by high concentrations of sulfate and ammonium with a strong seasonal variation peaking in summer; (2) nitrate and the associated ammonium with a seasonal maximum in winter; (3) “coal combustion/other” factor with presence of sulfate, EC, OC, and Se; (4) soil represented by Al, Ca, Fe, K, Si and Ti; and (5) wood smoke with the high concentrations of EC, OC and K. The motor vehicle factor with high concentrations of EC and OC and the presence of some soil dust components is found at the urban sites, but cannot be resolved for the two rural sites. Among the other factors, two similar industry factors are found at the two sites in each state. For the wood smoke factor, different seasonal trends are found between urban and rural sites, suggesting different wood burning patterns between urban and rural regions. For the industry factors, different seasonal variations are also found between urban and rural sites, suggesting that this factor may come from different sources or a common source may impact the two sites differently. Generally, sulfate, soil, and nitrate factors at the four sites showed similar chemical composition profiles and seasonal variation patterns reflecting the regional characteristics of these factors. These regional factors have predominantly low frequency variations while local factors such as coal combustion, motor vehicle, wood smoke, and industry factors have high frequency variations in addition to low frequency variations.     

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.     

Sources of Fine Particulate Species in Ambient Air over Lake Champlain Basin,VT

Abstract

This study is a part of an ongoing investigation of the types and locations of emission sources that contribute fine particulate air contaminants to Underhill, VT. The air quality monitoring data used for this study are from the Interagency Monitoring of Protected Visual Environments network for the period of 2001–2003 for the Underhill site. The main source-receptor modeling techniques used are the positive matrix factorization (PMF) and potential source contribution function (PSCF). This new study is intended as a comparison to a previous study of the 1988–1995 Underhill data that successfully revealed a total of 11 types of emission sources with significant contributions to this rural site. This new study has identified a total of nine sources: nitrate-rich secondary aerosol, wood smoke, East Coast oil combustion, automobile emission, metal working, soil/dust, sulfur-rich aerosol type I, sulfur-rich aerosol type II, and sea salt/road salt. Furthermore, the mass contributions from the PMF identified sources that correspond with sampling days with either good or poor visibility were analyzed to seek possible correlations. It has been shown that sulfur-rich aerosol type I, nitrate aerosol, and automobile emission are the most important contributors to visibility degradation. Soil/dust and sea salt/road salt also have an added effect.     

Identification of Sources Contributing to Mid-Atlantic Regional Aerosol

Abstract

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

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

Sources of fine particulate species in ambient air over lake Champlain Basin, VT

This study is a part of an ongoing investigation of the types and locations of emission sources that contribute fine particulate air contaminants to Underhill, VT. The air quality monitoring data used for this study are from the Interagency Monitoring of Protected Visual Environments network for the period of 2001-2003 for the Underhill site. The main source-receptor modeling techniques used are the positive matrix factorization (PMF) and potential source contribution function (PSCF). This new study is intended as a comparison to a previous study of the 1988-1995 Underhill data that successfully revealed a total of 11 types of emission sources with significant contributions to this rural site. This new study has identified a total of nine sources: nitrate-rich secondary aerosol, wood smoke, East Coast oil combustion, automobile emission, metal working, soil/dust, sulfur-rich aerosol type I, sulfur-rich aerosol type II, and sea salt/road salt. Furthermore, the mass contributions from the PMF identified sources that correspond with sampling days with either good or poor visibility were analyzed to seek possible correlations. It has been shown that sulfur-rich aerosol type I, nitrate aerosol, and automobile emission are the most important contributors to visibility degradation. Soil/dust and sea salt/road salt also have an added effect.     

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.     

Causes of Haze in the Columbia River Gorge

Abstract

Visibility impairment in the Columbia River Gorge National Scenic Area is an area of concern. A field study conducted from July 2003 to February 2005 was followed by data analysis and receptor modeling to better understand the temporal and spatial patterns of haze and the sources contributing to the haze in the Columbia River Gorge in the states of Washington and Oregon. The nephelometer light scattering and surface meteorological data at eight sites along the gorge showed five distinct wind patterns, each with its characteristic diurnal and spatial patterns in light scattering by particles (b_sp). In summer, winds were nearly always from west to east (upgorge) and showed decreasing b_sp with distance into the gorge and a pronounced effect of the Portland, OR, metropolitan area on haze, especially in the western portions of the gorge. Winter often had winds from the east with very high levels of b_sp, especially at the eastern gorge sites, with sources east of the gorge responsible for much of the haze. The major chemical components responsible for haze were organic carbon, sulfate, and nitrate. Positive matrix factorization (PMF) using chemically speciated Inter-agency Monitoring of Protected Visual Environments data indicated seven source factors in the western gorge and five factors in the eastern gorge. Organic mass is a large contributor to haze in the gorge in all seasons, with a peak in fall. The PMF analysis suggests that approximately half of the organic mass is biomass smoke, with mobile sources as the second largest contributor. PMF analysis showed nitrates (important in fall and winter) mainly attributed to a generic secondary nitrate factor, with the next largest contributor being oil combustion at Mt. Zion, WA and mobile sources at Wishram, WA. Sulfate is a significant contributor in all seasons, with peak sulfate concentrations in summer.     

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

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

Source 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.     

Causes of haze in the Columbia River Gorge

Visibility impairment in the Columbia River Gorge National Scenic Area is an area of concern. A field study conducted from July 2003 to February 2005 was followed by data analysis and receptor modeling to better understand the temporal and spatial patterns of haze and the sources contributing to the haze in the Columbia River Gorge in the states of Washington and Oregon. The nephelometer light scattering and surface meteorological data at eight sites along the gorge showed five distinct wind patterns, each with its characteristic diurnal and spatial patterns in light scattering by particles (bsp). In summer, winds were nearly always from west to east (upgorge) and showed decreasing bsp with distance into the gorge and a pronounced effect of the Portland, OR, metropolitan area on haze, especially in the western portions of the gorge. Winter often had winds from the east with very high levels of bsp, especially at the eastern gorge sites, with sources east of the gorge responsible for much of the haze. The major chemical components responsible for haze were organic carbon, sulfate, and nitrate. Positive matrix factorization (PMF) using chemically speciated Interagency Monitoring of Protected Visual Environments data indicated seven source factors in the western gorge and five factors in the eastern gorge. Organic mass is a large contributor to haze in the gorge in all seasons, with a peak in fall. The PMF analysis suggests that approximately half of the organic mass is biomass smoke, with mobile sources as the second largest contributor. PMF analysis showed nitrates (important in fall and winter) mainly attributed to a generic secondary nitrate factor, with the next largest contributor being oil combustion at Mt. Zion, WA and mobile sources at Wishram, WA. Sulfate is a significant contributor in all seasons, with peak sulfate concentrations in summer.     

Coarse Particulate Matter Apportionment around a Steel Smelter Plant

Abstract

The results from a study carried out in the urban area of Genoa, Italy, where a large steel smelter recently shut down are presented. We had the opportunity to sample particulate matter (PM) before and after plant closure and, therefore, to measure the changes in concentration and composition of PM₁₀ (atmospheric PM with aerodynamic diameter <10 µm). Elemental concentrations of Na to Pb were obtained through energy dispersive X-ray fluorescence (ED-XRF), and the contributions of specific sources of PM₁₀ were calculated by positive matrix factorization (PMF). The PM₁₀ average concentration turned out to be surprisingly similar before and after closing of the smelter. Nevertheless, the comparison among data collected in the two periods (plant operating and closed), even with the limited information provided by ED-XRF, allowed us to single out two sources of PM related to the smelter activities, to extract their emission profile, and to quantify the impact of the plant on PM₁₀ levels.     

Identification of sources contributing to Mid-Atlantic regional aerosol

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

Comparison between sample-species specific uncertainties and estimated uncertainties for the source apportionment of the speciation trends network data

In order to use the US Environmental Protection Agency's speciation trends networks (STN) data in source apportionment studies with positive matrix factorization (PMF), uncertainties for each of the measured data points are required. Since STN data were not accompanied by sample-species specific uncertainties (SSU) prior to July 2003, a comprehensive set of fractional uncertainties was estimated by Kim et al. [2005. Estimation of organic carbon blank values and error structures of the speciation trends network data for source apportionments. Journal of Air and Waste Management Association 55, 1190–1199]. The objective of this study is to compare the use of the estimated fractional uncertainties (EFU) for the source apportionment of PM_2.5 (particulate matter less than 2.5 μm in aerodynamic diameter) measured at the STN monitoring sites with the results obtained using SSU. Thus, the source apportionment of STN PM_2.5 data were performed and their contributions were estimated through the application of PMF for two selected STN sites, Elizabeth, NJ and Baltimore, MD with both SSU and EFU for the elements measured by X-ray fluorescence. The PMF resolved factor profiles and contributions using EFU were similar to those using SSU at both monitoring sites. The comparisons of normalized concentrations indicated that the STN SSU were not well estimated. This study supports the use of EFU for the STN samples to provide useful error structure for the source apportionment studies of the STN data.     

Evaluation of model simulated atmospheric constituents with observations in the factor projected space: CMAQ simulations of SEARCH measurements

Two-year CMAQ simulations of gases and aerosols over the southeast are evaluated using SEARCH observations for 2000 and 2001, both by direct comparison to observations and by projecting both datasets to the factor space using the Positive Matrix Factorization (PMF) model. Model performance for secondary species (sulfate, ozone) is generally better than for primary species (EC, CO). Nitrate concentrations are overestimated, mainly due to wintertime over-partitioning to the particulate phase. Projecting both observed and simulated constituents to the factor space using PMF, four common factors are resolved for each surface site (two urban sites and two rural sites). The resolved factors include (1) secondary sulfate, (2) secondary nitrate, (3) a fresh motor vehicle factor characterized by EC, OC, CO, NO and NO_y, and (4) a mixed factor characterized by EC, OC, and CO. Performance for the sulfate and nitrate factors follow that of the corresponding driving species, while the motor vehicle and “mixed” factors exhibit performance corresponding to that of primary species. Comparing observations and CMAQ simulations in the projected space allow for an evaluation of the co-variability between species, an indicator of source impacts. The fact that similar factors were resolved by PMF from both the observations and the CMAQ simulations suggests that temporal processes related to emissions from specific source categories, as well as the subsequent dispersion and reactivity, are well captured by the CMAQ model. The ability to identify additional factors can be enhanced by adding tracer species in CMAQ simulations.     

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.     

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

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

Source 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.     

PM2.5 source apportionment: reconciling receptor models for U.S. nonurban and urban long-term networks

Chemical mass balance (CMB) and trajectory receptor models were applied to speciated particulate matter with aerodynamic diameter < or =2.5 microm (PM2.5) measurements from Speciation Trends Network (STN; part of the Chemical Speciation Network [CSN]) and Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring network across the state of Minnesota as part of the Minnesota PM2.5 Source Apportionment Study (MPSAS). CMB equations were solved by the Unmix, positive matrix factorization (PMF), and effective variance (EV) methods, giving collective source contribution and uncertainty estimates. Geological source profiles developed from local dust materials were either incorporated into the EV-CMB model or used to verify factors derived from Unmix and PMF. Common sources include soil dust, calcium (Ca)-rich dust, diesel and gasoline vehicle exhausts, biomass burning, secondary sulfate, and secondary nitrate. Secondary sulfate and nitrate aerosols dominate PM2.5 mass (50-69%). Mobile sources outweigh area sources at urban sites, and vice versa at rural sites due to traffic emissions. Gasoline and diesel contributions can be separated using data from the STN, despite significant uncertainties. Major differences between MPSAS and earlier studies on similar environments appear to be the type and magnitude of stationary sources, but these sources are generally minor (<7%) in this and other studies. Ensemble back-trajectory analysis shows that the lower Midwestern states are the predominant source region for secondary ammoniated sulfate in Minnesota. It also suggests substantial contributions of biomass burning and soil dust from out-of-state on occasions, although a quantitative separation of local and regional contributions was not achieved in the current study. Supplemental materials are available for this article. Go to the publisher's online edition of the Journal of the Air & Waste Management Association for a summary of input data, Unmix and PMF factor profiles, and additional maps.