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Armistead G Russell - One of the best experts on this subject based on the ideXlab platform.
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spatial and temporal extension of a novel hybrid Source Apportionment model
2014Co-Authors: Cesunica Ivey, Heather A Holmes, Yongtao Hu, James A Mulholland, Armistead G RussellAbstract:Exposure assessment and development of control strategies are limited by the air pollutants measured and the spatial and temporal resolution of the observations. Air quality modeling can provide more comprehensive estimates of the temporal and spatial variation of pollutant concentrations, however with significant uncertainties. Source Apportionment, which can be conducted as part of the air quality modeling, provides estimates of the impacts of Sources on the mixtures of pollutants and contains surrogate estimates for pollutants that are not measured. This study details results using a novel spatiotemporal hybrid Source Apportionment method employed with interpolation techniques to quantify the impact of 33 PM2.5 Source categories. The hybrid model, which aims to reduce estimating uncertainties, adjusts original Source impact estimates from a chemical transport model at monitoring sites to closely reflect observed ambient concentrations of measured PM2.5 species. Daily Source impacts are calculated for the contiguous U.S. Two interpolation methods are used to generate the data needed for spatiotemporal hybrid Source Apportionment. Hybrid adjustment factors are spatially interpolated using kriging, and daily observations are calculated by temporally interpolating available monitoring data. Methods are evaluated by comparing daily simulated concentrations—generated by reconstruction of Source impact results—to observed species concentrations from monitors independent of model development. Results also elucidate U.S. regions with relatively higher impacts from specific Sources. Monitoring data in this study originated from the Chemical Speciation Network (CSN), EPA-funded supersites, and the Southeastern Aerosol Research Characterization (SEARCH) Network. Results are to be used in health impact assessments.
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fine particulate matter Source Apportionment using a hybrid chemical transport and receptor model approach
Atmospheric Chemistry and Physics, 2013Co-Authors: Sivarama Alachandra, Cesunica Ivey, James A Mulholland, Jorge E Pacho, Jaemee Aek, Heathe A Holmes, Mehme T Odma, Armistead G RussellAbstract:A hybrid fine particulate matter (PM 2.5) Source Apportionment approach based on a receptor model (RM) species balance and species specific Source impacts from a chemical transport model (CTM) equipped with a sensi- tivity analysis tool is developed to provide physically and chemically consistent relationships between Source emis- sions and receptor impacts. This hybrid approach enhances RM results by providing initial estimates of Source impacts from a much larger number of Sources than are typically used in RMs, and provides Source-receptor relationships for secondary species. Further, the method addresses issues of Source collinearities and accounts for emissions uncertain- ties. We apply this hybrid approach to conduct PM2.5 Source Apportionment at Chemical Speciation Network (CSN) sites across the US. Ambient PM2.5 concentrations at these re- ceptor sites were apportioned to 33 separate Sources. Hybrid method results led to large changes of impacts from CTM estimates for Sources such as dust, woodstoves, and other biomass-burning Sources, but limited changes to others. The refinements reduced the differences between CTM-simulated and observed concentrations of individual PM2.5 species by over 98 % when using a weighted least-squares error mini- mization. The rankings of Source impacts changed from the initial estimates, further demonstrating that CTM-only re- sults should be evaluated with observations. Assessment with RM results at six US locations showed that the hybrid results differ somewhat from commonly resolved Sources. The hy- brid method also resolved Sources that typical RM methods do not capture without extra measurement information for unique tracers. The method can be readily applied to large domains and long (such as multi-annual) time periods to pro- vide Source impact estimates for management- and health- related studies.
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tools for improving air quality management a review of top down Source Apportionment techniques and their application in developing countries
2011Co-Authors: Todd Johnson, Armistead G Russell, John G Watson, Sarath K Guttikunda, Gary J Wells, Paulo Artaxo, Tami C Bond, Jason J WestAbstract:Building an effective air quality management system (AQMS) requires a process of continual improvement, and the Source Apportionment techniques described in this report can contribute in a cost effective manner to improving existing systems or even as the first step to begin an AQMS. This is good news for many developing country cities where the combination of rapid growth, dirty fuels, and old and polluting technologies are overwhelming the capacities of cities to control air pollution. For these cities, Source Apportionment offers policymakers practical tools for identifying and quantifying the different Sources of air pollution, and thereby increasing the ability to put in place effective policy measures to reduce air pollution to acceptable levels. This report arises from a concern over the lack of objective and scientifically-based information on the contributions of different Sources of air pollution, especially for fi ne particulate matter (PM), in developing countries. PM is the air pollutant of most concern for adverse health effects, and in urban areas alone accounts for approximately 800,000 premature deaths worldwide each year.
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Source Apportionment of pm2 5 comparing pmf and cmb results for four ambient monitoring sites in the southeastern united states
Atmospheric Environment, 2008Co-Authors: Yuhang Wang, Armistead G Russell, Eric S EdgertonAbstract:Two commonly used receptor models, positive matrix factorization (PMF) and chemical mass balance (CMB), are applied to 3-year PM2.5 data at two urban sites (Atlanta, GA and Birmingham, AL) and two rural sites (Yorkville, GA and Centreville, AL). Source Apportionment results using the two receptor models are analyzed and compared. Both models are able to identify major Sources at all sites, though the degree of agreements and correlations between Source impacts estimated by PMF and CMB varies depending on Sources and receptor sites. Estimated contributions of secondary inorganic particles are the most comparable and highly correlated. The lesser comparability and correlations of estimated contributions of other Sources (mostly primary) may be attributed to several factors. Resolved Source profiles in PMF have more processed (or aged) characteristics resulting in part from atmospheric mixing and condensation of oxidized compounds, whereas Source profiles used in CMB are obtained from measurements of emission Sources with minimum amount of atmospheric processing. The PMF profiles vary from site to site; both atmospheric processing and local Source variability contribute. In comparison, the CMB profiles obtained from a limited number of emission measurements may not be locally representative even if they are regionally representative. The omission of possible known or unknown Sources due to lack of proper Source profiles or proper ‘‘marker’’ species may also cause the differences in the Source Apportionment results. In addition, the implication for PM time-series health study is discussed based on the results from this study. r 2008 Elsevier Ltd. All rights reserved.
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fine particle Sources and cardiorespiratory morbidity an application of chemical mass balance and factor analytical Source Apportionment methods
Environmental Health Perspectives, 2008Co-Authors: Jeremy A Sarnat, Armistead G Russell, James A Mulholland, Philip K Hopke, Eugene Kim, Amit Marmur, Mitchel Klein, Stefanie Ebelt Sarnat, Paige E TolbertAbstract:Recent interest in the health effects of particulate matter (PM) has focused on identifying Sources of PM that pose the greatest health risks. Because it is likely that not all PM is equally toxic, epidemiologic models that incorporate Source-resolved PM may provide a step toward targeting the most important causal agents and refining traditional mass-based PM standards. Quantifying health risks associated with Sources such as biomass burning, power plants, gasoline and diesel emissions, rather than individual pollutants, may also capture complex multipollutant interactions that more accurately reflect the etiologic relationships between PM and adverse health. Few epidemiologic studies, however, have included Source-Apportionment data in their examinations of PM health effects (Ito et al. 2006; Laden et al. 2000; Mar et al. 2000, 2006; Ozkaynak and Thurston 1987; Schreuder et al. 2006). The limited application of Source Apportionment may be attributable partly to uncertainties regarding optimal methods for conducting PM Source Apportionment, as well as the lack of suitable air quality data for analysis. Source-Apportionment methods used in previous studies have generally relied on factor analytic approaches. For example, Laden et al. (2000) grouped elemental PM concentrations from six U.S. cities into a small numbers of categories, or “factors” (Laden et al. 2000). Significant associations were found between mortality and the traffic and coal combustion factors, with the largest effect size for the traffic factor. No significant associations were observed for the oil and soil factors. A series of recent analyses examined the associations between Source-resolved PM estimates and mortality in Washington, DC, and Phoenix, Arizona, using several different multivariate factor analytic methods in each city (Hopke et al. 2006; Ito et al. 2006; Mar et al. 2006; Thurston et al. 2005). In these analyses, Source Apportionment was conducted on samples collected twice a week, using absolute principal components analysis (PCA), UNMIX (a multivariate receptor model), and positive matrix factorization (PMF). Results showed that variability among the methods was small when compared with overall Source-Apportionment model uncertainty, and suggested that these Apportionment methods may be useful in discerning Source-specific health effects. The authors note the relatively limited sample size for these data sets and their inability to robustly identify certain Source categories (e.g., specific mobile Source types). Questions also remain concerning the generalizability of these findings to other locations with different aerosol compositions, the marginal benefit of using Source-apportioned data over single-species tracers, and whether analyses using other Source-Apportionment methods, notably chemical mass balance (CMB), will show the same pattern of agreement. Here we present and compare results from epidemiologic analyses of emergency department (ED) visits and Source-resolved PM2.5 (PM with aerodynamic diameter ≤ 2.5 μm; fine PM) obtained using PMF, modified CMB, and a single-species tracer approach. This analysis is the first to compare epidemiologic findings generated using both factor analysis and mass balance Source-Apportionment methods. The data used in this analysis were collected in Atlanta, Georgia, a unique location for conducting this type of health-effects study given the existence of an extensive time-series of daily speciated PM2.5 measurements and corresponding hospital records. These data have been previously characterized in several Source-Apportionment and epidemiologic analyses (Kim et al. 2004; Marmur et al. 2005; Metzger et al. 2004; Peel et al. 2005). We compare results across methods and assess the robustness of health risk estimates for cardiopulmonary ED visits. The implications of using one or several methods for understanding the Sources of PM2.5-mediated health risks are also addressed.
Philip K Hopke - One of the best experts on this subject based on the ideXlab platform.
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Source Apportionment of size resolved particulate matter at a european air pollution hot spot
Science of The Total Environment, 2015Co-Authors: Petra Pokorna, Jan Hovorka, Miroslav Klan, Philip K HopkeAbstract:Positive Matrix Factorization-PMF was applied to hourly resolved elemental composition of fine (PM0.15-1.15) and coarse (PM1.15-10) aerosol particles to apportion their Sources in the airshed of residential district, Ostrava-Radvanice and Bartovice in winter 2012. Multiple-site measurement by PM2.5 monitors complements the Source Apportionment. As there were no statistical significant differences amongst the monitors, the Source Apportionment derived for the central site data is expected to apply to whole residential district. The apportioned Sources of the fine aerosol particles were coal combustion (58.6%), sinter production-hot phase (22.9%), traffic (15%), raw iron production (3.5%), and desulfurization slag processing (<0.5%) whilst road dust (47.3%), sinter production-cold phase (27.7%), coal combustion (16.8%), and raw iron production (8.2%) were resolved being Sources of the coarse aerosol particles. The shape and elemental composition of size-segregated aerosol airborne-sampled by an airship aloft presumed air pollution Sources helped to interpret the PMF solution.
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chapter 1 theory and application of atmospheric Source Apportionment
Developments in environmental science, 2009Co-Authors: Philip K HopkeAbstract:Abstract Source Apportionment is the estimation of the contributions to the airborne concentrations that arise from the emissions of natural and anthropogenic Sources. To obtain a Source Apportionment, data analysis tools called receptor models are applied to elicit information on the Sources of air pollutants from the measured constituent concentrations. Typically, they use the chemical composition data for airborne particulate matter samples. In such cases, the outcome is the identification of the pollution Source types and estimates of the contribution of each Source type to the observed concentrations. It can also involve efforts to identify the locations of the Sources through the use of ensembles of air parcel back trajectories. In recent years, there have been improvements in the factor analysis methods that are applied in receptor modeling, as well as easier application of trajectory methods. These developments are reviewed and typical applications to data from national parks, wilderness, and other Class 1 visibility locations in the United States are presented in this chapter.
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fine particle Sources and cardiorespiratory morbidity an application of chemical mass balance and factor analytical Source Apportionment methods
Environmental Health Perspectives, 2008Co-Authors: Jeremy A Sarnat, Armistead G Russell, James A Mulholland, Philip K Hopke, Eugene Kim, Amit Marmur, Mitchel Klein, Stefanie Ebelt Sarnat, Paige E TolbertAbstract:Recent interest in the health effects of particulate matter (PM) has focused on identifying Sources of PM that pose the greatest health risks. Because it is likely that not all PM is equally toxic, epidemiologic models that incorporate Source-resolved PM may provide a step toward targeting the most important causal agents and refining traditional mass-based PM standards. Quantifying health risks associated with Sources such as biomass burning, power plants, gasoline and diesel emissions, rather than individual pollutants, may also capture complex multipollutant interactions that more accurately reflect the etiologic relationships between PM and adverse health. Few epidemiologic studies, however, have included Source-Apportionment data in their examinations of PM health effects (Ito et al. 2006; Laden et al. 2000; Mar et al. 2000, 2006; Ozkaynak and Thurston 1987; Schreuder et al. 2006). The limited application of Source Apportionment may be attributable partly to uncertainties regarding optimal methods for conducting PM Source Apportionment, as well as the lack of suitable air quality data for analysis. Source-Apportionment methods used in previous studies have generally relied on factor analytic approaches. For example, Laden et al. (2000) grouped elemental PM concentrations from six U.S. cities into a small numbers of categories, or “factors” (Laden et al. 2000). Significant associations were found between mortality and the traffic and coal combustion factors, with the largest effect size for the traffic factor. No significant associations were observed for the oil and soil factors. A series of recent analyses examined the associations between Source-resolved PM estimates and mortality in Washington, DC, and Phoenix, Arizona, using several different multivariate factor analytic methods in each city (Hopke et al. 2006; Ito et al. 2006; Mar et al. 2006; Thurston et al. 2005). In these analyses, Source Apportionment was conducted on samples collected twice a week, using absolute principal components analysis (PCA), UNMIX (a multivariate receptor model), and positive matrix factorization (PMF). Results showed that variability among the methods was small when compared with overall Source-Apportionment model uncertainty, and suggested that these Apportionment methods may be useful in discerning Source-specific health effects. The authors note the relatively limited sample size for these data sets and their inability to robustly identify certain Source categories (e.g., specific mobile Source types). Questions also remain concerning the generalizability of these findings to other locations with different aerosol compositions, the marginal benefit of using Source-apportioned data over single-species tracers, and whether analyses using other Source-Apportionment methods, notably chemical mass balance (CMB), will show the same pattern of agreement. Here we present and compare results from epidemiologic analyses of emergency department (ED) visits and Source-resolved PM2.5 (PM with aerodynamic diameter ≤ 2.5 μm; fine PM) obtained using PMF, modified CMB, and a single-species tracer approach. This analysis is the first to compare epidemiologic findings generated using both factor analysis and mass balance Source-Apportionment methods. The data used in this analysis were collected in Atlanta, Georgia, a unique location for conducting this type of health-effects study given the existence of an extensive time-series of daily speciated PM2.5 measurements and corresponding hospital records. These data have been previously characterized in several Source-Apportionment and epidemiologic analyses (Kim et al. 2004; Marmur et al. 2005; Metzger et al. 2004; Peel et al. 2005). We compare results across methods and assess the robustness of health risk estimates for cardiopulmonary ED visits. The implications of using one or several methods for understanding the Sources of PM2.5-mediated health risks are also addressed.
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fine particle Sources and cardiorespiratory morbidity an application of chemical mass balance and factor analytical Source Apportionment methods
Environmental Health Perspectives, 2008Co-Authors: Jeremy A Sarnat, Armistead G Russell, James A Mulholland, Philip K Hopke, Amit Marmur, Mitchel Klein, Stefanie Ebelt Sarnat, Paige E TolbertAbstract:Recent interest in the health effects of particulate matter (PM) has focused on identifying Sources of PM that pose the greatest health risks. Because it is likely that not all PM is equally toxic, epidemiologic models that incorporate Source-resolved PM may provide a step toward targeting the most important causal agents and refining traditional mass-based PM standards. Quantifying health risks associated with Sources such as biomass burning, power plants, gasoline and diesel emissions, rather than individual pollutants, may also capture complex multipollutant interactions that more accurately reflect the etiologic relationships between PM and adverse health. Few epidemiologic studies, however, have included Source-Apportionment data in their examinations of PM health effects (Ito et al. 2006; Laden et al. 2000; Mar et al. 2000, 2006; Ozkaynak and Thurston 1987; Schreuder et al. 2006). The limited application of Source Apportionment may be attributable partly to uncertainties regarding optimal methods for conducting PM Source Apportionment, as well as the lack of suitable air quality data for analysis. Source-Apportionment methods used in previous studies have generally relied on factor analytic approaches. For example, Laden et al. (2000) grouped elemental PM concentrations from six U.S. cities into a small numbers of categories, or “factors” (Laden et al. 2000). Significant associations were found between mortality and the traffic and coal combustion factors, with the largest effect size for the traffic factor. No significant associations were observed for the oil and soil factors. A series of recent analyses examined the associations between Source-resolved PM estimates and mortality in Washington, DC, and Phoenix, Arizona, using several different multivariate factor analytic methods in each city (Hopke et al. 2006; Ito et al. 2006; Mar et al. 2006; Thurston et al. 2005). In these analyses, Source Apportionment was conducted on samples collected twice a week, using absolute principal components analysis (PCA), UNMIX (a multivariate receptor model), and positive matrix factorization (PMF). Results showed that variability among the methods was small when compared with overall Source-Apportionment model uncertainty, and suggested that these Apportionment methods may be useful in discerning Source-specific health effects. The authors note the relatively limited sample size for these data sets and their inability to robustly identify certain Source categories (e.g., specific mobile Source types). Questions also remain concerning the generalizability of these findings to other locations with different aerosol compositions, the marginal benefit of using Source-apportioned data over single-species tracers, and whether analyses using other Source-Apportionment methods, notably chemical mass balance (CMB), will show the same pattern of agreement. Here we present and compare results from epidemiologic analyses of emergency department (ED) visits and Source-resolved PM2.5 (PM with aerodynamic diameter ≤ 2.5 μm; fine PM) obtained using PMF, modified CMB, and a single-species tracer approach. This analysis is the first to compare epidemiologic findings generated using both factor analysis and mass balance Source-Apportionment methods. The data used in this analysis were collected in Atlanta, Georgia, a unique location for conducting this type of health-effects study given the existence of an extensive time-series of daily speciated PM2.5 measurements and corresponding hospital records. These data have been previously characterized in several Source-Apportionment and epidemiologic analyses (Kim et al. 2004; Marmur et al. 2005; Metzger et al. 2004; Peel et al. 2005). We compare results across methods and assess the robustness of health risk estimates for cardiopulmonary ED visits. The implications of using one or several methods for understanding the Sources of PM2.5-mediated health risks are also addressed.
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comparison between sample species specific uncertainties and estimated uncertainties for the Source Apportionment of the speciation trends network data
Atmospheric Environment, 2007Co-Authors: Eugene Kim, Philip K HopkeAbstract:Abstract 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.
Timothy V. Larson - One of the best experts on this subject based on the ideXlab platform.
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Source Apportionment of environmental combustion Sources using excitation emission matrix fluorescence spectroscopy and machine learning
Atmospheric Environment, 2021Co-Authors: Jay Rutherford, Timothy V. Larson, Timothy Gould, Edmund Seto, Igor Novosselov, Jonathan D PosnerAbstract:Abstract The link between particulate matter (PM) air pollution and negative health effects is well-established. Air pollution was estimated to cause 4.9 million deaths in 2017 and PM was responsible for 94% of these deaths. In order to inform effective mitigation strategies in the future, further study of PM and its health effects is important. Here, we present a method for identifying Sources of combustion generated PM using excitation-emission matrix (EEM) fluorescence spectroscopy and machine learning (ML) algorithms. PM samples were collected during a health effects exposure assessment panel study in Seattle. We use archived field samples from the exposure study and the associated positive matrix factorization (PMF) Source Apportionment based on X-ray fluorescence and light absorbing carbon measurements to train convolutional neural network and principal component regression algorithms. We show EEM spectra from cyclohexane extracts of the archived filter samples can be used to accurately apportion mobile and vegetative burning Sources but were unable to detect crustal dust, Cl-rich, secondary sulfate and fuel oil Sources. The use of this EEM-ML approach may be used to conduct PM exposure studies that include Source Apportionment of combustion Sources.
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Source Apportionment of pm2 5 and selected hazardous air pollutants in seattle
Science of The Total Environment, 2007Co-Authors: Timothy V. Larson, John Williamson, Hal Westberg, L Sally J LiuAbstract:The potential benefits of combining the speciated PM(2.5) and VOCs data in Source Apportionment analysis for identification of additional Sources remain unclear. We analyzed the speciated PM(2.5) and VOCs data collected at the Beacon Hill in Seattle, WA between 2000 and 2004 with the Multilinear Engine (ME-2) to quantify Source contributions to the mixture of hazardous air pollutants (HAPs). We used the 'missing mass', defined as the concentration of the measured total particle mass minus the sum of all analyzed species, as an additional variable and implemented an auxiliary equation to constrain the sum of all species mass fractions to be 100%. Regardless of whether the above constraint was implemented and/or the additional VOCs data were included with the PM(2.5) data, the models identified that wood burning (24%-31%), secondary sulfate (20%-24%) and secondary nitrate (15%-20%) were the main contributors to PM(2.5). Using only PM(2.5) data, the model distinguished two diesel features with the 100% constraint, but identified only one diesel feature without the constraint. When both PM(2.5) and VOCs data were used, one additional feature was identified as the major contributor (26%) to total VOC mass. Adding VOCs data to the speciated PM(2.5) data in Source Apportionment modeling resulted in more accurate Source contribution estimates for combustion related Sources as evidenced by the less 'missing mass' percentage in PM(2.5). Using the Source contribution estimates, we evaluated the validity of using black carbon (BC) as a surrogate for diesel exhaust. We found that BC measured with an aethalometer at 370 nm and 880 nm had reasonable correlations with the estimated concentrations of diesel particulate matters (r>0.7), as well as with the estimated concentrations of wood burning particles during the heating seasons (r=0.56-0.66). This indicates that the BC is not a unique tracer for either Source. The difference in BC between 370 and 880 nm, however, correlated well exclusively with the estimated wood smoke Source (r=0.59) and may be used to separate wood smoke from diesel exhaust. Thus, when multiple BC related Sources exist in the same monitoring environment, additional data processing or modeling of the BC measurements is needed before these measurements could be used to represent the diesel exhaust.
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PM Source Apportionment and health effects: 2. An investigation of intermethod variability in associations between Source-apportioned fine particle mass and daily mortality in Washington, DC
Journal of Exposure Science & Environmental Epidemiology, 2006Co-Authors: William F Christensen, Timothy V. Larson, Delbert J. Eatough, Ronald C. Henry, Francine Laden, Ramona Lall, Lucas Neas, Philip K Hopke, George D ThurstonAbstract:Source Apportionment may be useful in epidemiological investigation of PM health effects, but variations and options in these methods leave uncertainties. An EPA-sponsored workshop investigated Source Apportionment and health effects analyses by examining the associations between daily mortality and the investigators' estimated Source-apportioned PM_2.5 for Washington, DC for 1988–1997. A Poisson Generalized Linear Model (GLM) was used to estimate Source-specific relative risks at lags 0–4 days for total non-accidental, cardiovascular, and cardiorespiratory mortality adjusting for weather, seasonal/temporal trends, and day-of-week. Source-related effect estimates and their lagged association patterns were similar across investigators/methods. The varying lag structure of associations across Source types, combined with the Wednesday/Saturday sampling frequency made it difficult to compare the Source-specific effect sizes in a simple manner. The largest (and most significant) percent excess deaths per 5–95^th percentile increment of apportioned PM_2.5 for total mortality was for secondary sulfate (variance-weighted mean percent excess mortality=6.7% (95% CI: 1.7, 11.7)), but with a peculiar lag structure (lag 3 day). Primary coal-related PM_2.5 (only three teams) was similarly significantly associated with total mortality with the same 3-day lag as sulfate. Risk estimates for traffic-related PM_2.5, while significant in some cases, were more variable. Soil-related PM showed smaller effect size estimates, but they were more consistently positive at multiple lags. The cardiovascular and cardiorespiratory mortality associations were generally similar to those for total mortality. Alternative weather models generally gave similar patterns, but sometimes affected the lag structure (e.g., for sulfate). Overall, the variations in relative risks across investigators/methods were found to be much smaller than those across estimated Source types or across lag days for these data. This consistency suggests the robustness of the Source Apportionment in health effects analyses, but remaining issues, including accuracy of Source Apportionment and Source-specific sensitivity to weather models, need to be investigated.
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pm Source Apportionment and health effects 1 intercomparison of Source Apportionment results
Journal of Exposure Science and Environmental Epidemiology, 2006Co-Authors: Philip K Hopke, William F Christensen, Delbert J. Eatough, Ronald C. Henry, Francine Laden, Ramona Lall, Kazuhiko Ito, Therese F Mar, Eugene Kim, Timothy V. LarsonAbstract:During the past three decades, receptor models have been used to identify and apportion ambient concentrations to Sources. A number of groups are employing these methods to provide input into air quality management planning. A workshop has explored the use of resolved Source contributions in health effects models. Multiple groups have analyzed particulate composition data sets from Washington, DC and Phoenix, AZ. Similar Source profiles were extracted from these data sets by the investigators using different factor analysis methods. There was good agreement among the major resolved Source types. Crustal (soil), sulfate, oil, and salt were the Sources that were most unambiguously identified (generally highest correlation across the sites). Traffic and vegetative burning showed considerable variability among the results with variability in the ability of the methods to partition the motor vehicle contributions between gasoline and diesel vehicles. However, if the total motor vehicle contributions are estimated, good correspondence was obtained among the results. The Source impacts were especially similar across various analyses for the larger mass contributors (e.g., in Washington, secondary sulfate SE=7% and 11% for traffic; in Phoenix, secondary sulfate SE=17% and 7% for traffic). Especially important for time-series health effects assessment, the Source-specific impacts were found to be highly correlated across analysis methods/researchers for the major components (e.g., mean analysis to analysis correlation, r>0.9 for traffic and secondary sulfates in Phoenix and for traffic and secondary nitrates in Washington. The sulfate mean r value is >0.75 in Washington.). Overall, although these intercomparisons suggest areas where further research is needed (e.g., better division of traffic emissions between diesel and gasoline vehicles), they provide support the contention that PM(2.5) mass Source Apportionment results are consistent across users and methods, and that today's Source Apportionment methods are robust enough for application to PM(2.5) health effects assessments.
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workgroup report workshop on Source Apportionment of particulate matter health effects intercomparison of results and implications
Environmental Health Perspectives, 2005Co-Authors: George D Thurston, William F Christensen, Delbert J. Eatough, Ronald C. Henry, Francine Laden, Ramona Lall, Kazuhiko Ito, Therese F Mar, Eugene Kim, Timothy V. LarsonAbstract:Although the association between exposure to ambient fine particulate matter with aerodynamic diameter < 2.5 µm (PM2.5) and human mortality is well established, the most responsible particle types/Sources are not yet certain. In May 2003, the U.S. Environmental Protection Agency’s Particulate Matter Centers Program sponsored the Workshop on the Source Apportionment of PM Health Effects. The goal was to evaluate the consistency of the various Source Apportionment methods in assessing Source contributions to daily PM2.5 mass‐mortality associations. Seven research institutions, using varying methods, participated in the estimation of Source Apportionments of PM2.5 mass samples collected in Washington, DC, and Phoenix, Arizona, USA. Apportionments were evaluated for their respective associations with mortality using Poisson regressions, allowing a comparative assessment of the extent to which variations in the Apportionments contributed to variability in the Source-specific mortality results. The various research groups generally identified the same major Source types, each with similar elemental makeups. Intergroup correlation analyses indicated that soil-, sulfate-, residual oil-, and salt-associated mass were most unambiguously identified by various methods, whereas vegetative burning and traffic were less consistent. Aggregate Source-specific mortality relative risk (RR) estimate confidence intervals overlapped each other, but the sulfate-related PM2.5 component was most consistently significant across analyses in these cities. Analyses indicated that Source types were a significant predictor of RR, whereas Apportionment group differences were not. Variations in the Source Apportionments added only some 15% to the mortality regression uncertainties. These results provide supportive evidence that existing PM2.5 Source Apportionment methods can be used to derive
Francesco Canonaco - One of the best experts on this subject based on the ideXlab platform.
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characterization and Source Apportionment of organic aerosol using offline aerosol mass spectrometry
Atmospheric Measurement Techniques, 2016Co-Authors: Kaspar R Daellenbach, Francesco Canonaco, Monica Crippa, Carlo Bozzetti, Adela Křepelova, R Wolf, Peter Zotter, P Fermo, Jay G SlowikAbstract:Abstract. Field deployments of the Aerodyne Aerosol Mass Spectrometer (AMS) have significantly advanced real-time measurements and Source Apportionment of non-refractory particulate matter. However, the cost and complex maintenance requirements of the AMS make its deployment at sufficient sites to determine regional characteristics impractical. Furthermore, the negligible transmission efficiency of the AMS inlet for supermicron particles significantly limits the characterization of their chemical nature and contributing Sources. In this study, we utilize the AMS to characterize the water-soluble organic fingerprint of ambient particles collected onto conventional quartz filters, which are routinely sampled at many air quality sites. The method was applied to 256 particulate matter (PM) filter samples (PM1, PM2.5, and PM10, i.e., PM with aerodynamic diameters smaller than 1, 2.5, and 10 µm, respectively), collected at 16 urban and rural sites during summer and winter. We show that the results obtained by the present technique compare well with those from co-located online measurements, e.g., AMS or Aerosol Chemical Speciation Monitor (ACSM). The bulk recoveries of organic aerosol (60–91 %) achieved using this technique, together with low detection limits (0.8 µg of organic aerosol on the analyzed filter fraction) allow its application to environmental samples. We will discuss the recovery variability of individual hydrocarbon ions, ions containing oxygen, and other ions. The performance of such data in Source Apportionment is assessed in comparison to ACSM data. Recoveries of organic components related to different Sources as traffic, wood burning, and secondary organic aerosol are presented. This technique, while subjected to the limitations inherent to filter-based measurements (e.g., filter artifacts and limited time resolution) may be used to enhance the AMS capabilities in measuring size-fractionated, spatially resolved long-term data sets.
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actris acsm intercomparison part 2 intercomparison of me 2 organic Source Apportionment results from 15 individual co located aerosol mass spectrometers
Atmospheric Measurement Techniques, 2015Co-Authors: Roman Frohlich, Olivier Favez, Francesco Canonaco, Vincent Crenn, Ari Setyan, C A Belis, Veronique Riffault, Jay G Slowik, Mikko Aijala, Andres AlastueyAbstract:Abstract. Chemically resolved atmospheric aerosol data sets from the largest intercomparison of the Aerodyne aerosol chemical speciation monitors (ACSMs) performed to date were collected at the French atmospheric supersite SIRTA. In total 13 quadrupole ACSMs (Q-ACSM) from the European ACTRIS ACSM network, one time-of-flight ACSM (ToF-ACSM), and one high-resolution ToF aerosol mass spectrometer (AMS) were operated in parallel for about 3 weeks in November and December~2013. Part 1 of this study reports on the accuracy and precision of the instruments for all the measured species. In this work we report on the intercomparison of organic components and the results from factor analysis Source Apportionment by positive matrix factorisation (PMF) utilising the multilinear engine 2 (ME-2). Except for the organic contribution of mass-to-charge ratio m/z 44 to the total organics (f44), which varied by factors between 0.6 and 1.3 compared to the mean, the peaks in the organic mass spectra were similar among instruments. The m/z 44 differences in the spectra resulted in a variable f44 in the Source profiles extracted by ME-2, but had only a minor influence on the extracted mass contributions of the Sources. The presented Source Apportionment yielded four factors for all 15 instruments: hydrocarbon-like organic aerosol (HOA), cooking-related organic aerosol (COA), biomass burning-related organic aerosol (BBOA) and secondary oxygenated organic aerosol (OOA). ME-2 boundary conditions (profile constraints) were optimised individually by means of correlation to external data in order to achieve equivalent / comparable solutions for all ACSM instruments and the results are discussed together with the investigation of the influence of alternative anchors (reference profiles). A comparison of the ME-2 Source Apportionment output of all 15 instruments resulted in relative standard deviations (SD) from the mean between 13.7 and 22.7 % of the Source's average mass contribution depending on the factors (HOA: 14.3 ± 2.2 %, COA: 15.0 ± 3.4 %, OOA: 41.5 ± 5.7 %, BBOA: 29.3 ± 5.0 %). Factors which tend to be subject to minor factor mixing (in this case COA) have higher relative uncertainties than factors which are recognised more readily like the OOA. Averaged over all factors and instruments the relative first SD from the mean of a Source extracted with ME-2 was 17.2 %.
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organic aerosol components derived from 25 ams data sets across europe using a consistent me 2 based Source Apportionment approach
Atmospheric Chemistry and Physics, 2014Co-Authors: Monica Crippa, Francesco Canonaco, Mikko Aijala, V A Lanz, J D Allan, Samara Carbone, Gerard Capes, Darius Ceburnis, Manuel Dallosto, Douglas A DayAbstract:Abstract. Organic aerosols (OA) represent one of the major constituents of submicron particulate matter (PM1) and comprise a huge variety of compounds emitted by different Sources. Three intensive measurement field campaigns to investigate the aerosol chemical composition all over Europe were carried out within the framework of the European Integrated Project on Aerosol Cloud Climate and Air Quality Interactions (EUCAARI) and the intensive campaigns of European Monitoring and Evaluation Programme (EMEP) during 2008 (May–June and September–October) and 2009 (February–March). In this paper we focus on the identification of the main organic aerosol Sources and we define a standardized methodology to perform Source Apportionment using positive matrix factorization (PMF) with the multilinear engine (ME-2) on Aerodyne aerosol mass spectrometer (AMS) data. Our Source Apportionment procedure is tested and applied on 25 data sets accounting for two urban, several rural and remote and two high altitude sites; therefore it is likely suitable for the treatment of AMS-related ambient data sets. For most of the sites, four organic components are retrieved, improving significantly previous Source Apportionment results where only a separation in primary and secondary OA Sources was possible. Generally, our solutions include two primary OA Sources, i.e. hydrocarbon-like OA (HOA) and biomass burning OA (BBOA) and two secondary OA components, i.e. semi-volatile oxygenated OA (SV-OOA) and low-volatility oxygenated OA (LV-OOA). For specific sites cooking-related (COA) and marine-related Sources (MSA) are also separated. Finally, our work provides a large overview of organic aerosol Sources in Europe and an interesting set of highly time resolved data for modeling purposes.
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sofi an igor based interface for the efficient use of the generalized multilinear engine me 2 for the Source Apportionment me 2 application to aerosol mass spectrometer data
Atmospheric Measurement Techniques, 2013Co-Authors: Francesco Canonaco, Jay G Slowik, Monica Crippa, U Baltensperger, Andre S H PrevotAbstract:Abstract. Source Apportionment using the bilinear model through a multilinear engine (ME-2) was successfully applied to non-refractory organic aerosol (OA) mass spectra collected during the winter of 2011 and 2012 in Zurich, Switzerland using the aerosol chemical speciation monitor (ACSM). Five factors were identified: low-volatility oxygenated OA (LV-OOA), semivolatile oxygenated OA (SV-OOA), hydrocarbon-like OA (HOA), cooking OA (COA) and biomass burning OA (BBOA). A graphical user interface SoFi (Source Finder) was developed at PSI in order to facilitate the testing of different rotational techniques available within the ME-2 engine by providing a priori factor profiles for some or all of the expected factors. ME-2 was used to test the positive matrix factorization (PMF) model, the fully constrained chemical mass balance (CMB) model, and partially constrained models utilizing a values and pulling equations. Within the set of model solutions determined to be environmentally reasonable, BBOA and SV-OOA factor mass spectra and time series showed the greatest variability. This variability represents the uncertainty in the model solution and indicates that analysis of model rotations provides a useful approach for assessing the uncertainty of bilinear Source Apportionment models.
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Primary and secondary organic aerosol origin by combined gas-particle phase Source Apportionment
Atmospheric Chemistry and Physics, 2013Co-Authors: M. Crippa, Francesco Canonaco, J. G. Slowik, I. El Haddad, P. F. Decarlo, C. Mohr, M. F. Heringa, R. Chirico, Nicolas Marchand, B. Temime-rousselAbstract:Secondary organic aerosol (SOA), a prominent fraction of particulate organic mass (OA), remains poorly constrained. Its formation involves several unknown precursors, formation and evolution pathways and multiple natural and anthropogenic Sources. Here a combined gas-particle phase Source Apportionment is applied to wintertime and summertime data collected in the megacity of Paris in order to investigate SOA origin during both seasons. This was possible by combining the information provided by an aerosol mass spectrometer (AMS) and a proton transfer reaction mass spectrometer (PTR-MS). A better constrained Apportionment of primary OA (POA) Sources is also achieved using this methodology, making use of gas-phase tracers. These tracers made possible the discrimination between biogenic and continental/anthropogenic Sources of SOA. We found that continental SOA was dominant during both seasons (24–50% of total OA), while contributions from photochemistry-driven SOA (9% of total OA) and marine emissions (13% of total OA) were also observed during summertime. A semi-volatile nighttime component was also identified (up to 18% of total OA during wintertime). This approach was successfully applied here and implemented in a new Source Apportionment toolkit.
Jay G Slowik - One of the best experts on this subject based on the ideXlab platform.
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characterization and Source Apportionment of organic aerosol using offline aerosol mass spectrometry
Atmospheric Measurement Techniques, 2016Co-Authors: Kaspar R Daellenbach, Francesco Canonaco, Monica Crippa, Carlo Bozzetti, Adela Křepelova, R Wolf, Peter Zotter, P Fermo, Jay G SlowikAbstract:Abstract. Field deployments of the Aerodyne Aerosol Mass Spectrometer (AMS) have significantly advanced real-time measurements and Source Apportionment of non-refractory particulate matter. However, the cost and complex maintenance requirements of the AMS make its deployment at sufficient sites to determine regional characteristics impractical. Furthermore, the negligible transmission efficiency of the AMS inlet for supermicron particles significantly limits the characterization of their chemical nature and contributing Sources. In this study, we utilize the AMS to characterize the water-soluble organic fingerprint of ambient particles collected onto conventional quartz filters, which are routinely sampled at many air quality sites. The method was applied to 256 particulate matter (PM) filter samples (PM1, PM2.5, and PM10, i.e., PM with aerodynamic diameters smaller than 1, 2.5, and 10 µm, respectively), collected at 16 urban and rural sites during summer and winter. We show that the results obtained by the present technique compare well with those from co-located online measurements, e.g., AMS or Aerosol Chemical Speciation Monitor (ACSM). The bulk recoveries of organic aerosol (60–91 %) achieved using this technique, together with low detection limits (0.8 µg of organic aerosol on the analyzed filter fraction) allow its application to environmental samples. We will discuss the recovery variability of individual hydrocarbon ions, ions containing oxygen, and other ions. The performance of such data in Source Apportionment is assessed in comparison to ACSM data. Recoveries of organic components related to different Sources as traffic, wood burning, and secondary organic aerosol are presented. This technique, while subjected to the limitations inherent to filter-based measurements (e.g., filter artifacts and limited time resolution) may be used to enhance the AMS capabilities in measuring size-fractionated, spatially resolved long-term data sets.
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actris acsm intercomparison part 2 intercomparison of me 2 organic Source Apportionment results from 15 individual co located aerosol mass spectrometers
Atmospheric Measurement Techniques, 2015Co-Authors: Roman Frohlich, Olivier Favez, Francesco Canonaco, Vincent Crenn, Ari Setyan, C A Belis, Veronique Riffault, Jay G Slowik, Mikko Aijala, Andres AlastueyAbstract:Abstract. Chemically resolved atmospheric aerosol data sets from the largest intercomparison of the Aerodyne aerosol chemical speciation monitors (ACSMs) performed to date were collected at the French atmospheric supersite SIRTA. In total 13 quadrupole ACSMs (Q-ACSM) from the European ACTRIS ACSM network, one time-of-flight ACSM (ToF-ACSM), and one high-resolution ToF aerosol mass spectrometer (AMS) were operated in parallel for about 3 weeks in November and December~2013. Part 1 of this study reports on the accuracy and precision of the instruments for all the measured species. In this work we report on the intercomparison of organic components and the results from factor analysis Source Apportionment by positive matrix factorisation (PMF) utilising the multilinear engine 2 (ME-2). Except for the organic contribution of mass-to-charge ratio m/z 44 to the total organics (f44), which varied by factors between 0.6 and 1.3 compared to the mean, the peaks in the organic mass spectra were similar among instruments. The m/z 44 differences in the spectra resulted in a variable f44 in the Source profiles extracted by ME-2, but had only a minor influence on the extracted mass contributions of the Sources. The presented Source Apportionment yielded four factors for all 15 instruments: hydrocarbon-like organic aerosol (HOA), cooking-related organic aerosol (COA), biomass burning-related organic aerosol (BBOA) and secondary oxygenated organic aerosol (OOA). ME-2 boundary conditions (profile constraints) were optimised individually by means of correlation to external data in order to achieve equivalent / comparable solutions for all ACSM instruments and the results are discussed together with the investigation of the influence of alternative anchors (reference profiles). A comparison of the ME-2 Source Apportionment output of all 15 instruments resulted in relative standard deviations (SD) from the mean between 13.7 and 22.7 % of the Source's average mass contribution depending on the factors (HOA: 14.3 ± 2.2 %, COA: 15.0 ± 3.4 %, OOA: 41.5 ± 5.7 %, BBOA: 29.3 ± 5.0 %). Factors which tend to be subject to minor factor mixing (in this case COA) have higher relative uncertainties than factors which are recognised more readily like the OOA. Averaged over all factors and instruments the relative first SD from the mean of a Source extracted with ME-2 was 17.2 %.
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sofi an igor based interface for the efficient use of the generalized multilinear engine me 2 for the Source Apportionment me 2 application to aerosol mass spectrometer data
Atmospheric Measurement Techniques, 2013Co-Authors: Francesco Canonaco, Jay G Slowik, Monica Crippa, U Baltensperger, Andre S H PrevotAbstract:Abstract. Source Apportionment using the bilinear model through a multilinear engine (ME-2) was successfully applied to non-refractory organic aerosol (OA) mass spectra collected during the winter of 2011 and 2012 in Zurich, Switzerland using the aerosol chemical speciation monitor (ACSM). Five factors were identified: low-volatility oxygenated OA (LV-OOA), semivolatile oxygenated OA (SV-OOA), hydrocarbon-like OA (HOA), cooking OA (COA) and biomass burning OA (BBOA). A graphical user interface SoFi (Source Finder) was developed at PSI in order to facilitate the testing of different rotational techniques available within the ME-2 engine by providing a priori factor profiles for some or all of the expected factors. ME-2 was used to test the positive matrix factorization (PMF) model, the fully constrained chemical mass balance (CMB) model, and partially constrained models utilizing a values and pulling equations. Within the set of model solutions determined to be environmentally reasonable, BBOA and SV-OOA factor mass spectra and time series showed the greatest variability. This variability represents the uncertainty in the model solution and indicates that analysis of model rotations provides a useful approach for assessing the uncertainty of bilinear Source Apportionment models.
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wintertime aerosol chemical composition and Source Apportionment of the organic fraction in the metropolitan area of paris
Atmospheric Chemistry and Physics, 2013Co-Authors: Monica Crippa, Jay G Slowik, P. F. Decarlo, M. F. Heringa, R. Chirico, Claudia Mohr, Laurent PoulainAbstract:The effect of a post-industrial megacity on local and regional air quality was assessed via a month-long field measurement campaign in the Paris metropolitan area during winter 2010. Here we present Source Apportionment results from three aerosol mass spectrometers and two aethalometers deployed at three measurement stations within the Paris region. Submicron aerosol composition is dominated by the organic fraction (30-36%) and nitrate (28-29%), with lower contributions from sulfate (14-16%), ammonium (12-14%) and black carbon (7-13%). Organic Source Apportionment was performed using positive matrix factorization, resulting in a set of organic factors corresponding both to primary emission Sources and secondary production. The dominant primary Sources are traffic (11-15% of organic mass), biomass burning (13-15%) and cooking (up to 35% during meal hours). Secondary organic aerosol contributes more than 50% to the total organic mass and includes a highly oxidized factor from indeterminate and/or diverse Sources and a less oxidized factor related to wood burning emissions. Black carbon was apportioned to traffic and wood burning Sources using a model based on wavelength-dependent light absorption of these two combustion Sources. The time series of organic and black carbon factors from related Sources were strongly correlated. The similarities in aerosol composition, total mass and temporal variation between the three sites suggest that particulate pollution in Paris is dominated by regional factors, and that the emissions from Paris itself have a relatively low impact on its surroundings.
