Grantee Research Project Results
2005 Progress Report: Advancing ATOFMS to a Quantitative Tool for Source Apportionment
EPA Grant Number: R831083Title: Advancing ATOFMS to a Quantitative Tool for Source Apportionment
Investigators: Prather, Kimberly A. , Hopke, Philip K.
Institution: University of California - San Diego , Clarkson University
EPA Project Officer: Chung, Serena
Project Period: October 1, 2003 through September 30, 2006 (Extended to September 30, 2007)
Project Period Covered by this Report: October 1, 2004 through September 30, 2005
Project Amount: $450,000
RFA: Measurement, Modeling, and Analysis Methods for Airborne Carbonaceous Fine Particulate Matter (PM2.5) (2003) RFA Text | Recipients Lists
Research Category: Air , Air Quality and Air Toxics , Particulate Matter
Objective:
The objective of this research project is to explore fully the aerosol time-of-flight mass spectrometry (ATOFMS) data collected during the Supersite Program to ascertain if single particle mass spectrometry can: (1) measure quantitatively the carbonaceous component of the ambient aerosol including organic carbon (OC) and elemental carbon (EC), and specific compounds like polyaromatic hydrocarbons, and to ascertain if it is possible to develop a quantitative and universal calibration that provides results comparable to time resolved OC/EC measurements; (2) provide key markers that distinguish among sources of carbonaceous aerosol, including diesel and spark-ignition vehicles, mobile and stationary sources, fossil fuel sources versus biomass burning, and primary biological and/or secondary organics; (3) distinguish the difference between primary and secondary OC by examining if primary OC is more related to coemitted EC particles or gases, whereas secondary OC is found associated with more abundant sulfate or nitrate particles; and (4) provide critical insights into atmospheric processes that then can be represented better in air quality models such as the relationship of secondary OC with primary particle type. Particle type(s) like sulfate or nitrate may be particularly important for providing an effective surface on which the carbonaceous material can condense.
Progress Summary:
Work has been completed on a number of objectives. There has been progress on the development of additional approaches to analyzing ATOFMS data including developing better classification approaches for single-particle compositions measured using the ATOFMS, calibrations based on particle class populations and ambient particulate matter composition measurements, and direct approaches to determining quantitatively the amount of OC associated with EC in particles.
Particle Classification
Adaptive resonance theory (ART)-2a (Song, et al., 1999) and a density-based cluster method, Density-Based Spatial Clustering of Application with Noise (DBSCAN) (Daszykowski, et al., 2001, 2002), have been used for classification of the single particle mass spectra measured at New York City. ART-2a requires the selection of a critical parameter, the vigilance factor. Using too large of a vigilance factor in ART-2a leads to many similar clusters with overlap, and thus a low vigilance factor was used in this study. The DBSCAN method can identify clusters with complex shapes and various sizes, and representative spectra are chosen to identify different particle types within each cluster. The cluster structure of the single particle mass spectra were examined by DBSCAN. Both methods found that the major clusters were sea salt and anthropogenic combustion emissions. The continua in sulfate, potassium, and OC particles were found by DBSCAN and a large cluster was formed, whereas ART-2a broke it into several small clusters without finding this continuum. A detailed discussion of the cluster analysis results including representative mass spectra, size distributions, and temporal behavior are provided by Zhou, et al. (2006).
Multivariate Calibration Approaches
It is difficult for ATOFMS to provide quantitative estimation of chemical compositions of ambient aerosols, although it now has been accomplished as described in the next section. In an earlier study, the possibility of developing a calibration model to predict chemical compositions from ATOFMS data was demonstrated (Fergenson, et al., 2001), but because of the limited number of samples (only 12), the ability of the calibration model was not realized fully. In a more complete study (Zhao, et al., 2005), 50 samples were created to test further the prediction ability of the calibration model. The conceptual framework is to relate the mass concentrations of the particles in the identified classes to the average aerosol compositions for each sampling time interval using a calibration model based on ART-2a and multivariate analysis. There may be some nonlinearity between cluster mass concentrations and ambient species concentrations because of measurement errors, the scaling equations used to estimate particle mass, and various assumptions required for building the model. Thus, in this study, partial least squares (PLS) regression was integrated with radial basis functions (RBF-PLS) to obtain better prediction effects and compared to PLS regression alone. Compared with an earlier study, these results provided better and a more convincing demonstration of the ability of the calibration model to estimate the chemical compositions from ATOFMS data. The results also suggested that the model would be able to provide carbon data and thus substitute for thermal optical reflectance measurements. Additionally, the calibration model based on RBF-PLS showed more accurate predictions in the cases with some nonlinearity. Some of the key steps in the modeling effect also are discussed in detail by Zhao, et al. (2005).
Direct Approach
Historically, obtaining quantitative chemical information using laser desorption ionization mass spectrometry for analyzing individual aerosol particles has been quite challenging. This is attributed in large part to fluctuations in the absolute ion signals resulting from inhomogeneities in the laser beam profile, as well as chemical matrix effects. Progress has been made in quantifying atomic species using high laser powers, but very few studies have been performed quantifying molecular species. In our studies (Spencer, et al., 2006), promising results were obtained using a new approach to measure the fraction of OC associated with EC in aerosol particles using single particle laser desorption ionization. A tandem differential mobility analyzer (DMA) was used to generate OC/EC particles by size selecting EC particles of a given mobility diameter and then coating them with known thicknesses of OC measured using a second DMA. The mass spectra of the OC/EC particles exiting the second DMA were measured using an ultrafine ultra fine-ATOFMS. A calibration curve was produced with a linear correlation (R2 = 0.98) over the range of OC/EC ion intensity ratios observed in source and ambient studies. Importantly, the OC/EC values measured in ambient field tests with the ultrafine-ATOFMS show a linear correlation (R2 = 0.69) with OC/EC mass ratios obtained using semi-continuous filter based thermo-optical measurements. The calibration procedure established herein represents a significant step toward quantification of OC and EC in submicron ambient particles using laser desorption ionization mass spectrometry.
Future Activities:
We will continue to explore the use of advanced factor analysis models to resolve sources of particles based on ATOFMS data. We will continue to resolve the various approaches to obtaining quantitative estimates of the airborne PM composition based on single particles characterized with the ATOFMS.
References:
Daszykowski M, Walczak B, Massart DL. Looking for natural patterns in data: part 1. density-based approach. Chemometrics and Intelligent Laboratory Systems 2001;56:83-92.
Daszykowski M, Walczak B, Massart DL. Representative subset selection. Analytica Chimica Acta 2002;468:91-103.
Fergenson DP, Song XH, Ramadan Z, Allen JO, Hughes LS, Cass GR, Hopke PK, Prather KA. The quantification of ATOFMS data by multivariate methods. Analytical Chemistry 2001;73:3535-3541.
Song X-H, Hopke PK, Fergenson DP, Prather KA. Classification of single particles analyzed by ATOFMS using an artificial neural network, ART-2a. Analytical Chemistry 1999;71:860-865.
Spencer M.T, Prather KA. Using ATOFMS to determine OC/EC mass fractions in particles. Aerosol Science and Technology 2006;40(8):585-594.
Zhao WX, Hopke PK, Qin XY, Prather KA. Predicting bulk ambient aerosol compositions from ATOFMS data with ART-2a and multivariate analysis. Analytica Chimica Acta 2005;549(1-2):179-187.
Zhou LM, Hopke PK, Venkatarachi P. Cluster analysis of single particle mass spectra at flushing, NY. Analytica Chimica Acta 2006;555(1):47-56.
Journal Articles:
No journal articles submitted with this report: View all 8 publications for this projectSupplemental Keywords:
OC/EC, secondary organic aerosol, supersite, air, environmental chemistry, air toxics, particulate matter, PM, aerosol time-of-flight mass spectrometry, air quality models, atmospheric measurements, human health risks,, RFA, Scientific Discipline, Air, Ecosystem Protection/Environmental Exposure & Risk, particulate matter, air toxics, Environmental Chemistry, Monitoring/Modeling, Environmental Monitoring, Environmental Engineering, atmospheric particulate matter, atmospheric dispersion models, atmospheric measurements, source apportionment, aerosol particles, human health effects, secondary organic aerosols, air quality models, monitoring stations, air sampling, carbon particles, air quality model, emissions, modeling, particulate matter mass, human exposure, secondary organic aerosol, particle phase molecular markers, transport modeling, modeling studies, aerosol analyzers, measurement methodsProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.