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SOURCE APPORTIONMENT OF PM2.5 IN SEATTLE, WA URBAN IMPROVE SITE: COMPARISON OF THREE RECEPTOR MODELS AND SOURCE PROFILES
Citation:
Lewtas, J, N. Maykut, E. Kim, AND T. Larson. SOURCE APPORTIONMENT OF PM2.5 IN SEATTLE, WA URBAN IMPROVE SITE: COMPARISON OF THREE RECEPTOR MODELS AND SOURCE PROFILES. Presented at 2003 AAAR PM Meeting, Pittsburgh, PA, March 31-April 4, 2003.
Impact/Purpose:
The objective of this task is to develop and evaluate personal exposure and biomarker methods for toxic components associated with PM2.5 and SVOC in population exposures. Specific sub-objectives include the following:
1) Identification and quantification of either toxic or tracer organic chemicals associated with PM2.5 and associated SVOC.
2) Measurement of personal airborne exposure of selected toxic/tracer organic species in population based human exposure studies.
3) Development and application of urinary metabolite and other biomarker methods for these toxic/tracer organic species in human exposure studies.
4) Evaluation of multivariant receptor models for apportioning personal exposure using biomarker data.
Description:
IMPROVE protocol data were collected at the urban Beacon Hill monitoring site in Seattle, WA from 1996-99. The 289 sets of PM2.5 filters were analyzed for: metals using PIXIE and XRF, anions using ion chromatography, elemental hydrogen (H) by proton scattering, and elemental and organic carbon fractions (OC1-OC4 and EC1-EC3) by thermal optical reflectance (TOR). The data was analyzed by CMB8, Positive Matrix Factorization (PMF) and UNMIX. The CMB8 model determined the contribution of minor industrial sources (7%), two combustion sources, vegetative burning (16%) and mobile sources (44%), soil (4%), and 3 marine and secondary sources. The PMF model was able to utilize all of the data (30 species) to derive 8 sources. The sources with the highest contribution of the 5 most abundant carbon fractions (OC1-OC4 and EC1), all appear to be combustion sources. We have designated those as gasoline vehicles, diesel, vegetative burning, and fuel oil based on the profiles derived from the PMF model. The following components are found in relatively high abundance for each of these sources: OC3, Pb, Zn, K, and Ti in the gasoline profile; EC1, Fe, Zn, and Mn in the diesel profile; OC3, OC4, EC1, OC2, and K in the vegetative burning profile; OC4 and V in the fuel oil profile. The other 4 profiles derived from the PMF model we have designated as follows with the distinguishing elements indicated in parentheses: soil (Si, Al, Ti), marine (Na, Cl), Na rich (nitrate, Na, and both OC and EC fractions, Ca, and K), and a sulfate (secondary) source (sulfate, nitrate, and EC1). UNMIX was more restrictive in deriving 6 sources based on a statistically acceptable model solution using 15 out of the 30 available species including OC2, OC3, OC4 and EC1 but not OC1 or EC2. Both of these receptor models derived source profiles for 4 different combustion sources containing OC fractions and EC1 whose abundance differ between the sources. Both models derived a profile for soil (Si and Al) and marine/sulfate source(s) (sulfate, Ca, and K). The marine and sodium rich source(s) containing EC1/EC2 fractions may contain some marine diesel combustion emissions. Both of these multivariate models agree in the estimated relative contributions of the combustion sources to the PM2.5 mass as follows: vegetative (28-37%), diesel (18-19%), fuel oil (10-15%), gasoline (4-9%).
This work has been funded by the U S Environmental Protection Agency. It has been subjected to Agency review and approved for publication.