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