Literature DB >> 16187588

Estimation of organic carbon blank values and error structures of the speciation trends network data for source apportionment.

Eugene Kim1, Philip K Hopke, Youjun Qin.   

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 < or = 2.5 microm in aerodynamic diameter (PM2.5) regression against OC concentrations. The estimated OC blank concentrations ranged from 1 to 2.4 microg/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 identified in this study with the estimated error structures and min values among different MDL values from the five 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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Year:  2005        PMID: 16187588     DOI: 10.1080/10473289.2005.10464705

Source DB:  PubMed          Journal:  J Air Waste Manag Assoc        ISSN: 1096-2247            Impact factor:   2.235


  3 in total

1.  Chemical characterization of PM1.0 aerosol in Delhi and source apportionment using positive matrix factorization.

Authors:  Amrita Singhai; Gazala Habib; Ramya Sunder Raman; Tarun Gupta
Journal:  Environ Sci Pollut Res Int       Date:  2016-10-10       Impact factor: 4.223

2.  Emergency admissions for cardiovascular and respiratory diseases and the chemical composition of fine particle air pollution.

Authors:  Roger D Peng; Michelle L Bell; Alison S Geyh; Aidan McDermott; Scott L Zeger; Jonathan M Samet; Francesca Dominici
Journal:  Environ Health Perspect       Date:  2009-02-11       Impact factor: 9.031

3.  Contribution of point and small-scaled sources to the PM10 emission using positive matrix factorization model.

Authors:  Zohre Farahmandkia; Faramarz Moattar; Farid Zayeri; Mohamad Sadegh Sekhavatjou; Nabiollah Mansouri
Journal:  J Environ Health Sci Eng       Date:  2017-01-14
  3 in total

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