Source identification of PM 25 in an arid Northwest US City by positive matrix factorization
Kim, E; Larson, TV; Hopke, PK; Slaughter, C; Sheppard, LE; Claiborn, C
| HERO ID | 130968 |
|---|---|
| In Press | No |
| Year | 2003 |
| Title | Source identification of PM 25 in an arid Northwest US City by positive matrix factorization |
| Authors | Kim, E; Larson, TV; Hopke, PK; Slaughter, C; Sheppard, LE; Claiborn, C |
| Journal | Atmospheric Research |
| Volume | 66 |
| Issue | 4 |
| Page Numbers | 291-305 |
| Abstract | Spokane, WA is prone to frequent particulate pollution episodes due to dust storms, biomass burning, and periods of stagnant meteorological conditions. Spokane is the location of a long-term study examining the association between health effects and chemical or physical constituents of particulate pollution. Positive matrix factorization (PMF) was used to deduce the sources Of PM2.5 (particulate matter less than or equal to2.5 mum in aerodynamic diameter) at a residential site in Spokane from 1995 through 1997. A total of 16 elements in 945 daily PM2.5 samples were measured. The PMF results indicated that seven sources independently contribute to the observed PM2.5 mass: vegetative burning (44%), sulfate aerosol (19%), motor vehicle (11%), nitrate aerosol (9%), airborne soil (9%), chlorine-rich source (6%) and metal processing (3%). Conditional probability functions were computed using surface wind data and the PMF deduced mass contributions from each source and were used to identify local point sources. Concurrently measured carbon monoxide and nitrogen oxides were correlated with the PM2.5 from both motor vehicles and vegetative burning. (C) 2003 Elsevier Science B.V. All rights reserved. |
| Doi | 10.1016/S0169-8095(03)00025-5 |
| Wosid | WOS:000184448500006 |
| Url | http://linkinghub.elsevier.com/retrieve/pii/S0169809503000255 |
| Is Certified Translation | No |
| Dupe Override | No |
| Comments | Source: Web of Science WOS:000184448500006 |
| Is Public | Yes |
| Keyword | source apportionment; receptor modeling; positive matrix factorization; PM2.5; conditional probability function |
| Is Qa | No |