Source Identification of PM2.5 in Steubenville, Ohio Using a Hybrid Method for Highly Time-Resolved Data
| Autor: | Joseph Patrick Pancras, Ram Vedantham, David A. Olson, Matthew S. Landis |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Aerosols
Air Pollutants Fine particulate Environmental engineering Coal combustion products Sampling (statistics) Objective data Time resolution Wind General Chemistry Particulates Atmospheric sciences National Ambient Air Quality Standards Time resolved data Coal Metallurgy Environmental Chemistry Environmental science Particulate Matter Seasons Particle Size Algorithms Environmental Monitoring Ohio Power Plants |
| Zdroj: | Environmental Science & Technology. 48:1718-1726 |
| ISSN: | 1520-5851 0013-936X |
| Popis: | A new source-type identification method, Reduction and Species Clustering Using Episodes (ReSCUE), was developed to exploit the temporal synchronicity typically observed between ambient species in high time resolution fine particulate matter (PM2.5) data to form clusters that vary together. High time-resolution (30 min) PM2.5 sampling was conducted for a month during the summer of 2006 in Steubenville, OH, an EPA designated nonattainment area for the U.S. National Ambient Air Quality Standards (NAAQS). When the data were evaluated, the species clusters from ReSCUE matched extremely well with the source types identified by EPA Unmix demonstrating that ReSCUE is a valuable tool in identifying source types. Results from EPA Unmix show that contributions to PM2.5 are mostly from iron/steel manufacturing (36% ± 9%), crustal matter (33% ± 11%), and coal combustion (11% ± 19%). More importantly, ReSCUE was useful in (i) providing objective data driven guidance for the number of source factors and key fitting species for EPA Unmix, and (ii) detecting tenuous associations between some species and source types in the results derived by EPA Unmix. |
| Databáze: | OpenAIRE |
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