Positive matrix factorization of PM(2.5): comparison and implications of using different speciation data sets
Mingjie Xie1, Michael P Hannigan, Steven J Dutton
1Department of Mechanical Engineering, College of Engineering and Applied Science, University of Colorado, Boulder, Colorado 80309, USA.
Environmental Science & Technology
|September 19, 2012
Summary
Different speciation data sets were analyzed for particulate matter (PM2.5) source apportionment using positive matrix factorization. Including all species provided consistent results, aiding health studies.
Area of Science:
- Environmental Science
- Atmospheric Chemistry
- Chemical Engineering
Background:
- Particulate matter (PM2.5) poses significant health risks.
- Accurate source apportionment is crucial for effective mitigation strategies.
- The impact of different chemical speciation data sets on PM2.5 source apportionment remains an area of active research.
Purpose of the Study:
- To evaluate the utility and consistency of various speciation data sets for PM2.5 source apportionment.
- To compare the effectiveness of positive matrix factorization (PMF) with different input data combinations.
- To assess the influence of data selection on the identification and contribution of PM2.5 sources.
Main Methods:
- Applied positive matrix factorization (PMF) with a bootstrap technique for uncertainty assessment.
- Utilized four distinct 1-year data sets: bulk species, bulk species with water-soluble elements (WSE), bulk species with organic molecular markers (OMM), and all species.
- Compared the PMF solutions derived from each data set to evaluate factor profiles and contributions.
Main Results:
- The PMF solution using only bulk species best reproduced observed PM2.5 concentrations.
- Combining WSE with bulk species yielded five factors, with soil, road dust, and processed dust contributing 26.0% to the PM2.5 mass.
- A 7-factor solution using OMM and bulk species identified EC/sterane and aliphatic factors as major contributors to EC (39.0%) and OC (53.8%).
- The 9-factor solution including all species showed high consistency (r = 0.88-1.00) with previous solutions.
Conclusions:
- The choice of input data set significantly influences PM2.5 source apportionment results.
- Including all available species in PMF analysis provides robust and consistent source identification.
- Data set selection should be tailored to the specific PM components or sources relevant to source-oriented health studies.
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