AAAR 31st Annual Conference
October 8-12, 2012
Hyatt Regency Minneapolis
Minneapolis, Minnesota, USA
Abstract View
Positive Matrix Factorization of PM2.5 -- Uncertainty and Bias Assessment of Factor Contribution
MINGJIE XIE, Joshua Hemann, Steven Dutton, Jana Milford, Shelly Miller, Michael Hannigan, University of Colorado at Boulder
Abstract Number: 609 Working Group: Source Apportionment
Abstract The Denver Aerosol Source and Health (DASH) study aims to identify and quantify the sources of ambient PM2.5 that are detrimental to human health given short-term exposure. A 2.8-year series of daily PM2.5 compositional data from Denver, CO, including concentrations of sulfate, nitrate, bulk elemental (EC) and organic carbon (OC), and 51 organic molecular markers (OMMs), was analyzed using positive matrix factorization (PMF). A novel method has been developed by Hemann et al. (2009) to estimate the uncertainty and bias related to factor contributions at the daily time scale. A stationary bootstrap technique is used to create replicate datasets, and then analyzed with PMF for each. Neural networks are trained to align the factor profiles from each PMF bootstrap solution to that of the original solution based upon the observed data (known as the base case). A PMF bootstrap solution is recorded only when each factor of that solution could be uniquely matched to a base case factor. The measurement days resampled in each recorded solution are tracked to examine the bias and uncertainty in contribution of each factor on each day. This method was applied to the 2.8-year series of PM2.5 characterization; results will be presented with an emphasis on improving the understanding of uncertainty, bias and robustness of PMF solutions for long time series.