AAAR 31st Annual Conference
October 8-12, 2012
Hyatt Regency Minneapolis
Minneapolis, Minnesota, USA
Abstract View
Daily Trends of Ultrafine Particulate Matter at Sacramento, California
Toshihiro Kuwayama, Chris Ruehl, MICHAEL KLEEMAN, UC Davis
Abstract Number: 598 Working Group: Source Apportionment
Abstract Ultrafine particulate matter (PM0.1) represent a potential health risk, but very little monitoring data exists for source contributions to PM0.1 over an annual cycle. The present study investigates the 2009 daily trends of ultrafine particulate matter emissions with aerodynamic diameter between 0.056-0.1 micro-meter at Sacramento, California, using Positive Matrix Factorization (PMF) version 3.0. Samples were collected with Micro Orifice Uniform Deposit Impactors (MOUDIs) followed by analysis for organic carbon (OC) and elemental carbon (EC) using thermal-optical analysis and metals analysis using ICP-MS.
PM0.1 chemical composition changed as a function of season, with higher concentrations of EC during the winter months and lower concentrations during the summer. Metals such as Na, K, Fe, and As also followed a similar seasonal pattern, pointing to the importance of reduced mixing depth during winter months along with new sources associated with home heating. Wind direction and wind speeds also affected the collection of Ni, Cu, and Pb since point sources dominated these emissions.
Contributions to PM0.1 mass were identified from major sources including diesel truck emissions, railway traffic, background mixed highway traffic emissions, and residential wood burning emissions using PMF. PM0.1 factors for diesel trucks and railway traffic were lower than factors for residential wood burning emissions and background mixed highway traffic emissions at the sampling site. PM0.1 source contributions displayed significant seasonal variation, with the majority of PM0.1 biomass combustion observed during the cooler winter months.
The time-series of PM0.1 composition and source apportionment results provides a basis for future comparisons to PM0.1 predictions from regional grid models. This technique of model predictions and comparisons to point measurements represents the most plausible methods for PM0.1 epidemiological analysis in the coming years.