PM2.5 characterization for time series studies: Pointwise uncertainty estimation and bulk speciation methods applied in Denver

Many studies have identified associations between adverse health effects and short-term exposure to particulate matter less than 2.5 mu m in diameter (PM2.5). These effects, however, are not consistent across geographical regions. This may be due in part to variations in the chemical make-up of PM2.5 resulting from unique combinations of sources, both primary and secondary, in different regions. The Denver Aerosol Sources and Health (DASH) study is a multi-year time series study designed to characterize the daily chemical composition of PM2.5 in Denver, identify the major contributing sources, and investigate associations between sources and a broad array of adverse health outcomes. Measurement methodology, field blank correction, pointwise uncertainty estimation and detection limit consideration are discussed in the context of bulk speciation for the DASH study. Results are presented for the first 4.5 years of mass, inorganic ion and bulk carbon speciation. The derived measurement uncertainties were propagated using the root sum of squares method and show good agreement with precision estimates derived from bi-weekly duplicate samples collected on collocated samplers. Gravimetric mass has the most uncertainty of any measurement and reconstructed mass generated from the sum of the individual species shows less uncertainty than measured mass on average. The methods discussed provide a good framework for PM2.5 speciation measurements and are generalizable to analysis of other environmental measures. (C) 2008 Elsevier Ltd. All rights reserved.