PM2.5 characterization for time series studies: Pointwise uncertainty estimation and bulk speciation methods applied in Denver
Introduction
Numerous studies have identified adverse health effects from both short-term and long-term exposures to particulate matter less than 2.5 μm in diameter (PM2.5) including, but not limited to, worsening of asthma (Ko et al., 2007, Rabinovitch et al., 2006) and increased cardiopulmonary mortality (Dominici et al., 2005, Dockery et al., 1993, Pope et al., 1995). Largely because of these health concerns, the Environmental Protection Agency promulgated National Ambient Air Quality Standards (NAAQS) for PM2.5 in 1997 (US-EPA, 1997). Observed health effects with particulate matter, however, are not consistent across geographical regions (Dominici et al., 2005). This may be due in part to variations in sources, both primary and secondary, in different regions. The Denver Aerosol Sources and Health (DASH) study is one of a handful of studies designed to investigate associations between PM2.5 sources and a range of adverse health outcomes including mortality, hospitalizations and asthma control (Vedal et al., in press). The specific goals of the DASH study are to: (1) measure speciated PM2.5 daily over multiple years in Denver, Colorado, (2) determine the major contributing sources through receptor modeling, and (3) investigate associations between the identified sources and health outcomes.
Since source apportionment is a major aspect of the DASH study, an in-depth exploration of measurement uncertainty was undertaken. Pointwise uncertainty estimates are frequently used in source apportionment models such as Positive Matrix Factorization (PMF) (Paatero, 1997, Paatero, 2000) and they can have an important effect on the performance of the model (Kim and Hopke, 2007). For long time series studies where sampling protocols and analysis techniques may change with time, pointwise uncertainty estimates are critical for unbiased model results. In this situation, applying a single absolute or relative uncertainty across all measurements may not be appropriate. This paper describes the bulk chemistry measurements and uncertainty estimation tactics used for daily PM2.5 speciation in the DASH study.
The PM2.5 components measured daily over multiple years include nitrate, sulfate, elemental carbon, bulk organic carbon and 84 different organic species which serve as molecular markers for source identification. In addition, analysis for trace metals was performed on a one year subset of the samples. This paper focuses on measurement techniques, uncertainty estimation and results from the bulk PM2.5 speciation (mass, inorganic ions and bulk carbon). The first 4.5 years of daily concentration data for these species are presented and the pointwise propagated uncertainties are compared to precision estimates derived from bi-weekly duplicate measurements using collocated samplers. The propagated uncertainty estimates are broken down by their origin (analytical, field blank correction and volume calculation) revealing areas where methodology improvements would have the most impact. Studies in relatively low concentration cities such as Denver are of particular interest as regulatory agencies consider promulgating ever more stringent air quality standards.
Section snippets
PM2.5 sampling protocol
The core sampling site for the DASH study is located on the rooftop of a two story elementary school in a residential neighborhood 5.3 km east of downtown Denver. This site was chosen for its central location in a highly populated residential region of Denver. The site is far from the influence of any major industrial or point sources. The closest large highway is 5.2 km south-west of the site and the nearest commuter street is 0.6 km away.
Sample collection commenced on July 1, 2002 and is
Time series and summary statistics
Fig. 1 contains concentration time series plots of PM2.5 mass, nitrate, sulfate, EC, OC and TC for the first 4.5 years of the DASH study (July 1, 2002–December 31, 2006). The error bars depict the pointwise propagated uncertainty estimates (+/−1 SD) derived using the techniques discussed in this paper. Table 2 contains a list of aggregate statistics for each species covering the 4.5 year period including the mean value of the field blank correction applied to each sample. To help illustrate the
PM2.5 bulk chemistry in Denver
The source apportionment and health modeling goals of the DASH study both rely heavily on accurate measurement of speciated PM2.5 as well as careful determination of pointwise uncertainty estimates. This paper has provided a first look at the bulk chemical make-up and seasonality of the PM2.5 measured daily for 4.5 years at the DASH receptor site in Denver. It has also outlined the measurement methods and uncertainty estimation techniques used in the mass, inorganic ion and bulk carbon
Acknowledgements
This research is supported by NIEHS research grant number RO1 ES010197. Additional support for student assistance was provided by NSF Research Experience for Undergraduates award number EEC 0552895. We would like to thank Brendon Rudack for his help constructing the weigh chamber; Dan Williams, James Schroeder, Bobby Irmiger, Brian Cone, Rachel Bryant and Toni Newville for their contributions to the mass measurements; Paul Schuster, Fatimah Matalkah, Mary Beth Oshnack and Stacy Louie for their
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