title: Errors in coarse particulate matter mass concentrations and spatiotemporal characteristics when using subtraction estimation methods.
abstract: In studies of coarse particulate matter (PM10-2.5), mass concentrations are often estimated through the subtraction of PM2.5 from collocated PM10 tapered element oscillating microbalance (TEOM) measurements. Though all field instruments have yet to be updated, the Filter Dynamic Measurement System (FDMS) was introduced to account for the loss of semivolatile material from heated TEOM filters. To assess errors in PM10-2.5 estimation when using the possible combinations of PM10 and PM2.5 TEOM units with and without FDMS, data from three monitoring sites of the Colorado Coarse Rural-Urban Sources and Health (CCRUSH) study were used to simulate four possible subtraction methods for estimating PM10-2.5 mass concentrations. Assuming all mass is accounted for using collocated TEOMs with FDMS, the three other subtraction methods were assessed for biases in absolute mass concentration, temporal variability, spatial correlation, and homogeneity. Results show collocated units without FDMS closely estimate actual PM10-2.5 mass and spatial characteristics due to the very low semivolatile PM10-2.5 concentrations in Colorado. Estimation using either a PM2.5 or PM10 monitor without FDMS introduced absolute biases of 2.4 microg/m3 (25%) to -2.3 microg/m3 (-24%), respectively. Such errors are directly related to the unmeasured semivolatile mass and alter measures of spatiotemporal variability and homogeneity, all of which have implications for the regulatory and epidemiology communities concerned about PM10-2.5. Two monitoring sites operated by the state of Colorado were considered for inclusion in the CCRUSH acute health effects study, but concentrations were biased due to sampling with an FDMS-equipped PM2.5 TEOM and PM10 TEOM not corrected for semivolatile mass loss. A regression-based model was developed for removing the error in these measurements by estimating the semivolatile concentration of PM2.5 from total PM2.5 concentrations. By estimating nonvolatile PM2.5 concentrations from this relationship, PM10-2.5 was calculated as the difference between nonvolatile PM10 and PM2.5 concentrations.
authors.affiliation: Department of Mechanical Engineering, College of Engineering and Applied Science, University of Colorado at Boulder Boulder, Colorado 80309, USA. nsclements@gmail.com
authors.forename: Nicholas
authors.initials: N
authors.lastname: Clements
authors.affiliation: Department of Mechanical Engineering, College of Engineering and Applied Science, University of Colorado at Boulder Boulder, Colorado 80309, USA.
authors.forename: Jana B
authors.initials: JB
authors.lastname: Milford
authors.affiliation: Department of Mechanical Engineering, College of Engineering and Applied Science, University of Colorado at Boulder Boulder, Colorado 80309, USA.
authors.forename: Shelly L
authors.initials: SL
authors.lastname: Miller
authors.affiliation: Department of Applied Mathematics and Statistics, Colorado School of Mines, Golden, Colorado, USA.
authors.forename: William
authors.initials: W
authors.lastname: Navidi
authors.affiliation: Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado, USA.
authors.forename: Jennifer L
authors.initials: JL
authors.lastname: Peel
authors.affiliation: Department of Mechanical Engineering, College of Engineering and Applied Science, University of Colorado at Boulder Boulder, Colorado 80309, USA.
authors.forename: Michael P
authors.initials: MP
authors.lastname: Hannigan
mesh_terms: D003120:Colorado; D008962:Models, Theoretical; D052638:Particulate Matter; D014835:Volatilization
chemical_list: D052638:Particulate Matter