Errors in coarse particulate matter mass concentrations and spatiotemporal characteristics when using subtraction estimation methods.

In studies of coarse particulate matter (PM10-2.5), mass concentrations are often estimated through the subtraction of PM2.5from collocated PM10tapered 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 PM10-2.5estimation when using the possible combinations of PM10and PM2.5TEOM 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 PM10-2.5mass 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 PM10-2.5mass and spatial characteristics due to the very low semivolatile PM10-2.5concentrations in Colorado. Estimation using either a PM2.5or PM10monitor without FDMS introduced absolute biases of 2.4 µg/m3(25%) to –2.3 µg/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 PM2.5TEOM and PM10TEOM 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 PM2.5from total PM2.5concentrations. By estimating nonvolatile PM2.5concentrations from this relationship, PM10-2.5was calculated as the difference between nonvolatile PM10and PM2.5concentrations. Implications:Errors in the estimation of PM10-2.5 concentrations using subtraction methods were shown to be related to the unmeasured semivolatile mass when using certain combinations of TEOM instruments. For the northeastern Colorado region, the absolute bias associated with this error significantly affects mean and 95th percentile values, which would affect assessment of compliance if PM10-2.5 is regulated in the future. Estimating PM10-2.5 mass concentrations using nonvolatile mass concentrations from collocated PM10 and PM2.5 TEOM monitors closely estimates the total PM10-2.5 mass concentrations. A corrective model that removes the described error was developed and applied to data from two sites in Denver. Supplemental Materials:Supplemental materials are available for this paper. Go to the publisher's online edition of theJournal of the Air & Waste Management Association. [ABSTRACT FROM PUBLISHER]