Comparison of exposure estimation methods for air pollutants: Ambient monitoring data and regional air quality simulation

Abstract

Air quality modeling could potentially improve exposure estimates for use in epidemiological studies. We investigated this application of air quality modeling by estimating location-specific (point) and spatially-aggregated (county level) exposure concentrations of particulate matter with an aerodynamic diameter less than or equal to 2.5 µm (PM2.5) and ozone (O3) for the eastern U.S. in 2002 using the Community Multi-scale Air Quality (CMAQ) modeling system and a traditional approach using ambient monitors. The monitoring approach produced estimates for 370 and 454 counties for PM2.5 and O3, respectively. Modeled estimates included 1861 counties, covering 50% more population. The population uncovered by monitors differed from those near monitors (e.g., urbanicity, race, education, age, unemployment, income, modeled pollutant levels). CMAQ overestimated O3 (annual normalized mean bias = 4.30%), while modeled PM2.5 had an annual normalized mean bias of −2.09%, although bias varied seasonally, from 32% in November to −27% in July. Epidemiology may benefit from air quality modeling, with improved spatial and temporal resolution and the ability to study populations far from monitors that may differ from those near monitors. However, model performance varied by measure of performance, season, and location. Thus, the appropriateness of using such modeled exposures in health studies depends on the pollutant and metric of concern, acceptable level of uncertainty, population of interest, study design, and other factors.

Associations between prenatal exposure to air pollution, small for gestational age, and term low birthweight in a state-wide birth cohort

“…O 3 ) or subset of days (e.g., every 3 days, PM). Recently, in an analysis covering the Eastern United States, Bravo et al (2012) demonstrated that use of the CMAQ model provided increased spatial and temporal resolution compared to the use of monitoring data. Additionally, Bravo et al (2012) observed that a number of demographic characteristics (self-identification as Black, median income, poverty, age, and educational attainment) differed between counties with and without AQS monitors.…”

“…Recently, in an analysis covering the Eastern United States, Bravo et al (2012) demonstrated that use of the CMAQ model provided increased spatial and temporal resolution compared to the use of monitoring data. Additionally, Bravo et al (2012) observed that a number of demographic characteristics (self-identification as Black, median income, poverty, age, and educational attainment) differed between counties with and without AQS monitors. Bell and Dominici (2008) previously reported that some communitylevel characteristics (including percent Black population and unemployment) can act as effect measure modifiers in epidemiologic studies of air pollution.…”

Fusing Observational, Satellite Remote Sensing and Air Quality Model Simulated Data to Estimate Spatiotemporal Variations of PM2.5 Exposure in China

“…However, the accuracy of air quality models depends on the uncertainty of emission inventories and meteorological inputs and has thus been reported to vary with seasons and locations [33].…”

Long-term particulate matter modeling for health effect studies in California – Part 1: Model performance on temporal and spatial variations

“…Chemical transport models (CTMs) have recently been used as one of the alternative approaches to address the limitations of central site monitors (Anenberg et al, 2010;Bravo et al, 2012;Sarnat et al, 2011;Tainio et al, 2013). The latest generation of CTMs represents a "state-of-the-science" understanding of emissions, transport, and atmospheric chemistry.…”