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Comparative Study
. 2012 Jul:116:1-10.
doi: 10.1016/j.envres.2012.04.008. Epub 2012 May 10.

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

Mercedes A Bravo  1 , Montserrat Fuentes, Yang Zhang, Michael J Burr, Michelle L Bell
Affiliations
Comparative Study

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

Mercedes A Bravo et al. Environ Res. 2012 Jul.

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 (PM(2.5)) and ozone (O(3)) 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 PM(2.5) and O(3), 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 O(3) (annual normalized mean bias=4.30%), while modeled PM(2.5) 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.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Fig. 1
Fig. 1
Monthly normalized mean bias in simulated concentrations of PM2.5 and O3
Fig. 2
Fig. 2
Annual average normalized mean bias (by monitor location)
Fig. 3
Fig. 3
Annual average correlation between observed and simulated concentrations (by monitor location)
Fig. 4
Fig. 4
County-level annual average exposure estimates for 24-hour PM2.5 (a) Monitor-derived and (b) model-derived
Fig. 5
Fig. 5
County-level seasonal average (April–Sept.) exposure estimates for 8-hour O3: (a) Monitor-derived and (b) model-derived
See this image and copyright information in PMC

References

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