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

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• 作者：Mercedes A. Bravo^a ; ^{mercedes.bravo@yale.edu} ; Montserrat Fuentes^b ; ¹ ; ^{fuentes@stat.ncsu.edu} ; Yang Zhang^c ; ² ; ^{yang_zhang@ncsu.edu} ; Michael J. Burr^c ; ^{mikeburr3@gmail.com} ; Michelle L. Bell^a ; ³ ; ^{michelle.bell@yale.edu}
• 关键词：PM2.5 ; Ozone ; Community multiscale air quality model ; CMAQ ; Exposure assessment ; Health assessment
• 刊名：Environmental Research
• 出版年：2012
• 期刊代码：78_00139351
• 类别：env
• 出版时间：July, 2012
• 卷：116
• 期：Complete
• 页码：1-10
• 文件大小：1321 K

摘要

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 渭m (PM_2.5) 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 PM_2.5 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 PM_2.5 had an annual normalized mean bias of 鈭?.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.