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

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Title: Comparison of exposure estimation methods for air pollutants: Ambient monitoring data and regional air quality simulation
Authors: Bravo, Mercedes A.1 mercedes.bravo@yale.edu, Fuentes, Montserrat2 fuentes@stat.ncsu.edu, Zhang, Yang3 yang_zhang@ncsu.edu, Burr, Michael J.3 mikeburr3@gmail.com, Bell, Michelle L.1 michelle.bell@yale.edu
Source: Environmental Research. Jul2012, Vol. 116, p1-10. 10p.
Subjects: Epidemiology research methodology, Mathematical models of air quality, Environmental exposure, Simulation methods & models, Air quality monitoring, Health, Air pollution, Factor analysis
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 2.5μm (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. [Copyright &y& Elsevier]
Copyright of Environmental Research is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 76333433
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PubType: Academic Journal
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  Data: Comparison of exposure estimation methods for air pollutants: Ambient monitoring data and regional air quality simulation
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  Data: <searchLink fieldCode="AR" term="%22Bravo%2C+Mercedes+A%2E%22">Bravo, Mercedes A.</searchLink><relatesTo>1</relatesTo><i> mercedes.bravo@yale.edu</i><br /><searchLink fieldCode="AR" term="%22Fuentes%2C+Montserrat%22">Fuentes, Montserrat</searchLink><relatesTo>2</relatesTo><i> fuentes@stat.ncsu.edu</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yang%22">Zhang, Yang</searchLink><relatesTo>3</relatesTo><i> yang_zhang@ncsu.edu</i><br /><searchLink fieldCode="AR" term="%22Burr%2C+Michael+J%2E%22">Burr, Michael J.</searchLink><relatesTo>3</relatesTo><i> mikeburr3@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Bell%2C+Michelle+L%2E%22">Bell, Michelle L.</searchLink><relatesTo>1</relatesTo><i> michelle.bell@yale.edu</i>
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  Data: <searchLink fieldCode="DE" term="%22Epidemiology+research+methodology%22">Epidemiology research methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models+of+air+quality%22">Mathematical models of air quality</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+exposure%22">Environmental exposure</searchLink><br /><searchLink fieldCode="DE" term="%22Simulation+methods+%26+models%22">Simulation methods & models</searchLink><br /><searchLink fieldCode="DE" term="%22Air+quality+monitoring%22">Air quality monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Health%22">Health</searchLink><br /><searchLink fieldCode="DE" term="%22Air+pollution%22">Air pollution</searchLink><br /><searchLink fieldCode="DE" term="%22Factor+analysis%22">Factor analysis</searchLink>
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  Data: 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 2.5μm (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. [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Environmental Research is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.envres.2012.04.008
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        Text: English
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      – SubjectFull: Epidemiology research methodology
        Type: general
      – SubjectFull: Mathematical models of air quality
        Type: general
      – SubjectFull: Environmental exposure
        Type: general
      – SubjectFull: Simulation methods & models
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      – SubjectFull: Air quality monitoring
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      – SubjectFull: Health
        Type: general
      – SubjectFull: Air pollution
        Type: general
      – SubjectFull: Factor analysis
        Type: general
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      – TitleFull: Comparison of exposure estimation methods for air pollutants: Ambient monitoring data and regional air quality simulation
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            NameFull: Bravo, Mercedes A.
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              Text: Jul2012
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              Y: 2012
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