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Fine-Scale Air Pollution Models for Epidemiologic Research: Insights From Approaches Developed in the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

  • Early Life Environmental Health (H Volk and J Buckley, Section Editors)
  • Published:
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Abstract

Purpose of Review

Epidemiological studies of short- and long-term health impacts of ambient air pollutants require accurate exposure estimates. We describe the evolution in exposure assessment and assignment in air pollution epidemiology, with a focus on spatiotemporal techniques first developed to meet the needs of the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Initially designed to capture the substantial variation in pollutant levels and potential health impacts that can occur over small spatial and temporal scales in metropolitan areas, these methods have now matured to permit fine-scale exposure characterization across the contiguous USA and can be used for understanding long- and short-term health effects of exposure across the lifespan. For context, we highlight how the MESA Air models compare to other available exposure models.

Recent Findings

Newer model-based exposure assessment techniques provide predictions of pollutant concentrations with fine spatial and temporal resolution. These validated models can predict concentrations of several pollutants, including particulate matter less than 2.5 μm in diameter (PM2.5), oxides of nitrogen, and ozone, at specific locations (such as at residential addresses) over short time intervals (such as 2 weeks) across the contiguous USA between 1980 and the present. Advances in statistical methods, incorporation of supplemental pollutant monitoring campaigns, improved geographic information systems, and integration of more complete satellite and chemical transport model outputs have contributed to the increasing validity and refined spatiotemporal spans of available models.

Summary

Modern models for predicting levels of outdoor concentrations of air pollutants can explain a substantial amount of the spatiotemporal variation in observations and are being used to provide critical insights into effects of air pollutants on the prevalence, incidence, progression, and prognosis of diseases across the lifespan. Additional enhancements in model inputs and model design, such as incorporation of better traffic data, novel monitoring platforms, and deployment of machine learning techniques, will allow even further improvements in the performance of pollutant prediction models.

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Funding

This publication was developed under a STAR research assistance agreements RD831697 (MESA Air), RD-83830001 (MESA Air Next Stage), and RD83479601 (UW Center for Clean Air Research), awarded by the US Environmental Protection Agency. It has not been formally reviewed by the EPA. Research reported in this publication was also supported by the University of Washington EDGE Center of the NIA under award number: P30ES007033, by ECHO PATHWAYS (NIH grants: 1UG3OD023271-01 and 4UH3OD023271-03) and by grants R56ES026528 and P30ES007033 from NIEHS and R01ES026187 from NIA and NIEHS. This work was supported in part by the UW NIEHS sponsored Biostatistics, Epidemiologic and Bioinformatics Training in Environmental Health (BEBTEH) Training Grant, Grant #: NIEHS T32ES015459.

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Authors and Affiliations

  1. Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, WA, USA

    Kipruto Kirwa, Meng Wang, Michael T. Young & Sun-Young Kim

  2. Department of Biostatistics, University of Washington School of Public Health, Seattle, WA, USA

    Adam A. Szpiro

  3. Departments of Biostatistics and Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, WA, USA

    Lianne Sheppard

  4. Department of Statistics, University of Washington School of Public Health, Seattle, WA, USA

    Paul D. Sampson

  5. Department of Epidemiology and Environmental Health, School of Public Health and Health Professions Research and Education in Energy, Environment and Water Institute, University at Buffalo, Buffalo, NY, USA

    Meng Wang

  6. Department of Statistics, Colorado State University, Fort Collins, CO, USA

    Joshua P. Keller

  7. Institute of Health and Environment, Seoul National University, Seoul, South Korea

    Sun-Young Kim

  8. Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA

    Timothy V. Larson

  9. Departments of Environmental and Occupational Health Sciences, Epidemiology, and Medicine, University of Washington, Seattle, WA, USA

    Joel D. Kaufman

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  1. Kipruto Kirwa
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Correspondence to Kipruto Kirwa.

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Kirwa, K., Szpiro, A.A., Sheppard, L. et al. Fine-Scale Air Pollution Models for Epidemiologic Research: Insights From Approaches Developed in the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Curr Envir Health Rpt 8, 113–126 (2021). https://doi.org/10.1007/s40572-021-00310-y

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