Estimating individualized exposure impacts from ambient ozone levels: A synthetic information approach

Estimating individualized exposure impacts from ambient ozone levels: A synthetic information... There is ample evidence that short-term ozone exposure is associated with increased respiratory symptoms. Many studies, however, aggregate the population, activities, or concentration levels of the pollutant across space and/or time, failing to capture critical variations in the exposure levels. We couple spatiotemporal air quality estimates of ozone with a synthetic information model of the Houston Metropolitan Area, allowing us to attach exposure levels to individuals based on exact times, geo-locations, and microenvironments of activities. Several scenarios of the model are run at different levels of resolution. When we maintain the spatiotemporal resolution of the data, the proportion of the population that experiences sharp increases in short-term exposure increases substantially. This can be particularly important if experienced by sensitive populations given the increased risk for adverse health effects. We find that individuals in the same zip code, neighborhood, and even household have varying levels of exposure. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Environmental Modelling & Software Elsevier

Estimating individualized exposure impacts from ambient ozone levels: A synthetic information approach

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
1364-8152
eISSN
1873-6726
D.O.I.
10.1016/j.envsoft.2018.02.007
Publisher site
See Article on Publisher Site

Abstract

There is ample evidence that short-term ozone exposure is associated with increased respiratory symptoms. Many studies, however, aggregate the population, activities, or concentration levels of the pollutant across space and/or time, failing to capture critical variations in the exposure levels. We couple spatiotemporal air quality estimates of ozone with a synthetic information model of the Houston Metropolitan Area, allowing us to attach exposure levels to individuals based on exact times, geo-locations, and microenvironments of activities. Several scenarios of the model are run at different levels of resolution. When we maintain the spatiotemporal resolution of the data, the proportion of the population that experiences sharp increases in short-term exposure increases substantially. This can be particularly important if experienced by sensitive populations given the increased risk for adverse health effects. We find that individuals in the same zip code, neighborhood, and even household have varying levels of exposure.

Journal

Environmental Modelling & SoftwareElsevier

Published: May 1, 2018

References

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