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Limitations of Remotely Sensed Aerosol as a Spatial Proxy for Fine Particulate Matter

BACKGROUND: Recent research highlights the promise of remotely sensed aerosol optical depth (AOD) as a proxy for ground-level particulate matter with aerodynamic diameter ≤ 2.5 μm (PM(2.5)). Particular interest lies in estimating spatial heterogeneity using AOD, with important application to estimat...

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Autores principales: Paciorek, Christopher J., Liu, Yang
Formato: Texto
Lenguaje:English
Publicado: National Institute of Environmental Health Sciences 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2702404/
https://www.ncbi.nlm.nih.gov/pubmed/19590681
http://dx.doi.org/10.1289/ehp.0800360
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author Paciorek, Christopher J.
Liu, Yang
author_facet Paciorek, Christopher J.
Liu, Yang
author_sort Paciorek, Christopher J.
collection PubMed
description BACKGROUND: Recent research highlights the promise of remotely sensed aerosol optical depth (AOD) as a proxy for ground-level particulate matter with aerodynamic diameter ≤ 2.5 μm (PM(2.5)). Particular interest lies in estimating spatial heterogeneity using AOD, with important application to estimating pollution exposure for public health purposes. Given the correlations reported between AOD and PM(2.5), it is tempting to interpret the spatial patterns in AOD as reflecting patterns in PM(2.5). OBJECTIVES: We evaluated the degree to which AOD can help predict long-term average PM(2.5) concentrations for use in chronic health studies. METHODS: We calculated correlations of AOD and PM(2.5) at various temporal aggregations in the eastern United States in 2004 and used statistical models to assess the relationship between AOD and PM(2.5) and the potential for improving predictions of PM(2.5) in a subregion, the mid-Atlantic. RESULTS: We found only limited spatial associations of AOD from three satellite retrievals with daily and yearly PM(2.5). The statistical modeling shows that monthly average AOD poorly reflects spatial patterns in PM(2.5) because of systematic, spatially correlated discrepancies between AOD and PM(2.5). Furthermore, when we included AOD as a predictor of monthly PM(2.5) in a statistical prediction model, AOD provided little additional information in a model that already accounts for land use, emission sources, meteorology, and regional variability. CONCLUSIONS: These results suggest caution in using spatial variation in currently available AOD to stand in for spatial variation in ground-level PM(2.5) in epidemiologic analyses and indicate that when PM(2.5) monitoring is available, careful statistical modeling outperforms the use of AOD.
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spelling pubmed-27024042009-07-09 Limitations of Remotely Sensed Aerosol as a Spatial Proxy for Fine Particulate Matter Paciorek, Christopher J. Liu, Yang Environ Health Perspect Research BACKGROUND: Recent research highlights the promise of remotely sensed aerosol optical depth (AOD) as a proxy for ground-level particulate matter with aerodynamic diameter ≤ 2.5 μm (PM(2.5)). Particular interest lies in estimating spatial heterogeneity using AOD, with important application to estimating pollution exposure for public health purposes. Given the correlations reported between AOD and PM(2.5), it is tempting to interpret the spatial patterns in AOD as reflecting patterns in PM(2.5). OBJECTIVES: We evaluated the degree to which AOD can help predict long-term average PM(2.5) concentrations for use in chronic health studies. METHODS: We calculated correlations of AOD and PM(2.5) at various temporal aggregations in the eastern United States in 2004 and used statistical models to assess the relationship between AOD and PM(2.5) and the potential for improving predictions of PM(2.5) in a subregion, the mid-Atlantic. RESULTS: We found only limited spatial associations of AOD from three satellite retrievals with daily and yearly PM(2.5). The statistical modeling shows that monthly average AOD poorly reflects spatial patterns in PM(2.5) because of systematic, spatially correlated discrepancies between AOD and PM(2.5). Furthermore, when we included AOD as a predictor of monthly PM(2.5) in a statistical prediction model, AOD provided little additional information in a model that already accounts for land use, emission sources, meteorology, and regional variability. CONCLUSIONS: These results suggest caution in using spatial variation in currently available AOD to stand in for spatial variation in ground-level PM(2.5) in epidemiologic analyses and indicate that when PM(2.5) monitoring is available, careful statistical modeling outperforms the use of AOD. National Institute of Environmental Health Sciences 2009-06 2009-02-21 /pmc/articles/PMC2702404/ /pubmed/19590681 http://dx.doi.org/10.1289/ehp.0800360 Text en http://creativecommons.org/publicdomain/mark/1.0/ Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
spellingShingle Research
Paciorek, Christopher J.
Liu, Yang
Limitations of Remotely Sensed Aerosol as a Spatial Proxy for Fine Particulate Matter
title Limitations of Remotely Sensed Aerosol as a Spatial Proxy for Fine Particulate Matter
title_full Limitations of Remotely Sensed Aerosol as a Spatial Proxy for Fine Particulate Matter
title_fullStr Limitations of Remotely Sensed Aerosol as a Spatial Proxy for Fine Particulate Matter
title_full_unstemmed Limitations of Remotely Sensed Aerosol as a Spatial Proxy for Fine Particulate Matter
title_short Limitations of Remotely Sensed Aerosol as a Spatial Proxy for Fine Particulate Matter
title_sort limitations of remotely sensed aerosol as a spatial proxy for fine particulate matter
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2702404/
https://www.ncbi.nlm.nih.gov/pubmed/19590681
http://dx.doi.org/10.1289/ehp.0800360
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