We have located links that may give you full text access.
JOURNAL ARTICLE
RESEARCH SUPPORT, U.S. GOV'T, NON-P.H.S.
Limitations of remotely sensed aerosol as a spatial proxy for fine particulate matter.
Environmental Health Perspectives 2009 June
BACKGROUND: Recent research highlights the promise of remotely sensed aerosol optical depth (AOD) as a proxy for ground-level particulate matter with aerodynamic diameter
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.
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.
Full text links
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app