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Estimating Regional Spatial and Temporal Variability of PM(2.5) Concentrations Using Satellite Data, Meteorology, and Land Use Information
BACKGROUND: Studies of chronic health effects due to exposures to particulate matter with aerodynamic diameters ≤ 2.5 μm (PM(2.5)) are often limited by sparse measurements. Satellite aerosol remote sensing data may be used to extend PM(2.5) ground networks to cover a much larger area. OBJECTIVES: In...
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Formato: | Texto |
Lenguaje: | English |
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National Institute of Environmental Health Sciences
2009
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2702401/ https://www.ncbi.nlm.nih.gov/pubmed/19590678 http://dx.doi.org/10.1289/ehp.0800123 |
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author | Liu, Yang Paciorek, Christopher J. Koutrakis, Petros |
author_facet | Liu, Yang Paciorek, Christopher J. Koutrakis, Petros |
author_sort | Liu, Yang |
collection | PubMed |
description | BACKGROUND: Studies of chronic health effects due to exposures to particulate matter with aerodynamic diameters ≤ 2.5 μm (PM(2.5)) are often limited by sparse measurements. Satellite aerosol remote sensing data may be used to extend PM(2.5) ground networks to cover a much larger area. OBJECTIVES: In this study we examined the benefits of using aerosol optical depth (AOD) retrieved by the Geostationary Operational Environmental Satellite (GOES) in conjunction with land use and meteorologic information to estimate ground-level PM(2.5) concentrations. METHODS: We developed a two-stage generalized additive model (GAM) for U.S. Environmental Protection Agency PM(2.5) concentrations in a domain centered in Massachusetts. The AOD model represents conditions when AOD retrieval is successful; the non-AOD model represents conditions when AOD is missing in the domain. RESULTS: The AOD model has a higher predicting power judged by adjusted R(2) (0.79) than does the non-AOD model (0.48). The predicted PM(2.5) concentrations by the AOD model are, on average, 0.8–0.9 μg/m(3) higher than the non-AOD model predictions, with a more smooth spatial distribution, higher concentrations in rural areas, and the highest concentrations in areas other than major urban centers. Although AOD is a highly significant predictor of PM(2.5), meteorologic parameters are major contributors to the better performance of the AOD model. CONCLUSIONS: GOES aerosol/smoke product (GASP) AOD is able to summarize a set of weather and land use conditions that stratify PM(2.5) concentrations into two different spatial patterns. Even if land use regression models do not include AOD as a predictor variable, two separate models should be fitted to account for different PM(2.5) spatial patterns related to AOD availability. |
format | Text |
id | pubmed-2702401 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2009 |
publisher | National Institute of Environmental Health Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-27024012009-07-09 Estimating Regional Spatial and Temporal Variability of PM(2.5) Concentrations Using Satellite Data, Meteorology, and Land Use Information Liu, Yang Paciorek, Christopher J. Koutrakis, Petros Environ Health Perspect Research BACKGROUND: Studies of chronic health effects due to exposures to particulate matter with aerodynamic diameters ≤ 2.5 μm (PM(2.5)) are often limited by sparse measurements. Satellite aerosol remote sensing data may be used to extend PM(2.5) ground networks to cover a much larger area. OBJECTIVES: In this study we examined the benefits of using aerosol optical depth (AOD) retrieved by the Geostationary Operational Environmental Satellite (GOES) in conjunction with land use and meteorologic information to estimate ground-level PM(2.5) concentrations. METHODS: We developed a two-stage generalized additive model (GAM) for U.S. Environmental Protection Agency PM(2.5) concentrations in a domain centered in Massachusetts. The AOD model represents conditions when AOD retrieval is successful; the non-AOD model represents conditions when AOD is missing in the domain. RESULTS: The AOD model has a higher predicting power judged by adjusted R(2) (0.79) than does the non-AOD model (0.48). The predicted PM(2.5) concentrations by the AOD model are, on average, 0.8–0.9 μg/m(3) higher than the non-AOD model predictions, with a more smooth spatial distribution, higher concentrations in rural areas, and the highest concentrations in areas other than major urban centers. Although AOD is a highly significant predictor of PM(2.5), meteorologic parameters are major contributors to the better performance of the AOD model. CONCLUSIONS: GOES aerosol/smoke product (GASP) AOD is able to summarize a set of weather and land use conditions that stratify PM(2.5) concentrations into two different spatial patterns. Even if land use regression models do not include AOD as a predictor variable, two separate models should be fitted to account for different PM(2.5) spatial patterns related to AOD availability. National Institute of Environmental Health Sciences 2009-06 2009-01-28 /pmc/articles/PMC2702401/ /pubmed/19590678 http://dx.doi.org/10.1289/ehp.0800123 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 Liu, Yang Paciorek, Christopher J. Koutrakis, Petros Estimating Regional Spatial and Temporal Variability of PM(2.5) Concentrations Using Satellite Data, Meteorology, and Land Use Information |
title | Estimating Regional Spatial and Temporal Variability of PM(2.5) Concentrations Using Satellite Data, Meteorology, and Land Use Information |
title_full | Estimating Regional Spatial and Temporal Variability of PM(2.5) Concentrations Using Satellite Data, Meteorology, and Land Use Information |
title_fullStr | Estimating Regional Spatial and Temporal Variability of PM(2.5) Concentrations Using Satellite Data, Meteorology, and Land Use Information |
title_full_unstemmed | Estimating Regional Spatial and Temporal Variability of PM(2.5) Concentrations Using Satellite Data, Meteorology, and Land Use Information |
title_short | Estimating Regional Spatial and Temporal Variability of PM(2.5) Concentrations Using Satellite Data, Meteorology, and Land Use Information |
title_sort | estimating regional spatial and temporal variability of pm(2.5) concentrations using satellite data, meteorology, and land use information |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2702401/ https://www.ncbi.nlm.nih.gov/pubmed/19590678 http://dx.doi.org/10.1289/ehp.0800123 |
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