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 <or= 2.5 microm (PM(2.5)). Particular interest lies in estimating spatial heterogeneity using AOD, with important application to...
| Publicado en: | Environmental Health Perspectives Vol. 117; no. 6; pp. 904 - 910 |
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| Autores principales: | , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
National Institute of Environmental Health Sciences
Jun2009
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105395296&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105395296 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00916765 3B5 jtl: Environmental Health Perspectives issn: 00916765 maglogo: N pubinfo: dt: Jun2009 vid: 117 iid: 6 pid: 56539 pub: National Institute of Environmental Health Sciences place: Research Triangle Park, North Carolina artinfo: ui: 105395296 2010358434 10.1289/ehp.0800360 NLM19590681 105395296 ppf: 904 ppct: 6 formats: fmt: @attributes: type: P tig: atl: Limitations of remotely sensed aerosol as a spatial proxy for fine particulate matter. aug: au: Paciorek CJ Liu Y affil: Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA. paciorek@alumni.cmu.edu sug: subj: Aerosols Analysis Air Pollution Chronic Disease Particulate Matter Analysis Models, Statistical Regression United States United States Environmental Protection Agency Human ab: BACKGROUND: Recent research highlights the promise of remotely sensed aerosol optical depth (AOD) as a proxy for ground-level particulate matter with aerodynamic diameter <or= 2.5 microm (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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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