Comparison of Geostatistical Interpolation and Remote Sensing Techniques for Estimating Long-Term Exposure to Ambient PM2.5 Concentrations across the Continental United States.
BACKGROUND: A better understanding of the adverse health effects of chronic exposure to fine particulate matter (PM[sub 2.5]) requires accurate estimates of PM[sub 2.5] variation at fine spatial scales. Remote sensing has emerged as an important means of estimating PM[sub 2.5] exposures, but relativ...
| Publicado en: | Environmental Health Perspectives Vol. 120; no. 12; pp. 1727 - 1733 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
National Institute of Environmental Health Sciences
Dec2012
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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=104403976&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104403976 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00916765 3B5 jtl: Environmental Health Perspectives issn: 00916765 maglogo: N pubinfo: dt: Dec2012 vid: 120 iid: 12 pid: 56539 pub: National Institute of Environmental Health Sciences place: Research Triangle Park, North Carolina artinfo: ui: 104403976 84495996 10.1289/ehp.1205006 NLM23033456 PMC3546366 104403976 ppf: 1727 ppct: 6 formats: fmt: @attributes: type: P tig: atl: Comparison of Geostatistical Interpolation and Remote Sensing Techniques for Estimating Long-Term Exposure to Ambient PM2.5 Concentrations across the Continental United States. aug: au: Lee, Seung-Jae Serre, Marc L. van Donkelaar, Aaron Martin, Randall V. Burnett, Richard T. Jerrett, Michael affil: Geospatial Development Department, Risk Management Solutions Inc., Newark, California, USA sug: subj: Environmental Exposure Methods Particulate Matter Adverse Effects Environmental Monitoring Methods Models, Statistical United States Funding Source Linear Regression Descriptive Statistics Pearson's Correlation Coefficient Spearman's Rank Correlation Coefficient Human ab: BACKGROUND: A better understanding of the adverse health effects of chronic exposure to fine particulate matter (PM[sub 2.5]) requires accurate estimates of PM[sub 2.5] variation at fine spatial scales. Remote sensing has emerged as an important means of estimating PM[sub 2.5] exposures, but relatively few studies have compared remote-sensing estimates to those derived from monitor-based data. OBJECTIVE: We evaluated and compared the predictive capabilities of remote sensing and geostatistical interpolation. METHODS: We developed a space -- time geostatistical kriging model to predict PM[sub 2.5] over the continental United States and compared resulting predictions to estimates derived from satellite retrievals. RESULTS: The kriging estimate was more accurate for locations that were about 100 km from a monitoring station, whereas the remote sensing estimate was more accurate for locations that were > 100 km from a monitoring station. Based on this finding, we developed a hybrid map that combines the kriging and satellite-based PM[sub 2.5] estimates. CONCLUSIONS: We found that for most of the populated areas of the continental United States, geostatistical interpolation produced more accurate estimates than remote sensing. The differences between the estimates resulting from the two methods, however, were relatively small. In areas with extensive monitoring networks, the interpolation may provide more accurate estimates, but in the many areas of the world without such monitoring, remote sensing can provide useful exposure estimates that perform nearly as well. 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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