Document Detail


Predicting residential indoor concentrations of nitrogen dioxide, fine particulate matter, and elemental carbon using questionnaire and geographic information system based data.
MedLine Citation:
PMID:  19830252     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Previous studies have identified associations between traffic-related air pollution and adverse health effects. Most have used measurements from a few central ambient monitors and/or some measure of traffic as indicators of exposure, disregarding spatial variability and/or factors influencing personal exposure-ambient concentration relationships. This study seeks to utilize publicly available data (i.e., central site monitors, geographic information system (GIS), and property assessment data) and questionnaire responses to predict residential indoor concentrations of traffic-related air pollutants for lower socioeconomic status (SES) urban households.As part of a prospective birth cohort study in urban Boston, we collected indoor and outdoor 3-4 day samples of nitrogen dioxide (NO(2)) and fine particulate matter (PM(2.5)) in 43 low SES residences across multiple seasons from 2003 - 2005. Elemental carbon concentrations were determined via reflectance analysis. Multiple traffic indicators were derived using Massachusetts Highway Department data and traffic counts collected outside sampling homes. Home characteristics and occupant behaviors were collected via a standardized questionnaire. Additional housing information was collected through property tax records, and ambient concentrations were collected from a centrally-located ambient monitor.The contributions of ambient concentrations, local traffic and indoor sources to indoor concentrations were quantified with regression analyses. PM(2.5) was influenced less by local traffic but had significant indoor sources, while EC was associated with traffic and NO(2) with both traffic and indoor sources. Comparing models based on covariate selection using p-values or a Bayesian approach yielded similar results, with traffic density within a 50m buffer of a home and distance from a truck route as important contributors to indoor levels of NO(2) and EC, respectively. The Bayesian approach also highlighted the uncertanity in the models. We conclude that by utilizing public databases and focused questionnaire data we can identify important predictors of indoor concentrations for multiple air pollutants in a high-risk population.
Authors:
Lisa K Baxter; Jane E Clougherty; Chritopher J Paciorek; Rosalind J Wright; Jonathan I Levy
Publication Detail:
Type:  JOURNAL ARTICLE    
Journal Detail:
Title:  Atmospheric environment (Oxford, England : 1994)     Volume:  41     ISSN:  -     ISO Abbreviation:  Atmos Environ     Publication Date:  2007 Oct 
Date Detail:
Created Date:  2009-10-15     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9888534     Medline TA:  Atmos Environ     Country:  -    
Other Details:
Languages:  ENG     Pagination:  6561-6571     Citation Subset:  -    
Affiliation:
Harvard School of Public Health, Department of Environmental Health, Landmark Center-4 Floor West, P.O. Box 15677, Boston, MA 02215, USA.
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MeSH Terms
Descriptor/Qualifier:
Grant Support
ID/Acronym/Agency:
R03 ES013988-01//NIEHS NIH HHS; R03 ES013988-02//NIEHS NIH HHS; U01 HL072494-01//NHLBI NIH HHS; U01 HL072494-02//NHLBI NIH HHS

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