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ABSTRACT: Source contributions to urban fine particulate matter (PM(2.5) ) have been modelled using land use regression (LUR) and factor analysis (FA). However, people spend more time indoors, where these methods are less explored. We collected 3-4- day samples of nitrogen dioxide and PM(2.5) inside and outside of 43 homes in summer and winter, 2003-2005, in and around Boston, Massachusetts. Particle filters were analysed for black carbon and trace element concentrations using reflectometry, X-ray fluorescence (XRF), and high-resolution inductively coupled mass spectrometry (ICP-MS). We regressed indoor against outdoor concentrations modified by ventilation, isolating the indoor-attributable fraction, and then applied constrained FA to identify source factors in indoor concentrations and residuals. Finally, we developed LUR predictive models using GIS-based outdoor source indicators and questionnaire data on indoor sources. FA using concentrations and residuals reasonably separated outdoor (long-range transport/meteorology, fuel oil/diesel, road dust) from indoor sources (combustion, smoking, cleaning). Multivariate LUR regression models for factors from concentrations and indoor residuals showed limited predictive power, but corroborated some indoor and outdoor factor interpretations. Our approach to validating source interpretations using LUR methods provides direction for studies characterizing indoor and outdoor source contributions to indoor cocentrations. PRACTICAL IMPLICATIONS: By merging indoor-outdoor modeling, factor analysis, and LUR-style predictive regression modeling, we have added to previous source apportionment studies by attempting to corroborate factor interpretations. Our methods and results support the possibility that indoor exposures may be modeled for epidemiologic studies, provided adequate sample size and variability to identify indoor and outdoor source contributions. Using these techniques, epidemiologic studies can more clearly examine exposures to indoor sources and indoor penetration of source-specific components, reduce exposure misclassification, and improve the characterization of the relationship between particle constituents and health effects.
Indoor Air 09/2010; 21(1):53-66. · 2.55 Impact Factor
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Epidemiology. 01/2007; 18(5):S49-S50.
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Epidemiology. 01/2007; 18(5):S69-S69.
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Healthy Buildings 2006Healthy Buildings 2006, Lisboa, Portugal; 01/2006
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Epidemiology. 01/2005; 16(5):S117-S118.
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Epidemiology. 01/2005; 16(5):S118-S118.
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ABSTRACT: In an environmental intervention study in public housing, we examined monthly Juniper Paediatric Asthma Quality of Life (QOL) Questionnaires for 51 children. Longitudinal analysis and spline models were used to identify time periods with significant improvements in QOL to inform judgments about causality. We found significant improvements in QOL, with moderate improvements before environmental interventions, increased rates of improvement immediately after, and reduced rates more than 5 months post-intervention. Effect modification analyses identified high-risk subpopulations and emphasized the importance of environmental, social, and economic conditions. Our results demonstrate the value of longitudinal techniques in evaluating the benefits of environmental interventions for asthma.
Journal of Asthma 43(5):335-43. · 1.52 Impact Factor