Source apportionment of indoor residential fine particulate matter using land use regression and constrained factor analysis.

Harvard School of Public Health, Department of Environmental Health, Boston, MA, USA.
Indoor Air (Impact Factor: 4.9). 09/2010; 21(1):53-66. DOI: 10.1111/j.1600-0668.2010.00682.x
Source: PubMed


Abstract Source contributions to urban fine particulate matter (PM2.5) 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 PM2.5 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.
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.

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Available from: Jane Clougherty, Nov 24, 2014
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    • "Indoor concentrations are a composite of outdoor concentrations (which vary by residential location) and indoor sources, modified by ventilation characteristics (Baxter et al., 2007a; Abt et al., 2000a). Spatial variance in outdoor concentrations of fine particulate matter (PM 2.5 ) can vary by orders of magnitude across an urban area, attributable to proximity to industrial and traffic sources, and modifying factors such as elevation or meteorology (Clougherty et al., 2011; Adgate et al., 2002). While this variance in outdoor air pollution may result in substantial indoor concentration variability, indoor sources, such as cooking, smoking, and cleaning activities, can contribute significantly to indoor air pollution (Abt et al., 2000a; Semple et al., 2012). "
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    ABSTRACT: Impacts of industrial emissions on outdoor air pollution in nearby communities are well-documented. Fewer studies, however, have explored impacts on indoor air quality in these communities. Because persons in northern climates spend a majority of their time indoors, understanding indoor exposures, and the role of outdoor air pollution in shaping such exposures, is a priority issue. Braddock and Clairton, Pennsylvania, industrial communities near Pittsburgh, are home to an active steel mill and coke works, respectively, and the population experiences elevated rates of childhood asthma. Twenty-one homes were selected for 1-week indoor sampling for fine particulate matter (PM2.5) and black carbon (BC) during summer 2011 and winter 2012. Multivariate linear regression models were used to examine contributions from both outdoor concentrations and indoor sources. In the models, an outdoor infiltration component explained 10 to 39% of variability in indoor air pollution for PM2.5, and 33 to 42% for BC. For both PM2.5 models and the summer BC model, smoking was a stronger predictor than outdoor pollution, as greater pollutant concentration increases were identified. For winter BC, the model was explained by outdoor pollution and an open windows modifier. In both seasons, indoor concentrations for both PM2.5 and BC were consistently higher than residence-specific outdoor concentration estimates. Mean indoor PM2.5 was higher, on average, during summer (25.8±22.7μg/m(3)) than winter (18.9±13.2μg/m(3)). Contrary to the study's hypothesis, outdoor concentrations accounted for only little to moderate variability (10 to 42%) in indoor concentrations; a much greater proportion of PM2.5 was explained by cigarette smoking. Outdoor infiltration was a stronger predictor for BC compared to PM2.5, especially in winter. Our results suggest that, even in industrial communities of high outdoor pollution concentrations, indoor activities - particularly cigarette smoking - may play a larger role in shaping indoor exposures. Copyright © 2015. Published by Elsevier B.V.
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    • "Spatial saturation monitoring and land use regression (LUR) modeling are standard exposure assessment methodologies for characterizing intra-urban variability in air pollution concentrations [1,4-6,11] and pollution source apportionment [15]. For spatial saturation studies, Geographic Information System (GIS)-based indicators of local air pollution sources are used to systematically allocate monitoring locations to saturate hypothesized pollution concentration gradients across complex domains. "
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