Source apportionment of indoor residential fine particulate matter using land use regression and constrained factor analysis.
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.
- SourceAvailable from: Jane Clougherty[Show abstract] [Hide abstract]
ABSTRACT: Characterizing intra-urban variation in air quality is important for epidemiological investigation of health outcomes and disparities. To date, however, few studies have been designed to capture spatial variation during select hours of the day, or to examine the roles of meteorology and complex terrain in shaping intra-urban exposure gradients. We designed a spatial saturation monitoring study to target local air pollution sources, and to understand the role of topography and temperature inversions on fine-scale pollution variation by systematically allocating sampling locations across gradients in key local emissions sources (vehicle traffic, industrial facilities) and topography (elevation) in the Pittsburgh area. Street-level integrated samples of fine particulate matter (PM2.5), black carbon (BC), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3) were collected during morning rush and probable inversion hours (6-11 AM), during summer and winter. We hypothesized that pollution concentrations would be: 1) higher under inversion conditions, 2) exacerbated in lower-elevation areas, and 3) vary by season. During July - August 2011 and January - March 2012, we observed wide spatial and seasonal variability in pollution concentrations, exceeding the range measured at regulatory monitors. We identified elevated concentrations of multiple pollutants at lower-elevation sites, and a positive association between inversion frequency and NO2 concentration. We examined temporal adjustment methods for deriving seasonal concentration estimates, and found that the appropriate reference temporal trend differs between pollutants. Our time-stratified spatial saturation approach found some evidence for modification of inversion-concentration relationships by topography, and provided useful insights for refining and interpreting GIS-based pollution source indicators for Land Use Regression modeling.Environmental Health 04/2014; 13(1):28. · 2.71 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Cooking is a significant source of indoor particulate matter that can cause adverse health effects. In this study, a 5-stage cascade impactor was used to collect particulate matter from 14 restaurants that cooked with charcoal in Kocaeli, the second largest city in Turkey. A total of 24 elements were quantified using ICP-MS. All of the element contents except for Mn were higher for fine particles (PM2.5) than coarse particles (PM>2.5), and the major trace elements identified in the PM2.5 included V, Se, Zn, Cr, As, Cu, Ni, and Pb. Principle component analysis (PCA) and enrichment factor (EF) calculations were used to determine the sources of PM2.5. Four factors that explained over 77% of the total variance were identified by the PCA. These factors included charcoal combustion, indoor activities, crustal components, and road dust. The Se, As, Cd, and V contents in the PM2.5 were highly enriched (EF>100). The health risks posed by the individual metals were calculated to assess the potential health risks associated with inhaling the fine particles released during charcoal cooking. The total hazard quotient (total HQ) for a PM2.5 of 4.09 was four times greater than the acceptable limit (i.e., 1.0). In addition, the excess lifetime cancer risk (total ELCR) for PM2.5 was 1.57×10(-4), which is higher than the acceptable limit of 1.0×10(-6). Among all of the carcinogenic elements present in the PM2.5, the cancer risks resulting from Cr(VI) and As exposure were the highest (i.e., 1.16×10(-4) and 3.89×10(-5), respectively). Overall, these results indicate that the lifetime cancer risk associated with As and Cr(VI) exposure is significant at selected restaurants, which is of concern for restaurant workers.Science of The Total Environment 03/2013; 454-455C:79-87. · 3.26 Impact Factor