Examining intra-urban variation in fine particle mass constituents using GIS and constrained factor analysis Jane E. Clougherty a, * , E. Andres Houseman b, c , Jonathan I. Levy a a Harvard School of Public Health, Department of Environmental Health, Landmark Center 4th Floor West, Boston, MA, 02215, USA b Harvard School of Public Health, Department of Biostatistics, 665 Huntington Avenue, Boston, MA, 02115, USA c Center for Environmental Health and Technology, the Warren Alpert Medical School of Brown University,121 South Main Street, Room 217, Providence, RI 02903, USA article info Article history: Received 5 September 2008 Received in revised form 28 March 2009 Accepted 4 May 2009 Keywords: Source apportionment PM 2.5 Land use regression Factor analysis GIS abstract Recent studies have used land use regression (LUR) techniques to explain spatial variability in exposures to PM 2.5 and traffic-related pollutants. Factor analysis has been used to determine source contributions to measured concentrations. Few studies have combined these methods, however, to construct and explain latent source effects. In this study, we derive latent source factors using confirmatory factor analysis constrained to non-negative loadings, and develop LUR models to predict the influence of outdoor sources on latent source factors using GIS-based measures of traffic and other local sources, central site monitoring data, and meteorology. We collected 3–4 day samples of nitrogen dioxide (NO 2 ) and PM 2.5 outside of 44 homes in summer and winter, from 2003 to 2005 in and around Boston, Massachusetts. Reflectance analysis, X-ray fluorescence spectroscopy (XRF), and high-resolution inductively-coupled plasma mass spectrometry (ICP-MS) were performed on particle filters to estimate elemental carbon (EC), trace element, and water-soluble metals concentrations. Within our constrained factor analysis, a five-factor model was optimal, balancing statistical robustness and physical interpretability. This model produced loadings indicating long-range transport, brake wear/traffic exhaust, diesel exhaust, fuel oil combustion, and resuspended road dust. LUR models largely corroborated factor interpretations through covariate significance. For example, ‘long-range transport’ was predicted by central site PM 2.5 and season; ‘brake wear/traffic exhaust’ and ‘resuspended road dust’ by traffic and residential density; ‘diesel exhaust’ by percent diesel traffic on nearest major road; and ‘fuel oil combustion’ by population density. Results suggest that outdoor residential PM 2.5 source contributions can be partially predicted using GIS- based terms, and that LUR techniques can support factor interpretation for source apportionment. Together, LUR and factor analysis facilitate source identification, assessment of spatial and temporal variability, and more refined source exposure assignment for evaluation of source contributions to health outcomes in epidemiological studies. Ó 2009 Elsevier Ltd. All rights reserved. 1. Introduction Epidemiological studies have demonstrated relationships between fine particulate matter (PM 2.5 ) and respiratory and cardio- vascular health (Brunekreef, 2002; Hoek et al., 2002; Pope et al., 2009). In urban settings, there is specific interest in traffic and local sources (Brauer et al., 2003; Jerrett et al., 2007), though disentangling source contributions to PM 2.5 concentrations, and differentiating the health effects of various constituents, is decidedly complex. Two general approaches have been used to explore source contributions to urban PM 2.5 . Land use regression (LUR) has been used to explain intra-urban variability in PM 2.5 , using geographic information systems (GIS) and spatial modeling (Jerrett et al., 2005). Source apportionment techniques, including factor analysis (FA), have been used to differentiate source signals contributing to PM 2.5 constituents (Laden et al., 2000). In the LUR literature, most studies have focused on long-term exposure differentials in spatially-varying traffic-related pollutants, usually nitrogen dioxide (NO 2 )(Brunekreef et al., 1997; Arain et al., 2007; Gilbert et al., 2007; Jerrett et al., 2007; Rosenlund et al., 2008; Su et al., 2008). LUR studies of PM 2.5 have reported less spatial variability (Hochadel et al., 2006; Ross et al., 2007; Clougherty et al., 2008; Wheeler et al., 2008). Studies of elemental carbon (EC), a constituent of PM 2.5 , report higher intra-urban variability and stronger associations with traffic (Hochadel et al., 2006; Henderson et al., 2007; Clougherty et al., 2008), especially in Europe, where * Corresponding author. E-mail address: jcloughe@hsph.harvard.edu (J.E. Clougherty). Contents lists available at ScienceDirect Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv 1352-2310/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2009.05.003 Atmospheric Environment 43 (2009) 5545–5555