The association of LUR modeled PM2.5 elemental composition with personal exposure

Abstract

Background and aims: Land use regression (LUR) models predict spatial variation of ambient concentrations, but little is known about the validity in predicting personal exposures. In this study, the association of LUR modeled concentrations of PM2.5 components with measured personal concentrations was determined. The elements of interest were copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V) and zinc (Zn). Methods: In Helsinki (Finland), Utrecht (the Netherlands) and Barcelona (Spain) five participants from urban background, five from suburban background and five from busy street sites were selected in each city (15 participants per city). Outdoor, indoor and personal 96-hour PM2.5 samples were collected by the participants over periods of two weeks in three different seasons (winter, summer and spring/autumn) and the overall average was calculated. Elemental composition was measured by ED-XRF spectrometry. The LUR models for the average ambient concentrations of each element were developed by the ESCAPE project. Results: LUR models predicted the within-city variation of average outdoor Cu and Fe concentrations moderately well (range in R(2)27-67% for Cu and 24-54% for Fe). The outdoor concentrations of the other elements were not well predicted. The LUR modeled concentration only significantly correlated with measured personal Fe exposure in Utrecht and Ni and V in Helsinki. The LUR model predictions did not correlate with measured personal Cu exposure. After excluding observations with an indoor/outdoor ratio of >1.5, modeled Cu outdoor concentrations correlated with indoor concentrations in Helsinki and Utrecht and personal concentrations in Utrecht. The LUR model predictions were associated with measured outdoor, indoor and personal concentrations for all elements when the data for the three cities was pooled. Conclusions: Within-city modeled variation of elemental composition of PM2.5 did not predict measured variation in personal exposure well. (C) 2014 Published by Elsevier B.V.