Land use regression models for crustal and traffic-related PM2.5 constituents in four areas of the SAPALDIA study

Abstract

Many studies have documented adverse health effects of long-term exposure to fine particulate matter (PM2.5), but there is still limited knowledge regarding the causal relationship between specific sources of PM2.5 and such health effects. The spatial variability of PM2.5 constituents and sources, as a exposure assessment strategy for investigating source contributions to health effects, has been little explored so far. Between 2011 and 2012, three measurement campaigns of PM and nitrogen dioxide (NO2) were performed in 80 sites across four areas of the Swiss Study on Air Pollution and Lung and heart Diseases in Adults (SAPALDIA). Reflectance analysis and energy dispersive X-ray fluorescence (XRF) were performed on PM2.5 filter samples to estimate light absorbance and trace element concentrations, respectively. Three air pollution source factors were identified using principal-component factor analysis: vehicular, crustal, and long-range transport. Land use regression (LUR) models were developed for temporally-adjusted scores of each factor, combining the four study areas. Model performance was assessed using two cross-validation methods. Model explained variance was high for the vehicular factor (R(2)=0.76), moderate for the crustal factor (R(2)=0.46), and low for the long-range transport factor (R(2)=0.19). The cross-validation methods suggested that models for the vehicular and crustal factors moderately accounted for both the between and within-area variability, and therefore can be applied to the four study areas to estimate long-term exposures within the SAPALDIA study population. The combination of source apportionment techniques and LUR modelling may help in identifying air pollution sources and disentangling their contribution to observed health effects in epidemiologic studies.

Keywords: Factor analysis; Land use regression; PM(2.5); Sapaldia; Source apportionment.