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

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