Land use regression models for the oxidative potential of fine particles (PM_2.5) in five European areas

Oxidative potential (OP) of particulate matter (PM) is proposed as a biologically-relevant exposure metric for studies of air pollution and health. We aimed to evaluate the spatial variability of the OP of measured PM_2.5 using ascorbate (AA) and (reduced) glutathione (GSH), and develop land use regression (LUR) models to explain this spatial variability. We estimated annual average values (m^-3) of OP^AA and OP^GSH for five areas (Basel, CH; Catalonia, ES; London-Oxford, UK (no OP^GSH); the Netherlands; and Turin, IT) using PM_2.5 filters. OP^AA and OP^GSH LUR models were developed using all monitoring sites, separately for each area and combined-areas. The same variables were then used in repeated sub-sampling of monitoring sites to test sensitivity of variable selection; new variables were offered where variables were excluded (p > .1). On average, measurements of OP^AA and OP^GSH were moderately correlated (maximum Pearson's maximum Pearson's R = = .7) with PM_2.5 and other metrics (PM_2.5absorbance, NO₂, Cu, Fe). HOV (hold-out validation) R² for OP^AA models was .21, .58, .45, .53, and .13 for Basel, Catalonia, London-Oxford, the Netherlands and Turin respectively. For OP^GSH, the only model achieving at least moderate performance was for the Netherlands (R² = .31). Combined models for OP^AA and OP^GSH were largely explained by study area with weak local predictors of intra-area contrasts; we therefore do not endorse them for use in epidemiologic studies. Given the moderate correlation of OP^AA with other pollutants, the three reasonably performing LUR models for OP^AA could be used independently of other pollutant metrics in epidemiological studies.