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 PM2.5 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 PM2.5 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 PM2.5 and other metrics (PM2.5absorbance, NO2, Cu, Fe). HOV (hold-out validation) R(2) 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 (R(2) = .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.