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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 2.5 absorbance, NO 2 , 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. [ABSTRACT FROM AUTHOR]

Background Tobacco smoke exposure increases the risk of cancer in the liver, but little is known about the possible risk associated with exposure to ambient air pollution. Objectives We evaluated the association between residential exposure to air pollution and primary liver cancer incidence. Methods We obtained data from four cohorts with enrolment during 1985–2005 in Denmark, Austria and Italy. Exposure to nitrogen oxides (NO 2 and NO X ), particulate matter (PM) with diameter of less than 10 µm (PM 10 ), less than 2.5 µm (PM 2.5 ), between 2.5 and 10 µm (PM 2.5–10 ) and PM 2.5 absorbance (soot) at baseline home addresses were estimated using land-use regression models from the ESCAPE project. We also investigated traffic density on the nearest road. We used Cox proportional-hazards models with adjustment for potential confounders for cohort-specific analyses and random-effects meta-analyses to estimate summary hazard ratios (HRs) and 95% confidence intervals (CIs). Results Out of 174,770 included participants, 279 liver cancer cases were diagnosed during a mean follow-up of 17 years. In each cohort, HRs above one were observed for all exposures with exception of PM 2.5 absorbance and traffic density. In the meta-analysis, all exposures were associated with elevated HRs, but none of the associations reached statistical significance. The summary HR associated with a 10-μg/m 3 increase in NO 2 was 1.10 (95% confidence interval (CI): 0.93, 1.30) and 1.34 (95% CI: 0.76, 2.35) for a 5-μg/m 3 increase in PM 2.5 . Conclusions The results provide suggestive evidence that ambient air pollution may increase the risk of liver cancer. Confidence intervals for associations with NO 2 and NO X were narrower than for the other exposures. [ABSTRACT FROM AUTHOR]

Satellite-derived (SAT) and chemical transport model (CTM) estimates of PM 2.5 and NO 2 are increasingly used in combination with Land Use Regression (LUR) models. We aimed to compare the contribution of SAT and CTM data to the performance of LUR PM 2.5 and NO 2 models for Europe. Four sets of models, all including local traffic and land use variables, were compared (LUR without SAT or CTM, with SAT only, with CTM only, and with both SAT and CTM). LUR models were developed using two monitoring data sets: PM 2.5 and NO 2 ground level measurements from the European Study of Cohorts for Air Pollution Effects (ESCAPE) and from the European AIRBASE network. LUR PM 2.5 models including SAT and SAT+CTM explained ~60% of spatial variation in measured PM 2.5 concentrations, substantially more than the LUR model without SAT and CTM (adjR 2 : 0.33–0.38). For NO 2 CTM improved prediction modestly (adjR 2 : 0.58) compared to models without SAT and CTM (adjR 2 : 0.47–0.51). Both monitoring networks are capable of producing models explaining the spatial variance over a large study area. SAT and CTM estimates of PM 2.5 and NO 2 significantly improved the performance of high spatial resolution LUR models at the European scale for use in large epidemiological studies. [ABSTRACT FROM AUTHOR]