Empirical spatial air pollution models have been applied extensively to assess exposure in epidemiological studies with increasingly sophisticated and complex statistical algorithms beyond ordinary linear regression. However, different algorithms have rarely been compared in terms of their predictive ability. This study compared 16 algorithms to predict annual average fine particle (PM2.5) and nitrogen dioxide (NO2) concentrations across Europe. The evaluated algorithms included linear stepwise regression, regularization techniques and machine learning methods. Air pollution models were developed based on the 2010 routine monitoring data from the AIRBASE dataset maintained by the European Environmental Agency (543 sites for PM2.5 and 2399 sites for NO2), using satellite observations, dispersion model estimates and land use variables as predictors. We compared the models by performing five-fold cross-validation (CV) and by external validation (EV) using annual average concentrations measured at 416 (PM2.5) and 1396 sites (NO2) from the ESCAPE study. We further assessed the correlations between predictions by each pair of algorithms at the ESCAPE sites. For PM2.5, the models performed similarly across algorithms with a mean CV R2 of 0.59 and a mean EV R2 of 0.53. Generalized boosted machine, random forest and bagging performed best (CV R2~0.63; EV R2 0.58-0.61), while backward stepwise linear regression, support vector regression and artificial neural network performed less well (CV R2 0.48-0.57; EV R2 0.39-0.46). Most of the PM2.5 model predictions at ESCAPE sites were highly correlated (R2 > 0.85, with the exception of predictions from the artificial neural network). For NO2, the models performed even more similarly across different algorithms, with CV R2s ranging from 0.57 to 0.62, and EV R2s ranging from 0.49 to 0.51. The predicted concentrations from all algorithms at ESCAPE sites were highly correlated (R2 > 0.9). For both pollutants, biases were low for all models except the artificial neural network. Dispersion model estimates and satellite observations were two of the most important predictors for PM2.5 models whilst dispersion model estimates and traffic variables were most important for NO2 models in all algorithms that allow assessment of the importance of variables. Different statistical algorithms performed similarly when modelling spatial variation in annual average air pollution concentrations using a large number of training sites.

Air pollutants such as NO2 and PM2.5 have consistently been linked to mortality, but only few previous studies have addressed associations with long-term exposure to black carbon (BC) and ozone (O3). We investigated the association between PM2.5, PM10, BC, NO2, and O3 and mortality in a Danish cohort of 49,564 individuals who were followed up from enrollment in 1993-1997 through 2015. Residential address history from 1979 onwards was combined with air pollution exposure obtained by the state-of-the-art, validated, THOR/AirGIS air pollution modelling system, and information on residential traffic noise exposure, lifestyle and socio-demography. We observed higher risks of all-cause as well as cardiovascular disease (CVD) mortality with higher long-term exposure to PM2.5, PM10, BC, and NO2. For PM2.5 and CVD mortality, a hazard ratio (HR) of 1.29 (95% CI: 1.13-1.47) per 5 μg/m3 was observed, and correspondingly HRs of 1.16 (95% CI: 1.05-1.27) and 1.11 (95% CI: 1.04-1.17) were observed for BC (per 1 μg/m3) and NO2 (per 10 μg/m3), respectively. Adjustment for noise gave slightly lower estimates for the air pollutants and CVD mortality. Inverse relationships were observed for O3. None of the investigated air pollutants were related to risk of respiratory mortality. Stratified analyses suggested that the elevated risks of CVD and all-cause mortality in relation to long-term PM, NO2 and BC exposure were restricted to males. This study supports a role of PM, BC, and NO2 in all-cause and CVD mortality independent of road traffic noise exposure.

Ambient air pollution has been associated with lung cancer, but the shape of the exposure-response function - especially at low exposure levels - is not well described. The aim of this study was to address the relationship between long-term low-level air pollution exposure and lung cancer incidence. The "Effects of Low-level Air Pollution: a Study in Europe" (ELAPSE) collaboration pools seven cohorts from across Europe. We developed hybrid models combining air pollution monitoring, land use data, satellite observations, and dispersion model estimates for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and ozone (O3) to assign exposure to cohort participants' residential addresses in 100 m by 100 m grids. We applied stratified Cox proportional hazards models, adjusting for potential confounders (age, sex, calendar year, marital status, smoking, body mass index, employment status, and neighborhood-level socio-economic status). We fitted linear models, linear models in subsets, Shape-Constrained Health Impact Functions (SCHIF), and natural cubic spline models to assess the shape of the association between air pollution and lung cancer at concentrations below existing standards and guidelines. The analyses included 307,550 cohort participants. During a mean follow-up of 18.1 years, 3956 incident lung cancer cases occurred. Median (Q1, Q3) annual (2010) exposure levels of NO2, PM2.5, BC and O3 (warm season) were 24.2 µg/m3 (19.5, 29.7), 15.4 µg/m3 (12.8, 17.3), 1.6 10-5m-1 (1.3, 1.8), and 86.6 µg/m3 (78.5, 92.9), respectively. We observed a higher risk for lung cancer with higher exposure to PM2.5 (HR: 1.13, 95% CI: 1.05, 1.23 per 5 µg/m3). This association was robust to adjustment for other pollutants. The SCHIF, spline and subset analyses suggested a linear or supra-linear association with no evidence of a threshold. In subset analyses, risk estimates were clearly elevated for the subset of subjects with exposure below the EU limit value of 25 µg/m3. We did not observe associations between NO2, BC or O3 and lung cancer incidence. Long-term ambient PM2.5 exposure is associated with lung cancer incidence even at concentrations below current EU limit values and possibly WHO Air Quality Guidelines.

Background— Light to moderate alcohol consumption is associated with a reduced risk of coronary heart disease. This protective effect of alcohol, however, may be confined to middle-aged or older individuals. Coronary heart disease incidence is low in men <40 years of age and in women <50 years of age; for this reason, study cohorts rarely have the power to investigate the effects of alcohol on coronary heart disease risk in younger adults. This study examined whether the beneficial effect of alcohol on coronary heart disease depends on age. Methods and Results— In this pooled analysis of 8 prospective studies from North America and Europe including 192 067 women and 74 919 men free of cardiovascular diseases, diabetes, and cancers at baseline, average daily alcohol intake was assessed at baseline with a food frequency or diet history questionnaire. An inverse association between alcohol and risk of coronary heart disease was observed in all age groups; hazard ratios among moderately drinking men (5.0 to 29.9 g/d) 39 to 50, 50 to 59, and ≥60 years of age were 0.58 (95% confidence interval [CI], 0.36 to 0.93), 0.72 (95% CI, 0.60 to 0.86), and 0.85 (95% CI, 0.75 to 0.97) compared with abstainers. However, the analyses indicated a smaller incidence rate difference between abstainers and moderate consumers in younger adults (incidence rate difference, 45 per 100 000; 90% CI, 8 to 84) than in middle-aged (incidence rate difference, 64 per 100 000; 90% CI, 24 to 102) and older (incidence rate difference, 89 per 100 000; 90% CI, 44 to 140) adults. Similar results were observed in women. Conclusion— Alcohol is also associated with a decreased risk of coronary heart disease in younger adults; however, the absolute risk was small compared with middle-aged and older adults.

Studies on health effects of long-term exposure to specific PM2.5 constituents are few. Previous studies have reported an association between black carbon (BC) exposure and cardiovascular diseases (CVD) and a few studies have found an association between sulfate exposure and mortality. These studies, however, relied mainly on exposure data from centrally located air-monitoring stations, which is a crude approximation of personal exposure. We focused on specific chemical constituents of PM2.5, i.e. elemental and primary organic carbonaceous particles (BC/OC), sea salt, secondary inorganic aerosols (SIA, i.e. NO3-, NH4+, and SO42-), and secondary organic aerosols (SOA), in relation to all-cause, CVD and respiratory disease mortality. We followed a Danish cohort of 49,564 individuals from enrollment in 1993-1997 through 2015. We combined residential address history from 1979 onwards with mean annual air pollution concentrations obtained by the AirGIS air pollution modelling system, lifestyle information from baseline questionnaires and socio-demography obtained by register linkage. During 895,897 person-years of follow-up, 10,193 deaths from all causes occurred - of which 2319 were CVD-related and 870 were related to respiratory disease. The 15-year time-weighted average concentrations of PM2.5, BC/OC, sea salt, SIA and SOA were 13.8, 2.8, 3.4, 4.9, and 0.3 µg/m3, respectively. For all-cause mortality, a higher risk was observed with higher exposure to PM2.5, BC/OC and SOA with adjusted hazard ratios of 1.03 (95% confidence intervals: 1.01, 1.05), 1.06 (1.03, 1.09), and 1.08 (1.03, 1.13) per interquartile range, respectively. The associations for BC/OC and SOA remained after adjustment for PM2.5 in two-pollutant models. For CVD mortality, we observed elevated risks with higher exposure to PM2.5, BC/OC and SIA. The results showed no clear relationship between sea salt and mortality. In this study, we observed a relationship between long-term exposure to PM2.5, BC/OC, and SOA and all-cause mortality and between PM2.5, BC/OC, and SIA and CVD mortality.