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BACKGROUND: Inconsistent associations between long-term exposure to particles with an aerodynamic diameter ≤ 2.5 μ m [fine particulate matter ( PM 2.5 )] components and mortality have been reported, partly related to challenges in exposure assessment. OBJECTIVES: We investigated the associations between long-term exposure to PM 2.5 elemental components and mortality in a large pooled European cohort; to compare health effects of PM 2.5 components estimated with two exposure modeling approaches, namely, supervised linear regression (SLR) and random forest (RF) algorithms. METHODS: We pooled data from eight European cohorts with 323,782 participants, average age 49 y at baseline (1985-2005). Residential exposure to 2010 annual average concentration of eight PM 2.5 components [copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)] was estimated with Europe-wide SLR and RF models at a 100 × 100 m scale. We applied Cox proportional hazards models to investigate the associations between components and natural and cause-specific mortality. In addition, two-pollutant analyses were conducted by adjusting each component for PM 2.5 mass and nitrogen dioxide ( NO 2 ) separately. RESULTS: We observed 46,640 natural-cause deaths with 6,317,235 person-years and an average follow-up of 19.5 y. All SLR-modeled components were statistically significantly associated with natural-cause mortality in single-pollutant models with hazard ratios (HRs) from 1.05 to 1.27. Similar HRs were observed for RF-modeled Cu, Fe, K, S, V, and Zn with wider confidence intervals (CIs). HRs for SLR-modeled Ni, S, Si, V, and Zn remained above unity and (almost) significant after adjustment for both PM 2.5 and NO 2 . HRs only remained (almost) significant for RF-modeled K and V in two-pollutant models. The HRs for V were 1.03 (95% CI: 1.02, 1.05) and 1.06 (95% CI: 1.02, 1.10) for SLR- and RF-modeled exposures, respectively, per 2 ng / m 3 , adjusting for PM 2.5 mass. Associations with cause-specific mortality were less consistent in two-pollutant models. CONCLUSION: Long-term exposure to V in PM 2.5 was most consistently associated with increased mortality. Associations for the other components were weaker for exposure modeled with RF than SLR in two-pollutant models. https://doi.org/10.1289/EHP8368.

BACKGROUND: Whether there is a causal relation between long-term exposure to traffic and asthma development is so far not clear. This may be explained by inaccurate exposure assessment. OBJECTIVE: We investigated the associations of long-term traffic-related exposures with asthma onset assessed retrospectively and respiratory symptoms in 9- to 10-year-old children. METHODS: We collected information on respiratory outcomes and potential confounding variables by parental questionnaire in 2,871 children in Oslo. Nitrogen dioxide exposure was assessed by the EPISODE dispersion model and assigned at updated individual addresses during lifetime. Distance to major road was assigned at birth address and address by date of questionnaire. Cox proportional hazard regression and logistic regression were used. RESULTS: We did not find positive associations between any long-term traffic-related exposure and onset of doctor-diagnosed asthma. An interquartile range (IQR) increase of NO(2) exposure before asthma onset was associated with an adjusted risk ratio of 0.82 [95% confidence interval (CI), 0.67-1.02]. Handling early asthma cases (children /= 4 years of age) were positive but not statistically significant. For current symptoms, an IQR increase of previous year's NO(2) exposure was associated with adjusted odds ratios of 1.01 (95% CI, 0.83-1.23) for wheeze, 1.10 (95% CI, 0.79-1.51) for severe wheeze, and 1.01 (95% CI, 0.84-1.21) for dry cough. CONCLUSIONS: We were not able to find positive associations of long-term traffic-related exposures with asthma onset or with current respiratory symptoms in 9- to 10-year-old children in Oslo.