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Background: Exposure to particulate matter air pollution (PM) has been associated with cardiovascular diseases., Objectives: In this study we evaluated whether annual exposure to ambient air pollution is associated with systemic inflammation, which is hypothesized to be an intermediate step to cardiovascular disease., Methods: Six cohorts of adults from Central and Northern Europe were used in this cross-sectional study as part of the larger ESCAPE project (European Study of Cohorts for Air Pollution Effects). Data on levels of blood markers for systemic inflammation-high-sensitivity C-reactive protein (CRP) and fibrinogen-were available for 22,561 and 17,428 persons, respectively. Land use regression models were used to estimate cohort participants' long-term exposure to various size fractions of PM, soot, and nitrogen oxides (NOx). In addition, traffic intensity on the closest street and traffic load within 100 m from home were used as indicators of traffic air pollution exposure., Results: Particulate air pollution was not associated with systemic inflammation. However, cohort participants living on a busy (> 10,000 vehicles/day) road had elevated CRP values (10.2%; 95% CI: 2.4, 18.8%, compared with persons living on a quiet residential street with < 1,000 vehicles/day). Annual NOx concentration was also positively associated with levels of CRP (3.2%; 95% CI: 0.3, 6.1 per 20 μg/m3), but the effect estimate was more sensitive to model adjustments. For fibrinogen, no consistent associations were observed., Conclusions: Living close to busy traffic was associated with increased CRP concentrations, a known risk factor for cardiovascular diseases. However, it remains unclear which specific air pollutants are responsible for the association.

Background: Studies have shown associations between mortality and long-term exposure to particulate matter air pollution. Few cohort studies have estimated the effects of the elemental composition of particulate matter on mortality., Objectives: Our aim was to study the association between natural-cause mortality and long-term exposure to elemental components of particulate matter., Methods: Mortality and confounder data from 19 European cohort studies were used. Residential exposure to eight a priori-selected components of particulate matter (PM) was characterized following a strictly standardized protocol. Annual average concentrations of copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc within PM size fractions ≤ 2.5 μm (PM2.5) and ≤ 10 μm (PM10) were estimated using land-use regression models. Cohort-specific statistical analyses of the associations between mortality and air pollution were conducted using Cox proportional hazards models using a common protocol followed by meta-analysis., Results: The total study population consisted of 291,816 participants, of whom 25,466 died from a natural cause during follow-up (average time of follow-up, 14.3 years). Hazard ratios were positive for almost all elements and statistically significant for PM2.5 sulfur (1.14; 95% CI: 1.06, 1.23 per 200 ng/m3). In a two-pollutant model, the association with PM2.5 sulfur was robust to adjustment for PM2.5 mass, whereas the association with PM2.5 mass was reduced., Conclusions: Long-term exposure to PM2.5 sulfur was associated with natural-cause mortality. This association was robust to adjustment for other pollutants and PM2.5.

Background: Land use regression (LUR) models have been developed mostly to explain intraurban variations in air pollution based on often small local monitoring campaigns. Transferability of LUR models from city to city has been investigated, but little is known about the performance of models based on large numbers of monitoring sites covering a large area., Objectives: We aimed to develop European and regional LUR models and to examine their transferability to areas not used for model development., Methods: We evaluated LUR models for nitrogen dioxide (NO2) and particulate matter (PM; PM2.5, PM2.5 absorbance) by combining standardized measurement data from 17 (PM) and 23 (NO2) ESCAPE (European Study of Cohorts for Air Pollution Effects) study areas across 14 European countries for PM and NO2. Models were evaluated with cross-validation (CV) and hold-out validation (HV). We investigated the transferability of the models by successively excluding each study area from model building., Results: The European model explained 56% of the concentration variability across all sites for NO2, 86% for PM2.5, and 70% for PM2.5 absorbance. The HV R2s were only slightly lower than the model R2 (NO2, 54%; PM2.5, 80%; PM2.5 absorbance, 70%). The European NO2, PM2.5, and PM2.5 absorbance models explained a median of 59%, 48%, and 70% of within-area variability in individual areas. The transferred models predicted a modest-to-large fraction of variability in areas that were excluded from model building (median R2: NO2, 59%; PM2.5, 42%; PM2.5 absorbance, 67%)., Conclusions: Using a large data set from 23 European study areas, we were able to develop LUR models for NO2 and PM metrics that predicted measurements made at independent sites and areas reasonably well. This finding is useful for assessing exposure in health studies conducted in areas where no measurements were conducted.

Background: Associations between traffic noise and sleep problems have been detected in experimental studies, but population-level evidence is scarce., Objectives: We studied the relationship between the levels of nighttime traffic noise and sleep disturbances and identified vulnerable population groups., Methods: Noise levels of nighttime-outdoor traffic were modeled based on the traffic intensities in the cities of Helsinki and Vantaa, Finland. In these cities, 7,019 public sector employees (81% women) responded to postal surveys on sleep and health. We linked modeled outdoor noise levels to the residences of the employees who responded to the postal survey. We used logistic regression models to estimate associations of noise levels with subjectively assessed duration of sleep and symptoms of insomnia (i.e., difficulties falling asleep, waking up frequently during the night, waking up too early in the morning, nonrestorative sleep). We also used stratified models to investigate the possibility of vulnerable subgroups., Results: For the total study population, exposure to levels of nighttime-outside (L(night, outside)) traffic noise > 55 dB was associated with any insomnia symptom ≥ 2 nights per week [odds ratio (OR) = 1.32; 95% confidence interval (CI): 1.05, 1.65]. Among participants with higher trait anxiety scores, which we hypothesized were a proxy for noise sensitivity, the ORs for any insomnia symptom at exposures to L(night, outside) traffic noises 50.1-55 dB and > 55 dB versus ≤ 45 dB were 1.34 (95% CI: 1.00, 1.80) and 1.61 (95% CI: 1.07, 2.42), respectively., Conclusions: Nighttime traffic noise levels > 50 dB L(night, outside) was associated with insomnia symptoms among persons with higher scores for trait anxiety. For the total study population, L(night, outside) > 55 dB was positively associated with any symptoms.