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Background: Epidemiological evidence on the association between ambient air pollution and brain tumor risk is sparse and inconsistent., Methods: In 12 cohorts from 6 European countries, individual estimates of annual mean air pollution levels at the baseline residence were estimated by standardized land-use regression models developed within the ESCAPE and TRANSPHORM projects: particulate matter (PM) ≤2.5, ≤10, and 2.5-10 μm in diameter (PM2.5, PM10, and PMcoarse), PM2.5 absorbance, nitrogen oxides (NO2 and NOx) and elemental composition of PM. We estimated cohort-specific associations of air pollutant concentrations and traffic intensity with total, malignant, and nonmalignant brain tumor, in separate Cox regression models, adjusting for risk factors, and pooled cohort-specific estimates using random-effects meta-analyses., Results: Of 282194 subjects from 12 cohorts, 466 developed malignant brain tumors during 12 years of follow-up. Six of the cohorts also had data on nonmalignant brain tumor, where among 106786 subjects, 366 developed brain tumor: 176 nonmalignant and 190 malignant. We found a positive, statistically nonsignificant association between malignant brain tumor and PM2.5 absorbance (hazard ratio and 95% CI: 1.67; 0.89-3.14 per 10-5/m3), and weak positive or null associations with the other pollutants. Hazard ratio for PM2.5 absorbance (1.01; 0.38-2.71 per 10-5/m3) and all other pollutants were lower for nonmalignant than for malignant brain tumors., Conclusion: We found suggestive evidence of an association between long-term exposure to PM2.5 absorbance indicating traffic-related air pollution and malignant brain tumors, and no association with overall or nonmalignant brain tumors., (© The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.)

Objectives: Dust is generally sampled on a filter using air pumps, but passive sampling could be a cost-effective alternative. One promising passive sampler is the University of North Carolina passive aerosol sampler (UNC sampler). The aim of this study is to characterize and compare the UNC sampler's performance with PM10 and PM2.5 impactors in a working environment., Methods: Area sampling was carried out at different mining locations using UNC samplers in parallel with PM2.5 and PM10 impactors. Two different collection surfaces, polycarbonate (PC) and carbon tabs (CT), were employed for the UNC sampling. Sampling was carried out for 4-25 hours., Results: The UNC samplers underestimated the concentrations compared to PM10 and PM2.5 impactor data. At the location with the highest aerosol concentration, the time-averaged mean of PC showed 24% and CT 35% of the impactor result for PM2.5. For PM10, it was 39% with PC and 58% with CT. Sample blank values differed between PC and CT. For PM2.5, PC blank values were ~7 times higher than those of CT, but only 1.8 times higher for PM10. The blank variations were larger for PC than for CT., Conclusions: Particle mass concentrations appear to be underestimated by the UNC sampler compared to impactors, more so for PM2.5 than for PM10. CT may be preferred as a collection surface because the blank values were lower and less variable than for PC. Future validations in the working environment should include respirable dust sampling., (© The Author 2017. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

Background: Long-term exposure to air pollution has been hypothesized to elevate arterial blood pressure (BP). The existing evidence is scarce and country specific., Objectives: We investigated the cross-sectional association of long-term traffic-related air pollution with BP and prevalent hypertension in European populations., Methods: We analyzed 15 population-based cohorts, participating in the European Study of Cohorts for Air Pollution Effects (ESCAPE). We modeled residential exposure to particulate matter and nitrogen oxides with land use regression using a uniform protocol. We assessed traffic exposure with traffic indicator variables. We analyzed systolic and diastolic BP in participants medicated and nonmedicated with BP-lowering medication (BPLM) separately, adjusting for personal and area-level risk factors and environmental noise. Prevalent hypertension was defined as ≥ 140 mmHg systolic BP, or ≥ 90 mmHg diastolic BP, or intake of BPLM. We combined cohort-specific results using random-effects meta-analysis., Results: In the main meta-analysis of 113,926 participants, traffic load on major roads within 100 m of the residence was associated with increased systolic and diastolic BP in nonmedicated participants [0.35 mmHg (95% CI: 0.02, 0.68) and 0.22 mmHg (95% CI: 0.04, 0.40) per 4,000,000 vehicles × m/day, respectively]. The estimated odds ratio (OR) for prevalent hypertension was 1.05 (95% CI: 0.99, 1.11) per 4,000,000 vehicles × m/day. Modeled air pollutants and BP were not clearly associated., Conclusions: In this first comprehensive meta-analysis of European population-based cohorts, we observed a weak positive association of high residential traffic exposure with BP in nonmedicated participants, and an elevated OR for prevalent hypertension. The relationship of modeled air pollutants with BP was inconsistent.

The aim of this study was to make a preliminary evaluation of the University of North Carolina passive aerosol sampler (UNC sampler) for personal air sampling of particles. Nine personal air samplings of respirable fraction were conducted in an open-pit mine, with pairwise UNC samplers and a respirable cyclone mounted on the chest of workers. UNC samples were analyzed with scanning electron microscopy (SEM) and to some extent energy dispersive X-ray spectroscopy (EDS). Respirable cyclone filter samples were weighed. Correlations and particle elemental compositions were described. Microscopic imaging of the collection surface showed that the particles were heterogeneously deposited across the surface of the UNC sampler. Collected particles were shaped as gravel particles and the resulting particle size distribution in air showed a peak at ca. 3 µm aerodynamic diameter, similarly to what has previously been reported from the same mine. The elemental composition indicated mineral origin. All correlations between the airborne mass concentrations from UNC samplers and respirable cyclones (Pearson = 0.54 and Spearman = 0.43) and between pairs of parallel UNC samplers (Pearson = 0.55 and Spearman = 0.67) were weak. The UNC sampler mass concentrations were approximately 30 times higher than those measured with the respirable cyclone. In conclusion, the UNC sampler, when used for personal sampling in a mine, provides a reasonable particle size distribution and the deposited particles appeared to be of mineral origin and not from textile or skin but the approximately 30-fold overestimation of mass concentrations when comparing with respirable cyclone sampling indicates that further improvements are necessary. Positioning of the sampler may be critical and moving the UNC sampler from the chest to e.g. the top of a helmet might be an improvement. Grounding of the sampler in order to avoid static electricity might also be useful. The UNC sampler should continue to be researched for personal sampling, as passive sampling might become a useful alternative to more laborious sampling techniques. [ABSTRACT FROM AUTHOR]

Objectives: In an occupational environment, passive sampling could be an alternative to active sampling with pumps for sampling of dust. One passive sampler is the University of North Carolina passive aerosol sampler (UNC sampler). It is often analysed by microscopic imaging. Promising results have been shown for particles above 2.5 µm, but indicate large underestimations for PM2.5. The aim of this study was to evaluate, and possibly improve, the UNC sampler for stationary sampling in a working environment. Methods: Sampling was carried out at 8-h intervals during 24 h in four locations in an open pit mine with UNC samplers, respirable cyclones, PM10 and PM2.5 impactors, and an aerodynamic particle sizer (APS). The wind was minimal. For quantification, two modifications of the UNC sampler analysis model, UNC sampler with hybrid model and UNC sampler with area factor, were compared with the original one, UNC sampler with mesh factor derived from wind tunnel experiments. The effect of increased resolution for the microscopic imaging was examined. Results: Use of the area factor and a higher resolution eliminated the underestimation for PM10 and PM2.5. The model with area factor had the overall lowest deviation versus the impactor and the cyclone. The intraclass correlation (ICC) showed that the UNC sampler had a higher precision and better ability to distinguish between different exposure levels compared to the cyclone (ICC: 0.51 versus 0.24), but lower precision compared to the impactor (PM10: 0.79 versus 0.99; PM2.5: 0.30 versus 0.45). The particle size distributions as calculated from the different UNC sampler analysis models were visually compared with the distributions determined by APS. The distributions were obviously different when the UNC sampler with mesh factor was used but came to a reasonable agreement when the area factor was used. Conclusions: High resolution combined with a factor based on area only, results in no underestimation of small particles compared to impactors and cyclones and a better agreement with the APS's particle size distributions. The UNC sampler had lower precision than the impactors, but higher than the respirable cyclone. The UNC sampler with area factor could be used for PM2.5, PM10 and respirable fraction measurements in this working environment without wind. [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) incidence have been assessed in connection with air pollution exposure in several studies; however, few have investigated associations with source-specific local emissions. This study aims to estimate the risk of DM incidence associated with source-specific air pollution in a Swedish cohort with relatively low exposure. Individuals in the Västerbotten intervention programme cohort were followed until either a DM diagnosis or initiation of treatment with glucose-lowering medication occurred. Dispersion models with high spatial resolution were used to estimate annual mean concentrations of particulate matter (PM) with aerodynamic diameter ≤10 μm (PM 10) and ≤2.5 μm (PM 2.5) at individual addresses. Hazard ratios were estimated using Cox regression models in relation to moving averages 1–5 years preceding the outcome. During the study period, 1479 incident cases of DM were observed during 261,703 person-years of follow-up. Increased incidence of DM was observed in association with PM 10 (4% [95% CI: -54–137%] per 10 μg/m3), PM 10 -traffic (2% [95% CI: -6–11%] per 1 μg/m3) and PM 2.5 -exhaust (11% [95% CI: -39–103%] per 1 μg/m3). A negative association was found for both PM 2.5 (-18% [95% CI: -99–66%] per 5 μg/m3), but only in the 2nd exposure tertile (-10% [95% CI: -25–9%] compared to the first tertile), and PM 2.5 -woodburning (-30% [95% CI: -49–4%] per 1 μg/m3). In two-pollutant models including PM 2.5 -woodburning, there was an 11% [95% CI: -11–38%], 6% [95% CI: -16–34%], 13% [95% CI: -7–36%] and 17% [95% CI: 4–41%] higher risk in the 3rd tertile of PM 10 , PM 2.5 , PM 10 -traffic and PM 2.5 -exhaust, respectively, compared to the 1st. Although the results lacked in precision they are generally in line with the current evidence detailing particulate matter air pollution from traffic as an environmental risk factor for DM. • Risk increases for DM were estimated in relation to long-term PM exposure. • The highest risk increase per μg/m3 was in relation to PM 2.5 -exhaust. • Associations with PM 2.5 -exhaust remained after adjusting for PM-woodburning. [ABSTRACT FROM AUTHOR]