Exposure to long-term source-specific transportation noise and incident breast cancer: A pooled study of eight Nordic cohorts.

BACKGROUND: Environmental noise is an important environmental exposure that can affect health. An association between transportation noise and breast cancer incidence has been suggested, although current evidence is limited. We investigated the pooled association between long-term exposure to transportation noise and breast cancer incidence. METHODS: Pooled data from eight Nordic cohorts provided a study population of 111,492 women. Road, railway, and aircraft noise were modelled at residential addresses. Breast cancer incidence (all, estrogen receptor (ER) positive, and ER negative) was derived from cancer registries. Hazard ratios (HR) were estimated using Cox Proportional Hazards Models, adjusting main models for sociodemographic and lifestyle variables together with long-term exposure to air pollution. RESULTS: A total of 93,859 women were included in the analyses, of whom 5,875 developed breast cancer. The median (5th-95th percentile) 5-year residential road traffic noise was 54.8 (40.0-67.8) dB Lden, and among those exposed, the median railway noise was 51.0 (41.2-65.8) dB Lden. We observed a pooled HR for breast cancer (95 % confidence interval (CI)) of 1.03 (0.99-1.06) per 10 dB increase in 5-year mean exposure to road traffic noise, and 1.03 (95 % CI: 0.96-1.11) for railway noise, after adjustment for lifestyle and sociodemographic covariates. HRs remained unchanged in analyses with further adjustment for PM2.5 and attenuated when adjusted for NO2 (HRs from 1.02 to 1.01), in analyses using the same sample. For aircraft noise, no association was observed. The associations did not vary by ER status for any noise source. In analyses using <60 dB as a cutoff, we found HRs of 1.08 (0.99-1.18) for road traffic and 1.19 (0.95-1.49) for railway noise. CONCLUSIONS: We found weak associations between road and railway noise and breast cancer risk. More high-quality prospective studies are needed, particularly among those exposed to railway and aircraft noise before conclusions regarding noise as a risk factor for breast cancer can be made.

Long-term exposure to traffic noise and risk of incident colon cancer: A pooled study of eleven Nordic cohorts.

Background Colon cancer incidence is rising globally, and factors pertaining to urbanization have been proposed involved in this development. Traffic noise may increase colon cancer risk by causing sleep disturbance and stress, thereby inducing known colon cancer risk-factors, e.g. obesity, diabetes, physical inactivity, and alcohol consumption, but few studies have examined this. Objectives The objective of this study was to investigate the association between traffic noise and colon cancer (all, proximal, distal) in a pooled population of 11 Nordic cohorts, totaling 155,203 persons. Methods We identified residential address history and estimated road, railway, and aircraft noise, as well as air pollution, for all addresses, using similar exposure models across cohorts. Colon cancer cases were identified through national registries. We analyzed data using Cox Proportional Hazards Models, adjusting main models for harmonized sociodemographic and lifestyle data. Results During follow-up (median 18.8 years), 2757 colon cancer cases developed. We found a hazard ratio (HR) of 1.05 (95% confidence interval (CI): 0.99-1.10) per 10-dB higher 5-year mean time-weighted road traffic noise. In sub-type analyses, the association seemed confined to distal colon cancer: HR 1.06 (95% CI: 0.98-1.14). Railway and aircraft noise was not associated with colon cancer, albeit there was some indication in sub-type analyses that railway noise may also be associated with distal colon cancer. In interaction-analyses, the association between road traffic noise and colon cancer was strongest among obese persons and those with high NO2-exposure. Discussion A prominent study strength is the large population with harmonized data across eleven cohorts, and the complete address-history during follow-up. However, each cohort estimated noise independently, and only at the most exposed façade, which may introduce exposure misclassification. Despite this, the results of this pooled study suggest that traffic noise may be a risk factor for colon cancer, especially of distal origin.

Self-reported health in the vicinity of five wind power production areas in Finland.

In many countries, some people living in the vicinity of wind power production areas report having symptoms that they intuitively associate with wind turbines. Recently public discussions have focused especially on wind turbine infrasound. However, scientific evidence supporting an association is lacking. The aim of this study was to assess the association between exposure to wind turbines and the prevalence of self-reported symptoms, diseases and medications. A cross-sectional questionnaire study (n = 2,828) was conducted in the vicinity of five wind power production areas in Finland in 2015-2016. Each area had 3-16 turbines with a nominal power of 2.4-3.3 MW. The response rate was 50% (n = 1,411). Continuous and categorised (≤ 2.5, > 2.5-5, > 5-10 km) distance between the respondents' home and the closest wind turbine was used to represent exposure to wind turbines. Wind turbine sound pressure level outdoors could be reliably modelled only for the closest distance zone where the yearly average was 34 dB and maximum 43 dB. The data on symptoms (headache, nausea, dizziness, tinnitus, ear fullness, arrhythmia, fatigue, difficulties in falling asleep, waking up too early, anxiety, stress), diseases (hypertension, heart insufficiency, diabetes), and medications (analgesics for headache, joint/muscle pain and other pain, and medication for sleep disturbance, anxiety and depression, and hypertension) was obtained from the questionnaire. Logistic regression analyses were adjusted for age, sex, marital status, education, work situation, smoking, alcohol consumption, physical activity, body mass index, and hearing problems. Annoyance and sleep disturbance due to wind turbine noise were inversely associated with the distance to the closest wind turbine. The prevalence of symptoms, diseases and medications was essentially the same in all distance categories. In multivariate regression modelling, the odds ratio estimates were generally close to unity and statistically non-significant. Beyond annoyance and sleep disturbance, there were no consistent associations between exposure to wind turbines and self-reported health problems. The results do not support the hypothesis that broadband sound or infrasound from wind turbines could cause the proposed health problems.

Air Pollution from Road Traffic and Systemic Inflammation in Adults: A Cross-Sectional Analysis in the European ESCAPE Project.

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.

Driver and passenger exposure to aerosol particles in buses and trams in Helsinki, Finland.

This study investigates commuter and driver exposure to aerosol particles in buses and trams in Helsinki, Finland. Particle number and PM(2.5) concentrations were determined in the cabin and the driver's compartment. In addition, the <2.5 microm black carbon concentration was measured in the driver's compartment and PM(2.5) was collected for elemental analysis in the cabin. The measurements were repeated on two generations of buses and trams including two measurement days in each vehicle type. Fine particle number and mass concentrations in the driver's compartments were only slightly increased compared to Helsinki background air. Daily average ratios of number and mass to the background varied in range 0.8-4.3 and 1.0-2.9, respectively, both being the highest in the older bus type. However, the drivers were exposed to elevated levels of black carbon, which some studies have addressed to be strongly correlated with adverse health effects. The daily average ratio of black carbon to the background varied between 2.4 and 11.4. Additionally, the black carbon concentration had spatial variation. The drivers were exposed to higher peak concentrations of black carbon in downtown area. Particle concentrations were smaller in the driver's compartment than in the cabin. The newer technology in the newer model of the tram and bus seemed to decrease driver exposure to aerosol particles.

A simple procedure for correcting loading effects of aethalometer data.

A simple method for correcting for the loading effects of aethalometer data is presented. The formula BC(CORRECTED) = (1 + k x ATN) x BC(NONCORRECTED), where ATN is the attenuation and BC is black carbon, was used for correcting aethalometer data obtained from measurements at three different sites: a subway station in Helsinki, an urban background measurement station in Helsinki, and a rural station in Hyytiälä in central Finland. The BC data were compared with simultaneously measured aerosol volume concentrations (V). After the correction algorithm, the BC-to-V ratio remained relatively stable between consequent filter spots, which can be regarded as indirect evidence that the correction algorithm works. The k value calculated from the outdoor sites had a clear seasonal cycle that could be explained by darker aerosol in winter than in summer. When the contribution of BC to the total aerosol volume was high, the k factor was high and vice versa. In winter, the k values at all wavelengths were very close to that obtained from the subway station data. In summer, the k value was wavelength dependent and often negative. When the k value is negative, the noncorrected BC concentrations overestimated the true concentrations.