High life in the sky? Mortality by floor of residence in Switzerland

Living in high-rise buildings could influence the health of residents. Previous studies focused on structural features of high-rise buildings or characteristics of their neighbourhoods, ignoring differences within buildings in socio-economic position or health outcomes. We examined mortality by floor of residence in the Swiss National Cohort, a longitudinal study based on the linkage of December 2000 census with mortality and emigration records 2001-2008. Analyses were based on 1.5 million people living in buildings with four or more floors and 142,390 deaths recorded during 11.4 million person-years of follow-up. Cox models were adjusted for age, sex, civil status, nationality, language, religion, education, professional status, type of household and crowding. The rent per m² increased with higher floors and the number of persons per room decreased. Mortality rates decreased with increasing floors: hazard ratios comparing the ground floor with the eighth floor and above were 1.22 [95% confidence interval (CI) 1.15-1.28] for all causes, 1.40 (95% CI 1.11-1.77) for respiratory diseases, 1.35 (95% CI 1.22-1.49) for cardiovascular diseases and 1.22 (95% CI 0.99-1.50) for lung cancer, but 0.41 (95% CI 0.17-0.98) for suicide by jumping from a high place. There was no association with suicide by any means (hazard ratio 0.81; 95% CI 0.57-1.15). We conclude that in Switzerland all-cause and cause-specific mortality varies across floors of residence among people living in high-rise buildings. Gradients in mortality suggest that floor of residence captures residual socioeconomic stratification and is likely to be mediated by behavioural (e.g. physical activity), and environmental exposures, and access to a method of suicide.

All-sky Meteor Orbit System AMOS and preliminary analysis of three unusual meteor showers

All-sky Meteor Orbit System (AMOS) is a semi-autonomous video observatory for detection of transient events on the sky, mostly the meteors. Its hardware and software development and permanent placement on several locations in Slovakia allowed the establishment of Slovak Video Meteor Network (SVMN) monitoring meteor activity above the Central Europe. The data reduction, orbital determination and additional results from AMOS cameras–the SVMN database– as well as from observational expeditions on Canary Islands and in Canada provided dynamical and physical data for better understanding of mutual connections between parent bodies of asteroids and comets and their meteoroid streams. We present preliminary results on exceptional and rare meteor streams such as September ε Perseids (SPE) originated from unknown long periodic comet on a retrograde orbit, suspected asteroidal meteor stream of April α Comae Berenicids (ACO) in the orbit of meteorites Příbram and Neuschwanstein and newly observed meteor stream Camelopardalids (CAM) originated from Jupiter family comet 209P/Linear.

Cloud observations in Switzerland using hemispherical sky cameras

We present observations of total cloud cover and cloud type classification results from a sky camera network comprising four stations in Switzerland. In a comprehensive intercomparison study, records of total cloud cover from the sky camera, long-wave radiation observations, Meteosat, ceilometer, and visual observations were compared. Total cloud cover from the sky camera was in 65–85% of cases within ±1 okta with respect to the other methods. The sky camera overestimates cloudiness with respect to the other automatic techniques on average by up to 1.1 ± 2.8 oktas but underestimates it by 0.8 ± 1.9 oktas compared to the human observer. However, the bias depends on the cloudiness and therefore needs to be considered when records from various observational techniques are being homogenized. Cloud type classification was conducted using the k-Nearest Neighbor classifier in combination with a set of color and textural features. In addition, a radiative feature was introduced which improved the discrimination by up to 10%. The performance of the algorithm mainly depends on the atmospheric conditions, site-specific characteristics, the randomness of the selected images, and possible visual misclassifications: The mean success rate was 80–90% when the image only contained a single cloud class but dropped to 50–70% if the test images were completely randomly selected and multiple cloud classes occurred in the images.