Browsing by Subject "TIME-SERIES ANALYSIS"
Now showing items 1-5 of 5
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(2018)Pilot aircraft-assisted suicides (AAS) are rare, and there is limited understanding of copycat phenomenon among aviators. The aim of this study was to evaluate the possible effect the 11 September 2001, terrorist attacks had on pilot AASs in the U.S. Fatal aviation accidents in the National Transportation Safety Board (NTSB) database were searched using the following search words: "suicide", "murder-suicide" and "homicide-suicide". The timeline between 11 September 1996, and 11 September 2004, was analyzed. Only those accidents in which NTSB judged that the cause of the accident was suicide were included in the final analysis. The relative risk (RR) of the pilot AASs in all fatal accidents in the U.S. was calculated in order to compare the one, two, and three-year periods after the September 11 terrorist attacks with five years preceding the event. The RR of a fatal general aviation aircraft accident being due to pilot suicide was 3.68-fold (95% confidence interval 1.04-12.98) during the first year after 11 September 2001, but there was not a statistically significant increase in the later years. This study showed an association, albeit not determinate causal effect, of a very specific series of simultaneous terrorist murder-suicides with subsequent pilot AASs.
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(2020)Urbanisation and industrialisation led to the increase of ambient particulate matter (PM) concentration. While subsequent regulations may have resulted in the decrease of some PM matrices, the simultaneous changes in climate affecting local meteorological conditions could also have played a role. To gain an insight into this complex matter, this study investigated the long-term trends of two important matrices, the particle mass (PM2.5) and particle number concentrations (PNC), and the factors that influenced the trends. Mann-Kendall test, Sen's slope estimator, the generalised additive model, seasonal decomposition of time series by LOESS (locally estimated scatterplot smoothing) and the Buishand range test were applied. Both PM2.5 and PNC showed significant negative monotonic trends (0.03-0.6 mg m(-3).yr(-1) and 0.40-3.8 x 10(3) particles. cm(-3). yr(-1), respectively) except Brisbane (+0.1 mg m(-3). yr(-1) and +53 particles. cm(-3). yr(-1), respectively). For the period covered in this study, temperature increased (0.03-0.07 degrees C.yr(-1)) in all cities except London; precipitation decreased (0.02-1.4 mm.yr(-1)) except in Helsinki; and wind speed was reduced in Brisbane and Rochester but increased in Helsinki, London and Augsburg. At the change-points, temperature increase in cold cities influenced PNC while shifts in precipitation and wind speed affected PM2.5. Based on the LOESS trend, extreme events such as dust storms and wildfires resulting from changing climates caused a positive step-change in concentrations, particularly for PM2.5. In contrast, among the mitigation measures, controlling sulphur in fuels caused a negative step-change, especially for PNC. Policies regarding traffic and fleet management (e.g. low emission zones) that were implemented only in certain areas or in a progressive uptake (e.g. Euro emission standards), resulted to gradual reductions in concentrations. Therefore, as this study has clearly shown that PM2.5 and PNC were influenced differently by the impacts of the changing climate and by the mitigation measures, both metrics must be considered in urban air quality management. (C) 2020 Elsevier Ltd. All rights reserved.
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(2021)The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality sensors, however, presents several challenges: they suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques. We also identify open research challenges and present directions for future research.
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(2017)It is controversial if global warming will result into increased crime and conflict rate, and no causal neurobiological mechanisms have been proposed for the putative association between ambient temperature and aggressive behavior. This study shows that during 1996-2013, ambient temperature explained 10% of variance in the violent crime rate in Finland, corresponding to a 1.7% increase/degree centigrade. Ambient temperature also correlated with a one month delay in circannual changes in peripheral serotonin transporter density among both offenders and healthy control subjects, which itself correlated strongly with the monthly violent crime rate. This suggests that rise in temperature modulates serotonergic transmission which may increase impulsivity and general human activity level, resulting into increase in social interaction and risk of violent incidents. Together, these results suggest that the effect of ambient temperature on occurrence of violent crime is partly mediated through the serotonergic system, and that a 2 degrees C increase in average temperatures would increase violent crime rates by more than 3% in non-tropical and non-subtropical areas, if other contributing factors remained constant.
