Suchergebnisse

Abstract. Background: Climate factors may offer a stronger explanation of the variations in suicide rates compared with economic variables, even in the case of patients admitted involuntarily. Aims: We assessed the role of temperature as a determinant of the increased prevalence of suicide attempts (SA). Method: The sample comprised all cases of hospitalization for SA at the Psychiatric Clinic of the IRCCS Ospedale Policlinico San Martino between August 2013 and July 2018. For ambient temperature, data were provided by the Meteorological Observatory of the University of Genoa. Results: We noted a peak in suicides that was typically found in late spring and early summer due to global warming. Limitations: Other environmental/psychological factors contributing to the onset of an acute clinical event were not considered. The cross-sectional design of the study is another limitation. Conclusion: Further studies are needed to clarify the impact of climatic factors on suicide behavior and implement early intervention and preventive strategies for mental health.

Abstract. The e-Science environment developed in the framework of the EU-funded DRIHM project was used to demonstrate its ability to provide relevant, meaningful hydrometeorological forecasts. This was illustrated for the tragic case of 4 November 2011, when Genoa, Italy, was flooded as the result of heavy, convective precipitation that inundated the Bisagno catchment. The Meteorological Model Bridge (MMB), an innovative software component developed within the DRIHM project for the interoperability of meteorological and hydrological models, is a key component of the DRIHM e-Science environment. The MMB allowed three different rainfall-discharge models (DRiFt, RIBS and HBV) to be driven by four mesoscale limited-area atmospheric models (WRF-NMM, WRF-ARW, Meso-NH and AROME) and a downscaling algorithm (RainFARM) in a seamless fashion. In addition to this multi-model configuration, some of the models were run in probabilistic mode, thus giving a comprehensive account of modelling errors and a very large amount of likely hydrometeorological scenarios (> 1500). The multi-model approach proved to be necessary because, whilst various aspects of the event were successfully simulated by different models, none of the models reproduced all of these aspects correctly. It was shown that the resulting set of simulations helped identify key atmospheric processes responsible for the large rainfall accumulations over the Bisagno basin. The DRIHM e-Science environment facilitated an evaluation of the sensitivity to atmospheric and hydrological modelling errors. This showed that both had a significant impact on predicted discharges, the former being larger than the latter. Finally, the usefulness of the set of hydrometeorological simulations was assessed from a flash flood early-warning perspective.

Abstract. During the autumn of 2011 two catastrophic, very intense rainfall events affected two different parts of the Liguria Region of Italy causing various flash floods. The first occurred in October and the second at the beginning of November. Both the events were characterized by very high rainfall intensities (> 100 mm h−1) that persisted on a small portion of territory causing local huge rainfall accumulations (> 400 mm 6 h−1). Two main considerations were made in order to set up this work. The first consideration is that various studies demonstrated that the two events had a similar genesis and similar triggering elements. The second very evident and coarse concern is that two main elements are needed to have a flash flood: a very intense and localized rainfall event and a catchment (or a group of catchments) to be affected. Starting from these assumptions we did the exercise of mixing the two flash flood ingredients by putting the rainfall field of the first event on the main catchment struck by the second event, which has its mouth in the biggest city of the Liguria Region: Genoa. A complete framework was set up to quantitatively carry out a "what if" experiment with the aim of evaluating the possible damages associated with this event. A probabilistic rainfall downscaling model was used to generate possible rainfall scenarios maintaining the main characteristics of the observed rainfall fields while a hydrological model transformed these rainfall scenarios in streamflow scenarios. A subset of streamflow scenarios is then used as input to a 2-D hydraulic model to estimate the hazard maps, and finally a proper methodology is applied for damage estimation. This leads to the estimation of the potential economic losses and of the risk level for the people that stay in the affected area. The results are interesting, surprising and in a way worrying: a rare but not impossible event (it occurred about 50 km away from Genoa) would have caused huge damages estimated between 120 and EUR 230 million for the affected part of the city of Genoa, Italy, and more than 17 000 potentially affected people.

COVID-19 spreads mainly among people who are in close contact. Policymakers mostly resorted to normative measures to limit close contacts and impose social distancing. Our study aimed to estimate the risk of exposure to COVID-19 by location and activity in crowded metropolitan areas. The risk of exposure to COVID-19 was defined as the product of crowding (people within a six feet distance) and exposure duration (fraction of 15 min). Our epidemiological investigation used aggregated and anonymized mobility data from Google Maps to estimate the visit duration. We collected visit duration data for 561 premises in the metropolitan area of Genoa, Italy from October 2020 to January 2021. The sample was then clustered into 14 everyday activities, from grocery shopping to the post office. Crowding data by activity were obtained from pre-existing building norms and new government measures to contain the pandemic. The study found significant variance in the risk of exposure to COVID-19 among activities and, for the same activity, among locations. The empirical determination of the risk of exposure to COVID-19 can inform national and local public health policies to contain the pandemic's diffusion. Its simple numerical form can help policymakers effectively communicate difficult decisions affecting our daily lives. Most importantly, risk data by location can help us rethink our daily routine and make informed, responsible choices when we decide to go out.