Addressing coastal risks related to sea storms requires an integrative approach which combines monitoring stations, forecasting models, early warning systems, and coastal management and planning. Such great effort is sometimes possible only through transnational cooperation, which becomes thus vital to face, effectively and promptly, the marine events which are responsible for damage impacting the environment and citizens' life. Here we present a shared and interoperable system to allow a better exchange of and elaboration on information related to sea storms among countries. The proposed integrated web system (IWS) is a combination of a common data system for sharing ocean observations and forecasts, a multi-model ensemble system, a geoportal, and interactive geo-visualisation tools to make results available to the general public. The multi-model ensemble mean and spread for sea level height and wave characteristics are used to describe three different sea condition scenarios. The IWS is designed to provide sea state information required for issuing coastal risk alerts over the analysed region as well as for being easily integrated into existing local early warning systems. This study describes the application of the developed system to the exceptional storm event of 29 October 2018 that caused severe flooding and damage to coastal infrastructure in the Adriatic Sea. The forecasted ensemble products were successfully compared with in situ observations. The hazards estimated by integrating IWS results in existing early warning systems were confirmed by documented storm impacts along the coast of Slovenia, Emilia-Romagna and the city of Venice. For the investigated event, the most severe simulated scenario results provide a realistic and conservative estimation of the peak storm conditions to be used in coastal risk management.

Air quality in Europe continues to remain poor in many areas, with regulation limits often exceeded by many countries. The EU Life-IP PREPAIR Project, involving administrations and environmental protection agencies of eight regions and three municipalities in Northern Italy and Slovenia, was designed to support the implementation of the regional air quality plans in the Po Valley, one of the most critical areas in Europe in terms of pollution levels. In this study, four air quality modelling systems, based on three chemical transport models (CHIMERE, FARM and CAMx) were applied over the Po Valley to assess the sensitivity of PM_2.5 concentrations to NO_x and NH₃ emission reductions. These two precursors were reduced (individually and simultaneously) from 25% up to 75% for a total of 10 scenarios, aimed at identifying the most efficient emission reduction strategies and to assess the non-linear response of PM_2.5 concentrations to precursor changes. The multi-model analysis shows that reductions across multiple emission sectors are necessary to achieve optimal results. In addition, the analysis of non-linearities revealed that during the cold season, the efficiency of PM_2.5 abatement tends to increase by increasing the emission reductions, while during summertime, the same efficiency remains almost constant, or slightly decreases towards higher reduction strengths. Since the concentrations of PM_2.5 are greater in winter than in summer, it is reasonable to infer that significant emission reductions should be planned to maximise reduction effectiveness.

The goal of this paper is to present an original real-time algorithm devised for detection of tsunami or tsunami-like waves we call TEDA (Tsunami Early Detection Algorithm), and to introduce a methodology to evaluate its performance. TEDA works on the sea level records of a single station and implements two distinct modules running concurrently: one to assess the presence of tsunami waves ("tsunami detection") and the other to identify high-amplitude long waves ("secure detection"). Both detection methods are based on continuously updated time functions depending on a number of parameters that can be varied according to the application. In order to select the most adequate parameter setting for a given station, a methodology to evaluate TEDA performance has been devised, that is based on a number of indicators and that is simple to use. In this paper an example of TEDA application is given by using data from a tide gauge located at the Adak Island in Alaska, USA, that resulted in being quite suitable since it recorded several tsunamis in the last years using the sampling rate of 1 min.

Coastal tide gauges play a very important role in a tsunami warning system, since sea-level data are needed for a correct evaluation of the tsunami threat, and the tsunami arrival has to be recognized as early as possible. Real-time tsunami detection algorithms serve this purpose. For an efficient detection, they have to be calibrated and adapted to the specific local characteristics of the site where they are installed, which is easily done when the station has recorded a sufficiently large number of tsunamis. In this case the recorded database can be used to select the best set of parameters enhancing the discrimination power of the algorithm and minimizing the detection time. This chance is however rare, since most of the coastal tide-gauge stations, either historical or of new installation, have recorded only a few tsunamis in their lifetimes, if any. In this case calibration must be carried out by using synthetic tsunami signals, which poses the problem of how to generate them and how to use them. This paper investigates this issue and proposes a calibration approach by using as an example a specific case, which is the calibration of a real-time detection algorithm called TEDA (Tsunami Early Detection Algorithm) for two stations (namely Tremestieri and Catania) in eastern Sicily, Italy, which were recently installed in the frame of the Italian project TSUNET, aiming at improving the tsunami monitoring capacity in a region that is one of the most hazardous tsunami areas of Italy and of the Mediterranean.