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In this work, we study a Mediterranean cyclone, that was called Helios and took place during the period 9–11 February 2023 in the southeastern part of Sicily and Malta Island, by a multiparametric approach combining microseism results with sea state and meteorological data provided by wavemeter buoy, HF Radar, hindcast maps and satellite SEVIRI images. The sub-tropical system Helios caused heavy rainfall, strong wind gusts and violent storm surges with significant wave heights greater than 5 meters. We deal with the relationships between such a system and the features of microseism (the most continuous and ubiquitous seismic signal on the Earth) in terms of spectral content, space-time variation of the amplitude and source locations tracked by means of two different methods (amplitude-based grid search and array techniques). By comparing the location of the microseism sources and the area affected by significant storm surges, derived from sea state data, we note that the microseism location results are in agreement with the real position of the storm surges. In addition, we are able to obtain the seismic signature of Helios using a method that exploits the coherence of continuous seismic noise. Hence, we show how an innovative monitoring system of the Mediterranean cyclones can be designed by integrating microseism information with other techniques routinely used to study meteorological phenomena.

Age and growth of the horse mackerel Trachurus trachurus, caught in the two sides of the Strait of Sicily, central Mediterranean Sea (Italy: GSA16 and Tunisia: GSA13), were estimated by length-frequency analysis using FISAT II software. The estimated Von Bertalanffy growth parameters, compared with likelihood ratio test showed significant differences in horse mackerel growth, caught from the two areas. Trachurus trachurus from Tunisia tended to be greater in size than those from Italy. Satellite sea surface temperature revealed a constant higher temperature regime in Tunisian waters, which resulted 1°C warmer than Italian waters. Chlorophyll a concentration showed a higher primary production (+ 12%) in the Italian waters compared to Tunisian waters. Relationships between growth and geographical distribution denote the effect of environmental conditions on growth especially water temperature and primary production, although the role of other factors could not be excluded, among them genetic and/or epigenetic ones.

A meteorological tsunami, commonly called Meteotsunami, is a tsunami-like wave originated by rapid changes in barometric pressure that involve the displacement of a body of water. This phenomenon is usually present in the sea cost area of Mazara del Vallo (Sicily, Italy), in particular in the internal part of the seaport canal, sometimes making local population at risk. The Institute for Coastal Marine Environment (IAMC) of the National Research Council in Italy (CNR) have already conducted several studies upon meteotsunami phenomenon. One of the project has regarded the creation of a sensors network composed by micro-barometric sensors, located in 4 different stations close to the seaport of Mazara del Vallo, for the purpose of studying meteotsunami phenomenon. Each station sends all the measurements to a collecting one that elaborates them with the purpose of identifying the direction and speed of pressure fronts. Unfortunately, four stations provide the minimum amount of data necessary to a reliable characterization of pressure fronts so that the failure of only one is a serious issue. Such failures regard blackouts, connection loss, hardware failures or maintenances. In this context we have developed a fault tolerance system that is based on neural networks. A feed forward neural network i is associated with each station i, and is trained to predict its measurements using as inputs the ones of the other three station j with j ≠i. In the normal condition, the collecting station receives the measurements from each station. In the case of failure of only one station k, the related neural network k can be used to predict the missing measurements. We have conducted preliminary experiments using a two layer feed forward neural network with sigmoid and linear activation functions for the hidden and output layer respectively. In order to simulate failures, we have removed group of data from each station measurements that follow inside a fixed temporal range. The related networks have been used to predict the missing measurements, and the mean square error (MSE) from the real measured value has been computed as performance index. The very low values of obtained MSE lead to the suggestion of a certain effectiveness of the proposed system.

Information and communications technologies promise to have a significant impact on safety at sea. This is particularly true for smaller ships and boats that rarely have active on board safety systems. We are currently developing a system for computer-aided maritime search and rescue operations within the ICT-E3 Project (ICT excellence programme of Western Sicily funded by the Sicilian Regional Government).

La conoscenza dei flussi energetici scambiati fra oceano e bassa atmosfera è un requisito utile per modellare e comprendere il sistema climatico e la dinamica degli oceani. L’elevata difficoltà di effettuare misure in situ ed in continuo in ambiente marino induce spesso all’utilizzo ausiliario di strumentazione satellitare. Nel presente lavoro è stato effettuato un confronto tra la stima dei flussi energetici superficiali valutati con dati provenienti da satellite e quelli ottenuti da misure in situ. In particolare, per il confronto, sono stati utilizzati i dati SST acquisiti dal satellite NOAA e i dati raccolti in situ dai sensori istallati a bordo della Nave Oceanografica Urania nel corso della Campagna Oceanografica effettuata nel periodo 3 Luglio – 9 Agosto 2005 nel Mediterraneo Centrale. È stata utilizzata la Bulk Formula per stimare i flussi di calore sensibile e latente nelle medesime stazioni di campionamento. Nonostante la risoluzione spaziale dei dati provenienti da satellite permetta di disporre di valori di SST mediati su 1.1 km2 di superficie di mare, i flussi energetici calcolati con le due metodologie mostrano di avere un buon accordo. Il coefficiente di correlazione tra i flussi di calore latente calcolati con i valori di SST acquisiti attraverso i due differenti metodi è 0.91, mentre per i flussi di calore sensibile è 0.94.

According to the protocols for the reduction of the production of the gases responsible of the greenhouse effect, it makes necessary to develop systems able to forecast and to evaluate such gases. The dump of Bellolampo, situated in proximity of the urban area in Palermo, is a source of CO2 and CH4. It was officially created in the eighties and only some years later it was decided to convert it into a controlled landfill, soon becoming a catch basin of refuse for a large hinterland constituted by approximately 50 small towns. The city of Palermo has been equipped with a network of weather and air-quality stations far monitoring the main components that are responsible for atmospherlc pollution. The network consists of 10 fixed stations located both in peripheral and centrai parts of the city. The gases produced by the landfill, under particular meteorologica! conditions, reach the nearby inhabited centre, making to record many peak events, dangerous for the population health. The purpose of this research is to develop a pure predictive model, using an Artificial Neural Network (ANN), to forecast, with a lead time of a dai, the CH4 concentration in the site of Palermo and to help the administrations to rationalize the preventive intervention to reduce the occurrence of this pollutant. The mode! uses an available time series which was recorded by the network of monitoring stations, on the urban area of Palermo, far the period 2003-2004. This study has some important implications for health warning systems environmental management in places with high pollution concentration

Many landfills for urban solid waste have been developed in sites very close to urbanised areas thus exposing the population to various dangerous pollutants. The landfill of "Bellolampo", located a few kilometres from the city ofPalermo is one of these. It was officially created in the eighties and only some years later it was decided convert it into a controlled landfill, soon becoming a catch basin of refuse for a large hinterland constituted by approximately 50 small towns. In recent years the city of Palermo has been equipped with a network of weather and air-quality stations for monitoring the main components that afe responsible for atmospheric pollution; it consists of 10 fixed stations located both in peripheral and centraI parts ofthe city. Four ofthem afe able to measure the CH4 concentration. In this work an analysis of the meteorological data and of the CH4 concentrations, acquired downwind the emission source, has been carried out. A dispersion Gaussian model of pollutants was applied in order to estimate the amount of the CH4 released by the landfill. The estimated values were validated by utilising the methane concentration values recorded by the biogas capture system installed in the landfill area.

In this paper a new method is presented for the evaluation of methane concentration near urban sites. The work is based on field measurements taken for a lon period of time on selected sites of the town of Palermo. The main finding of the work is that the landfill seriously affects the air quality of the town, at least around the selected sites