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The objective of this study was to determine the relationship between weather conditions and hospital admissions for cardiovascular diseases (CVD). The analysed data of CVD hospital admissions were part of the database of the Policlinico Giovanni XXIII of Bari (southern Italy) within a reference period of 4 years (2013–2016). CVD hospital admissions have been aggregated with daily meteorological recordings for the reference time interval. The decomposition of the time series allowed us to filter trend components; consequently, the non-linear exposure–response relationship between hospitalizations and meteo-climatic parameters was modelled with the application of a Distributed Lag Non-linear model (DLNM) without smoothing functions. The relevance of each meteorological variable in the simulation process was determined by means of machine learning feature importance technique. The study employed a Random Forest algorithm to identify the most representative features and their respective importance in predicting the phenomenon. As a result of the process, the mean temperature, maximum temperature, apparent temperature, and relative humidity have been determined to be the most suitable meteorological variables as the best variables for the process simulation. The study examined daily admissions to emergency rooms for cardiovascular diseases. Using a predictive analysis of the time series, an increase in the relative risk associated with colder temperatures was found between 8.3 °C and 10.3 °C. This increase occurred instantly and significantly 0–1 days after the event. The increase in hospitalizations for CVD has been shown to be correlated to high temperatures above 28.6 °C for lag day 5.

Artificial intelligence, in particular a supervised and unsupervised machine learning approach, has been becoming an interest in the field of measurement and instrumentation. Many problems of classification can be faced by a machine learning approach. We know machine learning is a broad area of artificial intelligence that comprises some other lines of research and activities such as deep learning. Synthetic aperture radar (SAR) measurements by means of its sensors are of great interest in environmental monitoring, in particular in land classification. This paper presents findings related to measurements and characterization through land classification of an environmentally sensitive area in Italy over two different time periods in order to assess changing parameters. A deep learning algorithm has been designed and implemented, and a comparison has been established with a spectral density approach.

The paper discusses advances in coupling satellite driven soil water balance model and meteorological forecast as support for precision smart irrigation use in a case study of an operative farm in the South of Italy where semi-arid climatic conditions holds. Crop water needs forecast are computed with the intuitive idea of forcing the soil water balance model with the meteorological model outlooks. Discussion on the methodology approach is presented, comparing, for a reanalysis period between June and September 2014, the forecast system outputs with observed soil moisture and crop water needs. Two main issues are here in emphasized: the characteristic of soil moisture water balance model, that due to its state variables may be directly calibrated and validated using satellite or near sensing land surface temperatures; the accuracy of those forecast meteorological variables that are the most important in driving the soil water and energy balance. The soil water balance model performances are then discussed highlighting the importance of using a model which state variable (the pixel surface equilibrium temperature) is the same as the data detected by satellite (Land Surface Temperature), so that it can be used for calibrating and validating soil hydrological parameters. Model outputs are also validated with a comparison of ground latent and sensible heat fluxes from an eddy covariance station and soil moisture data. Problems insight into the meteorological modeling, such as temporal and spatial scale, and their influence on soil moisture forecast are discussed showing on the base of several observation periods the need to increase the meteorological forcings accuracy for this type of applications. The obtained results show how the proposed methodology of the forecasting system is able to have a high reliability in soil moisture forecast correctly providing irrigation suggestion.

Climate change increasingly affects every aspect of human life. Recent studies report a close correlation with human health and it is estimated that global death rates will increase by 73 per 100,000 by 2100 due to changes in temperature. In this context, the present work aims to study the correlation between climate change and human health, on a global scale, using artificial intelligence techniques. Starting from previous studies on a smaller scale, that represent climate change and which at the same time can be linked to human health, four factors were chosen. Four causes of mortality, strongly correlated with the environment and climatic variability, were subsequently selected. Various analyses were carried out, using neural networks and machine learning to find a correlation between mortality due to certain diseases and the leading causes of climate change. Our findings suggest that anthropogenic climate change is strongly correlated with human health, some diseases are mainly related to risk factors while others require a more significant number of variables to derive a correlation. In addition, a forecast of victims related to climate change was formulated. The predicted scenario confirms that a prevalently increasing trend in climate change factors corresponds to an increase in victims.

A recent report “The Future is Now: Science for Achieving Sustainable Development” Global Sustainable Development Report 2019 - SDG Summit’ as part of the activity of Agenda 2030 of UN, highlights the opportunity to develop Early warning system for drought, floods and other meteorological events, that by providing timely information can be used by vulnerable countries to build resilience, reduce risks and prepare more effective responses. Following the suggestion, combining outputs from Global Circulation models, remote sensing, hydraulic models and machine learning tools, a local scale flooding Early Warning System (EWS) is proposed for the St. Lucia island ( Caribbean). The objective of the EWS is to provide forecasts of potentially dangerous flooding phenomena at different time scale: a) 0-2 hours, nowcasting; b) 24-48 hours, short range; c) 3-10 days, middle to long range. Data used to build the model are: Geopotential Height (GPH) fields at 850 hPa and Integrated Vapor Transport (IVT) fields from European Centre for Medium-range Weather Forecasts (ECMWF) - Reanalysis v5 (ERA5); Tropical Cyclone tracks from NOAA-NHC; 18 weather stations homogeneously distributed in the island; rainfall map data from the weather radar in Saint Lucia. GPH and IVT fields were defined between 110°W - 10°W and 45°N - 10°S. The EWS is constituted by an ensemble of flooding risk forecast subsystems which is potentially applicable to Atlantic tropical and extra-tropical regions. Different approaches are used for each subsystem to link large scale atmospheric features to local rainfall and flooding: a) Non-homogeneous Hidden Markov and Event Synchronization models to translate IVT and GPH at 850 hPa fields (from ECMWF-Set II- Atmospheric Model Ensemble) in local daily rainfall amount and probability of exceedance of a prefixed heavy rainfall threshold; b) a physical based cyclone/rainfall model to convert Tropical cyclone attributes – position and maximum wind velocity (provided from National Hurricane Center)- in rainfall intensity spatial distribution on the island; c) a surrogate model for a fast and accurate prediction of flooding events that is obtained from a multi-layer perceptron neural network (MLPNN), which is trained on a high-fidelity dataset relying on solution of the full two-dimensional shallow water equations with direct rainfall application. Results show an excellent ability of the models to identify the climatic configurations that determine the occurrence of extreme events and the exceeding of threshold values ​​that generate floods. In particular, during the late hurricane season September-October-November, when is highest the probability of flood events, the EWS was able to forecast the occurrence of critical climatic configurations 86% of the times they occurred. The EWS was able to predict the exceeding of the rainfall threshold that generated floods 80% of times.

Meteorological conditions play a crucial role in air pollution by affecting both directly and indirectly the emissions, transport, formation, and deposition of air pollutants. Extreme weather events can strongly affect surface air quality. Understanding relations between air pollutant concentrations and extreme weather events is a fundamental step toward improving the knowledge of how excessive heat impacts on air quality. In this work, we developed a statistical procedure for investigating the variations in the correlation structure of four air pollutants (NOx, O3, PM10, PM2.5) during extreme temperature events measured in monitoring sites located of Emilia Romagna region, Northern Italy, in summer (June–August) from 2015 to 2017. For the selected stations, Hot Days (HDs) and Heat Waves (HWs) were identified with respect to historical series of maximum temperature measured for a 30-year period (1971–2000). This method, based on multivariate techniques, allowed us to highlight the variations in air quality of study area due to the occurrence of HWs. The examined data, including PM concentrations, show higher values, whereas NOx and O3 concentrations seem to be not influenced by HWs. This operative procedure can be easily exported in other geographical areas for studying effects of climate change on a local scale.

Environmental monitoring networks are essential for understanding environmental dynamics of a given region if dedicated sensors and sensing systems are used. They are sensitive to external and internal fluctuations that can produce loss of capabilities of correct detection and retrieval of desired environmental quantities. Recent findings, in terms of smart grids and Internet of Things, have allowed the implementation of testing and characterization of such networks using new computation and simulation platforms that can deliver excellent results. This paper presents a procedure for evaluating the informative content of an actual network and a new approach of implementing a characterization of hydrological and environmental networks of sensors to be “stress-tested.” The proposed characterization is implemented by using Ptolemy II tool. It is an open-source simulation and modeling tool intended for experimenting with system design techniques, particularly those that involve combinations of different types of models. It is user friendly as described in final comments. This paper does not deal with power systems, intended as smart grid, but with smart sensors networking.

In the last decades, Mediterranean rural landscapes have undergone significant changes, with relevant considerable environmental and socio-economic impacts. These phenomena are often triggered by agricultural abandonment, especially in environmentally-sensitive areas, which are usually located in marginal and less profitable regions, and which could indeed irremediably compromise the identity and role of these Mediterranean landscapes. On the other hand, the progressive increase of available multi-source geodata allows to reconstruct the landscape original structure, providing new tools able to prevent negative impacts on environment. Hence, thanks to the development of increasingly advanced and open-source GIS tools, it is possible to implement several geodata typologies that can be mutually integrated in an increasingly efficient approach. In this paper the process of landscape reshaping pattern is analyzed in a study area of Basilicata region (Southern Italy) using remote sensing. In particular, the vegetation component of a landscape has been assessed by means of SAR images by using an artificial intelligence approach, that is machine learning to understand landscape dynamics in two different time periods. In this way, it has been possible to integrate data of different source and composition into landscape analysis methodologies, hence developing a suitable tool for planning and managing the rural landscape.

The Basilicata region summarizes many basic features of the biogeographic complexity characterizing Mediterranean countries. The intricate geomorphology and the long history of human management generated the current landscapes, which include both high-value ecosystems and areas prone to desertification. Preserving goods and services provided by such composite land cover mosaics poses many problems due to the interference/overlap of diverse natural and anthropic factors which make the correct selection of relevant parameters and the interpretation of observational data rather difficult. Here, we study interconnections between local climate and vegetation activity by correlating parameters characterizing the interannual statistics of the NDVI (Normalized Difference Vegetation Index), derived from satellite data, with a recently devised multivariate statistical index of meteoclimatic variability. We used a 15-year sequence of remote images concerning a set of plots located around meteorological ground stations of the central-eastern part of the region to pick up spatial structures in the vegetation–climate relationships. Our analyses were able to correlate spatial heterogeneity to variations in water exchanges between vegetation and atmosphere. This study represents a first step to improve the description of relevant processes to protect natural habitats and quality agriculture, therefore combating land degradation and climate change detrimental effects.

Flow slow-down in rivers and artificial canals is a basic aspect to be monitored and kept strictly under control. Flow slow-downs can become a major concern in the event of extreme phenomena. The paper illustrates an advanced image processing method that uses particle tracking velocimetry in conjunction with a monadic approach to better characterize water flow in the presence of waste or debris that block normal water flow within a river. An high-speed camera installed beneath a bridge takes periodic images of the water flow. The measured water level and the images taken by the camera are sent to a central system in real-time. Results demonstrate the capability of the proposed method to accurately detect the presence of debris from the measured water flow.

Life satisfaction has been widely used in recent studies to evaluate the effect of environmental factors on individuals’ well-being. In the last few years, many studies have shown that the potential impact of climate change on cities depends on a variety of social, economic, and environmental determinants. In particular, extreme events, such as flood and heat waves, may cause more severe impacts and induce a relatively higher level of vulnerability in populations that live in urban areas. Therefore, the impact of climate change and related extreme events certainly influences the economy and quality of life in affected cities. Heat wave frequency, intensity, and duration are increasing in global and local climate change scenarios. The association between high temperatures and morbidity is well-documented, but few studies have examined the role of meteo-climatic variables on hospital admissions. This study investigates the effects of temperature, relative humidity, and barometric pressure on health by linking daily access to a Matera (Italy) hospital with meteorological conditions in summer 2012. Extreme heat wave episodes that affected most of the city from 1 June to 31 August 2012 (among the selected years 2003, 2012, and 2017) were analyzed. Results were compared with heat waves from other years included in the base period (1971–2000) and the number of emergency hospital admissions on each day was considered. The meteorological data used in this study were collected from two weather stations in Matera. In order to detect correlations between the daily emergency admissions and the extreme health events, a combined methodology based on a heat wave identification technique, multivariate analysis (PCA), and regression analysis was applied. The results highlight that the role of relative humidity decreases as the severity level of heat waves increases. Moreover, the combination of temperatures and daily barometric pressure range (DPR) has been identified as a precursor for a surveillance system of risk factors in hospital admissions.

In this study an innovative statistical procedure based on multivariate techniques is applied. It is able to identify and to interpret the correlation structure among different meteo-climatic descriptors measured in different sampling sites. The test case of Agri valley (Southern Italy), is presented, analyzing daily data of temperature, relative humidity and precipitation measured in seven monitoring sites, from 2000 to 2013. The Principal Component Analysis technique is recursively applied for each year and for each station, for calculating the synthetic Normalized Principal Component Index. This index allows to characterize quantitatively the information content of descriptors and/or of sites and to compare the behavior of the different variables in the correlation structure. The results show that temperature and relative humidity have high weights in the correlation structure, while the precipitation represents a singular variable. Moreover sampling sites may be classified according to their degree of stability in the correlation structure. A low degree of stability may be correlated with the occurrence of climate changes at the microscale.

Algorithms based on a clever exploitation of artificial intelligence (AI) techniques are the key for modern multidisciplinary applications that are being developed in the last decades. AI approaches’ ability of extracting relevant information from data is essential to perform comprehensive studies in new multidisciplinary topics such as ecological informatics. For example, improving knowledge on cetaceans’ distribution patterns enables the acquisition of a strategic expertise for developing tools aimed to the preservation of the marine environment. In this paper we present an innovative approach, based on Random Forest and RUSBoost, aimed to define predictive models for presence/absence and abundance estimation of two classes of cetaceans: the striped dolphin Stenella coeruleoalba and the common bottlenose dolphin Tursiops truncatus. Sightings data from 2009 to 2017 have been collected and enriched by geo-morphological and meteorological data in order to build a comprehensive dataset of real observations used to train and validate the proposed algorithms. Results in terms of classification and regression accuracy demonstrate the feasibility of the proposed approach and suggest the application of such artificial intelligence based techniques to larger datasets, with the aim of enabling large scale studies as well as improving knowledge on data deficient species.

Rural pipelines dedicated to water distribution, that is, waterworks, are essential for agriculture, notably plantations and greenhouse cultivation. Water is a primary resource for agriculture, and its optimized management is a key aspect. Saving water dispersion is not only an economic problem but also an environmental one. Spectral estimation of leakage is based on processing signals captured from sensors and/or transducers generally mounted on pipelines. There are different techniques capable of processing signals and displaying the actual position of leaks. Not all algorithms are suitable for all signals. That means, for pipelines located underground, for example, external vibrations affect the spectral response quality, then, depending on external vibrations/noises and flow velocity within pipeline, one should choose a suitable algorithm that fits better with the expected results in terms of leak position on the pipeline and expected time for localizing the leak. This paper presents findings related to the application of a decimated linear prediction (DLP) algorithm for agriculture and rural environments. In a certain manner, the application also detects the hydrodynamics of the water transportation. A general statement on the issue, DLP illustration, a real application and results are also included.

In the context of climate change and variability, there is considerable interest in how large scale climate indicators influence regional precipitation occurrence and its seasonality. Seasonal and longer climate projections from coupled ocean-atmosphere models need to be downscaled to regional levels for hydrologic applications, and the identification of appropriate state variables from such models that can best inform this process is also of direct interest. Here, a Non-Homogeneous Hidden Markov Model (NHMM) for downscaling daily rainfall is developed for the Agro-Pontino Plain, a coastal reclamation region very vulnerable to changes of hydrological cycle. The NHMM, through a set of atmospheric predictors, provides the link between large scale meteorological features and local rainfall patterns. Atmospheric data from the NCEP/NCAR archive and 56-years record (1951-2004) of daily rainfall measurements from 7 stations in Agro-Pontino Plain are analyzed. A number of validation tests are carried out, in order to: 1) identify the best set of atmospheric predictors to model local rainfall; 2) evaluate the model performance to capture realistically relevant rainfall attributes as the inter-annual and seasonal variability, as well as average and extreme rainfall patterns. Validation tests show that the best set of atmospheric predictors are the following: mean sea level pressure, temperature at 1000 hPa, meridional and zonal wind at 850 hPa and precipitable water, from 20°N to 80°N of latitude and from 80°W to 60°E of longitude. Furthermore, the validation tests show that the rainfall attributes are simulated realistically and accurately. The capability of the NHMM to be used as a forecasting tool to quantify changes of rainfall patterns forced by alteration of atmospheric circulation under climate change and variability scenarios is discussed.

Control and monitoring of pipelines, dedicated to liquids transportation and distribution, are generally performed by active and passive techniques. With active techniques, we intend the possibility of using external sources to target pipelines and/or liquid inside the infrastructure. The used radiation can be partly reflected by the liquid and pipeline. Passive techniques are related to spontaneous emissions from liquids within the pipelines. However, capturing water displacements and fluctuations within the pipeline, by means of sensors and transducers, is somehow a passive technique. This latter is here illustrated thanks to pressure sensors mounted on an experimental pipeline. The basic idea is to detect leaks but also to monitor water flow regime by means of imaging without using a camera. That is possible thanks to the algorithm here developed and based on 2D/3D Decimated Signal Diagonalization (DSD). It is a technique used in nuclear magnetic resonance, and susceptible to bring to excellent results if water flow within a pipeline is considered as blood flow inside an artery or a vein. The approach has been applied to an experimental hydraulic circuit.

Pipelines conveying fluids are considered strategic infrastructures to be protected and maintained. They generally serve for transportation of important fluids such as drinkable water, waste water, oil, gas, chemicals, etc. Monitoring and continuous testing, especially on-line, are necessary to assess the condition of pipelines. The paper presents findings related to a comparison between two spectral response algorithms based on the decimated signal diagonalization (DSD) and decimated Pad&eacute, approximant (DPA) techniques that allow to one to process signals delivered by pressure sensors mounted on an experimental pipeline.

The present study provides an assessment of the climate variability at a subnational scale, focusing on the case of the Apulia region, in Southeastern Italy. The variables considered for the purpose of a trend analysis were the minimum, maximum, and mean temperatures, and reference evapotranspiration. These are very important in an urban–rural planning context. The study was based on 38 monitoring stations and consisted in the application of the nonparametric Mann–Kendall test and a progressive trend analysis, both used to detect the changes. The 1950–2003 period was investigated on seasonal and annual scales. The results generally showed a warming process and an acceleration of the atmospheric evaporative demand which took place especially since the mid-1970s. The latter had a significant positive trend, while the period before the break point of the 70s had a cooling effect. Finally, the warming effect was more pronounced for minimum temperatures.

This study aimed to evaluate the antibacterial action of KTP (potassium-titanyl-phosphate) laser irradiations (compared with 980 nm diode laser), associated with conventional endodontic procedures, on Enterococcus faecalis biofilms. Fifty-six dental roots with single canals were prepared with Ni-Ti rotary instruments, autoclaved, inoculated with an E. faecalis suspension and incubated for 72 h. They were randomly allocated to control and treatment groups. Laser parameters were as follows: power 2.5 W, Ton 35 ms, Toff 50 ms (KTP laser); power 2.5 W, Ton 30 ms, Toff 30 ms (980 nm diode laser). To evaluate the residual bacterial load, BioTimer Assay was employed. The chemo-mechanical treatment together with laser irradiations (KTP and 980 nm diode lasers) achieved a considerable reduction of bacterial load (higher than 96% and 93%, respectively). Regarding both laser systems, comparisons with conventional endodontic procedures (mortality rate of about 67%) revealed statistically highly significant differences (P ≤ 0.01). This study confirms that laser systems can provide an additional aid in endodontic disinfection.

Los satelites geoestacionarios son capaces de resolver el ciclo diurno, proporcionando secuencias temporales de las observaciones con una resolucion temporal muy alta. Se desarrolla una metodologia basada en filtro de Kalman para explotar esta continuidad temporal y recuperar simultaneamente la temperatura superficial y la emisividad, a partir de datos de SEVIRI (Spinning Enhanced Visible and Infrared Imager). La metodologia se aplica y se prueba sobre una region geografica en el sur de Italia que se caracteriza por diferentes tipos de superficies: arida, cultivada, con vegetacion, zonas urbanas, y marina. El objetivo es implementar un monitoreo continuo y en tiempo real de los parametros de superficie, que podrian ser utiles para sectores como el turismo y la agronomia, la vigilancia de los suelos, y el analisis y evaluacion de los riesgos naturales. La recuperacion de los parametros de superficie se realizo para el conjunto de 2013 y los resultados se compararon con otras observaciones de satelites similares, tales como los derivados de AVHRR (Advanced Very High Resolution Radiometer) y MODIS (Moderate Resolution Imaging Spectroradiometer). Tambien se muestran comparaciones con analisis sobre la superficie del mar proporcionados por el ECMWF (European Centre for Medium range Weather Forecasts).

Mass and energy transfer between soil, vegetation and atmosphere is the process that allows to maintain an adequate energy and water balance in the earth–atmosphere system. However, the evaluation of the energy balance components, such as the net radiation and the sensible and latent heat fluxes, is characterized by significant uncertainties related to both the dynamic nature of heat transfer processes and surfaces heterogeneity. Therefore, a detailed land use classification and an accurate evaluation of vegetation spatial distribution are required for an accurate estimation of these variables. For this purpose, in the present article, a pixel-oriented supervised classification was applied to obtain land use maps of the Basilicata region in Southern Italy by processing three Landsat TM and ETM+ satellite images. An accuracy analysis based on the overall accuracy index and the agreement Khat of Cohen coefficient showed a good performance of the applied classification methodology and a good quality of the obtained maps. Subsequently, these maps were used in the application of a simplified two-source energy balance model for estimating the actual evapotranspiration at a regional scale. The comparison between the simulations made by applying the simplified two-source energy balance model and the measurements of evapotranspiration at a lysimetric station located in the study area showed the applicability and the validity of the proposed methodology. Copyright © 2012 John Wiley & Sons, Ltd.

Summary The increasing interest of hydrological, climatic and meteorological models in the different components of the surface energy balance has encouraged the development of operational methods for estimating surface energy fluxes at a regional scale. In this paper, a sequence of three high-resolution satellite-based surface energy fluxes images are analyzed over an extensive area with a large variety of land uses. Two images from Landsat 7-ETM+ (1999, 2002) and one from Landsat 5-TM (2004) are collected covering the whole Basilicata region (Southern Italy). A Simplified version of a Two-Source Energy Balance (STSEB) model is used to retrieve the surface sensible heat flux. A balance between the long-wave and short-wave radiation is applied to extract the net radiation flux. The evapotranspiration ( LE ) is obtained as a residual term of the energy balance equation. The different croplands are characterized from the CORINE Land Cover maps, and the required meteorological variables are obtained by interpolating the data of a network of agro-meteorological stations distributed within the region. Atmospheric profiles from radiosoundings are used in the radiative transfer model MODTRAN 4.0 to correct the satellite data. Maps of the different fluxes are produced. Daily LE results are compared with some ground measurements, and an analysis is made taking the land use classification as a basis. An accuracy close to 1 mm day −1 is obtained. This value is in agreement with the typical uncertainties reported in the literature.

In this work we present the first results of a controlled experiment carried out for evaluating the capability of SP technique for water fluxes detection in the vadose zone. The aim of the research is to improve the detection of water fluxes in the vadose zone by using a self-potential tomographic technique which gives a 4D (3D + time) high resolution image of water movements within the soil subsurface. Several water infiltration experiments were performed in both field and lab conditions. In this case, particularly, the experiments were performed by using a sand box (7.0 x 3.5 x 2.0 meters) which allows to be reproduced a medium-large scale natural environment. SP signals were recorded continuously by using an array of 32 unpo-larizable electrodes connected to a multiplexed high impedance and resolution multimeter. Better results came out from laboratory experiments where most of the so called cultural noise was reduced at a negligible amount. The results have proved that SP tomographic images represent a good tool for monitoring the changes of water fluxes in the vadose zone under the boundary conditions which are discussed in the work.

This study aimed to evaluate the antibacterial action of KTP (potassium-titanyl-phosphate) laser irradiations (compared with 980 nm diode laser), associated with conventional endodontic procedures, on Enterococcus faecalis biofilms. Fifty-six dental roots with single canals were prepared with Ni-Ti rotary instruments, autoclaved, inoculated with an E. faecalis suspension and incubated for 72 h. They were randomly allocated to control and treatment groups. Laser parameters were as follows: power 2.5 W, Ton 35 ms, Toff 50 ms (KTP laser); power 2.5 W, Ton 30 ms, Toff 30 ms (980 nm diode laser). To evaluate the residual bacterial load, BioTimer Assay was employed. The chemo-mechanical treatment together with laser irradiations (KTP and 980 nm diode lasers) achieved a considerable reduction of bacterial load (higher than 96% and 93%, respectively). Regarding both laser systems, comparisons with conventional endodontic procedures (mortality rate of about 67%) revealed statistically highly significant differences (P ≤ 0.01). This study confirms that laser systems can provide an additional aid in endodontic disinfection.

In recent years the study of water quality in lakes and reservoir has been subject to several research programmes for both ecosystem evolution and water management. Analysis of hydraulic-environmental characteristics in terms of temporal trend has shown the hydraulics and physical-chemical structure of water but in terms of spatial distribution it is necessary to characterize the difference between point and non-point processes. A mobile monitoring system able to measure several vertical profiles along the reservoir surface and a geostatistical model are employed in order to evaluate the spatial (3-D) distribution of environmental indicators (temperature, dissolved oxygen, electrical conductivity, pH and redox potential). This approach has been demonstrated on the Camastra reservoir (Southern Italy). Through these computations the evolution of water thermal structure and hypolimnetic anoxic phenomena are described. The latter has been recognized to be dependent on the local hydraulic characteristics and not by diffuse anoxic processes.

The paper deals some important aspects concerning the study of desertification phenomenon that has significantly interested all European countries of the Mediterranean basin in the last decades. The territorial sensibility, qualitatively and/or quantitatively, of the risk related to the desertification processes has been valued as grade of susceptibility to the phenomenon in the study area, with respect to the hydraulic and hydrologic conditioning factors. The methodological approach followed is based on the classification of desertification indicators by means of a suitable conceptual model applied to the study area in Basilicata region. The classical desertification indexes have been evaluate referring to the study area generating temporal informative map. To achieve such objective, the historical series of temperatures and rainfall of several gauge-stations have been analysed, and the first results show an increasing trend for the temperature in last years, according to the global scale. An increase of aridity and erosion index values is, also, outlined by the collected data with slight reductions at higher altitudes.

In the last years the new electrical imaging techniques have been largely applied to support hydrogeological investigations, in particular the analysis of Self-Potential ground surface anomalous pattems has been revealed as a powerful tooI to study pollutant diffusion processes in aquifers, groundwater circulation systems in landslide body, seepage phenomena in dam embankment and in all other cases concerning fluid flows in the subsoil (Sharma, 1997 and references therein).

We describe the design and the development of a new prototype to obtain Self-Potential (SP) tomographic images (2D, 3D and 4D) of subsoil. The method of SP is a passive geophysical prospecting method: the data are generally measured on the earth surface as potential drops across a passive dipole, normally consisting of a pair of grounded electrodes, moving along a selected direction. Starting from the measure of the natural electric field along selected profiles and/or maps, we evaluated the probability to identify in subsoil positive and/or negative charge accumulation zones. The prototype has been implemented assembling some hardware components of National Instruments and developing ad hoc a software in C++ and VB language. The system allows to measure the SP voltage differences between electrodes regularly distributed along selected profiles and/or maps (maximum 128 channels). In real time the software produces a high resolution tomographic image representing the probability to find electrical sources in subsoil. The main applications of this prototype regard the near surface geophysical exploration in areas characterized by high hydrogeological hazard. In particular the system is able to depict the groundwater circulation system in landslide body, to describe the spatial and temporal dynamics of pollutant diffusion processes in aquifers and to localize seepage phenomena in dam embankment.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A geophysical survey, based on unconventional geoelectrical prospecting methods, was carried out in a controlled test site located in Montalto Uffugo (CS), Southem Italy. The main objective of the study is to outline the complex geological features of subsurface and to gain information about the hydrogeological features of the investigated area. Subsequently, the geoelectrical measurements will be used to control the time evolution and spatial distribution of contaminants and to analyze the correlation between the soil resistivity and the hydraulic characteristics of aquifer.

A method for combining monitoring, mathematical modeling, and the use of a geographical information system (GIS) is proposed to investigate the transport, dispersion and diffusion of pollutants in reservoirs. Preliminary and partial results obtained from an extensive and continous observation and measurement activity on the Camastra reservoir have confirmed the validity of the proposed approach.