Maria Ragosta

Source origin and parameters influencing levels of heavy metals in TSP, in an industrial background area of Southern Italy

In this paper, we investigate the relationships among atmospheric concentration of trace elements and some meteorological parameters. In particular, the effects of different meteorological conditions on heavy metal levels are interpreted by means of a multivariate statistical approach. The analysed variables were measured during a monitoring survey that started in 1997, and this survey was carried out in order to evaluate the atmospheric concentrations of heavy metals in the industrial area of Tito Scalo (Basilicata Region, Southern Italy). Here we present and analyse the data set collected from 1997 to 1999. The data set includes daily concentrations of total suspended particulates (TSP), daily concentrations of eight metals (Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn) in TSP and daily meteoclimatic data (temperature, rainfall, speed and wind directions). Both the concentration level and the occurrence of peak concentration events are consistent with the characteristics of the study area: abundant small and medium industrial plants in a mountainous and unpolluted zone. Regarding the origin of sources of heavy metals in TSP, the statistical procedure allows us to identify three profiles: SP1 and SP2 related to industrial sources and SP3 related to other sources (natural and/or anthropogenic). In particular, taking into account the effect of different meteorological conditions, we are able to distinguish the contribution of different fractions of the same metal in the detected source profiles.

Heavy Metal Concentrations in Dairy Products from Sheep Milk Collected in Two Regions of Southern Italy

Inorganic or aggregated forms of chemical substances (metalloids, heavy metals etc.) in feed and food represent a severe risk for their long-term toxicological effects. Heavy metals are widely dispersed in the environment. The toxicity induced by excessive levels of some of these elements, such as chromium (Cr), cadmium (Cd), lead (Pb) and mercury (Hg), are well known [8]. The toxic metal content of milk and dairy products is due to several factors – in particular – environmental conditions, the manufacturing process and the possible contamination during several steps of the manufacturing processes. Southern Italy with more than 1.000.000 sheep represents an important source of income for rural areas of this territory. The milk from sheep is almost entirely used to produce cheese. At present, no data are available concerning levels of heavy metal contamination in milk and dairy products from sheep in Calabria and Campania – two regions of southern Italy. The aim of this work was to detect the concentrations of some heavy metals in milk collected from ewes in several farms in Calabria and Campania and to evaluate to what extent these metals may be present in dairy products for human consumption. This study is a part of a work published elsewhere [1].

Characterization of nucleotidic sequences using maximum entropy techniques

A statistical method for characterizing nucleotidic sequences based on maximum entropy techniques is presented. The method uses only codon usage tables and takes into account the length of sequences, and preserves the information contained in each codon by a punctual index. We present the methodological aspects of the analysis, showing an application relative to nucleotidic sequences of eukaryotes.

Input strategy analysis for an air quality data modelling procedure at a local scale based on neural network

In recent years, a significant part of the studies on air pollutants has been devoted to improve statistical techniques for forecasting the values of their concentrations in the atmosphere. Reliable predictions of pollutant trends are essential not only for setting up preventive measures able to avoid risks for human health but also for helping stakeholders to take decision about traffic limitations. In this paper, we present an operating procedure, including both pollutant concentration measurements (CO, SO2, NO2, O3, PM10) and meteorological parameters (hourly data of atmospheric pressure, relative humidity, wind speed), which improves the simple use of neural network for the prediction of pollutant concentration trends by means of the integration of multivariate statistical analysis. In particular, we used principal component analysis in order to define an unconstrained mix of variables able to improve the performance of the model. The developed procedure is particularly suitable for characterizing the investigated phenomena at a local scale.

Supervised and Characterized Smart Monitoring Network for Sensing Environmental Quantities

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.

Integrated Indicators for the Estimation of Vulnerability to Land Degradation

The setting up of sustainable development strategies, able to balance the opposite demands of economic growth and environmental protection, is one of the fundamental challenges for the international community. Our developing world is experiencing growing pressures on its land, water, and food production systems and the role of the human society in determin‐ ing change within the Earth environment is becoming ever more central [1]. In this context, preserving the land productivity is a prior goal, especially in those areas, such as drylands, which are particularly fragile from an ecological point of view.