Background level of heavy-metals soil concentrations in an industrial area of Basilicata Region (Southern Italy)

In the framework of Basilicata region air quality monitoring, we are investigating the industrial area of Melfi town. This area has been chosen as test site to evaluate the environmental impact of anthropic activities on a rather unpolluted agricultural area. In this paper we discuss the procedure to characterize the background level of heavy-metals soil concentrations. The topsoil bioavailable fraction of eight elements (Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn) has been collected in two annual surveys (1993 and 1994) on a georeferenced grid, translated in GIS informative layer. Statistical analysis of spatial and temporal patterns is shown in detail

Background level of heavy-metals soil concentrations in an industrial area of Basilicata Region (Southern Italy)

In the framework of Basilicata region air quality monitoring, we are investigating the industrial area of Melfi town. This area has been chosen as test site to evaluate the environmental impact of anthropic activities on a rather unpolluted agricultural area. In this paper we discuss the procedure to characterize the background level of heavy-metals soil concentrations. The topsoil bioavailable fraction of eight elements (Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn) has been collected in two annual surveys (1993 and 1994) on a georeferenced grid, translated in GIS informative layer. Statistical analysis of spatial and temporal patterns is shown in detail

Experimental and statistical investigations on atmospheric heavy metals concentrations in an industrial area of Southern Italy

In this paper we present experimental protocol and statistical proceduresfor evaluating atmospheric concentrationsof TSP and heavy metals(Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn) in the area of Tito Scalo (PZ, Southern Italy), a small industrial site, far from urban areas and surrounded by agricultural and naturalistic sites. In order to characterise the concentrations level and the temporal pattern of each pollutant, we have analysed data collected in two field surveys (April-December 1997, April-December 1998) with univariate and multivariate statistical methods. Furthermore we have investigated the relationships between pollutants concentrationsand meteoclimatic parameters(temp erature and rainfalls)

Estimation of vegetation cover resilience from satellite time series

Resilience is a fundamental concept for understanding vegetation as a dynamic component of the climate system. It expresses the ability of ecosystems to tolerate disturbances and to recover their initial state. Recovery times are basic parameters of the vegetation's response to forcing and, therefore, are essential for describing realistic vegetation within dynamical models. Healthy vegetation tends to rapidly recover from shock and to persist in growth and expansion. On the contrary, climatic and anthropic stress can reduce resilience thus favouring persistent decrease in vegetation activity. <br><br> In order to characterize resilience, we analyzed the time series 1982–2003 of 8 km GIMMS AVHRR-NDVI maps of the Italian territory. Persistence probability of negative and positive trends was estimated according to the vegetation cover class, altitude, and climate. Generally, mean recovery times from negative trends were shorter than those estimated for positive trends, as expected for vegetation of healthy status. Some signatures of inefficient resilience were found in high-level mountainous areas and in the Mediterranean sub-tropical ones. This analysis was refined by aggregating pixels according to phenology. This multitemporal clustering synthesized information on vegetation cover, climate, and orography rather well. The consequent persistence estimations confirmed and detailed hints obtained from the previous analyses. Under the same climatic regime, different vegetation resilience levels were found. In particular, within the Mediterranean sub-tropical climate, clustering was able to identify features with different persistence levels in areas that are liable to different levels of anthropic pressure. Moreover, it was capable of enhancing reduced vegetation resilience also in the southern areas under Warm Temperate sub-continental climate. The general consistency of the obtained results showed that, with the help of suited analysis methodologies, 8 km AVHRR-NDVI data could be useful for capturing details on vegetation cover activity at local scale even in complex territories such as that of the Italian peninsula

A preliminary studyof the site-dependence of the multifractalfeatures of geoelectric measurements

Multifractal analysis was performed to characterize the fluctuations in dynamics of the hourly time variability of self-potential signals measured from January 2001 to September 2002 by three stations installed in the Basilicata region (Southern Italy). Two stations (Giuliano and Tito) are located in a seismic area, and one (Laterza) in an aseismic area. Multifractal formalism leads to the identification of a set of parameters derived from the shape of the multifractal spectrum (the maximum a0, the asymmetry B and the width W) and measuring the «complexity» of the signals. Furthermore, the multifractal parameters seem to discriminate self-potential signals measured in seismic areas from those recorded in aseismic areas

AVHRR Automated detection of volcanic clouds.

A new satellite‐based technique has recently been proposed which seems suitable for an automatic detection of volcanic clouds in daytime conditions. In this paper the robustness of such a new approach, in particular in detecting early eruptive clouds, is evaluated, on several eruptive events at Mt Etna, by using five years of Advanced Very High Resolution Radiometer (AVHRR) data. The detection scheme is discussed together with its possible extension to night‐time monitoring and the improvements expected by its application to the next generation of satellite sensors (in particular Spinning Enhanced Visible and Infrared Imager (SEVIRI)) with enhanced spectral and temporal resolution. The proposed approach seems to overcome the limitations related to other proposed methods which, in some conditions (very fresh eruptive clouds, cold‐backgrounds, etc.), give false or missed detection and will no longer be applicable to the next generation of Geostationary Operational Environmental Satellites (GOES) due to the planned reduction of their thermal infrared channels until 2010

Time dynamics of background noise in geoelectrical and geochemical signals: an application in a seismic area of Southern Italy

We analyse geoelectrical and geochemical time series jointly measured by means of a multiparametric automatic station close to an anomalous fluid emission in Val d’Agri (Basilicata, Italy). The investigated area is located on Southern Apennine chain that in past and recent years was interested by destructive earthquakes. After a complete pre-processing of time series, we analyse the fluctuations triggered by the seasonal cycles and focus our attention on the possible link between geoelectrical and geochemical signals. In order to extract quantitative dynamical information from experimental time series, we detect scaling laws in power spectra that are typical fingerprints of fractional Brownian processes. After this analysis, the problem of the identification of extreme events in the time series has been approached. We consider significant anomalous patterns only when more consecutive values are above/below a fixed threshold in almost two of the time series jointly measured. We give the first preliminary results about the comparison between anomalous patterns detected in geoelectrical and geochemical parameters and the local seismic activity and, finally, analyse the implications with the earthquake prediction problem

Wavelet analysis as a tool to characteriseand remove environmental noisefrom self-potential time series

Multiresolution wavelet analysis of self-potential signals and rainfall levels is performed for extracting fluctuations in electrical signals, which might be addressed to meteorological variability. In the time-scale domain of the wavelet transform, rain data are used as markers to single out those wavelet coefficients of the electric signal which can be considered relevant to the environmental disturbance. Then these coefficients are filtered out and the signal is recovered by anti-transforming the retained coefficients. Such methodological approach might be applied to characterise unwanted environmental noise. It also can be considered as a practical technique to remove noise that can hamper the correct assessment and use of electrical techniques for the monitoring of geophysical phenomena

Soil magnetic susceptibility measurements for characterising heavy metal patterns in industrial areas (Chapt.3).

The development of innovative monitoring strategies, able to provide detailed information on temporal and spatial evolution of contaminants in superficial soil, combining fast, not invasive and low cost acquisition procedures for in situ monitoring pollution, is a crucial task for environmental research. In this context, electromagnetic parameter measurements represent suitable tools for identifying innovative experimental procedures. Particularly, magnetic susceptibility measurements of superficial soil may be used as a simple, rapid, cheap and non-destructive proxy variable to evaluate heavy metal patterns in industrial, urban and agricultural areas. We developed an experimental procedure to carry out field surveys for characterizing heavy metal patterns in superficial soil. This procedure is based on in situ soil magnetic susceptibility measurements supported by analytical determinations of metal concentrations. The well defined experimental protocol allows to reduce intrinsic environmental biases and to standardize the measurement procedure making simpler data interpretation. In this chapter we present a review of case studies concerning the characterization of contamination patterns in different industrial areas of Basilicata region in Southern Italy. Basilicata is an inner region, rather unpolluted, in which the main land use is agriculture The investigated sites show different geomorphologic and anthropogenic features, so they represent optimal sites for studying the potentiality of this kind of approach in different environmental conditions

Searching for low dimensionality in air pollution time series

Time series of atmospheric pollutants (${\rm NO}_x$, CO and ${\rm O}_3$) have been analyzed and the possible presence of low-dimensional chaos has been checked. The analysis has been performed by exploiting the concept of short-term predictability of chaotic systems. Although the time series exhibit statistical characteristics which mimic those typically explained by complex systems, no sign of chaos has been evidenced