1,721,112 research outputs found
Multivariate indices for analysing correlation structures in environmental datasets
No full textIn the most recent literature, we may find many studies concerning biogeochemical features of ecosystems, communities' ecological structure, analysis of multi-temporal and multi-scales data sets coming from remote observations, atmospheric pollution modeling and forecasting in which multivariate procedures are applied . In all these cases multivariate statistical techniques have to be sequentially and recursively applied and tools able to compare the role of different variables in different correlation structures are necessary. In this study we present the formulation of two new indices based on the joined application of cluster analysis and principal component analysis. These indices are able to evaluate, quantitatively, a standardized weight for measured variables (descriptors) and object-observations or object-samples (objects), characterizing different correlation structures
Relazione tra esposizione a particolato e russamento in una popolazione di pazienti con sospette apnee ostruttive nel sonno
No full textSoil magnetic susceptibility measurements for characterising heavy metal patterns in industrial areas (Chapt.3).
No full textThe 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
Neural network model for forecasting atmospheric particulate levels
No full textProcedures based on artificial neural network (ANN) have been applied with success to forecast levels of atmospheric pollutants. These techniques show a capability to make regressive approximation of non-linear functions in high-dimensional space and they are more flexible in comparison to traditional statistical techniques. In this paper we present a short review of recent applications of ANN models for forecasting atmospheric particulate levels and the results of a study carried out to forecast hourly levels of PM10 in urban area starting from data measured in N-previous days. In particular we analyze PM10 hourly concentrations measured from March 2001 to February 2002 in three stations of the air quality monitoring network of Potenza town (southern Italy). The applied ANN model is a feed-forward multi-layer perceptron (MLP) with an only hidden layer. The conjugate gradient learning algorithm is used. The learning capability of the model and the average goodness of the prediction are evaluated by Mean Absolute Percentage Error (MAPE) and by the number of concentration values that the model is not able to predict (NP). The results indicate that, in the study area, a simple model of ANN is able to forecast PM10 hourly levels with a good approximation but the quality of data, in terms of presence of data missing, represents the main limit of these forecasting techniques at local scale. In order to improve the model performance increasing the number of input variables, the results suggest not only to take into account meteorological parameters but also to better characterize the dynamic features of emission source pattern
Modelling study for forecasting gaseous pollutants levels in a urban area
No full textAtmospheric pollution is an important topic in environmental sciences. Nowadays the quality and the quantity of the data from air quality monitoring networks are significantly increased, but, for an effective management and assessment of this information, innovative data analysis methodologies have been developed. Approaches coming from advanced statistical methods were introduced in modeling and forecasting procedure to define operational techniques for atmospheric pollutants characterization at different scales. In this paper we present an application of artificial neural networks (ANN) for forecasting atmospheric gaseous pollutants. Starting from hourly data collected in Basilicata (southern Italy), from 1998 to 2007, we select the best dataset in terms of minimum data missing percentage. The applied model is a feed-forward multi-layer perceptron with an only hidden layer. The conjugate gradient learning algorithm is used. The learning capability of the model and the average goodness of the prediction are evaluated by Mean Absolute Percentage Error. The goal is to evaluate the performance the ANN model for forecasting 24-hourly data on the base of only the 24-hourly data collected in the previous day and to quantify the improvement obtained with different input strategies (optimal mix of pollutants defined by data correlation structure analysis). The preliminary results suggest that the dynamical characteristics of the gaseous pollutants may play a fundamental role in the definition of the forecasting procedure. Moreover, results confirm that the correlation structure analysis may be usefully applied for identifying the optimal strategy of data input selection. Nevertheless the quality of data represents the main limit of forecasting techniques at local scale
NPC-index application for PM sources apportionment study in two monitoring sites of Spain.
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