Giuseppina Anna Giorgio

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.