0000000000067084
AUTHOR
L. Pignato
Analytic solutions of the diffusion-deposition equation for fluids heavir than atmospheric air
A steady-state bi-dimensional turbulent diffusion equation was studied to find the concentration distribution of a pollutant near the ground. We have considered the air pollutant emitted from an elevated point source in the lower atmosphere in adiabatic conditions. The wind velocity and diffusion coefficient are given by power laws. We have found analytical solutions using or the Lie Group Analysis or the Method of Separation of Variables. The classical diffusion equation has been modified introducing the falling term with non-zero deposition velocity. Analytical solutions are essential to test numerical models for the great difficulty in validating with experiments.
Hourly Forecasting of SO2 Pollutant Concentration Using an Elman Neural Network
In this paper the first results produced by an Elman neural network for hourly SO2 ground concentration forecasting are presented. Time series has been recorded between 1998 and 2001 and are referred to a monitoring station of SO2 in the industrial site of Priolo, Syracuse, Italy. Data has been kindly provided by CIPA (Consorzio Industriale per la Protezione dell'Ambiente, Siracusa, Italia). Time series parameters are the horizontal and vertical wind velocity, the wind direction, the stability classes of Thomas, the base level of the layer of the atmospheric stability, the gradient of the potential temperature and the difference of the potential temperature of reference.
Three Hours Ahead Prevision of SO2 Pollutant Concentration Using an Elman Neural based Forecaster
Abstract Indoor air quality near the industrial site is tightly joined to pollutant concentration level, since outdoor pollution heavily influences air quality and, consequently, inhabitants health. A pollution management system is essential for health protection. Automatic air quality management systems have became an important research issue with strong implications for inhabitants’ health. In this paper an automatic forecaster based on neural networks for SO 2 concentration prevision is proposed. The analyzed area covers different small towns near the industrial site of Priolo, in the south of the world. Among these towns, Melilli was the first town in Italy that was evacuated for high l…
Simulation with a Torque Dynamometer of Pollutants Emission from Vehicles Near Urban Cross-Road
Nowadays, the influence of the vehicular traffic on the degradation of urban atmosphere is focused on by researchers belonging to different fields in view of its prime importance for public health. The control of exhaust gases emission is primary in order to keep air pollution below the specified limits of the laws in force. In previous works the “medium car” characterises a country in modelling a pollutant in urban areas. This is the central parameter of a forecasting model proposed by Pignato and LoMastro, of pollutant concentration near urban cross-road with traffic lights in continue queue and platoon flow. The proofs have been carried on two classes of displacement vehicles and four cl…
Estimation of biogas produced by the landfill of Palermo, applying a Gaussian model
Abstract In this work, a procedure is suggested to assess the rate of biogas emitted by the Bellolampo landfill (Palermo, Italy), starting from the data acquired by two of the stations for monitoring meteorological parameters and polluting gases. The data used refer to the period November 2005–July 2006. The methane concentration, measured in the CEP suburb of Palermo, has been analysed together with the meteorological data collected by the station situated inside the landfill area. In the present study, the methane has been chosen as a tracer of the atmospheric pollutants produced by the dump. The data used for assessing the biogas emission refer to night time periods characterized by weak…
Two-days ahead prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the urban area of Palermo, Italy
Abstract Artificial neural networks are functional alternative techniques in modelling the intricate vehicular exhaust emission dispersion phenomenon. Pollutant predictions are notoriously complex when using either deterministic or stochastic models, which explains why this model was developed using a neural network. Neural networks have the ability to learn about non-linear relationships between the used variables. In this paper a recurrent neural network (Elman model) based forecaster for the prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the city of Palermo is proposed. The effectiveness of the presented forecaster was tested using a time series recorded between …