An alternative wind profile formulation for urban areas in neutral conditions

On the basis of meteorological observations conducted within the city of Rome, Italy, a new formulation of the wind-speed profile valid in urban areas and neutral conditions is developed. It is found that the role played by the roughness length in the canonical log-law profile can be taken by a local length scale, depending on both the surface cover and the distance above the ground surface, which follows a pattern of exponential decrease with height. The results show that the proposed model leads to increased performance compared with that obtained by using other approaches found in the literature

The Gram-Charlier method to evaluate the probability density function in monodimensional case

In many experimental applications, starting from a random variable, it is possible to evaluate the moments and to define the probability density function(PDF) in different ways. In this paper a new approach is shown in order to estimate the PDF by moments according to Gram-Charlier method (GCm). The approach consists of a choice of standard deviation (s new) in GCm which optimizes the values of the input moments. In particular three s new are selected in order to minimize: 1) the sum of absolute relative deviations among theoretical and experimental moments; 2) the relative per cent of negative probabilities coming from GC expansion; 3) the product between the two previous functions. A theoretical application of the above approach is made where the input moments data set comes from the vertical velocity distribution estimated for one level of the convective mixed layer. This application consists of two different simulations. The first evaluates the moments up to 10th order, having as input data the moments up to 3rd order. The second gives the moments up to 10th order considering both the moments of the previous simulation and the 4th-order moment calculated with Gaussian closure as input data

A new formulation of the Gram-Charlier method: Performance for fitting non-normal distribution

The Gram-Charlier expansion was derived in an attempt to express non-normal densities as infinite series involving the normal density and its derivatives, using the moments data as input terms. In classic Gram-Charlier expansion the random variable is standardized, so that the Gaussian parameters are Always fixed and referred to the mean equal to zero and to the standard deviation equal to one. This assumption seems to be too strong. An improvement of Gram-Charlier expansion was obtained by an optimization process, directed to choose new values of Gaussian parameters. In order to check the performance of the new approach, an estimate of the gamma probability density function was calculated. Two probability density functions, characterized by a different degree of skewness and kurtosis, were considered. The study has shown that in comparison with the classic assumption, the new one always gives the best results in terms of probability density function reproducibility and allows the best evaluation of the input moments. Further the comparison between estimated moments of order higher than the input ones and the theoretical moments shows a good reproduction. Finally the method seems to suggest that a less restrictive condition can be considered respect to the usual convergence criterium of the Gram-Charlier expansion

METHODOLOGICAL CONSIDERATION ON PRE-PROCESSING DATA OPTIMIZATION CONCERNING AIR DISPERSION MODEL AND NEURAL NETWORKS: A CASE-STUDY OF OZONE PREDICTION LEVEL

This work analyzes the results of a Neural Network model applied to air pollution data. In particular, we forecast ozone pollutants levels in a short term using both air dispersion models and neural network methods. The purpose of this work is to provide a novel methodological procedure to analyze environmental data by using a neural net as forecast technique for ozone levels in the urban area of Rome. Results show that the model performance can be improved by pre-processing input data using typical datamining techniques and coupling air dispersion model with neural net

METHODOLOGICAL CONSIDERATION ON PRE-PROCESSING DATA OPTIMIZATION CONCERNING AIR DISPERSION MODEL AND NEURAL NETWORKS: A CASE-STUDY OF OZONE PREDICTION LEVEL

This work analyzes the results of a Neural Network model applied to air pollution data. In particular, we forecast ozone pollutants levels in a short term using both air dispersion models and neural network methods. The purpose of this work is to provide a novel methodological procedure to analyze environmental data by using a neural net as forecast technique for ozone levels in the urban area of Rome. Results show that the model performance can be improved by pre-processing input data using typical datamining techniques and coupling air dispersion model with neural net

DEFINITION OF NOVEL HEALTH AND AIR POLLUTION INDEX BASED ON SHORT TERM EXPOSURE AND AIR CONCENTRATION LEVELS

Health impact assessment has become important in the development of air quality policies and in finding the relationships between pollutants concentration and health effects. In our work we presented a novel index able to evaluate the effects on the human exposure caused by ambient air pollution in urban areas. The index is able to link both health risk factors and pollutants levels. The indexes is of additive type and is composed by two terms: the former is based on pollutants concentration and is connected with EPA air quality index (AQI), while the latter is composed by an adimensional term based on the exposure levels. We tested the methodology using PM10 as studied pollutants. The spatial and temporal variation of its health impact was evaluated by means of index maps applying the above methodology in the city of Rome during three selected episodes. Our study shows index maps for all episodes linked to population and to pollutants

Analysis of the influence of geometric and ventilation factors on indoor pollutant dispersion: a numerical study

The aim of this study is to delineate the role played by natural ventilation and room geometry on indoor dispersion. To this end, particle material (PM) concentration fields obtained using Computational Fluid Dynamics (CFD) concerning a series of ideal cases regarding parallelepiped rooms of different sizes and inlet velocities at the openings have been analysed. The numerical results have been compared with the concentrations obtained using a Box Model based on the mass balance. The results show a reasonably good agreement between the emptying times of the rooms calculated by the CFD and the Box Model, particularly when the room is square shaped. It was also found that the emptying time assumes an almost constant value once normalized with the inlet velocity and room diagonal. Since these are known values, it is possible to infer the emptying time avoiding the use of highly time-consuming numerical simulations

Modelling the influence of shielding on physical and biological organ doses.

Distributions of "physical" and "biological" dose in different organs were calculated by coupling the FLUKA MC transport code with a geometrical human phantom inserted into a shielding box of variable shape, thickness and material. While the expression "physical dose" refers to the amount of deposited energy per unit mass (in Gy), "biological dose" was modelled with "Complex Lesions" (CL), clustered DNA strand breaks calculated in a previous work based on "event-by-event" track-structure simulations. The yields of complex lesions per cell and per unit dose were calculated for different radiation types and energies, and integrated into a version of FLUKA modified for this purpose, allowing us to estimate the effects of mixed fields. As an initial test simulation, the phantom was inserted into an aluminium parallelepiped and was isotropically irradiated with 500 MeV protons. Dose distributions were calculated for different values of the shielding thickness. The results were found to be organ-dependent. In most organs, with increasing shielding thickness the contribution of primary protons showed an initial flat region followed by a gradual decrease, whereas secondary particles showed an initial increase followed by a decrease at large thickness values. Secondary particles were found to provide a substantial contribution, especially to the biological dose. In particular, the decrease of their contribution occurred at larger depths than for primary protons. In addition, their contribution to biological dose was generally greater than that of primary protons