MODELLING THE CONCENTRATION FLUCTUATION AND INDIVIDUAL EXPOSURE IN COMPLEX URBAN ENVIRONMENTS

The concentrations fluctuations of a dispersing hazardous gaseous pollutant in the atmospheric boundary layer, and the hazard associated with short-term concentration levels, demonstrate the necessity of estimating the magnitude of these fluctuations using predicting models. Moreover the computation of concentration fluctuations and individual exposure in case of dispersion in realistic situations, such as built-up areas or street canyons, is of special practical interest for hazard assessment purposes. In order to predict or/and estimate the maximum expected dosage and the exposure time within which the dosage exceeds certain health limits, the knowledge of the behaviour of concentration fluctuations at the point under consideration is needed. In this study the whole effort is based on the ‘Mock Urban Setting Test – MUST’, an extensive field test carried out on a test site of the US Army in the Great Basin Desert in 2001 (Biltoft, 2001; Yee, 2004). The experimental data that was used for the model evaluation concerned the dispersion of a passive gas between street canyons which have been created by 120 standard size shipping containers. The computational simulations have been performed using the laboratory CFD code ADREA, which has been developed for simulating the dispersion and exposure of pollutants over complex geometries. The ADREA model is evaluated by comparing the model’s predictions with the observations utilizing statistical metrics and scatter plots. The present study has been performed in the frame of the Action COST 732 “Quality Assurance and Improvement of Micro-Scale Meteorological Models”

Extracting Formations from Long Financial Time Series Using Data Mining

Technical analysis has become a custom decision support tool for traders and analysts, though not widely accepted by the academic community. It is based on the identification of a series of well-defined formations appearing over irregular intervals. The same principle forms the basis for the application of data mining methodologies as a tool to discover hidden patterns that exist in a time series, which is achieved by a detailed breakdown of historic information. This paper introduces a methodology for the discovery of formations that exist within a time series and have high probability of reoccurrence. The methodology was developed in an efficient manner requiring only a small number of user-specified parameters. Its two main stages are (a) a modified bottom-up segmentation algorithm with an optimization stage to reach the optimal number of segments, and (b) a rule extraction algorithm. The developed methodology is tested on two major financial series, the daily closing values of the SP500 Index and the GB Pound to US Dollar exchange rates.Numéro Spécial « Special Issue on Nonlinear Financial Analysis :Editorial Introduction » Guest Editor :Catherine Kyrtsouinfo:eu-repo/semantics/publishe