Tritium clouds environmental impact in air into the Western Mediterranean Basin evaluation

The paper considers short-term releases of tritium (mainly but not only tritium hydride (HT)) to the atmosphere from a potential ITER-like fusion reactor located in the Mediterranean Basin and explores if the short range legal exposure limits are exceeded (both locally and downwind). For this, a coupled Lagrangian ECMWF/FLEXPART model has been used to follow real time releases of tritium. This tool was analyzed for nominal tritium operational conditions under selected incidental conditions to determine resultant local and Western Mediterranean effects, together with hourly observations of wind, to provide a short-range approximation of tritium cloud behavior. Since our results cannot be compared with radiological station measurements of tritium in air, we use the NORMTRI Gaussian model. We demonstrate an overestimation of the sequence of tritium concentrations in the atmosphere, close to the reactor, estimated with this model when compared with ECMWF/FLEXPART results. A Gaussian “mesoscale” qualification tool has been used to validate the ECMWF/FLEXPART for winter 2010/spring 2011 with a database of the HT plumes. It is considered that NORMTRI allows evaluation of tritium-in-air-plume patterns and its contribution to doses.

ANN and Fuzzy c-Means applied to environmental pollution prediction

Salamanca, situated in center of Mexico is among the cities which suffer most from the air pollution in Mexico. The vehicular park and the industry, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Sulphur Dioxide (SO2). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables and air pollutant concentrations of SO2. Before the prediction, Fuzzy c-Means and K-means clustering algorithms have been implemented in order to find relationship among pollutant and meteorological variables. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of SO2 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results showed that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hours.