odour b learning master university valparaiso 2020   The Chilean Catholic University of Valparaiso is launching the second undergraduate Degree on Management, Control and Evaluation of atmospheric odour emissions. This was, to our knowledge, the first official Degree given by a University on odour management and it was so successful that the initiative is taken place again this year. The Degree on Odour Management will be given on the modality of blended-learning (b-learning) with both in-person and on-line formal education.

   This Official Degree in Management, Control and Evaluation of Atmospheric Odour emissions, has been designed and developed by the Pontifical Catholic University of Valparaiso (PUCV). The content of this Degree has been configured to prepare the students to learn new abilities, related to different areas of the environmental engineering, so that they can set strategies for a better odour management inside their organizations

Silsoe   After the success of their last Odour Study Day, that took place on the 23rd January 2020, held at their odour laboratory at Wrest Park in Bedfordshire, UK, Silsoe Odours has announced the next one-day course for January 2021.

   In its fourteenth year, the Silsoe Odours Study Day Course, organized by the Silsoe Odours Ltd, has been developed by leading odour professionals based on their own extensive experience. This course designed for regulators, operators of odorous industries, local authorities, consultants, air quality professionals and abatement equipment suppliers provides clear, up-to-date guidance on the principles, concepts and methods of odour management.

Santiago Marco    The Department of Chemistry, Materials and Chemical Engineering "Giulio Natta" of the Politecnico di Milano is organizing a Virtual Seminar Series, and this Friday June 19, 2020, Mr. Santiago Marco from the University of Barcelona is presenting a free seminar at 11:30 AM CET.

   The seminar “Signal and Data Processing Workflows for Untargeted Chemical Analysis: Sensor Array and Mass Spectrometry Analysis of Complex Gas Samples” will focus on the problemas of the instrumental issues and sampling conditions of complex gases for the correct interpretation of the results by building mega-variate predictive models using tools from machine learning.

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