During March, 2020, most European countries implemented lockdowns to restrict the transmission of SARS-CoV-2, the virus which causes COVID-19 through their populations. These restrictions had positive impacts for air quality due to a dramatic reduction of economic activity and emissions. In this work, a machine learning approach was designed and implemented to analyze local air quality improvements during the COVID-19 lockdown in Graz, Austria. The machine learning approach was used as a robust alternative to simple, historical measurement comparisons for various individual pollutants. Concentrations of NO2 (nitrogen dioxide), PM10, O3 (ozone) and Ox (total oxidant) were selected from five measurement sites in Graz and were set as target variables for random forest regression models to predict their expected values during the city's lockdown period. The true vs. expected difference is presented here as an indicator of true pollution during the lockdown. The machine learning models showed a high level of generalization for predicting the concentrations. Therefore, the approach was suitable for analyzing reductions in pollution concentrations. Results on the validation set showed very good performance for Ox and NO2 when compared to PM10 and O3. The analysis indicated that the city's average concentration reductions for the lockdown period were:-36.9 to-41.6%, and-6.6 to-14.2% for NO2 and PM10, respectively. However, an increase of 11.6 to 33.8% for O3 was estimated. The reduction in pollutant concentration, especially NO2 can be explained by significant drops in traffic-flows during the lockdown period (-51.6 to-43.9%). The results presented give a real-world example of what pollutant concentration reductions can be achieved by reducing traffic-flows and other economic activities.
pharmazeutischen Industrie wird unterschieden zwischen theoretischem, technischem, stofflichem und wirtschaftlichem Potenzial. Die Potenzialtheorie wird systematisch auf die Prozessentwicklung angewendet.
Für die Gestaltung nachhaltiger Produktionsverfahren in allen Branchen der Prozessindustrie sind neben sozialen und ökonomischen auch ökologische Aspekte von entscheidender Bedeutung. Vorgestellt wird ein Ansatz auf Basis eines 3-Ebenen-Modells, der die Analyse von Produktionsprozessen in Mehrproduktanlagen im Hinblick auf Energie-und Ressourcenverbrauch verbunden mit ökologischen Betrachtungen ermöglicht sowie die Identifizierung und Quantifizierung von Verbesserungspotenzialen unterstützt. Der Ansatz ist modular aufgebaut und basiert auf einer Stoffstromnetzmodellierung der betrachteten Prozesse.For a sustainable process design in the chemical, specialty chemical, and pharmaceutical industries, ecological aspects alongside the social and economic ones play a significant role. An approach based on a three level model is presented that allows an analysis of production processes in multi-product plants with respect to consumption of energy and resources combined with ecological assessment. The approach has a modular structure and builds on a material flow modeling of the assessed processes.
The integration of innovative equipment technologies in production processes offers efficiency potentials with regards to energy and resource demand as well as investment and operating costs. For the estimation of these potentials, a sufficiently detailed database is necessary as well as the possibility to consider particular boundary conditions of the production process, infrastructure of the production site, and the overall plant design. A structured concept for the selection of suitable equipment technol-ogies and sizing rules for the design of the equipment for a desired process task are presented in this review. The combination with an appropriate modeling approach allows a holistic assessment of technically feasible solutions to quantify economic and ecological efficiency potentials of the equipment technologies. The application of this concept is demonstrated on the task of a typical heat transfer problem in the special chemicals sector.
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