Our Head of Customer Success and Research at Meteosim, Raúl Arasa, PhD in Physics from the University of Barcelona and expert in meteorological and air quality modeling, has collaborated with a research group from the Department of Chemical Engineering and Analytical Chemistry at the University of Barcelona, specialized in Computational Fluid Dynamics (CFD), on a technical study focused on comparing the performance of CFD models versus traditional Gaussian plume models. With extensive experience in developing air quality management systems across Europe, Latin America, Africa, and the Middle East, Raúl applied this analysis to the Prairie Grass experiment, a reference case in atmospheric dispersion simulation under flat terrain and neutral atmospheric conditions.
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Why this study?
Our clients often require highly accurate estimates of pollutant concentrations at locations very close to emission sources. However, Gaussian models do not take into account physical obstacles such as buildings or infrastructures typically found in industrial environments. This gap highlighted the opportunity to explore the added value that CFD modeling can provide, offering a more detailed representation of complex physical surroundings.
Study objectives
• Compare the accuracy and applicability of CFD models versus Gaussian models using empirical data from the Prairie Grass experiment.
• Identify the strengths and limitations of each modeling approach, with particular attention to the balance between precision and computational cost.
Key conclusions
CFD models demonstrated strong capabilities in accurately replicating pollutant dispersion, even accounting for nearby obstacles. However, this level of detail comes at a high computational cost. By contrast, Gaussian models, while less detailed, remain effective and significantly more efficient for source-receptor distances greater than 100 meters.
Want to learn more about air quality or pollutant dispersion modeling? Need to apply this type of simulation to your project?
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