Środowiskowe Seminarium Fizyki Atmosfery
2006/2007 | 2007/2008 | 2008/2009 | 2009/2010 | 2010/2011 | 2011/2012 | 2012/2013 | 2013/2014 | 2014/2015 | 2015/2016 | 2016/2017 | 2017/2018 | 2018/2019 | 2019/2020 | 2020/2021 | 2021/2022 | 2022/2023 | 2023/2024 | 2024/2025 | 2025/2026 | Strona własna seminarium
2025-10-24 (Piątek)
prof. dr hab. Małgorzata Werner (Wydział Nauk o Ziemi i Kształtowania Środowiska, Uniwersytet Wrocławski)
Anthropogenic and natural aerosols in the atmosphere - examples of practical applications of modelling and measurements
This presentation will focus on the application of numerical modelling and measurement methods to assess air quality in Europe, with a focus on Poland. The modelling will include the state-of-the-art Eulerian chemical transport models dedicated for regional scale analysis as well as Gaussian type models used for city scale. In addition, the application of machine learning methods and the combination of chemical transport model results with observations to predict air pollution concentrations will be presented. The talk will cover the most problematic chemical pollutants such as PM2.5 (particulate matter with a diameter of less than 2.5 m), BaP (benzo(L)piren), NO2 (nitrogen dioxide) as well as biological particles such as allergenic pollen grains. Examples of the application of modelling to answer questions related to air quality management will be shown. The presentation will conclude with current challenges and opportunities for further development.
Join Zoom Meeting
https://uw-edu-pl.zoom.us/j/98168055999?pwd=BWRrOr06kAFjQaIri757V8Fi1bqyCQ.1
Meeting ID: 981 6805 5999
Passcode: 016069
Join Zoom Meeting
https://uw-edu-pl.zoom.us/j/98168055999?pwd=BWRrOr06kAFjQaIri757V8Fi1bqyCQ.1
Meeting ID: 981 6805 5999
Passcode: 016069
2025-10-17 (Piątek)
dr Mehri Davtalab (Department of Environmental Research, Center for Physical Sciences and Technology, Vilnius, Lithuania)
Integrating GIS and machine learning for urban air quality assessment
Rapid urbanization and industrial growth have intensified air quality challenges in cities worldwide. Understanding how urban structure influences air pollution requires analytical frameworks capable of handling complex spatial and temporal data. This seminar presents how the integration of Geographic Information Systems (GIS) and Machine Learning (ML) can enhance urban air quality research. GIS provides a spatial foundation for managing and visualizing environmental parameters such as land use, traffic density, meteorological conditions, and emission sources. ML techniques make it possible to identify complex relationships between urban structural characteristics such as urban built-up areas (UBAs) and urban green spaces (UGSs) and pollutant concentrations. Moreover, ML enables air quality assessment by predicting pollutant levels, estimating missing data, and determining the influence of environmental and urban factors. By integrating GIS-based spatial analysis with ML-based predictive modeling, it becomes possible to generate high-resolution air quality maps, identify pollution hotspots, and evaluate the role of urban morphology in pollutant dispersion. This integrated approach improves the interpretability of air quality assessments and informs strategies for sustainable urban and environmental management.
Join Zoom Meeting
https://uw-edu-pl.zoom.us/j/97848514361?pwd=pcbE5IPFUz5241S7SpZVbnfom5jF8e.1
Meeting ID: 978 4851 4361
Passcode: 005211
Join Zoom Meeting
https://uw-edu-pl.zoom.us/j/97848514361?pwd=pcbE5IPFUz5241S7SpZVbnfom5jF8e.1
Meeting ID: 978 4851 4361
Passcode: 005211
2025-10-09 (Czwartek)
prof. Martin Oberlack (Department of Mechanical Engineering, Technical University of Darmstadt, Germany)
Recent progress in the symmetry based theory for near-surface shear flows
2025-10-03 (Piątek)
dr Simon Görtz (Technical University of Darmstadt)
Wave propagation and causality in atmospheric free shear layers
We investigate the reflection and transmission of waves through a shear layer. We use a hyperbolic tangent to model the shear layer, and reflection and transmission coefficients to quantify its interaction with pressure waves. Using causality arguments in the inverse Laplace transform, we demonstrate that the direction of the incident wave in relation to the mean velocity gradient is crucial. A wave that is incident from the upper far field and propagates in the opposite direction to the increasing velocity gradient is amplified during the reflection process, and a distinct resonance frequency exists. Conversely, waves incident from the lower far field propagate in the direction of the increasing velocity gradient. Causality reveals that these waves are damped during reflection and that a distinct total absorption frequency exists. Therefore, causality arguments underline the observation that the problem is not fully symmetric from the perspective of an incident wave, even if a symmetric mean flow profile is chosen.
Join Zoom Meeting:
https://uw-edu-pl.zoom.us/j/95929888109?pwd=gsOXOpvJ0bXl5aYrK7ppjzIy0vjqLd.1
Meeting ID: 959 2988 8109
Passcode: 874810
Join Zoom Meeting:
https://uw-edu-pl.zoom.us/j/95929888109?pwd=gsOXOpvJ0bXl5aYrK7ppjzIy0vjqLd.1
Meeting ID: 959 2988 8109
Passcode: 874810


