AtmoSTEM: From high-resolution emission mapping to environmental intelligence
Event date: 2026-11-05
Event location:
Welcome to this week’s Learning Machines seminar.
This seminar is a collaboration between RISE and Climate AI Nordics – climateainordics.com.
Title: AtmoSTEM: From high-resolution emission mapping to environmental intelligence
Speaker: Anastasia Kakouri, University of Aegean
Abstract: The spatial and temporal representation of anthropogenic emissions is essential for atmospheric modelling, yet its influence on the predictive performance of data-driven air quality models remains insufficiently assessed. This study introduces AtmoSTEM, a modular European framework integrating high-resolution emission modelling with machine learning-based concentration prediction.
AtmoSTEM combines CAMS-REG emission inventories, sector-specific geospatial proxies, and CAMS-TEMPO profiles to generate spatially and temporally resolved anthropogenic emissions. The initial application covers daily PM2.5 and PM10 emissions at 1 km spatial resolution across Europe for 2015–2024.
The resulting emissions are integrated with meteorological, satellite-derived, atmospheric composition, and geospatial information to support machine learning-based predictions of ground-level pollutant concentrations. An Extreme Gradient Boosting (XGBoost) approach is employed to investigate the relationships between anthropogenic emissions, environmental conditions, and observed air pollution levels, with particular emphasis on the contribution of high-resolution emission information to predictive performance.
Model predictions are evaluated against independent ground-based observations from air quality monitoring stations across Europe. The study aims to assess the potential of integrating high-resolution spatiotemporal emissions with machine learning for air quality prediction, providing a basis for more spatially detailed population exposure assessments and epidemiological research.
About the speaker: Anastasia Kakouri is a researcher with a PhD in Atmospheric Sciences and Sustainability, working at the intersection of atmospheric science, geospatial analysis, and environmental intelligence. She has over six years of experience in national and European research projects, with expertise in geospatial analytics, satellite remote sensing, machine learning, and environmental modelling. Her research focuses on integrating atmospheric and environmental datasets to advance high-resolution emission mapping, air quality assessment, population exposure estimation, and environmental health applications. She also develops automated and reproducible computational workflows for large-scale environmental data processing and analysis.
Location: This is an online seminar. Connect using Zoom.
Date: 2026-11-05 15:00
Upcoming seminars:
- 2026-12-03: Miguel Costa, Technical University of Denmark
- 2026-12-17: Lester Jame Miranda, University of Cambridge
- 2027-01-14: Maria Perez Ortiz, University College London
- 2027-01-28: Nicola Messina, ISTI-CNR
- All seminars are 15:00 CET.
More information and coming seminars: https://ri.se/lm-sem
– The Learning Machines Team