Aerosol particles are key components of the Earth system; important in dictating radiative balance, human health, and other areas of key societal concern. Understanding their formation, evolution, properties and impacts relies on developments from multiple disciplines covering both experimental laboratory work, field studies and numerical modelling. This session covers all aspects of Aerosol Chemistry and Physics. Contributions from aerosol laboratory, field, remote sensing and model studies are all highly encouraged.
Beyond the general topics, we recognize the rapid development of digital technologies has begun to transform and even lead new directions in aerosol research. Cloud computing, digital twins, and artificial intelligence are providing unprecedented capabilities for this field. These approaches span multiple scales, from single particles to global systems, and from process-level understanding to impact attribution. This session will spotlight the growing role of digital technologies in aerosol chemistry and physics. We invite contributions that explore the application and highlight key discoveries enabled by digital technologies. At the same time, we also emphasize the importance of balancing innovation with rigor: conclusions and processes must be carefully validated, uncertainties explicitly assessed, and data-driven methods integrated with theory, process models, and experimental observations to ensure reliability and reproducibility. Through this lens, this session aims to discuss both the opportunities and responsibilities of integrating digital technologies into aerosol research.
We welcome submissions that fall under a broad range of atmospheric aerosol applications. This could include work on the role and impact of:
- Advance fundamental understanding of aerosol chemistry and physics
- Development of hybrid process-machine learning based aerosol models
- Increased resolution and/or computational efficiency of numerical methods
- Applications of AI-enabled (e.g., GenAI, foundation models) and new-generation tools in aerosol research
- AI-enabled interpretation/prediction of aerosol variability and consequences, from characterizing properties to forecasting extreme events and quantifying impacts
- Development of new physical and digital platforms/technologies for aerosol research
- Open science practices: benchmark datasets, reproducible workflows, model sharing, and evaluation standards
Song Guo, Hanna Vehkamäki