Aerosol particles are key components of the Earth system, with important roles in atmospheric chemistry, climate and radiative balance, air quality, human health, and other areas of societal concern. Understanding their formation, evolution, properties, and impacts requires contributions from multiple disciplines, including laboratory experiments, field observations, remote sensing, theory, and numerical modelling. This session welcomes contributions across the full breadth of aerosol chemistry and physics, and submissions do not need to address the focus topic described below.

Alongside this broad scope, this year we particularly welcome studies highlighting emerging technologies for aerosol research, spanning both physical and digital approaches. These may include advances in aerosol sensing and measurement, instrumentation and ionisation techniques, new experimental platforms, high-resolution and computationally efficient modelling, cloud computing, digital twins, and AI. Such technologies are creating new opportunities across scales, from individual particles and molecular processes to regional and global aerosol systems, and from fundamental process understanding to prediction and impact assessment.

We welcome submissions across a broad range of aerosol topics, including but not limited to:
- Fundamental aerosol chemistry and physics, including particle formation, composition, mixing state, phase state, morphology, and physicochemical properties
- Primary and secondary aerosol formation and evolution, including new particle formation, gas-to-particle conversion, photochemistry, aqueous and heterogeneous chemistry, and aerosol ageing
- Aerosol observations from laboratory experiments, field campaigns, long-term monitoring, and remote sensing
- Aerosol interactions with radiation, clouds, climate, air quality, ecosystems, and human health
- Development and application of aerosol measurement, sensing, instrumentation, ionisation, and experimental technologies
- Development of process-based, data-driven, and hybrid process–machine-learning aerosol models
- Advances in numerical methods, model resolution, computational efficiency, and multiscale aerosol modelling
- Applications of AI and emerging digital approaches, including machine learning, generative AI, foundation models, cloud computing, and digital twins
- Open science approaches, including benchmark datasets, reproducible workflows, model and data sharing, and evaluation standards