Understanding the sources and variability of atmospheric composition, including ambient particulate matter, volatile organic compounds, greenhouse gases, and complementary chemical and isotopic tracers, is central to addressing air quality and climate challenges. Reliable observational data and robust source apportionment methods are both essential for attributing emissions, detecting changes over time, and supporting effective environmental and climate policy.
This session brings together data-driven approaches for analysing atmospheric composition, combining recent advances in source apportionment methodologies with developments in atmospheric measurements. Contributions addressing receptor-oriented models, multivariate analysis techniques, and novel or improved approaches for source attribution of air pollutants and greenhouse gases are welcomed, alongside studies that focus on measurement quality, calibration, uncertainty quantification, and harmonisation across observational platforms and monitoring networks.
The session encourages case studies, methodological developments, and intercomparison exercises that include high-quality in situ, mobile, airborne, and emerging measurement platforms (e.g. low-cost sensors, UAVs, mobile laboratories), receptor modelling, satellite observations, and supporting model simulations. Of particular interest are studies that improve the consistency, interpretability, and comparability of observational datasets across temporal and spatial scales, new methodologies on source apportionment of air pollution, application of receptor models and studies that strengthen the link between atmospheric composition measurements, emissions attribution, and science–policy applications. By fostering dialogue between observational and modelling communities, the session aims to advance a more integrated understanding of atmospheric composition relevant to both air quality management and climate mitigation.