Lagrangian particle dispersion models (LPDMs) are powerful tools to simulate the atmospheric transport of trace gases. They have gained increased popularity over the recent decades, particularly in emission quantification. This increased popularity is due to several key advantages, including minimal numerical diffusion, approximation on subgrid-scale transport variability, and simulated transport independence of modeled chemical species. In the past ten years, substantial advances have been made in refining model physics and enhancing computational efficiency by leveraging advanced technology such as high-performance computing and machine learning/artificial intelligence (AI). This session invites presentations on methodological/technological advances and novel applications of LPDMs. We are particularly interested in research focused on quantifying emissions of greenhouse gases (e.g., CO2, CH4, N2O, F-gases), ozone-depleting substances, and air pollutants across urban to national scales. Topics may cover inverse modeling, source attribution, model validation/intercomparison, and LPDM integration with diverse measurement platforms (surface, aircraft, satellite).