High-Resolution Modeling of Urban Air Quality: From Multi-Source Emissions to Scalable Prediction Systems
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 23
Special Issue Editor
Special Issue Information
Dear Colleagues,
Addressing the critical challenge of urban air pollution—where complex mixtures from traffic, industrial activities, and secondary aerosols interact across multiple spatial and temporal scales—requires advances in predictive modeling. This Special Issue of Atmosphere invites authors to submit transformative research that advances air quality modeling (AQM), an indispensable tool for public health protection and regulatory science, moving beyond conventional deterministic Gaussian plume approaches. We seek innovations in high-resolution (<500 m) Eulerian chemical transport models (e.g., WRF-Chem, CMAQ); GPU-accelerated Lagrangian systems (e.g., FLEXPART-WRF); AI–physics hybrid models (e.g., neural ODEs, physics-informed neural operators); and dispersion models (e.g., Gaussian plume models, WindTrax 2.0) capable of resolving mixed-source dynamics in urban canyons, integrating real-time emission inventories, and identifying pollution hotspots. Contributions should demonstrate the use of exascale computational techniques (e.g., adaptive meshes, GPU-optimized chemical solvers), include rigorous validation against hyperspectral remote sensing data (e.g., TEMPO, Sentinel-5P) and multi-platform field campaigns, and apply machine learning frameworks for uncertainty-aware prediction of primary and secondary pollutant interactions.
Dr. Khalid Mehmood
Guest Editor
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Keywords
- air quality
- urban air pollution
- modeling
- high-resolution air quality modeling
- Lagrangian and Eulerian models
- dispersion modeling
- machine learning and AI in AQM
- pollution source apportionment
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