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High-Resolution Modeling of Urban Air Quality: From Multi-Source Emissions to...
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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 725

Special Issue Editor

Dr. Khalid Mehmood

E-Mail Website
Guest Editor
Mediterranean Institute of Biodiversity and Ecology Marine and Terrestrial (IMBE), Aix-Marseille University, 13284 Marseille, France
Interests: air quality; modelling, emissions; health risk assessment and climate change

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

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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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Published Papers (1 paper)

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Research

24 pages, 7992 KB  
Article
Ensemble Artificial Intelligence Fusing Satellite, Reanalysis, and Ground Observations for Improved PM2.5 Prediction
by Muhammad Haseeb, Zainab Tahir, Syed Amer Mehmood, Hania Arif, Sumaira Kousar, Sundas Ghafoor and Khalid Mehmood
Atmosphere 2026, 17(4), 411; https://doi.org/10.3390/atmos17040411 - 18 Apr 2026
Viewed by 325
Abstract
Air pollution caused by fine particulate matter (PM2.5) poses a serious public health threat in many South Asian megacities where monitoring networks remain limited. Lahore, Pakistan—frequently ranked among the world’s most polluted cities—still lacks reliable short-term PM2.5 forecasting systems. This [...] Read more.
Air pollution caused by fine particulate matter (PM2.5) poses a serious public health threat in many South Asian megacities where monitoring networks remain limited. Lahore, Pakistan—frequently ranked among the world’s most polluted cities—still lacks reliable short-term PM2.5 forecasting systems. This study develops a performance-weighted ensemble machine learning framework that integrates satellite observations, meteorological reanalysis data, and ground monitoring measurements to improve daily PM2.5 prediction. Eleven predictor variables were processed using a unified Google Earth Engine pipeline, including MODIS aerosol optical depth, Sentinel-5P trace gases (CO, NO2, SO2), and ERA5 meteorological parameters. Four tree-based machine learning algorithms—Random Forest, XGBoost, LightGBM, and CatBoost—were trained using daily observations from 2019 to 2023. Model evaluation using an independent 2024 dataset showed strong predictive capability, with Random Forest achieving R2 = 0.77 (RMSE = 24.75 µg m−3), XGBoost R2 = 0.76 (RMSE = 26.32 µg m−3), CatBoost R2 = 0.73 (RMSE = 30.39 µg m−3), and LightGBM R2 = 0.70 (RMSE = 32.75 µg m−3). To further enhance performance, the best models were combined into a weighted ensemble (RF 0.5, XGBoost 0.3, and CatBoost 0.2), which produced the highest validation accuracy (R2 = 0.77; RMSE = 23.37 µg m−3). Statistical testing using paired t-tests and Diebold–Mariano tests confirmed that the ensemble significantly reduced forecast errors compared with individual models. Feature importance analysis revealed that surface pressure, temperature, CO, and NO2 were the most influential predictors of PM2.5 variability. The proposed framework demonstrates that combining satellite data, reanalysis meteorology, and ground observations through ensemble learning can provide accurate and scalable air quality forecasting for data-limited urban environments. Full article
(This article belongs to the Special Issue High-Resolution Modeling of Urban Air Quality: From Multi-Source Emissions to Scalable Prediction Systems)
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