21 citations as recorded by crossref.

1. Impact of high-resolution soil erodibility datasets on dust simulations in WRF-Chem with the GOCART scheme L. Segado-Moreno et al. https://doi.org/10.5194/acp-26-12641-2026
2. Modeling the impact of wildfire on air quality in Nepal using WRF-Chem and Sentinel-5P K. Shrestha et al. https://doi.org/10.1016/j.envpol.2025.126545
3. Validation of a 10-meter wind in simulating a dust event in northwestern Iran mountains (October 28–31, 2017) E. Mobarak Hassan et al. https://doi.org/10.1016/j.aeolia.2025.101002
4. Assessment of quasi-global surface ozone simulations with WRF-chem using multi-dataset evaluation L. Wu et al. https://doi.org/10.1016/j.envpol.2026.128230
5. Integrating WRF-CHEM simulation with Random Forest algorithm to evaluate the spatio-temporal variation of PM10 in Western Himalayan region of India H. Tripathi et al. https://doi.org/10.1080/02786826.2026.2666422
6. From cause to consequence: examining the historic April 2024 rainstorm in the United Arab Emirates through the lens of climate change D. Francis et al. https://doi.org/10.1038/s41612-025-01073-1
7. Systematic Review and Meta-Analysis of Urban Air Quality in the Arabian Peninsula E. Irankunda et al. https://doi.org/10.3390/atmos16080990
8. Impact of AIFS and GFS Initialization on WRF Operational Forecasts During High-Impact Storms in Spain (2025) R. Arasa Agudo et al. https://doi.org/10.3390/earth7030077
9. Short-term association of ground-level ozone with 50,000 hospital admissions in Delhi: A fused data and distributed lag modeling study P. Parmar et al. https://doi.org/10.1016/j.uclim.2026.102958
10. Impacts of future land use and land cover changes on air quality in China W. Liu et al. https://doi.org/10.1038/s41612-026-01420-w
11. Short-term exposure to PM2.5 and its components and neuropsychiatric hospitalizations in Delhi, India P. Parmar et al. https://doi.org/10.1016/j.envint.2026.110464
12. WRF-Chem simulations of CO2 over Belgium and surrounding countries assessed by ground-based measurements J. Wang et al. https://doi.org/10.5194/acp-26-3541-2026
13. Seasonality of dust optical depth over Arabian Peninsula using MIDAS data A. Alsubhi & Y. Alsubhi https://doi.org/10.1080/01431161.2025.2496534
14. A Novel Spatio-Temporal Graph Convolutional Network with Attention Mechanism for PM2.5 Concentration Prediction X. Guan et al. https://doi.org/10.3390/make7030088
15. The June 2024 middle east compound heatwave: Dynamical drivers and AI-weather forecast models' evaluation N. Nelli et al. https://doi.org/10.1016/j.atmosres.2026.108779
16. A pan-European WRF–CMAQ framework for air quality assessment: Model evaluation and policy baseline N. Traka et al. https://doi.org/10.1016/j.apr.2026.102992
17. From sensitivity regimes to policy action: Evaluating EKMA curve effectiveness with WRF-Chem, OBM, and machine learning X. Ye et al. https://doi.org/10.1016/j.atmosenv.2025.121689
18. Validation of the 2021 Emission Inventory for Cuenca, Ecuador, Through Weather and Air Quality Modeling in the Framework of WRF-Chem R. Parra et al. https://doi.org/10.3390/atmos17060569
19. Global high-resolution ultrafine particle number concentrations through data fusion with machine learning P. Georgiades et al. https://doi.org/10.1038/s41597-025-06055-9
20. Modeling and Analysis of Long-Range Dust Transport from the Sahara and Arabian Deserts To West Asia Using WRF-Chem and Remote Sensing: Insights from the Spring 2015 Dust Event Y. Aydin et al. https://doi.org/10.1007/s41748-026-01043-4
21. Understanding the elevated PM2.5 pollution in the Middle East during May 2022: Insights from numerical simulations I. Sulaymon et al. https://doi.org/10.1016/j.atmosres.2025.108527