12 citations as recorded by crossref.

1. Technical note: Accurate, reliable, and high-resolution air quality predictions by improving the Copernicus Atmosphere Monitoring Service using a novel statistical post-processing method A. Riccio & E. Chianese 10.5194/acp-24-1673-2024
2. Daily high-resolution surface PM2.5 estimation over Europe by ML-based downscaling of the CAMS regional forecast S. Shetty et al. 10.1016/j.envres.2024.120363
3. Correction of CAMS PM10 Reanalysis Improves AI-Based Dust Event Forecast R. Sarafian et al. 10.3390/rs17020222
4. Copernicus Atmosphere Monitoring Service – Regional Air Quality Production System v1.0 A. Colette et al. 10.5194/gmd-18-6835-2025
5. Forecasting the Exceedances of PM2.5 in an Urban Area S. Logothetis et al. 10.3390/atmos15050594
6. Forecasting PM10 Levels Using Machine Learning Models in the Arctic: A Comparative Study P. Fazzini et al. 10.3390/rs15133348
7. High-resolution modelling of particulate matter chemical composition over Europe: brake wear pollution A. Upadhyay et al. 10.1016/j.envint.2025.109615
8. Data-driven modeling of environmental factors influencing Arctic methanesulfonic acid aerosol concentrations J. Pernov et al. 10.5194/acp-25-6497-2025
9. Machine learning for air quality prediction and data analysis: Review on recent advancements, challenges, and outlooks M. Karmoude et al. 10.1016/j.scitotenv.2025.180593
10. A Machine Learning Approach for High-Resolution Modeling and Apportionment of Black Carbon Concentrations in an Impacted Community S. Hamilton & R. Harley 10.1021/acsestair.3c00082
11. Prisma-Based Review Of Mis Solutions For Enhanced Disaster Response And Resource Allocation E. Haque & Z. Hasan 10.2139/ssrn.5051485
12. Real-time IoT-powered AI system for monitoring and forecasting of air pollution in industrial environment M. Ramadan et al. 10.1016/j.ecoenv.2024.116856