15 citations as recorded by crossref.

1. Non-uniform tropospheric NO2 level changes in European Union caused by governmental COVID-19 restrictions and geography G. Varga et al. https://doi.org/10.1016/j.cacint.2024.100145
2. Clustering of emission–dispersion dynamics in a state space defined by pollution sources and meteorological variables . Yuval et al. https://doi.org/10.1016/j.scitotenv.2025.180051
3. Multi-objective machine learning for health-oriented O3 and PM2.5 control: Integrating VOC photochemical consumption and source apportionment H. Jia et al. https://doi.org/10.1016/j.jhazmat.2026.141483
4. Estimating surface-level NO2 concentrations in the Madrid region using Sentinel-5P observations and ground-based meteorological data with machine learning approaches C. Morillas et al. https://doi.org/10.1016/j.atmosenv.2026.121904
5. Viabilidad e implicaciones de la Inteligencia Artificial para la estimación de la calidad del aire en España C. Morillasa et al. https://doi.org/10.59192/mapping.492
6. Spatiotemporal variations of PM2.5 and ozone in urban agglomerations of China and meteorological drivers for ozone using explainable machine learning Y. Lyu et al. https://doi.org/10.1016/j.envpol.2024.125380
7. Optimization of spatio-temporal ozone (O3) pollution modeling using an ensemble machine model learning with a swarm-based metaheuristic algorithm S. Razavi-Termeh et al. https://doi.org/10.1016/j.ecoenv.2025.118764
8. Co-evolving emission controls and climate impacts: A multi-decadal machine learning decomposition of urban O3 and NO2 air quality measurements M. Brancher https://doi.org/10.1016/j.atmosenv.2025.121655
9. Bias-corrected exceedance mapping of groundwater nitrate using geostatistical and machine-learning models A. Brenning et al. https://doi.org/10.1016/j.envsoft.2026.107097
10. Geospatial-based estimation of NMHC concentrations through an ensemble stacking Geo-AI algorithm to advance air quality assessment T. Prahesti et al. https://doi.org/10.1016/j.jhazmat.2026.141210
11. TROPOMI-Based PM2.5 Estimates and Their Evaluation During a High-Pollution Event in Germany J. Handschuh et al. https://doi.org/10.3390/rs18040562
12. Exploring AI potential in optical analysis of organic and elemental carbons: A review of emerging applications in airborne particulate characterization A. Agibayeva et al. https://doi.org/10.1016/j.apr.2025.102839
13. Exploring the key meteorological drivers of air pollution by intrinsically interpretable deep learning C. Yan et al. https://doi.org/10.1016/j.envsoft.2025.106571
14. Satellite-driven modelling of NO2 and PM2.5 across Germany (2019–2024): A multi-sensor machine-learning approach R. Miller et al. https://doi.org/10.1016/j.envpol.2026.127898
15. Sentinel Data for Monitoring of Pollutant Emissions by Maritime Transport—A Literature Review T. Batista et al. https://doi.org/10.3390/rs17132202