13 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.
2. Clustering of emission–dispersion dynamics in a state space defined by pollution sources and meteorological variables . Yuval et al.
3. Multi-objective machine learning for health-oriented O3 and PM2.5 control: Integrating VOC photochemical consumption and source apportionment H. Jia et al.
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.
5. 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.
6. 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.
7. Co-evolving emission controls and climate impacts: A multi-decadal machine learning decomposition of urban O3 and NO2 air quality measurements M. Brancher
8. Geospatial-based estimation of NMHC concentrations through an ensemble stacking Geo-AI algorithm to advance air quality assessment T. Prahesti et al.
9. TROPOMI-Based PM2.5 Estimates and Their Evaluation During a High-Pollution Event in Germany J. Handschuh et al.
10. Exploring AI potential in optical analysis of organic and elemental carbons: A review of emerging applications in airborne particulate characterization A. Agibayeva et al.
11. Exploring the key meteorological drivers of air pollution by intrinsically interpretable deep learning C. Yan et al.
12. Satellite-driven modelling of NO2 and PM2.5 across Germany (2019–2024): A multi-sensor machine-learning approach R. Miller et al.
13. Sentinel Data for Monitoring of Pollutant Emissions by Maritime Transport—A Literature Review T. Batista et al.