14 citations as recorded by crossref.

1. Hybrid transformer and physics-informed neural operator for correcting TEMPO NO2 biases over North America S. Kayastha et al. https://doi.org/10.1038/s44407-026-00056-7
2. Advances in Satellite-Based Monitoring of Urban Emission Sources and Air Quality: A Review S. Naderi et al. https://doi.org/10.1007/s11270-025-09009-4
3. Tracking the path to cleaner cities using global urban NO2 monitoring from space A. Hassani et al. https://doi.org/10.1088/1748-9326/ae5e97
4. Mapping GHG Emission Vulnerability Using Convolutional Autoencoder And Multi-Sensor Satellite In Bali, Indonesia M. Saifulloh et al. https://doi.org/10.24057/2071-9388-2025-4005
5. Global NO2 changes between 2019 and 2024 as observed by TROPOMI in urban areas and emerging hotspots D. Huber et al. https://doi.org/10.5194/acp-26-3783-2026
6. Monitoring of total and off-road NOx emissions from Canadian oil sands surface mining using the Ozone Monitoring Instrument C. McLinden et al. https://doi.org/10.5194/acp-25-6093-2025
7. Satellite-Based Estimation of Urban CO2 Emissions in Shandong Province, China, Using TROPOMI NO2 Observations and Differential Evolution Algorithm Y. Xie et al. https://doi.org/10.3390/rs18101470
8. TANGO CO2 and NO2 observations: synergistic usage to improve emission quantification and characterize atmospheric chemistry T. Borsdorff et al. https://doi.org/10.5194/amt-19-4617-2026
9. Investigating Ozone Formation Regimes in the Metropolitan Area of São Paulo Using Five Years of TROPOMI HCHO/NO2 Ratios A. Freitas et al. https://doi.org/10.3390/rs18101603
10. Gap-filling XCO2 variability: A conditional framework to estimate XCO2 in data-sparse regions using dense TROPOMI NO2 observations over China J. Gu et al. https://doi.org/10.1016/j.envres.2026.124580
11. A novel framework for temporal data transformation in environmental risk assessment: Addressing data variability and gaps in monitoring T. Marum et al. https://doi.org/10.1016/j.eiar.2026.108391
12. 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
13. Spatial and Seasonal Variation of Nitric Acid (HNO3) and Understanding the Formation of NH4NO3 in the HNO3–NH3 system in Korea D. Jung et al. https://doi.org/10.1007/s44408-025-00090-2
14. Spatiotemporal estimation of daily surface NO2 concentrations over China from 2019 to 2024 based on TROPOMI data and MAPST-Net model Q. Zeng et al. https://doi.org/10.1016/j.atmosenv.2026.122203