12 citations as recorded by crossref.

1. The impact of the COVID-19 epidemic on anthropogenic emissions in the Beijing-Tianjin-Hebei region of China based on 3DVar sectoral emission inversion C. Huang et al. https://doi.org/10.1016/j.jhazmat.2025.138998
2. Adjustment of the Power of Model Emissions of Anthropogenic Atmospheric Pollution Sources Based on Measurement Data and Adjoint Problem Methods P. Antokhin et al. https://doi.org/10.1134/S1024856025700319
3. Quantification of Multi-Source Road Emissions in an Urban Environment Using Inverse Methods P. Gkirmpas et al. https://doi.org/10.3390/atmos16101184
4. 3DVar sectoral emission inversion based on source apportionment and machine learning C. Huang et al. https://doi.org/10.1016/j.envpol.2024.125140
5. Using a System of Models of Various Complexity in Inverse Modeling of the Pollutant Transport and Transformation in the Atmosphere A. Penenko et al. https://doi.org/10.3103/S1068373925060044
6. Sequential sensitivity analysis of WRF physics and surface specifications for air quality modeling in a coastal urban domain S. Delbari & V. Hosseini https://doi.org/10.1016/j.atmosres.2026.109093
7. Hybrid IFDMB/4D-Var inverse modeling to constrain the spatiotemporal distribution of CO and NO2 emissions using the CMAQ adjoint model J. Moon et al. https://doi.org/10.1016/j.atmosenv.2024.120490
8. Assessing mass balance-based inverse modeling methods via a pseudo-observation test to constrain NOx emissions over South Korea J. Mun et al. https://doi.org/10.1016/j.atmosenv.2022.119429
9. An improved Bayesian inversion to estimate daily NOx emissions of Paris from TROPOMI NO2 observations between 2018–2023 A. Mols et al. https://doi.org/10.5194/acp-26-1497-2026
10. Update of SO2 emission inventory in the Megacity of Chongqing, China by inverse modeling X. Feng et al. https://doi.org/10.1016/j.atmosenv.2022.119519
11. An evaluation of digital filtering and 4DVar data assimilation in the WRF model towards the simulation of tropical cyclones G. Tiwari et al. https://doi.org/10.1016/j.asr.2024.02.004
12. Spatiotemporal optimization of NOx and VOC emissions using a hybrid inversion framework and implications for ozone sensitivity-regime diagnosis J. Moon et al. https://doi.org/10.5194/acp-26-10379-2026