29 citations as recorded by crossref.

1. Towards hyperlocal source identification of pollutants in cities by combining mobile measurements with atmospheric modeling J. Lin et al. 10.1016/j.atmosenv.2023.119995
2. A new inverse modeling approach for emission sources based on the DDM-3D and 3DVAR techniques: an application to air quality forecasts in the Beijing–Tianjin–Hebei region X. Cheng et al. 10.5194/acp-21-13747-2021
3. Adaptive center constraint for joint release rate estimation and model correction: Multi-scenario validation against wind tunnel experiments X. Li et al. 10.1016/j.pnucene.2024.105413
4. Sequential optimal positioning of mobile sensors using mutual information K. Schmidt et al. 10.1002/sam.11431
5. Data-driven optimal closures for mean-cluster models: Beyond the classical pair approximation A. Ahmadi et al. 10.1103/PhysRevE.106.025313
6. Sensitivity of a Bayesian source-term estimation model to spatiotemporal sensor resolution D. Jensen et al. 10.1016/j.aeaoa.2019.100045
7. Automated release rate inversion and plume bias correction for atmospheric radionuclide leaks: A robust and general remediation to imperfect radionuclide transport modeling S. Fang et al. 10.1016/j.scitotenv.2020.142140
8. Comparative study on gradient-free optimization methods for inverse source-term estimation of radioactive dispersion from nuclear accidents S. Jang et al. 10.1016/j.jhazmat.2023.132519
9. Assessment of Caesium-137 Detections at CTBTO Radionuclide Monitoring Stations in East Asia and Their Relationship to Asian Dust Dispersion A. Furuno et al. 10.1007/s00024-024-03620-y
10. Examining the potential for detecting simultaneous noble gas and aerosol samples in the international monitoring system radionuclide network P. Eslinger et al. 10.1016/j.jenvrad.2023.107349
11. MCMC methods applied to the reconstruction of the autumn 2017 Ruthenium-106 atmospheric contamination source J. Dumont Le Brazidec et al. 10.1016/j.aeaoa.2020.100071
12. A machine learning emulator for Lagrangian particle dispersion model footprints: a case study using NAME E. Fillola et al. 10.5194/gmd-16-1997-2023
13. Source term inversion of nuclear accident with random release durations based on machine learning W. Yang et al. 10.1016/j.jhazmat.2025.137448
14. A spatiotemporally separated framework for reconstructing the sources of atmospheric radionuclide releases Y. Xu et al. 10.5194/gmd-17-4961-2024
15. Source term inversion of nuclear accident based on deep feedforward neural network W. Cui et al. 10.1016/j.anucene.2022.109257
16. Inferring Atmospheric Release Characteristics in a Large Computer Experiment Using Bayesian Adaptive Splines D. Francom et al. 10.1080/01621459.2018.1562933
17. Machine Learning Emulation of Spatial Deposition from a Multi-Physics Ensemble of Weather and Atmospheric Transport Models N. Gunawardena et al. 10.3390/atmos12080953
18. Source localisation and its uncertainty quantification after the third DPRK nuclear test P. De Meutter et al. 10.1038/s41598-018-28403-z
19. Atmospheric modeling and source reconstruction of radioactive ruthenium from an undeclared major release in 2017 O. Saunier et al. 10.1073/pnas.1907823116
20. A baseline for source localisation using the inverse modelling tool FREAR P. De Meutter et al. 10.1016/j.jenvrad.2024.107372
21. Surrogate tree ensemble model representing 2D population doses over complex terrain in the event of a radiological release into the air N. Hvala et al. 10.1016/j.pnucene.2023.104594
22. Bayesian atmospheric tomography for detection and quantification of methane emissions: application to data from the 2015 Ginninderra release experiment L. Cartwright et al. 10.5194/amt-12-4659-2019
23. Multi-scenario validation of the robust inversion method with biased plume range and values X. Dong et al. 10.1016/j.jenvrad.2023.107363
24. Can Delhi's Pollution be Affected by Crop Fires in the Punjab Region? M. Takigawa et al. 10.2151/sola.2020-015
25. EKI-based inverse modeling for multi-radionuclide source term estimation from time-series gamma dose rates during radiological emergencies S. Jang et al. 10.1016/j.measurement.2025.116938
26. Source estimation of an unexpected release of Ruthenium-106 in 2017 using an inverse modelling approach L. Western et al. 10.1016/j.jenvrad.2020.106304
27. Emulation of greenhouse‐gas sensitivities using variational autoencoders L. Cartwright et al. 10.1002/env.2754
28. The Application of an Evolutionary Programming Process to a Simulation of the ETEX Large-Scale Airborne Dispersion Experiment D. Werth et al. 10.1175/JAMC-D-18-0098.1
29. Methodology for the investigation of undeclared atmospheric releases of radionuclides: Application to recent radionuclide detections in Northern Europe from 2019 to 2022 O. Saunier et al. 10.1016/j.anucene.2023.109907