Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 26, issue 19
Articles | Volume 26, issue 19
https://doi.org/10.5194/acp-26-14185-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/acp-26-14185-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 19
Research article
 | 
09 Oct 2026
Research article |  | 09 Oct 2026

NeuPlume: generative inversion of atmospheric point-source emissions from sparse observations

Lei Wang and Xin Ma

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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-73,https://doi.org/10.5194/essd-2026-73, 2026
Revised manuscript accepted for ESSD
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Cited articles

Aghdasi, S., Rayner, P., Deutscher, N., and Silver, J.: Exploring uncertainty reduction in high-resolution methane emissions in Gippsland through in-situ data: a Bayesian inverse modeling and variational assimilation method, Atmos. Res., https://doi.org/10.1016/j.atmosres.2025.107911, 2025. a
Andersen, T., Zhao, Z., de Vries, M., Necki, J., Swolkien, J., Menoud, M., Röckmann, T., Roiger, A., Fix, A., Peters, W., and Chen, H.: Local-to-regional methane emissions from the Upper Silesian Coal Basin (USCB) quantified using UAV-based atmospheric measurements, Atmos. Chem. Phys., 23, 5191–5216, https://doi.org/10.5194/acp-23-5191-2023, 2023. a, b
Cassiani, M., Stohl, A., and Brioude, J.: Lagrangian stochastic modelling of dispersion in the convective boundary layer with skewed turbulence conditions and a vertical density gradient: formulation and implementation in the FLEXPART model, Bound.-Lay. Meteorol., https://doi.org/10.1007/s10546-014-9976-5, 2014. a
Chung, H., Kim, J., McCann, M. T., Klasky, M. L., and Ye, J. C.: Diffusion posterior sampling for general noisy inverse problems, in: International Conference on Learning Representations, arXiv [preprint], https://doi.org/10.48550/arXiv.2209.14687, 2023. a
Conley, S., Faloona, I., Mehrotra, S., Suard, M., Lenschow, D. H., Sweeney, C., Herndon, S., Schwietzke, S., Pétron, G., Pifer, J., Kort, E. A., and Schnell, R.: Application of Gauss's theorem to quantify localized surface emissions from airborne measurements of wind and trace gases, Atmos. Meas. Tech., 10, 3345–3358, https://doi.org/10.5194/amt-10-3345-2017, 2017. a, b
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Estimating greenhouse gas emissions from a single source is difficult when only a few air measurements are available. We developed NeuPlume, a method that combines atmospheric simulation with machine learning to estimate the emission rate and its uncertainty. Tests with simulated plumes and drone methane measurements show that it can give more realistic estimates and identify when wind information or flight coverage limits confidence.
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