Preprints
https://doi.org/10.5194/gmd-2021-438
https://doi.org/10.5194/gmd-2021-438
Submitted as: methods for assessment of models
21 Feb 2022
Submitted as: methods for assessment of models | 21 Feb 2022
Status: a revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

Recovery of sparse urban greenhouse gas emissions

Benjamin Zanger, Jia Chen, Man Sun, and Florian Dietrich Benjamin Zanger et al.
  • Environmental Sensing and Modeling, Technical University of Munich (TUM), Munich, Germany

Abstract. To localize and quantify greenhouse gas emissions from cities, gas concentrations are typically measured at a small number of sites and then linked to emission fluxes using atmospheric transport models. Solving this inverse problem is challenging because the system of equations is usually underdetermined. A common top-down approach to solving this problem is Bayesian inversion that uses a given Gaussian prior emission field. However, such an approach has drawbacks when the assumed spatial emission distribution is incorrect. In our work, we investigate sparse reconstruction (SR), an alternative reconstruction method that does not require a prior emission field, but only the assumption that the emission field is sparse. We show that this assumption is mostly true for the cities we investigated and that the use of the discrete wavelet transform helps to make the urban emission field even more sparse. To evaluate the performance of SR, we created concentration data by applying an atmospheric forward transport model to CO2 emission inventories of several major European cities. We used SR to locate and quantify the emission sources by applying compressed sensing theory and compared the results to regularized least squares (LS) methods. Our results show that SR requires fewer measurements than LS methods and that SR provides better localization and quantification of unknown emitters.

Benjamin Zanger et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-438', Anonymous Referee #1, 04 Mar 2022
  • RC2: 'Comment on gmd-2021-438', Anonymous Referee #2, 09 Mar 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-438', Anonymous Referee #1, 04 Mar 2022
  • RC2: 'Comment on gmd-2021-438', Anonymous Referee #2, 09 Mar 2022

Benjamin Zanger et al.

Data sets

Gaussian plume footprints Zanger, Benjamin and Chen, Jia and Sun, Man and Dietrich, Florian https://doi.org/10.5281/zenodo.5901298

Model code and software

tum-esm/recovery_of_sparse_urban_greeenhouse_gas_emissions: v0.1.0 Zanger, Benjamin and Chen, Jia and Sun, Man and Dietrich, Florian https://doi.org/10.5281/zenodo.5900738

Benjamin Zanger et al.

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Short summary
Gaussian priors (GPs) used in least-squares inversion do not well reflect the true distributions of greenhouse gas emissions. A method that does not rely on GPs is sparse reconstruction (SR). We show that necessary conditions for SR are satisfied for cities and that application of a wavelet transform can further enhance sparsity. We apply the theory of compressed sensing to SR. Our results show that SR needs fewer measurements and is superior for assessing unknown emitters compared to GPs.