1. Émissions de CO2 estimées par données satellitaires sur les villes à forte croissance démographique
- Author
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Danjou, Alexandre, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université Paris-Saclay, François-Marie Breon, Grégoire Broquet, and Thomas Lauvaux
- Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere ,Satellite ,Panache ,Co2 ,Ghg ,Urban emissions ,Émissions urbaines - Abstract
Cities are responsible for more than half of all greenhouse gas emissions. While many cities have committed to emission reduction trajectories, many lack the infrastructure to develop their emissions budgets. The measurement of CO2 plumes from cities by satellite imagery, coupled with atmospheric inversion methods, could allow quantifying direct CO2 emissions from cities, or at least detecting trends in their evolution.OCO-3, with its Snapshot Area Maps (SAMs) mode, is the first instrument to provide 2D (≈80km*80km) images of the total CO2 column at high resolution (≈2km*2km). In particular, these SAMs target atmospheric plumes of CO2 from cities and powerplants, with the goal of quantifying their emissions. Methods to estimate these emissions must be reliable and fast to process all available images (several thousands for OCO-3), whose number will increase with the CO2M and GeoCarb missions. The inversion methods by direct flux calculation (Integrated Mass Enhancement, Cross-Sectional and Source Pixel) or with a Gaussian plume model require little computation time. This thesis aims to evaluate the accuracy of these CO2 plume inversion methods and to study the favorable cases in terms of target and observation condition. This is done in a theoretical framework (atmospheric transport simulations) and by applying the methods to acquired SAMs.We quantify and analyze the different sources of error of these methods in detail using satellite pseudo-images of plumes, first over Paris and then over 31 cities in the world. The error of these methods is mainly due to errors in the estimation of the background concentration (XCO2 concentration that does not come from the city emissions) and in the estimation of the effective wind that carried the plume. We show, with a decision tree learning method, the sensitivity of the error on the emission estimate to the variability of the wind direction in the PBL and to the city's emission budget. The set of pseudo-images for which the emissions are large (>2.1ktCO2/h) and the wind direction variability low (2.1ktCO2/h) et la variabilité de la direction du vent faible (
- Published
- 2022