Cities play an important role in tackling climate change as they account for more than 70% of global anthropogenic CO2 emissions. In recent years, several efforts have attempted to quantify city-scale CO2 emissions and establish a high spatially and temporally resolved inventory for supporting urban emission mitigation strategies. The so-called "top-down" inverse estimation of CO2 emissions constrained by independent atmospheric observations could serve to evaluate the consistency of traditional "bottom-up" inventories. A novel CO2 monitoring technique, known as the Greenhouse gas Laser Imaging Tomography Experiment (GreenLITE™) trace gas measurement system, was deployed in central Paris for a 1-year monitoring of near-surface atmospheric CO2 concentrations along 30 horizontal chords. This system has a much wider spatial coverage than traditional in situ sampling and was expected to be more consistent with the spatial representativeness of the kilometer-scale resolution atmospheric transport models used for the city-scale atmospheric inversion.The primary objective of this thesis is to assess the potential contribution of this GreenLITE™ system, in addition to two urban and four peri-urban in situ CO2 measurement stations, for a better understanding of the spatiotemporal variations of CO2 concentrations within Paris and its vicinity. For this objective, I have developed a full modeling framework around the high-resolution Weather Research and Forecasting model (WRF) and its coupling with Chemistry (WRF-Chem), using CO2 emission inventories, estimates of the vegetation fluxes and boundary conditions provided by a large-scale simulation.Chapter 1 is a broad introduction to the subject while chapter 2-4 are built around three separate and publishable papers.Chapter 2 aims at evaluating whether the WRF model running at a 3-km horizontal resolution, with its various configurations, can reproduce the meteorological fields over the IdF region better than the 16-km resolution ECMWF global operational forecasts. The comparisons between WRF and ECMWF forecasts with respect to observations are carried out with a focus on three atmospheric variables (air temperature, wind and PBL height). The results of the sensitivity tests of different physics schemes and nudging options obtained in this chapter are used in subsequent research for the selection of appropriate WRF-Chem model setup in support of atmospheric CO2 transport modeling.Chapter 3 aims at understanding the spatiotemporal variations of CO2 concentrations within Paris and its vicinity during the 1-year GreenLITE™ operating period from September 2015 to December 2016. The analyses are based on CO2 data provided by GreenLITE™ together with six in situ stations and the 1 km-resolution WRF-Chem model coupled with two urban canopy schemes (Urban Canopy Model - UCM; Building Effect Parameterization - BEP). The GreenLITE™ data provide clear information that favors BEP over UCM in the description of vertical mixing and CO2 concentrations during the winter. However, there are indications of measurement noise in summer that limit the usefulness of the data. Furthermore, the model-observation mismatches clearly stress the difficulty of CO2 modeling within urban areas due to the large uncertainties both in the atmospheric transport and the emission inventory.Chapter 4 aims at investigating in detail the critical sources of errors that lead to the model-observation mismatches in the atmospheric CO2 modeling over Paris. These sources of misfit include uncertainties in the assumed distribution of anthropogenic emission, errors in the atmospheric transport, in biogenic CO2 fluxes and in CO2 boundary conditions at the edges of the atmospheric transport model domain. The lessons and insights from this chapter provide requirements and recommendations for the assimilation of CO2 measurements into the atmospheric inversion, when aiming at the quantification of CO2 emissions for the Paris region.