High-Resolution Lagrangian Inverse Modeling of CO2 Emissions Over the Paris Region During the First 2020 Lockdown Period

ABSTRACT

Stringent mobility restrictions across the world during the COVID 19 pandemic have impacted local economies and, consequently, city carbon budgets, offering a unique opportunity to evaluate the capability of scientific approaches to quantify emissions changes. Our study aims to quantify and map CO2 emissions from fossil fuel and biogenic CO2 fluxes over the Paris metropolitan area during the first lockdown period (March-May 2020) in France, in comparison with the same period in 2019. Our inversion system relies on transport model simulations initiated with the Weather Research and Forecasting chemistry transport model combined with a high-resolution fossil fuel CO2 emissions inventory, and biogenic CO2 fluxes from a vegetation model. The inversion with atmospheric observations from a network of six towers resulted in a positive re-adjustment of fossil fuel CO2 emissions in 2019 and 2020 compared to prior. In 2020, the inversion resulted in a large emission reduction (43%) compared to 2019, while the reductions were estimated to be 37% based on the prior inventory itself. By assimilating CO mixing ratios in addition to CO2, the traffic emission estimates were reduced by 68% in 2020, compared to nontraffic (29%). Various sensitivity tests show that prior emission uncertainty and different background conditions significantly impacted the emissions estimates. We conclude that our current inversion system with atmospheric CO2 monitoring makes it possible to identify the emission decrease in 2020 partly over the urban region. However, additional information on prior emission errors and a dense network will be needed to map emissions precisely. © 2022. American Geophysical Union. All Rights Reserved.