From fluxes to signals: A joint analysis of GHG and Air Quality over the Paris Megacity

Given the trajectory of the global climate crisis, the current emission allowances following the 2015 Paris Agreement require that GHG fluxes (sources/sinks) are quantified more accurately. Atmospheric inversion approaches have the potential to produce a semi-independent assessment of these fluxes. However, these approaches only provide an estimate of the total flux, with no information on the per sector distribution, which is a major shortcoming for policy makers.

Multiple emission datasets have been developed worldwide at various spatial scales in order to provide a better understanding of the global carbon cycle, but also more locally for large cities and other emission hot-spots. Due to the different methodologies and the quality of the surrogate data, large discrepancies are observed between these datasets, especially at the sectoral level. To overcome the aforementioned shortcoming of GHG inversions, we investigate Air Quality (AQ) data for additional information that could be useful if such data was assimilated jointly with GHG's to attribute atmospheric information to specific sectors of activity.

We focus here on the Paris metropolitan area and analyze ground-based observations as well as high-resolution emission inventory estimates for both GHG's and other reactive pollutants. The observations were acquired by the ICOS GHG monitoring network and the Airparif AQMN. Bottom-up emission estimates were provided by three different products for CO, CO₂, NO_X.

We analyzed the atmospheric signals using a backward-in-time Lagrangian Particle Dispersion Model (LPDM) driven by meteorological variables from mesoscale simulations (WRF-FDDA) at 1-km resolution to represent the origin of the emissions (so-called tower footprints).

The modelled concentrations were compared to observations over the year 2017 to assess the validity of the temporal variations of the various sectors, for each emissions dataset and for both seasonal and diurnal cycles. This comparison provides valuable insights on the sectoral contributions at the city-scale by comparing temporal cycles of CO, CO₂, and NO_X. We further examine traffic information and look into the Covid-19 lockdown period to confirm our findings, a first step toward providing process-based information from atmospheric observations.