Atmospheric concentration measurements are used to adjust the daily to monthly budget of fossil fuel CO₂ emissions of the Paris urban area from the prior estimates established by the Airparif local air quality agency. Five atmospheric monitoring sites are available, including one at the top of the Eiffel Tower. The atmospheric inversion is based on a Bayesian approach, and relies on an atmospheric transport model with a spatial resolution of 2 km with boundary conditions from a global coarse grid transport model. The inversion adjusts prior knowledge about the anthropogenic and biogenic CO₂ fluxes from the Airparif inventory and an ecosystem model, respectively, with corrections at a temporal resolution of 6 h, while keeping the spatial distribution from the emission inventory. These corrections are based on assumptions regarding the temporal autocorrelation of prior emissions uncertainties within the daily cycle, and from day to day. The comparison of the measurements against the atmospheric transport simulation driven by the a priori CO₂ surface fluxes shows significant differences upwind of the Paris urban area, which suggests a large and uncertain contribution from distant sources and sinks to the CO₂ concentration variability. This contribution advocates that the inversion should aim at minimising model–data misfits in upwind–downwind gradients rather than misfits in mole fractions at individual sites. Another conclusion of the direct model–measurement comparison is that the CO₂ variability at the top of the Eiffel Tower is large and poorly represented by the model for most wind speeds and directions. The model's inability to reproduce the CO₂ variability at the heart of the city makes such measurements ill-suited for the inversion. This and the need to constrain the budgets for the whole city suggests the assimilation of upwind–downwind mole fraction gradients between sites at the edge of the urban area only. The inversion significantly improves the agreement between measured and modelled concentration gradients. Realistic emissions are retrieved for two 30-day periods and suggest a significant overestimate by the AirParif inventory. Similar inversions over longer periods are necessary for a proper evaluation of the optimised CO₂ emissions against independent data.

The ability of a Bayesian atmospheric inversion to quantify the Paris region's fossil fuel CO₂ emissions on a monthly basis, based on a network of three surface stations operated for 1 year as part of the CO₂-MEGAPARIS experiment (August 2010–July 2011), is analysed. Differences in hourly CO₂ atmospheric mole fractions between the near-ground monitoring sites (CO₂ gradients), located at the north-eastern and south-western edges of the urban area, are used to estimate the 6 h mean fossil fuel CO₂ emission. The inversion relies on the CHIMERE transport model run at 2 km × 2 km horizontal resolution, on the spatial distribution of fossil fuel CO₂ emissions in 2008 from a local inventory established at 1 km × 1 km horizontal resolution by the AIRPARIF air quality agency, and on the spatial distribution of the biogenic CO₂ fluxes from the C-TESSEL land surface model. It corrects a prior estimate of the 6 h mean budgets of the fossil fuel CO₂ emissions given by the AIRPARIF 2008 inventory. We found that a stringent selection of CO₂ gradients is necessary for reliable inversion results, due to large modelling uncertainties. In particular, the most robust data selection analysed in this study uses only mid-afternoon gradients if wind speeds are larger than 3 m s⁻¹ and if the modelled wind at the upwind site is within ±15° of the transect between downwind and upwind sites. This stringent data selection removes 92 % of the hourly observations. Even though this leaves few remaining data to constrain the emissions, the inversion system diagnoses that their assimilation significantly reduces the uncertainty in monthly emissions: by 9 % in November 2010 to 50 % in October 2010. The inverted monthly mean emissions correlate well with independent monthly mean air temperature. Furthermore, the inverted annual mean emission is consistent with the independent revision of the AIRPARIF inventory for the year 2010, which better corresponds to the measurement period than the 2008 inventory. Several tests of the inversion's sensitivity to prior emission estimates, to the assumed spatial distribution of the emissions, and to the atmospheric transport modelling demonstrate the robustness of the measurement constraint on inverted fossil fuel CO₂ emissions. The results, however, show significant sensitivity to the description of the emissions' spatial distribution in the inversion system, demonstrating the need to rely on high-resolution local inventories such as that from AIRPARIF. Although the inversion constrains emissions through the assimilation of CO₂ gradients, the results are hampered by the improperly modelled influence of remote CO₂ fluxes when air masses originate from urbanised and industrialised areas north-east of Paris. The drastic data selection used in this study limits the ability to continuously monitor Paris fossil fuel CO₂ emissions: the inversion results for specific months such as September or November 2010 are poorly constrained by too few CO₂ measurements. The high sensitivity of the inverted emissions to the prior emissions' diurnal variations highlights the limitations induced by assimilating data only during the afternoon. Furthermore, even though the inversion improves the seasonal variation and the annual budget of the city's emissions, the assimilation of data during a limited number of suitable days does not necessarily yield robust estimates for individual months. These limitations could be overcome through a refinement of the data processing for a wider data selection, and through the expansion of the observation network.

Most of the global fossil fuel CO₂ emissions arise from urbanized and industrialized areas. Bottom-up inventories quantify them but with large uncertainties. In 2010–2011, the first atmospheric in situ CO₂ measurement network for Paris, the capital of France, began operating with the aim of monitoring the regional atmospheric impact of the emissions coming from this megacity. Five stations sampled air along a northeast–southwest axis that corresponds to the direction of the dominant winds. Two stations are classified as rural (Traînou – TRN; Montgé-en-Goële – MON), two are peri-urban (Gonesse – GON; Gif-sur-Yvette – GIF) and one is urban (EIF, located on top of the Eiffel Tower). In this study, we analyze the diurnal, synoptic and seasonal variability of the in situ CO₂ measurements over nearly 1 year (8 August 2010–13 July 2011). We compare these datasets with remote CO₂ measurements made at Mace Head (MHD) on the Atlantic coast of Ireland and support our analysis with atmospheric boundary layer height (ABLH) observations made in the center of Paris and with both modeled and observed meteorological fields. The average hourly CO₂ diurnal cycles observed at the regional stations are mostly driven by the CO₂ biospheric cycle, the ABLH cycle and the proximity to urban CO₂ emissions. Differences of several µmol mol⁻¹ (ppm) can be observed from one regional site to the other. The more the site is surrounded by urban sources (mostly residential and commercial heating, and traffic), the more the CO₂ concentration is elevated, as is the associated variability which reflects the variability of the urban sources. Furthermore, two sites with inlets high above ground level (EIF and TRN) show a phase shift of the CO₂ diurnal cycle of a few hours compared to lower sites due to a strong coupling with the boundary layer diurnal cycle. As a consequence, the existence of a CO₂ vertical gradient above Paris can be inferred, whose amplitude depends on the time of the day and on the season, ranging from a few tenths of ppm during daytime to several ppm during nighttime. The CO₂ seasonal cycle inferred from monthly means at our regional sites is driven by the biospheric and anthropogenic CO₂ flux seasonal cycles, the ABLH seasonal cycle and also synoptic variations. Enhancements of several ppm are observed at peri-urban stations compared to rural ones, mostly from the influence of urban emissions that are in the footprint of the peri-urban station. The seasonal cycle observed at the urban station (EIF) is specific and very sensitive to the ABLH cycle. At both the diurnal and the seasonal scales, noticeable differences of several ppm are observed between the measurements made at regional rural stations and the remote measurements made at MHD, that are shown not to define background concentrations appropriately for quantifying the regional ( ∼ 100 km) atmospheric impact of urban CO₂ emissions. For wind speeds less than 3 m s⁻¹, the accumulation of local CO₂ emissions in the urban atmosphere forms a dome of several tens of ppm at the peri-urban stations, mostly under the influence of relatively local emissions including those from the Charles de Gaulle (CDG) Airport facility and from aircraft in flight. When wind speed increases, ventilation transforms the CO₂ dome into a plume. Higher CO₂ background concentrations of several ppm are advected from the remote Benelux–Ruhr and London regions, impacting concentrations at the five stations of the network even at wind speeds higher than 9 m s⁻¹. For wind speeds ranging between 3 and 8 m s⁻¹, the impact of Paris emissions can be detected in the peri-urban stations when they are downwind of the city, while the rural stations often seem disconnected from the city emission plume. As a conclusion, our study highlights a high sensitivity of the stations to wind speed and direction, to their distance from the city, but also to the ABLH cycle depending on their elevation. We learn some lessons regarding the design of an urban CO₂ network: (1) careful attention should be paid to properly setting regional (∼ 100 km) background sites that will be representative of the different wind sectors; (2) the downwind stations should be positioned as symmetrically as possible in relation to the city center, at the peri-urban/rural border; (3) the stations should be installed at ventilated sites (away from strong local sources) and the air inlet set up above the building or biospheric canopy layer, whichever is the highest; and (4) high-resolution wind information should be available with the CO₂ measurements.

In June 2013, a ground-based mobile lidar performed the ~10 000 km ride from Paris to Ulan-Ude, near Lake Baikal, profiling for the first time aerosol optical properties all the way from western Europe to central Siberia. The instrument was equipped with N2-Raman and depolarization channels that enabled an optical speciation of aerosols in the low and middle troposphere. The extinction-to-backscatter ratio (also called lidar ratio or LR) and particle depolarization ratio (PDR) at 355 nm have been retrieved. The LR in the lower boundary layer (300–700 m) was found to be 63 ± 17 sr on average during the campaign with a distribution slightly skewed toward higher values that peaks between 50 and 55 sr. Although the difference is small, PDR values observed in Russian cities (>2%, except after rain) are systematically higher than the ones measured in Europe (<1%), which is probably an effect of the lifting of terrigenous aerosols by traffic on roads. Biomass burning layers from grassland or/and forest fires in southern Russia exhibit LR values ranging from 65 to 107 sr and from 3 to 4% for the PDR. During the route, desert dust aerosols originating from the Caspian and Aral seas regions were characterized for the first time, with a LR (PDR) of 43 ± 14 sr (23 ± 2%) for pure dust. The lidar observations also showed that this dust event extended over 2300 km and lasted for ~6 days. Measurements from the Moderate Resolution Imaging Spectrometer (MODIS) show that our results are comparable in terms of aerosol optical thickness (between 0.05 and 0.40 at 355 nm) with the mean aerosol load encountered throughout our route.