Abstract. 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.

Abstract. The ability of a Bayesian atmospheric inversion to quantify the Paris region's fossil fuel CO 2 emissions on a monthly basis, based on a network of three surface stations operated for 1 year as part of the CO 2 -MEGAPARIS experiment (August 2010-July 2011, is analysed. Differences in hourly CO 2 atmospheric mole fractions between the near-ground monitoring sites (CO 2 gradients), located at the north-eastern and south-western edges of the urban area, are used to estimate the 6 h mean fossil fuel CO 2 emission. The inversion relies on the CHIMERE transport model run at 2 km × 2 km horizontal resolution, on the spatial distribution of fossil fuel CO 2 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 CO 2 fluxes from the C-TESSEL land surface model. It corrects a prior estimate of the 6 h mean budgets of the fossil fuel CO 2 emissions given by the AIR-PARIF 2008 inventory. We found that a stringent selection of CO 2 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 midafternoon 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 CO 2 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 CO 2 gradients, the results are hampered by the improperly modelled influence of remote CO 2 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 CO 2 emissions: the inversion results for specific months such asPublished by Copernicus Publications on behalf of the European Geosciences Union. September or November 2010 are p...

Environmental context Volatile organic compounds are key compounds in atmospheric chemistry as precursors of ozone and secondary organic aerosols. To determine their impact at a megacity scale, a first important step is to characterise their sources. We present an estimate of volatile organic compound sources in Paris based on a combination of measurements and model results. The data suggest that the current emission inventory strongly overestimates the volatile organic compounds emitted from solvent industries, and thus needs to be corrected. Abstract A positive matrix factorisation model has been used for the determination of volatile organic compound (VOC) source contributions in Paris during an intensive campaign (May–June 2007). The major sources were traffic-related emissions (vehicle exhaust, 22% of the total mixing ratio of the measured VOCs, and fuel evaporation, 17%), with the remaining emissions from remote industrial sources (35%), natural gas and background (13%), local sources (7%), biogenic and fuel evaporation (5%) and wood-burning (2%). It was noted that the remote industrial contribution was highly dependent on the air-mass origin. During the period of oceanic influences (when only local and regional pollution was observed), this source made a relatively low contribution (<15%), whereas the source contribution linked to traffic was high (54%). During the period of continental influences (when additional continental pollution was observed), remote industrial sources played a dominant role, contributing up to 50% of measured VOCs. Finally, the positive matrix factorisation results obtained during the oceanic air mass-influenced period were compared with the local emission inventory. This comparison suggests that the VOC emission from solvent industries might be overestimated in the inventory, consistent with findings in other European cities.

Simulations with the chemistry transport model CHIMERE are compared to measurements performed during the MEGAPOLI (Megacities: Emissions, urban, regional and Global Atmospheric POLlution and climate effects, and Integrated tools for assessment and mitigation) summer campaign in the Greater Paris region in July 2009. The volatility-basis-set approach (VBS) is implemented into this model, taking into account the volatility of primary organic aerosol (POA) and the chemical aging of semi-volatile organic species. Organic aerosol is the main focus and is simulated with three different configurations with a modified treatment of POA volatility and modified secondary organic aerosol (SOA) formation schemes. In addition, two types of emission inventories are used as model input in order to test the uncertainty related to the emissions. Predictions of basic meteorological parameters and primary and secondary pollutant concentrations are evaluated, and four pollution regimes are defined according to the air mass origin. Primary pollutants are generally overestimated, while ozone is consistent with observations. Sulfate is generally overestimated, while ammonium and nitrate levels are well simulated with the refined emission data set. As expected, the simulation with non-volatile POA and a single-step SOA formation mechanism largely overestimates POA and underestimates SOA. Simulation of organic aerosol with the VBS approach taking into account the aging of semi-volatile organic compounds (SVOC) shows the best correlation with measurements. High-concentration events observed mostly after long-range transport are well reproduced by the model. Depending on the emission inventory used, simulated POA levels are either reasonable or underestimated, while SOA levels tend to be overestimated. Several uncertainties related to the VBS scheme (POA volatility, SOA yields, the aging parameterization), to emission input data, and to simulated OH levels can be responsible for this behavior. Despite these uncertainties, the implementation of the VBS scheme into the CHIMERE model allowed for much more realistic organic aerosol simulations for Paris during summertime. The advection of SOA from outside Paris is mostly responsible for the highest OA concentration levels. During advection of polluted air masses from northeast (Benelux and Central Europe), simulations indicate high levels of both anthropogenic and biogenic SOA fractions, while biogenic SOA dominates during periods with advection from Southern France and Spain

Abstract. Atmospheric concentration measurements are used to adjust the daily to monthly budget of CO2 emissions from the AirParif inventory of the Paris agglomeration. We use 5 atmospheric monitoring sites 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 tool adjusts the CO2 fluxes (anthropogenic and biogenic) with a temporal resolution of 6 h, assuming temporal correlation of emissions uncertainties within the daily cycle and from day to day, while keeping the a priori spatial distribution from the emission inventory. The inversion significantly improves the agreement between measured and modelled concentrations. However, the amplitude of the atmospheric transport errors is often large compared to the CO2 gradients between the sites that are used to estimate the fluxes, in particular for the Eiffel tower station. In addition, we sometime observe large model-measurement differences upwind from the Paris agglomeration, which confirms the large and poorly constrained contribution from distant sources and sinks included in the prescribed CO2 boundary conditions These results suggest that (i) the Eiffel measurements at 300 m above ground cannot be used with the current system and (ii) the inversion shall rely on the measured upwind-downwind gradients rather than the raw mole fraction measurements. With such setup, realistic emissions are retrieved for two 30 day periods. Similar inversions over longer periods are necessary for a proper evaluation of the results.