ALBERT

Description: 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.

Description: Most of the global fossil fuel CO2 emissions arise out of 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, has been operated with the aim of monitoring the regional atmospheric impact of the emissions out 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 (TRN and MON), two are peri-urban (GON and 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 one 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 centre 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 traffic, residential and commercial heating), the more the CO2 concentration is elevated, as is the associated variability which reflects the variability of the urban sources. Furthermore, two elevated sites (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 are driven by the biospheric and anthropogenic CO2 flux seasonal cycles, by the ABLH seasonal cycle and also by synoptic variations. Gradients of several ppm are observed between the rural and peri-urban stations, 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 can be 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 atmospheric impact of urban CO2 emissions. For wind speeds less than 3 m s−1, the accumulation of the 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 aircrafts 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 background sites that will be representative of the different wind sectors; 2/ the downwind stations should as much as possible be positioned symmetrically in relation to the city centre, 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 greatest; and 4/ high resolution wind information should be available with the CO2 measurements.

Description: 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 during one year as part of the CO2-MEGAPARIS experiment (August 2010–July 2011), is analysed. Differences in hourly CO2 atmospheric mole fraction 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 degrees of the transect between downwind and upwind site. 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 like September 2010 or November 2010 are poorly constrained by too few CO2 measurements. The high sensitivity of the inverted emissions to the prior emissions' day-to-day variations highlights the limitations induced by assimilating data during a limited number of suitable days. Therefore, even though the inversion improves the seasonal variation and the annual budget of the city's emissions, it 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.

Description: 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.

Description: This paper presents the first version of the regional-scale personal exposure model EXPLUME (EXposure to atmospheric PolLUtion ModEling). The model uses simulated gridded data of outdoor O3 and PM2.5 concentrations and several population and building-related datasets to simulate (1) space–time activity event sequences, (2) the infiltration of atmospheric contaminants indoors, and (3) daily aggregated personal exposure. The model is applied over the greater Paris region at 2 km×2 km resolution for the entire year of 2017. Annual averaged population exposure is discussed. We show that population mobility within the region, disregarding pollutant concentrations indoors, has only a small effect on average daily exposure. By contrast, considering the infiltration of PM2.5 in buildings decreases annual average exposure by 11 % (population average). Moreover, accounting for PM2.5 exposure during transportation (in vehicle, while waiting on subway platforms, and while crossing on-road tunnels) increases average population exposure by 5 %. We show that the spatial distribution of PM2.5 and O3 exposure is similar to the concentration maps over the region, but the exposure scale is very different when accounting for indoor exposure. We model large intra-population variability in PM2.5 exposure as a function of the transportation mode, especially for the upper percentiles of the distribution. Overall, 20 % of the population using bicycles or motorcycles is exposed to annual average PM2.5 concentrations above the EU target value (25 µg m−3), compared to 0 % for people travelling by car. Finally, we develop a 2050 horizon projection of the building stock to study how changes in the buildings' characteristics to comply with the thermal regulations will affect personal exposure. We show that exposure to ozone will decrease by as much as 14 % as a result of this projection, whereas there is no significant impact on exposure to PM2.5.