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Lagrangian tracer simulations are deployed to investigate processes influencing vertical and horizontal dispersion of anthropogenic pollution in Fairbanks, Alaska, during the Alaskan Layered Pollution and Chemical Analysis (ALPACA) 2022 field campaign. Simulated concentrations of carbon monoxide (CO), sulfur dioxide (SO2), and nitrogen oxides (NOx), including surface and elevated sources, are the highest at the surface under very cold stable conditions. Pollution enhancements above the surface (50–300 m) are mainly attributed to elevated power plant emissions. Both surface and elevated sources contribute to Fairbanks' regional pollution that is transported downwind, primarily to the south-west, and may contribute to wintertime Arctic haze. Inclusion of a novel power plant plume rise treatment that considers the presence of surface and elevated temperature inversion layers leads to improved agreement with observed CO and NOx plumes, with discrepancies attributed to, for example, displacement of plumes by modelled winds. At the surface, model results show that observed CO variability is largely driven by meteorology and, to a lesser extent, by emissions, although simulated tracers are sensitive to modelled vertical dispersion. Modelled underestimation of surface NOx during very cold polluted conditions is considerably improved following the inclusion of substantial increases in diesel vehicle NOx emissions at cold temperatures (e.g. a factor of 6 at −30 °C). In contrast, overestimation of surface SO2 is attributed mainly to model deficiencies in vertical dispersion of elevated (5–18 m) space heating emissions. This study highlights the need for improvements to local wintertime Arctic anthropogenic surface and elevated emissions and improved simulation of Arctic stable boundary layers.

In order to gain understanding on the vertical structure of atmospheric water vapour above mountain lakes and to assess its link with the isotopic composition of the lake water and with small-scale dynamics (i.e. valley winds, thermal convection above complex terrain), the L-WAIVE (Lacustrine-Water vApor Isotope inVentory Experiment) field campaign was conducted in the Annecy valley in the French Alps during 10 d in June 2019. This field campaign was based on an original experimental synergy between a suite of ground-based, boat-borne, and two ultra-light aircraft (ULA) measuring platforms implemented to characterize the thermodynamic and isotopic composition above and in the lake. A cavity ring-down spectrometer and an in-cloud liquid water collector were deployed aboard one of the ULA to characterize the vertical distribution of the main stable water isotopes (H216O, H218O and H2H16O) both in the air and in shallow cumulus clouds. The temporal evolution of the meteorological structures of the low troposphere was derived from an airborne Rayleigh–Mie lidar (embarked on a second ULA), a ground-based Raman lidar, and a wind lidar. ULA flight patterns were repeated several times per day to capture the diurnal evolution as well as the variability associated with the different weather events encountered during the field campaign, which influenced the humidity field, cloud conditions, and slope wind regimes in the valley. In parallel, throughout the campaign, liquid water samples of rain, at the air–lake water interface, and at 2 m depth in the lake were taken. A significant variability of the isotopic composition was observed along time, depending on weather conditions, linked to the transition from the valley boundary layer towards the free troposphere, the valley wind intensity, and the vertical thermal stability. Thus, significant gradients of isotopic content have been revealed at the transition to the free troposphere, at altitudes between 2.5 and 3.5 km. The influence of the lake on the atmosphere isotopic composition is difficult to isolate from other contributions, especially in the presence of thermal instabilities and valley winds. Nevertheless, such an effect appears to be detectable in a layer of about 300 m thickness above the lake in light wind conditions. We also noted similar isotopic compositions in cloud drops and rainwater.

Medium-to-large fluctuations and coherent structures (mlf-cs's) can be observed using horizontal scans from single Doppler lidar or radar systems. Despite the ability to detect the structures visually on the images, this method would be time-consuming on large datasets, thus limiting the possibilities to perform studies of the structures properties over more than a few days. In order to overcome this problem, an automated classification method was developed, based on the observations recorded by a scanning Doppler lidar (Leosphere WLS100) installed atop a 75 m tower in Paris's city centre (France) during a 2-month campaign (September–October 2014). The mlf-cs's of the radial wind speed are estimated using the velocity–azimuth display method over 4577 quasi-horizontal scans. Three structure types were identified by visual examination of the wind fields: unaligned thermals, rolls and streaks. A learning ensemble of 150 mlf-cs patterns was classified manually relying on in situ and satellite data. The differences between the three types of structures were highlighted by enhancing the contrast of the images and computing four texture parameters (correlation, contrast, homogeneity and energy) that were provided to the supervised machine-learning algorithm, namely the quadratic discriminant analysis. The algorithm was able to classify successfully about 91 % of the cases based solely on the texture analysis parameters. The algorithm performed best for the streak structures with a classification error equivalent to 3.3 %. The trained algorithm applied to the whole scan ensemble detected structures on 54 % of the scans, among which 34 % were coherent structures (rolls and streaks).

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.

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.

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 (

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 Alaskan Layered Pollution And Chemical Analysis (ALPACA) field experiment was a collaborative study designed to improve understanding of pollution sources and chemical processes during winter (cold climate and low-photochemical activity), to investigate indoor pollution, and to study dispersion of pollution as affected by frequent temperature inversions. A number of the research goals were motivated by questions raised by residents of Fairbanks, Alaska, where the study was held. This paper describes the measurement strategies and the conditions encountered during the January and February 2022 field experiment, and reports early examples of how the measurements addressed research goals, particularly those of interest to the residents. Outdoor air measurements showed high concentrations of particulate matter and pollutant gases including volatile organic carbon species. During pollution events, low winds and extremely stable atmospheric conditions trapped pollution below 73 m, an extremely shallow vertical scale. Tethered-balloon-based measurements intercepted plumes aloft, which were associated with power plant point sources through transport modeling. Because cold climate residents spend much of their time indoors, the study included an indoor air quality component, where measurements were made inside and outside a house to study infiltration and indoor sources. In the absence of indoor activities such as cooking and/or heating with a pellet stove, indoor particulate matter concentrations were lower than outdoors; however, cooking and pellet stove burns often caused higher indoor particulate matter concentrations than outdoors. The mass-normalized particulate matter oxidative potential, a health-relevant property measured here by the reactivity with dithiothreiol, of indoor particles varied by source, with cooking particles having less oxidative potential per mass than pellet stove particles., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)