Влияние турбулентности и восходящих потоков воздуха на дочерние продукты распада радона

In this paper, a simulation of the distribution of radon progeny over the height of the atmosphere, depending on the amount of turbulent mixing and the vertical air velocity, is presented. The obtained results are compared with the change in the activity ratio of Bi-214/Pb-214 isotopes recorded in rainwater during 3-year observations in Prague. It is found that the reasons for the most common values of Bi-214/Pb-214 can be the height of the lower edge of the cloud of 0.2-1.4 km and the vertical air velocity of 0.1 – 0.2 m / s. The ratio changes slightly from changes in the turbulent mixing, the value of the vertical air movement makes the main contribution. It is found that with the increase in the intensity of rain, a shift in the radioactive equilibrium should occur due to an increase in the velocity of vertical air. Atmospheric inversion is able to balance the volumetric activities of the descendants of atmospheric radon, atmospheric inversion can be identified by the equality between the activities of the radon progeny in the atmosphere at different altitudes or in rainwater. It is shown that the search for the relationship between precipitation intensity and gamma radiation is expose to error, without taking into account the influence of the АBi−214/АPb−214 ratio, due to the unequal activities of the atmospheric isotopes Bi-214 and Pb-214. This error of 7-14% when using gamma radiometry, and of 5-9% when using dosimeters is estimated. олучены результаты моделирования распределения дочерних продуктов радона в атмосферном столбе по высоте, объясняющие изменение концентраций радионуклидов в дождевой воде в зависимости от высоты нижней кромки облаков. Значения соотношений активностей АBi−214/АPb−214 радионуклидов дождевой воды от 0.6 до 0.8, могут возникать при высоте нижней кромки облаков от 0.2 до 1.4 км и адвекции от 0.1 до 0.2 м/с соответственно. Произведена оценка шибки от 7 до 14%, возникающая при использовании гамма радиометров, и от 5 до 9% — дозиметров, во время осадков с целью поиска корреляции роста гамма-фона и интенсивности жидких ливневых осадков.

The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies

Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface fluxes of greenhouse gases (GHGs), pollutants and other trace gases. In the past decade, the research community has developed various inversion software, mainly using variational or ensemble Bayesian optimization methods, with various assumptions on uncertainty structures and prior information and with various atmospheric chemistry–transport models. Each of them can assimilate some or all of the available observation streams for its domain area of interest: flask samples, in situ measurements or satellite observations. Although referenced in peer-reviewed publications and usually accessible across the research community, most systems are not at the level of transparency, flexibility and accessibility needed to provide the scientific community and policy makers with a comprehensive and robust view of the uncertainties associated with the inverse estimation of GHG and reactive species fluxes. Furthermore, their development, usually carried out by individual research institutes, may in the future not keep pace with the increasing scientific needs and technical possibilities. We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source Python-based tool to estimate the fluxes of various GHGs and reactive species both at the global and regional scales. It will allow for running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow for a comprehensive assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and we demonstrate how it operates in a simple academic case.

Suivi atmosphérique des émissions de CO2 de la région parisienne

Les avancées scientifiques permettent un suivi des émissions des villes à partir de mesures des concentrations atmosphériques de CO2 sur un réseau de stations et de méthodes d'inversion fondées sur des modèles de météorologie et de transport atmosphérique à méso-échelle. Nous prenons pour exemple l'agglomération de Paris. Les mesures atmosphériques collectées par un réseau de stations urbaines et périurbaines sont présentées, ainsi que les résultats d'une inversion des émissions et les directions de recherche pour affiner ces estimations. Enfin, la signature du premier confinement lié à la Covid-19 pendant le printemps 2020 sur les mesures atmosphériques de CO2 est présentée et suggère une forte réduction des émissions. Scientific advances enable the monitoring of urban CO2 emissions from in situ measurements of atmospheric CO2 concentrations and atmospheric inversion methods based on mesoscale meteorological models. We use the Paris urban area as an example. Atmospheric measurements collected at a network of urban and suburban stations are presented, as well as the results of an atmospheric inversion of the city emissions, and research directions to refine these estimates. The signature of the Covid-19 lockdown measures in the spring 2020 on the urban atmospheric CO2 signals is presented, indicative of a strong reduction of emissions.

The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies

Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface greenhouse gas (GHG) fluxes. In the past decade, the research community has developed various inversion softwares, mainly using variational or ensemble Bayesian optimization methods, with various assumptions on uncertainty structures and prior information and with various atmospheric chemistry-transport models. Each of them can assimilate some or all of the available observation streams for its domain area of interest: flask samples, in-situ measurements or satellite observations. Although referenced in peer-reviewed publications and usually accessible across the research community, most systems are not at the level of transparency, flexibility and accessibility needed to provide the scientific community and policy makers with a comprehensive and robust view of the uncertainties associated with the inverse estimation of GHG fluxes. Furthermore, their development, usually carried out by individual research institutes, may in the future not keep pace with the increasing scientific needs and technical possibilities. We present here a Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source python-based tool to estimate the fluxes of various GHGs both at global and regional scales. It will allow running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow a comprehensively assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and demonstrate how it operates in a simple academic case.

Quantitative evaluation of the uncertainty sources for the modeling of atmospheric CO₂ concentration within and in the vicinity of Paris city

The top-down atmospheric inversion method that couples atmospheric CO2 observations with an atmospheric transport model has been used extensively to quantify CO2 emissions from cities. However, the potential of the method is limited by several sources of misfits between the measured and modeled CO2 that are of different origins than the targeted CO2 emissions. This study investigates the critical sources of errors that can compromise the estimates of the city-scale emissions and identifies the signal of emissions that has to be filtered when doing inversions. A set of one-year forward simulations is carried out using the WRF-Chem model at a horizontal resolution of 1 km focusing on the Paris area with different anthropogenic emission inventories, physical parameterizations and CO2 boundary conditions. The simulated CO2 concentrations are compared with in situ observations from six continuous monitoring stations located within Paris and its vicinity. Results highlight large nighttime observation-model misfits, especially in winter within the city, which are attributed to large uncertainties in the diurnal profile of anthropogenic emissions as well as to errors in the vertical mixing near the surface in the WRF-Chem model. The nighttime biogenic respiration to the CO2 concentration is a significant source of modeling errors during the growing season outside the city. When winds are from continental Europe and the CO2 concentration of incoming air masses is influenced by remote emissions and large-scale biogenic fluxes, differences in the simulated CO2 induced by the two different boundary conditions (CAMS and CarbonTracker) can be of up to 5 ppm. Our results suggest three selection criteria for the CO2 data to be assimilated for the inversion of CO2 emissions from Paris (i) discard data that appear as statistical outliers in the model-data misfits which are interpreted as model's deficiencies under complex meteorological conditions; (ii) use only afternoon urban measurements in winter and suburban ones in summer; (iii) test the influence of different boundary conditions in inversions. If possible, using additional observations to constrain the boundary inflow, or using CO2 gradients of upwind-downwind stations, rather than absolute CO2 concentration, as atmospheric inversion inputs.