A study of the atmospheric dispersion of a high release of krypton-85 above a complex coastal terrain, comparison with the predictions of Gaussian models (Briggs, Doury, ADMS4)

First quantitative constraints on chlorine 36 dry deposition velocities on grassland: Comparing measurements and modelling results

Once released into the atmosphere, radionuclide dry deposition represents a major transfer process. It can be accurately characterized by its deposition velocity. However, this parameter is poorly constrained for most radionuclides, including chlorine 36. Chlorine 36 is a radionuclide of cosmogenic and anthropogenic origin. It may be discharged into the environment as gases and/or particles during the decommissioning of nuclear plants and the recycling of nuclear fuels. In this study, chlorine 36 deposition velocities are, for the first time, experimentally determined on grass downwind from the Orano La-Hague plant. The atmospheric chlorine 36 measurements were on average 50 nBq.m-3 for the gaseous fraction and 19 nBq.m3 for the particulate fraction. To measure the chlorine 36 transferred from the atmosphere to the grass, a method was devised for extracting the chlorides contained in solid matrices. With this method, chlorides were extracted with a mean efficiency of 83%. Chlorine 36 concentrations in the grass were on average 4 μBq.g-1, suggesting fast uptake of atmospheric chlorine 36. The yielded 36Cl dry deposition velocities varied with the season and were between 1 × 10-3 and 6 × 10-3 m s-1. The chlorine 36 depositions were modelled by adapting the existing deposition models and based on meteorological and micro-meteorological data. The dry deposition velocities calculated by the model showed less than one order of magnitude of difference with those determined experimentally. The deposition fluxes calculated by the model showed that the atmospheric depositions were predominantly gaseous chlorine 36 (>97%). However, on remote sites, the particulate fraction could be larger and have a greater influence on dry deposition. As chlorine 36 is a highly soluble and bioavailable element, these results will enable a better study of its behaviour in the environment and a more accurate evaluation of its dosimetric impact.

An open GIS based 3D simulation software to predict cooling tower drift diffusion

This paper developed XJCT-3D, a simulation software for cooling tower wet plume dispersion. By coupling it with the Open GIS component Dotspatial, we have achieved geospatial visual representation of the calculation results, which has solved the problems of low calculation efficiency and insufficient visual representation of the traditional CFD software in the calculation of cooling tower wet plume dispersion. In order to verify the validity of the XJCT-3D software simulation results, we have conducted tracer experimental data from the ChalkPoint power plant. XJCT-3D accurately models wet plume deposition during cooling tower operation. From the XJCT-3D calculation results, we have observed that the maximum value of the cooling tower thermal plume wet deposition occurs near 610 m with a maximum value of 6.9E-07 kg/m2 s. This finding suggests that the cooling tower emissions carry a significant load of particles or droplets that have settled on surfaces at this particular altitude. It provides insights into potential environmental and human health impacts and helps in identifying and assessing areas at relatively higher risk of deposition, such as nearby ecosystems, farmland, or urban areas. This information can contribute to the development of effective mitigation strategies and the implementation of appropriate measures to minimize the impact of cooling tower emissions.

Investigation of a Gaussian Plume in the Vicinity of an Urban Cyclotron Using Helium as a Tracer Gas

Studies focusing on the radiological impact of fluorine 18 on populations living near to cyclotrons (<200 m) frequently assume normal distribution of atmospheric concentration for simplification purposes. On this basis, Gaussian models are used, despite their limits, as deployment requires little input data and computing resources. To estimate the ability of a Gaussian model to predict atmospheric dispersion in an urban environment, we used helium as a new passive tracer of atmospheric dispersion in the near-field range (<500 m) of the Beuvry hospital cyclotron (France). The atmospheric transfer coefficients measured in the field were compared with those modeled using a Gaussian equation. According to the results, helium is an effective tracer of atmospheric dispersion when attempting to determine atmospheric transfer coefficients ( downwind of a discharge point. The Briggs-rural, Briggs-urban and Doury Gaussian models underestimate and sometimes maximum in the prevailing weather conditions during the experiments. By compiling the results of this study with data from the literature, it appears that the maximum observed obey a power law as a function of the distance from the discharge point, for distances from the discharge point in excess of 20 m.

Near-field krypton-85 measurements in stable meteorological conditions around the AREVA NC La Hague reprocessing plant: estimation of atmospheric transfer coefficients

The aim of this work was to study the near-field dispersion of (85)Kr around the nuclear fuel reprocessing plant at La Hague (AREVA NC La Hague - France) under stable meteorological conditions. Twenty-two (85)Kr night-time experimental campaigns were carried out at distances of up to 4 km from the release source. Although the operational Gaussian models predict for these meteorological conditions a distance to plume touchdown of several kilometers, we almost systematically observed a marked ground signal at distances of 0.5-4 km. The calculated atmospheric transfer coefficients (ATC) show values (1) higher than those observed under neutral conditions, (2) much higher than those proposed by the operational models, and (3) higher than those used in the impact assessments.

Comparison of RIMPUFF, HYSPLIT, ADMS atmospheric dispersion model outputs, using emergency response procedures, with (85)Kr measurements made in the vicinity of nuclear reprocessing plant

The Institut de Radioprotection et de Sureté Nucléaire (IRSN) performed a series of (85)Kr air sampling campaigns at mesoscale distances (18-50 km) from the AREVA NC La Hague nuclear reprocessing plant (North West France) between 2007 and 2009. The samples were collected in order to test and optimise a technique to measure low krypton-85 ((85)Kr) air concentrations and to investigate the performance of three atmospheric dispersion models (RIMPUFF, HYSPLIT, and ADMS), This paper presents the (85)Kr air concentrations measured at three sampling locations which varied from 2 to 8000 Bq m(-3), along with the (85)Kr air concentrations output by the dispersion models. The dispersion models made reasonable estimates of the mean concentrations of (85)Kr field measurements during steady wind conditions. In contrast, the models failed to accurately predict peaks in (85)Kr air concentration during periods of rapid and large changes in wind speed and/or wind direction. At distances where we made the comparisons (18-50 km), in all cases, the models underestimated the air concentration activities.

The TOCATTA-χ model for assessing 14C transfers to grass: an evaluation for atmospheric operational releases from nuclear facilities

Radioactive (14)C is formed as a by-product of nuclear power generation and from the operation of nuclear fuel reprocessing plants like AREVA-NC La Hague (North France), which releases about 15 TBq per year of (14)C into the atmosphere. This article evaluates a recently improved radioecology model (TOCATTA-χ) to assess (14)C transfers to grassland ecosystems under normal operating conditions. The new version of the TOCATTA model (TOCATTA-χ) includes developments that were derived from PaSiM, a pasture model for simulating grassland carbon and radiocarbon cycling. The TOCATTA-χ model has been tested against observations of (14)C activity concentrations in grass samples collected monthly from six plots which are located around the periphery of the reprocessing plant. Simulated (14)C activities are consistent with observations on both intensively managed and poorly managed grasslands, but an adaptation of the mean turn-over time for (14)C within the plant is necessary in the model to account for different management practices. When atmospheric (14)C activity concentrations are directly inferred from observations, TOCATTA-χ performs better than TOCATTA (the root mean square error is decreased by 45%), but when atmospheric (14)C activity concentrations are not known and must be calculated, the uncertainty associated with the TOCATTA-χ model outcomes is estimated to be larger than the standard deviation of the observations.

Simulating industrial emissions using atmospheric dispersion modeling system: model performance and source emission factors

In this paper, the Gaussian Atmospheric Dispersion Modeling System (ADMS4) was coupled with field observations of surface meteorology and concentrations of several air quality indicators (nitrogen oxides (NOx), carbon monoxide (CO), fine particulate matter (PM10) and sulfur dioxide (SO2)) to test the applicability of source emission factors set by the European Environment Agency (EEA) and the United States Environmental Protection Agency (USEPA) at an industrial complex. Best emission factors and data groupings based on receptor location, type of terrain and wind speed, were relied upon to examine model performance using statistical analyses of simulated and observed data. The model performance was deemed satisfactory for several scenarios when receptors were located at downwind sites with index of agreement 'd' values reaching 0.58, fractional bias 'FB' and geometric mean bias 'MG' values approaching 0 and 1, respectively, and normalized mean square error 'NMSE' values as low as 2.17. However, median ratios of predicted to observed concentrations 'Cp/Co' at variable downstream distances were 0.01, 0.36, 0.76 and 0.19 for NOx, CO, PM10 and SO2, respectively, and the fraction of predictions within a factor of two of observations 'FAC2' values were lower than 0.5, indicating that the model could not adequately replicate all observed variations in emittant concentrations. Also, the model was found to be significantly sensitive to the input emission factor bringing into light the deficiency in regulatory compliance modeling which often uses internationally reported emission factors without testing their applicability.