scholarly journals Ranking uncertainties in atmospheric dispersion modelling following the accidental release of radioactive material

2020 ◽  
Vol 55 ◽  
pp. S51-S55 ◽  
Author(s):  
S.J. Leadbetter ◽  
S. Andronopoulos ◽  
P. Bedwell ◽  
K. Chevalier-Jabet ◽  
G. Geertsema ◽  
...  
Keyword(s):  
Early Phase ◽  
Source Term ◽  
Dispersion Model ◽  
Atmospheric Dispersion ◽  
Radioactive Material ◽  
Dispersion Modelling ◽  
Dispersion Models ◽  
Sources Of Uncertainty ◽  
Accidental Release ◽  
Modelling Uncertainty

During the pre-release and early phase of an accidental release of radionuclides into the atmosphere there are few or no measurements, and dispersion models are used to assess the consequences and assist in determining appropriate countermeasures. However, uncertainties are high during this early phase and it is important to characterise these uncertainties and, if possible, include them in any dispersion modelling. In this paper we examine three sources of uncertainty in dispersion modelling; uncertainty in the source term, uncertainty in the meteorological information used to drive the dispersion model and intrinsic uncertainty within the dispersion model. We also explore the possibility of ranking these uncertainties dependent on their impact on the dispersion model outputs.

scholarly journals INFLUENCE OF MODELLING CHOICES ON THE RESULTS OF LANDFILL ODOUR DISPERSION

Detritus ◽  
2020 ◽  
pp. 92-99
Author(s):  
Francesca Tagliaferri ◽  
Marzio Invernizzi ◽  
Selena Sironi ◽  
Laura Capelli
Keyword(s):  
Source Term ◽  
Dispersion Model ◽  
Dispersion Modelling ◽  
Dispersion Models ◽  
Large Area ◽  
Chemical Mechanisms ◽  
Modelling Studies ◽  
Odour Dispersion ◽  
Definition Of ◽  
Dynamic Olfactometry

Landfills are an important source of odour pollution, potentially causing nuisance to adjacent populations. The most commonly used odour impact assessment for this type of plants usually involves a combination of dynamic olfactometry with dispersion modelling. Despite the advantages associated with the use of dispersion models, there are still some important issues related to their uncertainty. The dispersion model requires the Odour Emission Rate (OER) as input, expressed as units of odour emitted per unit time. Source term characterization and the estimation of the OER are typically the most important steps in the model’s implementation, accounting for the highest contribution to the overall uncertainty. Another important element of uncertainty when modelling emissions from landfill surfaces is the geometrical implementation of the emission source in the dispersion model. This entails the definition of the initial dimensions of the emission, which is critical in the case of large area sources. This paper discusses issues related to uncertainty in the use of dispersion models for the evaluation of landfill odour impacts, particularly focusing on the estimation of the OER and the emission’s initial vertical dimension. This study shows that modelling choices may lead to a variance in the resulting modelled odour concentrations at receptors differing by up to a factor 3. This variability should not cause distrust in the method, but rather indicates the importance of having odour dispersion modelling studies carried out by experts with deep knowledge of the physical-chemical mechanisms underlying atmospheric emissions.

A Research Reactor Source Term and Dose Results in the Event of a LOCA

2014 ◽  
Author(s):  
Charalampos Pappas ◽  
Andreas Ikonomopoulos ◽  
Athanasios Sfetsos ◽  
Spyros Andronopoulos ◽  
Melpomeni Varvayanni ◽  
...  
Keyword(s):  
Research Reactor ◽  
Source Term ◽  
Dispersion Model ◽  
Ventilation System ◽  
Atmospheric Dispersion ◽  
Reactor Core ◽  
Forecast Model ◽  
Scale Model ◽  
Continuous Reactor ◽  
Reactor Building

The present study discusses the source term derivation and dose result calculation for a hypothetical accident sequence in the Greek Research Reactor – 1 (GRR-1). A loss-of-coolant accident (LOCA) has been selected as a credible accident sequence. The source term derivation has been based on the GRR-1 confinement performance where the inventory has been computed assuming continuous reactor operation. A core damage fraction of 30% has been considered for the calculations while conservative core release fractions have been employed. The radionuclides released from the reactor core to the confinement atmosphere have been subjected to natural decay, deposition on and resuspension from various internal surfaces before being led to the release pathway. It has been assumed that an emergency shutdown is initiated immediately after the beginning of the accident sequence and the emergency ventilation system is also activated. Subsequently, the source term has been derived comprising of noble gases, iodine and aerosol. The JRODOS computational software for off-site nuclear emergency management has been utilized to estimate the dose results from the LOCA-initiated source term that is released in its entirety from the reactor stack at ambient temperature. The Local Scale Model Chain in conjunction with the DIPCOT atmospheric dispersion model that is embedded in JRODOS have been used with proper parameterization of the calculation settings. Five weather scenarios have been selected as representative of typical meteorological conditions at the reactor site. The scenarios have been assessed with the use of the Weather Research and Forecast model. Total effective, skin, thyroid, lung and inhalation doses downwind of the reactor building and up to a distance of 10 km have been calculated for each weather scenario and are presented. The total effective gamma dose rate at a fixed distance from the reactor building has been assessed. The radiological consequences of the dose results are discussed.

scholarly journals Atmospheric Dispersion Modelling at the London VAAC: A Review of Developments since the 2010 Eyjafjallajökull Volcano Ash Cloud

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 352 ◽  
Author(s):  
Frances M. Beckett ◽  
Claire S. Witham ◽  
Susan J. Leadbetter ◽  
Ric Crocker ◽  
Helen N. Webster ◽  
...  
Keyword(s):  
Volcanic Ash ◽  
Dispersion Model ◽  
Source Parameters ◽  
Atmospheric Dispersion ◽  
Lessons Learned ◽  
Dispersion Modelling ◽  
Atmospheric Dispersion Modelling ◽  
Volcanic Ash Cloud ◽  
Atmospheric Dispersion Model ◽  
Ash Cloud

It has been 10 years since the ash cloud from the eruption of Eyjafjallajökull caused unprecedented disruption to air traffic across Europe. During this event, the London Volcanic Ash Advisory Centre (VAAC) provided advice and guidance on the expected location of volcanic ash in the atmosphere using observations and the atmospheric dispersion model NAME (Numerical Atmospheric-Dispersion Modelling Environment). Rapid changes in regulatory response and procedures during the eruption introduced the requirement to also provide forecasts of ash concentrations, representing a step-change in the level of interrogation of the dispersion model output. Although disruptive, the longevity of the event afforded the scientific community the opportunity to observe and extensively study the transport and dispersion of a volcanic ash cloud. We present the development of the NAME atmospheric dispersion model and modifications to its application in the London VAAC forecasting system since 2010, based on the lessons learned. Our ability to represent both the vertical and horizontal transport of ash in the atmosphere and its removal have been improved through the introduction of new schemes to represent the sedimentation and wet deposition of volcanic ash, and updated schemes to represent deep moist atmospheric convection and parametrizations for plume spread due to unresolved mesoscale motions. A good simulation of the transport and dispersion of a volcanic ash cloud requires an accurate representation of the source and we have introduced more sophisticated approaches to representing the eruption source parameters, and their uncertainties, used to initialize NAME. Finally, upper air wind field data used by the dispersion model is now more accurate than it was in 2010. These developments have resulted in a more robust modelling system at the London VAAC, ready to provide forecasts and guidance during the next volcanic ash event.

Take one dispersing plume and add some precipitation: using ensembles to simulate deposition uncertainty

2020 ◽  
Author(s):  
Susan Leadbetter ◽  
Peter Bedwell ◽  
Gertie Geertsema ◽  
Irene Korsakissok ◽  
Jasper Tomas ◽  
...  
Keyword(s):  
High Resolution ◽  
Dispersion Model ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Radioactive Material ◽  
Positional Error ◽  
Multiple Model ◽  
Dispersion Models ◽  
Air Concentration ◽  
Initial State

<p>In the event of an accidental airborne release of radioactive material, dispersion models would be used to simulate the spread of the pollutant in the atmosphere and its subsequent deposition. Typically, meteorological information is provided to dispersion models from numerical weather prediction (NWP) models. As these NWP models have increased in resolution their ability to resolve short-lived, heavy precipitation events covering smaller areas has improved. This has led to more realistic looking precipitation forecasts. However, when traditional statistics comparing precipitation predictions to measurements at a point (e.g. an observation site) are used, these high-resolution models appear to have a lower skill in predicting precipitation due to small differences in the location and timing of the precipitation with respect to the observations. This positional error is carried through to the dispersion model resulting in predictions of high deposits where none are observed and vice versa; a problem known as the double penalty problem in meteorology.</p><p>Since observations are not available at the onset of an event, it is crucial to gain insight into the possible location and timing errors. One method to address this issue is to use ensemble meteorological data as input to the dispersion model. Meteorological ensembles are typically generated by running multiple model integrations where each model integration starts from a perturbed initial state and uses slightly different model parametrisations to represent uncertainty in the atmospheric state and its evolution. Ensemble meteorological data provide several possible predictions of the precipitation that are all considered to be equally likely and this allows the dispersion model to produce several possible predictions of the deposits of radioactive material.</p><p>As part of the Euratom funded project, CONFIDENCE, a case study involving the passage of a warm front, where the timing of the front is uncertain in relation to a hypothetical nuclear accident in Europe was examined. In this study a ten-member meteorological ensemble was generated using time lagged forecasts to simulate perturbations in the initial state and two different model parameterisations. This meteorological ensemble was used as input to a single dispersion model to generate a dispersion model ensemble. The resulting ensemble dispersion output and methods to communicate the uncertainty in the deposition and the resulting uncertainty in the air concentration predictions are presented. The results demonstrate how high-resolution meteorological ensembles can be combined with dispersion models to simulate the maximum impact of precipitation and the uncertainty in its position and timing.</p>

scholarly journals Detailed source term estimation of the atmospheric release for the Fukushima Daiichi Nuclear Power Station accident by coupling simulations of an atmospheric dispersion model with an improved deposition scheme and oceanic dispersion model

2015 ◽  
Vol 15 (2) ◽  
pp. 1029-1070 ◽  
Author(s):  
G. Katata ◽  
M. Chino ◽  
T. Kobayashi ◽  
H. Terada ◽  
M. Ota ◽  
...  
Keyword(s):  
Dose Rate ◽  
Nuclear Power ◽  
Source Term ◽  
Dispersion Model ◽  
Atmospheric Dispersion ◽  
Air Concentration ◽  
Power Station ◽  
Release Rates ◽  
Surface Deposition ◽  
Fukushima Daiichi

Abstract. Temporal variations in the amount of radionuclides released into the atmosphere during the Fukushima Daiichi Nuclear Power Station (FNPS1) accident and their atmospheric and marine dispersion are essential to evaluate the environmental impacts and resultant radiological doses to the public. In this paper, we estimate the detailed atmospheric releases during the accident using a reverse estimation method which calculates the release rates of radionuclides by comparing measurements of air concentration of a radionuclide or its dose rate in the environment with the ones calculated by atmospheric and oceanic transport, dispersion and deposition models. The atmospheric and oceanic models used are WSPEEDI-II (Worldwide version of System for Prediction of Environmental Emergency Dose Information) and SEA-GEARN-FDM (Finite difference oceanic dispersion model), both developed by the authors. A sophisticated deposition scheme, which deals with dry and fog-water depositions, cloud condensation nuclei (CCN) activation, and subsequent wet scavenging due to mixed-phase cloud microphysics (in-cloud scavenging) for radioactive iodine gas (I2 and CH3I) and other particles (CsI, Cs, and Te), was incorporated into WSPEEDI-II to improve the surface deposition calculations. The results revealed that the major releases of radionuclides due to the FNPS1 accident occurred in the following periods during March 2011: the afternoon of 12 March due to the wet venting and hydrogen explosion at Unit 1, midnight of 14 March when the SRV (safety relief valve) was opened three times at Unit 2, the morning and night of 15 March, and the morning of 16 March. According to the simulation results, the highest radioactive contamination areas around FNPS1 were created from 15 to 16 March by complicated interactions among rainfall, plume movements, and the temporal variation of release rates. The simulation by WSPEEDI-II using the new source term reproduced the local and regional patterns of cumulative surface deposition of total 131I and 137Cs and air dose rate obtained by airborne surveys. The new source term was also tested using three atmospheric dispersion models (Modèle Lagrangien de Dispersion de Particules d'ordre zéro: MLDP0, Hybrid Single Particle Lagrangian Integrated Trajectory Model: HYSPLIT, and Met Office's Numerical Atmospheric-dispersion Modelling Environment: NAME) for regional and global calculations, and the calculated results showed good agreement with observed air concentration and surface deposition of 137Cs in eastern Japan.

scholarly journals GEOSTATISTICS FOR AIR QUALITY MAPPING: CASE OF BAGUIO CITY, PHILIPPINES

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 353-359
Author(s):  
R. V. Ramos ◽  
A. C. Blanco
Keyword(s):  
Air Quality ◽  
Dispersion Model ◽  
Atmospheric Dispersion ◽  
Spatial Variations ◽  
Dispersion Modeling ◽  
Dispersion Models ◽  
Night Time ◽  
Smoothing Factor ◽  
Pollutant Concentrations ◽  
Atmospheric Dispersion Models

Abstract. Mapping of air quality are often based on ground measurements using gravimetric and air portable sensors, remote sensing methods and atmospheric dispersion models. In this study, Geographic Information Systems (GIS) and geostatistical techniques are employed to evaluate coarse particulate matter (PM10) concentrations observed in the Central Business District of Baguio City, Philippines. Baguio City has been reported as one of the most polluted cities in the country and several studies have already been conducted in monitoring its air quality. The datasets utilized in this study are based on hourly simulations from a Gaussian-based atmospheric dispersion model that considers the impacts of vehicular emissions. Dispersion modeling results, i.e., PM10 concentrations at 20-meter interval, show that high values range from 135 to 422 μg/mm3. The pollutant concentrations are evident within 40 meters from the roads. Spatial variations and PM10 estimates at unsampled locations are determined using Ordinary Kriging. Geostatistical modeling estimates are evaluated based on recommended values for mean error (ME), root mean square error (RMSE) and standardized errors. Optimal predictors for pollutant concentrations at 5-meter interval include 2 to 5 search neighbors and variable smoothing factor for night-time datasets while 2 to 10 search neighbors and smoothing factors 0.3 to 0.5 were used for daytime datasets. Results from several interpolation tests indicate small ME (0.0003 to 0.0008 μg/m3) and average standardized errors (4.24 to 8.67 μg/m3). RMSE ranged from 2.95 to 5.43 μg/m3, which are approximately 2 to 3% of the maximum pollutant concentrations in the area. The methodology presented in this paper may be integrated with atmospheric dispersion models in refining estimates of pollutant concentrations, in generating surface representations, and in understanding the spatial variations of the outputs from the model simulations.

scholarly journals Atmospheric Dispersion of PM10 in an Urban Area

2014 ◽  
Vol 8 (2) ◽  
pp. 91-99 ◽  
Author(s):  
Carmelia Mariana Dragomir ◽  
Daniel Eduard Constantin ◽  
Mirela Voiculescu ◽  
Lucian Puiu Georgescu Georgescu
Keyword(s):  
Environmental Protection Agency ◽  
Dispersion Model ◽  
Atmospheric Dispersion ◽  
City Centre ◽  
Anthropogenic Emissions ◽  
Dispersion Models ◽  
Protection Agency ◽  
Using Data ◽  
The City ◽  
Pm10 Emissions

Abstract One way of monitoring the atmospheric pollution is to estimate anthropogenic emissions. This paper presents a study of PM10 emissions in a city SE of Romania (Braila) for the period 2009-2012. PM10 emissions decrease from 304.75 t in 2009 to 78.01 t in 2012. Using data from the Environmental Protection Agency Braila and the METI-LIS dispersion model, four maps were produced in order to estimate the spatial distribution of PM10 emission in each year. Results of dispersion models show that the air quality can change abruptly between points at few meters away. Expectedly, PM10 emissions increase towards the centre of the city centre, are generally higher in the vicinity of busy streets and roads.

A source term estimation method for a nuclear accident using atmospheric dispersion models

2015 ◽  
Vol 58 (1/2) ◽  
pp. 39 ◽  
Author(s):  
Minsik Kim ◽  
Ryohji Ohba ◽  
Masamichi Oura ◽  
Shinsuke Kato ◽  
Masayuki Takigawa ◽  
...  
Keyword(s):  
Source Term ◽  
Atmospheric Dispersion ◽  
Estimation Method ◽  
Nuclear Accident ◽  
Dispersion Models ◽  
Atmospheric Dispersion Models ◽  
Source Term Estimation
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