scholarly journals Source term estimate assorted with uncertainties using backward atmospheric transport modelling, Bayesian reconstruction and an ensemble of weather predictions

2020 ◽  
Author(s):  
P. Armand
2021 ◽  
Author(s):  
Giuseppe Ottaviano ◽  
Antonietta Rizzo ◽  
Chiara Telloli ◽  
Alberto Ubaldini ◽  
Barbara Ferrucci ◽  
...  

<div><span>In June 2020, the Swedish station SEP63 of the International Monitoring System (IMS) of the Comprehensive Nuclear Test Ban Treaty Organization (CTBTO) recorded anomalous values of a mixture of some fission products and neutron activation products not present in the natural background of the station itself. Some concentration activity values above the statistical range of the station were measured. An online search for any relevant news reports was carried out and atmospheric transport modelling (ATM) conducted to identify the possible source of the emissions and to assess the related source-term. The aim of this work is to sketch out a preliminary forensic approach to characterize the event.</span></div>


Author(s):  
Ingeborg Levin ◽  
Samuel Hammer ◽  
Elke Eichelmann ◽  
Felix R. Vogel

Independent verification of greenhouse gas emissions reporting is a legal requirement of the Kyoto Protocol, which has not yet been fully accomplished. Here, we show that dedicated long-term atmospheric measurements of greenhouse gases, such as carbon dioxide (CO 2 ) and methane (CH 4 ), continuously conducted at polluted sites can provide the necessary tool for this undertaking. From our measurements at the semi-polluted Heidelberg site in the upper Rhine Valley, we find that in the catchment area CH 4 emissions decreased on average by 32±6% from the second half of the 1990s until the first half of the 2000s, but the observed long-term trend of emissions is considerably smaller than that previously reported for southwest Germany. In contrast, regional fossil fuel CO 2 levels, estimated from high-precision 14 CO 2 observations, do not show any significant decreasing trend since 1986, in agreement with the reported emissions for this region. In order to provide accurate verification, these regional measurements would best be accompanied by adequate atmospheric transport modelling as required to precisely determine the relevant catchment area of the measurements. Furthermore, reliable reconciliation of reported emissions will only be possible if these are known at high spatial resolution in the catchment area of the observations. This information should principally be available in all countries that regularly report their greenhouse gas emissions to the United Nations Framework Convention on Climate Change.


1998 ◽  
Vol 32 (24) ◽  
pp. 4167-4186 ◽  
Author(s):  
Jørgen Brandt ◽  
Annemarie Bastrup-birk ◽  
Jesper H. Christensen ◽  
Torben Mikkelsen ◽  
Søren Thykier-Nielsen ◽  
...  

2021 ◽  
Author(s):  
Jolanta Kusmierczyk-Michulec ◽  
Anne Tipka ◽  
Martin Kalinowski

<p>For every atmospheric radionuclide sample taken by the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty Organisation (CTBTO), the CTBTO makes use of operational Atmospheric Transport Modelling (ATM) to assist States Signatories in localization of possible source regions of any measured substance. Currently, ATM is accomplished by using the Lagrangian particle dispersion model (LPDM) FLEXPART driven by global meteorological fields with a spatial resolution of 0.5 degrees and 1 hourly temporal resolution. Meteorological fields are provided by the European Centre for Medium-Range Weather Forecasts (ECMWF ) and the National Centers for Environmental Prediction (NCEP).  </p><p>Recent studies to increase the accuracy in the CTBTO’s localization process to be applied for specific detection events, utilizes High-Resolution Atmospheric Transport Modelling (HRATM) by using the Weather Research and Forecasting model (WRF) to generate high-resolution meteorological input data for the LPDM version Flexpart-WRF.   </p><p>This presentation uses measurements from the International Monitoring System (IMS) station DEX33 in Germany of seven episodes of elevated Xe-133 concentrations in 2014 in combination with with the stack emission data of the medical isotope production facility IRE in Fleurus, Belgium. Each episode consists of 6 to 11 subsequent 24-hour samples. Backward simulations for each sample are conducted and the sensitivity to the stack emission data are analysed. All samples determined to represent a detection of IRE releases are selected to be used for an evaluation study. </p><p>Evaluating the CTBTO’s utilization of HRATM requires to investigate the ability to localize the source region as well as the accuracy of the match and the computational performance to accomplish these results. The evaluation of HRATM results is done by using statistical metrics established during former ATM challenges. Concerning the computational performance and to account for uncertainties, sensitivity studies with varying spatial resolutions, physical parameterization variations and different regional domain setups for WRF were accomplished. This comprises a reference comparison to the operational ATM FLEXPART model with an increased spatial resolution to 0.1 degrees.   </p>


2017 ◽  
Vol 10 (12) ◽  
pp. 5017-5037 ◽  
Author(s):  
Sébastien Ars ◽  
Grégoire Broquet ◽  
Camille Yver Kwok ◽  
Yelva Roustan ◽  
Lin Wu ◽  
...  

Abstract. This study presents a new concept for estimating the pollutant emission rates of a site and its main facilities using a series of atmospheric measurements across the pollutant plumes. This concept combines the tracer release method, local-scale atmospheric transport modelling and a statistical atmospheric inversion approach. The conversion between the controlled emission and the measured atmospheric concentrations of the released tracer across the plume places valuable constraints on the atmospheric transport. This is used to optimise the configuration of the transport model parameters and the model uncertainty statistics in the inversion system. The emission rates of all sources are then inverted to optimise the match between the concentrations simulated with the transport model and the pollutants' measured atmospheric concentrations, accounting for the transport model uncertainty. In principle, by using atmospheric transport modelling, this concept does not strongly rely on the good colocation between the tracer and pollutant sources and can be used to monitor multiple sources within a single site, unlike the classical tracer release technique. The statistical inversion framework and the use of the tracer data for the configuration of the transport and inversion modelling systems should ensure that the transport modelling errors are correctly handled in the source estimation. The potential of this new concept is evaluated with a relatively simple practical implementation based on a Gaussian plume model and a series of inversions of controlled methane point sources using acetylene as a tracer gas. The experimental conditions are chosen so that they are suitable for the use of a Gaussian plume model to simulate the atmospheric transport. In these experiments, different configurations of methane and acetylene point source locations are tested to assess the efficiency of the method in comparison to the classic tracer release technique in coping with the distances between the different methane and acetylene sources. The results from these controlled experiments demonstrate that, when the targeted and tracer gases are not well collocated, this new approach provides a better estimate of the emission rates than the tracer release technique. As an example, the relative error between the estimated and actual emission rates is reduced from 32 % with the tracer release technique to 16 % with the combined approach in the case of a tracer located 60 m upwind of a single methane source. Further studies and more complex implementations with more advanced transport models and more advanced optimisations of their configuration will be required to generalise the applicability of the approach and strengthen its robustness.


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