| bibtype |
J -
Journal Article
|
| ARLID |
0539239 |
| utime |
20240103225350.3 |
| mtime |
20210204235959.9 |
| SCOPUS |
85100380780 |
| WOS |
000617993400002 |
| DOI |
10.5194/amt-14-803-2021 |
| title
(primary) (eng) |
Real-time measurement of radionuclide concentrations and its impact on inverse modeling of 106Ru release in the fall of 2017 |
| specification |
| page_count |
16 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0361778 |
| ISSN |
1867-1381 |
| title
|
Atmospheric Measurement Techniques |
| volume_id |
14 |
| volume |
2 (2021) |
| page_num |
803-818 |
| publisher |
|
|
| keyword |
inverse modeling |
| keyword |
radionuclide measurement |
| keyword |
source term estimation |
| author
(primary) |
| ARLID |
cav_un_auth*0267768 |
| name1 |
Tichý |
| name2 |
Ondřej |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| full_dept |
Department of Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0352443 |
| name1 |
Hýža |
| name2 |
M. |
| country |
CZ |
|
| author
|
| ARLID |
cav_un_auth*0363740 |
| name1 |
Evangeliou |
| name2 |
N. |
| country |
NO |
|
| author
|
| ARLID |
cav_un_auth*0101207 |
| name1 |
Šmídl |
| name2 |
Václav |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| full_dept |
Department of Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
GA20-27939S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0391986 |
|
| abstract
(eng) |
Low concentrations of 106Ru were detected across Europe at the turn of September and October 2017. The origin of 106Ru has still not been confirmed, however, current studies agree that the release occurred probably near Mayak in the southern Urals. The source reconstructions are mostly based on an analysis of concentration measurements coupled with an atmospheric transport model. Since reasonable temporal resolution of concentration measurements is crucial for proper source term reconstruction, the standard 1-week sampling interval could be limiting. In this paper, we present an investigation of the usability of the newly developed AMARA (Autonomous Monitor of Atmospheric Radioactive Aerosol) and CEGAM (carousel gamma spectrometry) real-time monitoring systems, which are based on the gamma-ray counting of aerosol filters and allow for determining the moment when 106Ru arrived at the monitoring site within approx. 1 h and detecting activity concentrations as low as several mBq m−3 in 4 h intervals. These high-resolution data were used for inverse modeling of the 106Ru release. We perform backward runs of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) atmospheric transport model driven with meteorological data from the Global Forecast System (GFS), and we construct a source–receptor sensitivity (SRS) matrix for each grid cell of our domain. Then, we use our least squares with adaptive prior covariance (LS-APC) method to estimate possible locations of the release and the source term of the release. With Czech monitoring data, the use of concentration measurements from the standard regime and from the real-time regime is compared, and a better source reconstruction for the real-time data is demonstrated in the sense of the location of the source and also the temporal resolution of the source. The estimated release location, Mayak, and the total estimated source term, 237±107 TBq, are in agreement with previous studies. Finally, the results based on the Czech monitoring data are validated with the IAEA-reported (International Atomic Energy Agency) dataset with a much better spatial resolution, and the agreement between the IAEA dataset and our reconstruction is demonstrated. In addition, we validated our findings also using the FLEXPART (FLEXible PARTicle dispersion) model coupled with meteorological analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF). |
| result_subspec |
WOS |
| RIV |
BC |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10102 |
| reportyear |
2022 |
| num_of_auth |
4 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0317537 |
| confidential |
S |
| mrcbC86 |
3+4 Article Meteorology Atmospheric Sciences |
| mrcbC91 |
A |
| mrcbT16-e |
METEOROLOGYATMOSPHERICSCIENCES |
| mrcbT16-j |
1.06 |
| mrcbT16-s |
1.551 |
| mrcbT16-D |
Q3 |
| mrcbT16-E |
Q2 |
| arlyear |
2021 |
| mrcbU14 |
85100380780 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000617993400002 WOS |
| mrcbU63 |
cav_un_epca*0361778 Atmospheric Measurement Techniques 1867-1381 1867-8548 Roč. 14 č. 2 2021 803 818 Copernicus |
|