| bibtype |
J -
Journal Article
|
| ARLID |
0616896 |
| utime |
20250224104736.9 |
| mtime |
20250213235959.9 |
| SCOPUS |
85217128649 |
| DOI |
10.1016/j.apr.2025.102419 |
| title
(primary) (eng) |
Inverse modeling of 137Cs during Chernobyl 2020 wildfires without the first guess |
| specification |
| page_count |
11 s. |
| media_type |
E |
|
| serial |
| ARLID |
cav_un_epca*0348310 |
| ISSN |
1309-1042 |
| title
|
Atmospheric Pollution Research |
| volume_id |
16 |
| publisher |
|
|
| keyword |
Chernobyl wildfires |
| keyword |
Inverse modeling |
| keyword |
Plume bias correction |
| keyword |
Multi-species emissions |
| keyword |
JRODOS |
| 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*0363740 |
| name1 |
Evangeliou |
| name2 |
N. |
| country |
NO |
|
| author
|
| ARLID |
cav_un_auth*0483338 |
| name1 |
Selivanova |
| name2 |
A. |
| country |
CZ |
|
| 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 |
|
| cas_special |
| project |
| project_id |
GA24-10400S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0464279 |
|
| abstract
(eng) |
This study estimates 137Cs emissions from Chernobyl wildfires in April 2020 using inverse modeling. Emissions are resolved with daily resolution by particle sizes (0.4 µm, 8 µm, 16 µm) and altitudes (up to 3 km). The inverse problem's complexity requires regularization due to its ill-posed nature. One potential way to regularize the problem is the use of the so-called first guess, i.e. emission taken from expert knowledge or previous literature. However, inappropriately chosen first guess may lead to serious bias in results or its availability may be limited for rapid response. We rather follow a Bayesian approach where all model parameters are considered as variables to be estimated from available data. We aim to combine three key principles: modeling of sparsity and smoothness of the emission vector, modeling of bounded ratios between released particle size/altitude fractions, and bias correction of the atmospheric transport model. All these principles proved their significance separately, however, we combine them in one comprehensive method to estimate the 137Cs emissions from the Chernobyl wildfires. The total released activity was estimated to be 458 GBq with uncertainty estimated to be 69 GBq. Our estimates also suggest that most of the activity has been released below a one-kilometer altitude with the more dominant role towards the smallest particle fraction than was considered in other studies. Using our estimate, we calculate the time-integrated volumetric activities of 137Cs over the domain using the JRODOS system and our findings well agrees with previous results. |
| result_subspec |
WOS |
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2025 |
| num_of_auth |
4 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0364268 |
| cooperation |
| ARLID |
cav_un_auth*0339477 |
| name |
NILU-Norwegian Institute for Air Research |
| country |
NO |
|
| cooperation |
| ARLID |
cav_un_auth*0352445 |
| name |
National Radiation Protection Institute |
| institution |
SÚRO |
| country |
CZ |
|
| cooperation |
| ARLID |
cav_un_auth*0400308 |
| name |
Czech University of Life Sciences |
| country |
CZ |
|
| confidential |
S |
| article_num |
102419 |
| mrcbC91 |
C |
| mrcbT16-e |
ENVIRONMENTALSCIENCES |
| mrcbT16-j |
0.715 |
| mrcbT16-s |
0.95 |
| mrcbT16-D |
Q3 |
| mrcbT16-E |
Q3 |
| arlyear |
2025 |
| mrcbU14 |
85217128649 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0348310 Atmospheric Pollution Research 16 4 2025 1309-1042 1309-1042 Elsevier |
|