| bibtype |
J -
Journal Article
|
| ARLID |
0647123 |
| utime |
20260310073203.7 |
| mtime |
20260309235959.9 |
| DOI |
10.1016/j.jhazmat.2026.141523 |
| title
(primary) (eng) |
Intuitively tuned elastic bias correction of atmospheric inversion using Gaussian process prior: Application to accidental radioactive emissions |
| specification |
| page_count |
17 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0257168 |
| ISSN |
0304-3894 |
| title
|
Journal of Hazardous Materials |
| volume_id |
506 |
| publisher |
|
|
| keyword |
Source inversion |
| keyword |
Bias correction |
| keyword |
Gaussian process prior |
| keyword |
Radionuclide emissions |
| keyword |
Hyper-parameter tuning |
| author
(primary) |
| ARLID |
cav_un_auth*0464277 |
| name1 |
Brožová |
| name2 |
Antonie |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| 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. |
|
| author
|
| ARLID |
cav_un_auth*0267768 |
| name1 |
Tichý |
| name2 |
Ondřej |
| 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. |
|
| author
|
| ARLID |
cav_un_auth*0363740 |
| name1 |
Evangeliou |
| name2 |
N. |
| country |
NO |
|
| source |
|
| source |
|
| cas_special |
| project |
| project_id |
GA24-10400S |
| agency |
GA ČR |
| country |
CZ |
| ARLID |
cav_un_auth*0464279 |
|
| project |
| project_id |
101008004 |
| agency |
EC |
| country |
XE |
| ARLID |
cav_un_auth*0437505 |
|
| project |
| project_id |
SGS24/141/OHK4/3T/14 |
| agency |
Studentska soutěž ČVUT |
| country |
CZ |
| ARLID |
cav_un_auth*0483231 |
|
| abstract
(eng) |
Precise estimation of atmospheric pollutant releases is crucial for assessing the impact of environmental accidents. Atmospheric inversion typically relies on a linear model with a source–receptor sensitivity (SRS) matrix, which may contain significant errors or even completely fail to capture the real magnitude of the event. We propose a correction of the SRS matrix formulated as slight shifts in the observation locations, effectively warping the sensitivity field. To constrain these shifts and ensure data-driven corrections, we model them using a Gaussian process prior. This prior not only enforces smoothness and sparsity, but also enables posterior prediction of shifts at previously unseen locations. This key feature provides a mechanism for hyperparameter tuning: the predicted shift field can be visualized on a map and assessed by an expert. We present a user-friendly framework that combines a Bayesian inversion model with correction and a tuning algorithm based on L-curve-like plots and the maps of predicted shifts. The proposed method is demonstrated on three case studies: the ETEX-I experiment, the 137Cs emissions during the 2020 Chernobyl wildfires, and the 106Ru release in 2017. |
| result_subspec |
WOS |
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10102 |
| reportyear |
2027 |
| num_of_auth |
4 |
| inst_support |
RVO:67985556 |
| permalink |
https://hdl.handle.net/11104/0376749 |
| confidential |
S |
| article_num |
141523 |
| mrcbC91 |
A |
| mrcbC96 |
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5760363 |
| mrcbT16-e |
ENVIRONMENTALSCIENCES|ENGINEERING.ENVIRONMENTAL |
| mrcbT16-f |
12.4 |
| mrcbT16-g |
1.9 |
| mrcbT16-h |
3.9 |
| mrcbT16-i |
0.18877 |
| mrcbT16-j |
1.88 |
| mrcbT16-k |
239998 |
| mrcbT16-q |
375 |
| mrcbT16-s |
3.078 |
| mrcbT16-y |
66.57 |
| mrcbT16-x |
13.99 |
| mrcbT16-3 |
125960 |
| mrcbT16-4 |
Q1 |
| mrcbT16-5 |
10.100 |
| mrcbT16-6 |
3589 |
| mrcbT16-7 |
Q1 |
| mrcbT16-C |
94.9 |
| mrcbT16-M |
1.75 |
| mrcbT16-N |
Q1 |
| mrcbT16-P |
95.1 |
| arlyear |
2026 |
| mrcbU14 |
SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
WOS |
| mrcbU63 |
cav_un_epca*0257168 Journal of Hazardous Materials 506 1 2026 0304-3894 1873-3336 Elsevier |
|