Standard
Sensitivity Operator-Based Approach to the Interpretation of Heterogeneous Air Quality Monitoring Data. / Penenko, Alexey; Penenko, Vladimir; Tsvetova, Elena et al.
Large-Scale Scientific Computing - 13th International Conference, LSSC 2021, Revised Selected Papers. ed. / Ivan Lirkov; Svetozar Margenov. 1. ed. Springer Science and Business Media Deutschland GmbH, 2022. p. 164-171 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13127 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Harvard
Penenko, A, Penenko, V, Tsvetova, E
, Gochakov, A, Pyanova, E & Konopleva, V 2022,
Sensitivity Operator-Based Approach to the Interpretation of Heterogeneous Air Quality Monitoring Data. in I Lirkov & S Margenov (eds),
Large-Scale Scientific Computing - 13th International Conference, LSSC 2021, Revised Selected Papers. 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13127 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 164-171, 13th International Conference on Large-Scale Scientific Computations, LSSC 2021, Sozopol, Bulgaria,
07.06.2021.
https://doi.org/10.1007/978-3-030-97549-4_19
APA
Penenko, A., Penenko, V., Tsvetova, E.
, Gochakov, A., Pyanova, E., & Konopleva, V. (2022).
Sensitivity Operator-Based Approach to the Interpretation of Heterogeneous Air Quality Monitoring Data. In I. Lirkov, & S. Margenov (Eds.),
Large-Scale Scientific Computing - 13th International Conference, LSSC 2021, Revised Selected Papers (1 ed., pp. 164-171). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13127 LNCS). Springer Science and Business Media Deutschland GmbH.
https://doi.org/10.1007/978-3-030-97549-4_19
Vancouver
Penenko A, Penenko V, Tsvetova E
, Gochakov A, Pyanova E, Konopleva V.
Sensitivity Operator-Based Approach to the Interpretation of Heterogeneous Air Quality Monitoring Data. In Lirkov I, Margenov S, editors, Large-Scale Scientific Computing - 13th International Conference, LSSC 2021, Revised Selected Papers. 1 ed. Springer Science and Business Media Deutschland GmbH. 2022. p. 164-171. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-97549-4_19
Author
BibTeX
@inproceedings{2990a3e48920453c8e596f13b988563b,
title = "Sensitivity Operator-Based Approach to the Interpretation of Heterogeneous Air Quality Monitoring Data",
abstract = "The joint use of atmospheric chemistry transport and transformation models and observational data makes it possible to solve a wide range of environment protection tasks, including pollution sources identification and reconstruction of the pollution fields in unobserved areas. Seamless usage of different measurement data types can improve the accuracy of air quality forecasting systems. The approach considered is based on sensitivity operators and adjoint equations solutions ensembles. The ensemble construction allows for the natural combination of various measurement data types in one operator equation. In the paper, we consider combining image-type, integral-type, pointwise, and time series-type measurement data for the air pollution source identification. The synergy effect is numerically illustrated in the inverse modeling scenario for the Baikal region.",
keywords = "Advection-diffusion-reaction model, Air quality, Heterogeneous measurements, Inverse modeling, Sensitivity operator",
author = "Alexey Penenko and Vladimir Penenko and Elena Tsvetova and Alexander Gochakov and Elza Pyanova and Viktoriia Konopleva",
note = "Funding Information: Supported by the grant №075-15-2020-787 in the form of a subsidy for a Major scientific project from Ministry of Science and Higher Education of Russia (project “Fundamentals, methods and technologies for digital monitoring and forecasting of the environmental situation on the Baikal natural territory”). Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 13th International Conference on Large-Scale Scientific Computations, LSSC 2021 ; Conference date: 07-06-2021 Through 11-06-2021",
year = "2022",
doi = "10.1007/978-3-030-97549-4_19",
language = "English",
isbn = "978-3-030-97548-7",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "164--171",
editor = "Ivan Lirkov and Svetozar Margenov",
booktitle = "Large-Scale Scientific Computing - 13th International Conference, LSSC 2021, Revised Selected Papers",
address = "Germany",
edition = "1",
}
RIS
TY - GEN
T1 - Sensitivity Operator-Based Approach to the Interpretation of Heterogeneous Air Quality Monitoring Data
AU - Penenko, Alexey
AU - Penenko, Vladimir
AU - Tsvetova, Elena
AU - Gochakov, Alexander
AU - Pyanova, Elza
AU - Konopleva, Viktoriia
N1 - Funding Information:
Supported by the grant №075-15-2020-787 in the form of a subsidy for a Major scientific project from Ministry of Science and Higher Education of Russia (project “Fundamentals, methods and technologies for digital monitoring and forecasting of the environmental situation on the Baikal natural territory”).
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - The joint use of atmospheric chemistry transport and transformation models and observational data makes it possible to solve a wide range of environment protection tasks, including pollution sources identification and reconstruction of the pollution fields in unobserved areas. Seamless usage of different measurement data types can improve the accuracy of air quality forecasting systems. The approach considered is based on sensitivity operators and adjoint equations solutions ensembles. The ensemble construction allows for the natural combination of various measurement data types in one operator equation. In the paper, we consider combining image-type, integral-type, pointwise, and time series-type measurement data for the air pollution source identification. The synergy effect is numerically illustrated in the inverse modeling scenario for the Baikal region.
AB - The joint use of atmospheric chemistry transport and transformation models and observational data makes it possible to solve a wide range of environment protection tasks, including pollution sources identification and reconstruction of the pollution fields in unobserved areas. Seamless usage of different measurement data types can improve the accuracy of air quality forecasting systems. The approach considered is based on sensitivity operators and adjoint equations solutions ensembles. The ensemble construction allows for the natural combination of various measurement data types in one operator equation. In the paper, we consider combining image-type, integral-type, pointwise, and time series-type measurement data for the air pollution source identification. The synergy effect is numerically illustrated in the inverse modeling scenario for the Baikal region.
KW - Advection-diffusion-reaction model
KW - Air quality
KW - Heterogeneous measurements
KW - Inverse modeling
KW - Sensitivity operator
UR - http://www.scopus.com/inward/record.url?scp=85127183284&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/6689ca19-741c-3f5d-9c79-5f5a6a7c2f9c/
U2 - 10.1007/978-3-030-97549-4_19
DO - 10.1007/978-3-030-97549-4_19
M3 - Conference contribution
AN - SCOPUS:85127183284
SN - 978-3-030-97548-7
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 164
EP - 171
BT - Large-Scale Scientific Computing - 13th International Conference, LSSC 2021, Revised Selected Papers
A2 - Lirkov, Ivan
A2 - Margenov, Svetozar
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th International Conference on Large-Scale Scientific Computations, LSSC 2021
Y2 - 7 June 2021 through 11 June 2021
ER -
