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Sensitivity Operator-Based Approach to the Interpretation of Heterogeneous Air Quality Monitoring Data. / Penenko, Alexey; Penenko, Vladimir; Tsvetova, Elena и др.

Large-Scale Scientific Computing - 13th International Conference, LSSC 2021, Revised Selected Papers. ред. / Ivan Lirkov; Svetozar Margenov. 1. ред. Springer Science and Business Media Deutschland GmbH, 2022. стр. 164-171 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 13127 LNCS).

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование

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. в I Lirkov & S Margenov (ред.), Large-Scale Scientific Computing - 13th International Conference, LSSC 2021, Revised Selected Papers. 1 изд., Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Том. 13127 LNCS, Springer Science and Business Media Deutschland GmbH, стр. 164-171, 13th International Conference on Large-Scale Scientific Computations, LSSC 2021, Sozopol, Болгария, 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. в I. Lirkov, & S. Margenov (Ред.), Large-Scale Scientific Computing - 13th International Conference, LSSC 2021, Revised Selected Papers (1 ред., стр. 164-171). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 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. в Lirkov I, Margenov S, Редакторы, Large-Scale Scientific Computing - 13th International Conference, LSSC 2021, Revised Selected Papers. 1 ред. Springer Science and Business Media Deutschland GmbH. 2022. стр. 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

Penenko, Alexey ; Penenko, Vladimir ; Tsvetova, Elena и др. / Sensitivity Operator-Based Approach to the Interpretation of Heterogeneous Air Quality Monitoring Data. Large-Scale Scientific Computing - 13th International Conference, LSSC 2021, Revised Selected Papers. Редактор / Ivan Lirkov ; Svetozar Margenov. 1. ред. Springer Science and Business Media Deutschland GmbH, 2022. стр. 164-171 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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 -

ID: 35810796