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.
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”).
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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: Lirkov, I., Margenov, S. (eds) Large-Scale Scientific Computing. LSSC 2021. Lecture Notes in Computer Science, vol 13127. Springer, Cham. https://doi.org/10.1007/978-3-030-97549-4_19
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DOI: https://doi.org/10.1007/978-3-030-97549-4_19
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