Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems

Air quality monitoring systems differ in composition and accuracy of observations and their temporal and spatial coverage. A monitoring system’s performance can be assessed by evaluating the accuracy of the emission sources identified by its data. In the considered inverse modeling approach, a sourc...

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Bibliographic Details
Published in:Atmosphere Vol. 12; no. 12; p. 1697
Main Authors: Penenko, Alexey, Penenko, Vladimir, Tsvetova, Elena, Gochakov, Alexander, Pyanova, Elza, Konopleva, Viktoriia
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.12.2021
Subjects:
Accuracy
Air
Air monitoring
Air pollution
Air quality
Algorithms
Data assimilation
Decision making
Emission analysis
emission source identification
Forecasting
Identification
inverse problem
Inverse problems
Linear operators
Measurement
Monitoring systems
Numerical experiments
Sensitivity analysis
sensitivity operator
transport and transformation of impurities
Lake Baikal
ISSN:2073-4433, 2073-4433
Online Access:Get full text
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Summary:Air quality monitoring systems differ in composition and accuracy of observations and their temporal and spatial coverage. A monitoring system’s performance can be assessed by evaluating the accuracy of the emission sources identified by its data. In the considered inverse modeling approach, a source identification problem is transformed to a quasi-linear operator equation with the sensitivity operator. The sensitivity operator is composed of the sensitivity functions evaluated on the adjoint ensemble members. The members correspond to the measurement data element aggregates. Such ensemble construction allows working in a unified way with heterogeneous measurement data in a single-operator equation. The quasi-linear structure of the resulting operator equation allows both solving and predicting solutions of the inverse problem. Numerical experiments for the Baikal region scenario were carried out to compare different types of inverse problem solution accuracy estimates. In the considered scenario, the projection to the orthogonal complement of the sensitivity operator’s kernel allowed predicting the source identification results with the best accuracy compared to the other estimate types. Our contribution is the development and testing of a sensitivity-operator-based set of tools for analyzing heterogeneous air quality monitoring systems. We propose them for assessing and optimizing observational systems and experiments.
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ISSN:2073-4433
2073-4433
DOI:10.3390/atmos12121697