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 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.

Automated summary

Air quality monitoring systems vary in composition and accuracy, with performance assessed by evaluating the accuracy of emission sources identified by data. In an inverse modeling approach, a source identification problem is transformed using a quasi-linear operator equation with a sensitivity operator. Numerical experiments in the Baikal region show that projecting to the orthogonal complement of the sensitivity operator's kernel provides the most accurate source identification results. Our contribution lies in developing and testing a set of tools based on sensitivity operators for analyzing diverse air quality monitoring systems.