SAMIRA-SAtellite Based Monitoring Initiative for Regional Air Quality

Estimation of Aerosol Extinction Coefficient Using Camera Images and Application in Mass Extinction Efficiency Retrieval

In this study, we attempted to calculate the extinction parameters of PM2.5 using images from a commercial camera. The photo pixels provided information on the characteristics of the objects (i.e., the reflectivity, transmittance, or extinction efficiency) and ambient brightness. Using the RGB values of pixels, we calculated the extinction coefficient and efficiency applied to the mass concentration of PM2.5. The calculated extinction coefficient of PM2.5 determined from the camera images had a higher correlation with the PM2.5 mass concentration (R2 = 0.7) than with the visibility data, despite the limited mass range. Finally, we identified that the method of calculating extinction parameters using the effective wavelength of RGB images could be applied to studies of changes in the atmosphere and aerosol characteristics. The mass extinction efficiency of PM2.5, derived from images, and the mass concentration of PM2.5 was (10.8 ± 6.9) m2 g−1, which was higher than the values obtained in Northeast Asia by previous studies. We also confirmed that the dry extinction efficiency of PM2.5, applied with a DRH of 40%, was reduced to (6.9 ± 5.0) m2 g−1. The extinction efficiencies of PM2.5, calculated in this study, were higher than those reported in previous other studies. We inferred that high extinction efficiency is related to changes in size or the composition of aerosols; therefore, an additional long-term study must be conducted.

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.