Abstract: Pandora UV-visible spectrometers are a suite of instruments that are distributed globally as part of the Pandonia Global Network (PGN), and operate in direct-Sun, zenith-sky, and multi-axis viewing geometries to probe different parts of the atmosphere. In this study, we explore the application of optimal estimation to Pandora multi-axis measurements of NO2 and SO2 to obtain vertical profile information. For the NO2 study, we derive, for the first time, NO2 profiles (0-4 km) from Pandora multi-axis data using the Heidelberg Profile (HeiPro) retrieval algorithm from 2018-2020 in Toronto, a suburban region subject to local traffic emissions and urban influences. The bias and correlation of this unique Pandora-HeiPro dataset to Pandora direct-Sun, TROPOMI, and in-situ observations, as well as GEM-MACH modelled data, are assessed. We find that, for partial column comparisons, the Pandora-HeiPro dataset tends to overestimate TROPOMI and Pandora Direct-Sun observations and the GEM-MACH modelled data, with a similar seasonal cycle. Additionally, we find that Pandora-HeiPro surface NO2 captures the diurnal variability of in-situ surface NO2, though with an underestimation. The results from this work aim to (i) further improve the quality of Pandora data products to better meet the needs of air quality studies, and (ii) better understand the spatiotemporal heterogeneity of NO2. For the SO2 study, we aim to utilize Pandora multi-axis data, combined with optimal estimation, at sites with high SO2 conditions (e.g., plumes from volcanic emissions, sites near power plants) to investigate its vertical distribution.
Host: Aleksandra Elias