For more than 2 decades, satellite observations from instruments such as GOME, SCIAMACHY, GOME-2, and OMI have been used for the monitoring of bromine monoxide (BrO) distributions on global and regional scales. In October 2017, the TROPOspheric Monitoring Instrument (TROPOMI) was launched on board the Copernicus Sentinel-5 Precursor platform with the goal of continuous daily global trace gas observations with unprecedented spatial resolution. In this study, sensitivity tests were performed to find an optimal wavelength range for TROPOMI BrO retrievals under various measurement conditions. From these sensitivity tests, a wavelength range for TROPOMI BrO retrievals was determined and global data for April 2018 as well as for several case studies were retrieved. Comparison with GOME-2 and OMI BrO retrievals shows good consistency and low scatter of the columns. The examples of individual TROPOMI overpasses show that due to the better signal-to-noise ratio and finer spatial resolution of 3.5 × 7 km 2 , TROPOMI BrO retrievals provide good data quality with low fitting errors and unique information on small-scale variabilities in various BrO source regions such as Arctic sea ice, salt marshes, and volcanoes.

Abstract. Every polar spring, phenomena called bromine explosions occur over sea ice. These bromine explosions comprise photochemical heterogeneous chain reactions that release bromine molecules, Br2, to the troposphere and lead to tropospheric plumes of bromine monoxide, BrO. This autocatalytic mechanism depletes ozone, O3, in the boundary layer and troposphere and thereby changes the oxidizing capacity of the atmosphere. The phenomenon also leads to accelerated deposition of metals (e.g., Hg). In this study, we present a 22-year (1996 to 2017) consolidated and consistent tropospheric BrO dataset north of 70∘ N, derived from four different ultraviolet–visible (UV–VIS) satellite instruments (GOME, SCIAMACHY, GOME-2A and GOME-2B). The retrieval data products from the different sensors are compared during periods of overlap and show good agreement (correlations of 0.82–0.98 between the sensors). From our merged time series of tropospheric BrO vertical column densities (VCDs), we infer changes in the bromine explosions and thus an increase in the extent and magnitude of tropospheric BrO plumes during the period of Arctic warming. We determined an increasing trend of about 1.5 % of the tropospheric BrO VCDs per year during polar springs, while the size of the areas where enhanced tropospheric BrO VCDs can be found has increased about 896 km2 yr−1. We infer from comparisons and correlations with sea ice age data that the reported changes in the extent and magnitude of tropospheric BrO VCDs are moderately related to the increase in first-year ice extent in the Arctic north of 70∘ N, both temporally and spatially, with a correlation coefficient of 0.32. However, the BrO plumes and thus bromine explosions show significant variability, which also depends, apart from sea ice, on meteorological conditions.

The analysis of air quality and the continuous monitoring of air pollution levels are important subjects of the environmental science and research. This problem actually has real impact in the human health and quality of life. The determination of the conditions which favor high concentration of pollutants and most of all the timely forecast of such cases is really crucial, as it facilitates the imposition of specific protection and prevention actions by civil protection. This research paper discusses an innovative threefold intelligent hybrid system of combined machine learning algorithms HISYCOL (henceforth). First, it deals with the correlation of the conditions under which high pollutants concentrations emerge. On the other hand, it proposes and presents an ensemble system using combination of machine learning algorithms capable of forecasting the values of air pollutants. What is really important and gives this modeling effort a hybrid nature is the fact that it uses clustered datasets. Moreover, this approach improves the accuracy of existing forecasting models by using unsupervised machine learning to cluster the data vectors and trace hidden knowledge. Finally, it employs a Mamdani fuzzy inference system for each air pollutant in order to forecast even more effectively its concentrations.

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