Organic aerosols affect the planet's radiative balance by absorbing and scattering light as well as by activating cloud droplets. These organic aerosols contain chromophores, termed brown carbon (BrC), and can undergo indirect photochemistry, affecting their ability to act as cloud condensation nuclei (CCN). Here, we investigated the effect of photochemical aging by tracking the conversion of organic carbon into inorganic carbon, termed the photomineralization mechanism, and its effect on the CCN abilities in four different types of BrC samples: (1) laboratorygenerated (NH 4 ) 2 SO 4 -methylglyoxal solutions, (2) dissolved organic matter isolate from Suwannee River fulvic acid (SRFA), (3) ambient firewood smoke aerosols, and (4) ambient urban wintertime particulate matter in Padua, Italy. Photomineralization occurred in all BrC samples albeit at different rates, evidenced by photobleaching and by loss of organic carbon up to 23% over a simulated 17.6 h of sunlight exposure. These losses were correlated with the production of CO up to 4% and of CO 2 up to 54% of the initial organic carbon mass, monitored by gas chromatography. Photoproducts of formic, acetic, oxalic and pyruvic acids were also produced during irradiation of the BrC solutions, but at different yields depending on the sample. Despite these chemical changes, CCN abilities did not change substantially for the BrC samples. In fact, the CCN abilities were dictated by the salt content of the BrC solution, trumping a photomineralization effect on the CCN abilities for the hygroscopic BrC samples. Solutions of (NH 4 ) 2 SO 4 -methylglyoxal, SRFA, firewood smoke, and ambient Padua samples had hygroscopicity parameters κ of 0.6, 0.1, 0.3, and 0.6, respectively. As expected, the SRFA solution with a κ of 0.1 was most impacted by the photomineralization mechanism. Overall, our results suggest that the photomineralization mechanism is expected in all BrC samples and can drive changes in the optical properties and chemical composition of aging organic aerosols.

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Small humps on the floor go beyond the detectable scope of laser scanners and are therefore not integrated into SLAM based maps of mobile robots. However, even such small irregularities can have a tremendous effect on the robot's stability and the path quality. As a basis to develop anomaly detection algorithms, kinematics data is collected exemplarily for an overrun of a cable channel and a bulb plate. A recurrent neuronal network (RNN), based on the autoencoder principle, could be trained successfully with this data. The described RNN architecture looks promising to be used for realtime anomaly detection and also to quantify path quality.

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