Abstract / truncated to 115 words (read the full abstract)

Air pollution remains a major global challenge, particularly in urban areas where high pollutant concentrations negatively impact public health and contribute to climate change. Among the various pollutants, biogenic volatile organic compounds (BVOCs) play a critical role in atmospheric chemistry, influencing the formation of secondary organic aerosols and ground-level ozone, affecting air quality and climate dynamics. Accurately estimating BVOC emissions at high spatial resolution is challenging due to the limitations of satellite observations and computational models. Additionally, forecasting nitrogen dioxide (NO2) concentrations in urban environments is vital for effective air quality management, yet existing models often struggle to capture complex spatiotemporal dependencies. The thesis aims to address these challenges by proposing novel deep learning (DL) ... toggle 11 keywords

biogenic emissions – BVOC – isoprene – super-resolution – domain adaptation – spatiotemporal forecast – graph neural network – image-to-image translation – generative adversarial network – remote sensing – downscaling

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