PhD thesis supervisor: dr. Kristina Plauškaitė-Šukienė (apply for recommendation)
Application of machine learning methods for complex environmental pollution assessment
The dissertation work will aim to comprehensively assess environmental pollution by applying machine learning models to long-term atmospheric processes and pollution dispersion data sets, considering the impact of the impact of different types of anthropogenic and biogenic sources (primary and secondary) on the formation of aerosol particles. The study of these processes will be based on the application of machine learning tools and their comparison with conventional analytical methods, such as aerosol particle size distribution, chemical composition, including mass spectra, and molecular markers of known compounds (especially for BC source apportionment).