In his PhD project, Kasper developed a method to improve the resolution with which bacteria can be identified in complex environments using modern DNA sequencing technologies and bioinformatics. At the same time, this method resulted in a microbial DNA database of wastewater treatment plants worldwide that use biological treatment of wastewater. This made it possible to name previously unknown bacteria, which make up the majority of the bacterial composition in the plants and water environments in general, creating the basis for linking identity and biological function.

Kasper also developed and trained machine learning models based on neural networks on a large dataset of bacterial composition in Danish wastewater treatment plants over 5-6 years. This can be used to predict the temporal dynamics in the frequency of the most important bacterial species in treatment plants and their activity. This provides the opportunity to continuously monitor processes before operational problems and pollution of waterways occur.

The results are important for people in the water sector and researchers investigating complex microbial environments in general. The results are significant in reducing human impact on the global ecosystem, as wastewater is one of human's largest waste products, and bacteria are the primary workhorses in all of the world's treatment plants.

The PhD study was completed at Department of Chemistry and Bioscience, Aalborg University.

This summary was prepared by the PhD student.