I am currently working on the COMBO research project (PNR EST 2023-033), which aims to assess the environmental factors responsible for the spatial heterogeneity of the periphyton meta-metabolome - a proxy for microbial activity - particularly in the context of chemical stress associated with micropollutants, using a predictive meta-metabolomic approach based on machine learning. Ultimately, this method would make it possible to identify metabolites, or groups of metabolites (i.e., fingerprints), capable of predicting certain environmental conditions and water quality, while highlighting the parts of the meta-metabolome influenced by confounding factors related to chemical stress. This information could be particularly useful for the biomonitoring of aquatic environments.