Advanced monitoring and computational strategies for water quality assessment and CECs management

Contaminants of Emerging Concern or CECs encompass a wide range of natural and human-made substances, recently introduced into the environment or long-present but only recently recognized as widespread and potentially harmful. Detected at trace concentrations, they pose potential risks to the "One Health" triad: the environment, humans, and animals. To defuse the ticking ’timebomb’ of water pollution, it is essential to investigate the fate and transport of these contaminants in aquatic systems. This helps to unveil their potential reactivity and toxicity, and facilitates the development of new monitoring and remediation strategies, that allow for continuous data collection and more effective risk management. In this context, the European Commission aims to protect the hydrosphere through continuous water monitoring and the digitalization of water supply networks. The goal is to achieve a 30-50% reduction in the pollution of rivers and lakes by 2030. Starting from the premise that "Blue gold is not scarce, but the real problem is our ability to use it", this dissertation aligns with the current objective, proposing two innovative electrochemical sensors: a "green" sensor for analyzing chemical oxygen demand (COD), a key parameter for assessing water quality, and another based on methylene-blue gold nanoparticles, capable of detecting a class of CECs, namely per- and polyfluoroalkyl substances (PFAS) and anionic surfactants, at trace level concentrations. Current methods for detecting PFAS and CECs are based on liquid chromatography coupled with mass spectrometry. These methods are time-consuming and expensive; moreover, undesired adsorption of contaminants, specifically PFAS, to containers can lead to errors. The adsorption of PFAS to plastic containers suggests a higher interaction with nanoplastics and plastic debris in aquatic environments. To gain deeper insight into the behavior of these contaminants in water and to understand their "cocktail toxic effect", a new computational strategy was developed for elucidating the co-transport of PFAS and microplastics, another widespread class of CECs. Additionally, a chemical risk probabilistic model based on Bayesian statistic is proposed, which describes the risk exposure to PFAS along the wastewater treatment plant, thereby assisting in water management. Social implications, cost-benefit analysis, and the principles of the circular economy are integrated and evaluated in each aspect of this research.