Małgorzata Kida, BEng, PhD, DSc, Assoc. Prof., to lead an OPUS 29 project funded by the National Science Centre
The project entitled “The Impact of Water Disinfection Processes on the Emission of Contaminants from Microplastics and the Possibilities of Their Reduction Using Natural Sorbents” will be carried out over 36 months, with a total budget of PLN 819,800.
Research Team
The project is led by Małgorzata Kida, BEng, PhD, DSc, Assoc. Prof., from the Department of Environmental Engineering and Environmental Chemistry at the Faculty of Civil and Environmental Engineering and Architecture. The research team includes Sabina Ziembowicz, BEng, PhD; Professor Piotr Koszelnik, BEng, PhD, DSc, Prof.Tit.; Marcin Chutkowski, BEng, PhD; Kamil Pochwat, BEng, PhD; Andżelika Domoń, BEng, PhD; and Wojciech Strojny, MSc, BEng.
Research Overview
The project addresses one of the key challenges of contemporary environmental protection and drinking water safety: the presence of microplastics (MPs) and their impact on water quality during disinfection processes. As widespread environmental contaminants, microplastics can release harmful substances into water, including plasticisers, stabilisers, and other chemical additives. More than 16,000 different substances are used in plastic production, yet only around 6% are covered by international regulations. Disinfection processes play a crucial role in ensuring the sanitary safety of water; however, they may simultaneously lead to the release of toxic compounds from microplastics and the formation of new, potentially hazardous disinfection by-products (DBPs).
The research will involve a detailed analysis of various disinfection methods—such as UV irradiation and ozonation—as well as key operational parameters, including pH, contact time, and the type of disinfectant used. The study will examine how these factors influence both the migration of compounds from microplastics and the formation of DBPs. In addition, the project will assess the effectiveness of natural and modified sorbents, with particular emphasis on magnetite, in removing microplastics and limiting the emission of toxic substances during disinfection. The use of such sorbents may provide an innovative and environmentally friendly solution to reducing the risk of secondary water contamination.
Based on experimental data, an advanced predictive model will be developed using machine learning techniques to identify the most important factors influencing contaminant emissions and to optimise disinfection and sorption process parameters. Laboratory data will be analysed using methods such as artificial neural networks (ANN), support vector machines (SVM), k-nearest neighbours (k-NN), random forest regression, and boosted regression trees. Classical statistical approaches, including multiple regression and ANOVA, will be applied for comparison. The analysis will focus on interactions between parameters affecting both toxic substance emissions and sorption efficiency. Predictive emission models will be developed for different environmental scenarios and calibrated to improve their accuracy and reliability. An ecotoxicological risk assessment related to the presence of microplastics and associated contaminant emissions will also be conducted.
The project will deepen understanding of interactions between microplastics and water treatment processes and provide practical tools for minimising their negative impact on the environment and public health. Its results will support the development of effective strategies for managing drinking water quality in the face of increasing microplastic pollution.
