Sulfamethoxazole degradation pathways in wastewater treatment: Bayesian network-based approach for a meta-analysis of scientific papers.

Autor: Ouaret R; Laboratoire de Génie Chimique, Université de Toulouse, CNRS, INPT, UPS, Toulouse, 31000, France., Minta AB; Laboratoire de Génie Chimique, Université de Toulouse, CNRS, INPT, UPS, Toulouse, 31000, France.; INRAE, UR REVERSAAL, 5 Rue de La Doua, CS, 20244, F-69625, Villeurbanne Cedex, France., Albasi C; Laboratoire de Génie Chimique, Université de Toulouse, CNRS, INPT, UPS, Toulouse, 31000, France., Choubert JM; INRAE, UR REVERSAAL, 5 Rue de La Doua, CS, 20244, F-69625, Villeurbanne Cedex, France., Azaïs A; INRAE, UR REVERSAAL, 5 Rue de La Doua, CS, 20244, F-69625, Villeurbanne Cedex, France. antonin.azais@inrae.fr.
Jazyk: angličtina
Zdroj: Environmental science and pollution research international [Environ Sci Pollut Res Int] 2024 Oct 02. Date of Electronic Publication: 2024 Oct 02.
DOI: 10.1007/s11356-024-34982-4
Abstrakt: Given the widespread presence of micropollutants in urban water systems, it is imperative to gain a comprehensive understanding of their degradation pathways. This paper focuses on sulfamethoxazole (SMX) as a model molecule due to its extensive study, aiming to elucidate its degradation pathways in biological (BIO) and oxidative (AOP) processes. Numerous reaction pathways are outlined in scientific papers. However, a significant deficiency in current methodologies has led to the development of a novel meta-analytical approach, seeking consensus among researchers by synthesizing data from studies characterized by their heterogeneity and contradictions. As an innovative alternative, probabilistic graphical models such as Bayesian networks (BNs) could illuminate the relationships and dependencies between various transformation products, providing a holistic view of the degradation process. Based on the analysis of an extensive bibliography gathering more than 45 articles for more than 140 molecules and 177 reaction pathways, this study proposes a meta-analysis methodology based on Bayesian networks.
(© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Databáze: MEDLINE
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