Sulfamethoxazole degradation pathways in wastewater treatment: Bayesian network-based approach for a meta-analysis of scientific papers - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement
Article Dans Une Revue Environmental Science and Pollution Research Année : 2024

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

Schéma réactionnel de l'élimination de la sulfaméthoxazole: analyse des articles scientifiques par réseau bayésien

Résumé

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.
Fichier non déposé

Dates et versions

hal-04722054 , version 1 (04-10-2024)

Identifiants

Citer

Rachid Ouaret, Ali Badara Minta, Claire Albasi, Jean-Marc Choubert, Antonin Azaïs. Sulfamethoxazole degradation pathways in wastewater treatment: Bayesian network-based approach for a meta-analysis of scientific papers. Environmental Science and Pollution Research, 2024, ⟨10.1007/s11356-024-34982-4⟩. ⟨hal-04722054⟩
18 Consultations
0 Téléchargements

Altmetric

Partager

More