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

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Title: Sulfamethoxazole degradation pathways in wastewater treatment: Bayesian network-based approach for a meta-analysis of scientific papers.
Authors: Ouaret, Rachid1 (AUTHOR) rachid.ouaret@toulouse-inp.fr, Minta, Ali Badara1,2 (AUTHOR) ali-badara.minta@inrae.fr, Albasi, Claire1 (AUTHOR) claire.albasi@ensiacet.fr, Choubert, Jean-Marc2 (AUTHOR) jean-marc.choubert@inrae.fr, Azaïs, Antonin2 (AUTHOR) antonin.azais@inrae.fr
Source: Environmental Science & Pollution Research. Jun2026, Vol. 33 Issue 22, p11219-11232. 14p.
Subject Terms: *Sulfamethoxazole, *Bayesian analysis, *Oxidases, *Micropollutants, *Biodegradation, *Wastewater treatment, *Evidence synthesis
Abstract: 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. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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PubType: Academic Journal
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  Data: Sulfamethoxazole degradation pathways in wastewater treatment: Bayesian network-based approach for a meta-analysis of scientific papers.
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  Data: <searchLink fieldCode="AR" term="%22Ouaret%2C+Rachid%22">Ouaret, Rachid</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rachid.ouaret@toulouse-inp.fr</i><br /><searchLink fieldCode="AR" term="%22Minta%2C+Ali+Badara%22">Minta, Ali Badara</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> ali-badara.minta@inrae.fr</i><br /><searchLink fieldCode="AR" term="%22Albasi%2C+Claire%22">Albasi, Claire</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> claire.albasi@ensiacet.fr</i><br /><searchLink fieldCode="AR" term="%22Choubert%2C+Jean-Marc%22">Choubert, Jean-Marc</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> jean-marc.choubert@inrae.fr</i><br /><searchLink fieldCode="AR" term="%22Azaïs%2C+Antonin%22">Azaïs, Antonin</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> antonin.azais@inrae.fr</i>
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  Data: <searchLink fieldCode="JN" term="%22Environmental+Science+%26+Pollution+Research%22">Environmental Science & Pollution Research</searchLink>. Jun2026, Vol. 33 Issue 22, p11219-11232. 14p.
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  Data: *<searchLink fieldCode="DE" term="%22Sulfamethoxazole%22">Sulfamethoxazole</searchLink><br />*<searchLink fieldCode="DE" term="%22Bayesian+analysis%22">Bayesian analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Oxidases%22">Oxidases</searchLink><br />*<searchLink fieldCode="DE" term="%22Micropollutants%22">Micropollutants</searchLink><br />*<searchLink fieldCode="DE" term="%22Biodegradation%22">Biodegradation</searchLink><br />*<searchLink fieldCode="DE" term="%22Wastewater+treatment%22">Wastewater treatment</searchLink><br />*<searchLink fieldCode="DE" term="%22Evidence+synthesis%22">Evidence synthesis</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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        Value: 10.1007/s11356-024-34982-4
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      – Code: eng
        Text: English
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        PageCount: 14
        StartPage: 11219
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      – SubjectFull: Sulfamethoxazole
        Type: general
      – SubjectFull: Bayesian analysis
        Type: general
      – SubjectFull: Oxidases
        Type: general
      – SubjectFull: Micropollutants
        Type: general
      – SubjectFull: Biodegradation
        Type: general
      – SubjectFull: Wastewater treatment
        Type: general
      – SubjectFull: Evidence synthesis
        Type: general
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            NameFull: Minta, Ali Badara
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            NameFull: Choubert, Jean-Marc
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              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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