Biodegradation of CAHs and BTEX in groundwater at a multi-polluted pesticide site undergoing natural attenuation: Insights from identifying key bioindicators using machine learning methods based on microbiome data
Groundwater pollution, particularly in retired pesticide sites, is a significant environmental concern due to the presence of chlorinated aliphatic hydrocarbons (CAHs) and benzene, toluene, ethylbenzene, and xylene (BTEX). These contaminants pose serious risks to ecosystems and human health. Natural...
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Elsevier
2025-02-01
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| Series: | Ecotoxicology and Environmental Safety |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0147651324016853 |
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| author | Feiyang Xia Tingting Fan Mengjie Wang Lu Yang Da Ding Jing Wei Yan Zhou Dengdeng Jiang Shaopo Deng |
| author_facet | Feiyang Xia Tingting Fan Mengjie Wang Lu Yang Da Ding Jing Wei Yan Zhou Dengdeng Jiang Shaopo Deng |
| author_sort | Feiyang Xia |
| collection | DOAJ |
| description | Groundwater pollution, particularly in retired pesticide sites, is a significant environmental concern due to the presence of chlorinated aliphatic hydrocarbons (CAHs) and benzene, toluene, ethylbenzene, and xylene (BTEX). These contaminants pose serious risks to ecosystems and human health. Natural attenuation (NA) has emerged as a sustainable solution, with microorganisms playing a crucial role in pollutant biodegradation. However, the interpretation of the diverse microbial communities in relation to complex pollutants is still challenging, and there is limited research in multi-polluted groundwater. Advanced machine learning (ML) algorithms help identify key microbial indicators for different pollution types (CAHs, BTEX plumes, and mixed plumes). The accuracy and Area Under the Curve (AUC) achieved by Support Vector Machines (SVM) were impressive, with values of 0.87 and 0.99, respectively. With the assistance of model explanation methods, we identified key bioindicators for different pollution types which were then analyzed using co-occurrence network analysis to better understand their potential roles in pollution degradation. The identified key genera indicate that oxidation and co-metabolism predominantly drive dechlorination processes within the CAHs group. In the BTEX group, the primary mechanism for BTEX degradation was observed to be anaerobic degradation under sulfate-reducing conditions. However, in the CAHs&BTEX groups, the indicative genera suggested that BTEX degradation occurred under iron-reducing conditions and reductive dechlorination existed. Overall, this study establishes a framework for harnessing the power of ML alongside co-occurrence network analysis based on microbiome data to enhance understanding and provide a robust assessment of the natural attenuation degradation process at multi-polluted sites. |
| format | Article |
| id | doaj-art-50c85e7c86a545178ef614f800b063fe |
| institution | Kabale University |
| issn | 0147-6513 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecotoxicology and Environmental Safety |
| spelling | doaj-art-50c85e7c86a545178ef614f800b063fe2025-02-03T04:16:27ZengElsevierEcotoxicology and Environmental Safety0147-65132025-02-01291117609Biodegradation of CAHs and BTEX in groundwater at a multi-polluted pesticide site undergoing natural attenuation: Insights from identifying key bioindicators using machine learning methods based on microbiome dataFeiyang Xia0Tingting Fan1Mengjie Wang2Lu Yang3Da Ding4Jing Wei5Yan Zhou6Dengdeng Jiang7Shaopo Deng8State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, ChinaState Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, ChinaState Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, ChinaState Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, ChinaState Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, ChinaState Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, ChinaState Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, ChinaState Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, ChinaCorresponding author.; State Environmental Protection Key Laboratory of Soil Environmental Management and Pollution Control, Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, Nanjing 210042, ChinaGroundwater pollution, particularly in retired pesticide sites, is a significant environmental concern due to the presence of chlorinated aliphatic hydrocarbons (CAHs) and benzene, toluene, ethylbenzene, and xylene (BTEX). These contaminants pose serious risks to ecosystems and human health. Natural attenuation (NA) has emerged as a sustainable solution, with microorganisms playing a crucial role in pollutant biodegradation. However, the interpretation of the diverse microbial communities in relation to complex pollutants is still challenging, and there is limited research in multi-polluted groundwater. Advanced machine learning (ML) algorithms help identify key microbial indicators for different pollution types (CAHs, BTEX plumes, and mixed plumes). The accuracy and Area Under the Curve (AUC) achieved by Support Vector Machines (SVM) were impressive, with values of 0.87 and 0.99, respectively. With the assistance of model explanation methods, we identified key bioindicators for different pollution types which were then analyzed using co-occurrence network analysis to better understand their potential roles in pollution degradation. The identified key genera indicate that oxidation and co-metabolism predominantly drive dechlorination processes within the CAHs group. In the BTEX group, the primary mechanism for BTEX degradation was observed to be anaerobic degradation under sulfate-reducing conditions. However, in the CAHs&BTEX groups, the indicative genera suggested that BTEX degradation occurred under iron-reducing conditions and reductive dechlorination existed. Overall, this study establishes a framework for harnessing the power of ML alongside co-occurrence network analysis based on microbiome data to enhance understanding and provide a robust assessment of the natural attenuation degradation process at multi-polluted sites.http://www.sciencedirect.com/science/article/pii/S0147651324016853Microbial communityGroundwaterMulti-solvents contaminationNatural attenuationMachine learning |
| spellingShingle | Feiyang Xia Tingting Fan Mengjie Wang Lu Yang Da Ding Jing Wei Yan Zhou Dengdeng Jiang Shaopo Deng Biodegradation of CAHs and BTEX in groundwater at a multi-polluted pesticide site undergoing natural attenuation: Insights from identifying key bioindicators using machine learning methods based on microbiome data Ecotoxicology and Environmental Safety Microbial community Groundwater Multi-solvents contamination Natural attenuation Machine learning |
| title | Biodegradation of CAHs and BTEX in groundwater at a multi-polluted pesticide site undergoing natural attenuation: Insights from identifying key bioindicators using machine learning methods based on microbiome data |
| title_full | Biodegradation of CAHs and BTEX in groundwater at a multi-polluted pesticide site undergoing natural attenuation: Insights from identifying key bioindicators using machine learning methods based on microbiome data |
| title_fullStr | Biodegradation of CAHs and BTEX in groundwater at a multi-polluted pesticide site undergoing natural attenuation: Insights from identifying key bioindicators using machine learning methods based on microbiome data |
| title_full_unstemmed | Biodegradation of CAHs and BTEX in groundwater at a multi-polluted pesticide site undergoing natural attenuation: Insights from identifying key bioindicators using machine learning methods based on microbiome data |
| title_short | Biodegradation of CAHs and BTEX in groundwater at a multi-polluted pesticide site undergoing natural attenuation: Insights from identifying key bioindicators using machine learning methods based on microbiome data |
| title_sort | biodegradation of cahs and btex in groundwater at a multi polluted pesticide site undergoing natural attenuation insights from identifying key bioindicators using machine learning methods based on microbiome data |
| topic | Microbial community Groundwater Multi-solvents contamination Natural attenuation Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S0147651324016853 |
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