{"title":"机器学习在微生物修复中的应用与挑战:现状与未来方向综述","authors":"Yueyan Zhang, Jun Sasaki, Ang Li, Jundong Chen","doi":"10.1080/10643389.2025.2560435","DOIUrl":null,"url":null,"abstract":"Microbial remediation is crucial in environmental pollution control. However, targeted intervention is challenging due to the complex and dynamic interactions between microbial communities and external stressors. Machine learning (ML) can be used to deeply analyze the connections between microbial processes and contaminant removal through data mining. Microbial remediation lies at the intersection of microbiology and environmental science, with its diverse scope offering high flexibility for ML applications. Despite the potential of ML, limited attention has been given to its applications within this specific field, and there is a lack of structured reviews to guide the development of ML frameworks in microbial remediation. This review examines the role and current status of ML in microbial remediation. Application modes are presented and compared with a clear hierarchy, including initial monitoring, strategy formulation, and system design. It provides access to established frameworks and alternative solutions to address relevant challenges. Two primary application modes are identified among the seemingly diverse approaches: mapping-based inference and importance-based identification of key agents. The first mode establishes a mapping between two causally linked datasets to predict various outcomes such as remedial effects and microbial growth. Accordingly, the second mode identifies predictors that significantly contribute to mapping accuracies as key microbes or environmental variables. Emerging issues related to the limited accessibility and interpretability are discussed. Finally, using multi-modal learning for pipeline development and applying knowledge graphs (KGs) and a deep reinforcement learning framework to enhance interpretability are proposed as promising solutions.","PeriodicalId":10823,"journal":{"name":"Critical Reviews in Environmental Science and Technology","volume":"22 1","pages":""},"PeriodicalIF":13.2000,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application and challenges of machine learning in microbial remediation: A review of current status and future directions\",\"authors\":\"Yueyan Zhang, Jun Sasaki, Ang Li, Jundong Chen\",\"doi\":\"10.1080/10643389.2025.2560435\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Microbial remediation is crucial in environmental pollution control. However, targeted intervention is challenging due to the complex and dynamic interactions between microbial communities and external stressors. Machine learning (ML) can be used to deeply analyze the connections between microbial processes and contaminant removal through data mining. Microbial remediation lies at the intersection of microbiology and environmental science, with its diverse scope offering high flexibility for ML applications. Despite the potential of ML, limited attention has been given to its applications within this specific field, and there is a lack of structured reviews to guide the development of ML frameworks in microbial remediation. This review examines the role and current status of ML in microbial remediation. Application modes are presented and compared with a clear hierarchy, including initial monitoring, strategy formulation, and system design. It provides access to established frameworks and alternative solutions to address relevant challenges. Two primary application modes are identified among the seemingly diverse approaches: mapping-based inference and importance-based identification of key agents. The first mode establishes a mapping between two causally linked datasets to predict various outcomes such as remedial effects and microbial growth. Accordingly, the second mode identifies predictors that significantly contribute to mapping accuracies as key microbes or environmental variables. Emerging issues related to the limited accessibility and interpretability are discussed. Finally, using multi-modal learning for pipeline development and applying knowledge graphs (KGs) and a deep reinforcement learning framework to enhance interpretability are proposed as promising solutions.\",\"PeriodicalId\":10823,\"journal\":{\"name\":\"Critical Reviews in Environmental Science and Technology\",\"volume\":\"22 1\",\"pages\":\"\"},\"PeriodicalIF\":13.2000,\"publicationDate\":\"2025-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Critical Reviews in Environmental Science and Technology\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1080/10643389.2025.2560435\",\"RegionNum\":1,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Critical Reviews in Environmental Science and Technology","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1080/10643389.2025.2560435","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
Application and challenges of machine learning in microbial remediation: A review of current status and future directions
Microbial remediation is crucial in environmental pollution control. However, targeted intervention is challenging due to the complex and dynamic interactions between microbial communities and external stressors. Machine learning (ML) can be used to deeply analyze the connections between microbial processes and contaminant removal through data mining. Microbial remediation lies at the intersection of microbiology and environmental science, with its diverse scope offering high flexibility for ML applications. Despite the potential of ML, limited attention has been given to its applications within this specific field, and there is a lack of structured reviews to guide the development of ML frameworks in microbial remediation. This review examines the role and current status of ML in microbial remediation. Application modes are presented and compared with a clear hierarchy, including initial monitoring, strategy formulation, and system design. It provides access to established frameworks and alternative solutions to address relevant challenges. Two primary application modes are identified among the seemingly diverse approaches: mapping-based inference and importance-based identification of key agents. The first mode establishes a mapping between two causally linked datasets to predict various outcomes such as remedial effects and microbial growth. Accordingly, the second mode identifies predictors that significantly contribute to mapping accuracies as key microbes or environmental variables. Emerging issues related to the limited accessibility and interpretability are discussed. Finally, using multi-modal learning for pipeline development and applying knowledge graphs (KGs) and a deep reinforcement learning framework to enhance interpretability are proposed as promising solutions.
期刊介绍:
Two of the most pressing global challenges of our era involve understanding and addressing the multitude of environmental problems we face. In order to tackle them effectively, it is essential to devise logical strategies and methods for their control. Critical Reviews in Environmental Science and Technology serves as a valuable international platform for the comprehensive assessment of current knowledge across a wide range of environmental science topics.
Environmental science is a field that encompasses the intricate and fluid interactions between various scientific disciplines. These include earth and agricultural sciences, chemistry, biology, medicine, and engineering. Furthermore, new disciplines such as environmental toxicology and risk assessment have emerged in response to the increasing complexity of environmental challenges.
The purpose of Critical Reviews in Environmental Science and Technology is to provide a space for critical analysis and evaluation of existing knowledge in environmental science. By doing so, it encourages the advancement of our understanding and the development of effective solutions. This journal plays a crucial role in fostering international cooperation and collaboration in addressing the pressing environmental issues of our time.