{"title":"Integrating artificial intelligence with microbial biotechnology for sustainable environmental remediation","authors":"Firoozeh Alavian, Fatemeh Khodabakhshi","doi":"10.1007/s10661-025-14666-3","DOIUrl":null,"url":null,"abstract":"<div><p>This narrative review examines the significant advances of artificial intelligence (AI) in enhancing the identification and microbial degradation of environmentally persistent compounds, addressing major issues in pollution monitoring and management. Persistent pollutants, including microplastics, heavy metals, and synthetic pesticides, pose significant threats to environmental sustainability due to their resistance to natural degradation and their adverse effects on ecosystems and human health. Through the qualitative synthesis of over 50 recent peer-reviewed studies, this review highlights notable AI-driven developments representing substantial innovations in environmental biotechnology. Enhanced detection capabilities are demonstrated by AI models, which achieve exceptional detection accuracies exceeding 90% for microplastic classification, enabling precise ecological monitoring that was previously difficult with traditional methods. Compared with conventional methods, advanced enzyme engineering is exemplified by the AI-enabled design of engineered enzymes that increase the degradation rates of polyethylene terephthalate (PET) polymers by up to 46-fold, representing a significant increase in bioremediation technology. Innovative predictive frameworks emerge from AI integration, accelerating the design of microbial enzymes and predicting pollutant behaviors with remarkable accuracy, providing a novel framework for pollution control that is not achievable through conventional approaches. This study demonstrates substantial improvements in the accuracy and efficiency of identifying and monitoring environmental pollutants, enabling more precise assessment and proactive management strategies. The strategic integration of AI in environmental applications has accelerated microbial enzyme design, enhanced ecological risk assessments, and provided innovative solutions for addressing persistent pollution challenges. The findings of this review emphasize AI’s crucial and creative role of AI in environmental biotechnology, offering valuable insights for developing sustainable remediation strategies to combat persistent pollutants and protect ecosystem health.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 11","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Monitoring and Assessment","FirstCategoryId":"93","ListUrlMain":"https://link.springer.com/article/10.1007/s10661-025-14666-3","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
This narrative review examines the significant advances of artificial intelligence (AI) in enhancing the identification and microbial degradation of environmentally persistent compounds, addressing major issues in pollution monitoring and management. Persistent pollutants, including microplastics, heavy metals, and synthetic pesticides, pose significant threats to environmental sustainability due to their resistance to natural degradation and their adverse effects on ecosystems and human health. Through the qualitative synthesis of over 50 recent peer-reviewed studies, this review highlights notable AI-driven developments representing substantial innovations in environmental biotechnology. Enhanced detection capabilities are demonstrated by AI models, which achieve exceptional detection accuracies exceeding 90% for microplastic classification, enabling precise ecological monitoring that was previously difficult with traditional methods. Compared with conventional methods, advanced enzyme engineering is exemplified by the AI-enabled design of engineered enzymes that increase the degradation rates of polyethylene terephthalate (PET) polymers by up to 46-fold, representing a significant increase in bioremediation technology. Innovative predictive frameworks emerge from AI integration, accelerating the design of microbial enzymes and predicting pollutant behaviors with remarkable accuracy, providing a novel framework for pollution control that is not achievable through conventional approaches. This study demonstrates substantial improvements in the accuracy and efficiency of identifying and monitoring environmental pollutants, enabling more precise assessment and proactive management strategies. The strategic integration of AI in environmental applications has accelerated microbial enzyme design, enhanced ecological risk assessments, and provided innovative solutions for addressing persistent pollution challenges. The findings of this review emphasize AI’s crucial and creative role of AI in environmental biotechnology, offering valuable insights for developing sustainable remediation strategies to combat persistent pollutants and protect ecosystem health.
期刊介绍：
Environmental Monitoring and Assessment emphasizes technical developments and data arising from environmental monitoring and assessment, the use of scientific principles in the design of monitoring systems at the local, regional and global scales, and the use of monitoring data in assessing the consequences of natural resource management actions and pollution risks to man and the environment.
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