Zhuling Guo , Lei Huang , Jia Yan , Hongguo Zhang , Xue Jia , Meng Li , Hao Li
{"title":"环境研究与健康中的人工智能技术:发展与展望","authors":"Zhuling Guo , Lei Huang , Jia Yan , Hongguo Zhang , Xue Jia , Meng Li , Hao Li","doi":"10.1016/j.envint.2025.109788","DOIUrl":null,"url":null,"abstract":"<div><div>As the research in the environmental field continues to delve into deeper mechanisms, the traditional research paradigm has become inadequate. The introduction of artificial intelligence technology has provided a clear definition for the “green technology revolution”. Compared with traditional methods, AI has achieved a significant improvement in computational efficiency in environmental data analysis, reducing decision-making time by more than 60%. This effectively supports the efficient resolution of complex environmental issues. Meanwhile, the rapid leap-forward development of artificial intelligence (AI) technology is giving rise to a green technological revolution in environmental research. Based on this survey and the developing Digital Catalysis Platform, this commentary systematically summarizes the typical applications of AI in five major areas: treatment of water pollution, control of air pollution, disposal of solid waste, remediation of soil, and environmental health. It focuses on analyzing the remarkable effectiveness demonstrated by AI through machine learning (ML) methods. Such effectiveness is shown in aspects like material screening, performance prediction, instant detection, global distribution simulation of pollutants, and the control of human health. Notably, the current large-scale application of AI technology in the environmental field still faces multiple challenges, especially the scarcity of data samples in complex environmental systems. For instance, small-sample models to overfit, and the uneven geographical coverage of observational data in global pollutant distribution prediction. They all urgently need to be solved. In response to the above-mentioned bottlenecks, this study proposes corresponding solutions. As the technological bottlenecks are gradually overcome, AI is expected to become the core driving force for promoting environmentally sustainable development and contribute to the achievement of the “dual carbon” goals and the restoration of the global ecosystem.</div></div>","PeriodicalId":308,"journal":{"name":"Environment International","volume":"203 ","pages":"Article 109788"},"PeriodicalIF":9.7000,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence technology in environmental research and health: Development and prospects\",\"authors\":\"Zhuling Guo , Lei Huang , Jia Yan , Hongguo Zhang , Xue Jia , Meng Li , Hao Li\",\"doi\":\"10.1016/j.envint.2025.109788\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>As the research in the environmental field continues to delve into deeper mechanisms, the traditional research paradigm has become inadequate. The introduction of artificial intelligence technology has provided a clear definition for the “green technology revolution”. Compared with traditional methods, AI has achieved a significant improvement in computational efficiency in environmental data analysis, reducing decision-making time by more than 60%. This effectively supports the efficient resolution of complex environmental issues. Meanwhile, the rapid leap-forward development of artificial intelligence (AI) technology is giving rise to a green technological revolution in environmental research. Based on this survey and the developing Digital Catalysis Platform, this commentary systematically summarizes the typical applications of AI in five major areas: treatment of water pollution, control of air pollution, disposal of solid waste, remediation of soil, and environmental health. It focuses on analyzing the remarkable effectiveness demonstrated by AI through machine learning (ML) methods. Such effectiveness is shown in aspects like material screening, performance prediction, instant detection, global distribution simulation of pollutants, and the control of human health. Notably, the current large-scale application of AI technology in the environmental field still faces multiple challenges, especially the scarcity of data samples in complex environmental systems. For instance, small-sample models to overfit, and the uneven geographical coverage of observational data in global pollutant distribution prediction. They all urgently need to be solved. In response to the above-mentioned bottlenecks, this study proposes corresponding solutions. As the technological bottlenecks are gradually overcome, AI is expected to become the core driving force for promoting environmentally sustainable development and contribute to the achievement of the “dual carbon” goals and the restoration of the global ecosystem.</div></div>\",\"PeriodicalId\":308,\"journal\":{\"name\":\"Environment International\",\"volume\":\"203 \",\"pages\":\"Article 109788\"},\"PeriodicalIF\":9.7000,\"publicationDate\":\"2025-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environment International\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0160412025005392\",\"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":"Environment International","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0160412025005392","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}
Artificial intelligence technology in environmental research and health: Development and prospects
As the research in the environmental field continues to delve into deeper mechanisms, the traditional research paradigm has become inadequate. The introduction of artificial intelligence technology has provided a clear definition for the “green technology revolution”. Compared with traditional methods, AI has achieved a significant improvement in computational efficiency in environmental data analysis, reducing decision-making time by more than 60%. This effectively supports the efficient resolution of complex environmental issues. Meanwhile, the rapid leap-forward development of artificial intelligence (AI) technology is giving rise to a green technological revolution in environmental research. Based on this survey and the developing Digital Catalysis Platform, this commentary systematically summarizes the typical applications of AI in five major areas: treatment of water pollution, control of air pollution, disposal of solid waste, remediation of soil, and environmental health. It focuses on analyzing the remarkable effectiveness demonstrated by AI through machine learning (ML) methods. Such effectiveness is shown in aspects like material screening, performance prediction, instant detection, global distribution simulation of pollutants, and the control of human health. Notably, the current large-scale application of AI technology in the environmental field still faces multiple challenges, especially the scarcity of data samples in complex environmental systems. For instance, small-sample models to overfit, and the uneven geographical coverage of observational data in global pollutant distribution prediction. They all urgently need to be solved. In response to the above-mentioned bottlenecks, this study proposes corresponding solutions. As the technological bottlenecks are gradually overcome, AI is expected to become the core driving force for promoting environmentally sustainable development and contribute to the achievement of the “dual carbon” goals and the restoration of the global ecosystem.
期刊介绍:
Environmental Health publishes manuscripts focusing on critical aspects of environmental and occupational medicine, including studies in toxicology and epidemiology, to illuminate the human health implications of exposure to environmental hazards. The journal adopts an open-access model and practices open peer review.
It caters to scientists and practitioners across all environmental science domains, directly or indirectly impacting human health and well-being. With a commitment to enhancing the prevention of environmentally-related health risks, Environmental Health serves as a public health journal for the community and scientists engaged in matters of public health significance concerning the environment.