Artificial intelligence-aided new paradigm of environmental research

Artificial intelligence (AI) is revolutionizing a paradigm shift in environmental research from exploratory investigation to predictive study, facilitating the development of precision-guided research to tackle global environmental challenges at an unprecedented scale and speed. Against this backdrop, this perspective introduces the transformative impact of AI across various interconnected environmental domains. These technologies, including machine learning (ML), deep learning (DL), and large language models (LLMs), uncover latent environmental research patterns, integrate cross-scale data, and generate novel scientific insights. Systematically, we review advances in AI applications across water, soil, and atmospheric environments, as well as solid waste management. To further facilitate the construction and application of AI models, we summarize key considerations for AI model development, including data curation, data preprocessing, model training, validation, and interpretability. Significant challenges regarding AI application, including data-related issues, model selection, and ethical considerations are finally addressed. Surmounting these obstacles is a prerequisite for transitioning AI from passive analytical tools to autonomous research partners. Consequently, more innovative AI scientific agents are expected to promote cross-disciplinary collaboration and innovation to facilitate high-throughput, large-scale, and precision-guided composite investigations that integrate multi-source data across spatial-temporal scales to enable predictive and systemic insights, and to support holistic environmental governance.