Bridging Earth, ecological & environmental sciences with artificial intelligence

Artificial Intelligence & Environment (AI&E), officially launched in November 2025, is co-edited by Professor Guang-Guo Ying (South China Normal University) and Professor James P. Lewis (The University of Hong Kong).

The journal is dedicated to advancing the innovative applications of AI in ecological protection, climate change mitigation, water resource management, pollution control, and sustainable development. AI&E provides a cutting-edge, efficient, and professional platform for global researchers to exchange academic insights. We are currently open for submissions worldwide. As a quarterly publication, we accept various manuscript types and warmly welcome proposals for Special Issues from domain experts.

When AI Enters Environmental Science: A Paradigm Shift

In this inaugural issue, six seminal papers delineate the initial coordinates of this emerging interdisciplinary field:

📘 Editorial | Bridging Earth, Ecology, and Environmental Sciences with AI: A Message to the Future

We posit that AI is not intended to “replace” environmental scientists, but rather to expand the frontiers of human cognition. It transforms high-dimensional data processing, complex pattern recognition, and causal inference into standard analytical tools, moving them beyond the exclusive domain of specialized computational labs.

The mission of AI&E is clear: To empower environmental experts with state-of-the-art computational tools and to provide cutting-edge algorithms with the most urgent real-world application scenarios.

📘 From “Passive Analysis” to “Proactive Design”

• Intelligent Identification of Non-target Pollutants (Review): In an environment containing tens of thousands of organic pollutants where reference standards are perpetually lagging, machine learning is establishing a new paradigm for “standard-free” qualitative and quantitative analysis. By predicting mass spectra, inferring molecular formulas, and generating unknown structures, this represents the “autonomous driving” moment for environmental analytical chemistry.
• AI-driven Transformation of the Microplastics Research Chain (Perspective): From hyperspectral identification and sedimentation modeling to neurotoxicity mechanism inference and global exposure risk assessment, we propose a “Pan-Microplastics AI Framework.” This framework leverages AI to holistically address the “Triple Crisis” of microplastics, climate change, and biodiversity loss.

📘 From “Single Medium” to “Holistic Intelligence”

• AI Methodologies Across Water, Soil, Air, and Solid Waste (Perspective): This article systematically reviews AI applications across Earth’s spheres and proposes a “Five-Step Criterion” for AI model deployment—ranging from data cleaning to interpretability—effectively transforming “black boxes” into transparent scientific tools.
• Urban Exposomics in Pet Hair (Research): Domestic pet hair is emerging as an “intelligent sensor” for indoor pollution. Through text mining, machine learning, and high-resolution mass spectrometry (HRMS), researchers found that pets and their owners share over 50% of chemical exposure characteristics. Your pet’s fur may offer a more precise diagnostic of your health risks than a standard air monitor.

📘 From “Technical Tools” to “Global Governance”

• The Irreversible Divergence of AI Development Paradigms in China, the US, and the EU (Policy): With the US prioritizing foundation models and hardware, China excelling in application deployment, and the EU focusing on “Trustworthy AI” and regulatory standards, three distinct technological ecosystems are forming. For global environmental governance, this suggests a complex challenge: we must find collective solutions to the climate crisis within three parallel and potentially incompatible AI frameworks.

Read the Full Issue: https://www.the-newpress.com/aie/