Bridging earth, ecology, environmental science and artificial intelligence

Artificial Intelligence and Environment (AI&E)will be officially launched in November 2025 and is co-editor. Professor Guangguo Ying (South China Normal University) Professor James P. Lewis (University of Hong Kong).

This journal is dedicated to advancing 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 researchers around the world to exchange academic insights. We are currently accepting applications from all over the world. As a quarterly journal, we accept a wide variety of manuscripts and warmly welcome proposals for special issues from experts in their field.

When AI enters environmental science: A paradigm shift

In this inaugural issue, six seminal papers outline an early overview of this emerging interdisciplinary field.

📘 Editorial | Bridging earth, ecology, and environmental science with AI: A message for the future

We argue 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.

AI&E’s mission is clear. We provide environmental professionals with cutting-edge computational tools and deliver cutting-edge algorithms for the most pressing real-world application scenarios.

📘 From “passive analysis” to “aggressive design”

• Intelligent identification of non-target contaminants (review): In an environment containing tens of thousands of organic contaminants where reference standards consistently lag, machine learning is establishing a new paradigm for “standard-free” qualitative and quantitative analysis. This represents a “self-driving” moment in environmental analytical chemistry by predicting mass spectra, inferring molecular formulas, and generating unknown structures.
• Transforming the microplastics research chain using AI (Outlook): From hyperspectral identification and sedimentation modeling to inference of neurotoxicity mechanisms and global exposure risk assessment, we propose a “pan-microplastics AI framework”. The framework leverages AI to comprehensively address the “triple crisis” of microplastics, climate change, and biodiversity loss.

📘 From “single media” to “holistic intelligence”

• AI methods across water, soil, air, and solid waste (perspectives): In this article, we systematically review AI applications across the globe and propose a “five-step standard” for AI model deployment that effectively transforms “black boxes” into transparent scientific tools, from data cleaning to interpretability.
• Urban exposomics in pet hair (research): Pet hair is attracting attention 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 more than 50% of chemical exposure characteristics. Your pet’s fur may be able to diagnose health risks more accurately than standard air monitors.

📘 From “technical tools” to “global governance”

• Irreversible differences in AI development paradigms in China, the US, and the EU (policy): Three distinct technology ecosystems are forming, with the US prioritizing foundational models and hardware, China excelling at application deployment, and the EU focusing on “trustworthy AI” and regulatory standards. This suggests complex challenges for global environmental governance. 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/