Artificial Intelligence & Environment is an international multidisciplinary platform for communicating advances in fundamental and applied research on the intersection of environmental science and artificial intelligence (AI). It is dedicated to serving as an innovative, efficient and professional platform for researchers in the cross-discipline fields of earth and environmental sciences, big data science and AI around the world to deliver findings from this rapidly expanding field of science. It is a peer-reviewed, open-access journal that publishes critical review, original research, rapid communication, view-point, commentary and perspective papers, with an emphasis on, but not limited to the following topics:

(1) Environmental Monitoring and Data Analysis with AI

-  Development and application of AI algorithms for analyzing large-scale environmental data, such as remote sensing data, in-situ sensor data, and historical environmental records.

-  Machine learning models for predicting environmental variables like air quality indices, water quality parameters, and climate change indicators.

-  AI-driven data fusion techniques to integrate multi-source environmental information for comprehensive environmental assessments.

(2) AI Application in Ecosystem Modeling and Management

-  Use of artificial intelligence to simulate complex ecosystem processes, including species interactions, nutrient cycling, and ecosystem succession.

-  AI-based decision support systems for ecosystem management, conservation planning, and biodiversity protection.

-  Optimization of natural resource management strategies using machine learning and optimization algorithms.

(3) AI Application in Chemical Fate Modeling and Management

- AI applications in emission, environmental process, multi-media fate and management modeling of contaminants at different scales.

- AI-assisted modeling of water resources (e.g. surface water, ground water) and various contaminants.

- AI- and data-driven river basin management systems and chemical management.

(4) Climate Change and AI

-  AI applications in climate modeling, including improving the accuracy and resolution of climate predictions.

-  Analysis of climate change impacts on various environmental systems using AI techniques.

-  Development of AI-powered mitigation and adaptation strategies for climate change.

(5) Pollution Control and Waste Management with AI

-  AI-driven monitoring and prediction of pollutant dispersion in air, water, and soil.

-  Intelligent systems for waste classification, recycling, and disposal, improving the efficiency and effectiveness of waste management practices.

-  AI-based process optimization for pollution control technologies, such as wastewater and drinking water treatment and air purification systems.

(6) AI in Renewable Energy and Environmental Sustainability

-  Optimization of renewable energy systems, including solar, wind, and hydro energy, using AI algorithms for resource assessment, system design, and operation management.

-  AI applications in energy storage and grid integration of renewable energy sources, enhancing the stability and reliability of the energy supply.

-  Development of sustainable urban planning and smart city concepts with the aid of AI, focusing on reducing environmental impacts and promoting resource efficiency.

(7) Environmental Risk and Impact Assessment and AI

-  AI application in prediction of chemical identification, degradation and transformation, toxicity and mechanisms.

-  AI-driven risk assessment models for evaluating the potential impacts of environmental hazards, such as natural disasters, chemical spills, and invasive species.

-  Development of early warning systems using AI techniques to detect and predict environmental risks in real-time.

-  AI-based risk management strategies for mitigating environmental hazards and protecting human health and the environment.

-  AI-facilitated environmental impact assessment such as life cycle assessment to improve accuracy, flexibility and robustness.

(8) AI in Environmental Policy and Decision-Making

-  Analysis of environmental policies and regulations using AI techniques to assess their effectiveness and identify potential improvements.

-  AI-assisted decision-making frameworks for balancing environmental, economic, and social objectives in policy formulation.

-  Development of AI-powered tools for public participation and stakeholder engagement in environmental decision-making processes.