Artificial intelligence technology in environmental research and health: Development and prospects

Area of Science:

• Environmental Science
• Computer Science
• Green Technology

Background:

• Traditional environmental research paradigms are insufficient for complex mechanisms.
• Artificial intelligence (AI) is driving a green technology revolution in environmental science.
• AI enhances computational efficiency in environmental data analysis by over 60%.

Purpose of the Study:

• To systematically summarize AI applications in key environmental areas.
• To analyze the effectiveness of AI, particularly machine learning (ML), in environmental solutions.
• To identify challenges and propose solutions for large-scale AI implementation in environmental research.

Main Methods:

• Survey of AI applications in water pollution treatment, air pollution control, solid waste disposal, soil remediation, and environmental health.
• Analysis of machine learning (ML) methods for material screening, performance prediction, and pollutant detection.
• Identification of challenges such as data scarcity and uneven data coverage in complex environmental systems.

Main Results:

• AI demonstrates significant effectiveness in material screening, performance prediction, and instant detection.
• AI aids in simulating global pollutant distribution and controlling human health impacts.
• AI applications show promise in advancing environmental protection and sustainable development.

Conclusions:

• AI is a powerful tool for addressing complex environmental issues and accelerating the green technology revolution.
• Overcoming challenges like data scarcity is crucial for widespread AI adoption in environmental science.
• AI is poised to be a core driver for achieving environmental sustainability and "dual carbon" targets.