Metal contamination - a global environmental issue: sources, implications & advances in mitigation.

Area of Science:

• Environmental Science
• Biotechnology
• Data Science

Background:

• Metal contamination (MC) is an escalating global environmental concern, impacting human health and ecosystems through various exposure routes.
• Industrialization and population growth are intensifying the risks associated with MC, necessitating advanced remediation and monitoring solutions.
• Existing bio-based remediation methods like phytoextraction show promise but face challenges in complex environmental scenarios.

Purpose of the Study:

• To explore the potential of artificial intelligence (AI) in addressing the complexities of metal contamination detection and management.
• To investigate the integration of AI with advanced sensor technologies for enhanced phytoremediation strategies.
• To highlight the interdisciplinary approach for sustainable mitigation of metal contamination impacts.

Main Methods:

• Systematic screening of metallophytes for effective phytoremediation candidates.
• Application of artificial intelligence (AI) algorithms for modeling metallophyte growth and activity.
• Integration of AI with advanced sensor technologies and field-based trials.

Main Results:

• AI algorithms offer powerful tools for modeling complex metal-environment interactions and managing metallophytes.
• The combination of AI and sensor technology can improve the detection and prediction of metal contamination.
• AI can help fill knowledge gaps in understanding metallophyte efficacy in diverse contaminated environments.

Conclusions:

• AI presents a transformative potential for revolutionizing metal contamination remediation strategies.
• An interdisciplinary approach combining AI, sensors, and field trials is crucial for efficient and sustainable environmental cleanup.
• Future research integrating these technologies can significantly mitigate the detrimental effects of metal contamination.