Enhancing Environmental and Human Health Management Through the Integration of Advanced Revitalization Technologies...

Area of Science:

• Environmental Science
• Toxicology
• Public Health

Background:

• Pollution, defined as detrimental contaminants or energy sources, poses risks to ecosystems and human health.
• The human body acts as a key indicator of environmental contamination.
• Linking specific pollutants to human diseases is complex and time-consuming.

Purpose of the Study:

• To propose a comprehensive model for rapid and precise understanding of physiological impairments caused by contaminants.
• To integrate ecosystem contamination data with human biomedical data.
• To support conservation efforts by adapting the model to all ecosystem components.

Main Methods:

• Systematic collection and analysis of data from ecosystem contamination monitoring.
• Integration of biomedical data on human physiological conditions.
• Utilization of artificial intelligence (AI) tools for data analysis and decision-making.

Main Results:

• The proposed model facilitates a more efficient correlation between environmental contaminants and human health outcomes.
• AI enables handling large datasets for strategic decision-making in pollution impact assessment.
• The model's adaptability extends beyond human health to ecosystem component conservation.

Conclusions:

• The AI-powered integrated model accelerates the understanding of pollution's impact on human health.
• This approach is crucial for timely identification and mitigation of health risks associated with environmental contaminants.
• The model's versatility supports broader ecological conservation strategies.