Leveraging AI for Real-Time Monitoring and Prediction of Environmental Health Hazards: Protecting Public Health in the USA

Abstract

The rapid escalation of environmental health hazards, driven by industrialization, urbanization, and climate change, poses a significant risk to public health in the United States. Leveraging advancements in artificial intelligence (AI), this study explores a framework for realtime monitoring and predictive analytics to identify and mitigate these hazards. By integrating AI with Internet of Things (IoT) sensors, geospatial data, and epidemiological models, the proposed system enables early detection of threats such as air pollution spikes, water contamination, and heatwaves. Machine learning algorithms analyze massive datasets to forecast hazard trends and their potential health impacts, facilitating preemptive interventions. This approach also employs natural language processing (NLP) to synthesize public health advisories and disseminate actionable insights through digital platforms. A case study on urban air quality demonstrates the system's efficacy in reducing exposure and improving response times. The findings underscore the potential of AI to transform environmental health management, safeguard communities, and support policymakers in proactive decision-making.