Enhancing Carbon Capture And CO2 Reduction Processes Using Machine Learning And AI Technologies To Improve Biomedical Outcomes

Abstract

Enhancing carbon capture and CO₂ reduction processes using Machine Learning and AI technologies to mitigate health risks and improve biomedical outcomes is a critical area of research that combines environmental science with healthcare innovation. By leveraging advanced algorithms and data analytics, researchers can optimize carbon capture systems to operate more efficiently, thereby reducing greenhouse gas emissions. Additionally, these technologies can be employed to model the impact of air quality on public health. Biomedical outcomes such as asthma rates, respiratory diseases, and cardiovascular conditions can be better understood through the analysis of pollutant exposure levels. By integrating environmental data with health records, researchers can identify correlations between air quality and disease prevalence. This approach not only aids in developing targeted interventions but also informs policymakers about the potential health benefits of improving air quality.