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

Aug 6, 2026

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Enhancing carbon capture and CO2 reduction processes using machine learning and AI technologies addresses health risks while advancing biomedical outcomes. Advanced algorithms and data analytics optimize carbon capture systems to improve operational efficiency and lower greenhouse gas emissions. Integrated environmental and health data also support modeling of how air quality influences public health and disease prevalence, enabling a clearer understanding of asthma rates, respiratory illness, and cardiovascular conditions through pollutant exposure patterns. The framework supports targeted interventions and informs health-focused policy for air-quality improvements.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Abstract

Introduction

Health impacts of gas flaring

Premature deliveries

Respiratory disorders

Carcinogenic and ozone-related effects

Broader environmental impacts

Mitigation approaches

Metallurgical and Materials Engineering Research paper Enhancing Carbon Capture And CO2 Reduction Processes Using Machine Learning And AI Technologies To Improve Biomedical Outcomes Ali Shafaghat Department of Chemical & Petroleum Engineering, Faculty of Graduate Studies, Branch University of Calgary, Canada 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. Keywords: Carbon Capture, CO2, Machine Learning, AI, emissions, air pollution, climate change, greenhouse gases, catalyst, hydrogen, biomedical outcomes. INTRODUCTION A significant association between elevated exposure to hydrocarbon flaring a process involving the combustion of excess natural gas, resulting in substantial CO₂ and pollutant emissions—and a 50% increased risk of preterm birth compared to individuals residing outside the exposure range. It is true that high exposure was defined as experiencing 10 or more nocturnal flaring events within a 5-kilometer (approximately 3-mile) radius of the individual's residence, however new study reveals that the gas flaring Can Impact Individuals Up to 60 Miles away underscoring the potential adverse health impacts of flaring-related greenhouse gas, CO2 and pollutant emissions. New research from USC and UCLA reveals that over half million Americans face potential health hazards due to their proximity to oil and gas production sites where excess natural gas is burned off, a process known as flaring[2]. This practice, which involves the combustion of surplus gas at extraction locations, poses significant health concerns for nearby residents [1] . This becomes more highlighted, in areas with high oil and gas and industrial manufacturing facilities such as Texas and Louisiana in the USA. Gas flaring can have a number of negative health impacts in those area special Texas and Louisiana in the USA which leads to umber of health diseases including but not limited to: Premature Deliveries: Research has identified a correlation between proximity to gas flaring activities and a heightened likelihood of premature childbirth. This association raises concerns about the potential health impacts of environmental pollutants emitted during the flaring process. Studies have suggested that the release of volatile organic compounds and particulate matter may contribute to adverse pregnancy outcomes by affecting maternal health and fetal development. Furthermore, the stress associated with living near industrial sites may exacerbate these health risks, leading to increased anxiety and depression among pregnant individuals. This psychological stress can further impact maternal behaviors, such as prenatal care utilization and lifestyle choices, which are crucial for healthy fetal development. Additionally, exposure to pollutants from flaring can disrupt endocrine functions, potentially leading to complications such as low birth weight, preterm birth, and developmental delays. As such, it is essential to consider both the environmental and social factors that contribute to maternal and fetal healt

FAQs

How do machine learning and AI technologies improve carbon capture and CO2 reduction?

They use advanced algorithms and data analytics to optimize carbon capture system operation, improving efficiency and reducing greenhouse gas emissions.

How can these technologies connect air quality with biomedical outcomes?

By integrating environmental data with health records, researchers can analyze correlations between pollutant exposure levels and disease prevalence.

What health risks are discussed in relation to gas flaring emissions?

The document highlights risks such as premature births, respiratory disorders like asthma exacerbations, and increased cancer risk from carcinogenic compounds such as benzene, along with other air-quality related impacts.

Aug 6, 2026

Upload by Violet