Urban Air Quality and Machine Learning

 Ugwu Chinyere N., Val Hyginus Udoka Eze and Ogenyi Fabian C.

Department of Publication and Extension, Kampala International University Uganda

ABSTRACT

Urbanization has led to significant air quality degradation due to increased emissions from industrial activities, fossil fuel combustion, and vehicular traffic. This degradation poses severe health risks, including respiratory and cardiovascular diseases. Predicting and monitoring urban air quality is complex, influenced by both human activities and natural processes. Machine learning (ML) techniques offer promising solutions for modeling and predicting air quality metrics. This paper reviews the current state of urban air quality monitoring, explores the applications of ML in this field, and discusses the associated challenges and limitations. It highlights successful case studies and suggests future research directions to improve urban air quality monitoring using advanced ML techniques.

Keywords: Urban air quality, machine learning, air pollution, predictive modeling, health impact.

CITATION: Ugwu Chinyere N., Val Hyginus Udoka Eze and Ogenyi Fabian C. Urban Air Quality and Machine Learning. Research Output Journal of Engineering and Scientific Research. 2024 3(1):101-104.