MACHINE LEARNING IN AIR POLLUTION MONITORING: TRANSFORMING ENVIRONMENTAL PROTECTION THROUGH ADVANCED ANALYTICS
Volume 2, Issue 2, Pp 52-56, 2024
DOI: https://doi.org/10.61784/ajes3005
Author(s)
David Kim1, Lisa Wang2, Robert Johnson3*
Affiliation(s)
1Department of Environmental Engineering, Oregon State University, US.
2School of Computing University of Central Florida, United States.
3Department of Environmental Science, University of Nevada Reno, US.
Corresponding Author
Robert Johnson
ABSTRACT
Air pollution is a global challenge with significant implications for public health and the environment. Traditional pollution monitoring methods often suffer from limitations in spatial coverage, real-time data availability, and the ability to identify specific pollution sources. This paper explores the transformative potential of machine learning techniques in revolutionizing pollution monitoring and management. By leveraging advanced data analytics, predictive modeling, and intelligent sensing, machine learning-powered pollution monitoring systems can provide unprecedented insights, enabling more effective decision-making and targeted intervention strategies.
The paper delves into the key applications of machine learning in pollution monitoring, including source identification, air quality forecasting, emission pattern analysis, and personalized exposure assessment. It examines the integration of machine learning with emerging technologies, such as Internet of Things (IoT) sensors and satellite imagery, to enhance the breadth and granularity of pollution data. Additionally, the review addresses the technical considerations, data challenges, and ethical implications surrounding the deployment of machine learning in this domain.
Through a comprehensive analysis of case studies and industry trends, this paper serves as a guide for policymakers, environmental agencies, and technology providers seeking to harness the power of machine learning to tackle the pressing issue of air pollution. By embracing these innovative approaches, organizations can develop more robust, responsive, and data-driven pollution monitoring and mitigation strategies, ultimately improving air quality and safeguarding public health.
KEYWORDS
Air pollution; Pollution monitoring; Machine learning; Data analytics; IoT sensors; Satellite imagery; Predictive modeling; Source identification; Air quality forecasting
CITE THIS PAPER
David Kim, Lisa Wang, Robert Johnson. Machine learning in air pollution monitoring: transforming environmental protection through advanced analytics. Academic Journal of Earth Sciences. 2024, 2(2): 52-56. DOI: https://doi.org/10.61784/ajes3005.
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