PM2.5: Air Quality Index Prediction Using Machine Learning: Evidence from Kuwait’s Air Quality Monitoring Stations

Huda Alrashidi (), Fadi N. Sibai (), Abdullah Abonamah, Mufreh Alrashidi and Ahmad Alsaber
Additional contact information
Huda Alrashidi: Department of Computer Science and Engineering (CSE), Kuwait College of Science and Technology, Block 4, Doha District, Kuwait City 35004, Kuwait
Fadi N. Sibai: Electrical & Computer Engineering Department and Center for Sustainable Development, Gulf University for Science and Technology, Kuwait City 32093, Kuwait
Abdullah Abonamah: GWU Environmental and Energy Management Institute, School of Engineering and Applied Science, George Washington University, Washington, DC 20052, USA
Mufreh Alrashidi: Quality, Health, Safety & Work Environment Department (QHSWED), Kuwait Institute for Scientific Research, P.O. Box 24885, Safat 13109, Kuwait
Ahmad Alsaber: Department of Management, American University of Kuwait, 15 Salem Al Mubarak St, Salmiya, Safat 13034, Kuwait

Sustainability, 2025, vol. 17, issue 20, 1-25

Abstract: Air pollution poses a significant threat to public health and the environment, particularly fine particulate matter (PM2.5). Machine learning (ML) models have proven their accuracy in classifying and predicting air pollution levels. This research trains and compares the performance of eight machine learning regression models on a time series air quality dataset containing data from 12 dispersed air quality stations in Kuwait, to predict the PM2.5 Air Quality Index (AQI). After cleaning then trimming the large dataset to about 13.4% of its original size, we performed thorough data visualization and analysis of the dataset to identify important patterns. Next, in a set of five experiments exploring feature pruning, the tree-based models, namely Gradient Boosting and AdaBoost, generated mean square errors below 1.5 and R 2 numbers above 0.998, outperforming the other ML models. By integrating meteorological data, pollution source information, and geographical factors specific to Kuwait, these models provide a precise prediction of air quality levels. This research contributes to a deeper understanding and visualization of Kuwait’s air pollution challenges, and draws some public policy recommendations to mitigate environmental and health impacts.

Keywords: air pollution modeling; machine learning regression; PM2.5; air quality monitoring systems; public health policy; industrial air quality (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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