An Analysis on Environmental Justice and Air Quality Using Machine Learning Techniques

Authors : Gorkem Demircan, Gülsüm Çiğdem Çavdaroğlu

Pages : 1-23

View : 115 | Download : 147

Publication Date : 2025-12-24

Article Type : Research Paper

Abstract :The study aims to analyze air quality using machine learning and data analysis methods, focusing on environmental justice and air quality. With rapid urbanization, industrial growth, and global environmental challenges, air quality studies are becoming increasingly important. The United Nations Framework Convention on Climate Change has broadened the definition of climate change to encompass human impact, and this closely links to the intensifying climate crisis. The study presents a comprehensive overview of air pollution by analyzing air quality data from various countries. We develop machine learning models using methodologies like Random Forest, Decision Tree, XGBoost, and Adaboost to predict future air quality trends and analyze and interpret their results. We made predictions for the next 10 years using the XGBoost model, which demonstrated the highest prediction performance among these methods. According to these predictions, Bhutan and North Korea have the highest increases, while countries such as India, Pakistan, and Nepal have noticeable decreases, reflecting diverse air quality trends. The analysis reveals that Laos, Indonesia, and North Korea will experience the most crucial changes in air quality, with changes of 0.183878, 0.116214, and 0.114642, respectively. These countries will have notable increases in their air quality from 2018 to 2028. The study emphasizes the concept of environmental injustice and uses effective data visualization techniques to visually present complex air quality data in an understandable manner.
Keywords : Hava Kalitesi Tahmini, CO Seviyesi Tahmini, Makine Öğrenimi

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