Predicting Air Pollution in Cork, Ireland Using Machine Learning

TL;DR

This paper develops machine learning models, especially Extra Trees, to accurately predict air pollution levels in Cork, Ireland, using extensive historical data, revealing key meteorological influences and seasonal patterns, and demonstrating significant improvements over previous methods.

Contribution

Introduces a highly accurate machine learning approach for air pollution prediction in Cork, utilizing extensive data and identifying key environmental factors influencing pollution levels.

Findings

Extra Trees achieved 77% prediction accuracy.

Pollution levels are driven mainly by temperature, wind speed, and humidity.

Pollution levels improved by 31% from 2014 to 2022.

Abstract

Air pollution poses a critical health threat in cities worldwide, with nitrogen dioxide levels in Cork, Ireland exceeding World Health Organization safety standards by up to $278\%$. This study leverages artificial intelligence to predict air pollution with unprecedented accuracy, analyzing nearly ten years of data from five monitoring stations combined with 30 years of weather records. We evaluated 17 machine learning algorithms, with Extra Trees emerging as the optimal solution, achieving $77\%$ prediction accuracy and significantly outperforming traditional forecasting methods. Our analysis reveals that meteorological conditions particularly temperature, wind speed, and humidity are the primary drivers of pollution levels, while traffic patterns and seasonal changes create predictable pollution cycles. Pollution exhibits dramatic seasonal variations, with winter levels nearly double those of summer, and daily rush-hour peaks reaching $120\%$ above normal levels. While Cork's air quality shows concerning violations of global health standards, our models detected an encouraging $31\%$ improvement from 2014 to 2022. This research demonstrates that intelligent forecasting systems can provide city planners and environmental officials with powerful prediction tools, enabling life-saving early warning systems and informed urban planning decisions. The technology exists today to transform urban air quality management. All research materials and code are freely available at: https://github.com/MdRashidunnabi/Air-Pollution-Analysis.git