Predicting Air Pollution in Cork, Ireland Using Machine Learning

2025-07-05

AI Summary

This study addresses frequent exceedances of World Health Organization (WHO) nitrogen oxides (NOₓ) limits in Cork, Ireland. To enable high-accuracy pollution forecasting, we develop a machine learning–based system integrating ten years of local air quality monitoring data with thirty years of meteorological records. We systematically quantify the influences of meteorology, traffic patterns, and seasonality—finding winter NOₓ concentrations are twice those in summer and morning rush-hour exceedance rates reach 120%. Long-term trend analysis reveals a 31% improvement in air quality between 2014 and 2022. Among 17 candidate algorithms, Extra Trees achieves the highest performance, attaining 77% accuracy in predicting NOₓ exceedance events—substantially outperforming conventional statistical approaches. All code and datasets are publicly released under open-source licenses, enabling reproducible research and supporting urban air quality early-warning systems, intelligent environmental management, and evidence-based public health interventions.

Technology Category

• Machine Learning: Evaluation and Analysis
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Algorithm Configuration

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

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

Problem

• Predicting air pollution in Cork using machine learning
• Identifying key drivers of pollution like weather and traffic
• Evaluating 17 algorithms to improve forecasting accuracy

Innovation

• Uses Extra Trees algorithm for pollution prediction
• Combines 10 years of pollution and 30 years of weather data
• Achieves 77% accuracy in pollution forecasting