用机器学习精准预测科克市空气污染，助力城市治理
Predicting Air Pollution in Cork, Ireland Using Machine Learning
- 基于10年监测数据与30年气象记录，筛选出最优的随机森林类模型
- 模型预测准确率达77%，冬季污染水平是夏季的近两倍，高峰时段超标120%
- 适合城市规划者、环保部门及关注空气质量的研究者使用
空气污染对全球城市健康构成重大威胁，爱尔兰科克市的二氧化氮浓度曾高达世卫组织标准的278%。本研究利用人工智能分析近十年五处监测站数据及三十年气象记录，评估17种机器学习算法，发现额外树（Extra Trees）表现最佳，预测准确率达77%，显著优于传统方法。结果表明，气象条件（尤其是温度、风速和湿度）是污染主因，交通模式与季节变化导致可预测的污染周期。冬季污染水平接近夏季两倍，早晚高峰期浓度比正常值高出120%。尽管存在严重超标情况，但2014至2022年间空气质量已改善31%。该研究证明智能预测系统可为城市规划与环境管理提供有力工具，实现早期预警与科学决策。所有代码与数据公开于：https://github.com/MdRashidunnabi/Air-Pollution-Analysis.git
原文摘要 · Abstract (English)
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
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