标题： 使用机器学习预测爱尔兰科克的空气污染 显示英文标题

标题： Predicting Air Pollution in Cork, Ireland Using Machine Learning

摘要： 空气污染在全球城市中构成了严重的健康威胁，爱尔兰科克市的二氧化氮水平超过世界卫生组织安全标准高达$278\%$。 本研究利用人工智能以前所未有的准确性预测空气质量，分析了来自五个监测站近十年的数据以及三十年的天气记录。 我们评估了17种机器学习算法，其中Extra Trees表现出最佳效果，达到了$77\%$的预测准确率，并显著优于传统预测方法。 我们的分析表明，气象条件，特别是温度、风速和湿度是污染水平的主要驱动因素，而交通模式和季节变化则形成了可预测的污染周期。 污染呈现出显著的季节性变化，冬季水平几乎是夏季的两倍，每日高峰时段的污染水平高出正常水平$120\%$。 尽管科克市的空气质量显示出令人担忧的全球健康标准违规情况，但我们的模型检测到从2014年到2022年出现了令人鼓舞的$31\%$改善。 这项研究表明，智能预测系统可以为城市规划者和环境官员提供强大的预测工具，从而实现挽救生命的早期预警系统和明智的城市规划决策。 如今已有技术可以改变城市空气质量的管理方式。 所有研究材料和代码均可免费在以下地址获取：https://github.com/MdRashidunnabi/Air-Pollution-Analysis.git 显示英文摘要

摘要： 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