Air pollution imposes great costs on productivity, safety and health of individuals and dictates necessity of a proactive air pollution management. This, in turn, requires powerful tools for air quality modeling. In this article we develop a two-stage procedure for predicting exceedances of the EU legal limits for PM10 and O3${}_3$${}_3$ concentrations using hourly data. Within the first stage we deploy machine learning methods to produce accurate 24-h-ahead forecasts of hourly pollutant concentrations at seven specific locations in the cities of Augsburg and Munich, Germany. The best performance was shown by the Stochastic Gradient Boosting Model—an ensemble tree-based method, especially convenient because of its computational efficiency and robustness to overfitting. Its predictive ability was largely superior to that reported by similar studies. In the second stage, the hourly forecasts were used to predict the exceedances of the EU daily limits for PM10 and O3${}_3$${}_3$ concentrations. For both pollutants we could achieve the average probability of exceedances detection above 80%, while keeping the probability of false alarms at a reasonably low level. Such satisfactory results show that our approach can be successfully applied to anticipate the shocks, which would allow authorities to manage them in the most effective manner.

中文翻译：

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管理空气质量：使用机器学习方法预测 PM10 和 O3 浓度超过法定限值

空气污染给个人的生产力、安全和健康带来了巨大的成本，并要求进行积极的空气污染管理。反过来，这需要强大的空气质量建模工具。在本文中，我们开发了一个两阶段程序，用于预测 PM10 和 O 是否超出欧盟法律限制3${}_3$${}_3$使用每小时数据的浓度。在第一阶段，我们部署机器学习方法，对德国奥格斯堡和慕尼黑市的七个特定地点的每小时污染物浓度进行准确的 24 小时预测。随机梯度提升模型（一种基于集成树的方法）显示了最佳性能，由于其计算效率和对过度拟合的鲁棒性而特别方便。其预测能力大大优于类似研究报告的预测能力。在第二阶段，每小时预测用于预测 PM10 和 O 超过欧盟每日限值3${}_3$${}_3$浓度。对于这两种污染物，我们可以实现超过 80% 的平均超标检测概率，同时将误报概率保持在相当低的水平。如此令人满意的结果表明，我们的方法可以成功地应用于预测冲击，这将使当局能够以最有效的方式管理它们。