Covid-19 pandemic lock-down has resulted significant differences in air quality levels all over the world. In contrary to decrease seen in primary pollutant species, many of the countries have experienced elevated ground-level ozone levels in this period. Air pollution forecast gains more importance to achieve air quality management and take measures against the risks under such extra-ordinary conditions. Statistical models are indispensable tools for predicting air pollution levels. Considering the complex photochemical reactions involved in tropospheric ozone formation, modeling this pollutant requires efficient non-linear approaches. In this study, deep learning methods were applied to forecast hourly ozone levels during pandemic lock-down for an industrialized region in Turkey. With this aim, different deep learning methods were tested and efficiencies of the models were compared considering the calculated RMSE, MAE, $R^2$ and loss values.

Covid-19 大流行封锁导致世界各地的空气质量水平存在显着差异。与主要污染物种类减少相反，许多国家在此期间经历了地面臭氧水平升高。空气污染预测对于实现空气质量管理和采取措施应对此类非常规条件下的风险越来越重要。统计模型是预测空气污染水平不可或缺的工具。考虑到对流层臭氧形成过程中涉及的复杂光化学反应，对这种污染物进行建模需要采用高效的非线性方法。在这项研究中，深度学习方法被用于预测土耳其工业化地区在大流行病封锁期间的每小时臭氧水平。为了这个目标，$R^{\mathrm{2个}}$和损失值。