Over the years, many organizations across the globe have conducted various studies pertaining to air pollution and its ill effects. The results of these studies substantially conclude that a plethora of people succumbs to the adversities caused by the ever-increasing air pollutants. In this investigation, M5P, random forest (RF)- and Gaussian process (GP)-based approaches are used to predict the tropospheric ozone for Amritsar, Punjab state of India, metropolitan area. The models proposed were based on ten input parameters viz. particulate matter PM2.5, particulate matter PM10, sulphur dioxide (SO₂), nitrogen dioxide (NO₂), nitric oxide (NO), ammonia (NH₃), temperature (T), solar radiation (SR), wind direction (WD) and wind speed (WS), while the tropospheric ozone (O₃) was an output parameter. Three most popular statistical parameters such as correlation coefficient (CC), mean absolute error (MAE) and root mean square error (RMSE) were used for the assessment of the developed models. In comparison, it was found that better results were achieved with random forest-based model with CC value as 0.8850, MAE value as 0.0593 and RMSE value as 0.0772 for testing stage. The suggested models are expected to save cost of instrument, cost of labour work, time and contribute to greater accuracy. A result of sensitivity investigation concludes that the solar radiation is the most influencing parameter in estimating the actual values of O₃ based on the current data set.

多年来，全球许多组织开展了有关空气污染及其不良影响的各种研究。这些研究的结果基本上得出结论，许多人屈服于不断增加的空气污染物造成的逆境。在本次调查中，使用基于 M5P、随机森林 (RF) 和高斯过程 (GP) 的方法来预测印度旁遮普邦大都市区阿姆利则的对流层臭氧。提出的模型基于十个输入参数，即。颗粒物 PM2.5、颗粒物 PM10、二氧化硫 (SO ₂ )、二氧化氮 (NO ₂ )、一氧化氮 (NO)、氨气 (NH ₃)、温度 (T)、太阳辐射 (SR)、风向 (WD) 和风速 (WS)，而对流层臭氧 (O ₃ ) 是输出参数。三个最流行的统计参数，如相关系数 (CC)、平均绝对误差 (MAE) 和均方根误差 (RMSE) 用于评估开发的模型。相比之下，测试阶段的CC值为0.8850，MAE值为0.0593，RMSE值为0.0772的随机森林模型取得了更好的结果。建议的模型有望节省仪器成本、劳动力成本、时间并有助于提高准确性。敏感性调查的结果得出结论，太阳辐射是估计 O ₃实际值的最大影响参数 基于当前数据集。