Prediction of daytime and nighttime ground-level ozone using the hybrid regression models

Ozone is one of the major challenges for the air quality community due to its adverse impact on the environment and human health. This study seeks to improve the understanding of underlying mechanisms for several developed models for ozone prediction. We aim to establish a robust prediction model for ozone concentration up to the next four hours. Three years dataset including ozone (O3), nitrogen oxide (NOx), nitric oxide (NO), sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), particulate matter (PM10, PM2.5), wind speed, solar radiation, temperature, and relative humidity (RH) were used in this study. The data were analyzed by using Multiple Linear Regression (MLR), Principal Component Regression (PCR), and Cluster-Multiple Linear Regression (CMLR) in predicting the next hours of O3 concentration. Results show that the MLR models executed high accuracy for O3t+1 (R2= 0.313), O3,t+2 (R2= 0.265), O3,t+3 (R2= 0.227) and O3,t+4 (R2= 0.217) as the best fitted-model. In conclusion, the MLR model is suitable for the next hour's O3 concentration prediction.