forecasting the short-term changes of surface ozone and no2 during a festival event using stochastic and neural network models

Air pollution is one of the most destructive environmental issues on the local, regional, and global level. its negative influences go far beyond ecosystems and the economy, harming human health and environmental sustainability. by these facts, efficient and accurate modelling and forecasting the concentration of air pollutants are vital. hence, this work explores investigate the time series components of surface ozone (o3) and its precursor nitrogen dioxide (no2) and develops a model for predicting o3 variations produced by intense fireworks during the vishu festival over kannur. time series methods using stochastic and recurrent neural network (rnn) and seasonal autoregressive integrated moving average (sarima) models are considered the most accurate tools for estimating air pollution trends due to their logical flexibility. model performance is evaluated based on statistical measurements indicating an increasing trend in o3 concentration of 0.11 ppb/year and no2 of 0.18 ppb/year. based on the analysis, we found that the sarima model shows better accuracy with a mean squared error (mse) of 0.55 and a root mean squared error (rmse) of 0.74. the broader implications of this study highlight the applicability of advanced time series forecasting techniques for air quality monitoring during short-term pollution events.