  Accurate air pollution forecasting plays a crucial role in controlling air
quality and minimizing adverse effects on human life. Among pollutants,
atmospheric particulate matter (PM) is particularly significant, affecting both
visibility and human health. In this study the concentration of air pollutants
and comprehensive air quality index (CAI) data collected from 2015 to 2018 in
Seoul, South Korea was analyzed. Using two different statistical models: error,
trend, season (ETS) and autoregressive moving-average (ARIMA), measured monthly
average PM2.5 concentration were used as input to forecast the monthly averaged
concentration of PM2.5 12 months ahead. To evaluate the performance of the ETS
model, five evaluation criteria were used: mean error (ME), root mean squared
error (RMSE), mean absolute error (MAE), mean percentage error (MPE), and mean
absolute percentage error (MAPE). Data collected from January 2019 to December
2019 were used for cross-validation check of ETS model. The best fitted ARIMA
model was determined by examining the AICc (Akaike Information Criterion
corrected) value. The results indicated that the ETS model outperforms the
ARIMA model.
