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.

翻译：精确的空气污染预测在控制空气质量、减少对人类生活的不利影响方面发挥着关键作用。在污染物中，大气颗粒物（PM）尤其显著，影响着能见度与人类健康。本研究分析了韩国首尔2015年至2018年收集的空气污染物浓度和综合空气质量指数（CAI）数据。采用误差-趋势-季节模型（ETS）和自回归移动平均模型（ARIMA）两种统计模型，以测得的月平均PM2.5浓度作为输入，预测未来12个月的月平均PM2.5浓度。为评估ETS模型性能，使用了五项评价指标：平均误差（ME）、均方根误差（RMSE）、平均绝对误差（MAE）、平均百分比误差（MPE）和平均绝对百分比误差（MAPE）。选取2019年1月至2019年12月的数据进行ETS模型的交叉验证。通过检验AICc（校正后赤池信息准则）值确定最优拟合ARIMA模型。结果表明，ETS模型优于ARIMA模型。