Evaluating the Effectiveness of ARIMA and SARIMA Models for PM2.5 Forecasting in Bangladesh: A Time-Series Study (2000–2026)

Abstract Background and Aim: Particulate matter ≤ 2.5 micrometers (PM2.5) is a major air pollutant linked to serious environmental and public health hazards. In Bangladesh, PM2.5 levels often exceed WHO guidelines due to unplanned urbanization, deforestation, industrial emissions, and vehicular pollution. This study explores long-term trends and seasonal variations in PM2.5 concentrations in Bangladesh and forecasts future levels using time-series models—Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA). Methods Monthly average PM2.5 data (2000–2024) were obtained from NASA’s Giovanni platform. Forecasts for 2025 and 2026 were generated using R’s auto.arima() function, which selected the best models based on Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). Results ARIMA(3,0,1) and SARIMA(1,0,0)(1,1,2)[12] were identified as optimal models based on statistical criteria. Both projected similar overall trends, but while the ARIMA model showed a stable trend, the SARIMA model captured seasonal fluctuations in PM2.5 levels. The Ljung-Box test confirmed SARIMA’s superior performance in accounting for white noise, highlighting the importance of seasonal components in accurate forecasting. Conclusion This study demonstrates the value of ARIMA and SARIMA models for analyzing and predicting air pollution trends in Bangladesh. These models, supported by strong statistical validation, provide effective tools for environmental monitoring and policymaking. Accurate PM2.5 forecasts can support timely interventions, inform public health strategies, and guide the development of early warning systems to reduce pollution-related health risks.