A new dynamic approach using data-driven and machine learning models for forecasting particulate matter in Dhaka megacity

This study conducts a comprehensive examination of six machine learning models for forecasting PM 2.5 and PM 10 concentrations in Dhaka, Bangladesh, employing average data from three air monitoring stations - Darus Salam, Parliament Area, and BARC established by the Department of Environment (DoE). The analysis utilizes average data from three air monitoring stations spanning January 2016 to December 2022, with meticulous pre-processing to ensure data quality. The employed models for analysis include ARIMA, ANN, ELM, ETS, NAÏVE, and TBATS. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are used to validate and rigorously compare model performance. ARIMA shows the best performance for PM 2.5 , while TBATS is slightly better for PM 10 predictions. These insights hold significant value for air quality management in Dhaka, enabling informed and proactive measures to counter particulate pollution and its adverse health implications. Furthermore, this study demonstrates the potential of machine learning models in accounting for local factors influencing air quality, complementing existing research on combining air quality models. This opens doors for further developing even better hybrid models, including weather data and exploring advanced ensemble techniques. • Utilized advanced machine learning model to predict air pollution of Dhaka. • ARIMA and TBATS outperformed other models for PM 2.5 and PM 10 prediction, respectively. • Leveraged real-world air quality data to capture local factors influencing pollution levels. • Demonstrated the potential of machine learning for developing sophisticated air quality forecasting tools. • Findings provide valuable guidance for policymakers to address air pollution challenges.