Short-term Predictions of PM 10 Using Bayesian Regression Models

• Published in: E3S web of conferences Vol. 437; p. 1006
• Main Authors: Ramli, Norazrin, Abdul Hamid, Hazrul, Yahaya, Ahmad Shukri, Noor, Norazian Mohamed, Elena, Holban
• Format: Journal Article
• Language: English
• Published: 2023
• ISSN: 2267-1242; 2267-1242
• DOI: 10.1051/e3sconf/202343701006

One of the air pollutants that poses the greatest threat to human health is PM 10 . The objectives of this study are to develop a prediction model for PM 10 . The Multiple Linear Regression (MLR) and Bayesian Regression (BRM) models were constructed to forecast the following day’s (Day 1) and next two days’ (Day 2) PM 10 concentration. To choose the optimal model, the performance metrics (NAE, RMSE, PA, IA, and R 2 ) are applied to each model. Jerantut, Nilai, Shah Alam, and Klang were chosen as monitoring sites. Data from the Department of Environment Malaysia (DOE) was utilised as a case study for five years, with seven parameters (PM 10 , temperature, relative humidity, NO 2 , SO 2 , CO, and O 3 ) chosen. According to the findings, the key factors responsible for the unhealthy levels of air quality at the Klang station include carbon monoxide (CO), nitrogen dioxide (NO 2 ), sulphur dioxide (SO 2 ), and ozone (O 3 ) from industrial and maritime activities, which are thought to influence PM 10 concentrations in the area. When compared to MLR models, the results demonstrate that BRM are the best model for predicting the next day and next two days PM 10 concentration at all locations.