Short-term Predictions of PM₁₀ Using Bayesian Regression Models

¹ Faculty of Civil Engineering & Technology, Universiti Malaysia Perlis, Jejawi, 02600 Arau, Perlis, Malaysia
² Sustainable Environment Research Group (SERG), Centre of Excellence Geopolymer and Green Technology (CEGeoGTech), Universiti Malaysia Perlis, Jejawi, 02600 Arau, Perlis, Malaysia
³ School of Distance Education, Universiti Sains Malaysia, 11800 Gelugor, Penang, Malaysia
⁴ School of Civil Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Pulau Pinang, Malaysia
⁵ National Instiute for Research and Development in Environmental Protection, Splaiul Independenţei 294, Bucharest, Romania, 060031

^* Corresponding author: norazrin@unimap.edu.my
hazrul@usm.my

Abstract

One of the air pollutants that poses the greatest threat to human health is PM₁₀. The objectives of this study are to develop a prediction model for PM₁₀. 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) PM₁₀ concentration. To choose the optimal model, the performance metrics (NAE, RMSE, PA, IA, and R²) 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 (PM₁₀, temperature, relative humidity, NO₂, SO₂, CO, and O₃) 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 (NO₂), sulphur dioxide (SO₂), and ozone (O₃) from industrial and maritime activities, which are thought to influence PM₁₀ 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 PM₁₀ concentration at all locations.