Performance of Bayesian Model Averaging (BMA) for Short-Term Prediction of PM10 Concentration in the Peninsular Malaysia

Abstract

In preparation for the Fourth Industrial Revolution (IR 4.0) in Malaysia, the government envisions a path to environmental sustainability and an improvement in air quality. Air quality measurements were initiated in different backgrounds including urban, suburban, industrial and rural to detect any significant changes in air quality parameters. Due to the dynamic nature of the weather, geographical location and anthropogenic sources, many uncertainties must be considered when dealing with air pollution data. In recent years, the Bayesian approach to fitting statistical models has gained more popularity due to its alternative modelling strategy that accounted for uncertainties for all air quality parameters. Therefore, this study aims to evaluate the performance of Bayesian Model Averaging (BMA) in predicting the next-day PM10 concentration in Peninsular Malaysia. A case study utilized seventeen years’ worth of air quality monitoring data from nine (9) monitoring stations located in Peninsular Malaysia, using eight air quality parameters, i.e., PM10, NO2, SO2, CO, O3, temperature, relative humidity and wind speed. The performances of the next-day PM10 prediction were calculated using five models’ performance evaluators, namely Coefficient of Determination (R2), Index of Agreement (IA), Kling-Gupta efficiency (KGE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The BMA models indicate that relative humidity, wind speed and PM10 contributed the most to the prediction model for the majority of stations with (R2 = 0.752 at Pasir Gudang monitoring station), (R2 = 0.749 at Larkin monitoring station), (R2 = 0.703 at Kota Bharu monitoring station), (R2 = 0.696 at Kangar monitoring station) and (R2 = 0.692 at Jerantut monitoring station), respectively. Furthermore, the BMA models demonstrated a good prediction model performance, with IA ranging from 0.84 to 0.91, R2 ranging from 0.64 to 0.75 and KGE ranging from 0.61 to 0.74 for all monitoring stations. According to the results of the investigation, BMA should be utilised in research and forecasting operations pertaining to environmental issues such as air pollution. From this study, BMA is recommended as one of the prediction tools for forecasting air pollution concentration, especially particulate matter level.

Marketing Strategy of Private Enterprises Based on Bayesian Dynamic Panel Model of Machine Learning Algorithms

“…The bargaining power of suppliers. If suppliers want to affect the profitability of existing enterprises in the industry and the competitiveness of products, they generally achieve this by improving the price of input factors and reducing the quality of unit value (Ramli et al, 2023). However, what input factors it provides to the buyer can mainly determine the strength of the supplier in the competition.…”

Relationships between Air Pollutants and Meteorological Factors during Southwest and Northeast Monsoon at Urban Areas in Peninsular Malaysia

“…In 2016, high levels of SO2 and CO were also detected at the Pasir Gudang monitoring station. In the Bayesian Model Averaging prediction model, Ramli et al [11] discovered that NO2 and PM10 were the most prevalent pollution at Pasir Gudang. Within a radius of two to three kilometres, both residential and business areas can be found in close proximity to the Pasir Gudang monitoring station.…”

İklimsel Değişkenlerin Partikül Madde ve Kükürt Dioksit Üzerindeki Etkisi

“…There have been many studies about the influence of climate variables on air pollutants i.e. particulate matter and sulphur dioxide [38][39][40][41][42][43][44][45][46][47]. In another research, a relationship was found between sulphur dioxide and some climate elements [48].…”

“…In addition, the researchers, Nejadkoorki and Baroutian [66], and Chaloulakou et al [67] operated an artificial neural network method whereas Chen et al [68] used stepwise regression and wavelet analysis in prediction. Furthermore, Ramli et al used the Bayesian Model Averaging method to estimate the concentration of particulate matter in Malaysia [46]. They used some air pollutants, i.e., NO 2 , SO 2 , CO, O 3 and climate variables i.e., air temperature, wind speed and relative humidity.…”