Predicting particulate matter (PM2.5) in Malaysia using Multiple Linear Regression and Artificial Neural Network

Abstract

Air pollution is a well-known issue for all countries, including Malaysia. It has been stated that particulate matter that less than 2.5mm known as PM2.5 has a greater effect on health as the smaller particulate size can penetrate deep into the respiratory system and affect the cardiovascular system significantly. Therefore, it is necessary to estimate the concentration of PM2.5 for haze precautions. This study characterizes the pattern of PM2.5 concentrations involving seven stations including Alor Setar, Shah Alam, Pasir Gudang, Ipoh, Kuantan, Kuala Terengganu and Miri with seven indicator parameters (Carbon Monoxide, Ozone, Sulphur Dioxide, Nitrogen Dioxide, Humidity, Temperature and Wind Speed). PM2.5 concentrations were predicted for each station using Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). Descriptive and trend analysis using Mann-Kandell Trend analysis was used to describe the haze characteristics and identify significant trends in the haze selected locations in Malaysia. MLR and ANN were fitted for the data. The performance of both prediction models was compared based on R2 and Mean Square Error (MSE). The results show ANN performed better than MLR with a high value of coefficient determination (R2) and low error measure. The ANN model was used to predict the occurrence of haze for the next day in the Air Quality Index (API).