Modified linear regression for predicting ambient particulate pollutants (PM10) during High Particulate Event

Abstract

Particulate Matter (PM10) is one of the most significant contributors towards haze or high particulate event (HPE) that occurs in Malaysia. HPE can severely affect human health, environment and economic so it is important to create a reliable prediction model in predicting future PM10 concentration especially during HPE. Therefore, the aim of this study is to investigate the performance of modified linear regression models in predicting the next-day Particulate Matter (PM10+24) concentration at two areas in the peninsular Malaysia namely, Bukit Rambai and Nilai. Hourly air quality dataset during historic HPE in 1997, 2005, 2013 and 2015 were used for analysis. Pearson correlation was used to select the input of the PM10 prediction model where only parameters with moderate (0.6 > r > 0.3) and strong (r > 0.6) correlation with PM10 concentration were selected as independent variables input in creating the multiple linear regression (MLR) model. The performance of modified linear regression model was evaluated by using several performance indicator which is Prediction Accuracy (PA), Index of Agreement (d 2), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results show that the modified MLR (parameter with r > 0.6 included as input) gave the best prediction model for the next-day PM10 concentration in both Bukit Rambai and Nilai.