Prediction Of Pm10 Concentrations Using Logistic Regression Analysis: Case Study In Jerantut
Ahmad Shukri Yahaya, Abdul Hamid Hazrul, Mohamad Hazlami Abdul Hamid · The European Proceedings of Social & Behavioural Sciences · 2020
Particulate matter (PM10) can cause several serious negative health effects to humans when it is present in the environment. Thus, it is important for us to forecast its concentration levels in the environment so that we can reduce the risk of exposure towards particulate matter. Secondary data on the concentration of PM10, sulphur dioxide (SO2), nitrogen dioxide (NO2), ground level ozone (O3), carbon monoxide (CO) along with temperature and relative humidity at Jerantut monitoring stations between 2010 to 2012 obtained from Department of Environment. The main objective of this study is to describe the relationship between PM10 with other gases and weather conditions by using correlation. It also aims to determine the best prediction categories. Furthermore, this research aims to find a model for predicting the concentration of PM10 using logistic regression. PM10 and O3 at Jerantut monitoring station were found to have a strong positive correlation. The best logistic regression model was obtained at Jerantut station in 2010 with an R2 value of 0.565. The best prediction category for Jerantut monitoring stations was shown to be healthy with a correct percentage of more than 85% obtained from the analysis of the overall and annual results between 2010 to 2012.