Multiple Linear Regression Model for Prediction of Pupils Exposure to PM2.5

Particulate matter, as one of the biggest problems of air pollution in metropolises, is the cause of many respiratory and cardiovascular diseases, adverse effects of which on human health can be reduced through timely awareness and announcement. Therefore, considering that children are more exposed and more sensitive to this pollution, this research was conducted to introduce an evaluated mathematical model to predict PM2.5 concentration levels, indoor selected preschools located in one of central districts of Tehran (district 6), using determination of related factors to PM2.5 concentration. Classroom environmental information, Meteorological information and urban fixed monitoring stations data were collected through measuring indoor and outdoor classroom PM2.5 concentrations using direct-reading instruments, adjusted questionnaire and conducted organizations, simultaneously. Results showed the spring and autumn had the lowest and highest indoor and outdoor concentrations (17.1 and 20.5 μg m-3 & 48.7 and 78 μg m-3) respectively. Also, multiple linear regression model was introduced by statistical analysis. The results of predicted indoor PM2.5 concentration were compared and evaluated to measured data and showed that introduced model, consisting of seven main factors affecting the mean concentrations of indoor PM2.5, including outdoor PM2.5, the number of pupils, ambient temperature, wind speed, wind direction and open area of the doors and windows has good accuracy (R2 = 0.705) and significantly correlated (p < 0.001). The Multiple Linear Regression Model can be used with good accuracy to predict indoor PM2.5 concentration of preschool classes in Tehran.