Prediction of Hourly Indoor Carbon Monoxide Concentrations by Using Multivariate Methods with Sensitivity Analysis Technique

Abstract

Precise site prediction of indoor hourly carbon monoxide (CO) concentrations in school buildings is a key issue in air quality research nowadays due to its impact on children’s health. In this study multivariate statistical methods, multiple linear regression (MLR) and principle component analysis (PCA), were employed to predict hourly indoor CO concentration in Gaza Strip, Palestine. Measurements were carried in 12 schools from October 2012 to May 2013 (one academic year). The results suggested that the selected models are effective forecasting tools and hence can be applicable for short-term forecasting of indoor CO level. The predicted indoor CO concentration values agree strongly well with the measured data with high coefficients of determination (R2) 0.869, 0.870 for MLR and PCA-MLR (PCR) respectively. Overall, results showed that PCA model combined with MLR improved MLR model of predicting indoor CO concentration, with reduced errors by as much as 7.14%.