Prediction of phthalates concentration in household dust based on back propagation neural network

Field or laboratory measurements are commonly conducted to determine phthalates concentrations in spaces. This study investigated the association between various influencing factors and indoor phthalates concentrations. Back-propagation (BP) Neural Network was employed to verify a prediction model of indoor phthalates concentration with 80% of experimental data and 20% remaining data. The validation of remaining data shows a reasonable accuracy for model application, where the ratios of standard deviations were all greater than 0.45, most E-RMS were close to 0 and all the E-MR were less than 15.5%. In addition, we used relevant data from the China, Children, Homes, Health (CCHH) study conducted in Tianjin for further inspection. The prediction on di (2-ethylhexyl) phthalate (DEHP) concentration was performed, which indicated a high accuracy. Furthermore, the Monte-Carlo simulation was applied to quantify the effect of temperature on phthalates concentration combining with the prediction model. When the temperature increment value was 4 degrees C, the average relative decrease ratio of dibutyl phthalate (DBP), DEHP, diisobutyl phthalate (DIBP) concentration was about 7.8%, 12.8% and 9.3%, respectively. The findings have established the validity of the prediction model and provided a quantification of the influence of temperature on the concentration of phthalates in the indoor dust phase.