Distribution law and exposure risk prediction of VOC in vehicles based on machine learning

Experimental sampling and testing of VOC in the vehicle is carried out, and it is found that the main pollutants in the vehicle are toluene and formaldehyde. The Monte Carlo simulation is used to assess the exposure risk of toluene and formaldehyde in the vehicle. On this basis, a BP neural network optimized by the genetic algorithm (GA-BP neural network) is used to establish a prediction model for the concentration of pollutants in the vehicle, which improves the prediction accuracy and stability. The results show that according to the Monte Carlo simulation results, the non-carcinogenic risk hazard quotient of formaldehyde and toluene in the vehicle is less than 1, and the carcinogenic risk is greater in women than in men. The predicted values of the GA-BP neural network model on the concentration of pollutants are in good agreement with the measured values. The accuracy of the toluene and formaldehyde prediction models is about 33% and 41% respectively higher than that of the model before optimization. The gray correlation degree is used to analyse the correlation degree of five influence factors on the concentration of pollutants in the vehicle. The results show that the influence degree is ranked as temperature in the vehicle, outdoor temperature, humidity in the vehicle, light intensity and outdoor wind speed.