In The Science of the total environment
Ambient monitoring may cause estimation errors, and wearable monitoring is expensive and labor-intensive when assessing PM2.5 personal exposure. Estimation errors have limited the development of exposure science and environmental epidemiology. Thus, we developed a scenario-based exposure (SBE) model that covered 8 outdoor exposure scenarios and 1 indoor scenario with corresponding time-activity patterns in Baoding City. The linear regression analysis of the SBE yielded an R2 value of 0.913 with satisfactory accuracy and reliability. To apply the SBE model to large-scale studies, we predicted time-activity patterns with the random forest model and atmosphere-to-scenario ratios with the linear regression model to obtain the essential parameters of the SBE model; their R2 was 0.65-0.93. The developed model would economize the study expenditure of field sampling for personal PM2.5 and deepen the understanding of the influences of indoor and outdoor factors on personal PM2.5. Using this method, we found that the personal PM2.5 exposure of Chinese residents was 10.50-347.02 μg/m3 in 2020, higher than the atmospheric PM2.5 concentration. Residents in North and Central China, especially the Beijing-Tianjin-Hebei region and the Fen-Wei Plains, had higher personal PM2.5 exposure than those in other areas.
Li Zhenglei, Chen Yu, Tao Yan, Zhao Xiuge, Wang Danlu, Wei Tong, Hou Yaxuan, Xu Xiaojing
2023-Feb-08
Exposure scenario, Machine learning, Personal PM(2.5) exposure, Scenario-based exposure, Time-activity patterns
