Mapping the personal PM_2.5 exposure of China's population using random forest

Ambient monitoring may cause estimation errors, and wearable monitoring is expensive and labor-intensive when assessing PM_2.5 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 PM_2.5 and deepen the understanding of the influences of indoor and outdoor factors on personal PM_2.5. Using this method, we found that the personal PM_2.5 exposure of Chinese residents was 10.50-347.02 μg/m³ in 2020, higher than the atmospheric PM_2.5 concentration. Residents in North and Central China, especially the Beijing-Tianjin-Hebei region and the Fen-Wei Plains, had higher personal PM_2.5 exposure than those in other areas.