Estimating elemental composition of personal PM2.5 by a modeling approach in two megacities, China

Personal exposure to PM2.5, and the elemental composition therein, may vary greatly from ambient measurements at fixed monitoring sites. Here, we characterized the differences between personal, indoor, and outdoor concentrations of PM2.5- bound elements, and predicted personal exposures to 21 PM2.5-bound elements. Personal-indoor-outdoor PM2.5 filter sam-ples were collected for five consecutive days across two seasons from 66 healthy non-smoking retired adults in Beijing (BJ) and Nanjing (NJ), China. Personal element-specific models were developed using liner mixed effects models and evaluated by R2 and root mean square error (RMSE). The mean (SD) concentrations of personal exposures varied by element and city, ranging from 2.5 (1.4) ng/m3 for Ni in BJ to 4271.2 (1614.8) ng/m3 for S in NJ. Personal exposures to PM2.5 and most elements were significantly correlated with both indoor and outdoor (except Ni in BJ) measurements, but frequently exceeded indoor levels and fell below outdoor levels. Indoor and outdoor PM2.5 elemental concentrations were the stron-gest determinants of most personal elemental exposures, with R2M ranging from 0.074 to 0.975 for indoor and from 0.078 to 0.917 for outdoor levels, respectively. Home ventilation conditions (especially window opening behavior), time-activity patterns, meteorological factors, household characteristics, and season were also key factors influencing personal exposure levels. The final models accounted for 24.2 %-94.0 % (RMSE: 0.135-0.718) of the variance in personal PM2.5 elemental exposures. By incorporating these crucial determinants, the modeling approach used here can improve PM2.5-bound elemental exposure estimates and better associate compositionally dependent PM2.5 exposures and health risks.