Abstract
PM2.5 is an air pollutant that can cause various adverse health effects. Although the fixed ambient air monitoring stations provides ambient PM2.5 concentration within a community, it is still weak to assess actual exposure of population account for time-activity patterns. However, the exposure of the entire population in a region of interest may be estimated by classifying the population according to time-activity pattern and modeling their exposure. In this study, we tried to suggest the methodology to assess exposure to PM2.5 of entire population in a region of interest. The five field technicians representing similar time-activity groups (STG) of preschool children, students, housewives, office workers, and the elderly conducted exposure simulation with PM2.5 personal exposure monitor in Guro-gu, Seoul, Korea. The PM2.5 exposure concentrations (cs) were modeled by interpolation (point in polygon, inverse distance weighted and ordinary kriging) and regression model using GPS data and sensor-based air monitoring instruments network and compared with MicroPEM data (cm). The exposure of the entire population to PM2.5 was estimated by population-weighted average through Monte-Carlo simulation. The elderly had the highest average cm follows by office workers, housewives, preschool children, and students. The correlation between c¬m and cs was good in order of ordinary kriging (R2=0.822), inverse distance weighted (R2=0.747), and point in polygon method (R2=0.721). The 33.8% of the entire population exposed to PM2.5 higher than Atmospheric Environmental Standard of PM2.5 for 24-hour average. In this study, the possibility of assessing the exposure of the entire population for real-time and long-term cumulative exposure was suggested by applying this methodology, and it is expected that the exposure surveillance system can be developed based on these results.
Published Version
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