Assessment of PM2.5 population exposure of a community using sensor based air monitoring instruments and similar time-activity groups

Özet

Although fixed ambient air monitoring stations provide data regarding ambient PM2.5 concentrations within agiven community, they present limitations in terms of assessing the actual exposure to individuals and populations using time-activity patterns. The population exposure of a community may be estimated by classifyingthe population according to the time-activity pattern and modeling their exposure. In this study, we provide apossible methodology to assess the population exposure to PM2.5 in a given community. Five field techniciansconducted exposure simulations for similar time-activity groups of preschool children, school students, homemakers, office workers, and elderly, with PM2.5 personal exposure monitoring (PEM) equipment, in Guro-gu,Seoul, Korea. The PM2.5 exposure concentrations were modeled by interpolation (point in polygon, inversedistance weighted, and ordinary kriging methods) and regression models using GPS data and a sensor-based airmonitoring instrument network, and were compared with the PEM data. The population exposure to PM2.5 wasestimated using the population-weighted average through Monte–Carlo simulation. Elderly presented thehighest average PM2.5 exposure concentration followed by office workers, homemakers, preschool children, andschool students. The correlation between the measured and modeled exposure was good, in the order of ordinarykriging (R2 = 0.822), inverse distance weighted (R2 = 0.747), and point in polygon methods (R2 = 0.721).Thirty-seven percent of the population in a community was exposed to PM2.5 concentrations higher than 35 μg/m3, which is the Korean Atmospheric Environmental Standard for PM2.5 (24-h average). It is suggested that thismethodology could be applied to assess the real-time and long-term cumulative exposures of a given community.It is expected that an exposure surveillance system can be developed based on these results.