Experimental And Theoretical Analysis On Atmospheric PM_2.5 Pollution And Its Impact On Indoor Environment

In recent years, the relatively developed regions or cities in China are frequently shrouded in fog and haze weather. The primary pollutant is fine particulate matter(PM2.5), whose size is less than 2.5μm. Considering that urban population generally spends most of time indoors, people living in urban indoor environment are exposed to significant PM2.5 concentrations. It is therefore important to study the pollution of atmospheric PM2.5 and its influencing degree on indoor environment.This work studied the variation characteristics of ambient atmospheric PM2.5 and its influence on indoor environment, and considered an office building in Beijing for monitoring. Conditions were considered for the office by natural ventilation, with windows closed and minimal indoor particle sources. Real-time monitoring of indoor and outdoor PM2.5 mass concentrations was respectively made for one year from July 2013 to June 2014. Meteorological parameters were monitored simultaneously to be correlated with PM2.5 mass concentrations and indoor-outdoor(I/O) concentration ratios. The results of the study indicated that:1) Except for spring, both outdoor and indoor PM2.5 mass concentrations were higher at night than daytime and showed an upward trend from Monday to Friday on workdays.2) The monthly-average concentrations of outdoor and indoor PM2.5 tended to be highest in winter, followed by autumn, spring and summer. And indoor-outdoor(I/O) concentration ratios in spring, autumn and winter were higher than those in summer.3) For all seasons, PM2.5 mass concentrations and I/O ratios were closely correlated with wind speed and relative humidity. However, outdoor temperature played a negligible role in PM2.5 mass concentrations and I/O ratios.4) Based on the measured and theoretical analysis and the early successes of our team, indoor PM2.5 human exposure level was evaluated in the office building, then compared with the measured values and calculated values using linear regression method. These findings can provide meaningful reference for the exact cognition of atmospheric PM2.5 and scientific evaluation of indoor PM2.5 human exposure levels, and further provide methods to control indoor environment.