Estimating Long-Term Indoor PM2.5 of Outdoor and Indoor Origin using Low-Cost Sensors
Keywords:Indoor PM2.5 exposure, ndoor emission, real building monitoring, I/O ratio, year-round distribution, natural ventilation
To evaluate the separate impacts on human health and establish indoor control strategies, it is crucial to estimate the contribution of outdoor infiltration and indoor emission to indoor PM2.5 in the built environment. This study applied an algorithm to automatically estimate the long-term time-resolved indoor PM2.5 of outdoor and indoor origin in real apartments with natural ventilation. The inputs for the algorithm were only the time-resolved indoor/outdoor PM2.5 concentrations and occupants’ window actions, which were easily obtained from the low-cost sensors. This study first applied the algorithm in an apartment in Tianjin, China. The indoor/outdoor contribution to the gross indoor exposure and time-resolved infiltration factor were automatically estimated using the algorithm. Due to the year-round monitoring, the probabilistic distribution of the time-resolved PM2.5 infiltration factor and indoor PM2.5 emission can be given over a year. The influence of outdoor PM2.5 data source on the estimated results was compared using the data from the low-cost light-scattering sensor and official monitoring station. Besides, the sensitive parameters to the algorithm were analyzed and their effects on the indoor emission contribution and estimated infiltration factor were investigated. Through the analysis, this study identified the practical applications that robust long-term outdoor PM2.5 monitoring for a specific building can use the data from nearest official monitoring station. This study demonstrated an algorithm for estimating long-term time-resolved indoor PM2.5 of outdoor and indoor origin in real naturally ventilated apartments with only the time-resolved indoor/outdoor PM2.5 concentrations and window behaviors.
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