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Factors affecting variability in gaseous and particle microenvironmental air pollutant concentrations in Hong Kong primary and secondary schools.
Indoor Air ( IF 5.8 ) Pub Date : 2020-07-30 , DOI: 10.1111/ina.12725 Wenwei Che 1 , Alison T Y Li 1 , Henry Christopher Frey 1, 2 , Kimberly Tasha Jiayi Tang 1 , Li Sun 1 , Peng Wei 1 , Md Shakhaoat Hossain 1 , Tilman Leo Hohenberger 1 , King Wai Leung 1 , Alexis K H Lau 1, 3, 4
Indoor Air ( IF 5.8 ) Pub Date : 2020-07-30 , DOI: 10.1111/ina.12725 Wenwei Che 1 , Alison T Y Li 1 , Henry Christopher Frey 1, 2 , Kimberly Tasha Jiayi Tang 1 , Li Sun 1 , Peng Wei 1 , Md Shakhaoat Hossain 1 , Tilman Leo Hohenberger 1 , King Wai Leung 1 , Alexis K H Lau 1, 3, 4
Affiliation
School‐age children are particularly susceptible to exposure to air pollutants. To quantify factors affecting children's exposure at school, indoor and outdoor microenvironmental air pollutant concentrations were measured at 32 selected primary and secondary schools in Hong Kong. Real‐time PM10, PM2.5, NO2, and O3 concentrations were measured in 76 classrooms and 23 non‐classrooms. Potential explanatory factors related to building characteristics, ventilation practice, and occupant activities were measured or recorded. Their relationship with indoor measured concentrations was examined using mixed linear regression models. Ten factors were significantly associated with indoor microenvironmental concentrations, together accounting for 74%, 61%, 46%, and 38% of variations observed for PM2.5, PM10, O3, and NO2 microenvironmental concentrations, respectively. Outdoor concentration is the single largest predictor for indoor concentrations. Infiltrated outdoor air pollution contributes to 90%, 70%, 75%, and 50% of PM2.5, PM10, O3, and NO2 microenvironmental concentrations, respectively, in classrooms during school hours. Interventions to reduce indoor microenvironmental concentrations can be prioritized in reducing ambient air pollution and infiltration of outdoor pollution. Infiltration factors derived from linear regression models provide useful information on outdoor infiltration and help address the gap in generalizable parameter values that can be used to predict school microenvironmental concentrations.
更新日期：2020-07-30
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