Factors affecting variability in gaseous and particle microenvironmental air pollutant concentrations in Hong Kong primary and secondary schools. (5th September 2020)

• Record Type: Journal Article
• Authors: Che, Wenwei; Li, Alison T. Y.; Frey, Henry Christopher; Tang, Kimberly Tasha Jiayi; Sun, Li; Wei, Peng; Hossain, Md Shakhaoat; Hohenberger, Tilman Leo; Leung, King Wai; Lau, Alexis K. H.
• Is Part Of: Indoor air. Volume 31:Number 1(2021)
• Page Start: 170
• Page End: 187
• Publication Date: 2020-09-05
• Subjects: fine particulate matter (PM2.5) -- indoor air quality -- nitrogen dioxide (NO2) -- ozone (O3) -- respirable particulate matter (PM10) -- school exposure; Indoor air pollution -- Periodicals; Sick building syndrome -- Periodicals; Ventilation -- Periodicals; 613.5
• DOI: 10.1111/ina.12725
• Languages: English
• ISSNs: 0905-6947

Abstract

Abstract: 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.