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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.

With increased efforts in developing air quality index, there is still a significant lack of effort in effectively communicating air pollution risk information to the general public. Research has shown that there is a magnitude of approaches and theories that make up risk communication, and the method of presenting risk information will be different depending on the method of presenting the information. This study aims to identify the different communication strategies that will be beneficial in air pollution risk communication as well as develop a risk communication tool that can effectively present both short-term and long-term air quality indexes. By referring to research on risk communication and air quality indexes, our objective is to identify the following questions: What are the hindering factors from achieving effective risk communication towards air pollution in Hong Kong? Furthermore, what is the most effective communication tool in presenting both short-term and long-term air quality health index? In this situation, risk communication is defined as the distribution of information regarding specific risk to the population at risk. Based on multiple literature reviews on air quality indexes around the world and theories of risk communication and risk perception, a small scale survey was conducted to Hong Kong residents in regards to their knowledge about air pollution, their perception towards air pollution and the current air quality health index (AQHI) in Hong Kong. A newly designed air quality index was also constructed by reviewing several works of literature on identifying factors in an air quality index by considering the differences between short-term and long-term air pollution indexes along with different presentation methods. Analysis of the responses demonstrated that even though the participants are knowledgeable about air pollution, their concern towards air pollution exposure remains low. Such results indicate that much effort in risk communi

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. © 2020 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd