Factors affecting variability in gaseous and particle microenvironmental air pollutant concentrations in Hong Kong primary and secondary schools.

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.

Sensitivity analysis of influence factors on multi-zone indoor airflow CFD simulation.

Computational fluid dynamics (CFD) is a powerful tool for performing indoor airflow analysis. The simulation results are usually validated with measurement results for accuracy in reflecting reality. When conducting CFD for simulating air flow in a multiple-zone indoor environment with different boundary conditions in different regions, the validation of the CFD model becomes sophisticated. To improve the accuracy of the simulation, boundary conditions need to be adjusted based on how significant the influence factors are affecting the multi-zone CFD model, which few studies have been conducted on. The objective of this study is to investigate the impact of influence factors on temperature and carbon dioxide concentration distribution of a validated CFD model of a typical office floor using ANSYS Fluent. This study provides insights on how to fine-tune a complex model to reflect the actual air flow and how the air quality and human comfort in different zones on the same floor could be affected by influence factors. The influence factors investigated are: (1) size of door gaps, (2) solar radiation and (3) number and orientation of occupants. The velocity variations caused by different door gap sizes were studied for improving multi-zone simulation accuracy by adjusting door gap sizes. To study the significant impact of solar heat on multi-zone environment, the sensitivity of different regions of the office floor to solar heat amount and distribution was analyzed by conducting solar analysis under different weather conditions. Impact of occupants on temperature and carbon dioxide concentration distributions in multi-zone environment were investigated by considering different numbers and facing directions of occupants in different regions of the office floor. In addition, this study demonstrates how to modify the influence factors efficiently using building information modeling (BIM).