Factors Influencing Classroom Exposures to Fine Particles, Black Carbon, and Nitrogen Dioxide in Inner-City Schools and Their Implications for Indoor Air Quality

Abstract

Background:School classrooms, where students spend the majority of their time during the day, are the second most important indoor microenvironment for children.Objective:We investigated factors influencing classroom exposures to fine particulate matter (), black carbon (BC), and nitrogen dioxide () in urban schools in the northeast United States.Methods:Over the period of 10 y (2008–2013; 2015–2019) measurements were conducted in 309 classrooms of 74 inner-city schools during fall, winter, and spring of the academic period. The data were analyzed using adaptive mixed-effects least absolute shrinkage and selection operator (LASSO) regression models. The LASSO variables included meteorological-, school-, and classroom-based covariates.Results:LASSO identified 10, 10, and 11 significant factors () that were associated with indoor , BC, and exposures, respectively. The overall variability explained by these models was , 0.687, and 0.621 for , BC, and , respectively. Of the model’s explained variability, outdoor air pollution was the most important predictor, accounting for 53.9%, 63.4%, and 34.1% of the indoor , BC, and concentrations. School-based predictors included furnace servicing, presence of a basement, annual income, building type, building year of construction, number of classrooms, number of students, and type of ventilation that, in combination, explained 18.6%, 26.1%, and 34.2% of , BC, and levels, whereas classroom-based predictors included classroom floor level, classroom proximity to cafeteria, number of windows, frequency of cleaning, and windows facing the bus area and jointly explained 24.0%, 4.2%, and 29.3% of , BC, and concentrations, respectively.Discussion:The adaptive LASSO technique identified significant regional-, school-, and classroom-based factors influencing classroom air pollutant levels and provided robust estimates that could potentially inform targeted interventions aiming at improving children’s health and well-being during their early years of development. https://doi.org/10.1289/EHP10007