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

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 ([Formula: see text]), black carbon (BC), a...

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Detalles Bibliográficos
Autores principales: Matthaios, Vasileios N., Kang, Choong-Min, Wolfson, Jack M., Greco, Kimberly F., Gaffin, Jonathan M., Hauptman, Marissa, Cunningham, Amparito, Petty, Carter R., Lawrence, Joy, Phipatanakul, Wanda, Gold, Diane R., Koutrakis, Petros
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Environmental Health Perspectives 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022782/
https://www.ncbi.nlm.nih.gov/pubmed/35446676
http://dx.doi.org/10.1289/EHP10007
Descripción
Sumario: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 ([Formula: see text]), black carbon (BC), and nitrogen dioxide ([Formula: see text]) 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 ([Formula: see text]) that were associated with indoor [Formula: see text] , BC, and [Formula: see text] exposures, respectively. The overall variability explained by these models was [Formula: see text] , 0.687, and 0.621 for [Formula: see text] , BC, and [Formula: see text] , 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 [Formula: see text] , BC, and [Formula: see text] 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 [Formula: see text] , BC, and [Formula: see text] 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 [Formula: see text] , BC, and [Formula: see text] 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