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 (${\mathrm{PM}}_{2.5}$), black carbon (BC), and nitrogen dioxide (${\mathrm{NO}}_2$) 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 ($p<0.05$) that were associated with indoor ${\mathrm{PM}}_{2.5}$, BC, and ${\mathrm{NO}}_2$ exposures, respectively. The overall variability explained by these models was $R^2=0.679$, 0.687, and 0.621 for ${\mathrm{PM}}_{2.5}$, BC, and ${\mathrm{NO}}_2$, 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 ${\mathrm{PM}}_{2.5}$, BC, and ${\mathrm{NO}}_2$ 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 ${\mathrm{PM}}_{2.5}$, BC, and ${\mathrm{NO}}_2$ 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 ${\mathrm{PM}}_{2.5}$, BC, and ${\mathrm{NO}}_2$ 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

中文翻译：

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影响市中心学校课堂接触细颗粒物、黑碳和二氧化氮的因素及其对室内空气质量的影响

摘要

背景：

学生白天大部分时间都呆在学校教室里，是儿童第二重要的室内微环境。

客观的：

我们调查了影响课堂暴露于细颗粒物的因素（${\mathrm{下午}}_{2.5}$)、黑碳 (BC) 和二氧化氮 (${\mathrm{不}}_2$) 在美国东北部的城市学校。

方法：

在 10 年（2008-2013 年；2015-2019 年）期间，在学年的秋季、冬季和春季，对 74 所市中心学校的 309 间教室进行了测量。使用自适应混合效应最小绝对收缩和选择算子 (LASSO) 回归模型分析数据。LASSO 变量包括基于气象、学校和课堂的协变量。

结果：

LASSO 确定了 10、10 和 11 个重要因素（$p<0.05$) 与室内相关的${\mathrm{下午}}_{2.5}$, BC 和${\mathrm{不}}_2$曝光，分别。这些模型解释的总体可变性是$R^2=0.679$, 0.687 和 0.621${\mathrm{下午}}_{2.5}$, BC 和${\mathrm{不}}_2$， 分别。在模型的解释变量中，室外空气污染是最重要的预测因子，分别占室内空气污染的 53.9%、63.4% 和 34.1%${\mathrm{下午}}_{2.5}$, BC 和${\mathrm{不}}_2$浓度。基于学校的预测因素包括熔炉维修、地下室的存在、年收入、建筑类型、建筑年份、教室数量、学生人数和通风类型，这些因素结合起来解释了 18.6%、26.1% 和 34.2 ％ 的${\mathrm{下午}}_{2.5}$, BC 和${\mathrm{不}}_2$水平，而基于教室的预测因素包括教室地板水平、教室与自助餐厅的距离、窗户数量、清洁频率和面向公交区域的窗户，共同解释了 24.0%、4.2% 和 29.3%${\mathrm{下午}}_{2.5}$, BC 和${\mathrm{不}}_2$浓度，分别。

讨论：

自适应 LASSO 技术确定了影响教室空气污染物水平的重要区域、学校和教室因素，并提供了可靠的估计值，这些估计值可能为旨在改善儿童早期发展阶段健康和福祉的有针对性的干预措施提供信息。https://doi.org/10.1289/EHP10007