Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
School of Geography Earth and Environmental Science, University of Birmingham, Birmingham, UK.
Environ Health Perspect. 2022 Apr;130(4):47005. doi: 10.1289/EHP10007. Epub 2022 Apr 21.
School classrooms, where students spend the majority of their time during the day, are the second most important indoor microenvironment for children.
We investigated factors influencing classroom exposures to fine particulate matter (), black carbon (BC), and nitrogen dioxide () in urban schools in the northeast United States.
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.
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.
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.
教室是学生白天大部分时间待的地方，是儿童的第二大重要室内微环境。
我们研究了影响美国东北部城市学校教室内细颗粒物（）、黑碳（BC）和二氧化氮（）暴露的因素。
在 10 年（2008-2013 年；2015-2019 年）期间，在学术期间的秋季、冬季和春季，对 74 所市内学校的 309 间教室进行了测量。数据分析采用自适应混合效应最小绝对收缩和选择算子（LASSO）回归模型。LASSO 变量包括气象、学校和教室为基础的协变量。
LASSO 确定了 10、10 和 11 个与室内、BC 和暴露相关的显著因素（）。这些模型解释的总变异性分别为 0.539、0.687 和 0.621。在模型解释的变异性中，室外空气污染是最重要的预测因素，占室内、BC 和浓度的 53.9%、63.4%和 34.1%。基于学校的预测因素包括熔炉维修、地下室存在、年收入、建筑类型、建筑建设年份、教室数量、学生数量和通风类型，这些因素共同解释了 18.6%、26.1%和 34.2%的、BC 和水平，而基于教室的预测因素包括教室楼层、教室与自助餐厅的距离、窗户数量、清洁频率以及面向公共汽车区域的窗户，这些因素共同解释了 24.0%、4.2%和 29.3%的、BC 和浓度。
自适应 LASSO 技术确定了影响教室空气污染物水平的显著区域、学校和教室为基础的因素，并提供了稳健的估计，这可能为旨在改善儿童在早期发育阶段的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.
