Mendoza Daniel L, Pirozzi Cheryl S, Crosman Erik T, Liou Theodore G, Zhang Yue, Cleeves Jessica J, Bannister Stephen C, Anderegg William R L, Robert Paine
Division of Respiratory, Critical Care and Occupational Pulmonary Medicine, School of Medicine, University of Utah, 26 North 1900 East, Salt Lake City, UT 84132, United States of America.
Department of Atmospheric Sciences, University of Utah, 135 S 1460 E, RM 819, Salt Lake City, UT 84112, United States of America.
Environ Res Lett. 2020 Nov;15(11). doi: 10.1088/1748-9326/abbf7a. Epub 2020 Nov 18.
High air pollution levels are associated with school absences. However, low level pollution impacts on individual school absences are under-studied. Understanding the variability of pollution at individual schools within an urban region could improve school recess decisions, better identify local pollution sources, and improve local economic impact assessments by providing granular information relevant to specific schools. We modelled PM and ozone concentrations at 36 schools from July 2015 to June 2018 using data from a dense, research grade regulatory sensor network. We determined exposures and daily absences at each school. We used a generalized estimating equations model to retrospectively estimate rate ratios for association between outdoor pollutant concentrations and school absences. We estimated lost school revenue, productivity, and family economic burden. PM and ozone concentrations and absence rates vary across the School District. Pollution exposure was associated with a rate ratio as high as 1.02 absences per g m and 1.01 per ppb increase for PM and ozone, respectively. Significantly, even PM and ozone exposure below the air quality index breakpoints for good air quality (<12.1 g m and <55 ppb, respectively) was associated with positive rate ratios of absences: 1.04 per g m and 1.01 per ppb increase, respectively. Granular local measurements enabled demonstration of air pollution impacts that varied between schools and were undetectable with averaged pollution levels. Reducing pollution by 50% would save $426000 per year districtwide. Pollution reduction benefits would be greatest in schools located in socioeconomically disadvantaged areas. Heterogeneity in exposure, disproportionately affecting socioeconomically disadvantaged schools, points to the need for fine resolution exposure estimation. The economic cost of absences associated with air pollution is substantial even excluding indirect costs such as hospital visits and medication. These findings may help elucidate the differential burden on individual schools and inform local decisions about recess and regulatory considerations for localized pollution sources.
高空气污染水平与学生缺课有关。然而，低水平污染对个体缺课的影响研究不足。了解城市区域内各学校污染的变异性可以改善学校课间休息决策，更好地识别当地污染源，并通过提供与特定学校相关的详细信息来改进当地经济影响评估。我们使用密集的研究级监管传感器网络的数据，对2015年7月至2018年6月期间36所学校的细颗粒物（PM）和臭氧浓度进行了建模。我们确定了每所学校的暴露情况和每日缺课情况。我们使用广义估计方程模型回顾性估计室外污染物浓度与学校缺课之间关联的率比。我们估计了学校收入损失、生产力损失和家庭经济负担。整个学区的PM和臭氧浓度以及缺课率各不相同。污染暴露与率比相关，PM每增加1微克/立方米缺课率比高达1.02，臭氧每增加1 ppb缺课率比为1.01。值得注意的是，即使PM和臭氧暴露低于空气质量指数中良好空气质量的断点（分别为<12.1微克/立方米和<55 ppb），也与缺课的正率比相关：分别为每增加1微克/立方米1.04和每增加1 ppb 1.01。详细的本地测量能够证明各学校之间空气污染影响的差异，而平均污染水平无法检测到这些差异。将污染降低50%每年可在整个学区节省426000美元。在社会经济条件不利地区的学校，减少污染的益处最大。暴露的异质性对社会经济条件不利的学校影响尤甚，这表明需要进行高分辨率的暴露估计。即使不包括诸如就医和用药等间接成本，与空气污染相关的缺课经济成本也相当可观。这些发现可能有助于阐明各学校的不同负担，并为有关课间休息的本地决策以及针对本地污染源的监管考量提供参考。
