Abstract：
Neglecting indoor air quality in exposure assessments may lead to biased exposure estimates and erroneous conclusions about the health impacts of exposure and environmental health disparities. This study assessed these biases by comparing two types of personal exposure estimates for 100 individuals: one derived from real-time particulate matter (PM2.5) measurements collected both indoors and outdoors using a low-cost portable air monitor (GeoAir2.0) and the other from PurpleAir sensor network data collected exclusively outdoors. The PurpleAir measurement data were used to create smooth air pollution surfaces using geostatistical methods. To obtain mobility-based exposure estimates, both sets of air pollution data were combined with the individuals\' GPS tracking data. Paired-sample t-tests were then performed to examine the differences between these two estimates. This study also investigated whether GeoAir2.0- and PurpleAir-based estimates yielded consistent conclusions about gender and economic disparities in exposure by performing Welch\'s t-tests and ANOVAs and comparing their t-values and F-values. The study revealed significant discrepancies between GeoAir2.0- and PurpleAir-based estimates, with PurpleAir data consistently overestimating exposure (t = 5.94; p < 0.001). It also found that females displayed a higher average exposure than males (15.65 versus. 8.55 μg/m3) according to GeoAir2.0 data (t = 4.654; p = 0.055), potentially due to greater time spent indoors engaging in pollution-generating activities traditionally associated with females, such as cooking. This contrasted with the PurpleAir data, which indicated higher exposure for males (43.78 versus. 46.26 μg/m3) (t = 3.793; p = 0.821). Additionally, GeoAir2.0 data revealed significant economic disparities (F = 7.512; p < 0.002), with lower-income groups experiencing higher exposure-a disparity not captured by PurpleAir data (F = 0.756; p < 0.474). These findings highlight the importance of considering both indoor and outdoor air quality to reduce bias in exposure estimates and more accurately represent environmental disparities.
摘要：
在暴露评估中忽略室内空气质量可能会导致有偏差的暴露估计和关于暴露对健康的影响和环境健康差异的错误结论。这项研究通过比较100个人的两种类型的个人暴露估计值来评估这些偏差：一种来自使用低成本便携式空气监测仪（GeoAir2.0）在室内和室外收集的实时颗粒物（PM2.5）测量结果，另一种来自PurpleAir传感器网络数据仅在室外收集。PurpleAir测量数据用于使用地统计学方法创建光滑的空气污染表面。为了获得基于移动性的暴露估计，两组空气污染数据与个人GPS跟踪数据相结合。然后进行配对样本t检验以检查这两个估计之间的差异。这项研究还调查了基于GeoAir2.0和PurpleAir的估计是否通过进行Welcht检验和ANOVA并比较其t值和F值，得出了有关性别和经济差异的一致结论。这项研究揭示了基于GeoAir2.0和PurpleAir的估计之间的显著差异，PurpleAir数据始终高估暴露(t=5.94；p<0.001)。研究还发现，女性的平均暴露量高于男性（15.65。8.55μg/m3），根据GeoAir2.0数据（t=4.654；p=0.055），可能是由于在室内花费更多时间参与传统上与女性相关的污染产生活动，比如做饭。这与PurpleAir的数据形成对比,这表明男性的暴露量较高（43.78对比。46.26μg/m3)(t=3.793；p=0.821)。此外,GeoAir2.0数据显示出显著的经济差异(F=7.512；p<0.002)，低收入群体经历更高的暴露-PurpleAir数据没有捕捉到的差异(F=0.756；p<0.474)。这些发现强调了同时考虑室内和室外空气质量以减少暴露估计偏差并更准确地表示环境差异的重要性。
