Assessing personal exposure to air pollution is challenging due to the limited availability of human movement data and the complexity of modeling air pollution at high spatiotemporal resolution. Most health studies rely on residential estimates of outdoor air pollution instead which introduces exposure measurement error. Personal exposure for 100,784 individuals in Los Angeles County was estimated by integrating human movement data simulated from the Southern California Association of Governments (SCAG) activity-based travel demand model with hourly PM_2.5 predictions from my 500 m gridded model incorporating low-cost sensor monitoring data. Individual exposures were assigned considering PM_2.5 levels at homes, workplaces, and other activity locations. These dynamic exposures were compared to the residence-based exposures, which do not consider human movement, to examine the degree of exposure estimation bias. The results suggest that exposures were underestimated by 13% (range 5–22%) on average when human movement was not considered, and much of the error was eliminated by accounting for work location. Exposure estimation bias increased for people who exhibited higher mobility levels, especially for workers with long commute distances. Overall, the personal exposures of workers were underestimated by 22% (5–61%) relative to their residence-based exposures. For workers who commute >20 miles, their exposure levels can be at most underestimated by 61%. Omitting mobility resulted in underestimating exposures for people who reside in areas with cleaner air but work in more polluted areas. Similarly, exposures were overestimated for people living in areas with poorer air quality and working in cleaner areas. These could lead to differential estimation biases across racial, ethnic and socioeconomic lines that typically correlate with where people live and work and lead to important exposure and health disparities. This study demonstrates that ignoring human movement and spatiotemporal variability of air pollution could lead to differential exposure misclassification potentially biasing health risk assessments. These improved dynamic approaches can help planners and policymakers identify disadvantaged populations for which exposures are typically misrepresented and might lead to targeted policy and planning implications.

由于人类运动数据的可用性有限以及以高时空分辨率模拟空气污染的复杂性，评估个人空气污染暴露具有挑战性。大多数健康研究依赖于室外空气污染的住宅估计，而不是引入暴露测量误差。洛杉矶县 100,784 人的个人暴露是通过将南加州政府协会 (SCAG) 基于活动的旅行需求模型模拟的人体运动数据与我的 500 m 网格模型中的每小时 PM _2.5预测相结合来估计的，该模型结合了低成本传感器监测数据。考虑 PM _2.5分配个人暴露家庭、工作场所和其他活动地点的水平。将这些动态暴露与不考虑人体运动的基于居住的暴露进行比较，以检查暴露估计偏差的程度。结果表明，在不考虑人员移动的情况下，暴露平均被低估了 13%（范围 5-22%），并且通过考虑工作地点消除了大部分错误。对于表现出较高流动性的人，尤其是通勤距离长的工人，暴露估计偏差会增加。总体而言，工人的个人暴露相对于其居住地暴露被低估了 22%（5-61%）。对于通勤 >20 英里的工人，他们的暴露水平最多被低估 61%。忽略流动性导致对居住在空气更清洁地区但在污染更严重地区工作的人的暴露估计不足。同样，生活在空气质量较差地区和在清洁地区工作的人们的暴露量也被高估了。这些可能导致跨种族、民族和社会经济界线的不同估计偏差，这些偏差通常与人们生活和工作的地点相关，并导致重要的暴露和健康差异。这项研究表明，忽略人类活动和空气污染的时空变化可能导致不同的暴露错误分类，从而可能使健康风险评估产生偏差。