Unit Healthy Living and Work, Department Risk Assessment for Products in Development, Netherlands Organization for Applied Scientific Research (TNO), Utrecht, The Netherlands.
Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands.
J Occup Environ Hyg. 2024 Oct;21(10):696-708. doi: 10.1080/15459624.2024.2389279. Epub 2024 Aug 29.
Occupational exposure to particulate matter (PM) can result in multiple adverse health effects and should be minimized to protect workers' health. PM exposure at the workplace can be complex with many potential sources and fluctuations over time, making it difficult to control. Dynamic maps that visualize how PM is distributed throughout a workplace over time can help in gaining better insights into when and where exposure occurs. This study explored the use of spatiotemporal modeling followed by kriging for the development of dynamic PM concentration maps in an experimental setting and a workplace setting. Data was collected using personal low-cost PM sensors and an indoor location tracking system, mounted on a moving robot or worker. Maps were generated for an experimental study with one simulated robot worker and a workplace study with four workers. Cross-validation was performed to evaluate the performance and robustness of three types of spatiotemporal models (metric, separable, and summetric) and, as an additional external validation, model estimates were compared with measurements from sensors that were placed stationary in the laboratory or workplace. Spatiotemporal models and maps were generated for both the experimental and workplace studies, with average root mean squared error (RMSE) from 10-fold cross-validation ranging from 7-12 and 73-127 µg/m, respectively. Workplace models were relatively more robust compared to the experimental study (relative SD ranging from 8-14% of the average RMSE 27-56%, respectively), presumably due to the larger number of parallel measurements. Model estimates showed low to moderate fits compared to stationary sensor measurements (R ranging from 0.1-0.5), indicating maps should be interpreted with caution and only used indicatively. Together, these findings show the feasibility of using spatiotemporal modeling for generating dynamic concentration maps based on personal data. The described method could be applied for exposure characterization within comparable study designs or can be expanded further, for example by developing real-time, location-based worker feedback systems, as efficient tools to visualize and communicate exposure risks.
职业性暴露于颗粒物（PM）可能导致多种健康不良影响，应尽量减少以保护工人健康。工作场所的 PM 暴露可能很复杂，存在许多潜在的来源且随时间波动，因此难以控制。可视化 PM 随时间在整个工作场所分布的动态地图可以帮助更好地了解何时何地发生暴露。本研究探讨了在实验和工作场所环境中使用时空建模和克里金法来开发动态 PM 浓度图。使用个人低成本 PM 传感器和室内位置跟踪系统收集数据，该系统安装在移动机器人或工人身上。为一项实验研究和一项有四名工人的工作场所研究生成了地图。进行交叉验证以评估三种时空模型（度量、可分离和和度量）的性能和稳健性，并作为额外的外部验证，将模型估计值与放置在实验室或工作场所的固定传感器的测量值进行比较。为实验和工作场所研究生成了时空模型和地图，十折交叉验证的平均均方根误差（RMSE）分别为 10-12 和 73-127μg/m。与实验研究相比，工作场所模型相对更稳健（平均 RMSE 的相对标准差范围为 27-56%，分别为 8-14%），可能是由于测量次数较多。与固定传感器测量值相比，模型估计值的拟合度较低（R 范围为 0.1-0.5），表明地图应谨慎解释，仅用作指示性工具。总之，这些发现表明，使用时空建模基于个人数据生成动态浓度图是可行的。该方法可应用于类似研究设计中的暴露特征描述，也可进一步扩展，例如开发基于位置的实时工人反馈系统，作为可视化和传达暴露风险的有效工具。
