National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA.
J Expo Sci Environ Epidemiol. 2013 Nov-Dec;23(6):654-9. doi: 10.1038/jes.2013.62. Epub 2013 Oct 2.
Many epidemiologic studies of the health effects of exposure to ambient air pollution use measurements from central-site monitors as their exposure estimate. However, measurements from central-site monitors may lack the spatial and temporal resolution required to capture exposure variability in a study population, thus resulting in exposure error and biased estimates. Articles in this dedicated issue examine various approaches to predict or assign exposures to ambient pollutants. These methods include combining existing central-site pollution measurements with local- and/or regional-scale air quality models to create new or "hybrid" models for pollutant exposure estimates and using exposure models to account for factors such as infiltration of pollutants indoors and human activity patterns. Key findings from these articles are summarized to provide lessons learned and recommendations for additional research on improving exposure estimation approaches for future epidemiological studies. In summary, when compared with use of central-site monitoring data, the enhanced spatial resolution of air quality or exposure models can have an impact on resultant health effect estimates, especially for pollutants derived from local sources such as traffic (e.g., EC, CO, and NO(x)). In addition, the optimal exposure estimation approach also depends upon the epidemiological study design. We recommend that future research develops pollutant-specific infiltration data (including for PM species) and improves existing data on human time-activity patterns and exposure to local source (e.g., traffic), in order to enhance human exposure modeling estimates. We also recommend comparing how various approaches to exposure estimation characterize relationships between multiple pollutants in time and space and investigating the impact of improved exposure estimates in chronic health studies.
许多关于暴露于环境空气污染对健康影响的流行病学研究使用中心站监测器的测量值作为其暴露估计值。然而，中心站监测器的测量值可能缺乏捕获研究人群中暴露变异性所需的空间和时间分辨率，从而导致暴露误差和有偏差的估计。本期专题文章探讨了预测或分配环境污染物暴露的各种方法。这些方法包括将现有的中心站污染测量值与局部和/或区域尺度空气质量模型相结合，以创建新的或“混合”模型来进行污染物暴露估计，并使用暴露模型来解释污染物在室内的渗透和人类活动模式等因素。这些文章的主要发现被总结出来，为改进未来流行病学研究中暴露估计方法提供了经验教训和建议。总之，与使用中心站监测数据相比，空气质量或暴露模型的增强空间分辨率可以对健康效应估计产生影响，特别是对于来自交通等本地来源的污染物（例如 EC、CO 和 NOx）。此外，最佳的暴露估计方法还取决于流行病学研究设计。我们建议未来的研究开发特定污染物的渗透数据（包括 PM 物种），并改进有关人类时间-活动模式和暴露于本地源（例如交通）的现有数据，以增强人体暴露模型估计。我们还建议比较各种暴露估计方法如何在时间和空间上描述多种污染物之间的关系，并研究改善暴露估计对慢性健康研究的影响。
