Department of Environmental and Occupational Health Sciences, University of Washington School of Public Health, Seattle, WA, USA.
Department of Biostatistics, University of Washington School of Public Health, Seattle, WA, USA.
Curr Environ Health Rep. 2021 Jun;8(2):113-126. doi: 10.1007/s40572-021-00310-y.
Epidemiological studies of short- and long-term health impacts of ambient air pollutants require accurate exposure estimates. We describe the evolution in exposure assessment and assignment in air pollution epidemiology, with a focus on spatiotemporal techniques first developed to meet the needs of the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Initially designed to capture the substantial variation in pollutant levels and potential health impacts that can occur over small spatial and temporal scales in metropolitan areas, these methods have now matured to permit fine-scale exposure characterization across the contiguous USA and can be used for understanding long- and short-term health effects of exposure across the lifespan. For context, we highlight how the MESA Air models compare to other available exposure models.
Newer model-based exposure assessment techniques provide predictions of pollutant concentrations with fine spatial and temporal resolution. These validated models can predict concentrations of several pollutants, including particulate matter less than 2.5 μm in diameter (PM), oxides of nitrogen, and ozone, at specific locations (such as at residential addresses) over short time intervals (such as 2 weeks) across the contiguous USA between 1980 and the present. Advances in statistical methods, incorporation of supplemental pollutant monitoring campaigns, improved geographic information systems, and integration of more complete satellite and chemical transport model outputs have contributed to the increasing validity and refined spatiotemporal spans of available models. Modern models for predicting levels of outdoor concentrations of air pollutants can explain a substantial amount of the spatiotemporal variation in observations and are being used to provide critical insights into effects of air pollutants on the prevalence, incidence, progression, and prognosis of diseases across the lifespan. Additional enhancements in model inputs and model design, such as incorporation of better traffic data, novel monitoring platforms, and deployment of machine learning techniques, will allow even further improvements in the performance of pollutant prediction models.
环境空气污染物短期和长期健康影响的流行病学研究需要准确的暴露评估。我们描述了暴露评估和分配在空气污染流行病学中的演变，重点介绍了最初为满足多民族动脉粥样硬化研究和空气污染（MESA 空气）的需求而开发的时空技术。这些方法最初旨在捕捉大都市地区小空间和时间尺度上可能发生的污染物水平和潜在健康影响的巨大变化，现在已经成熟，可以在整个美国大陆进行精细尺度的暴露特征描述，并可用于了解整个生命周期暴露对长期和短期健康的影响。为了说明问题，我们强调了 MESA 空气模型如何与其他可用暴露模型进行比较。
基于模型的新型暴露评估技术可提供具有精细时空分辨率的污染物浓度预测。这些经过验证的模型可以预测几种污染物的浓度，包括直径小于 2.5μm 的颗粒物（PM）、氮氧化物和臭氧，在特定地点（如居住地址），在短时间间隔（如 2 周）内，在美国大陆各地，时间范围从 1980 年到现在。统计方法的进步、补充污染物监测活动的纳入、改进的地理信息系统以及更完整的卫星和化学输送模型输出的整合，都有助于提高现有模型的有效性和细化时空范围。用于预测室外空气污染物浓度水平的现代模型可以解释观察到的大量时空变化，并被用于提供关于空气污染物对整个生命周期疾病的流行、发病、进展和预后的影响的关键见解。模型输入和模型设计的额外改进，如更好的交通数据、新型监测平台和机器学习技术的部署，将进一步提高污染物预测模型的性能。
