Modelling spatial variability in concentrations of single pollutants and composite air quality indicators in health effects studies
Modelling spatial variability in concentrations of single pollutants and composite air quality indicators in health effects studies
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DOI：
10.1111/rssa.12034
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发表时间：
2014-06-01
影响因子：
2
通讯作者：
Lee, Duncan
中科院分区：
文献类型：
--
作者：
Powell, Helen;Lee, Duncan
空气污染浓度升高几天所造成的健康影响一直是最近许多研究的重点，其中大多数研究评估的是单一污染物的影响，而不是综合空气质量指标。这些污染物在整个研究区域的平均浓度通常是通过对现有监测网络的测量结果进行平均来估计的，这种简单化的方法有几个不足之处。首先，由于监测网络的位置可能不是随机的，因此不可能是所研究区域的平均浓度。其次，真正的空间平均数是一个未知数，因此在估计其健康影响时，应考虑到任何估计的不确定性。本文提出了一种新的贝叶斯层次框架来解决这些问题，其中包括统计模型估计空间代表性措施的单一污染物和复合空气质量指标，这些污染措施的健康影响，同时正确地允许他们的不确定性。这一方法的发展是由一项流行病学研究的空气污染对呼吸系统死亡率的影响，在大伦敦，英格兰，2003年和2005年之间。这项研究的主要发现是，传统方法可能会低估空气污染对健康影响的不确定性，与本文提出的方法相比，1.4%至3.1%的风险增加与臭氧，颗粒物（PM10）和此处采用的综合空气质量指标浓度的1个标准偏差增加有关。
The health impact resulting from a few days of elevated air pollution concentrations has been the focus of much recent research, most of which assesses the effects of single pollutants rather than composite air quality indicators. The average concentrations of these pollutants across the study region are typically estimated by averaging the measurements from the available network of monitors, and this simplistic approach has several deficiencies. Firstly, it is unlikely to be the average concentration across the region under study, owing to the likely non-random placement of the monitoring network. Secondly, the true spatial average is an unknown quantity, and hence the uncertainty in any estimate should be allowed for when estimating its health effects. This paper proposes a novel Bayesian hierarchical framework for addressing these problems, which consists of statistical models for estimating spatially representative measures of single pollutants and composite air quality indicators, and the health effects of these pollution measures while correctly allowing for their uncertainty. This methodological development is motivated by an epidemiological study of the effects of air pollution on respiratory mortality in Greater London, England, between 2003 and 2005. The key findings from this study are that traditional approaches are likely to underestimate the uncertainty in the health effects of air pollution compared with the approach proposed here and increased risks of between 1.4% and 3.1% are associated with 1-standard-deviation increases in the concentrations of ozone, particulate matter (PM10) and the composite air quality indicator that is adopted here.