Global Effect Factors for Exposure to Fine Particulate Matter
Global Effect Factors for Exposure to Fine Particulate Matter
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DOI：
10.1021/acs.est.9b01800
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发表时间：
2019-06-18
影响因子：
11.4
通讯作者：
Evans, John S.
中科院分区：
文献类型：
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作者：
Fantke, Peter;McKone, Thomas E.;Evans, John S.
我们对细颗粒物（PM2.5）暴露响应模型进行了评估，为跨空间尺度和城乡环境的产品和政策评估提供了一套一致的全球影响因素。暴露浓度与pm2.5可归因健康影响之间的关系在很大程度上取决于地点、人口密度和死亡率。现有的影响因素主要建立在一个本质上是线性的暴露反应函数上，其系数来自美国癌症协会的研究。相比之下，全球疾病负担分析提供了一个非线性综合暴露反应（IER）模型，其系数来自涵盖大范围暴露浓度的大量流行病学研究。我们探索了IER，并提供了PM2.5水平、死亡率和严重程度的简化回归函数，并将结果与最近发表的全球暴露死亡率模型（GEMM）得出的影响因素进行了比较。影响因素的不确定性主要是暴露反应形状、背景死亡率和地理变异性。我们对不同地区的中央基于ir的影响因子估计与以前的估计没有很大的不同。然而，IER估计在不同地点之间以及城市和农村环境之间表现出显著的差异，这主要是由PM2.5浓度和死亡率的变化所驱动的。利用国际环境影响指数作为影响因素的基础，可为产品和政策评估框架提供与pm2.5相关的全球影响的一致情况。
We evaluate fine particulate matter (PM2.5) exposure-response models to propose a consistent set of global effect factors for product and policy assessments across spatial scales and across urban and rural environments. Relationships among exposure concentrations and PM2.5-attributable health effects largely depend on location, population density, and mortality rates. Existing effect factors build mostly on an essentially linear exposure response function with coefficients from the American Cancer Society study. In contrast, the Global Burden of Disease analysis offers a nonlinear integrated exposure response (IER) model with coefficients derived from numerous epidemiological studies covering a wide range of exposure concentrations. We explore the IER, additionally provide a simplified regression as a function of PM2.5 level, mortality rates, and severity, and compare results with effect factors derived from the recently published global exposure mortality model (GEMM). Uncertainty in effect factors is dominated by the exposure response shape, background mortality, and geographic variability. Our central IER-based effect factor estimates for different regions do not differ substantially from previous estimates. However, IER estimates exhibit significant variability between locations as well as between urban and rural environments, driven primarily by variability in PM2.5 concentrations and mortality rates. Using the IER as the basis for effect factors presents a consistent picture of global PM2.5-related effects for use in product and policy assessment frameworks.