Predicting chronic fine and coarse particulate exposures using spatiotemporal models for the Northeastern and Midwestern United States.
Predicting chronic fine and coarse particulate exposures using spatiotemporal models for the Northeastern and Midwestern United States.
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DOI：
10.1289/ehp.11692
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发表时间：
2009-04
影响因子：
10.4
通讯作者：
Suh HH
中科院分区：
文献类型：
--
作者：
Yanosky JD;Paciorek CJ;Suh HH
由于缺乏监测数据，对颗粒物（PM）的慢性流行病学研究受到限制，而是依赖于城市范围内的环境浓度来估计暴露。该方法忽略了城市内部的空间梯度，并将研究限制在具有附近监测数据的区域。这种数据的缺乏尤其限制了细颗粒（空气动力学直径< 2.5 μm的PM； PM2.5）和粗颗粒（空气动力学直径2.5 - 10 μm的PM； PM10-2.5）， 1999年之前对它们的监测是有限的。为了解决这些限制，我们开发了时空模型来预测美国东北部和中西部月度室外PM2.5和PM10-2.5浓度。对于PM2.5，我们开发了两个时期的模型：1988-1998年和1999-2002年。这两个模型都包含了基于地理信息系统和气象预测器的平滑空间和回归项。为了弥补监测数据的稀疏性，1999年以前的模型还包括了PM10（空气动力直径< 10 μm的PM）和消光系数（km−1）。PM10 - 2.5水平被估计为每月预测PM10和PM2.5的差异，预测PM10来自我们之前开发的PM10模型。对PM2.5的预测效果较好（1999年后和1999年前模型的交叉验证R2分别为0.77和0.69），精度较高（分别为2.2和2.7 μg/m3）。无论种群密度和季节如何，模型都表现良好。PM10-2.5的预测效果较弱（交叉验证R2 = 0.39），精密度较低（5.5 μg/m3）。PM10 - 2.5水平比PM10或PM2.5表现出更大的局部空间变异性，这表明环境监测点的PM2.5测量值比PM10，特别是PM10 - 2.5更能代表周围人群。我们提供了半经验模型来预测PM2.5和PM10-2.5在空间和时间上的长期平均室外浓度，以估计居住在美国东北部和中西部的人群的暴露情况。
Chronic epidemiologic studies of particulate matter (PM) are limited by the lack of monitoring data, relying instead on citywide ambient concentrations to estimate exposures. This method ignores within-city spatial gradients and restricts studies to areas with nearby monitoring data. This lack of data is particularly restrictive for fine particles (PM with aerodynamic diameter < 2.5 μm; PM2.5) and coarse particles (PM with aerodynamic diameter 2.5–10 μm; PM10–2.5), for which monitoring is limited before 1999. To address these limitations, we developed spatiotemporal models to predict monthly outdoor PM2.5 and PM10–2.5 concentrations for the northeastern and midwestern United States. For PM2.5, we developed models for two periods: 1988–1998 and 1999–2002. Both models included smooth spatial and regression terms of geographic information system-based and meteorologic predictors. To compensate for sparse monitoring data, the pre-1999 model also included predicted PM10 (PM with aerodynamic diameter < 10 μm) and extinction coefficients (km−1). PM10–2.5 levels were estimated as the difference in monthly predicted PM10 and PM2.5, with predicted PM10 from our previously developed PM10 model. Predictive performance for PM2.5 was strong (cross-validation R2 = 0.77 and 0.69 for post-1999 and pre-1999 PM2.5 models, respectively) with high precision (2.2 and 2.7 μg/m3, respectively). Models performed well irrespective of population density and season. Predictive performance for PM10–2.5 was weaker (cross-validation R2 = 0.39) with lower precision (5.5 μg/m3). PM10–2.5 levels exhibited greater local spatial variability than PM10 or PM2.5, suggesting that PM2.5 measurements at ambient monitoring sites are more representative for surrounding populations than for PM10 and especially PM10–2.5. We provide semiempirical models to predict spatially and temporally resolved long-term average outdoor concentrations of PM2.5 and PM10–2.5 for estimating exposures of populations living in the northeastern and midwestern United States.
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影响因子：
37.8
作者：
Pope, CA;Burnett, RT;Godleski, JJ
通讯作者：
Godleski, JJ
DOI：
10.3155/1047-3289.58.2.254
发表时间：
2008-02-01
影响因子：
2.7
作者：
Allen, David T.;Turner, Jay R.
通讯作者：
Turner, Jay R.
影响因子：
5
作者：
Yanosk, Jeff D.;Paciorek, Christopher J.;Suh, Helen H.
通讯作者：
Suh, Helen H.
影响因子：
5.4
作者：
Brauer, M;Hoek, G;Brunekreef, B
通讯作者：
Brunekreef, B
影响因子：
120.7
作者：
Pope, CA;Burnett, RT;Thurston, GD
通讯作者：
Thurston, GD