Predicting Intra-Urban Variation in Air Pollution Concentrations with Complex Spatio-Temporal Dependencies.
Predicting Intra-Urban Variation in Air Pollution Concentrations with Complex Spatio-Temporal Dependencies.
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DOI：
10.1002/env.1014
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发表时间：
2009-09-01
期刊：
影响因子：
1.7
通讯作者：
Kaufman, Joel D.
中科院分区：
文献类型：
--
作者：
Szpiro, Adam A.;Sampson, Paul D.;Sheppard, Lianne;Lumley, Thomas;Adar, Sara D.;Kaufman, Joel D.
关键词：
我们描述了一种方法，用于分配长期平均空气污染浓度的个人估计，占一个复杂的时空相关性结构，可以容纳时空错位的观测。这种方法是作为动脉粥样硬化和空气污染多种族研究（梅萨空气）的一部分开发的，这是一项由美国EPA资助的前瞻性队列研究，旨在调查长期暴露于空气污染和心血管疾病之间的关系。我们的分层模型将时空场分解为“平均值”，包括对协变量和空间变化的季节性和长期趋势的依赖，以及“残差”，该残差占平均值模型的空间相关偏差。该模型通过将每个位置的时间趋势表征为经验推导的时间基函数的线性组合，并将基函数系数的空间域嵌入具有空间相关残差的单独线性回归模型（通用克里金法），从而适应复杂的时空模式。这种方法使我们能够实现一个可扩展的单阶段估计过程，很容易容纳大量的缺失观测在一些监测位置。我们应用该模型来预测长期的氮氧化物（NOx）的平均浓度从2005-2007年在洛杉矶地区，从18个EPA空气质量系统监管监测的数据为基础。交叉验证的R2为0.67。梅萨空气研究也在收集更多的浓度数据，作为补充监测活动的一部分。我们描述了采样计划，并在模拟研究中证明，额外的数据将有助于改善长期平均浓度的预测。
We describe a methodology for assigning individual estimates of long-term average air pollution concentrations that accounts for a complex spatio-temporal correlation structure and can accommodate spatio-temporally misaligned observations. This methodology has been developed as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air), a prospective cohort study funded by the U.S. EPA to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. Our hierarchical model decomposes the space-time field into a “mean” that includes dependence on covariates and spatially varying seasonal and long-term trends and a “residual” that accounts for spatially correlated deviations from the mean model. The model accommodates complex spatio-temporal patterns by characterizing the temporal trend at each location as a linear combination of empirically derived temporal basis functions, and embedding the spatial fields of coefficients for the basis functions in separate linear regression models with spatially correlated residuals (universal kriging). This approach allows us to implement a scalable single-stage estimation procedure that easily accommodates a significant number of missing observations at some monitoring locations. We apply the model to predict long-term average concentrations of oxides of nitrogen (NOx) from 2005–2007 in the Los Angeles area, based on data from 18 EPA Air Quality System regulatory monitors. The cross-validated R2 is 0.67. The MESA Air study is also collecting additional concentration data as part of a supplementary monitoring campaign. We describe the sampling plan and demonstrate in a simulation study that the additional data will contribute to improved predictions of long-term average concentrations.
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影响因子：
5.8
作者：
Durbin, B;Rocke, DM
通讯作者：
Rocke, DM
影响因子：
1.8
作者：
Paciorek, Christopher J.;Yanosky, Jeff D.;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：
4.4
作者：
Damian, D;Sampson, PD;Guttorp, P
通讯作者：
Guttorp, P
DOI：
10.1038/sj.jea.7500388
发表时间：
2005-03-01
期刊：
JOURNAL OF EXPOSURE ANALYSIS AND ENVIRONMENTAL EPIDEMIOLOGY
影响因子：
--
作者：
Jerrett, M;Arain, A;Giovis, C
通讯作者：
Giovis, C