动脉粥样硬化和空气污染多民族研究中空气污染浓度的降秩时空模拟
Reduced-rank spatio-temporal modeling of air pollution concentrations in the Multi-Ethnic Study of Atherosclerosis and Air Pollution
摘要：
流行病学文献中越来越多的证据表明空气污染与不良健康结果之间的关系。在环境保护局（EPA）资助的动脉粥样硬化和空气污染的多种族研究（MESA空气）研究中，个人空气污染暴露的预测依赖于一个灵活的时空预测模型，该模型将土地利用回归与克里格法相结合，以考虑污染物浓度的空间依赖性。时间变异性捕获使用时间趋势估计通过修改奇异值分解和随时间变化的空间残差。该模型利用来自现有监管网络的监测数据和MESA空气监测补充数据来预测单个群组成员的浓度。在一般情况下，时空模型是有限的，由于计算的棘手性，在其大数据集的功效。我们开发的MESA空气时空模型的降阶版本。要做到这一点，我们应用低秩克里金占空间变化的平均过程，并讨论这种方法的局限性。作为一种替代方案，我们表示空间变化使用薄板回归样条。我们比较了使用EPA和MESA空气监测数据预测的氮氧化物（NO $_x$）的浓度的概述模型的性能-在MESA空气中的主要利益的污染物-在洛杉矶大都市区通过交叉验证的$R^2$。我们的研究结果表明，在某些情况下，使用降秩模型可以提高计算效率。低秩克里金法和薄板回归样条在所考虑的配方中具有竞争力，尽管TPRS在某些情况下似乎更稳健。
英文摘要：
There is growing evidence in the epidemiologic literature of the relationship between air pollution and adverse health outcomes. Prediction of individual air pollution exposure in the Environmental Protection Agency (EPA) funded Multi-Ethnic Study of Atheroscelerosis and Air Pollution (MESA Air) study relies on a flexible spatio-temporal prediction model that integrates land-use regression with kriging to account for spatial dependence in pollutant concentrations. Temporal variability is captured using temporal trends estimated via modified singular value decomposition and temporally varying spatial residuals. This model utilizes monitoring data from existing regulatory networks and supplementary MESA Air monitoring data to predict concentrations for individual cohort members. In general, spatio-temporal models are limited in their efficacy for large data sets due to computational intractability. We develop reduced-rank versions of the MESA Air spatio-temporal model. To do so, we apply low-rank kriging to account for spatial variation in the mean process and discuss the limitations of this approach. As an alternative, we represent spatial variation using thin plate regression splines. We compare the performance of the outlined models using EPA and MESA Air monitoring data for predicting concentrations of oxides of nitrogen (NO$_x$)-a pollutant of primary interest in MESA Air-in the Los Angeles metropolitan area via cross-validated $R^2$. Our findings suggest that use of reduced-rank models can improve computational efficiency in certain cases. Low-rank kriging and thin plate regression splines were competitive across the formulations considered, although TPRS appeared to be more robust in some settings.
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