Semiparametric latent variable regression models for spatiotemporal modelling of mobile source particles in the greater Boston area
Semiparametric latent variable regression models for spatiotemporal modelling of mobile source particles in the greater Boston area
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DOI：
10.1111/j.1467-9876.2007.00573.x
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发表时间：
2007-01-01
影响因子：
1.6
通讯作者：
Suh, Helen H.
中科院分区：
文献类型：
--
作者：
Gryparis, Alexandros;Coull, Brent A.;Suh, Helen H.
交通颗粒物浓度在大都市区域内表现出相当大的空间变异性。我们认为潜变量半参数回归模型的黑碳和元素碳浓度在大波士顿地区的空间和时间变异性建模。这些污染物的测量，这是交通颗粒物的标志，从几个单独的暴露研究，在特定的家庭位置，以及在该地区的15个环境监测点进行。该模型允许灵活的非线性效应的协变量和无法解释的空间和时间的变化暴露。此外，不同的个体暴露研究记录了不同的交通颗粒物替代物，其中一些只记录了黑碳或元素碳的室外浓度，一些记录了黑碳的室内浓度，另一些记录了黑碳的室内和室外浓度。指定空间变化潜变量的室外和室内暴露的联合模型在感兴趣的区域中提供更大的空间覆盖。我们提出了一个惩罚样条制定的模型，涉及到广义克里金的潜在的交通污染变量，并导致一个自然的贝叶斯马尔可夫链蒙特卡罗算法模型拟合。我们提出的方法，使我们能够控制自由度的平滑贝叶斯框架。最后，我们提出了一个分析的结果，该模型分别适用于夏季和冬季的数据。
Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modelling the spatial and temporal variability of black carbon and elemental carbon concentrations in the greater Boston area. Measurements of these pollutants, which are markers of traffic particles, were obtained from several individual exposure studies that were conducted at specific household locations as well as 15 ambient monitoring sites in the area. The models allow for both flexible non-linear effects of covariates and for unexplained spatial and temporal variability in exposure. In addition, the different individual exposure studies recorded different surrogates of traffic particles, with some recording only outdoor concentrations of black or elemental carbon, some recording indoor concentrations of black carbon and others recording both indoor and outdoor concentrations of black carbon. A joint model for outdoor and indoor exposure that specifies a spatially varying latent variable provides greater spatial coverage in the area of interest. We propose a penalized spline formulation of the model that relates to generalized kriging of the latent traffic pollution variable and leads to a natural Bayesian Markov chain Monte Carlo algorithm for model fitting. We propose methods that allow us to control the degrees of freedom of the smoother in a Bayesian framework. Finally, we present results from an analysis that applies the model to data from summer and winter separately.