Choi Jungsoon, Fuentes Montserrat, Reich Brian J, Davis Jerry M
J. Choi is a Graduate Student at the Department of Statistics, North Carolina State University. M. Fuentes is a Associate Professor at the Department of Statistics, North Carolina State University. (Email:
J Stat Theory Pract. 2009 Jun 1;3(2):407-418. doi: 10.1080/15598608.2009.10411933.
Fine particulate matter (PM(2.5)) is an atmospheric pollutant that has been linked to serious health problems, including mortality. PM(2.5) is a mixture of pollutants, and it has five main components: sulfate, nitrate, total carbonaceous mass, ammonium, and crustal material. These components have complex spatial-temporal dependency and cross dependency structures. It is important to gain insight and better understanding about the spatial-temporal distribution of each component of the total PM(2.5) mass, and also to estimate how the composition of PM(2.5) might change with space and time, by spatially interpolating speciated PM(2.5). This type of analysis is needed to conduct spatial-temporal epidemiological studies of the association of these pollutants and adverse health effect.We introduce a multivariate spatial-temporal model for speciated PM(2.5). We propose a Bayesian hierarchical framework with spatiotemporally varying coefficients. In addition, a linear model of coregionalization is developed to account for spatial and temporal dependency structures for each component as well as the associations among the components. We also introduce a statistical framework to combine different sources of data, which accounts for bias and measurement error. We apply our framework to speciated PM(2.5) data in the United States for the year 2004. Our study shows that sulfate concentrations are the highest during the summer while nitrate concentrations are the highest during the winter. The results also show total carbonaceous mass.
细颗粒物（PM₂.₅）是一种大气污染物，与包括死亡率在内的严重健康问题有关。PM₂.₅是多种污染物的混合物，它有五个主要成分：硫酸盐、硝酸盐、总碳质质量、铵和地壳物质。这些成分具有复杂的时空依赖性和交叉依赖性结构。通过对特定成分的PM₂.₅进行空间插值，深入了解和更好地理解总PM₂.₅质量中各成分的时空分布，并估计PM₂.₅的组成如何随空间和时间变化，这很重要。进行这些污染物与不良健康影响关联的时空流行病学研究需要这种类型的分析。我们为特定成分的PM₂.₅引入了一个多变量时空模型。我们提出了一个具有时空变化系数的贝叶斯层次框架。此外，还开发了一个核心区域化线性模型，以考虑每个成分的空间和时间依赖性结构以及各成分之间的关联。我们还引入了一个统计框架来整合不同来源的数据，该框架考虑了偏差和测量误差。我们将我们的框架应用于2004年美国特定成分的PM₂.₅数据。我们的研究表明，硫酸盐浓度在夏季最高，而硝酸盐浓度在冬季最高。结果还显示了总碳质质量。
