定量阐明我国大气污染与人群健康效应的暴露-反应关系规律是大气污染健康风险评估和环境空气质量标准制修订的重要科学依据。公众健康受到多样气象条件及多种大气污染物的影响，而目前统计方法的不足限制了不同气象条件下多元大气污染物健康效应的精确估计。.本项目拟提出并构建一套新的层次贝叶斯统计模型及其推断方法：①先验分布引入多阶段高斯过程协方差函数与高维数据贝叶斯变量选择两种方法，并分别运用多种先验分布结构以探讨各方法的适用性；②提出多种基于后验预测分布的模型诊断统计量及贝叶斯P值计算方法并获得最优检验策略；③建立复杂交互非线性模型下单一污染物的健康效应新指标评价方法。后验分布运用Hybrid Gibbs抽样结合自适应规则与潜变量设计两种改进优化算法。项目将揭示多元大气污染在不同气象条件下健康影响的暴露-反应规律，具有重要科学意义；还为我国建立大气污染暴露基准建议值及相关政策提供技术支持，具有实用价值。

Outdoor air pollution in China has aroused wide health concerns, and air pollution is a complex mixture of gaseous, liquid, and solid components that vary greatly in composition and concentration across the China and around the world due to differences in sources, weather, and topography. The challenges of determining whether effects are additive, synergistic, or less-than-additive, and of identifying climatic effect modification in epidemiologic studies, are substantial. Often, a high degree of correlation exists among levels of different pollutants emitted from similar sources or generated through similar atmospheric processes; and there may be nonlinear interactions among pollutants in relation to health outcomes. These issues complicate and may even preclude the use of classic regression approaches. .The aim of this study is to develop innovative statistical methods for studying the combined effects of individual pollutants under different climate condition. Specifically, this project will develop a family of Bayesian hierarchical models to simultaneously account for multivariate pollutants, effect modification of climatic variables. We first construct the stage 1 likelihood model using semiparametric Gaussian Process and high dimensional tensor product, which will be the basis for the stage 2 prior model. In the prior model, multi-stage Gaussian covariance function and Spike-and-Slab prior will be used for effective regularization of the multivariate exposure–response surface. Next, we developed an extension of hybrid Gibbs sampling approach that incorporates two MCMC optimization strategies, adaptive sampling and latent variable augmentation. Then, a class of Bayesian predictive model checking methods will be established. Finally, health effect of single pollutant under complex model will be constructed under the framework of average predictive comparisons..All methods proposed were required to include validation of the approach either by using simulation studies or by conducting a thorough sensitivity analysis with widely available data sets. The proposed statistical methods can facilitate the understanding of the health impact of multivariate air pollutants under variable climate condition, thereby informing policy making.