空间贝叶斯方法及在空气质量及其健康效果评估中的运用

项目名称： 空间贝叶斯方法及在空气质量及其健康效果评估中的运用

项目编号： No.41471376

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 天文学、地球科学

项目作者： 李连发

作者单位： 中国科学院地理科学与资源研究所

项目金额： 90万元

中文摘要： 国内外常用的空气质量及健康影响评估包括克里格插值、概化累加模型（GAM）、逻辑斯特回归及采用土地利用变量的回归（LUR）。传统方法主要不足：空间估值方法考虑辅助变量较少，GAM及LUR模型没有考虑空间相关性，LUR及逻辑斯特回归基于线性关系，与机理结合较薄弱，样本过少，可能会导致有偏结果。针对已有方法不足，本研究提出空间贝叶斯方法，该方法基于贝叶斯原理，加入空间聚类或空间相关性因素，引入累加光滑函数建立模型。该方法将结合机理或已有文献获取先验知识，充分考虑空间混淆因素影响，减少偏差，提高估计精度；采用累加模型提取影响因素同目标变量（暴露度/健康效果）之间线性/非线性关系，在健康效果评估中更符合实际地量化剂量-反应关系。算法将对相关海量数据进行处理，提高提高评估效率；对未来气候变化情景进行效果模拟，为空气质量政策提供信息支持。

中文关键词： 贝叶斯；空间相关性；空间聚集；空气质量；健康效果

英文摘要： The existing methods of evaluating air quality and its health effects are mainly based on spatial interpolation, generalized additive model (GAM), logistic regression or land-use regression (LUR). The shortcomings of these traditional methods include: (1) consideration of few predictive factors in spatial interpolations such as kriging or no spatial correlation in GAM and LUR; (2) linear regression mostly used to establish the association between predictive factors and exposure or health effects; (3) difficulty of combination and explanation with a priori knowledge; (4) use of a limited number of samples to train the models, probably resulting in biased results. Under this context, this study proposes spatial Bayesian approaches that are based on the Bayesian theorem, incorporate the information of spatial clustering or spatial correlation and employ additional linear/non-linear spline functions to establish the network links or regression associations. Our Bayesian approaches are able to employ a priori knowledge to improve justification of the results, take full consideration of spatial confounding effect to reduce the bias in estimation, and adopt additional terms to establish the non-linear association between predictive factors and the target variable (exposure to air pollutants and health effect) to reflect the dose-response relationship in practice. The parallel version of the algorithms will be also realized to deal with massive datasets to improve the computation efficiency and estimation accuracy; the scenario of exposure to air pollution and the corresponding effects for the climate changes in the future will also be simulated and the output will provide informative support for the relevant policies of air quality.

英文关键词： Bayesian;Spatial correlation;Spatial clustering;Air quality;Health effect