报告人：黄国文 副教授（汕头大学）

报告时间：2021年12月09日15:00

报告方式：工西416

摘要：We study the associations between daily mortality and short-term variations in the ambient concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2) and ozone (O3) in four cities in Canada. Firstly, a novel multivariate time series model within Bayesian framework is proposed for exposure assessment, where the response is a mixture of Gamma and Half-Cauchy distributions and the correlations between pollutants vary seasonally. A case-crossover design and conditional logistic regression model is used to relate exposure to disease data for each city, which then are combined to obtain a global estimate of exposure health effects allowing exposure uncertainty. The results suggest that every 10 ppb increase of O3 is associated with a 3.88\% (95\% credible interval[CI], 2.5\%, 5.18\%) increase of all-cause mortality, a 5.04\% (2.84\%, 7.43\%) increase of circulatory mortality, a 7.87\% (2.4\%, 12.9\%) increase of respiratory mortality, a 0.76\% (0.19\%, 1.35\%) increase of all-cause morbidity, a 6.6\% (0.58\%, 12.7\%) increase of respiratory morbidity. Similarly, every 10 ppb increase of NO2 is associated with a 2.13\% (0.42\%, 3.87\%) increase of circulatory morbidity. The health impacts of PM2.5 are not found to be present once other pollutants are accounted for.

个人简介：黄国文，副教授，2016年博士毕业于英国格拉斯哥大学，曾先后在台湾清华大学和加拿大多伦多大学从事博士后研究，主要研究空间统计学，对空气污染数据进行统计建模并定量分析其对人类健康的影响；代表性论文发表在Stat. Med., Spat. Stat., J. Comput. Graph Stat., J. R. Stat. Soc. Ser. C Appl. Stat., Environmetrics等学术期刊。