报告题目：Air-Quality Assessment with Spatial and Temporal Adjustment to Meteorological Confounding

报告人：陈松蹊、教授、williamhill威廉希尔官网

邀请人：冶继民 教授

报告时间：2016年4月24号上午10:30

报告地点：信远楼II206williamhill威廉希尔官网报告厅

报告人简介：北京大学统计科学中心联席主任, 商务统计与经济计量系联合系主任。美国科学促进会fellow， Institute of Mathematical Statistics（IMS）fellow，美国统计学会fellow，国际统计学会当选会员。The Annals of Statistics编委（2010-2018年）；美国统计学会会刊编委（自2018年）；Environmentrics编委（自2018年）.

报告摘要：Although air pollution is caused by emission of pollutants to the atmosphere, the observed pollution levels are largely affected by meteorological conditions which determine the dispersion condition of the pollutants. Effective air quality management requires statistical measures that are immune to the meteorological confounding in order to evaluate {spatial and temporal} changes of the pollution concentration objectively. Motivated by a challenging task of assessing changes and trends in the underlying pollution concentration in a region near Beijing。

We propose a spatial and temporal adjustment approach for the PM2.5 and other five pollutants with respect to the meteorological conditions by constructing a spatial and temporal baseline weather condition based on historic data to remove the meteorological confounding.

The adjusted mean pollution concentration is shown to be able to capture changes in the underlying emission while being able to control the meteorological variation. Estimation of the adjusted average is proposed together with asymptotic and numerical analyzes. We apply the approach to conduct assessments on six pollutants in the Beijing region from Year 2013 to Year 2016, which reveal some intriguing patterns and trends that are useful for the air quality management.