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Title: Regional Air Quality Assessment that Adjusts for Meteorological Confounding Authors:  Shuyi Zhang - Peking University (China) [presenting]
Songxi Chen - Peking University (China)
Bin Guo - Southwestern University of Finance and Economics (China)
Wei Lin - Peking University (China)
Hengfang Wang - Iowa State University (China)
Abstract: 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 by constructing a spatial and temporal baseline weather condition based on historical data to remove the meteorological confounding. The adjusted averaged pollution concentration over space and time 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 analysis. We apply the approach to conducting assessments on six pollutants in the Beijing region from Year 2013 to 2016, which reveal some intriguing patterns and trends that are useful for the air quality management.