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Activity Number: 636 - Statistical Methods of Air Quality and Exposure
Type: Contributed
Date/Time: Thursday, August 2, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract #329587
Title: Regional Air Quality Assessment That Adjusts for Meterological Confounding
Author(s): Shuyi Zhang* and Song Xi Chen and Bin Guo and Wei Lin and Hengfang Wang
Companies: Peking University and Peking University and Southwestern University of Finance and Economics and Peking University and Iowa State University
Keywords: air pollution; meteorological confounding; nonparametric regression; regional assessment; spatio-temporal bootstrap

Although air pollution is caused by emission of pollutants to the atmosphere, observed pollution levels are largely affected by meteorological conditions which determine the dispersion condition. Effective air quality management requires statistical measures immune to the meteorological confounding to evaluate spatial and temporal changes of pollution concentration objectively. Motivated by a challenging task of assessing changes and trends in the underlying pollution concentration in Beijing, we propose a spatial and temporal adjustment approach for 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 underlying emission while being able to control 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 2013 to 2016, which reveal some intriguing patterns and trends that are useful for air quality management.

Authors who are presenting talks have a * after their name.

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