Regional air-quality assessment that adjusts for meteorological confounding

Although air pollution is caused by emission of pollutants to the atmosphere, the observed pollution levels are confounded by meteorological conditions, which largely determine the dispersion of the pollutants. Hence, effective air-quality management requires the evaluation index and statistical measures that are immune to meteorological confounding and reflect changes in pollutant concentrations accurately and objectively. Motivated by the task of assessing changes in the underlying emission in a region near Beijing, we propose a spatial and temporal adjustment approach to remove meteorological confounding. The adjusted average pollutant concentration over space and time can capture changes in the underlying emission by controlling the meteorological variation. Estimation of the adjusted average is proposed together with theoretical and numerical analysis. We apply the approach to conducting air-quality assessments in the Beijing region, which reveals some intriguing patterns and trends that are useful for air-quality management.