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 PM$_{2.5}$  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.

I will also discuss on the air quality standards in China and advocate of using more strict standard for public health consideration.