Evaluation of NOx emissions before, during, and after the COVID-19 lockdowns in China: A comparison of meteorological normalization methods.

Meteorological normalization refers to the removal of meteorological effects on air pollutant concentrations for evaluating emission changes. There currently exist various meteorological normalization methods, yielding inconsistent results. This study aims to identify the state-of-the-art method of meteorological normalization for characterizing the spatiotemporal variation of NOx emissions caused by the COVID-19 pandemic in China.

A data-augmentation approach to deriving long-term surface SO across Northern China: Implications for interpretable machine learning.

Until recently, Northern China was one of the most SO polluted regions in the world. The lack of long-term and spatially resolved surface SO data hinders retrospective evaluation of relevant environmental policies and human health effects. This study aims to derive the spatiotemporal distribution of surface SO across Northern China during 2005-2019.