Highly Resolved Dynamic Emissions of Air Pollutants and Greenhouse Gas CO 2 during COVID-19 Pandemic in East China.

The unintentional emission reductions caused by the COVID-19 pandemic provides an opportunity to investigate the impact of energy, industry, and transportation activities on air pollutants and CO 2 emissions and their synergy. Here, we constructed an approach to estimate city-level high resolution dynamic emissions of both anthropogenic air pollutants and CO 2 by introducing dynamic temporal allocation coefficients based on real-time multisource activity data. We first apply this approach to estimate the spatiotemporal evolution of sectoral emissions in eastern China, focusing on the period around the COVID-19 lockdown. Comparisons with observational data show that our approach can well capture the spatiotemporal changes of both short-lived precursors (NO x and NMVOCs) and CO 2 emissions. Our results show that air pollutants (SO 2 , NO x , and NMVOCs) were reduced by up to 31%-53% during the lockdown period accompanied by simultaneous changes of 40% CO 2 emissions. The declines in power and heavy industry sectors dominated regional SO 2 and CO 2 reductions. NO x reductions were mainly attributed to mobile sources, while NMVOCs emission reductions were mainly from light industry sectors. Our findings suggest that differentiated emission control strategies should be implemented for different source categories to achieve coordinated reduction goals.