Quantifying the Impact of COVID‐19 Pandemic on the Spatiotemporal Changes of CO 2 Concentrations in the Yangtze River Delta, China

While the reduction in anthropogenic emissions due to Coronavirus disease 2019 (COVID‐19) lockdown in China and its impact on air quality have been reported extensively, its impact on ambient carbon dioxide (CO 2 ) concentrations is still yet to be assessed. In this study, the impact of emission reductions on spatiotemporal changes of CO 2 concentrations during the COVID‐19 pandemic was quantified in the Yangtze River Delta region (YRD), using high‐resolution dynamic emission inventory and the Weather Research and Forecasting model coupled with the Vegetation Photosynthesis and Respiration Model (WRF‐VPRM). The simulated CO 2 concentrations from dynamic emission inventory shows a better agreement with surface observations compared with the Open‐source Data Inventory for Anthropogenic CO 2 and Emission Database for Global Atmospheric Research emission, providing confidence in the quantification of CO 2 concentrations variations. Our results show that emission reductions during the COVID‐19 pandemic lead to a CO 2 decrease by 4.6 ppmv (−1.1%) in Shanghai and 3.1 ppmv (−0.7%) in YRD region. For the column‐averaged CO 2 concentrations (denoted as XCO 2 ), it also decreases by 0.20 ppmv (−0.05%) in Shanghai and 0.15 ppmv (−0.04%) in YRD region. Furthermore, emission reductions from transportation and industry are major contributors to the decline in CO 2 concentrations at the near surface, accounting for 45.8% (41.1%) and 34.9% (41.0%) in Shanghai (YRD). Our study deepens the understanding of the response of CO 2 concentrations to different sectors, which is helpful for emission management and climate adaption policies. Carbon dioxide (CO 2 ) is the most important greenhouse gas in the atmosphere and has a profound impact on global climate change. It kept increasing over the last decades. Although previous studies have investigated the sources and sinks of air pollutants, the variations of CO 2 concentrations at regional to national scales remains poorly understood owing to a lack of long‐term observations and limited modeling studies. High‐resolution CO 2 emission inventory is in high demand in accurate CO 2 simulations. This work integrates a high‐resolution dynamic emission inventory with WRF‐VPRM model to quantify the influence of reduced emissions from different sectors on the spatiotemporal changes of CO 2 concentrations during the COVID‐19 pandemic. This modeling system can help to understand the response of CO 2 concentrations to emissions and serves as