Quantifying the Impact of COVID‐19 Pandemic on the Spatiotemporal Changes of CO2 Concentrations in the Yangtze River Delta, China.

Shanghai (China); 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 (CO2) concentrations is still yet to be assessed. In this study, the impact of emission reductions on spatiotemporal changes of CO2 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 CO2 concentrations from dynamic emission inventory shows a better agreement with surface observations compared with the Open‐source Data Inventory for Anthropogenic CO2 and Emission Database for Global Atmospheric Research emission, providing confidence in the quantification of CO2 concentrations variations. Our results show that emission reductions during the COVID‐19 pandemic lead to a CO2 decrease by 4.6 ppmv (−1.1%) in Shanghai and 3.1 ppmv (−0.7%) in YRD region. For the column‐averaged CO2 concentrations (denoted as XCO2), 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 CO2 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 CO2 concentrations to different sectors, which is helpful for emission management and climate adaption policies. Plain Language Summary: Carbon dioxide (CO2) 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 CO2 concentrations at regional to national scales remains poorly understood owing to a lack of long‐term observations and limited modeling studies. High‐resolution CO2 emission inventory is in high demand in accurate CO2 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 CO2 concentrations during the COVID‐19 pandemic. This modeling system can help to understand the response of CO2 concentrations to emissions and serves as a basis for atmospheric inversion of CO2 emissions. Key Points: High‐resolution CO2 dynamic emission inventory greatly improves CO2 simulations compared with Open‐source Data Inventory for Anthropogenic CO2 and Emission Database for Global Atmospheric ResearchAnthropogenic emission reductions cause CO2 concentrations decrease by 4.6 (3.1) ppmv in Shanghai (Yangtze River Delta) during the COVID‐19 pandemicIndustrial and transportation emissions mainly dominate the reduction in CO2 concentrations at the near surface and vertical altitude [ABSTRACT FROM AUTHOR]

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