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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]

Carbon dioxide (CO2) and methane (CH4) are the two major greenhouse gases (GHGs) in the atmosphere that contribute to global warming. Vehicle emissions on expressways cannot be neglected in the megacity Shanghai because oil accounts for 41% of the total primary energy consumption, and the expressway network carries 60% of the total traffic volume. The spatial distributions of CO2 and CH4 concentrations were monitored in situ on the expressways and in road tunnels using a mobile vehicle. The average CO2 and CH4 concentrations were 472.88 ± 34.48 ppm and 2033 ± 54 ppb on the expressways and 1308.92 ± 767.48 ppm and 2182 ± 112 ppb in the road tunnels in Shanghai, respectively. The highest CO2 and CH4 concentrations appeared on the Yan'an Elevated Road and the North-South Elevated Road, respectively, while their lowest values both occurred on the Huaxia Elevated Road passing through the suburban area. The hotspots of CO2 and CH4 were not consistent, suggesting that they have different sources. Tunnels had a "push-pull effect" on GHGs, and the traffic-congested Yan'an East Road Tunnel showed a dramatically increasing trend of GHG concentration from the entrance to the exit. This traffic-congested tunnel could accumulate a very high concentration of GHGs as well as other pollutants, which could introduce unhealthy conditions for both drivers and passengers. Significant correlations between CO2 and CH4 mostly appeared on the expressways and in the tunnels in Shanghai, suggesting the influences of vehicle exhaust. ΔCH4/ΔCO2 (the slope of the linear regression between CH4 and CO2) and the CH4/CO2 ratio could be used as indicators of vehicle exhaust sources because it increases from sources (e.g., road tunnels) to the observatories in the urban area. [ABSTRACT FROM AUTHOR]

CO 2 and CH 4 are the two most potent greenhouse gases (GHGs) in the atmosphere. They are frequently monitored at background observatories, but less so in urban areas, especially in China. In this study, the temporal variations of urban atmospheric CO 2 and CH 4 , including seasonal, weekly, and diurnal characteristics, were obtained based on one year of continuous measurement in the megacity Shanghai. Probable anthropogenic sources were further investigated through relationships among GHGs and atmospheric pollutants. Potential sources regions and transport pathways of GHGs were studied in association with meteorological conditions. The annual average concentrations of CO 2 and CH 4 were 428.36 ± 13.96 ppm and 2154 ± 190 ppb, respectively, which were higher than those at global and regional background observatories. The highest CO 2 concentration appeared in winter and the lowest in autumn, whereas CH 4 values were highest in summer and lowest in spring. Both CO 2 and CH 4 showed strong diurnal variations, but only CO 2 exhibited seasonal and weekly differences due to the variation of emission sources and plant photosynthesis in Shanghai and the surrounding Yangtze River Delta (YRD). CO 2 and combustion-derived air pollutants were co-emitted from similar sources and co-transported to the research site, as demonstrated by correlation analysis. Furthermore, the correlation analysis also suggested some neglected anthropogenic combustion sources of CH 4 in Shanghai, besides the traditional biogenic emissions from landfills, wetlands, paddy fields, and dairy farms. The anthropogenic CO 2 concentration in urban Shanghai estimated from a CO 2 -CO regression model was 14.98 ± 5.09 ppm, together with a background concentration of 413.31 ± 7.07 ppm, which was higher than at global background sites but lower than at the nearby regional background Lin'an station. GHGs were accumulated from local sources under stable weather conditions and transported from remote sources at high wind speeds. Air masses from "super-emitters" north and south of the observation site contributed most of the elevation in GHGs concentration above their annual averages. Considering the co-emissions of GHGs and air pollutants from carbon-containing material combustion and use, and the potential of methane to generate O 3 in the urban environment, reduction of CO 2 and CH 4 together with air pollutants could benefit both climate and air quality in Shanghai and the surrounding YRD. Unlabelled Image • CO 2 and CH 4 were measured together with air pollutants and meteorological conditions in urban Shanghai. • Both CO 2 and CH 4 showed diurnal variations, but only CO 2 showed seasonal and weekday effects. • Anthropogenic sources affected the elevated concentrations of GHGs in urban environment. • GHGs were accumulated from local sources in stable, together with transport from remote sources. • Joint control of GHGs and air pollutants could benefit both climate and air quality. [ABSTRACT FROM AUTHOR]