Pickers Penelope A, Manning Andrew C, Le Quéré Corinne, Forster Grant L, Luijkx Ingrid T, Gerbig Christoph, Fleming Leigh S, Sturges William T
Centre for Ocean and Atmospheric Sciences, School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK.
National Centre for Atmospheric Science, University of East Anglia, Norwich NR4 7TJ, UK.
Sci Adv. 2022 Apr 22;8(16):eabl9250. doi: 10.1126/sciadv.abl9250.
It is not currently possible to quantify regional-scale fossil fuel carbon dioxide (ffCO) emissions with high accuracy in near real time. Existing atmospheric methods for separating ffCO from large natural carbon dioxide variations are constrained by sampling limitations, so that estimates of regional changes in ffCO emissions, such as those occurring in response to coronavirus disease 2019 (COVID-19) lockdowns, rely on indirect activity data. We present a method for quantifying regional signals of ffCO based on continuous atmospheric measurements of oxygen and carbon dioxide combined into the tracer "atmospheric potential oxygen" (APO). We detect and quantify ffCO reductions during 2020-2021 caused by the two U.K. COVID-19 lockdowns individually using APO data from Weybourne Atmospheric Observatory in the United Kingdom and a machine learning algorithm. Our APO-based assessment has near-real-time potential and provides high-frequency information that is in good agreement with the spread of ffCO emissions reductions from three independent lower-frequency U.K. estimates.
目前还无法在近实时的情况下高精度地量化区域尺度的化石燃料二氧化碳（ffCO）排放。现有的从大气中巨大的自然二氧化碳变化中分离出ffCO的方法受到采样限制，因此，ffCO排放区域变化的估计，例如因2019年冠状病毒病（COVID-19）封锁而产生的变化，依赖于间接活动数据。我们提出了一种基于连续大气测量氧气和二氧化碳并将其组合成示踪剂“大气潜在氧”（APO）来量化ffCO区域信号的方法。我们利用来自英国韦本大气观测站的APO数据和一种机器学习算法，分别检测和量化了2020年至2021年期间英国两次COVID-19封锁导致的ffCO减少量。我们基于APO的评估具有近实时潜力，并提供了高频信息，这与来自英国三个独立低频估计的ffCO减排量的传播情况高度吻合。
