William T. Sturges

It is not currently possible to quantify regional-scale fossil fuel carbon dioxide (ffCO 2 ) emissions with high accuracy in near real time. Existing atmospheric methods for separating ffCO 2 from large natural carbon dioxide variations are constrained by sampling limitations, so that estimates of regional changes in ffCO 2 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 2 based on continuous atmospheric measurements of oxygen and carbon dioxide combined into the tracer “atmospheric potential oxygen” (APO). We detect and quantify ffCO 2 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 2 emissions reductions from three independent lower-frequency U.K. estimates.