Fossil fuel combustion and industrial processes are responsible for the majority of anthropogenic carbon dioxide (CO2) emissions. Almost 70% of CO2 is emitted from cities and urban areas.
Despite their critical importance, the ability to evaluate reported emissions and monitor and inform the effectiveness of emissions reduction policies over the coming decades is currently limited.
This limitation was recently highlighted by the 2020–2021 pandemic of the coronavirus disease 2019 (COVID-19). To mitigate the spread of the virus, many countries implemented social distancing measures at national or regional scales, resulting in sudden and severe temporary reductions in emissions of CO2 from fossil fuels (FFCO2) and anthropogenic air pollutants.
Numerous studies have successfully reported on-air pollutant COVID-19 reductions observed from atmospheric measurements. But determining fossil fuel CO2 (FFCO2), COVID-19 reductions in the atmosphere has been substantially more challenging, owing to the large variations in atmospheric CO2 caused by terrestrial biosphere fluxes.
In a study, they quantified regional fossil fuel CO2 emissions reductions during the COVID-19 lockdowns of 2020-2021, using atmospheric measurements of CO2 and oxygen (O2) from the Weybourne Atmospheric Observatory on the north Norfolk coast in the UK.
The estimate uses a new method for separating CO2 signals from land plants and fossil fuels in the atmosphere.
Existing atmospheric-based methods have largely been unsuccessful at separating fossil fuel CO2 from large natural CO2 variability, so estimates of changes, such as those occurring in response to the lockdowns, must rely on indirect data sources, which can take months or years to compile.
The atmospheric O2-based method, published in the journal Science Advances, is in good agreement with three lower frequency UK emissions estimates produced during the pandemic by the Department for Business, Energy and Industrial Strategy, the Global Carbon Budget, and Carbon Monitor, which used different methods and combinations of data, for example, those based on energy usage.
Crucially, as well as being completely independent of the other estimates, this approach can be calculated much more quickly.
The researchers are also able to detect changes in emissions with higher frequency, such as daily estimates, and can clearly see two periods of reductions associated with two UK lockdown periods, separated by a period of emissions recovery when Covid restrictions were eased during the summer of 2020.
Researchers at UEA – home of the UK’s only high-precision atmospheric O2 measurement laboratory – worked with colleagues at Wageningen University in the Netherlands and the Max Planck Institute for Biogeochemistry, Germany.
The study’s lead author, Dr. Penelope Pickers, of UEA’s Centre for Ocean and Atmospheric Sciences, said: “If humans are to reduce our CO2 emissions from fossil fuels and our impact on the climate, we first need to know how much emissions are changing.”
“Our study is a major achievement in atmospheric science. Several others, based solely on CO2 data, have been unsuccessful, owing to large emissions from land plants, which obscure fossil fuel CO2 signals in the atmosphere.
“Using atmospheric O2 combined with CO2 to isolate fossil fuel CO2 in the atmosphere has enabled us to detect and quantify these important signals using a ‘top-down’ approach for the first time. Our findings indicate that a network of continuous measurement sites has strong potential for providing this evaluation of fossil fuel CO2 at regional levels.”
“Currently, anthropogenic CO2 emissions are self-reported to the United Nations Framework Convention on Climate Change using an indirect “bottom-up” approach, based primarily on energy statistics and emission factors, and an agreed methodology; however, large inconsistencies in bottom-up approaches have been reported, arising from inaccuracies in energy statistics and/or emission factors.” Study quotes.
An alternative method of estimating GHG emissions is to use a ‘top-down’ approach based on atmospheric measurements and modelling.
The UK emissions inventory is already successfully informed and supported by independent top-down assessments for some key GHGs, such as methane and nitrous oxide.
But for CO2, the most important GHG for climate change, this has never before been feasible because of the difficulties distinguishing between CO2 emissions from fossil fuels and land plant sources in the atmosphere.
Dr. Pickers said: “The time taken for inventories to be completed makes it hard to characterise changes in emissions that happen suddenly, such as the reductions associated with the Covid pandemic lockdowns.”
“We need reliable fossil fuel CO2 emissions estimates quickly and at finer scales, so that we can monitor and inform climate change policies to prevent reaching 2°C of global warming.”
“Our O2-based approach is cost-effective and provides high frequency information, with the potential to provide fossil fuel CO2 estimates quickly and at finer spatial scales, such as for counties, states or cities.”
The team used 10 years of high-precision, hourly measurements of atmospheric O2 and CO2 from Weybourne Atmospheric Observatory, which is supported by the UK’s National Centre for Atmospheric Science. Having long-term measurements of these climatically important gases was crucial to the success of the study.
A new ground-based measurement approach for quantifying the regional FFCO2 component of the atmospheric CO2 mole fraction (in parts per million) using potential atmospheric oxygen (APO) data.
Researchers demonstrate the potential of APO as FFCO2 tracer by detecting and quantifying COVID-19 FFCO2 reductions in the atmosphere associated with the first two waves of the pandemic in the United Kingdom, using continuous data from the Weybourne Atmospheric Observatory (WAO) in the United Kingdom and a machine learning algorithm.
The APO-based assessment researchers presented in the study separates biospheric and anthropogenic signals in atmospheric CO2 with high frequency (e.g., daily or sub-daily scales) and in near real-time, which is an important first step toward robust quantification of absolute FFCO2 emissions using atmospheric data.
Their approach does not quantify absolute emissions, but with the use of machine learning, they are able to quantify relative changes in emissions using APO data, which represents a major achievement in top-down observation-based FFCO2 emissions quantification efforts.
Using a combined APO and machine learning approach, they have detected a local 1.6-ppm reduction in daily-mean FFCO2 observed at WAO from March to July 2020 compared to the non-pandemic “counterfactual scenario” (i.e., compared to the expected FFCO2 during 2020 if the COVID-19 pandemic had not occurred), and a 1.3-ppm daily-mean reduction during November 2020 to January 2021. These two UK lockdown periods are separated by a period of recovery, from August to October 2020, characterized by a little reduction in FFCO2. Their APO-based estimate is in good agreement with the spread of FFCO2 reductions determined from three independent bottom-up emissions estimates for the United Kingdom.
Limitations of Study
“The APO network of stations is currently sparse with few measurement sites ideally situated to capture anthropogenic emissions signals. Thus, using APO as a tool for top-down FFCO2 emissions quantification efforts at scale will require investment in precise and accurate atmospheric O2 and CO2 measurements, which are technically challenging, and improved knowledge of RAPO from emissions inventories.” Study quotes.
- Pickers Penelope A., Manning Andrew C., Le Quéré Corinne, Forster Grant L., Luijkx Ingrid T., Gerbig Christoph, Fleming Leigh S., Sturges William T., Novel quantification of regional fossil fuel CO2 reductions during COVID-19 lockdowns using atmospheric oxygen measurements. Science Advances, Vol 8. Issue 16 DOI: 10.1126/sciadv.abl9250