Observed Impacts of COVID-19 on Urban CO2 Emissions
TL;DR: In this article, the authors use CO2 observations from 35 Berkeley Environment, Air-quality and CO2 Network (BEACO(2)N) nodes and an atmospheric transport model to quantify changes in urban CO2 emissions due to the order.
Abstract: Governments restricted mobility and effectively shuttered much of the global economy in response to the COVID-19 pandemic Six San Francisco Bay Area counties were the first region in the United States to issue a "shelter-in-place" order asking non-essential workers to stay home Here we use CO2 observations from 35 Berkeley Environment, Air-quality and CO2 Network (BEACO(2)N) nodes and an atmospheric transport model to quantify changes in urban CO2 emissions due to the order We infer hourly emissions at 900-m spatial resolution for 6 weeks before and 6 weeks during the order We observe a 30% decrease in anthropogenic CO2 emissions during the order and show that this decrease is primarily due to changes in traffic (-48%) with pronounced changes to daily and weekly cycles;non-traffic emissions show small changes (-8%) These findings provide a glimpse into a future with reduced CO2 emissions through electrification of vehicles
Citations
More filters
California Institute of Technology1, University of California2, Universities Space Research Association3, Goddard Space Flight Center4, California Energy Commission5, University of Colorado Boulder6, University of California, Berkeley7, University of Michigan8, Tsinghua University9, University of Washington10, George Washington University11, California Air Resources Board12, Northern Arizona University13, South Coast Air Quality Management District14, University of California, Riverside15, University of Toronto16, Washington University in St. Louis17, Lamont–Doherty Earth Observatory18, Duke University19, Columbia University20, University of California, Los Angeles21
TL;DR: The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheric composition as discussed by the authors.
Abstract: The COVID-19 global pandemic and associated government lockdowns dramatically altered human activity, providing a window into how changes in individual behavior, enacted en masse, impact atmospheric composition. The resulting reductions in anthropogenic activity represent an unprecedented event that yields a glimpse into a future where emissions to the atmosphere are reduced. Furthermore, the abrupt reduction in emissions during the lockdown periods led to clearly observable changes in atmospheric composition, which provide direct insight into feedbacks between the Earth system and human activity. While air pollutants and greenhouse gases share many common anthropogenic sources, there is a sharp difference in the response of their atmospheric concentrations to COVID-19 emissions changes, due in large part to their different lifetimes. Here, we discuss several key takeaways from modeling and observational studies. First, despite dramatic declines in mobility and associated vehicular emissions, the atmospheric growth rates of greenhouse gases were not slowed, in part due to decreased ocean uptake of CO2 and a likely increase in CH4 lifetime from reduced NO x emissions. Second, the response of O3 to decreased NO x emissions showed significant spatial and temporal variability, due to differing chemical regimes around the world. Finally, the overall response of atmospheric composition to emissions changes is heavily modulated by factors including carbon-cycle feedbacks to CH4 and CO2, background pollutant levels, the timing and location of emissions changes, and climate feedbacks on air quality, such as wildfires and the ozone climate penalty.
40 citations
TL;DR: In this paper, the authors detect and estimate decreases in CO2 emissions in Los Angeles and Washington DC/Baltimore during March and April 2020, using an inverse model to estimate relative emissions changes in 2020 compared to 2018 and 2019.
Abstract: Responses to COVID-19 have resulted in unintended reductions of city-scale carbon dioxide (CO2) emissions. Here, we detect and estimate decreases in CO2 emissions in Los Angeles and Washington DC/Baltimore during March and April 2020. We present three lines of evidence using methods that have increasing model dependency, including an inverse model to estimate relative emissions changes in 2020 compared to 2018 and 2019. The March decrease (25%) in Washington DC/Baltimore is largely supported by a drop in natural gas consumption associated with a warm spring whereas the decrease in April (33%) correlates with changes in gasoline fuel sales. In contrast, only a fraction of the March (17%) and April (34%) reduction in Los Angeles is explained by traffic declines. Methods and measurements used herein highlight the advantages of atmospheric CO2 observations for providing timely insights into rapidly changing emissions patterns that can empower cities to course-correct CO2 reduction activities efficiently.
29 citations
TL;DR: In this article, machine learning models based on a Gradient Boosting Machine algorithm are built to assess the outbreak impact on air quality in Quito, Ecuador, and quantified the changes in pollution are quantified.
Abstract: The worldwide research initiatives on Corona Virus disease 2019 lockdown effect on air quality agree on pollution reduction, but the most reliable method to pollution reduction quantification is still in debate. In this paper, machine learning models based on a Gradient Boosting Machine algorithm are built to assess the outbreak impact on air quality in Quito, Ecuador. First, the precision of the prediction was evaluated by cross-validation on the four years prelockdown, showing a high accuracy to estimate the real pollution levels. Then, the changes in pollution are quantified. During the full lockdown, air pollution decreased by -53 ± 2%, -45 ± 11%, -30 ± 13%, and -15 ± 9% for NO2, SO2, CO, and PM2.5, respectively. The traffic-busy districts were the most impacted areas of the city. After the transition to the partial relaxation, the concentrations have nearly returned to the levels as before the pandemic. The quantification of pollution drop is supported by an assessment of the prediction confidence.
24 citations
TL;DR: In this paper , the authors present a framework for the latest near-real-time carbon emission accounting technology that can be widely used and provide strong database support to the policymaking for China's “carbon neutrality” strategy.
24 citations
TL;DR: Wang et al. as mentioned in this paper investigated the variation of surface thermal environment in terms of land surface temperature (LST) difference triggered by significant urban evolution of intra-urban division containing two primary classes: old urban areas developed by 1992 and new ones expanded in the 1992-2015 period.
23 citations
References
More filters
TL;DR: In this article, Chen et al. present a survey of the state of the art in the field of computer vision and artificial intelligence, including a discussion of the role of the human brain in computer vision.
Abstract: S. Solomon, D. Qin, M. Manning, M. Marquis, K. Averyt, M.M.B. Tignor, H. LeRoy Miller, Jr. and Z. Chen, Cambridge, Cambridge University Press, 2007, 996 pp. (paperback), ISBN-978-1-57718-033-3 This...
6,121 citations
"Observed Impacts of COVID-19 on Urb..." refers background in this paper
...Carbon dioxide (CO2) is an atmospheric trace gas responsible for most of the growth in anthropogenic radiative forcing (IPCC, 2013)....
[...]
24 May 2022
TL;DR: In this paper , the authors present a comprehensive assessment of our understanding of global warming of 1.5°C, future climate change, potential impacts and associated risks, emission pathways, and system transitions consistent with 1.0°C global warming, and strengthening the global response to climate change in the context of sustainable development and efforts to eradicate poverty.
Abstract: The Intergovernmental Panel on Climate Change (IPCC) is the leading international body for assessing the science related to climate change. It provides regular assessments of the scientific basis of climate change, its impacts and future risks, and options for adaptation and mitigation. This IPCC Special Report is a comprehensive assessment of our understanding of global warming of 1.5°C, future climate change, potential impacts and associated risks, emission pathways, and system transitions consistent with 1.5°C global warming, and strengthening the global response to climate change in the context of sustainable development and efforts to eradicate poverty. It serves policymakers, decision makers, stakeholders and all interested parties with unbiased, up-to-date, policy-relevant information. This title is also available as Open Access on Cambridge Core.
2,820 citations
TL;DR: In this paper, the authors compile government policies and activity data to estimate the decrease in CO2 emissions during forced confinements during the COVID-19 pandemic, which drastically altered patterns of energy demand around the world.
Abstract: Government policies during the COVID-19 pandemic have drastically altered patterns of energy demand around the world. Many international borders were closed and populations were confined to their homes, which reduced transport and changed consumption patterns. Here we compile government policies and activity data to estimate the decrease in CO2 emissions during forced confinements. Daily global CO2 emissions decreased by –17% (–11 to –25% for ±1σ) by early April 2020 compared with the mean 2019 levels, just under half from changes in surface transport. At their peak, emissions in individual countries decreased by –26% on average. The impact on 2020 annual emissions depends on the duration of the confinement, with a low estimate of –4% (–2 to –7%) if prepandemic conditions return by mid-June, and a high estimate of –7% (–3 to –13%) if some restrictions remain worldwide until the end of 2020. Government actions and economic incentives postcrisis will likely influence the global CO2 emissions path for decades.
1,461 citations
TL;DR: The Rapid Refresh (RAP) as mentioned in this paper is an hourly updated assimilation and model forecast system, which replaced the Rapid Update Cycle (RUC) as an operational regional analysis and forecast system among the suite of models at the NOAA/National Centers for Environmental Prediction (NCEP).
Abstract: The Rapid Refresh (RAP), an hourly updated assimilation and model forecast system, replaced the Rapid Update Cycle (RUC) as an operational regional analysis and forecast system among the suite of models at the NOAA/National Centers for Environmental Prediction (NCEP) in 2012. The need for an effective hourly updated assimilation and modeling system for the United States for situational awareness and related decision-making has continued to increase for various applications including aviation (and transportation in general), severe weather, and energy. The RAP is distinct from the previous RUC in three primary aspects: a larger geographical domain (covering North America), use of the community-based Advanced Research version of the Weather Research and Forecasting (WRF) Model (ARW) replacing the RUC forecast model, and use of the Gridpoint Statistical Interpolation analysis system (GSI) instead of the RUC three-dimensional variational data assimilation (3DVar). As part of the RAP development, modif...
762 citations
"Observed Impacts of COVID-19 on Urb..." refers methods in this paper
...…Lagrangian Transport model (STILT; Fasoli et al., 2018; Lin et al., 2003) with meteorology from the NOAA High-Resolution Rapid Refresh (HRRR Kenyon et al., 2016) to both estimate the sensitivity of each measurement to upwind emission sources and estimate the concentration upwind of our…...
[...]