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Wu C, Yang S, Jiao D, Chen Y, Yang J, Huang B. Estimation of daily XCO 2 at 1 km resolution in China using a spatiotemporal ResNet model. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 954:176171. [PMID: 39260497 DOI: 10.1016/j.scitotenv.2024.176171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 08/28/2024] [Accepted: 09/07/2024] [Indexed: 09/13/2024]
Abstract
Carbon dioxide (CO2) serves as a crucial greenhouse gas that traps heat and regulates the Earth's temperature. High spatiotemporal resolution CO2 estimation can provide valuable information to understand the characteristics of fine-scale climate change trends and to formulate more effective emission reduction strategies. This study presents a spatiotemporal ResNet model (ST-ResNet) specifically developed to estimate the highest resolution (1 km × 1 km) daily column-averaged dry-air mole fraction of CO2 (XCO2) in China from 2015 to 2020. The ST-ResNet model excels in estimating XCO2 by comprehensively considering the complex relationships between XCO2 and its various influencing factors, while efficiently capturing both temporal and spatial correlations, thereby demonstrating remarkable generalization capability. The results show that the ST-ResNet generates a highly accurate XCO2 dataset, outperforming the traditional ResNet. Ground-based validation results further confirm the high accuracy and spatiotemporal resolution of our estimated data product. Using this dataset, the spatial and temporal characteristics of XCO2 across the entire China and several urban agglomerations have been analyzed. The high spatiotemporal resolution estimated XCO2 dataset for China is made publicly available at [https://doi.org/10.6084/m9.figshare.25272868], offering substantial potential for fine-scale carbon research.
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Affiliation(s)
- Chao Wu
- School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Shuo Yang
- School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Donglai Jiao
- School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Yixiang Chen
- School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Jing Yang
- School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Bo Huang
- Department of Geography, The University of Hong Kong, Pokfulam, Hong Kong.
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2
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Asimow NG, Turner AJ, Cohen RC. Sustained Reductions of Bay Area CO 2 Emissions 2018-2022. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:6586-6594. [PMID: 38572839 PMCID: PMC11025126 DOI: 10.1021/acs.est.3c09642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 03/04/2024] [Accepted: 03/06/2024] [Indexed: 04/05/2024]
Abstract
Cities represent a significant and growing portion of global carbon dioxide (CO2) emissions. Quantifying urban emissions and trends over time is needed to evaluate the efficacy of policy targeting emission reductions as well as to understand more fundamental questions about the urban biosphere. A number of approaches have been proposed to measure, report, and verify (MRV) changes in urban CO2 emissions. Here we show that a modest capital cost, spatially dense network of sensors, the Berkeley Environmental Air Quality and CO2 Network (BEACO2N), in combination with Bayesian inversions, result in a synthesis of measured CO2 concentrations and meteorology to yield an improved estimate of CO2 emissions and provide a cost-effective and accurate assessment of CO2 emissions trends over time. We describe nearly 5 years of continuous CO2 observations (2018-2022) in a midsized urban region (the San Francisco Bay Area). These observed concentrations constrain a Bayesian inversion that indicates the interannual trend in urban CO2 emissions in the region has been a modest decrease at a rate of 1.8 ± 0.3%/year. We interpret this decrease as primarily due to passenger vehicle electrification, reducing on-road emissions at a rate of 2.6 ± 0.7%/year.
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Affiliation(s)
- Naomi G. Asimow
- Department
of Earth and Planetary Science, University
of California, Berkeley, Berkeley, California 94720, United States
| | - Alexander J. Turner
- Department
of Earth and Planetary Science, University
of California, Berkeley, Berkeley, California 94720, United States
| | - Ronald C. Cohen
- Department
of Earth and Planetary Science, University
of California, Berkeley, Berkeley, California 94720, United States
- College
of Chemistry, University of California,
Berkeley, Berkeley, California 94720, United States
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3
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Li H, Zheng B, Ciais P, Boersma KF, Riess TCVW, Martin RV, Broquet G, van der A R, Li H, Hong C, Lei Y, Kong Y, Zhang Q, He K. Satellite reveals a steep decline in China's CO 2 emissions in early 2022. SCIENCE ADVANCES 2023; 9:eadg7429. [PMID: 37478188 PMCID: PMC10361590 DOI: 10.1126/sciadv.adg7429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 06/16/2023] [Indexed: 07/23/2023]
Abstract
Response actions to the coronavirus disease 2019 perturbed economies and carbon dioxide (CO2) emissions. The Omicron variant that emerged in 2022 caused more substantial infections than in 2020 and 2021 but it has not yet been ascertained whether Omicron interrupted the temporary post-2021 rebound of CO2 emissions. Here, using satellite nitrogen dioxide observations combined with atmospheric inversion, we show a larger decline in China's CO2 emissions between January and April 2022 than in those months during the first wave of 2020. China's CO2 emissions are estimated to have decreased by 15% (equivalent to -244.3 million metric tons of CO2) during the 2022 lockdown, greater than the 9% reduction during the 2020 lockdown. Omicron affected most of the populated and industrial provinces in 2022, hindering China's CO2 emissions rebound starting from 2021. China's emission variations agreed with downstream CO2 concentration changes, indicating a potential to monitor CO2 emissions by integrating satellite and ground measurements.
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Affiliation(s)
- Hui Li
- Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration, Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Bo Zheng
- Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration, Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Philippe Ciais
- Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration, Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
| | - K. Folkert Boersma
- Department of Meteorology and Air Quality, Wageningen University, Wageningen, Netherlands
- Climate Observations Department, Royal Netherlands Meteorological Institute, De Bilt, Netherlands
| | | | - Randall V. Martin
- Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO, USA
- Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada
| | - Gregoire Broquet
- Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Ronald van der A
- R&D Satellite Observations, Royal Netherlands Meteorological Institute (KNMI), De Bilt, Netherlands
| | - Haiyan Li
- School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China
| | - Chaopeng Hong
- Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration, Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
| | - Yu Lei
- Chinese Academy of Environmental Planning, Beijing 100012, China
| | - Yawen Kong
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Qiang Zhang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
| | - Kebin He
- State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
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4
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Feldman AF, Zhang Z, Yoshida Y, Gentine P, Chatterjee A, Entekhabi D, Joiner J, Poulter B. A multi-satellite framework to rapidly evaluate extreme biosphere cascades: The Western US 2021 drought and heatwave. GLOBAL CHANGE BIOLOGY 2023; 29:3634-3651. [PMID: 37070967 DOI: 10.1111/gcb.16725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Accepted: 04/04/2023] [Indexed: 06/06/2023]
Abstract
The increasing frequency and intensity of climate extremes and complex ecosystem responses motivate the need for integrated observational studies at low latency to determine biosphere responses and carbon-climate feedbacks. Here, we develop a satellite-based rapid attribution workflow and demonstrate its use at a 1-2-month latency to attribute drivers of the carbon cycle feedbacks during the 2020-2021 Western US drought and heatwave. In the first half of 2021, concurrent negative photosynthesis anomalies and large positive column CO2 anomalies were detected with satellites. Using a simple atmospheric mass balance approach, we estimate a surface carbon efflux anomaly of 132 TgC in June 2021, a magnitude corroborated independently with a dynamic global vegetation model. Integrated satellite observations of hydrologic processes, representing the soil-plant-atmosphere continuum (SPAC), show that these surface carbon flux anomalies are largely due to substantial reductions in photosynthesis because of a spatially widespread moisture-deficit propagation through the SPAC between 2020 and 2021. A causal model indicates deep soil moisture stores partially drove photosynthesis, maintaining its values in 2020 and driving its declines throughout 2021. The causal model also suggests legacy effects may have amplified photosynthesis deficits in 2021 beyond the direct effects of environmental forcing. The integrated, observation framework presented here provides a valuable first assessment of a biosphere extreme response and an independent testbed for improving drought propagation and mechanisms in models. The rapid identification of extreme carbon anomalies and hotspots can also aid mitigation and adaptation decisions.
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Affiliation(s)
- Andrew F Feldman
- Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
- NASA Postdoctoral Program, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
| | - Zhen Zhang
- Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA
| | - Yasuko Yoshida
- Science Systems and Applications, Inc. (SSAI), Lanham, Maryland, USA
| | - Pierre Gentine
- Department of Earth and Environmental Engineering, Columbia University, New York, New York, USA
| | - Abhishek Chatterjee
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
| | - Dara Entekhabi
- Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Joanna Joiner
- Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
| | - Benjamin Poulter
- Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
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5
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Tohjima Y, Niwa Y, Patra PK, Mukai H, Machida T, Sasakawa M, Tsuboi K, Saito K, Ito A. Near-real-time estimation of fossil fuel CO 2 emissions from China based on atmospheric observations on Hateruma and Yonaguni Islands, Japan. PROGRESS IN EARTH AND PLANETARY SCIENCE 2023; 10:10. [PMID: 36879643 PMCID: PMC9978285 DOI: 10.1186/s40645-023-00542-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 02/17/2023] [Indexed: 06/18/2023]
Abstract
UNLABELLED We developed a near-real-time estimation method for temporal changes in fossil fuel CO2 (FFCO2) emissions from China for 3 months [January, February, March (JFM)] based on atmospheric CO2 and CH4 observations on Hateruma Island (HAT, 24.06° N, 123.81° E) and Yonaguni Island (YON, 24.47° N, 123.01° E), Japan. These two remote islands are in the downwind region of continental East Asia during winter because of the East Asian monsoon. Previous studies have revealed that monthly averages of synoptic-scale variability ratios of atmospheric CO2 and CH4 (ΔCO2/ΔCH4) observed at HAT and YON in JFM are sensitive to changes in continental emissions. From the analysis based on an atmospheric transport model with all components of CO2 and CH4 fluxes, we found that the ΔCO2/ΔCH4 ratio was linearly related to the FFCO2/CH4 emission ratio in China because calculating the variability ratio canceled out the transport influences. Using the simulated linear relationship, we converted the observed ΔCO2/ΔCH4 ratios into FFCO2/CH4 emission ratios in China. The change rates of the emission ratios for 2020-2022 were calculated relative to those for the preceding 9-year period (2011-2019), during which relatively stable ΔCO2/ΔCH4 ratios were observed. These changes in the emission ratios can be read as FFCO2 emission changes under the assumption of no interannual variations in CH4 emissions and biospheric CO2 fluxes for JFM. The resulting average changes in the FFCO2 emissions in January, February, and March 2020 were 17 ± 8%, - 36 ± 7%, and - 12 ± 8%, respectively, (- 10 ± 9% for JFM overall) relative to 2011-2019. These results were generally consistent with previous estimates. The emission changes for January, February, and March were 18 ± 8%, - 2 ± 10%, and 29 ± 12%, respectively, in 2021 (15 ± 10% for JFM overall) and 20 ± 9%, - 3 ± 10%, and - 10 ± 9%, respectively, in 2022 (2 ± 9% for JFM overall). These results suggest that the FFCO2 emissions from China rebounded to the normal level or set a new high record in early 2021 after a reduction during the COVID-19 lockdown. In addition, the estimated reduction in March 2022 might be attributed to the influence of a new wave of COVID-19 infections in Shanghai. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1186/s40645-023-00542-6.
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Affiliation(s)
- Yasunori Tohjima
- National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba, Ibaraki 305-8506 Japan
| | - Yosuke Niwa
- National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba, Ibaraki 305-8506 Japan
| | - Prabir K. Patra
- Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 3173-25 Showa-Machi, Kanazawa-Ku, Yokohama, Kanagawa 236-0001 Japan
| | - Hitoshi Mukai
- National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba, Ibaraki 305-8506 Japan
| | - Toshinobu Machida
- National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba, Ibaraki 305-8506 Japan
| | - Motoki Sasakawa
- National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba, Ibaraki 305-8506 Japan
| | - Kazuhiro Tsuboi
- Meteorological Research Institute (MRI), 1-1 Nagamine, Tsukuba, Ibaraki 305-0052 Japan
| | - Kazuyuki Saito
- Japan Meteorological Agency (JMA), 3-6-9 Toranomon, Minato-Ku, Tokyo, 105-8431 Japan
| | - Akihiko Ito
- National Institute for Environmental Studies (NIES), 16-2 Onogawa, Tsukuba, Ibaraki 305-8506 Japan
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6
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Hu C, Griffis TJ, Xia L, Xiao W, Liu C, Xiao Q, Huang X, Yang Y, Zhang L, Hou B. Anthropogenic CO 2 emission reduction during the COVID-19 pandemic in Nanchang City, China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 309:119767. [PMID: 35870528 PMCID: PMC9299519 DOI: 10.1016/j.envpol.2022.119767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 07/06/2022] [Accepted: 07/09/2022] [Indexed: 06/15/2023]
Abstract
China is the largest CO2 emitting country on Earth. During the COVID-19 pandemic, China implemented strict government control measures on both outdoor activity and industrial production. These control measures, therefore, were expected to significantly reduce anthropogenic CO2 emissions. However, large discrepancies still exist in the estimated anthropogenic CO2 emission reduction rate caused by COVID-19 restrictions, with values ranging from 10% to 40% among different approaches. Here, we selected Nanchang city, located in eastern China, to examine the impact of COVID-19 on CO2 emissions. Continuous atmospheric CO2 and ground-level CO observations from January 1st to April 30th, 2019 to 2021 were used with the WRF-STILT atmospheric transport model and a priori emissions. And a multiplicative scaling factor and Bayesian inversion method were applied to constrain anthropogenic CO2 emissions before, during, and after the COVID-19 pandemic. We found a 37.1-40.2% emission reduction when compared to the COVID-19 pandemic in 2020 with the same period in 2019. Carbon dioxide emissions from the power industry and manufacturing industry decreased by 54.5% and 18.9% during the pandemic period. The power industry accounted for 73.9% of total CO2 reductions during COVID-19. Further, emissions in 2021 were 14.3-14.9% larger than in 2019, indicating that economic activity quickly recovered to pre-pandemic conditions.
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Affiliation(s)
- Cheng Hu
- College of Biology and the Environment, Joint Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing, China.
| | - Timothy J Griffis
- Department of Soil, Water, and Climate, University of Minnesota-Twin Cities, St. Paul, Minnesota, USA
| | - Lingjun Xia
- Ecological Meteorology Center, Jiangxi Meteorological Bureau, Nanchang, 330096, China
| | - Wei Xiao
- Yale-NUIST Center on Atmospheric Environment, International Joint Laboratory on Climate and Environment Change (ILCEC), Nanjing University of Information, Science & Technology, Nanjing, 210044, China
| | - Cheng Liu
- Jiangxi Province Key Laboratory of the Causes and Control of Atmospheric Pollution/School of Water Resources and Environmental Engineering, East China University of Technology, Nanchang, 330013, China
| | - Qitao Xiao
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China
| | - Xin Huang
- Key Laboratory of Eco-Environmental and Meteorology for the Qinling Mountains and Loess Plateau, Shaanxi Meteorological Bureau, Xi'an, 710014, Shaanxi, China
| | - Yanrong Yang
- College of Biology and the Environment, Joint Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China
| | - Leying Zhang
- College of Biology and the Environment, Joint Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China
| | - Bo Hou
- College of Biology and the Environment, Joint Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China
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The Impact of COVID-19 on Investors’ Investment Intention of Sustainability-Related Investment: Evidence from China. SUSTAINABILITY 2022. [DOI: 10.3390/su14095325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
This paper investigates how investors respond to the COVID-19 pandemic, particularly regarding their intention to invest in sustainability-related investment (SRI) funds. We conduct two experiments online with participants who have experience with stock and fund investments. The first one includes 292 participants, which aims to explore investors’ attitudes and investment intention of different sustainability-related components, and the second one includes 432 participants, which aims to examine how the COVID-19 pandemic affects individuals’ attitudes and investment intention. Our results show that investors tend to invest in SRI funds when the threat of the COVID-19 pandemic is salient. Specifically, we find that although investors perceive environmental issues to be more important than economic and social issues, their investment intention of economic-focused SRI funds significantly increases in response to the COVID-19 pandemic threat. These findings suggest that fund managers can focus on particular types of investors when designing SRI funds, such as active investors with a preference for technical analysis and young female investors with a high level of income and education.
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8
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Li Y, Li M, Rice M, Yang C. Impact of COVID-19 containment and closure policies on tropospheric nitrogen dioxide: A global perspective. ENVIRONMENT INTERNATIONAL 2022; 158:106887. [PMID: 34563750 PMCID: PMC8452510 DOI: 10.1016/j.envint.2021.106887] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 09/13/2021] [Accepted: 09/17/2021] [Indexed: 06/13/2023]
Abstract
The containment and closure policies adopted in attempts to contain the spread of the 2019 coronavirus disease (COVID-19) have impacted nearly every aspect of our lives including the environment we live in. These influences may be observed when evaluating changes in pollutants such as nitrogen dioxide (NO2), which is an important indicator for economic, industrial, and other anthropogenic activities. We utilized a data-driven approach to analyze the relationship between tropospheric NO2 and COVID-19 mitigation measures by clustering regions based on pollution levels rather than constraining the study units by predetermined administrative boundaries as pollution knows no borders. Specifically, three clusters were discovered signifying mild, moderate, and poor pollution levels. The most severely polluted cluster saw significant reductions in tropospheric NO2, coinciding with lockdown periods. Based on the clustering results, qualitative and quantitative analyses were conducted at global and regional levels to investigate the spatiotemporal changes. In addition, panel regression analysis was utilized to quantify the impact of policy measures on the NO2 reduction. This study found that a 23.58 score increase in the stringency index (ranging from 0 to 100) can significantly reduce the NO2 TVCD by 3.2% (p < 0.05) in the poor cluster in 2020, which corresponds to a 13.1% maximum reduction with the most stringent containment and closure policies implemented. In addition, the policy measures of workplace closures and close public transport can significantly decrease the tropospheric NO2 in the poor cluster by 6.7% (p < 0.1) and 4.5% (p < 0.1), respectively. An additional heterogeneity analysis found that areas with higher incomes, CO2 emissions, and fossil fuel consumption have larger NO2 TVCD reductions regarding workplace closures and public transport closures.
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Affiliation(s)
- Yun Li
- Department of Geography and GeoInformation Science, George Mason University, Fairfax, VA, USA; NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USA
| | - Moming Li
- Department of Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, CA, USA
| | - Megan Rice
- Department of Chemistry, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Chaowei Yang
- Department of Geography and GeoInformation Science, George Mason University, Fairfax, VA, USA; NSF Spatiotemporal Innovation Center, George Mason University, Fairfax, VA, USA.
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9
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Lovenduski NS, Chatterjee A, Swart NC, Fyfe JC, Keeling RF, Schimel D. On the Detection of COVID-Driven Changes in Atmospheric Carbon Dioxide. GEOPHYSICAL RESEARCH LETTERS 2021; 48:e2021GL095396. [PMID: 34924639 PMCID: PMC8667626 DOI: 10.1029/2021gl095396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 10/25/2021] [Accepted: 11/07/2021] [Indexed: 06/14/2023]
Abstract
We assess the detectability of COVID-like emissions reductions in global atmospheric CO2 concentrations using a suite of large ensembles conducted with an Earth system model. We find a unique fingerprint of COVID in the simulated growth rate of CO2 sampled at the locations of surface measurement sites. Negative anomalies in growth rates persist from January 2020 through December 2021, reaching a maximum in February 2021. However, this fingerprint is not formally detectable unless we force the model with unrealistically large emissions reductions (2 or 4 times the observed reductions). Internal variability and carbon-concentration feedbacks obscure the detectability of short-term emission reductions in atmospheric CO2. COVID-driven changes in the simulated, column-averaged dry air mole fractions of CO2 are eclipsed by large internal variability. Carbon-concentration feedbacks begin to operate almost immediately after the emissions reduction; these feedbacks reduce the emissions-driven signal in the atmosphere carbon reservoir and further confound signal detection.
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Affiliation(s)
- Nicole S. Lovenduski
- Department of Atmospheric and Oceanic Sciences and Institute of Arctic and Alpine ResearchUniversity of ColoradoBoulderCOUSA
| | - Abhishek Chatterjee
- Carbon Cycle and Ecosystems GroupJet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
- USRA/NASA Goddard Space Flight CenterGreenbeltMDUSA
| | - Neil C. Swart
- Canadian Centre for Climate Modelling and AnalysisEnvironment and Climate Change CanadaVictoriaBCCanada
| | - John C. Fyfe
- Canadian Centre for Climate Modelling and AnalysisEnvironment and Climate Change CanadaVictoriaBCCanada
| | - Ralph F. Keeling
- Scripps Institution of OceanographyUniversity of California San DiegoLa JollaCAUSA
| | - David Schimel
- Carbon Cycle and Ecosystems GroupJet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
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