1
|
Pitt JR, Lopez-Coto I, Karion A, Hajny KD, Tomlin J, Kaeser R, Jayarathne T, Stirm BH, Floerchinger CR, Loughner CP, Commane R, Gately CK, Hutyra LR, Gurney KR, Roest GS, Liang J, Gourdji S, Mueller KL, Whetstone JR, Shepson PB. Underestimation of Thermogenic Methane Emissions in New York City. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:9147-9157. [PMID: 38743431 PMCID: PMC11137862 DOI: 10.1021/acs.est.3c10307] [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: 12/07/2023] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 05/16/2024]
Abstract
Recent studies have shown that methane emissions are underestimated by inventories in many US urban areas. This has important implications for climate change mitigation policy at the city, state, and national levels. Uncertainty in both the spatial distribution and sectoral allocation of urban emissions can limit the ability of policy makers to develop appropriately focused emission reduction strategies. Top-down emission estimates based on atmospheric greenhouse gas measurements can help to improve inventories and inform policy decisions. This study presents a new high-resolution (0.02 × 0.02°) methane emission inventory for New York City and its surrounding area, constructed using the latest activity data, emission factors, and spatial proxies. The new high-resolution inventory estimates of methane emissions for the New York-Newark urban area are 1.3 times larger than those for the gridded Environmental Protection Agency inventory. We used aircraft mole fraction measurements from nine research flights to optimize the high-resolution inventory emissions within a Bayesian inversion. These sectorally optimized emissions show that the high-resolution inventory still significantly underestimates methane emissions within the New York-Newark urban area, primarily because it underestimates emissions from thermogenic sources (by a factor of 2.3). This suggests that there remains a gap in our process-based understanding of urban methane emissions.
Collapse
Affiliation(s)
- Joseph R. Pitt
- School
of Marine and Atmospheric Sciences, Stony
Brook University, Stony
Brook, New York 11794, United States
| | - Israel Lopez-Coto
- School
of Marine and Atmospheric Sciences, Stony
Brook University, Stony
Brook, New York 11794, United States
- National
Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Anna Karion
- National
Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Kristian D. Hajny
- School
of Marine and Atmospheric Sciences, Stony
Brook University, Stony
Brook, New York 11794, United States
- Department
of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Jay Tomlin
- Department
of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Robert Kaeser
- Department
of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Thilina Jayarathne
- Department
of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| | - Brian H. Stirm
- School
of Aviation and Transportation Technology, Purdue University, West Lafayette, Indiana 47906, United States
| | - Cody R. Floerchinger
- Department
of Earth and Planetary Sciences, Harvard
University, Cambridge, Massachusetts 02138, United States
| | | | - Róisín Commane
- Department
of Earth and Environmental Sciences, Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York 10964, United States
| | - Conor K. Gately
- Department
of Earth and Planetary Sciences, Harvard
University, Cambridge, Massachusetts 02138, United States
- Department
of Earth and Environment, Boston University, Boston, Massachusetts 02215, United States
| | - Lucy R. Hutyra
- Department
of Earth and Environment, Boston University, Boston, Massachusetts 02215, United States
| | - Kevin R. Gurney
- School
of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, Arizona 86011, United States
| | - Geoffrey S. Roest
- School
of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, Arizona 86011, United States
| | - Jianming Liang
- Environmental Systems Research Institute, Redlands, California 92373, United States
| | - Sharon Gourdji
- National
Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Kimberly L. Mueller
- National
Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - James R. Whetstone
- National
Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Paul B. Shepson
- School
of Marine and Atmospheric Sciences, Stony
Brook University, Stony
Brook, New York 11794, United States
- Department
of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States
| |
Collapse
|
2
|
Bun R, Marland G, Oda T, See L, Puliafito E, Nahorski Z, Jonas M, Kovalyshyn V, Ialongo I, Yashchun O, Romanchuk Z. Tracking unaccounted greenhouse gas emissions due to the war in Ukraine since 2022. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 914:169879. [PMID: 38185145 DOI: 10.1016/j.scitotenv.2024.169879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 12/07/2023] [Accepted: 01/01/2024] [Indexed: 01/09/2024]
Abstract
Accounting and reporting of greenhouse gas (GHG) emissions are mandatory for Parties under the Paris Agreement. Emissions reporting is important for understanding the global carbon cycle and for addressing global climate change. However, in a period of open conflict or war, military emissions increase significantly and the accounting system is not currently designed to account adequately for this source. In this paper we analyze how, during the first 18 months of the 2022/2023 full-scale war in Ukraine, GHG national inventory reporting to the UNFCCC was affected. We estimated the decrease of emissions due to a reduction in traditional human activities. We identified major, war-related, emission processes from the territory of Ukraine not covered by current GHG inventory guidelines and that are not likely to be included in national inventory reports. If these emissions are included, they will likely be incorporated in a way that is not transparent with potentially high uncertainty. We analyze publicly available data and use expert judgment to estimate such emissions from (1) the use of bombs, missiles, barrel artillery, and mines; (2) the consumption of oil products for military operations; (3) fires at petroleum storage depots and refineries; (4) fires in buildings and infrastructure facilities; (5) fires on forest and agricultural lands; and (6) the decomposition of war-related garbage/waste. Our estimate of these war-related emissions of carbon dioxide, methane, and nitrous oxide for the first 18 months of the war in Ukraine is 77 MtCO2-eq. with a relative uncertainty of +/-22 % (95 % confidence interval).
Collapse
Affiliation(s)
- Rostyslav Bun
- Lviv Polytechnic National University, Bandery str., 12, Lviv 79013, Ukraine; WSB University, Cieplaka str., 1c, Dąbrowa Górnicza 41300, Poland.
| | | | - Tomohiro Oda
- Universities Space Research Association, Washington, DC, USA; University of Maryland, College Park, MD, USA; Osaka University, Suita, Osaka, Japan.
| | - Linda See
- International Institute for Applied Systems Analysis, Laxenburg, Austria.
| | - Enrique Puliafito
- Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina; Mendoza Regional Faculty, National Technological University (FRM-UTN), Mendoza, Argentina.
| | - Zbigniew Nahorski
- Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland; WIT University, Warsaw, Poland.
| | - Mathias Jonas
- International Institute for Applied Systems Analysis, Laxenburg, Austria.
| | | | | | - Orysia Yashchun
- Lviv Polytechnic National University, Bandery str., 12, Lviv 79013, Ukraine; International Institute for Applied Systems Analysis, Laxenburg, Austria.
| | - Zoriana Romanchuk
- Lviv Polytechnic National University, Bandery str., 12, Lviv 79013, Ukraine; International Institute for Applied Systems Analysis, Laxenburg, Austria.
| |
Collapse
|
3
|
Zhu E, Yao J, Zhang X, Chen L. Explore the spatial pattern of carbon emissions in urban functional zones: a case study of Pudong, Shanghai, China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:2117-2128. [PMID: 38049690 DOI: 10.1007/s11356-023-31149-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 11/17/2023] [Indexed: 12/06/2023]
Abstract
It is crucial for the development of carbon reduction strategies to accurately examine the spatial distribution of carbon emissions. Limited by data availability and lack of industry segmentation, previous studies attempting to model spatial carbon emissions still suffer from significant uncertainty. Taking Pudong New Area as an example, with the help of multi-source data, this paper proposed a research framework for the amount calculation and spatial distribution simulation of its CO2 emissions at the scale of urban functional zones (UFZs). The methods used in this study were based on mapping relations among the locations of geographic entities and data of multiple sources, using the coefficient method recommended by the Intergovernmental Panel on Climate Change (IPCC) to calculate emissions. The results showed that the emission intensity of industrial zones and transport zones was much higher than that of other UFZs. In addition, Moran's I test indicated that there was a positive spatial autocorrelation in high emission zones, especially located in industrial zones. The spatial analysis of CO2 emissions at the UFZ scale deepened the consideration of spatial heterogeneity, which could contribute to the management of low carbon city and the optimal implementation of energy allocation.
Collapse
Affiliation(s)
- Enyan Zhu
- College of Transport and Communications, Shanghai Maritime University, Shanghai, 201306, China.
| | - Jian Yao
- College of Transport and Communications, Shanghai Maritime University, Shanghai, 201306, China
| | - Xinghui Zhang
- College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, 201306, China
| | - Lisu Chen
- College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, 201306, China
| |
Collapse
|
4
|
Karion A, Ghosh S, Lopez-Coto I, Mueller K, Gourdji S, Pitt J, Whetstone J. Methane Emissions Show Recent Decline but Strong Seasonality in Two US Northeastern Cities. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:19565-19574. [PMID: 37941355 DOI: 10.1021/acs.est.3c05050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Urban methane emissions estimated using atmospheric observations have been found to exceed estimates derived by using traditional inventory methods in several northeastern US cities. In this work, we leveraged a nearly five-year record of observations from a dense tower network coupled with a newly developed high-resolution emissions map to quantify methane emission rates in Washington, DC, and Baltimore, Maryland. Annual emissions averaged over 2018-2021 were 80.1 [95% CI: 61.2, 98.9] Gg in the Washington, DC urban area and 47.4 [95% CI: 35.9, 58.5] Gg in the Baltimore urban area, with a decreasing trend of approximately 4-5% per year in both cities. We also find wintertime emissions 44% higher than summertime emissions, correlating with natural gas consumption. We further attribute a large fraction of total methane emissions to the natural gas sector using a least-squares regression on our spatially resolved estimates, supporting previous findings that natural gas systems emit the plurality of methane in both cities. This study contributes to the relatively sparse existing knowledge base of urban methane emissions sources and variability, adding to our understanding of how these emissions change in time and providing evidence to support efforts to mitigate natural gas emissions.
Collapse
Affiliation(s)
- Anna Karion
- Special Programs Office, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Subhomoy Ghosh
- Special Programs Office, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
- Center for Research Computing, University of Notre Dame, South Bend, Indiana 46556, United States
| | - Israel Lopez-Coto
- Special Programs Office, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
- School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, New York 11794, United States
| | - Kimberly Mueller
- Special Programs Office, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Sharon Gourdji
- Special Programs Office, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| | - Joseph Pitt
- School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, New York 11794, United States
- School of Chemistry, University of Bristol, Bristol BS8 1QU, U.K
| | - James Whetstone
- Special Programs Office, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, United States
| |
Collapse
|
5
|
Young HA, Turnbull JC, Keller ED, Domingues LG, Parry-Thompson J, Hilton TW, Brailsford GW, Gray S, Moss RC, Mikaloff-Fletcher S. Urban flask measurements of CO 2ff and CO to identify emission sources at different site types in Auckland, New Zealand. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2023; 381:20220204. [PMID: 37807684 PMCID: PMC10642768 DOI: 10.1098/rsta.2022.0204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 03/17/2023] [Indexed: 10/10/2023]
Abstract
As part of the CarbonWatch-NZ research programme, air samples were collected at 28 sites around Auckland, New Zealand, to determine the atmospheric ratio (RCO) of excess (local enhancement over background) carbon monoxide to fossil CO2 (CO2ff). Sites were categorized into seven types (background, forest, industrial, suburban, urban, downwind and motorway) to observe RCO around Auckland. Motorway flasks observed RCO of 14 ± 1 ppb ppm-1 and were used to evaluate traffic RCO. The similarity between suburban (14 ± 1 ppb ppm-1) and traffic RCO suggests that traffic dominates suburban CO2ff emissions during daytime hours, the period of flask collection. The lower urban RCO (11 ± 1 ppb ppm-1) suggests that urban CO2ff emissions are comprised of more than just traffic, with contributions from residential, commercial and industrial sources, all with a lower RCO than traffic. Finally, the downwind sites were believed to best represent RCO for Auckland City overall (11 ± 1 ppb ppm-1). We demonstrate that the initial discrepancy between the downwind RCO and Auckland's estimated daytime inventory RCO (15 ppb ppm-1) can be attributed to an overestimation in inventory traffic CO emissions. After revision based on our observed motorway RCO, the revised inventory RCO (12 ppb ppm-1) is consistent with our observations. This article is part of the Theo Murphy meeting issue 'Radiocarbon in the Anthropocene'.
Collapse
Affiliation(s)
| | - Jocelyn C. Turnbull
- GNS Science, Lower Hutt 5010, New Zealand
- CIRES, University of Colorado at Boulder, Boulder, CO, USA
| | - Elizabeth D. Keller
- GNS Science, Lower Hutt 5010, New Zealand
- Antarctic Research Centre, Victoria University of Wellington, Wellington, New Zealand
| | - Lucas Gatti Domingues
- GNS Science, Lower Hutt 5010, New Zealand
- Department of Atmospheric Sciences, Institute of Astronomy, Geophysics and Atmospheric Sciences, University of São Paulo, São Paulo, Brazil
| | - Jeremy Parry-Thompson
- GNS Science, Lower Hutt 5010, New Zealand
- Greater Wellington Regional Council, Wellington, New Zealand
| | | | - Gordon W. Brailsford
- National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand
| | - Sally Gray
- National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand
| | - Rowena C. Moss
- National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand
| | | |
Collapse
|
6
|
Tayewo R, Septier F, Nevat I, Peters GW. Graph Regression Model for Spatial and Temporal Environmental Data-Case of Carbon Dioxide Emissions in the United States. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1272. [PMID: 37761572 PMCID: PMC10529149 DOI: 10.3390/e25091272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/18/2023] [Accepted: 08/24/2023] [Indexed: 09/29/2023]
Abstract
We develop a new model for spatio-temporal data. More specifically, a graph penalty function is incorporated in the cost function in order to estimate the unknown parameters of a spatio-temporal mixed-effect model based on a generalized linear model. This model allows for more flexible and general regression relationships than classical linear ones through the use of generalized linear models (GLMs) and also captures the inherent structural dependencies or relationships of the data through this regularization based on the graph Laplacian. We use a publicly available dataset from the National Centers for Environmental Information (NCEI) in the United States of America and perform statistical inferences of future CO2 emissions in 59 counties. We empirically show how the proposed method outperforms widely used methods, such as the ordinary least squares (OLS) and ridge regression for this challenging problem.
Collapse
Affiliation(s)
- Roméo Tayewo
- Univ Bretagne Sud, CNRS UMR 6205, LMBA, F-56000 Vannes, France;
| | | | - Ido Nevat
- TUMCREATE, 1 Create Way, #10-02 CREATE Tower, Singapore 138602, Singapore;
| | - Gareth W. Peters
- Department of Statistics and Applied Probability, University of California Santa Barbara, Santa Barbara, CA 93106, USA;
| |
Collapse
|
7
|
Miyazaki K, Bowman K. Predictability of fossil fuel CO 2 from air quality emissions. Nat Commun 2023; 14:1604. [PMID: 36959192 PMCID: PMC10034258 DOI: 10.1038/s41467-023-37264-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 03/06/2023] [Indexed: 03/25/2023] Open
Abstract
Quantifying the coevolution of greenhouse gases and air quality pollutants can provide insight into underlying anthropogenic processes enabling predictions of their emission trajectories. Here, we classify the dynamics of historic emissions in terms of a modified Environmental Kuznets Curve (MEKC), which postulates the coevolution of fossil fuel CO2 (FFCO2) and NOx emissions as a function of macroeconomic development. The MEKC broadly captures the historic FFCO2-NOx dynamical regimes for countries including the US, China, and India as well as IPCC scenarios. Given these dynamics, we find the predictive skill of FFCO2 given NOx emissions constrained by satellite data is less than 2% error at one-year lags for many countries and less than 10% for 4-year lags. The proposed framework in conjunction with an increasing satellite constellation provides valuable guidance to near-term emission scenario development and evaluation at time-scales relevant to international assessments such as the Global Stocktake.
Collapse
Affiliation(s)
- Kazuyuki Miyazaki
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.
| | - Kevin Bowman
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| |
Collapse
|
8
|
Gao F, Wu J, Xiao J, Li X, Liao S, Chen W. Spatially explicit carbon emissions by remote sensing and social sensing. ENVIRONMENTAL RESEARCH 2023; 221:115257. [PMID: 36642123 DOI: 10.1016/j.envres.2023.115257] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 12/05/2022] [Accepted: 01/09/2023] [Indexed: 06/17/2023]
Abstract
Scientific simulation of carbon emissions is an important prerequisite for achieving low-carbon green development and carbon peak and carbon neutralization. This study proposed a carbon emissions spatialization method based on nighttime light (NTL) remote sensing and municipal electricity social sensing. First, the economics-energy comprehensive index (EECI) was proposed by integrating the NTL and municipal electricity consumption (EC) data. Second, the carbon emissions were spatialized at a fine scale based on NTL, EC, and EECI, respectively. Finally, the geographical detector model was applied to quantify the influencing factors on carbon emissions from the perspectives of individuals and interactions. Results show that combining remote sensing and social sensing data helps depict carbon emissions accurately. The factor analysis found that GDP and population were the basis of carbon emissions, while the secondary industry and urbanization rate were the direct factors. This study is expected to provide constructive suggestions and methods for emission reduction, carbon peak, and carbon neutrality in high-density cities in China.
Collapse
Affiliation(s)
- Feng Gao
- Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou, 510060, China; Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou, 510060, China
| | - Jie Wu
- Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou, 510060, China; Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou, 510060, China.
| | - Jinghao Xiao
- Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou, 510060, China; Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou, 510060, China
| | - Xiaohui Li
- Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou, 510060, China; Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou, 510060, China
| | - Shunyi Liao
- Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou, 510060, China; Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou, 510060, China
| | - Wangyang Chen
- Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou, 510060, China; Guangdong Enterprise Key Laboratory for Urban Sensing, Monitoring and Early Warning, Guangzhou, 510060, China
| |
Collapse
|
9
|
Stagakis S, Feigenwinter C, Vogt R, Kalberer M. A high-resolution monitoring approach of urban CO 2 fluxes. Part 1 - bottom-up model development. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 858:160216. [PMID: 36402316 DOI: 10.1016/j.scitotenv.2022.160216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 10/13/2022] [Accepted: 11/12/2022] [Indexed: 06/16/2023]
Abstract
Monitoring carbon dioxide (CO2) emissions of urban areas is increasingly important to assess the progress towards the Paris Agreement goals for climate neutrality. Cities are currently voluntarily developing their local inventories, however, the approaches used across different cities are not systematically assessed, present consistency issues, neglect the biogenic fluxes and have restricted spatial and temporal resolution. In order to assess the accuracy of the urban emission inventories and provide information which is useful for planning local climate change mitigation actions, high resolution modelling approaches combined or evaluated with atmospheric observations are needed. This study presents a new high-resolution bottom-up (BU) model which provides hourly maps of all major components contributing to the local urban surface CO2 flux (i.e. building emissions, traffic emissions, human respiration, soil respiration, plant respiration, plant photosynthetic uptake) and can therefore be used for direct comparison with in-situ atmospheric observations and development of local scale atmospheric inversion methodologies. The model design aims to be simple and flexible using inputs that are available in most cities, facilitating transferability to different locations. The inputs are primarily based on open geospatial datasets, census information, road traffic monitoring and basic meteorological parameters. The model is applied on the city centre of Basel, Switzerland, for the year 2018 and the results are compared to a local inventory. It is demonstrated that the model captures the highly dynamic spatiotemporal variability of the urban CO2 fluxes according to main environmental drivers, population activity dynamics and geospatial information proxies. The annual modelled emissions from buildings and traffic are estimated 14.8 % and 9 % lower than the respective information derived by the local inventory. The differences are mainly attributed to the emissions from the industrial areas and the highways which are beyond the geographical coverage of the model.
Collapse
Affiliation(s)
- Stavros Stagakis
- Department of Environmental Sciences, University of Basel, Klingelbergstrasse 27, 4056 Basel, Switzerland.
| | - Christian Feigenwinter
- Department of Environmental Sciences, University of Basel, Klingelbergstrasse 27, 4056 Basel, Switzerland.
| | - Roland Vogt
- Department of Environmental Sciences, University of Basel, Klingelbergstrasse 27, 4056 Basel, Switzerland.
| | - Markus Kalberer
- Department of Environmental Sciences, University of Basel, Klingelbergstrasse 27, 4056 Basel, Switzerland.
| |
Collapse
|
10
|
Huo D, Liu K, Liu J, Huang Y, Sun T, Sun Y, Si C, Liu J, Huang X, Qiu J, Wang H, Cui D, Zhu B, Deng Z, Ke P, Shan Y, Boucher O, Dannet G, Liang G, Zhao J, Chen L, Zhang Q, Ciais P, Zhou W, Liu Z. Near-real-time daily estimates of fossil fuel CO 2 emissions from major high-emission cities in China. Sci Data 2022; 9:684. [PMCID: PMC9648454 DOI: 10.1038/s41597-022-01796-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 10/17/2022] [Indexed: 11/12/2022] Open
Abstract
Cities in China are on the frontline of low-carbon transition which requires monitoring city-level emissions with low-latency to support timely climate actions. Most existing CO2 emission inventories lag reality by more than one year and only provide annual totals. To improve the timeliness and temporal resolution of city-level emission inventories, we present Carbon Monitor Cities-China (CMCC), a near-real-time dataset of daily CO2 emissions from fossil fuel and cement production for 48 major high-emission cities in China. This dataset provides territory-based emission estimates from 2020-01-01 to 2021-12-31 for five sectors: power generation, residential (buildings and services), industry, ground transportation, and aviation. CMCC is developed based on an innovative framework that integrates bottom-up inventory construction and daily emission estimates from sectoral activities and models. Annual emissions show reasonable agreement with other datasets, and uncertainty ranges are estimated for each city and sector. CMCC provides valuable daily emission estimates that enable low-latency mitigation monitoring for cities in China. Measurement(s) | carbon dioxide emissions | Technology Type(s) | fossil fuel consumption |
Collapse
Affiliation(s)
- Da Huo
- grid.12527.330000 0001 0662 3178Department of Earth System Science, Tsinghua University, Beijing, 100084 China ,grid.17063.330000 0001 2157 2938Department of Civil & Mineral Engineering, University of Toronto, Toronto, ON M5S 1A1 Canada
| | - Kai Liu
- Product and Solution and Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, 311121 China
| | - Jianwu Liu
- Product and Solution and Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, 311121 China
| | - Yingjian Huang
- Product and Solution and Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, 311121 China
| | - Taochun Sun
- grid.12527.330000 0001 0662 3178Department of Earth System Science, Tsinghua University, Beijing, 100084 China
| | - Yun Sun
- grid.33763.320000 0004 1761 2484School of Environmental Science and Engineering, Tianjin University, Tianjin, 300072 China
| | - Caomingzhe Si
- grid.12527.330000 0001 0662 3178Department of Electrical Engineering, Tsinghua University, Beijing, 100084 China
| | - Jinjie Liu
- grid.12527.330000 0001 0662 3178Department of Earth System Science, Tsinghua University, Beijing, 100084 China ,The Chinese University of Hongkong, Shenzhen, Guangdong, 518172 China
| | - Xiaoting Huang
- grid.12527.330000 0001 0662 3178Department of Earth System Science, Tsinghua University, Beijing, 100084 China
| | - Jian Qiu
- Product and Solution and Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, 311121 China
| | - Haijin Wang
- The Chinese University of Hongkong, Shenzhen, Guangdong, 518172 China ,grid.511521.3The Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, Guangdong 518172 China
| | - Duo Cui
- grid.12527.330000 0001 0662 3178Department of Earth System Science, Tsinghua University, Beijing, 100084 China
| | - Biqing Zhu
- grid.12527.330000 0001 0662 3178Department of Earth System Science, Tsinghua University, Beijing, 100084 China
| | - Zhu Deng
- grid.12527.330000 0001 0662 3178Department of Earth System Science, Tsinghua University, Beijing, 100084 China
| | - Piyu Ke
- grid.12527.330000 0001 0662 3178Department of Earth System Science, Tsinghua University, Beijing, 100084 China
| | - Yuli Shan
- grid.6572.60000 0004 1936 7486School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, B15 2TT UK
| | - Olivier Boucher
- grid.462844.80000 0001 2308 1657Institute Pierre-Simon Laplace, Sorbonne Université/CNRS, Paris, France
| | - Grégoire Dannet
- grid.462844.80000 0001 2308 1657Institute Pierre-Simon Laplace, Sorbonne Université/CNRS, Paris, France
| | - Gaoqi Liang
- The Chinese University of Hongkong, Shenzhen, Guangdong, 518172 China ,grid.511521.3The Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, Guangdong 518172 China
| | - Junhua Zhao
- The Chinese University of Hongkong, Shenzhen, Guangdong, 518172 China ,grid.511521.3The Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, Guangdong 518172 China
| | - Lei Chen
- grid.12527.330000 0001 0662 3178Department of Electrical Engineering, Tsinghua University, Beijing, 100084 China
| | - Qian Zhang
- grid.410356.50000 0004 1936 8331Robert M. Buchan Department of Mining, Queen’s University, Kingston, ON K7L 3N6 Canada
| | - Philippe Ciais
- Laboratoire des Sciences du Climate et de l’Environnement LSCE, Orme de Merisiers, 91191 Gif-sur-Yvette, France
| | - Wenwen Zhou
- Product and Solution and Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, 311121 China
| | - Zhu Liu
- grid.12527.330000 0001 0662 3178Department of Earth System Science, Tsinghua University, Beijing, 100084 China
| |
Collapse
|
11
|
Neighborhood Emission Mapping Operation (NEMO): A 1-km anthropogenic emission dataset in the United States. Sci Data 2022; 9:680. [PMID: 36351966 PMCID: PMC9646775 DOI: 10.1038/s41597-022-01790-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 10/18/2022] [Indexed: 11/11/2022] Open
Abstract
We present an unprecedented effort to map anthropogenic emissions of air pollutants at 1 km spatial resolution in the contiguous United States (CONUS). This new dataset, Neighborhood Emission Mapping Operation (NEMO), is produced at hourly intervals based on the United States Environmental Protection Agency (US EPA) National Emission Inventories 2017. Fine-scale spatial allocation was achieved through distributing the emission sources using 108 spatial surrogates, factors representing the portion of a source in each 1 km grid. Gaseous and particulate pollutants are speciated into model species for the Carbon Bond 6 chemical mechanism. All sources are grouped in 9 sectors and stored in NetCDF format for air quality models, and in shapefile format for GIS users and air quality managers. This dataset shows good consistency with the USEPA benchmark dataset, with a monthly difference in emissions less than 0.03% for any sector. NEMO provides the first 1 km mapping of air pollution over the CONUS, enabling new applications such as fine-scale air quality modeling, air pollution exposure assessment, and environmental justice studies. Measurement(s) | anthropogenic emission in United States | Technology Type(s) | Census survey and computer models |
Collapse
|
12
|
Huo D, Huang X, Dou X, Ciais P, Li Y, Deng Z, Wang Y, Cui D, Benkhelifa F, Sun T, Zhu B, Roest G, Gurney KR, Ke P, Guo R, Lu C, Lin X, Lovell A, Appleby K, DeCola PL, Davis SJ, Liu Z. Carbon Monitor Cities near-real-time daily estimates of CO 2 emissions from 1500 cities worldwide. Sci Data 2022; 9:533. [PMID: 36050332 PMCID: PMC9434530 DOI: 10.1038/s41597-022-01657-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 08/22/2022] [Indexed: 11/09/2022] Open
Abstract
Building on near-real-time and spatially explicit estimates of daily carbon dioxide (CO2) emissions, here we present and analyze a new city-level dataset of fossil fuel and cement emissions, Carbon Monitor Cities, which provides daily estimates of emissions from January 2019 through December 2021 for 1500 cities in 46 countries, and disaggregates five sectors: power generation, residential (buildings), industry, ground transportation, and aviation. The goal of this dataset is to improve the timeliness and temporal resolution of city-level emission inventories and includes estimates for both functional urban areas and city administrative areas that are consistent with global and regional totals. Comparisons with other datasets (i.e. CEADs, MEIC, Vulcan, and CDP-ICLEI Track) were performed, and we estimate the overall annual uncertainty range to be ±21.7%. Carbon Monitor Cities is a near-real-time, city-level emission dataset that includes cities around the world, including the first estimates for many cities in low-income countries.
Collapse
Affiliation(s)
- Da Huo
- Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
| | - Xiaoting Huang
- Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Xinyu Dou
- Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Philippe Ciais
- Laboratoire des Sciences du Climate et de l'Environnement LSCE, Orme de Merisiers 91191, Gif-sur-Yvette, France
| | - Yun Li
- Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Zhu Deng
- Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Yilong Wang
- Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
| | - Duo Cui
- Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Fouzi Benkhelifa
- Nexqt, City Climate Intelligence, 9 rue des colonnes, Paris, 75002, France
| | - Taochun Sun
- Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Biqing Zhu
- Department of Earth System Science, Tsinghua University, Beijing, 100084, China
- Laboratoire des Sciences du Climate et de l'Environnement LSCE, Orme de Merisiers 91191, Gif-sur-Yvette, France
| | - Geoffrey Roest
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - Kevin R Gurney
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, 86011, USA
| | - Piyu Ke
- Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Rui Guo
- Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Chenxi Lu
- Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | - Xiaojuan Lin
- Department of Earth System Science, Tsinghua University, Beijing, 100084, China
| | | | | | - Philip L DeCola
- Department of Atmospheric and Oceanic Sciences, University of Maryland, College Park, MD, 20742, USA
| | - Steven J Davis
- Department of Earth System Science, University of California, Irvine, 3232 Croul Hall, Irvine, CA, 92697-3100, USA
| | - Zhu Liu
- Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
| |
Collapse
|
13
|
Yadav V, Ghosh S, Mueller K, Karion A, Roest G, Gourdji SM, Lopez‐Coto I, Gurney KR, Parazoo N, Verhulst KR, Kim J, Prinzivalli S, Fain C, Nehrkorn T, Mountain M, Keeling RF, Weiss RF, Duren R, Miller CE, Whetstone J. The Impact of COVID-19 on CO 2 Emissions in the Los Angeles and Washington DC/Baltimore Metropolitan Areas. GEOPHYSICAL RESEARCH LETTERS 2021; 48:e2021GL092744. [PMID: 34149111 PMCID: PMC8206775 DOI: 10.1029/2021gl092744] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 04/12/2021] [Accepted: 04/14/2021] [Indexed: 05/29/2023]
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.
Collapse
Affiliation(s)
- Vineet Yadav
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - Subhomoy Ghosh
- Center for Research ComputingUniversity of Notre DameSouth BendINUSA
- National Institute of Standards and TechnologyGaithersburgMDUSA
| | | | - Anna Karion
- National Institute of Standards and TechnologyGaithersburgMDUSA
| | - Geoffrey Roest
- School of Informatics, Computing, and Cyber SystemsNorthern Arizona UniversityFlagstaffAZUSA
| | | | | | - Kevin R. Gurney
- School of Informatics, Computing, and Cyber SystemsNorthern Arizona UniversityFlagstaffAZUSA
| | - Nicholas Parazoo
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | | | - Jooil Kim
- Scripps Institution of OceanographyUniversity of CaliforniaSan Diego, La JollaCAUSA
| | | | | | | | | | - Ralph F. Keeling
- Scripps Institution of OceanographyUniversity of CaliforniaSan Diego, La JollaCAUSA
| | - Ray F. Weiss
- Scripps Institution of OceanographyUniversity of CaliforniaSan Diego, La JollaCAUSA
| | - Riley Duren
- Arizona Institutes for ResilienceThe University of ArizonaTucsonAZUSA
| | - Charles E. Miller
- Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaCAUSA
| | - James Whetstone
- National Institute of Standards and TechnologyGaithersburgMDUSA
| |
Collapse
|
14
|
Gurney KR, Liang J, Roest G, Song Y, Mueller K, Lauvaux T. Under-reporting of greenhouse gas emissions in U.S. cities. Nat Commun 2021; 12:553. [PMID: 33531471 PMCID: PMC7854656 DOI: 10.1038/s41467-020-20871-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 12/15/2020] [Indexed: 11/09/2022] Open
Abstract
Cities dominate greenhouse gas emissions. Many have generated self-reported emission inventories, but their value to emissions mitigation depends on their accuracy, which remains untested. Here, we compare self-reported inventories from 48 US cities to independent estimates from the Vulcan carbon dioxide emissions data product, which is consistent with atmospheric measurements. We found that cities under-report their own greenhouse gas emissions, on average, by 18.3% (range: -145.5% to +63.5%) - a difference which if extrapolated to all U.S. cities, exceeds California's total emissions by 23.5%. Differences arise because city inventories omit particular fuels and source types and estimate transportation emissions differently. These results raise concerns about self-reported inventories in planning or assessing emissions, and warrant consideration of the new urban greenhouse gas information system recently developed by the scientific community.
Collapse
Affiliation(s)
- Kevin Robert Gurney
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA.
| | | | - Geoffrey Roest
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
| | - Yang Song
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
| | - Kimberly Mueller
- National Institute for Standards and Technology, Gaithersburg, MD, USA
| | - Thomas Lauvaux
- Laboratoire des Sciences du Climat et de l'Environnement, Gif-sur-Yvette, France
| |
Collapse
|
15
|
Karion A, Lopez-Coto I, Gourdji SM, Mueller K, Ghosh S, Callahan W, Stock M, DiGangi E, Prinzivalli S, Whetstone J. Background conditions for an urban greenhouse gas network in the Washington, D.C. and Baltimore metropolitan region. ATMOSPHERIC CHEMISTRY AND PHYSICS 2021; 21:10.5194/acp-21-6257-2021. [PMID: 36873665 PMCID: PMC9982866 DOI: 10.5194/acp-21-6257-2021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
As city governments take steps towards establishing emissions reduction targets, the atmospheric research community is increasingly able to assist in tracking emissions reductions. Researchers have established systems for observing atmospheric greenhouse gases in urban areas with the aim of attributing greenhouse gas concentration enhancements (and thus, emissions) to the region in question. However, to attribute enhancements to a particular region, one must isolate the component of the observed concentration attributable to fluxes inside the region by removing the background, which is the component due to fluxes outside. In this study, we demonstrate methods to construct several versions of a background for our carbon dioxide and methane observing network in the Washington, DC and Baltimore, MD metropolitan region. Some of these versions rely on transport and flux models, while others are based on observations upwind of the domain. First, we evaluate the backgrounds in a synthetic data framework, then we evaluate against real observations from our urban network. We find that backgrounds based on upwind observations capture the variability better than model-based backgrounds, although care must be taken to avoid bias from biospheric carbon dioxide fluxes near background stations in summer. Model-based backgrounds also perform well when upwind fluxes can be modeled accurately. Our study evaluates different background methods and provides guidance determining background methodology that can impact the design of urban monitoring networks.
Collapse
Affiliation(s)
- Anna Karion
- Special Programs Office, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA
| | - Israel Lopez-Coto
- Special Programs Office, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA
- School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY 11794, USA
| | - Sharon M. Gourdji
- Special Programs Office, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA
| | - Kimberly Mueller
- Special Programs Office, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA
| | - Subhomoy Ghosh
- Special Programs Office, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA
- Center for Research Computing, University of Notre Dame, South Bend, IN, 46556, USA
| | | | | | | | | | - James Whetstone
- Special Programs Office, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA
| |
Collapse
|