Articles | Volume 17, issue 6
https://doi.org/10.5194/essd-17-2489-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-2489-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Four-dimensional aircraft emission inventory dataset of the landing-and-takeoff cycle in China (2019–2023)
Jianlei Lang
Key Laboratory of Beijing on Regional Air Pollution Control, College of Environmental Science and Engineering, Beijing University of Technology, Beijing, 100124, China
Beijing Laboratory for Intelligent Environmental Protection, Beijing University of Technology, Beijing, 100124, China
Zekang Yang
Key Laboratory of Beijing on Regional Air Pollution Control, College of Environmental Science and Engineering, Beijing University of Technology, Beijing, 100124, China
Key Laboratory of Beijing on Regional Air Pollution Control, College of Environmental Science and Engineering, Beijing University of Technology, Beijing, 100124, China
Chaoyu Wen
Key Laboratory of Beijing on Regional Air Pollution Control, College of Environmental Science and Engineering, Beijing University of Technology, Beijing, 100124, China
Xiaoqing Cheng
Key Laboratory of Beijing on Regional Air Pollution Control, College of Environmental Science and Engineering, Beijing University of Technology, Beijing, 100124, China
Related authors
Ying Zhou, Xiaofan Xing, Jianlei Lang, Dongsheng Chen, Shuiyuan Cheng, Lin Wei, Xiao Wei, and Chao Liu
Atmos. Chem. Phys., 17, 2839–2864, https://doi.org/10.5194/acp-17-2839-2017, https://doi.org/10.5194/acp-17-2839-2017, 2017
Short summary
Short summary
A 1 km gridded and comprehensive biomass burning emission inventory including domestic and in-field straw burning, firewood burning, livestock excrement burning, and forest and grassland fires is developed for mainland China in 2012 based on county-level activity data, satellite data, and updated source-specific emission factors. The detailed emission inventory could provide useful information for air-quality modelling and supports the development of appropriate pollution-control strategies.
Nana Wu, Guannan Geng, Ruochong Xu, Shigan Liu, Xiaodong Liu, Qinren Shi, Ying Zhou, Yu Zhao, Huan Liu, Yu Song, Junyu Zheng, Qiang Zhang, and Kebin He
Earth Syst. Sci. Data, 16, 2893–2915, https://doi.org/10.5194/essd-16-2893-2024, https://doi.org/10.5194/essd-16-2893-2024, 2024
Short summary
Short summary
The commonly used method for developing large-scale air pollutant emission datasets for China faces challenges due to limited availability of detailed parameter information. In this study, we develop an efficient integrated framework to gather such information by harmonizing seven heterogeneous inventories from five research institutions. Emission characterizations are analyzed and validated, demonstrating that the dataset provides more accurate emission magnitudes and spatiotemporal patterns.
Xiaopu Lyu, Nan Wang, Hai Guo, Likun Xue, Fei Jiang, Yangzong Zeren, Hairong Cheng, Zhe Cai, Lihui Han, and Ying Zhou
Atmos. Chem. Phys., 19, 3025–3042, https://doi.org/10.5194/acp-19-3025-2019, https://doi.org/10.5194/acp-19-3025-2019, 2019
Short summary
Short summary
Through analyses on the synoptic systems, pollution characteristics of O3 precursors, and modeling of local O3 formation and processes influencing O3 level, we found that this O3 pollution event was induced by a uniform pressure field over the Shandong Peninsula and also aggravated by a low-pressure trough in the last few days. This finding indicated that the NCP might be an O3 source region, which exported photochemical pollution to the adjoining regions or even to the neighboring countries.
Ying Zhou, Xiaofan Xing, Jianlei Lang, Dongsheng Chen, Shuiyuan Cheng, Lin Wei, Xiao Wei, and Chao Liu
Atmos. Chem. Phys., 17, 2839–2864, https://doi.org/10.5194/acp-17-2839-2017, https://doi.org/10.5194/acp-17-2839-2017, 2017
Short summary
Short summary
A 1 km gridded and comprehensive biomass burning emission inventory including domestic and in-field straw burning, firewood burning, livestock excrement burning, and forest and grassland fires is developed for mainland China in 2012 based on county-level activity data, satellite data, and updated source-specific emission factors. The detailed emission inventory could provide useful information for air-quality modelling and supports the development of appropriate pollution-control strategies.
Related subject area
Domain: ESSD – Atmosphere | Subject: Energy and Emissions
A daily sunshine duration (SD) dataset in China from Himawari AHI imagery (2016–2023)
Residential heating emissions for the Western Balkans
The global daily High Spatial–Temporal Coverage Merged tropospheric NO2 dataset (HSTCM-NO2) from 2007 to 2022 based on OMI and GOME-2
Global gridded NOx emissions using TROPOMI observations
In situ airborne measurements of atmospheric parameters and airborne sea surface properties related to offshore wind parks in the German Bight during the project X-Wakes
Modeling fuel-, vehicle-type-, and age-specific CO2 emissions from global on-road vehicles in 1970–2020
Comparison of observation- and inventory-based methane emissions for eight large global emitters
Constructing a measurement-based spatially explicit inventory of US oil and gas methane emissions (2021)
State of Wildfires 2023–2024
Global Emissions Inventory from Open Biomass Burning (GEIOBB): utilizing Fengyun-3D global fire spot monitoring data
Integrating Point Sources to Map Anthropogenic Atmospheric Mercury Emissions in China, 1978–2021
Development of a high-resolution integrated emission inventory of air pollutants for China
Brazilian Atmospheric Inventories – BRAIN: a comprehensive database of air quality in Brazil
Air pollution emission inventory using national high-resolution spatial parameters for the Nordic countries and analysis of PM2.5 spatial distribution for road transport and machinery and off-road sectors
A quality-assured dataset of nine radiation components observed at the Shangdianzi regional GAW station in China (2013–2022)
CoCO2-MOSAIC 1.0: a global mosaic of regional, gridded, fossil, and biofuel CO2 emission inventories
A global catalogue of CO2 emissions and co-emitted species from power plants, including high-resolution vertical and temporal profiles
Global Carbon Budget 2023
Spatiotemporally resolved emissions and concentrations of styrene, benzene, toluene, ethylbenzene, and xylenes (SBTEX) in the US Gulf region
High-resolution emission inventory of full-volatility organic compounds from cooking in China during 2015–2021
Global carbon uptake of cement carbonation accounts 1930–2021
A dense station-based, long-term and high-accuracy dataset of daily surface solar radiation in China
The consolidated European synthesis of CO2 emissions and removals for the European Union and United Kingdom: 1990–2020
Decadal growth in emission load of major air pollutants in Delhi
Improved catalog of NOx point source emissions (version 2)
The HTAP_v3 emission mosaic: merging regional and global monthly emissions (2000–2018) to support air quality modelling and policies
Indicators of Global Climate Change 2022: annual update of large-scale indicators of the state of the climate system and human influence
Emission trends of air pollutants and CO2 in China from 2005 to 2021
Ten years of 1 Hz solar irradiance observations at Cabauw, the Netherlands, with cloud observations, variability classifications, and statistics
The consolidated European synthesis of CH4 and N2O emissions for the European Union and United Kingdom: 1990–2019
Spatially resolved hourly traffic emission over megacity Delhi using advanced traffic flow data
Near-real-time CO2 fluxes from CarbonTracker Europe for high-resolution atmospheric modeling
Retrievals of XCO2, XCH4 and XCO from portable, near-infrared Fourier transform spectrometer solar observations in Antarctica
Carbon fluxes from land 2000–2020: bringing clarity to countries' reporting
DeepOWT: a global offshore wind turbine data set derived with deep learning from Sentinel-1 data
Zhanhao Zhang, Shibo Fang, and Jiahao Han
Earth Syst. Sci. Data, 17, 1427–1439, https://doi.org/10.5194/essd-17-1427-2025, https://doi.org/10.5194/essd-17-1427-2025, 2025
Short summary
Short summary
We generate a daily sunshine duration dataset in China at a spatial resolution of 5 km using Himawari AHI data from 2016 to 2023 fitted with an Ångström–Prescott model at different days of year (DOYs). These high-resolution sunshine duration data provide important support for accurate radiation resource assessments in China.
Christian Asker, Eef van Dongen, and Olivier Tasse
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-462, https://doi.org/10.5194/essd-2024-462, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Air pollution adversely affects health, ecosystems and infrastructure. In this work we have developed a methodology and created a dataset for describing air pollution sources for heating of individual houses for the Western Balkan region. The dataset has high spatial resolution and can help understand the air pollution situation in the region.
Kai Qin, Hongrui Gao, Xuancen Liu, Qin He, Pravash Tiwari, and Jason Blake Cohen
Earth Syst. Sci. Data, 16, 5287–5310, https://doi.org/10.5194/essd-16-5287-2024, https://doi.org/10.5194/essd-16-5287-2024, 2024
Short summary
Short summary
Satellites have brought new opportunities for monitoring atmospheric NO2, although the results are limited by clouds and other factors, resulting in missing data. This work proposes a new process to obtain reliable data products with high coverage by reconstructing the raw data from multiple satellites. The results are validated in terms of traditional methods as well as variance maximization and demonstrate a good ability to reproduce known polluted and clean areas around the world.
Anthony Rey-Pommier, Alexandre Héraud, Frédéric Chevallier, Philippe Ciais, Theodoros Christoudias, Jonilda Kushta, and Jean Sciare
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-410, https://doi.org/10.5194/essd-2024-410, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
In this study, we estimate emissions of nitrogen oxides (NOx) in 2022 at high-resolution at the global scale, using satellite observations. We provide maps of the emissions and identify several types of sources. Our results are similar to the EDGAR emission inventory. However, differences are found in countries with lower observation densities and lower emissions.
Astrid Lampert, Rudolf Hankers, Thomas Feuerle, Thomas Rausch, Matthias Cremer, Maik Angermann, Mark Bitter, Jonas Füllgraf, Helmut Schulz, Ulf Bestmann, and Konrad B. Bärfuss
Earth Syst. Sci. Data, 16, 4777–4792, https://doi.org/10.5194/essd-16-4777-2024, https://doi.org/10.5194/essd-16-4777-2024, 2024
Short summary
Short summary
We conducted flights above the North Sea and investigated changes in the wind field. The research aircraft measured wind speed, wind direction, temperature, humidity and sea surface at high resolution. Wind parks reduce the wind speed, and the data help to determine how long it takes for the wind speed to recover. The coast also plays an important role, and the wind speed varies with distance from the coast. The results help in wind park planning and better estimating the energy yield.
Liu Yan, Qiang Zhang, Bo Zheng, and Kebin He
Earth Syst. Sci. Data, 16, 4497–4509, https://doi.org/10.5194/essd-16-4497-2024, https://doi.org/10.5194/essd-16-4497-2024, 2024
Short summary
Short summary
A new database of fuel-, vehicle-type-, and age-specific CO2 emissions from global on-road vehicles from 1970 to 2020 is developed with the fleet turnover model built in this study. Based on this database, the evolution of the global vehicle stock over 50 years is analyzed, the dominant emission contributors by vehicle and fuel type are identified, and the age distribution of on-road CO2 emissions is characterized further.
Ana Maria Roxana Petrescu, Glen P. Peters, Richard Engelen, Sander Houweling, Dominik Brunner, Aki Tsuruta, Bradley Matthews, Prabir K. Patra, Dmitry Belikov, Rona L. Thompson, Lena Höglund-Isaksson, Wenxin Zhang, Arjo J. Segers, Giuseppe Etiope, Giancarlo Ciotoli, Philippe Peylin, Frédéric Chevallier, Tuula Aalto, Robbie M. Andrew, David Bastviken, Antoine Berchet, Grégoire Broquet, Giulia Conchedda, Stijn N. C. Dellaert, Hugo Denier van der Gon, Johannes Gütschow, Jean-Matthieu Haussaire, Ronny Lauerwald, Tiina Markkanen, Jacob C. A. van Peet, Isabelle Pison, Pierre Regnier, Espen Solum, Marko Scholze, Maria Tenkanen, Francesco N. Tubiello, Guido R. van der Werf, and John R. Worden
Earth Syst. Sci. Data, 16, 4325–4350, https://doi.org/10.5194/essd-16-4325-2024, https://doi.org/10.5194/essd-16-4325-2024, 2024
Short summary
Short summary
This study provides an overview of data availability from observation- and inventory-based CH4 emission estimates. It systematically compares them and provides recommendations for robust comparisons, aiming to steadily engage more parties in using observational methods to complement their UNFCCC submissions. Anticipating improvements in atmospheric modelling and observations, future developments need to resolve knowledge gaps in both approaches and to better quantify remaining uncertainty.
Mark Omara, Anthony Himmelberger, Katlyn MacKay, James P. Williams, Joshua Benmergui, Maryann Sargent, Steven C. Wofsy, and Ritesh Gautam
Earth Syst. Sci. Data, 16, 3973–3991, https://doi.org/10.5194/essd-16-3973-2024, https://doi.org/10.5194/essd-16-3973-2024, 2024
Short summary
Short summary
We review, analyze, and synthesize previous peer-reviewed measurement-based data on facility-level oil and gas methane emissions and use these data to develop a high-resolution spatially explicit inventory of US basin-level and national methane emissions. This work provides an improved assessment of national methane emissions relative to government inventories in support of accurate and comprehensive methane emissions assessment, attribution, and mitigation.
Matthew W. Jones, Douglas I. Kelley, Chantelle A. Burton, Francesca Di Giuseppe, Maria Lucia F. Barbosa, Esther Brambleby, Andrew J. Hartley, Anna Lombardi, Guilherme Mataveli, Joe R. McNorton, Fiona R. Spuler, Jakob B. Wessel, John T. Abatzoglou, Liana O. Anderson, Niels Andela, Sally Archibald, Dolors Armenteras, Eleanor Burke, Rachel Carmenta, Emilio Chuvieco, Hamish Clarke, Stefan H. Doerr, Paulo M. Fernandes, Louis Giglio, Douglas S. Hamilton, Stijn Hantson, Sarah Harris, Piyush Jain, Crystal A. Kolden, Tiina Kurvits, Seppe Lampe, Sarah Meier, Stacey New, Mark Parrington, Morgane M. G. Perron, Yuquan Qu, Natasha S. Ribeiro, Bambang H. Saharjo, Jesus San-Miguel-Ayanz, Jacquelyn K. Shuman, Veerachai Tanpipat, Guido R. van der Werf, Sander Veraverbeke, and Gavriil Xanthopoulos
Earth Syst. Sci. Data, 16, 3601–3685, https://doi.org/10.5194/essd-16-3601-2024, https://doi.org/10.5194/essd-16-3601-2024, 2024
Short summary
Short summary
This inaugural State of Wildfires report catalogues extreme fires of the 2023–2024 fire season. For key events, we analyse their predictability and drivers and attribute them to climate change and land use. We provide a seasonal outlook and decadal projections. Key anomalies occurred in Canada, Greece, and western Amazonia, with other high-impact events catalogued worldwide. Climate change significantly increased the likelihood of extreme fires, and mitigation is required to lessen future risk.
Yang Liu, Jie Chen, Yusheng Shi, Wei Zheng, Tianchan Shan, and Gang Wang
Earth Syst. Sci. Data, 16, 3495–3515, https://doi.org/10.5194/essd-16-3495-2024, https://doi.org/10.5194/essd-16-3495-2024, 2024
Short summary
Short summary
Open biomass burning has a significant impact on regional and global air quality. To enhance the quantification of global emissions from open biomass burning, we have developed the Global Emissions Inventory from Open Biomass Burning (GEIOBB) dataset, which provides a global daily-scale database at 1 km resolution of multiple pollutant emissions. This database aids global-scale environmental analysis of biomass burning.
Yuying Cui, Qingru Wu, Shuxiao Wang, Kaiyun Liu, Shengyue Li, Zhezhe Shi, Daiwei Ouyang, Zhongyan Li, Qinqin Chen, Changwei Lü, Fei Xie, Yi Tang, Yan Wang, and Jiming Hao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-252, https://doi.org/10.5194/essd-2024-252, 2024
Revised manuscript accepted for ESSD
Short summary
Short summary
A comprehensive emission inventory has been developed at a resolution of 0.25°×0.3125° for total mercury (HgT) and each mercury species, namely gaseous elemental mercury (Hg0), gaseous oxidized mercury (HgII), and particulate-bound mercury (HgP). The inventory stems from the Point-source Integrated China Atmospheric Mercury Emission Model, ensuring both temporal and spatial coherence.
Nana Wu, Guannan Geng, Ruochong Xu, Shigan Liu, Xiaodong Liu, Qinren Shi, Ying Zhou, Yu Zhao, Huan Liu, Yu Song, Junyu Zheng, Qiang Zhang, and Kebin He
Earth Syst. Sci. Data, 16, 2893–2915, https://doi.org/10.5194/essd-16-2893-2024, https://doi.org/10.5194/essd-16-2893-2024, 2024
Short summary
Short summary
The commonly used method for developing large-scale air pollutant emission datasets for China faces challenges due to limited availability of detailed parameter information. In this study, we develop an efficient integrated framework to gather such information by harmonizing seven heterogeneous inventories from five research institutions. Emission characterizations are analyzed and validated, demonstrating that the dataset provides more accurate emission magnitudes and spatiotemporal patterns.
Leonardo Hoinaski, Robson Will, and Camilo Bastos Ribeiro
Earth Syst. Sci. Data, 16, 2385–2405, https://doi.org/10.5194/essd-16-2385-2024, https://doi.org/10.5194/essd-16-2385-2024, 2024
Short summary
Short summary
We introduce the Brazilian Atmospheric Inventories (BRAIN), the first comprehensive database for air quality studies in Brazil. The database encompasses hourly datasets of meteorology, emission sources, and ambient concentrations of multiple air pollutants covering the Brazilian territory. It combines local inventories, consolidated datasets, and internationally recommended models to provide essential data for developing air pollution control policies, even in data-scarce areas.
Ville-Veikko Paunu, Niko Karvosenoja, David Segersson, Susana López-Aparicio, Ole-Kenneth Nielsen, Marlene Schmidt Plejdrup, Throstur Thorsteinsson, Dam Thanh Vo, Jeroen Kuenen, Hugo Denier van der Gon, Jukka-Pekka Jalkanen, Jørgen Brandt, and Camilla Geels
Earth Syst. Sci. Data, 16, 1453–1474, https://doi.org/10.5194/essd-16-1453-2024, https://doi.org/10.5194/essd-16-1453-2024, 2024
Short summary
Short summary
Air pollution is an important cause of adverse health effects, even in Nordic countries. To assess their health impacts, emission inventories with high spatial resolution are needed. We studied how national data and methods for the spatial distribution of the emissions compare to a European level inventory. For road transport the methods are well established, but for machinery and off-road emissions the current recommendations for the spatial distribution of these emissions should be improved.
Weijun Quan, Zhenfa Wang, Lin Qiao, Xiangdong Zheng, Junli Jin, Yinruo Li, Xiaomei Yin, Zhiqiang Ma, and Martin Wild
Earth Syst. Sci. Data, 16, 961–983, https://doi.org/10.5194/essd-16-961-2024, https://doi.org/10.5194/essd-16-961-2024, 2024
Short summary
Short summary
Radiation components play important roles in various fields such as the Earth’s surface radiation budget, ecosystem productivity, and human health. In this study, a dataset consisting of quality-assured daily data of nine radiation components is presented based on the in situ measurements at the Shangdianzi regional GAW station in China during 2013–2022. The dataset can be applied in the validation of satellite products and numerical models and investigation of atmospheric radiation.
Ruben Urraca, Greet Janssens-Maenhout, Nicolás Álamos, Lucas Berna-Peña, Monica Crippa, Sabine Darras, Stijn Dellaert, Hugo Denier van der Gon, Mark Dowell, Nadine Gobron, Claire Granier, Giacomo Grassi, Marc Guevara, Diego Guizzardi, Kevin Gurney, Nicolás Huneeus, Sekou Keita, Jeroen Kuenen, Ana Lopez-Noreña, Enrique Puliafito, Geoffrey Roest, Simone Rossi, Antonin Soulie, and Antoon Visschedijk
Earth Syst. Sci. Data, 16, 501–523, https://doi.org/10.5194/essd-16-501-2024, https://doi.org/10.5194/essd-16-501-2024, 2024
Short summary
Short summary
CoCO2-MOSAIC 1.0 is a global mosaic of regional bottom-up inventories providing gridded (0.1×0.1) monthly emissions of anthropogenic CO2. Regional inventories include country-specific information and finer spatial resolution than global inventories. CoCO2-MOSAIC provides harmonized access to these datasets and can be considered as a regionally accepted reference to assess the quality of global inventories, as done in the current paper.
Marc Guevara, Santiago Enciso, Carles Tena, Oriol Jorba, Stijn Dellaert, Hugo Denier van der Gon, and Carlos Pérez García-Pando
Earth Syst. Sci. Data, 16, 337–373, https://doi.org/10.5194/essd-16-337-2024, https://doi.org/10.5194/essd-16-337-2024, 2024
Short summary
Short summary
A global dataset of emissions from thermal power plants was created for the year 2018. The resulting catalogue reports annual emissions of CO2 and co-emitted species (NOx, CO, SO2 and CH4) for more than 16000 individual facilities at their exact geographical locations. Information on the temporal and vertical distributions of the emissions is also provided at the facility level. The dataset is intended to support current and future satellite emission monitoring and inverse modelling efforts.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
Short summary
Short summary
The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Chi-Tsan Wang, Bok H. Baek, William Vizuete, Lawrence S. Engel, Jia Xing, Jaime Green, Marc Serre, Richard Strott, Jared Bowden, and Jung-Hun Woo
Earth Syst. Sci. Data, 15, 5261–5279, https://doi.org/10.5194/essd-15-5261-2023, https://doi.org/10.5194/essd-15-5261-2023, 2023
Short summary
Short summary
Hazardous air pollutant (HAP) human exposure studies usually rely on local measurements or dispersion model methods, but those methods are limited under spatial and temporal conditions. We processed the US EPA emission data to simulate the hourly HAP emission patterns and applied the chemical transport model to simulate the HAP concentrations. The modeled HAP results exhibit good agreement (R is 0.75 and NMB is −5.6 %) with observational data.
Zeqi Li, Shuxiao Wang, Shengyue Li, Xiaochun Wang, Guanghan Huang, Xing Chang, Lyuyin Huang, Chengrui Liang, Yun Zhu, Haotian Zheng, Qian Song, Qingru Wu, Fenfen Zhang, and Bin Zhao
Earth Syst. Sci. Data, 15, 5017–5037, https://doi.org/10.5194/essd-15-5017-2023, https://doi.org/10.5194/essd-15-5017-2023, 2023
Short summary
Short summary
This study developed the first full-volatility organic emission inventory for cooking sources in China, presenting high-resolution cooking emissions during 2015–2021. It identified the key subsectors and hotspots of cooking emissions, analyzed emission trends and drivers, and proposed future control strategies. The dataset is valuable for accurately simulating organic aerosol formation and evolution and for understanding the impact of organic emissions on air pollution and climate change.
Zi Huang, Jiaoyue Wang, Longfei Bing, Yijiao Qiu, Rui Guo, Ying Yu, Mingjing Ma, Le Niu, Dan Tong, Robbie M. Andrew, Pierre Friedlingstein, Josep G. Canadell, Fengming Xi, and Zhu Liu
Earth Syst. Sci. Data, 15, 4947–4958, https://doi.org/10.5194/essd-15-4947-2023, https://doi.org/10.5194/essd-15-4947-2023, 2023
Short summary
Short summary
This is about global and regional cement process carbon emissions and CO2 uptake calculations from 1930 to 2019. The global cement production is rising to 4.4 Gt, causing processing carbon emission of 1.81 Gt (95% CI: 1.75–1.88 Gt CO2) in 2021. Plus, in 2021, cement’s carbon accumulated uptake (22.9 Gt, 95% CI: 19.6–22.6 Gt CO2) has offset 55.2% of cement process CO2 emissions (41.5 Gt, 95% CI: 38.7–47.1 Gt CO2) since 1930.
Wenjun Tang, Junmei He, Jingwen Qi, and Kun Yang
Earth Syst. Sci. Data, 15, 4537–4551, https://doi.org/10.5194/essd-15-4537-2023, https://doi.org/10.5194/essd-15-4537-2023, 2023
Short summary
Short summary
In this study, we have developed a dense station-based, long-term dataset of daily surface solar radiation in China with high accuracy. The dataset consists of estimates of global, direct and diffuse radiation at 2473 meteorological stations from the 1950s to 2021. Validation indicates that our station-based radiation dataset clearly outperforms the satellite-based radiation products. Our dataset will contribute to climate change research and solar energy applications in the future.
Matthew J. McGrath, Ana Maria Roxana Petrescu, Philippe Peylin, Robbie M. Andrew, Bradley Matthews, Frank Dentener, Juraj Balkovič, Vladislav Bastrikov, Meike Becker, Gregoire Broquet, Philippe Ciais, Audrey Fortems-Cheiney, Raphael Ganzenmüller, Giacomo Grassi, Ian Harris, Matthew Jones, Jürgen Knauer, Matthias Kuhnert, Guillaume Monteil, Saqr Munassar, Paul I. Palmer, Glen P. Peters, Chunjing Qiu, Mart-Jan Schelhaas, Oksana Tarasova, Matteo Vizzarri, Karina Winkler, Gianpaolo Balsamo, Antoine Berchet, Peter Briggs, Patrick Brockmann, Frédéric Chevallier, Giulia Conchedda, Monica Crippa, Stijn N. C. Dellaert, Hugo A. C. Denier van der Gon, Sara Filipek, Pierre Friedlingstein, Richard Fuchs, Michael Gauss, Christoph Gerbig, Diego Guizzardi, Dirk Günther, Richard A. Houghton, Greet Janssens-Maenhout, Ronny Lauerwald, Bas Lerink, Ingrid T. Luijkx, Géraud Moulas, Marilena Muntean, Gert-Jan Nabuurs, Aurélie Paquirissamy, Lucia Perugini, Wouter Peters, Roberto Pilli, Julia Pongratz, Pierre Regnier, Marko Scholze, Yusuf Serengil, Pete Smith, Efisio Solazzo, Rona L. Thompson, Francesco N. Tubiello, Timo Vesala, and Sophia Walther
Earth Syst. Sci. Data, 15, 4295–4370, https://doi.org/10.5194/essd-15-4295-2023, https://doi.org/10.5194/essd-15-4295-2023, 2023
Short summary
Short summary
Accurate estimation of fluxes of carbon dioxide from the land surface is essential for understanding future impacts of greenhouse gas emissions on the climate system. A wide variety of methods currently exist to estimate these sources and sinks. We are continuing work to develop annual comparisons of these diverse methods in order to clarify what they all actually calculate and to resolve apparent disagreement, in addition to highlighting opportunities for increased understanding.
Saroj Kumar Sahu, Poonam Mangaraj, and Gufran Beig
Earth Syst. Sci. Data, 15, 3183–3202, https://doi.org/10.5194/essd-15-3183-2023, https://doi.org/10.5194/essd-15-3183-2023, 2023
Short summary
Short summary
The developed emission inventory identifies all the potential anthropogenic sources active in the Delhi NCR. The decadal change (2010–2020) and the changing policies have also been illustrated to observe the modulation in the sectorial emission trend. Emission hotspots with possible source-specific mitigation strategies have also been highlighted to improve the air quality of the Delhi NCR. The provided dataset is a vital tool for air quality and chemical transport modeling studies.
Steffen Beirle, Christian Borger, Adrian Jost, and Thomas Wagner
Earth Syst. Sci. Data, 15, 3051–3073, https://doi.org/10.5194/essd-15-3051-2023, https://doi.org/10.5194/essd-15-3051-2023, 2023
Short summary
Short summary
We present a catalog of nitrogen oxide emissions from point sources (like power plants or metal smelters) based on satellite observations of NO2 combined with meteorological wind fields.
Monica Crippa, Diego Guizzardi, Tim Butler, Terry Keating, Rosa Wu, Jacek Kaminski, Jeroen Kuenen, Junichi Kurokawa, Satoru Chatani, Tazuko Morikawa, George Pouliot, Jacinthe Racine, Michael D. Moran, Zbigniew Klimont, Patrick M. Manseau, Rabab Mashayekhi, Barron H. Henderson, Steven J. Smith, Harrison Suchyta, Marilena Muntean, Efisio Solazzo, Manjola Banja, Edwin Schaaf, Federico Pagani, Jung-Hun Woo, Jinseok Kim, Fabio Monforti-Ferrario, Enrico Pisoni, Junhua Zhang, David Niemi, Mourad Sassi, Tabish Ansari, and Kristen Foley
Earth Syst. Sci. Data, 15, 2667–2694, https://doi.org/10.5194/essd-15-2667-2023, https://doi.org/10.5194/essd-15-2667-2023, 2023
Short summary
Short summary
This study responds to the global and regional atmospheric modelling community's need for a mosaic of air pollutant emissions with global coverage, long time series, spatially distributed data at a high time resolution, and a high sectoral resolution in order to enhance the understanding of transboundary air pollution. The mosaic approach to integrating official regional emission inventories with a global inventory based on a consistent methodology ensures policy-relevant results.
Piers M. Forster, Christopher J. Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Sonia I. Seneviratne, Blair Trewin, Xuebin Zhang, Myles Allen, Robbie Andrew, Arlene Birt, Alex Borger, Tim Boyer, Jiddu A. Broersma, Lijing Cheng, Frank Dentener, Pierre Friedlingstein, José M. Gutiérrez, Johannes Gütschow, Bradley Hall, Masayoshi Ishii, Stuart Jenkins, Xin Lan, June-Yi Lee, Colin Morice, Christopher Kadow, John Kennedy, Rachel Killick, Jan C. Minx, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sophie Szopa, Peter Thorne, Robert Rohde, Maisa Rojas Corradi, Dominik Schumacher, Russell Vose, Kirsten Zickfeld, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 15, 2295–2327, https://doi.org/10.5194/essd-15-2295-2023, https://doi.org/10.5194/essd-15-2295-2023, 2023
Short summary
Short summary
This is a critical decade for climate action, but there is no annual tracking of the level of human-induced warming. We build on the Intergovernmental Panel on Climate Change assessment reports that are authoritative but published infrequently to create a set of key global climate indicators that can be tracked through time. Our hope is that this becomes an important annual publication that policymakers, media, scientists and the public can refer to.
Shengyue Li, Shuxiao Wang, Qingru Wu, Yanning Zhang, Daiwei Ouyang, Haotian Zheng, Licong Han, Xionghui Qiu, Yifan Wen, Min Liu, Yueqi Jiang, Dejia Yin, Kaiyun Liu, Bin Zhao, Shaojun Zhang, Ye Wu, and Jiming Hao
Earth Syst. Sci. Data, 15, 2279–2294, https://doi.org/10.5194/essd-15-2279-2023, https://doi.org/10.5194/essd-15-2279-2023, 2023
Short summary
Short summary
This study compiled China's emission inventory of air pollutants and CO2 during 2005–2021 (ABaCAS-EI v2.0) based on unified emission-source framework. The emission trends and its drivers are analyzed. Key sectors and regions with higher synergistic reduction potential of air pollutants and CO2 are identified. Future control measures are suggested. The dataset and analyses provide insights into the synergistic reduction of air pollutants and CO2 emissions for China and other developing countries.
Wouter B. Mol, Wouter H. Knap, and Chiel C. van Heerwaarden
Earth Syst. Sci. Data, 15, 2139–2151, https://doi.org/10.5194/essd-15-2139-2023, https://doi.org/10.5194/essd-15-2139-2023, 2023
Short summary
Short summary
We describe a dataset of detailed measurements of sunlight reaching the surface, recorded at a rate of one measurement per second for 10 years. The dataset includes detailed information on direct and scattered sunlight; classifications and statistics of variability; and observations of clouds, atmospheric composition, and wind. The dataset can be used to study how the atmosphere influences sunlight variability and to validate models that aim to predict this variability with greater accuracy.
Ana Maria Roxana Petrescu, Chunjing Qiu, Matthew J. McGrath, Philippe Peylin, Glen P. Peters, Philippe Ciais, Rona L. Thompson, Aki Tsuruta, Dominik Brunner, Matthias Kuhnert, Bradley Matthews, Paul I. Palmer, Oksana Tarasova, Pierre Regnier, Ronny Lauerwald, David Bastviken, Lena Höglund-Isaksson, Wilfried Winiwarter, Giuseppe Etiope, Tuula Aalto, Gianpaolo Balsamo, Vladislav Bastrikov, Antoine Berchet, Patrick Brockmann, Giancarlo Ciotoli, Giulia Conchedda, Monica Crippa, Frank Dentener, Christine D. Groot Zwaaftink, Diego Guizzardi, Dirk Günther, Jean-Matthieu Haussaire, Sander Houweling, Greet Janssens-Maenhout, Massaer Kouyate, Adrian Leip, Antti Leppänen, Emanuele Lugato, Manon Maisonnier, Alistair J. Manning, Tiina Markkanen, Joe McNorton, Marilena Muntean, Gabriel D. Oreggioni, Prabir K. Patra, Lucia Perugini, Isabelle Pison, Maarit T. Raivonen, Marielle Saunois, Arjo J. Segers, Pete Smith, Efisio Solazzo, Hanqin Tian, Francesco N. Tubiello, Timo Vesala, Guido R. van der Werf, Chris Wilson, and Sönke Zaehle
Earth Syst. Sci. Data, 15, 1197–1268, https://doi.org/10.5194/essd-15-1197-2023, https://doi.org/10.5194/essd-15-1197-2023, 2023
Short summary
Short summary
This study updates the state-of-the-art scientific overview of CH4 and N2O emissions in the EU27 and UK in Petrescu et al. (2021a). Yearly updates are needed to improve the different respective approaches and to inform on the development of formal verification systems. It integrates the most recent emission inventories, process-based model and regional/global inversions, comparing them with UNFCCC national GHG inventories, in support to policy to facilitate real-time verification procedures.
Akash Biswal, Vikas Singh, Leeza Malik, Geetam Tiwari, Khaiwal Ravindra, and Suman Mor
Earth Syst. Sci. Data, 15, 661–680, https://doi.org/10.5194/essd-15-661-2023, https://doi.org/10.5194/essd-15-661-2023, 2023
Short summary
Short summary
This paper presents detailed emission estimates of on-road traffic exhaust emissions of nine major pollutants for Delhi. We use advanced traffic data and emission factors as a function of speed to estimate emissions for each hour and 100 m × 100 m spatial resolution. We examine the source contribution according to the vehicle, fuel, road and Euro types to identify the most polluting vehicles. These data are useful for high-resolution air quality modelling for developing suitable strategies.
Auke M. van der Woude, Remco de Kok, Naomi Smith, Ingrid T. Luijkx, Santiago Botía, Ute Karstens, Linda M. J. Kooijmans, Gerbrand Koren, Harro A. J. Meijer, Gert-Jan Steeneveld, Ida Storm, Ingrid Super, Hubertus A. Scheeren, Alex Vermeulen, and Wouter Peters
Earth Syst. Sci. Data, 15, 579–605, https://doi.org/10.5194/essd-15-579-2023, https://doi.org/10.5194/essd-15-579-2023, 2023
Short summary
Short summary
To monitor the progress towards the CO2 emission goals set out in the Paris Agreement, the European Union requires an independent validation of emitted CO2. For this validation, atmospheric measurements of CO2 can be used, together with first-guess estimates of CO2 emissions and uptake. To quickly inform end users, it is imperative that this happens in near real-time. To aid these efforts, we create estimates of European CO2 exchange at high resolution in near real time.
David F. Pollard, Frank Hase, Mahesh Kumar Sha, Darko Dubravica, Carlos Alberti, and Dan Smale
Earth Syst. Sci. Data, 14, 5427–5437, https://doi.org/10.5194/essd-14-5427-2022, https://doi.org/10.5194/essd-14-5427-2022, 2022
Short summary
Short summary
We describe measurements made in Antarctica using an EM27/SUN, a near-infrared, portable, low-resolution spectrometer from which we can retrieve the average atmospheric concentration of several greenhouse gases. We show that these measurements are reliable and comparable to other, similar ground-based measurements. Comparisons to the ESA's Sentinel-5 precursor (S5P) satellite demonstrate the usefulness of these data for satellite validation.
Giacomo Grassi, Giulia Conchedda, Sandro Federici, Raul Abad Viñas, Anu Korosuo, Joana Melo, Simone Rossi, Marieke Sandker, Zoltan Somogyi, Matteo Vizzarri, and Francesco N. Tubiello
Earth Syst. Sci. Data, 14, 4643–4666, https://doi.org/10.5194/essd-14-4643-2022, https://doi.org/10.5194/essd-14-4643-2022, 2022
Short summary
Short summary
Despite increasing attention on the role of land use CO2 fluxes in climate change mitigation, there are large differences in available databases. Here we present the most updated and complete compilation of land use CO2 data based on country submissions to United Nations Framework Convention on Climate Change and explain differences with other datasets. Our dataset brings clarity of land use CO2 fluxes and helps track country progress under the Paris Agreement.
Thorsten Hoeser, Stefanie Feuerstein, and Claudia Kuenzer
Earth Syst. Sci. Data, 14, 4251–4270, https://doi.org/10.5194/essd-14-4251-2022, https://doi.org/10.5194/essd-14-4251-2022, 2022
Short summary
Short summary
The DeepOWT (Deep-learning-derived Offshore Wind Turbines) data set provides offshore wind energy infrastructure locations and their temporal deployment dynamics from July 2016 until June 2021 on a global scale. It differentiates between offshore wind turbines, platforms under construction, and offshore wind farm substations. It is derived by applying deep-learning-based object detection to Sentinel-1 imagery.
Cited articles
Airbus: A320 Family-Unbeatable fuel efficiency, https://www.airbus.com/en/products-services/commercial-aircraft/passenger-aircraft/a320-family, last access: last access: 1 February 2025.
Aircraft Commerce: In-service performance of the PW1100G & CFM LEAP-1A, https://www.aircraft-commerce.com/articles/articles-by-issue-date/, last access: 1 February 2025.
Arunachalam, S., Wang, B., Davis, N., Baek, B. H., and Levy, J. I.: Effect of chemistry-transport model scale and resolution on population exposure to PM2.5 from aircraft emissions during landing and takeoff, Atmos. Environ., 45, 3294–3300, https://doi.org/10.1016/j.atmosenv.2011.03.029, 2011.
Badrinath, S., Balakrishnan, H., Joback, E., and Reynolds, T. G.: Impact of Off-Block Time Uncertainty on the Control of Airport Surface Operations, Transport. Sci., 54, 920–943, https://doi.org/10.1287/trsc.2019.0957, 2020.
Bao, D., Tian, S., Kang, D., Zhang, Z., and Zhu, T.: Impact of the COVID-19 pandemic on air pollution from jet engines at airports in central eastern China, Air. Qual. Atmos. Hlth., 16, 641–659, https://doi.org/10.1007/s11869-022-01294-w, 2023.
Barrett, S. R. H., Britter, R. E., and Waitz, I. A.: Global mortality attributable to aircraft cruise emissions, Environ. Sci. Technol., 44, 7736–7742, https://doi.org/10.1021/es101325r, 2010.
Bo, X., Xue, X., Xu, J., Du, X., Zhou, B., and Tang, T.: Aviation's emissions and contribution to the air quality in China, Atmos. Environ., 201, 121–471, https://doi.org/10.1016/j.atmosenv.2019.01.005, 2019.
Bombelli, A., Soler, L., Trumbauer, E., and Mease, K. D.: Strategic Air Traffic Planning with Fréchet Distance Aggregation and Rerouting, J. Guid. Control Dynam., 40, 1117–1129, https://doi.org/10.2514/1.G002308, 2017.
Boningari, T. and Smirniotis, P. G.: Impact of nitrogen oxides on the environment and human health: Mn-based materials for the NOX abatement, Curr. Opin. Chem. Eng., 13, 133-141, https://doi.org/10.1016/j.coche.2016.09.004, 2016.
CAAC (Civil Aviation Administration of China): Aerodrome technical standards, MH 5001-2021, https://www.caac.gov.cn/XXGK/XXGK/BZGF/HYBZ/202112/t20211201_210343.html (last access: 1 February 2025), 2021a.
CAAC (Civil Aviation Administration of China): Statistics of main production indicators of CAAC, http://www.caac.gov.cn/XXGK/XXGK/TJSJ/202106/t20210610_207915.html (last access: 1 July 2024), 2021b.
CAACNEWS: Why do aircraft take off and land against the wind, http://caacnews.com.cn/1/6/201902/t20190227_1268005.html (last access: 1 July 2024), 2019.
CALS: Civil Aviation Leisure Station, http://www.xmyzl.com/, last access: 1 July 2024.
Chen, Y., Zhou, L., Bouguila, N., Wang, C., Chen, Y., and Du, J.: BLOCK-DBSCAN: Fast clustering for large scale data, Pattern Recogn., 109, 107624, https://doi.org/10.1016/j.patcog.2020.107624, 2021a.
Chen, Y., Zhou, L., Pei, S., Yu, Z., Chen, Y., Liu, X., Du, J., and Xiong, N.: KNN-BLOCK DBSCAN: Fast Clustering for Large-Scale Data, IEEE T. Sys. Ma Cy. A, 51, 3939–3953, https://doi.org/10.1109/TSMC.2019.2956527, 2021b.
Christodoulakis, J., Karinou, F., Kelemen, M., Kouremadas, G., Fotaki, E. F., and Varotsos, C. A.: Assessment of air pollution from Athens International Airport and suggestions for adaptation to new aviation emissions restrictions, Atmos. Pollut. Res., 13, 101441, https://doi.org/10.1016/j.apr.2022.101441, 2022.
CMA (China Meteorological Administration): The main meteorological factors affecting flight, https://www.cma.gov.cn/2011xzt/2015zt/20150918/2015091805/201509/t20150918_293227.html (last access: 1 July 2024), 2015.
Cui, Q., Lei, Y., and Chen, B.: Impacts of the proposal of the CNG2020 strategy on aircraft emissions of China–foreign routes, Earth Syst. Sci. Data, 14, 4419–4433, https://doi.org/10.5194/essd-14-4419-2022, 2022.
Deng, X., Chen, W., Zhou, Q., Zheng, Y., Li, H., Liao, S., and Biljecki, F.: Exploring spatiotemporal pattern and agglomeration of road CO2 emissions in Guangdong, China, Sci. Total Environ., 871, 162134, https://doi.org/10.1016/j.scitotenv.2023.162134, 2023.
Dissanayaka, M., Ryley, T., Spasojevic, B., and Caldera, S.: Evaluating Methods That Calculate Aircraft Emission Impacts on Air Quality: A Systematic Literature Review, Sustainability, 15, 9741, https://doi.org/10.3390/su15129741, 2023.
Eastham, S. D. and Barrett, S. R. H.: Aviation-attributable ozone as a driver for changes in mortality related to air quality and skin cancer, Atmos. Environ., 144, 17–23, https://doi.org/10.1016/j.atmosenv.2016.08.040, 2016.
Eastham, S. D., Chossière, G. P., Speth, R. L., Jacob, D. J., and Barrett, S. R. H.: Global impacts of aviation on air quality evaluated at high resolution, Atmos. Chem. Phys., 24, 2687–2703, https://doi.org/10.5194/acp-24-2687-2024, 2024.
EEDB: ICAO Aircraft Engine Emissions Databank, https://www.easa.europa.eu/domains/environment/icao-aircraft-engine-emissions-databank, last access: 1 July 2024.
EPA: Procedures For Emission Inventory Preparation, Volume IV Mobile Sources, https://nepis.epa.gov/ (last access: 1 July 2024), 1981.
Federal Aviation Administration (FAA): Aeronautical Lighting and Other Airport Visual Aids. Chapter 5: Section 2. Departure Procedures, https://www.faa.gov/air_traffic/publications/atpubs/aim_html/chap5_section_2.html, last access: 1 July 2024.
Garg, S., Kaur, K., Batra, S., Kaddoum, G., Kumar, N., and Boukerche, A.: A multi-stage anomaly detection scheme for augmenting the security in IoT-enabled applications, Future Gener. Comp. Sy., 104, 105–118, https://doi.org/10.1016/j.future.2019.09.038, 2020.
Gariel, M., Srivastava, A. N., and Feron, E.: Trajectory Clustering and an Application to Airspace Monitoring, IEEE T. Intell. Transp., 12, 1511–1524, https://doi.org/10.1109/TITS.2011.2160628, 2011.
GB6537 – Standard for Jet Fuel No.3: Standardization Administration of the People's Republic of China, https://www.chinesestandard.net/PDFOpenLib/GB6537-2006EN-P10P-H8369H-144797.pdf (last access: 1 July 2024), 2018.
Giovanni, L. D., Lancia, C., and Lulli, G.: Data-Driven Optimization for Air Traffic Flow Management with Trajectory Preferences, Transport. Sci., 58, 540–556, https://doi.org/10.1287/trsc.2022.0309, 2024.
Graver, B., Rutherford, D., and Zheng S.: CO2 emissions from commercial aviation: 2013, 2018, and 2019, International Council on Clean Transportation, Washington, Dc., 36 pp., https://theicct.org/publication/co2-emissions-from-commercial-aviation-2013-2018-and-2019/ (last access: 1 July 2024), 2020.
Gui, X., Zhang, J., and Peng, Z.: Trajectory clustering for arrival aircraft via new trajectory representation, IEEE J. Syst. Eng. Electron., 32, 473-486, https://doi.org/10.23919/JSEE.2021.000040, 2021.
Hou, T., Zhu, L., Wang, Y., and Peng, L.: Oxidative stress is the pivot for PM2.5-induced lung injury, Food Chem. Toxicol., 184, 114362, https://doi.org/10.1016/j.fct.2023.114362, 2024.
Hu, J., Li, W., Gao, Y., Zhao, G., Jiang, Y., Wang, W., Cao, M., Zhu, Y., Niu, Y., Ge, J., and Chen, R.: Fine particulate matter air pollution and subclinical cardiovascular outcomes: A longitudinal study in 15 Chinese cities, Environ. Int., 163, 107218, https://doi.org/10.1016/j.envint.2022.107218, 2022.
ICAO: Airport Air Quality Manual, https://www.icao.int/environmental-protection/Documents/Doc 9889.SGAR.WG2.Initial Update.pdf (last access: 1 July 2024), 2011.
Klenner, J., Muri, H., and Strømman, A. H.: High-resolution modeling of aviation emissions in Norway, Transp. Res. D-Tr. E., 109, 103379, https://doi.org/10.1016/j.trd.2022.103379, 2022.
Köhler, M. O., Rädel, G., Dessens, O., Shine, K. P., Rogers, H. L., Wild, O., and Pyle, J. A.: Impact of perturbations to nitrogen oxide emissions from global aviation, J. Geophys. Res.-Atmos., 113, D11305, https://doi.org/10.1029/2007JD009140, 2008.
Koudis, G. S., Hu, S. J., Majumdar, A., Jones, R., and Stettler, M. E. J.: Airport emissions reductions from reduced thrust takeoff operations, Transp. Res. D-Tr. E., 52, 15–28, https://doi.org/10.1016/j.trd.2017.02.004, 2017.
Kumar, N., Odman, M. T., and Russell, A. G.: Multiscale air quality modeling: Application to southern California, J. Geophys. Res.-Atmos., 99, 5385–5397, https://doi.org/10.1029/93JD03197, 1994.
Kurniawan, J. S. and Khardi, S.: Comparison of methodologies estimating emissions of aircraft pollutants, environmental impact assessment around airports, Environ. Impact Asses., 31, 240–252, https://doi.org/10.1016/j.eiar.2010.09.001, 2011.
Lang, J., Yang, Z., Zhou, Y., Wen, C., and Cheng, X.: Four-dimensional aircraft emission inventory dataset of Landing and take-off cycle in China from 2019 to 2023, Zenodo [data set], https://doi.org/10.5281/zenodo.13908440, 2024.
Lawal, A. S., Russell, A. G., and Kaiser, J.: Assessment of Airport-Related Emissions and Their Impact on Air Quality in Atlanta, GA, Using CMAQ and TROPOMI, Environ. Sci. Technol., 56, 98–108, https://doi.org/10.1021/acs.est.1c03388, 2022.
Lee, H., Olsen, S. C., Wuebbles, D. J., and Youn, D.: Impacts of aircraft emissions on the air quality near the ground, Atmos. Chem. Phys., 13, 5505–5522, https://doi.org/10.5194/acp-13-5505-2013, 2013.
Li, H., Jia, P., Wang, X., Yang, Z., Wang, J., and Kuang, H.: Ship carbon dioxide emission estimation in coastal domestic emission control areas using high spatial-temporal resolution data: A China case, Ocean Coast. Manage., 232, 106419, https://doi.org/10.1016/j.ocecoaman.2022.106419, 2023.
Liu, Y., Hu, M., Yin, J., Su, J., and Qiao, P.: Adaptive airport taxiing rule management: Design, assessment, and configuration, Transp. Res. Part C: Emerg. Technol., 163, 104652, https://doi.org/10.1016/j.trc.2024.104652, 2024.
Ma, S., Wang, X., Han, B., Zhao, J., Guan, Z., Wang, J., Zhang, Y., Liu, B., Yu, J., Feng, Y., and Hopke, P. K.: Exploring emission spatiotemporal pattern and potential reduction capacity in China's aviation sector: Flight trajectory optimization perspective, Sci. Total Environ., 951, 175558, https://doi.org/10.1016/j.scitotenv.2024.175558, 2024.
Mokalled, T., Calvé, S. L., Badaro-Saliba, N., Abboud, M., Zaarour, R., Farah, W., and Adjizian-Gérard, j.: Identifying the impact of Beirut Airport's activities on local air quality – Part I: Emissions inventory of NO2 and VOCs, Atmos. Environ., 187, 435–444, https://doi.org/10.1016/j.atmosenv.2018.04.036, 2018.
Murça, M. C. R., Hansman, R. J., Li, L., and Ren, P.: Flight trajectory data analytics for characterization of air traffic flows: A comparative analysis of terminal area operations between New York, Hong Kong and Sao Paulo,Transpor. Res. C-Emer., 97, 324–347, https://doi.org/10.1016/j.trc.2018.10.021, 2018.
Nahlik, M. J., Chester, M. V., Ryerson, M. S., and Fraser, M. A.: Spatial Differences and Costs of Emissions at U.S. Airport Hubs, Environ. Sci. Technol., 50, 4149–4158, https://doi.org/10.1021/acs.est.5b04491, 2016.
Pandey, G., Venkatram, A., and Arunachalam, S.: Modeling the air quality impact of aircraft emissions: is area or volume the appropriate source characterization in AERMOD?, Air. Qual. Atmos. Hlth., 17, 1425–1434, https://doi.org/10.1007/s11869-024-01517-2, 2024.
Peace, H., Maughan, J., Owen, B., and Raper, D.: Identifying the contribution of different airport related sources to local urban air quality, Environ. Model. Softw., 21, 532–538, https://doi.org/10.1016/j.envsoft.2004.07.014, 2006.
Quadros, F. D. A., Snellen, M., and Dedoussi, I. C.: Regional sensitivities of air quality and human health impacts to aviation emissions, Environ. Res. Lett., 15, 105013, https://doi.org/10.1088/1748-9326/abb2c5, 2020.
Quadros, F. D. A., Snellen, M., Sun, J., and Dedoussi, I. C.: Global Civil Aviation Emissions Estimates for 2017–2020 Using ADS–B Data, J. Aircraft, 59, 1394–1405, https://doi.org/10.2514/1.C036763, 2022.
Sander, J., Ester, M., Kriegel, HP., and Xu, X.: Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications, Data Min. Knowl. Disc., 2, 169–194, https://doi.org/10.1023/A:1009745219419, 1998.
Sekine, K., Kato, F., Tatsukawa, T., Fujii, K., and Itoh, E.: Rule Design for Interpretable En Route Arrival Management via Runway-Flow and Inter-Aircraft Control, IEEE Access, 11, 75093–75111, https://doi.org/10.1109/ACCESS.2023.3297136, 2023.
Stettler, M. E. J., Eastham, S., and Barrett, S. R. G.: Air quality and public health impacts of UK airports. Part I: Emissions, Atmos. Environ., 45, 5415–5424, https://doi.org/10.1016/j.atmosenv.2011.07.012, 2011.
Tekin, A. T. and Sarı, C.: Carbon Monoxide and Nitrogen Oxide Emissions Analysis: Clustering-Based Approach, Springer, Cham, 1089, 338-346, https://doi.org/10.1007/978-3-031-67195-1_40, 2024.
Teoh, R., Engberg, Z., Shapiro, M., Dray, L., and Stettler, M. E. J.: The high-resolution Global Aviation emissions Inventory based on ADS-B (GAIA) for 2019–2021, Atmos. Chem. Phys., 24, 725–744, https://doi.org/10.5194/acp-24-725-2024, 2024.
Unal, A., Hu, Y., Chang, M. E., Odman, M. T., and Russell, A. G.: Airport related emissions and impacts on air quality: Application to the Atlanta International Airport, Atmos. Environ., 39, 5787–5798, https://doi.org/10.1016/j.atmosenv.2005.05.051, 2005.
VariFlight: Flight status data, http://www.variflight.com/, last access: July 1 2024.
Ventorim, I. M., Luchi, D., Rodrigues, A. L., and Varejão, F. M.: BIRCHSCAN: A sampling method for applying DBSCAN to large datasets, Expert Syst. Appl., 184, 115518, https://doi.org/10.1016/j.eswa.2021.115518, 2021.
Wang, K., Wang, X., Cheng, S., Cheng, L., and Wang, R.: National emissions inventory and future trends in greenhouse gases and other air pollutants from civil airports in China, Environ. Sci. Pollut. Res., 29, 81703–81712, https://doi.org/10.1007/s11356-022-21425-1, 2022.
Wang, X., Huang, P., Yu, X., and Huang C.: Near ground wind characteristics during typhoon Meari: Turbulence intensities, gust factors, and peak factors, J. Cent. South Univ., 24, 2421–2430, https://doi.org/10.1007/s11771-017-3653-z, 2017.
Wang, Y., Zou, C., Fang, T., Sun, N., Liang, X., Wu, L., and Mao, H.: Emissions from international airport and its impact on air quality: A case study of beijing daxing international airport (PKX), China, Environ. Pollut., 336, 122472, https://doi.org/10.1016/j.envpol.2023.122472, 2023.
Wayson, R. L., Fleming, G. G., and Iovinelli, R.: Methodology to estimate particulate matter emissions from certified commercial aircraft engines, J. Air Waste Manag. Assoc., 59, 91–100, https://doi.org/10.3155/1047-3289.59.1.91, 2009.
Wen, C., Lang, J., Zhou, Y., Fan, X., Bian, Z., Chen, D., Tian, J., and Wang, P.: Emission and influences of non-road mobile sources on air quality in China, 2000–2019, Environ. Pollut., 324, 121404, https://doi.org/10.1016/j.envpol.2023.121404, 2023.
Wolfe, P. J., Giang, A., Ashok, A., Selin, N. E., and Barrett, S. R. H.: Costs of IQ Loss from Leaded Aviation Gasoline Emissions, Environ. Sci. Technol., 50, 9026–9033, https://doi.org/10.1021/acs.est.6b02910, 2016.
Woody, M. C. and Arunachalam, S.: Secondary organic aerosol produced from aircraft emissions at the Atlanta Airport: An advanced diagnostic investigation using process analysis, Atmos. Environ., 79, 101–109, https://doi.org/10.1016/j.atmosenv.2013.06.007, 2013.
Woody, M. C., Wong, H.-W., West, J. J., and Arunachalam, S.: Multiscale predictions of aviation-attributable PM2.5 for U.S. airports modeled using CMAQ with plume-in-grid and an aircraft-specific 1-D emission model, Atmos. Environ., 147, 384–394, https://doi.org/10.1016/j.atmosenv.2016.10.016, 2016.
Xu, H., Fu, Q., Yu, Y., Liu, Q., Pan, J., Cheng, J., Wang, Z., and Liu, L.: Quantifying aircraft emissions of Shanghai Pudong International Airport with aircraft ground operational data, Environ. Pollut., 261, 114115, https://doi.org/10.1016/j.envpol.2020.114115, 2020.
Xu, H., Xiao, K., Cheng, J., Yu, Y., Liu, Q., Pan, J., Chen, J., Chen, F., and Fu, Q.: Characterizing aircraft engine fuel and emission parameters of taxi phase for Shanghai Hongqiao International Airport with aircraft operational data, Sci. Total Environ., 720, 137431, https://doi.org/10.1016/j.scitotenv.2020.137431, 2020.
Yang, X., Cheng, S., Lang, J., Xu, R., and Lv, Z.: Characterization of aircraft emissions and air quality impacts of an international airport, J. Environ. Sci., 72, 198–207, https://doi.org/10.1016/j.jes.2018.01.007, 2018.
Yim, S. H. L., Lee, G. L., Lee, I. H., Allroggen, F., Ashok, A., Caiazzo, F., Eastham, S. D., Malina, R., and Barrett, S. R. H.: Global, regional and local health impacts of civil aviation emissions, Environ. Res. Lett., 10, 034001, https://doi.org/10.1088/1748-9326/10/3/034001, 2015.
Yin, S., Han, K., Ochieng, W. Y., and Sanchez, D. R.: Joint apron-runway assignment for airport surface operations, Transport. Res. Pt. B, 156, 76–100, https://doi.org/10.1016/j.trb.2021.12.011, 2022.
Zhang, J., Zhang, S., Zhang, X., Wang, J., Wu, Y., and Hao, J.: Developing a High-Resolution Emission Inventory of China's Aviation Sector Using Real-World Flight Trajectory Data, Environ. Sci. Technol., 56, 5743–5752, https://doi.org/10.1021/acs.est.1c08741, 2022.
Zhang, J., Jiang, Y., Wang, Y., Zhang, S., Wu, Y., Wang, S., Nielsen, C. P., McElroy, M. B., and Hao, J.: Increased Impact of Aviation on Air Quality and Human Health in China, Environ. Sci. Technol., 57, 19575–19583, https://doi.org/10.1021/acs.est.3c05821, 2023.
Zhou, Y., Jiao, Y., Lang, J., Chen, D., Huang, C., Wei, P., Li, S., and Cheng, S.: Improved estimation of air pollutant emissions from landing and takeoff cycles of civil aircraft in China, Environ. Pollut., 249, 463–471, https://doi.org/10.1016/j.envpol.2019.03.088, 2019.
Short summary
This study established a four-dimensional (hourly; 0.03° × 0.03° × 34 height layers) aircraft emission inventory dataset in the landing-and-takeoff cycle for China during 2019–2023 considering actual running time and flight trajectory. The dataset reflects unique horizontal and height spatial characteristics and hourly temporal variations of aircraft emissions and the impact of COVID-19 on these emissions, providing essential information for environmental analysis and policy decisions.
This study established a four-dimensional (hourly; 0.03° × 0.03° × 34 height layers) aircraft...
Altmetrics
Final-revised paper
Preprint