Articles | Volume 16, issue 9
https://doi.org/10.5194/essd-16-4051-2024
© Author(s) 2024. 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-16-4051-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
12 Sep 2024
Data description paper |
| 12 Sep 2024
| 12 Sep 2024
PM2.5 concentrations based on near-surface visibility in the Northern Hemisphere from 1959 to 2022
Hongfei Hao
Global Change and Earth System Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Institute of Carbon Neutrality, Sino-French Institute of Earth System Science, College Urban and Environmental Sciences, Peking University, Beijing 100871, China
Guocan Wu
Global Change and Earth System Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Jianbao Liu
Institute of Carbon Neutrality, Sino-French Institute of Earth System Science, College Urban and Environmental Sciences, Peking University, Beijing 100871, China
Institute of Carbon Neutrality, Sino-French Institute of Earth System Science, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing 100871, China
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This paper provides an overview of the HALO–(AC)3 aircraft campaign data sets, the campaign-specific instrument operation, data processing, and data quality. The data set comprises in situ and remote sensing observations from three research aircraft: HALO, Polar 5, and Polar 6. All data are published in the PANGAEA database by instrument-separated data subsets. It is highlighted how the scientific analysis of the HALO–(AC)3 data benefits from the coordinated operation of three aircraft.
Janusz W. Krzyścin, Agnieszka Czerwińska, Bonawentura Rajewska-Więch, Janusz Jarosławski, Piotr S. Sobolewski, and Izabela Pawlak
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-622, https://doi.org/10.5194/essd-2024-622, 2025
Revised manuscript accepted for ESSD
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Time series (1976−2023) of biologically effective (for skin redness, vitamin D3 production and psoriasis healing) daily radiant exposure (RE) at Belsk from standard erythemal biometers are examined. Comparisons of the measured data for cloudless days with values from a radiation transfer model provide a basis for data homogenization. Averaged results from different versions of the recalculated data give the 1976−2004 trend of about 6 % per 10 years in annual RE for all biological effects.
Simo Tukiainen, Tuomas Siipola, Niko Leskinen, and Ewan O'Connor
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-605, https://doi.org/10.5194/essd-2024-605, 2025
Revised manuscript accepted for ESSD
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Measurement campaigns are crucial for advancing the understanding of complex cloud–aerosol interactions in the atmosphere. Ground-based remote sensing measurements were conducted in Kenttärova, Finland, during the PaCE 2022 campaign. These measurements were processed using the Cloudnet methodology, and the data are available through the ACTRIS Cloudnet data portal.
Yugo Kanaya, Roberto Sommariva, Alfonso Saiz-Lopez, Andrea Mazzeo, Theodore K. Koenig, Kaori Kawana, James E. Johnson, Aurélie Colomb, Pierre Tulet, Suzie Molloy, Ian E. Galbally, Rainer Volkamer, Anoop Mahajan, John W. Halfacre, Paul B. Shepson, Julia Schmale, Hélène Angot, Byron Blomquist, Matthew D. Shupe, Detlev Helmig, Junsu Gil, Meehye Lee, Sean C. Coburn, Ivan Ortega, Gao Chen, James Lee, Kenneth C. Aikin, David D. Parrish, John S. Holloway, Thomas B. Ryerson, Ilana B. Pollack, Eric J. Williams, Brian M. Lerner, Andrew J. Weinheimer, Teresa Campos, Frank M. Flocke, J. Ryan Spackman, Ilann Bourgeois, Jeff Peischl, Chelsea R. Thompson, Ralf M. Staebler, Amir A. Aliabadi, Wanmin Gong, Roeland Van Malderen, Anne M. Thompson, Ryan M. Stauffer, Debra E. Kollonige, Juan Carlos Gómez Martin, Masatomo Fujiwara, Katie Read, Matthew Rowlinson, Keiichi Sato, Junichi Kurokawa, Yoko Iwamoto, Fumikazu Taketani, Hisahiro Takashima, Monica Navarro Comas, Marios Panagi, and Martin G. Schultz
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-566, https://doi.org/10.5194/essd-2024-566, 2025
Revised manuscript accepted for ESSD
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The first comprehensive dataset of tropospheric ozone over oceans/polar regions is presented, including 77 ship/buoy and 48 aircraft campaign observations (1977–2022, 0–5000 m altitude), supplemented by ozonesonde and surface data. Air masses isolated from land for 72+ hours are systematically selected as essentially oceanic. Among the 11 global regions, they show daytime decreases of 10–16% in the tropics, while near-zero depletions are rare, unlike in the Arctic, implying different mechanisms.
Isolde Glissenaar, Klaas Folkert Boersma, Isidora Anglou, Pieter Rijsdijk, Tijl Verhoelst, Steven Compernolle, Gaia Pinardi, Jean-Christopher Lambert, Michel Van Roozendael, and Henk Eskes
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-616, https://doi.org/10.5194/essd-2024-616, 2025
Revised manuscript accepted for ESSD
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We developed a new global dataset of nitrogen dioxide (NO2) levels in the lower atmosphere, using data from TROPOMI for 2018–2021. This dataset offers improved accuracy and detail compared to earlier versions, meeting high international standards for climate data. By refining how measurement errors are calculated and reduced over time and space, we provide clearer insights into pollution patterns. This work supports better air quality monitoring and informs actions to address pollution globally.
Laura Köhler, Julia Windmiller, Dariusz Baranowski, Michał Brennek, Michał Ciuryło, Lennéa Hayo, Daniel Kȩpski, Stefan Kinne, Beata Latos, Bertrand Lobo, Tobias Marke, Timo Nischik, Daria Paul, Piet Stammes, Artur Szkop, and Olaf Tuinder
Earth Syst. Sci. Data, 17, 633–659, https://doi.org/10.5194/essd-17-633-2025, https://doi.org/10.5194/essd-17-633-2025, 2025
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We present atmospheric and oceanic data from the Atlantic References and Convection ship campaign with the Maria S. Merian from Mindelo to Punta Arenas observed with the integrated ship sensors; humidity and temperature profiler; ceilometer; aerosol instruments (Calitoo, Microtops, and DustTrak); radiosondes; uncrewed aircraft vehicles; and conductivity, temperature, and depth scans. The data include three complete profiles of the Intertropical Convergence Zone and a storm in the South Atlantic.
Emmanouil Proestakis, Vassilis Amiridis, Carlos Pérez García-Pando, Svetlana Tsyro, Jan Griesfeller, Antonis Gkikas, Thanasis Georgiou, María Gonçalves Ageitos, Jeronimo Escribano, Stelios Myriokefalitakis, Elisa Bergas Masso, Enza Di Tomaso, Sara Basart, Jan-Berend W. Stuut, and Angela Benedetti
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-43, https://doi.org/10.5194/essd-2025-43, 2025
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Quantification of dust deposition into the broader Atlantic Ocean is provided, with the estimates established on the basis of Earth Observations. The dataset is considered unique with respect to a range of applications, including compensating for spatiotemporal gaps of sediment-trap measurements, assessments of model simulations, shedding light into physical processes related to the dust cycle, and to better understand dust biogeochemical impacts on oceanic ecosystems, on weather, and climate.
Jürgen Gratzl, David Brus, Konstantinos Doulgeris, Alexander Böhmländer, Ottmar Möhler, and Hinrich Grothe
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-543, https://doi.org/10.5194/essd-2024-543, 2025
Revised manuscript accepted for ESSD
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Near-real time monitoring of airborne biological particles like fungal spores or pollen grains is of great interest for two main reasons: To improve atmospheric allergen forecasts and deepen the understanding of how bioaerosols influence cloud formation. Here, we measured fluorescent bioaerosols in the Finnish sub-Arctic with high time resolution. A data set that might improve our understanding of biosphere-cloud interactions and the dynamics of bioaerosols in the atmosphere.
Xiaoran Guo, Jianping Guo, Deli Meng, Yuping Sun, Zhen Zhang, Hui Xu, Liping Zeng, Juan Chen, Ning Li, and Tianmeng Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-589, https://doi.org/10.5194/essd-2024-589, 2025
Revised manuscript accepted for ESSD
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Optimal atmospheric dynamic condition is essential for convective storms. This study generates a dataset of high-resolution divergence and vorticity profiles using the measurements of radar wind profiler mesonet in Beijing. The negative divergence and positive vorticity are present in advance of rainfall events. This suggests that this dataset can help improve our understanding of prestorm environment and has the potential to be applied to weather forecasting.
Yueming Dong, Jing Li, Zhenyu Zhang, Chongzhao Zhang, and Qiurui Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-583, https://doi.org/10.5194/essd-2024-583, 2025
Revised manuscript accepted for ESSD
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This study develops two merged global land aerosol single scattering albedo (SSA) datasets by combining AERONET ground observations and two satellite datasets using an Ensemble Kalman Filter data synergy method. The merged datasets exhibit significantly improved accuracy compared to the original satellite data. These results can provide more reliable estimates of aerosol scattering and absorption properties, essential for improving climate modeling and assessing aerosol climate effects.
Israel Silber, Jennifer M. Comstock, Michael R. Kieburtz, and Lynn M. Russell
Earth Syst. Sci. Data, 17, 29–42, https://doi.org/10.5194/essd-17-29-2025, https://doi.org/10.5194/essd-17-29-2025, 2025
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We present ARMTRAJ, a set of multipurpose trajectory datasets, which augments cloud, aerosol, and boundary layer studies utilizing the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility data. ARMTRAJ data include ensemble run statistics that enhance consistency and serve as uncertainty metrics for air mass coordinates and state variables. ARMTRAJ will soon become a near real-time product that will accompany past, ongoing, and future ARM deployments.
Cheng Chen, Xuefeng Lei, Zhenhai Liu, Haorang Gu, Oleg Dubovik, Pavel Litvinov, David Fuertes, Yujia Cao, Haixiao Yu, Guangfeng Xiang, Binghuan Meng, Zhenwei Qiu, Xiaobing Sun, Jin Hong, and Zhengqiang Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-483, https://doi.org/10.5194/essd-2024-483, 2024
Revised manuscript accepted for ESSD
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POSP on board GF-5(02) satellite is the first space-borne UV-VIS-NIR-SWIR multi-spectral cross-track scanning polarimeter. Due to wide spectral range and polarimetric capabilities, POSP measurements provide rich information for aerosol and surface characterization. We present the aerosol/surface products generated from POSP first 18 months of operation using GRASP/Models, including spectral AOD, AODF, AODC, and AE, SSA, scale height, full surface BRDF, BPDF, black-/white-sky albedos, NDVI.
Peiyuan Jiao, Chengzhi Xing, Yikai Li, Xiangguang Ji, Wei Tan, Qihua Li, Haoran Liu, and Cheng Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-562, https://doi.org/10.5194/essd-2024-562, 2024
Revised manuscript accepted for ESSD
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This study provides a dataset of high-resolution vertical profiles of aerosol, NO2, and HCHO, observed over periods ranging from 5 months to 5 years at 32 sites across China between 2019 and 2023. The dataset captures the vertical distribution, diurnal pattern and seasonal variations of these compositions. It has been validated against TROPOMI satellite observations and ground-based CNEMC measurements, showing good correlations.
Fan Mei, Jennifer M. Comstock, Mikhail S. Pekour, Jerome D. Fast, Krista L. Gaustad, Beat Schmid, Shuaiqi Tang, Damao Zhang, John E. Shilling, Jason M. Tomlinson, Adam C. Varble, Jian Wang, L. Ruby Leung, Lawrence Kleinman, Scot Martin, Sebastien C. Biraud, Brian D. Ermold, and Kenneth W. Burk
Earth Syst. Sci. Data, 16, 5429–5448, https://doi.org/10.5194/essd-16-5429-2024, https://doi.org/10.5194/essd-16-5429-2024, 2024
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Our study explores a comprehensive dataset from airborne field studies (2013–2018) conducted using the US Department of Energy's Gulfstream 1 (G-1). The 236 flights span diverse regions, including the Arctic, US Southern Great Plains, US West Coast, eastern North Atlantic, Amazon Basin in Brazil, and Sierras de Córdoba range in Argentina. This dataset provides unique insights into atmospheric dynamics, aerosols, and clouds and makes data available in a more accessible format.
Yuanyuan Wu, Jihu Liu, Yannian Zhu, Yu Zhang, Yang Cao, Kang-En Huang, Boyang Zheng, Yichuan Wang, Yanyun Li, Quan Wang, Chen Zhou, Yuan Liang, Jianning Sun, Minghuai Wang, and Daniel Rosenfeld
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-536, https://doi.org/10.5194/essd-2024-536, 2024
Revised manuscript accepted for ESSD
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In this paper, based on deep learning method, we established a global classification dataset of daytime and nighttime marine low-cloud mesoscale morphology. It aims to promote a comprehensive understanding of the cloud dynamics and cloud-climate feedback. Closed mesoscale cellular convection (MCC) clouds occur more frequently at night, while suppressed cumulus exhibit remarkable decrease. Solid stratus and MCC cloud types show clear seasonal variations.
Viktoria F. Sofieva, Alexei Rozanov, Monika Szelag, John P. Burrows, Christian Retscher, Robert Damadeo, Doug Degenstein, Landon A. Rieger, and Adam Bourassa
Earth Syst. Sci. Data, 16, 5227–5241, https://doi.org/10.5194/essd-16-5227-2024, https://doi.org/10.5194/essd-16-5227-2024, 2024
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Climate-related studies need information about the distribution of stratospheric aerosols, which influence the energy balance of the Earth’s atmosphere. In this work, we present a merged dataset of vertically resolved stratospheric aerosol extinction coefficients, which is derived from data of six limb and occultation satellite instruments. The created aerosol climate record covers the period from October 1984 to December 2023. It can be used in various climate-related studies.
Hasna Chebaicheb, Joel F. de Brito, Tanguy Amodeo, Florian Couvidat, Jean-Eudes Petit, Emmanuel Tison, Gregory Abbou, Alexia Baudic, Mélodie Chatain, Benjamin Chazeau, Nicolas Marchand, Raphaële Falhun, Florie Francony, Cyril Ratier, Didier Grenier, Romain Vidaud, Shouwen Zhang, Gregory Gille, Laurent Meunier, Caroline Marchand, Véronique Riffault, and Olivier Favez
Earth Syst. Sci. Data, 16, 5089–5109, https://doi.org/10.5194/essd-16-5089-2024, https://doi.org/10.5194/essd-16-5089-2024, 2024
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Long-term (2015–2021) quasi-continuous measurements have been obtained at 13 French urban sites using online mass spectrometry, to acquire the comprehensive chemical composition of submicron particulate matter. The results show their spatial and temporal differences and confirm the predominance of organics in France (40–60 %). These measurements can be used for many future studies, such as trend and epidemiological analyses, or comparisons with chemical transport models.
Paola Formenti and Claudia Di Biagio
Earth Syst. Sci. Data, 16, 4995–5007, https://doi.org/10.5194/essd-16-4995-2024, https://doi.org/10.5194/essd-16-4995-2024, 2024
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Particles from deserts and semi-vegetated areas (mineral dust) are important for Earth's climate and human health, notably depending on their size. In this paper we collect and make a synthesis of a body of these observations since 1972 in order to provide researchers modeling Earth's climate and developing satellite observations from space with a simple way of confronting their results and understanding their validity.
Yichen Jiang, Su Shi, Xinyue Li, Chang Xu, Haidong Kan, Bo Hu, and Xia Meng
Earth Syst. Sci. Data, 16, 4655–4672, https://doi.org/10.5194/essd-16-4655-2024, https://doi.org/10.5194/essd-16-4655-2024, 2024
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Limited ultraviolet (UV) measurements hindered further investigation of its health effects. This study used a machine learning algorithm to predict UV radiation with a daily and 10 km resolution of high accuracy in mainland China in 2005–2020. Then, uneven spatial distribution and population exposure risks as well as increased temporal trend of UV radiation were found in China. The long-term and high-quality UV dataset could further facilitate health-related research in the future.
Dene Bowdalo, Sara Basart, Marc Guevara, Oriol Jorba, Carlos Pérez García-Pando, Monica Jaimes Palomera, Olivia Rivera Hernandez, Melissa Puchalski, David Gay, Jörg Klausen, Sergio Moreno, Stoyka Netcheva, and Oksana Tarasova
Earth Syst. Sci. Data, 16, 4417–4495, https://doi.org/10.5194/essd-16-4417-2024, https://doi.org/10.5194/essd-16-4417-2024, 2024
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GHOST (Globally Harmonised Observations in Space and Time) represents one of the biggest collections of harmonised measurements of atmospheric composition at the surface. In total, 7 275 148 646 measurements from 1970 to 2023, from 227 different components, and from 38 reporting networks are compiled, parsed, and standardised. Components processed include gaseous species, total and speciated particulate matter, and aerosol optical properties.
Lei Kong, Xiao Tang, Zifa Wang, Jiang Zhu, Jianjun Li, Huangjian Wu, Qizhong Wu, Huansheng Chen, Lili Zhu, Wei Wang, Bing Liu, Qian Wang, Duohong Chen, Yuepeng Pan, Jie Li, Lin Wu, and Gregory R. Carmichael
Earth Syst. Sci. Data, 16, 4351–4387, https://doi.org/10.5194/essd-16-4351-2024, https://doi.org/10.5194/essd-16-4351-2024, 2024
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A new long-term inversed emission inventory for Chinese air quality (CAQIEI) is developed in this study, which contains constrained monthly emissions of NOx, SO2, CO, PM2.5, PM10, and NMVOCs in China from 2013 to 2020 with a horizontal resolution of 15 km. Emissions of different air pollutants and their changes during 2013–2020 were investigated and compared with previous emission inventories, which sheds new light on the complex variations of air pollutant emissions in China.
Can Li, Nickolay A. Krotkov, Joanna Joiner, Vitali Fioletov, Chris McLinden, Debora Griffin, Peter J. T. Leonard, Simon Carn, Colin Seftor, and Alexander Vasilkov
Earth Syst. Sci. Data, 16, 4291–4309, https://doi.org/10.5194/essd-16-4291-2024, https://doi.org/10.5194/essd-16-4291-2024, 2024
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Sulfur dioxide (SO2), a poisonous gas from human activities and volcanoes, causes air pollution, acid rain, and changes to climate and the ozone layer. Satellites have been used to monitor SO2 globally, including remote areas. Here we describe a new satellite SO2 dataset from the OMPS instrument that flies on the N20 satellite. Results show that the new dataset agrees well with the existing ones from other satellites and can help to continue the global monitoring of SO2 from space.
Jacqueline E. Russell, Richard J. Bantges, Helen E. Brindley, and Alejandro Bodas-Salcedo
Earth Syst. Sci. Data, 16, 4243–4266, https://doi.org/10.5194/essd-16-4243-2024, https://doi.org/10.5194/essd-16-4243-2024, 2024
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We present a dataset of top-of-atmosphere diurnally resolved reflected solar and emitted thermal energy for Earth system model evaluation. The multi-year, monthly hourly dataset, derived from observations made by the Geostationary Earth Radiation Budget instrument, covers the range 60° N–60° S, 60° E–60° W at 1° resolution. Comparison with two versions of the Hadley Centre Global Environmental Model highlight how the data can be used to assess updates to key model parameterizations.
Dominique Gantois, Guillaume Payen, Michaël Sicard, Valentin Duflot, Nelson Bègue, Nicolas Marquestaut, Thierry Portafaix, Sophie Godin-Beekmann, Patrick Hernandez, and Eric Golubic
Earth Syst. Sci. Data, 16, 4137–4159, https://doi.org/10.5194/essd-16-4137-2024, https://doi.org/10.5194/essd-16-4137-2024, 2024
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We describe three instruments that have been measuring interactions between aerosols (particles of various origin) and light over Réunion Island since 2012. Aerosols directly or indirectly influence the temperature in the atmosphere and can interact with clouds. Details are given on how we derived aerosol properties from our measurements and how we assessed the quality of our data before sharing them with the scientific community. A good correlation was found between the three instruments.
Pierre Tulet, Joel Van Baelen, Pierre Bosser, Jérome Brioude, Aurélie Colomb, Philippe Goloub, Andrea Pazmino, Thierry Portafaix, Michel Ramonet, Karine Sellegri, Melilotus Thyssen, Léa Gest, Nicolas Marquestaut, Dominique Mékiès, Jean-Marc Metzger, Gilles Athier, Luc Blarel, Marc Delmotte, Guillaume Desprairies, Mérédith Dournaux, Gaël Dubois, Valentin Duflot, Kevin Lamy, Lionel Gardes, Jean-François Guillemot, Valérie Gros, Joanna Kolasinski, Morgan Lopez, Olivier Magand, Erwan Noury, Manuel Nunes-Pinharanda, Guillaume Payen, Joris Pianezze, David Picard, Olivier Picard, Sandrine Prunier, François Rigaud-Louise, Michael Sicard, and Benjamin Torres
Earth Syst. Sci. Data, 16, 3821–3849, https://doi.org/10.5194/essd-16-3821-2024, https://doi.org/10.5194/essd-16-3821-2024, 2024
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The MAP-IO program aims to compensate for the lack of atmospheric and oceanographic observations in the Southern Ocean by equipping the ship Marion Dufresne with a set of 17 scientific instruments. This program collected 700 d of measurements under different latitudes, seasons, sea states, and weather conditions. These new data will support the calibration and validation of numerical models and the understanding of the atmospheric composition of this region of Earth.
Siwei Li, Yu Ding, Jia Xing, and Joshua S. Fu
Earth Syst. Sci. Data, 16, 3781–3793, https://doi.org/10.5194/essd-16-3781-2024, https://doi.org/10.5194/essd-16-3781-2024, 2024
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Surface PM2.5 data have gained widespread application in health assessments and related fields, while the inherent uncertainties in PM2.5 data persist due to the lack of ground-truth data across the space. This study provides a novel testbed, enabling comprehensive evaluation across the entire spatial domain. The optimized deep-learning model with spatiotemporal features successfully retrieved surface PM2.5 concentrations in China (2013–2021), with reduced biases induced by sample imbalance.
Shuai Wang, Mengyuan Zhang, Hui Zhao, Peng Wang, Sri Harsha Kota, Qingyan Fu, Cong Liu, and Hongliang Zhang
Earth Syst. Sci. Data, 16, 3565–3577, https://doi.org/10.5194/essd-16-3565-2024, https://doi.org/10.5194/essd-16-3565-2024, 2024
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Long-term, open-source, gap-free daily ground-level PM2.5 and PM10 datasets for India (LongPMInd) were reconstructed using a robust machine learning model to support health assessment and air quality management.
Hongfei Hao, Kaicun Wang, Chuanfeng Zhao, Guocan Wu, and Jing Li
Earth Syst. Sci. Data, 16, 3233–3260, https://doi.org/10.5194/essd-16-3233-2024, https://doi.org/10.5194/essd-16-3233-2024, 2024
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In this study, we employed a machine learning technique to derive daily aerosol optical depth from hourly visibility observations collected at more than 5000 airports worldwide from 1959 to 2021 combined with reanalysis meteorological parameters.
Arndt Kaps, Axel Lauer, Rémi Kazeroni, Martin Stengel, and Veronika Eyring
Earth Syst. Sci. Data, 16, 3001–3016, https://doi.org/10.5194/essd-16-3001-2024, https://doi.org/10.5194/essd-16-3001-2024, 2024
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CCClim displays observations of clouds in terms of cloud classes that have been in use for a long time. CCClim is a machine-learning-powered product based on multiple existing observational products from different satellites. We show that the cloud classes in CCClim are physically meaningful and can be used to study cloud characteristics in more detail. The goal of this is to make real-world clouds more easily understandable to eventually improve the simulation of clouds in climate models.
David Winker, Xia Cai, Mark Vaughan, Anne Garnier, Brian Magill, Melody Avery, and Brian Getzewich
Earth Syst. Sci. Data, 16, 2831–2855, https://doi.org/10.5194/essd-16-2831-2024, https://doi.org/10.5194/essd-16-2831-2024, 2024
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Clouds play important roles in both weather and climate. In this paper we describe version 1.0 of a unique global ice cloud data product derived from over 12 years of global spaceborne lidar measurements. This monthly gridded product provides a unique vertically resolved characterization of the occurrence and properties, optical and physical, of thin ice clouds and the tops of deep convective clouds. It should provide significant value for cloud research and model evaluation.
Karam Mansour, Stefano Decesari, Darius Ceburnis, Jurgita Ovadnevaite, Lynn M. Russell, Marco Paglione, Laurent Poulain, Shan Huang, Colin O'Dowd, and Matteo Rinaldi
Earth Syst. Sci. Data, 16, 2717–2740, https://doi.org/10.5194/essd-16-2717-2024, https://doi.org/10.5194/essd-16-2717-2024, 2024
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We propose and evaluate machine learning predictive algorithms to model freshly formed biogenic methanesulfonic acid and sulfate concentrations. The long-term constructed dataset covers the North Atlantic at an unprecedented resolution. The improved parameterization of biogenic sulfur aerosols at regional scales is essential for determining their radiative forcing, which could help further understand marine-aerosol–cloud interactions and reduce uncertainties in climate models
Piers M. Forster, Chris Smith, Tristram Walsh, William F. Lamb, Robin Lamboll, Bradley Hall, Mathias Hauser, Aurélien Ribes, Debbie Rosen, Nathan P. Gillett, Matthew D. Palmer, Joeri Rogelj, Karina von Schuckmann, Blair Trewin, Myles Allen, Robbie Andrew, Richard A. Betts, Alex Borger, Tim Boyer, Jiddu A. Broersma, Carlo Buontempo, Samantha Burgess, Chiara Cagnazzo, Lijing Cheng, Pierre Friedlingstein, Andrew Gettelman, Johannes Gütschow, Masayoshi Ishii, Stuart Jenkins, Xin Lan, Colin Morice, Jens Mühle, Christopher Kadow, John Kennedy, Rachel E. Killick, Paul B. Krummel, Jan C. Minx, Gunnar Myhre, Vaishali Naik, Glen P. Peters, Anna Pirani, Julia Pongratz, Carl-Friedrich Schleussner, Sonia I. Seneviratne, Sophie Szopa, Peter Thorne, Mahesh V. M. Kovilakam, Elisa Majamäki, Jukka-Pekka Jalkanen, Margreet van Marle, Rachel M. Hoesly, Robert Rohde, Dominik Schumacher, Guido van der Werf, Russell Vose, Kirsten Zickfeld, Xuebin Zhang, Valérie Masson-Delmotte, and Panmao Zhai
Earth Syst. Sci. Data, 16, 2625–2658, https://doi.org/10.5194/essd-16-2625-2024, https://doi.org/10.5194/essd-16-2625-2024, 2024
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This paper tracks some key indicators of global warming through time, from 1850 through to the end of 2023. It is designed to give an authoritative estimate of global warming to date and its causes. We find that in 2023, global warming reached 1.3 °C and is increasing at over 0.2 °C per decade. This is caused by all-time-high greenhouse gas emissions.
Joshua L. Laughner, Geoffrey C. Toon, Joseph Mendonca, Christof Petri, Sébastien Roche, Debra Wunch, Jean-Francois Blavier, David W. T. Griffith, Pauli Heikkinen, Ralph F. Keeling, Matthäus Kiel, Rigel Kivi, Coleen M. Roehl, Britton B. Stephens, Bianca C. Baier, Huilin Chen, Yonghoon Choi, Nicholas M. Deutscher, Joshua P. DiGangi, Jochen Gross, Benedikt Herkommer, Pascal Jeseck, Thomas Laemmel, Xin Lan, Erin McGee, Kathryn McKain, John Miller, Isamu Morino, Justus Notholt, Hirofumi Ohyama, David F. Pollard, Markus Rettinger, Haris Riris, Constantina Rousogenous, Mahesh Kumar Sha, Kei Shiomi, Kimberly Strong, Ralf Sussmann, Yao Té, Voltaire A. Velazco, Steven C. Wofsy, Minqiang Zhou, and Paul O. Wennberg
Earth Syst. Sci. Data, 16, 2197–2260, https://doi.org/10.5194/essd-16-2197-2024, https://doi.org/10.5194/essd-16-2197-2024, 2024
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This paper describes a new version, called GGG2020, of a data set containing column-integrated observations of greenhouse and related gases (including CO2, CH4, CO, and N2O) made by ground stations located around the world. Compared to the previous version (GGG2014), improvements have been made toward site-to-site consistency. This data set plays a key role in validating space-based greenhouse gas observations and in understanding the carbon cycle.
Antonin Soulie, Claire Granier, Sabine Darras, Nicolas Zilbermann, Thierno Doumbia, Marc Guevara, Jukka-Pekka Jalkanen, Sekou Keita, Cathy Liousse, Monica Crippa, Diego Guizzardi, Rachel Hoesly, and Steven J. Smith
Earth Syst. Sci. Data, 16, 2261–2279, https://doi.org/10.5194/essd-16-2261-2024, https://doi.org/10.5194/essd-16-2261-2024, 2024
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Anthropogenic emissions are the result of transportation, power generation, industrial, residential and commercial activities as well as waste treatment and agriculture practices. This work describes the new CAMS-GLOB-ANT gridded inventory of 2000–2023 anthropogenic emissions of air pollutants and greenhouse gases. The methodology to generate the emissions is explained and the datasets are analysed and compared with publicly available global and regional inventories for selected world regions.
Declan L. Finney, Alan M. Blyth, Martin Gallagher, Huihui Wu, Graeme J. Nott, Michael I. Biggerstaff, Richard G. Sonnenfeld, Martin Daily, Dan Walker, David Dufton, Keith Bower, Steven Böing, Thomas Choularton, Jonathan Crosier, James Groves, Paul R. Field, Hugh Coe, Benjamin J. Murray, Gary Lloyd, Nicholas A. Marsden, Michael Flynn, Kezhen Hu, Navaneeth M. Thamban, Paul I. Williams, Paul J. Connolly, James B. McQuaid, Joseph Robinson, Zhiqiang Cui, Ralph R. Burton, Gordon Carrie, Robert Moore, Steven J. Abel, Dave Tiddeman, and Graydon Aulich
Earth Syst. Sci. Data, 16, 2141–2163, https://doi.org/10.5194/essd-16-2141-2024, https://doi.org/10.5194/essd-16-2141-2024, 2024
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The DCMEX (Deep Convective Microphysics Experiment) project undertook an aircraft- and ground-based measurement campaign of New Mexico deep convective clouds during July–August 2022. The campaign coordinated a broad range of instrumentation measuring aerosol, cloud physics, radar signals, thermodynamics, dynamics, electric fields, and weather. The project's objectives included the utilisation of these data with satellite observations to study the anvil cloud radiative effect.
Jianzhong Xu, Xinghua Zhang, Wenhui Zhao, Lixiang Zhai, Miao Zhong, Jinsen Shi, Junying Sun, Yanmei Liu, Conghui Xie, Yulong Tan, Kemei Li, Xinlei Ge, Qi Zhang, and Shichang Kang
Earth Syst. Sci. Data, 16, 1875–1900, https://doi.org/10.5194/essd-16-1875-2024, https://doi.org/10.5194/essd-16-1875-2024, 2024
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A comprehensive aerosol observation project was carried out in the Tibetan Plateau (TP) and its surroundings in recent years to investigate the properties and sources of atmospheric aerosols as well as their regional differences by performing multiple intensive field observations. The release of this dataset can provide basic and systematic data for related research in the atmospheric, cryospheric, and environmental sciences in this unique region.
Xiaoyong Zhuge, Xiaolei Zou, Lu Yu, Xin Li, Mingjian Zeng, Yilun Chen, Bing Zhang, Bin Yao, Fei Tang, Fengjiao Chen, and Wanlin Kan
Earth Syst. Sci. Data, 16, 1747–1769, https://doi.org/10.5194/essd-16-1747-2024, https://doi.org/10.5194/essd-16-1747-2024, 2024
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The Himawari-8/9 level-2 operational cloud product has a low spatial resolution and is available only during the daytime. To supplement this official dataset, a new dataset named the NJIAS Himawari-8/9 Cloud Feature Dataset (HCFD) was constructed. The NJIAS HCFD provides a comprehensive description of cloud features over the East Asia and west North Pacific regions for the years 2016–2022 by 30 retrieved cloud variables. The NJIAS HCFD has been demonstrated to outperform the official dataset.
Honglin Pan, Jianping Huang, Jiming Li, Zhongwei Huang, Minzhong Wang, Ali Mamtimin, Wen Huo, Fan Yang, Tian Zhou, and Kanike Raghavendra Kumar
Earth Syst. Sci. Data, 16, 1185–1207, https://doi.org/10.5194/essd-16-1185-2024, https://doi.org/10.5194/essd-16-1185-2024, 2024
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We applied several correction procedures and rigorously checked for data quality constraints during the long observation period spanning almost 14 years (2007–2020). Nevertheless, some uncertainties remain, mainly due to technical constraints and limited documentation of the measurements. Even though not completely accurate, this strategy is expected to at least reduce the inaccuracy of the computed characteristic value of aerosol optical parameters.
Julie Christin Schindlbeck-Belo, Matthew Toohey, Marion Jegen, Steffen Kutterolf, and Kira Rehfeld
Earth Syst. Sci. Data, 16, 1063–1081, https://doi.org/10.5194/essd-16-1063-2024, https://doi.org/10.5194/essd-16-1063-2024, 2024
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Volcanic forcing of climate resulting from major explosive eruptions is a dominant natural driver of past climate variability. To support model studies of the potential impacts of explosive volcanism on climate variability across timescales, we present an ensemble reconstruction of volcanic stratospheric sulfur injection over the last 140 000 years that is based primarily on tephra records.
Sabrina Schnitt, Andreas Foth, Heike Kalesse-Los, Mario Mech, Claudia Acquistapace, Friedhelm Jansen, Ulrich Löhnert, Bernhard Pospichal, Johannes Röttenbacher, Susanne Crewell, and Bjorn Stevens
Earth Syst. Sci. Data, 16, 681–700, https://doi.org/10.5194/essd-16-681-2024, https://doi.org/10.5194/essd-16-681-2024, 2024
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This publication describes the microwave radiometric measurements performed during the EUREC4A campaign at Barbados Cloud Observatory (BCO) and aboard RV Meteor and RV Maria S Merian. We present retrieved integrated water vapor (IWV), liquid water path (LWP), and temperature and humidity profiles as a unified, quality-controlled, multi-site data set on a 3 s temporal resolution for a core period between 19 January 2020 and 14 February 2020.
Daniela Meloni, Filippo Calì Quaglia, Virginia Ciardini, Annalisa Di Bernardino, Tatiana Di Iorio, Antonio Iaccarino, Giovanni Muscari, Giandomenico Pace, Claudio Scarchilli, and Alcide di Sarra
Earth Syst. Sci. Data, 16, 543–566, https://doi.org/10.5194/essd-16-543-2024, https://doi.org/10.5194/essd-16-543-2024, 2024
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Solar and infrared radiation are key factors in determining Arctic climate. Only a few sites in the Arctic perform long-term measurements of the surface radiation budget (SRB). At the Thule High Arctic Atmospheric Observatory (THAAO, 76.5° N, 68.8° W) in Northern Greenland, solar and infrared irradiance measurements were started in 2009. These data are of paramount importance in studying the impact of the atmospheric (mainly clouds and aerosols) and surface (albedo) parameters on the SRB.
Karoline Block, Mahnoosh Haghighatnasab, Daniel G. Partridge, Philip Stier, and Johannes Quaas
Earth Syst. Sci. Data, 16, 443–470, https://doi.org/10.5194/essd-16-443-2024, https://doi.org/10.5194/essd-16-443-2024, 2024
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Aerosols being able to act as condensation nuclei for cloud droplets (CCNs) are a key element in cloud formation but very difficult to determine. In this study we present a new global vertically resolved CCN dataset for various humidity conditions and aerosols. It is obtained using an atmospheric model (CAMS reanalysis) that is fed by satellite observations of light extinction (AOD). We investigate and evaluate the abundance of CCNs in the atmosphere and their temporal and spatial occurrence.
Jianping Guo, Jian Zhang, Jia Shao, Tianmeng Chen, Kaixu Bai, Yuping Sun, Ning Li, Jingyan Wu, Rui Li, Jian Li, Qiyun Guo, Jason B. Cohen, Panmao Zhai, Xiaofeng Xu, and Fei Hu
Earth Syst. Sci. Data, 16, 1–14, https://doi.org/10.5194/essd-16-1-2024, https://doi.org/10.5194/essd-16-1-2024, 2024
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A global continental merged high-resolution (PBLH) dataset with good accuracy compared to radiosonde is generated via machine learning algorithms, covering the period from 2011 to 2021 with 3-hour and 0.25º resolution in space and time. The machine learning model takes parameters derived from the ERA5 reanalysis and GLDAS product as input, with PBLH biases between radiosonde and ERA5 as the learning targets. The merged PBLH is the sum of the predicted PBLH bias and the PBLH from ERA5.
Karina E. Adcock, Penelope A. Pickers, Andrew C. Manning, Grant L. Forster, Leigh S. Fleming, Thomas Barningham, Philip A. Wilson, Elena A. Kozlova, Marica Hewitt, Alex J. Etchells, and Andy J. Macdonald
Earth Syst. Sci. Data, 15, 5183–5206, https://doi.org/10.5194/essd-15-5183-2023, https://doi.org/10.5194/essd-15-5183-2023, 2023
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We present a 12-year time series of continuous atmospheric measurements of O2 and CO2 at the Weybourne Atmospheric Observatory in the United Kingdom. These measurements are combined into the term atmospheric potential oxygen (APO), a tracer that is not influenced by land biosphere processes. The datasets show a long-term increasing trend in CO2 and decreasing trends in O2 and APO between 2010 and 2021.
Nikos Benas, Irina Solodovnik, Martin Stengel, Imke Hüser, Karl-Göran Karlsson, Nina Håkansson, Erik Johansson, Salomon Eliasson, Marc Schröder, Rainer Hollmann, and Jan Fokke Meirink
Earth Syst. Sci. Data, 15, 5153–5170, https://doi.org/10.5194/essd-15-5153-2023, https://doi.org/10.5194/essd-15-5153-2023, 2023
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This paper describes CLAAS-3, the third edition of the Cloud property dAtAset using SEVIRI, which was created based on observations from geostationary Meteosat satellites. CLAAS-3 cloud properties are evaluated using a variety of reference datasets, with very good overall results. The demonstrated quality of CLAAS-3 ensures its usefulness in a wide range of applications, including studies of local- to continental-scale cloud processes and evaluation of climate models.
Sandip S. Dhomse and Martyn P. Chipperfield
Earth Syst. Sci. Data, 15, 5105–5120, https://doi.org/10.5194/essd-15-5105-2023, https://doi.org/10.5194/essd-15-5105-2023, 2023
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There are no long-term stratospheric profile data sets for two very important greenhouse gases: methane (CH4) and nitrous oxide (N2O). Along with radiative feedback, these species play an important role in controlling ozone loss in the stratosphere. Here, we use machine learning to fuse satellite measurements with a chemical model to construct long-term gap-free profile data sets for CH4 and N2O. We aim to construct similar data sets for other important trace gases (e.g. O3, Cly, NOy species).
Cited articles
Albrecht, B. A.: Aerosols, cloud microphysics, and fractional cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989.
Ali, M. A., Bilal, M., Wang, Y., Nichol, J. E., Mhawish, A., Qiu, Z., de Leeuw, G., Zhang, Y., Zhan, Y., Liao, K., Almazroui, M., Dambul, R., Shahid, S., and Islam, M. N.: Accuracy assessment of CAMS and MERRA-2 reanalysis PM2.5 and PM10 concentrations over China, Atmos. Environ., 288, 119297, https://doi.org/10.1016/j.atmosenv.2022.119297, 2022.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2000), Zenodo [data set], https://doi.org/10.5281/zenodo.8307595, 2023a.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2001), Zenodo [data set], https://doi.org/10.5281/zenodo.8307597, 2023b.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2002), Zenodo [data set], https://doi.org/10.5281/zenodo.8307599, 2023c.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2003), Zenodo [data set], https://doi.org/10.5281/zenodo.8307601, 2023d.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2004), Zenodo [data set], https://doi.org/10.5281/zenodo.8307605, 2023e.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2005), Zenodo [data set], https://doi.org/10.5281/zenodo.8307607, 2023f.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2006), Zenodo [data set], https://doi.org/10.5281/zenodo.8308225, 2023g.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2007), Zenodo [data set], https://doi.org/10.5281/zenodo.8308227, 2023h.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2008), Zenodo [data set], https://doi.org/10.5281/zenodo.8308231, 2023i.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2009), Zenodo [data set], https://doi.org/10.5281/zenodo.8308233, 2023j.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2010), Zenodo [data set], https://doi.org/10.5281/zenodo.8308237, 2023k.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2011), Zenodo [data set], https://doi.org/10.5281/zenodo.8310586, 2023l.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2012), Zenodo [data set], https://doi.org/10.5281/zenodo.8310590, 2023m.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2013), Zenodo [data set], https://doi.org/10.5281/zenodo.8310702, 2023n.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2014), Zenodo [data set], https://doi.org/10.5281/zenodo.8310704, 2023o.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2015), Zenodo [data set], https://doi.org/10.5281/zenodo.8310706, 2023p.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2016), Zenodo [data set], https://doi.org/10.5281/zenodo.8310708, 2023q.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2017), Zenodo [data set], https://doi.org/10.5281/zenodo.8310711, 2023r.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2018), Zenodo [data set], https://doi.org/10.5281/zenodo.8313603, 2023s.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2019), Zenodo [data set], https://doi.org/10.5281/zenodo.8313611, 2023t.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2020), Zenodo [data set], https://doi.org/10.5281/zenodo.8313613, 2023u.
Bai, K. and Li, K.: LGHAP v2: Global daily 1-km gap-free PM2.5 grids (2021), Zenodo [data set], https://doi.org/10.5281/zenodo.8313615, 2023v.
Bai, K., Li, K., Shao, L., Li, X., Liu, C., Li, Z., Ma, M., Han, D., Sun, Y., Zheng, Z., Li, R., Chang, N.-B., and Guo, J.: LGHAP v2: a global gap-free aerosol optical depth and PM2.5 concentration dataset since 2000 derived via big Earth data analytics, Earth Syst. Sci. Data, 16, 2425–2448, https://doi.org/10.5194/essd-16-2425-2024, 2024.
Beckerman, B. S., Jerrett, M., Serre, M., Martin, R. V., Lee, S.-J., Van Donkelaar, A., Ross, Z., Su, J., and Burnett, R. T.: A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States, Environ. Sci. Technol., 47, 7233–7241, https://doi.org/10.1021/es400039u, 2013.
Bergstrom, R. W., Pilewskie, P., Russell, P. B., Redemann, J., Bond, T. C., Quinn, P. K., and Sierau, B.: Spectral absorption properties of atmospheric aerosols, Atmos. Chem. Phys., 7, 5937–5943, https://doi.org/10.5194/acp-7-5937-2007, 2007.
Boers, R., van Weele, M., van Meijgaard, E., Savenije, M., Siebesma, A. P., Bosveld, F., and Stammes, P.: Observations and projections of visibility and aerosol optical thickness (1956–2100) in the Netherlands: impacts of time-varying aerosol composition and hygroscopicity, Environ. Res. Lett., 10, 015003, https://doi.org/10.1088/1748-9326/10/1/015003, 2015.
Boys, B., Martin, R., Van Donkelaar, A., MacDonell, R., Hsu, N., Cooper, M., Yantosca, R., Lu, Z., Streets, D., and Zhang, Q.: Fifteen-year global time series of satellite-derived fine particulate matter, Environ. Sci. Technol., 48, 11109–11118, https://doi.org/10.1021/es502113p, 2014.
Browne, M. W.: Cross-validation methods, J. Math. Psychol., 44, 108–132, https://doi.org/10.1006/jmps.1999.1279, 2000.
Buchard, V., da Silva, A. M., Colarco, P. R., Darmenov, A., Randles, C. A., Govindaraju, R., Torres, O., Campbell, J., and Spurr, R.: Using the OMI aerosol index and absorption aerosol optical depth to evaluate the NASA MERRA Aerosol Reanalysis, Atmos. Chem. Phys., 15, 5743–5760, https://doi.org/10.5194/acp-15-5743-2015, 2015.
Buchard, V., da Silva, A. M., Randles, C. A., Colarco, P., Ferrare, R., Hair, J., Hostetler, C., Tackett, J., and Winker, D.: Evaluation of the surface PM2.5 in Version 1 of the NASA MERRA Aerosol Reanalysis over the United States, Atmos. Environ., 125, 100–111, https://doi.org/10.1016/j.atmosenv.2015.11.004, 2016.
Buchard, V., Randles, C. A., da Silva, A. M., Darmenov, A., Colarco, P. R., Govindaraju, R., Ferrare, R., Hair, J., Beyersdorf, A. J., Ziemba, L. D., and Yu, H.: The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part II: Evaluation and Case Studies, J. Climate, 30, 6851–6872, https://doi.org/10.1175/JCLI-D-16-0613.1, 2017.
Chafe, Z. A., Brauer, M., Klimont, Z., Van Dingenen, R., Mehta, S., Rao, S., Riahi, K., Dentener, F., and Smith, K. R.: Household Cooking with Solid Fuels Contributes to Ambient PM2.5 Air Pollution and the Burden of Disease, Environ. Health Persp., 122, 1314–1320, https://doi.org/10.1289/ehp.1206340, 2014.
Chang, K.-L., Petropavlovskikh, I., Cooper, O. R., Schultz, M. G., and Wang, T.: Regional trend analysis of surface ozone observations from monitoring networks in eastern North America, Europe and East Asia, Elementa: Science of the Anthropocene, 5, 50, https://doi.org/10.1525/elementa.243, 2017.
Che, H., Xia, X., Zhu, J., Hong, W., and Shi, G.: Aerosol optical properties under the condition of heavy haze over an urban site of Beijing, China, Environ. Sci. Pollut. R., 22, 1043–1053, https://doi.org/10.1007/s11356-014-3415-5, 2014.
Chen, A., Zhao, C., and Fan, T.: Spatio-temporal distribution of aerosol direct radiative forcing over mid-latitude regions in north hemisphere estimated from satellite observations, Atmos. Res., 266, 105938, https://doi.org/10.1016/j.atmosres.2021.105938, 2022.
Chen, Z., Chen, D., Zhao, C., Kwan, M.-p., Cai, J., Zhuang, Y., Zhao, B., Wang, X., Chen, B., Yang, J., Li, R., He, B., Gao, B., Wang, K., and Xu, B.: Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism, Environ. Int., 139, 105558, https://doi.org/10.1016/j.envint.2020.105558, 2020.
Chow, J. C., Doraiswamy, P., Watson, J. G., Chen, L. W. A., Ho, S. S. H., and Sodeman, D. A.: Advances in Integrated and Continuous Measurements for Particle Mass and Chemical Composition, Japca J. Air Waste Ma., 58, 141–163, https://doi.org/10.3155/1047-3289.58.2.141, 2008.
Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep, K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V., Freedman, G., Hubbell, B., Jobling, A., Kan, H., Knibbs, L., Liu, Y., Martin, R., Morawska, L., Pope, C. A., III, Shin, H., Straif, K., Shaddick, G., Thomas, M., van Dingenen, R., van Donkelaar, A., Vos, T., Murray, C. J. L., and Forouzanfar, M. H.: Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015, Lancet, 389, 1907–1918, https://doi.org/10.1016/s0140-6736(17)30505-6, 2017.
Dabek-Zlotorzynska, E., Dann, T. F., Martinelango, P. K., Celo, V., Brook, J. R., Mathieu, D., Ding, L., and Austin, C. C.: Canadian National Air Pollution Surveillance (NAPS) PM2.5 speciation program: Methodology and PM2.5 chemical composition for the years 2003–2008, Atmos. Environ., 45, 673-686, https://doi.org/10.1016/j.atmosenv.2010.10.024, 2011.
Davies, J.: CEPA – The Canadian. Environmental Protection Act, JAPCA, 38, 1111–1113, https://doi.org/10.1080/08940630.1988.10466452, 1988.
Demerjian, K. L.: A review of national monitoring networks in North America, Atmos. Environ., 34, 1861–1884, https://doi.org/10.1016/S1352-2310(99)00452-5, 2000.
Fan, H., Zhao, C., Yang, Y., and Yang, X.: Spatio-Temporal Variations of the
Friedman, J. H.: Greedy function approximation: A gradient boosting machine, Ann. Stat., 29, 1189–1232, https://doi.org/10.1214/aos/1013203451, 2001.
Ganguly, T., Selvaraj, K. L., and Guttikunda, S. K.: National Clean Air Programme (NCAP) for Indian cities: Review and outlook of clean air action plans, Atmospheric Environment X, 8, 100096, https://doi.org/10.1016/j.aeaoa.2020.100096, 2020.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Goff, J. A.: Saturation pressure of water on the new Kelvin temperature scale, Transactions of the American Society of Heating and Ventilating Engineers, 63, 347–354, 1957.
Granier, C., Bessagnet, B., Bond, T., D'Angiola, A., Denier van der Gon, H., Frost, G. J., Heil, A., Kaiser, J. W., Kinne, S., and Klimont, Z.: Evolution of anthropogenic and biomass burning emissions of air pollutants at global and regional scales during the 1980–2010 period, Climatic Change, 109, 163–190, https://doi.org/10.1007/s10584-011-0154-1, 2011.
Green, D. and Fuller, G. W.: The implications of tapered element oscillating microbalance (TEOM) software configuration on particulate matter measurements in the UK and Europe, Atmos. Environ., 40, 5608–5616, https://doi.org/10.1016/j.atmosenv.2006.04.052, 2006.
Gui, K., Che, H., Zeng, Z., Wang, Y., Zhai, S., Wang, Z., Luo, M., Zhang, L., Liao, T., and Zhao, H.: Construction of a virtual PM2.5 observation network in China based on high-density surface meteorological observations using the Extreme Gradient Boosting model, Environ. Int., 141, 105801, https://doi.org/10.1016/j.envint.2020.105801, 2020.
Guo, S., Hu, M., Zamora, M. L., Peng, J., Shang, D., Zheng, J., Du, Z., Wu, Z., Shao, M., Zeng, L., Molina, M. J., and Zhang, R.: Elucidating severe urban haze formation in China, P. Natl. Acad. Sci. USA, 111, 17373–17378, https://doi.org/10.1073/pnas.1419604111, 2014.
Hall, E. and Gilliam, J.: Reference and Equivalent Methods Used to Measure National Ambient Air Quality Standards (NAAQS) Criteria Air Pollutants – Volume I, https://doi.org/10.13140/RG.2.1.3471.8329, 2016.
Hammer, M. S., van Donkelaar, A., Li, C., Lyapustin, A., Sayer, A. M., Hsu, N. C., Levy, R. C., Garay, M. J., Kalashnikova, O. V., and Kahn, R. A.: Global estimates and long-term trends of fine particulate matter concentrations (1998–2018), Environ. Sci. Technol., 54, 7879–7890, https://doi.org/10.1021/acs.est.0c01764, 2020.
Hao, H., Wang, K., Wu, G., Liu, J., and Li, J.: PM2.5 concentrations based on near-surface visibility at 4011 sites in the Northern Hemisphere from 1959 to 2022, National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/Atmos.tpdc.301127, 2024.
Hastie, T. and Tibshirani, R.: Generalized Additive Models: Some Applications, J. Am. Stat. Assoc., 82, 371–386, https://doi.org/10.1080/01621459.1987.10478440, 1987.
He, Q., Gao, K., Zhang, L., Song, Y., and Zhang, M.: Satellite-derived 1-km estimates and long-term trends of PM2.5 concentrations in China from 2000 to 2018, Environ. Int., 156, 106726, https://doi.org/10.1016/j.envint.2021.106726, 2021.
Hsu, N., Lee, J., Sayer, A., Carletta, N., Chen, S. H., Tucker, C., Holben, B., and Tsay, S. C.: Retrieving near-global aerosol loading over land and ocean from AVHRR, J. Geophys. Res-Atmos., 122, 9968–9989, https://doi.org/10.1002/2017JD026932, 2017.
Huang, W., Tan, J., Kan, H., Zhao, N., Song, W., Song, G., Chen, G., Jiang, L., Jiang, C., and Chen, R.: Visibility, air quality and daily mortality in Shanghai, China, Sci. Total Environ., 407, 3295–3300, https://doi.org/10.1016/j.scitotenv.2009.02.019, 2009.
Husar, R. B., Husar, J. D., and Martin, L.: Distribution of continental surface aerosol extinction based on visual range data, Atmos. Environ., 34, 5067–5078, https://doi.org/10.1016/s1352-2310(00)00324-1, 2000.
Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019.
Jin, C., Wang, Y., Li, T., and Yuan, Q.: Global validation and hybrid calibration of CAMS and MERRA-2 PM2.5 reanalysis products based on OpenAQ platform, Atmos. Environ., 274, 118972, https://doi.org/10.1016/j.atmosenv.2022.118972, 2022.
Kammann, E. E. and Wand, M. P.: Geoadditive Models, J. R. Stat. Soc. C-Appl., 52, 1–18, https://doi.org/10.1111/1467-9876.00385, 2003.
Kendall, M. G.: Rank correlation methods, Griffin, https://psycnet.apa.org/record/1948-15040-000 (last access: 30 August 2024), 1948.
Kim, K.-H., Kabir, E., and Kabir, S.: A review on the human health impact of airborne particulate matter, Environ. Int., 74, 136–143, https://doi.org/10.1016/j.envint.2014.10.005, 2015.
Kuklinska, K., Wolska, L., and Namiesnik, J.: Air quality policy in the US and the EU – a review, Atmos. Pollut. Res., 6, 129–137, https://doi.org/10.5094/APR.2015.015, 2015.
Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., and Pozzer, A.: The contribution of outdoor air pollution sources to premature mortality on a global scale, Nature, 525, 367–371, https://doi.org/10.1038/nature15371, 2015.
Li, C., Martin, R. V., Boys, B. L., van Donkelaar, A., and Ruzzante, S.: Evaluation and application of multi-decadal visibility data for trend analysis of atmospheric haze, Atmos. Chem. Phys., 16, 2435–2457, https://doi.org/10.5194/acp-16-2435-2016, 2016.
Li, C., Martin, R. V., van Donkelaar, A., Boys, B. L., Hammer, M. S., Xu, J.-W., Marais, E. A., Reff, A., Strum, M., and Ridley, D. A.: Trends in chemical composition of global and regional population-weighted fine particulate matter estimated for 25 years, Environ. Sci. Technol., 51, 11185–11195, https://doi.org/10.1021/acs.est.7b02530, 2017.
Li, J., Han, X., Jin, M., Zhang, X., and Wang, S.: Globally analysing spatiotemporal trends of anthropogenic PM2.5 concentration and population's PM2.5 exposure from 1998 to 2016, Environ. Int., 128, 46–62, https://doi.org/10.1016/j.envint.2019.04.026, 2019.
Li, J., Garshick, E., Hart, J. E., Li, L., Shi, L., Al-Hemoud, A., Huang, S., and Koutrakis, P.: Estimation of ambient PM2.5 in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing, Environ. Int., 151, 106445, https://doi.org/10.1016/j.envint.2021.106445, 2021.
Li, J., Carlson, B. E., Yung, Y. L., Lv, D., Hansen, J., Penner, J. E., Liao, H., Ramaswamy, V., Kahn, R. A., Zhang, P., Dubovik, O., Ding, A., Lacis, A. A., Zhang, L., and Dong, Y.: Scattering and absorbing aerosols in the climate system, Nat. Rev. Earth. Environ., 3, 363–379, https://doi.org/10.1038/s43017-022-00296-7, 2022.
Li, S., Chen, L., Huang, G., Lin, J., Yan, Y., Ni, R., Huo, Y., Wang, J., Liu, M., and Weng, H.: Retrieval of surface PM2.5 mass concentrations over North China using visibility measurements and GEOS-Chem simulations, Atmos. Environ., 222, 117121, https://doi.org/10.1016/j.atmosenv.2019.117121, 2020.
Liao, H., Chang, W., and Yang, Y.: Climatic Effects of Air Pollutants over China: A Review, Adv. Atmos. Sci., 32, 115–139, https://doi.org/10.1007/s00376-014-0013-x, 2015.
Lim, C.-H., Ryu, J., Choi, Y., Jeon, S. W., and Lee, W.-K.: Understanding global PM2.5 concentrations and their drivers in recent decades (1998–2016), Environ. Int., 144, 106011, https://doi.org/10.1016/j.envint.2020.106011, 2020.
Liu, M., Bi, J., and Ma, Z.: Visibility-based PM2.5 concentrations in China: 1957–1964 and 1973–2014, Environ. Sci. Technol., 51, 13161–13169, https://doi.org/10.1021/acs.est.7b03468, 2017.
Liu, M., Huang, X., Song, Y., Tang, J., Cao, J., Zhang, X., Zhang, Q., Wang, S., Xu, T., Kang, L., Cai, X., Zhang, H., Yang, F., Wang, H., Yu, J. Z., Lau, A. K. H., He, L., Huang, X., Duan, L., Ding, A., Xue, L., Gao, J., Liu, B., and Zhu, T.: Ammonia emission control in China would mitigate haze pollution and nitrogen deposition, but worsen acid rain, P. Natl. Acad. Sci. USA, 116, 7760–7765, https://doi.org/10.1073/pnas.1814880116, 2019.
Ma, Z., Hu, X., Sayer, A. M., Levy, R., Zhang, Q., Xue, Y., Tong, S., Bi, J., Huang, L., and Liu, Y.: Satellite-based spatiotemporal trends in PM2.5 concentrations: China, 2004–2013, Environ. Health Persp., 124, 184–192, https://doi.org/10.1289/ehp.1409481, 2016.
Mandal, S., Madhipatla, K. K., Guttikunda, S., Kloog, I., Prabhakaran, D., Schwartz, J. D., and Team, G. H. I.: Ensemble averaging based assessment of spatiotemporal variations in ambient PM2.5 concentrations over Delhi, India, during 2010–2016, Atmos. Environ., 224, 117309, https://doi.org/10.1016/j.atmosenv.2020.117309, 2020.
Mann, H. B.: Nonparametric Tests Against Trend, Econometrica, 13, 245–259, https://doi.org/10.2307/1907187, 1945.
Meng, X., Hand, J. L., Schichtel, B. A., and Liu, Y.: Space-time trends of PM2.5 constituents in the conterminous United States estimated by a machine learning approach, 2005–2015, Environ. Int., 121, 1137–1147, https://doi.org/10.1016/j.envint.2018.10.029, 2018.
Miao, Y. and Liu, S.: Linkages between aerosol pollution and planetary boundary layer structure in China, Sci. Total Environ., 650, 288–296, https://doi.org/10.1016/j.scitotenv.2018.09.032, 2019.
Molnár, A., Mészáros, E., Imre, K., and Rüll, A.: Trends in visibility over Hungary between 1996 and 2002, Atmos. Environ., 42, 2621–2629, https://doi.org/10.1016/j.atmosenv.2007.05.012, 2008.
Nagaraja Rao, C., Stowe, L., and McClain, E.: Remote sensing of aerosols over the oceans using AVHRR data Theory, practice and applications, Int. J. Remote Sens., 10, 743–749, https://doi.org/10.1080/01431168908903915, 1989.
Pant, P., Lal, R. M., Guttikunda, S. K., Russell, A. G., Nagpure, A. S., Ramaswami, A., and Peltier, R. E.: Monitoring particulate matter in India: recent trends and future outlook, Air Qual. Atmos. Hlth., 12, 45–58, https://doi.org/10.1007/s11869-018-0629-6, 2019.
Park, A., Guillas, S., and Petropavlovskikh, I.: Trends in stratospheric ozone profiles using functional mixed models, Atmos. Chem. Phys., 13, 11473–11501, https://doi.org/10.5194/acp-13-11473-2013, 2013.
Polansky, L. and Robbins, M. M.: Generalized additive mixed models for disentangling long-term trends, local anomalies, and seasonality in fruit tree phenology, Ecol. Evol., 3, 3141–3151, https://doi.org/10.1002/ece3.707, 2013.
Pui, D. Y. H., Chen, S.-C., and Zuo, Z.: PM2.5 in China: Measurements, sources, visibility and health effects, and mitigation, Particuology, 13, 1–26, https://doi.org/10.1016/j.partic.2013.11.001, 2014.
Qi, G., Wei, W., Wang, Z., Wang, Z., and Wei, L.: The spatial-temporal evolution mechanism of PM2.5 concentration based on China's climate zoning, J. Environ. Manage., 325, 116671, https://doi.org/10.1016/j.jenvman.2022.116671, 2023.
Ramanathan, V., Crutzen, P. J., Kiehl, J., and Rosenfeld, D.: Aerosols, climate, and the hydrological cycle, Science, 294, 2119–2124, https://doi.org/10.1126/science.1064034, 2001.
Ravindra, K., Rattan, P., Mor, S., and Aggarwal, A. N.: Generalized additive models: Building evidence of air pollution, climate change and human health, Environ. Int., 132, 104987, https://doi.org/10.1016/j.envint.2019.104987, 2019.
Ravindra, K., Vakacherla, S., Singh, T., Upadhya, A. R., Rattan, P., and Mor, S.: Long-term trend of PM2.5 over five Indian megacities using a new statistical approach, Stoch. Env. Res. Risk A., 38, 715–725, https://doi.org/10.1007/s00477-023-02595-x, 2024.
Samset, B. H., Lund, M. T., Bollasina, M., Myhre, G., and Wilcox, L.: Emerging Asian aerosol patterns, Nat. Geosci., 12, 582–584, https://doi.org/10.1038/s41561-019-0424-5, 2019.
Sen, P. K.: Estimates of the Regression Coefficient Based on Kendall's Tau, J. Am. Stat. Assoc., 63, 1379–1389, https://doi.org/10.1080/01621459.1968.10480934, 1968.
Shen, Z., Cao, J., Zhang, L., Zhang, Q., Huang, R.-J., Liu, S., Zhao, Z., Zhu, C., Lei, Y., and Xu, H.: Retrieving historical ambient PM2.5 concentrations using existing visibility measurements in Xi'an, Northwest China, Atmos. Environ., 126, 15–20, https://doi.org/10.1016/j.atmosenv.2015.11.040, 2016.
Shi, Y., Matsunaga, T., Yamaguchi, Y., Li, Z., Gu, X., and Chen, X.: Long-term trends and spatial patterns of satellite-retrieved PM2.5 concentrations in South and Southeast Asia from 1999 to 2014, Sci. Total Environ., 615, 177–186, https://doi.org/10.1016/j.scitotenv.2017.09.241, 2018.
Singh, A., Avis, W. R., and Pope, F. D.: Visibility as a proxy for air quality in East Africa, Environ. Res. Lett., 15, 084002, https://doi.org/10.1088/1748-9326/ab8b12, 2020.
Singh, V., Singh, S., and Biswal, A.: Exceedances and trends of particulate matter (PM2.5) in five Indian megacities, Sci. Total Environ., 750, 141461, https://doi.org/10.1016/j.scitotenv.2020.141461, 2021.
Smith, A., Lott, N., and Vose, R.: The Integrated Surface Database: Recent Developments and Partnerships, B. Am. Meteorol. Soc., 92, 704–708, https://doi.org/10.1175/2011BAMS3015.1, 2011.
Su, L., Gao, C., Ren, X., Zhang, F., Cao, S., Zhang, S., Chen, T., Liu, M., Ni, B., and Liu, M.: Understanding the spatial representativeness of air quality monitoring network and its application to PM2.5 in the mainland China, Geosci. Front., 13, 101370, https://doi.org/10.1016/j.gsf.2022.101370, 2022.
Sun, E., Xu, X., Che, H., Tang, Z., Gui, K., An, L., Lu, C., and Shi, G.: Variation in MERRA-2 aerosol optical depth and absorption aerosol optical depth over China from 1980 to 2017, J. Atmos. Sol.-Terr. Phy., 186, 8–19, https://doi.org/10.1016/j.jastp.2019.01.019, 2019.
Tan, S., Wang, Y., Yuan, Q., Zheng, L., Li, T., Shen, H., and Zhang, L.: Reconstructing global PM2.5 monitoring dataset from OpenAQ using a two-step spatio-temporal model based on SES-IDW and LSTM, Environ. Res. Lett., 17, 034014, https://doi.org/10.1088/1748-9326/ac52c9, 2022.
Teixeira, A.: Analyse discrimante par arbre de décision binaire (CART: Classification And Regression Tree), Rev. Mal. Respir., 21, 1174–1176, https://doi.org/10.1016/S0761-8425(04)71596-X, 2004.
Theil, H.: A Rank-Invariant Method of Linear and Polynomial Regression Analysis, in: Henri Theil's Contributions to Economics and Econometrics: Econometric Theory and Methodology, edited by: Raj, B. and Koerts, J., Springer Netherlands, Dordrecht, 345–381, https://doi.org/10.1007/978-94-011-2546-8_20, 1992.
Van Donkelaar, A., Martin, R. V., and Park, R. J.: Estimating ground-level PM2.5 using aerosol optical depth determined from satellite remote sensing, J. Geophys. Res., 111, D21201, https://doi.org/10.1029/2005JD006996, 2006.
Van Donkelaar, A., Martin, R. V., Brauer, M., Kahn, R., Levy, R., Verduzco, C., and Villeneuve, P. J.: Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application, Environ. Health Persp., 118, 847–855, https://doi.org/10.1289/ehp.0901623, 2010.
Van Donkelaar, A., Martin, R. V., Brauer, M., and Boys, B. L.: Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter, Environ. Health Persp., 123, 135–143, https://doi.org/10.1289/ehp.1408646, 2015.
Van Donkelaar, A., Martin, R. V., Brauer, M., Hsu, N. C., Kahn, R. A., Levy, R. C., Lyapustin, A., Sayer, A. M., and Winker, D. M.: Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors, Environ. Sci. Technol., 50, 3762–3772, https://doi.org/10.1021/acs.est.5b05833, 2016.
van Donkelaar, A., Hammer, M. S., Bindle, L., Brauer, M., Brook, J. R., Garay, M. J., Hsu, N. C., Kalashnikova, O. V., Kahn, R. A., Lee, C., Levy, R. C., Lyapustin, A., Sayer, A. M., and Martin, R. V.: Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty, Environ. Sci. Technol., 55, 15287–15300, https://doi.org/10.1021/acs.est.1c05309, 2021.
Verbeke, G. and Lesaffre, E.: A Linear Mixed-Effects Model with Heterogeneity in the Random-Effects Population, J. Am. Stat. Assoc., 91, 217–221, https://doi.org/10.1080/01621459.1996.10476679, 1996.
Viana, M., Kuhlbusch, T. A. J., Querol, X., Alastuey, A., Harrison, R. M., Hopke, P. K., Winiwarter, W., Vallius, A., Szidat, S., Prevot, A. S. H., Hueglin, C., Bloemen, H., Wahlin, P., Vecchi, R., Miranda, A. I., Kasper-Giebl, A., Maenhaut, W., and Hitzenberger, R.: Source apportionment of particulate matter in Europe: A review of methods and results, J. Aerosol Sci., 39, 827–849, https://doi.org/10.1016/j.jaerosci.2008.05.007, 2008.
Wang, K., Dickinson, R. E., and Liang, S.: Clear Sky Visibility Has Decreased over Land Globally from 1973 to 2007, Science, 323, 1468–1470, https://doi.org/10.1126/science.1167549, 2009.
Wang, K. C., Dickinson, R. E., Su, L., and Trenberth, K. E.: Contrasting trends of mass and optical properties of aerosols over the Northern Hemisphere from 1992 to 2011, Atmos. Chem. Phys., 12, 9387–9398, https://doi.org/10.5194/acp-12-9387-2012, 2012.
Wang, Q., Kwan, M.-P., Zhou, K., Fan, J., Wang, Y., and Zhan, D.: The impacts of urbanization on fine particulate matter (PM2.5) concentrations: Empirical evidence from 135 countries worldwide, Environ. Pollut., 247, 989–998, https://doi.org/10.1016/j.envpol.2019.01.086, 2019.
Wang, Z., Li, J., Wang, Z., Yang, W., Tang, X., Ge, B., Yan, P., Zhu, L., Chen, X., Chen, H., Wand, W., Li, J., Liu, B., Wang, X., Wand, W., Zhao, Y., Lu, N., and Su, D.: Modeling study of regional severe hazes over mid-eastern China in January 2013 and its implications on pollution prevention and control, Sci. China Earth Sci., 57, 3–13, https://doi.org/10.1007/s11430-013-4793-0, 2014.
Wei, J., Li, Z., Peng, Y., and Sun, L.: MODIS Collection 6.1 aerosol optical depth products over land and ocean: validation and comparison, Atmos. Environ., 201, 428–440, https://doi.org/10.1016/j.atmosenv.2018.12.004, 2019a.
Wei, J., Huang, W., Li, Z., Xue, W., Peng, Y., Sun, L., and Cribb, M.: Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach, Remote Sens. Environ., 231, 111221, https://doi.org/10.1016/j.rse.2019.111221, 2019b.
Wei, J., Li, Z., Cribb, M., Huang, W., Xue, W., Sun, L., Guo, J., Peng, Y., Li, J., Lyapustin, A., Liu, L., Wu, H., and Song, Y.: Improved 1 km resolution PM2.5 estimates across China using enhanced space–time extremely randomized trees, Atmos. Chem. Phys., 20, 3273–3289, https://doi.org/10.5194/acp-20-3273-2020, 2020.
Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and Cribb, M.: Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications, Remote Sens. Environ., 252, 112136, https://doi.org/10.1016/j.rse.2020.112136, 2021.
Wood, S. N., Pya, N., and Säfken, B.: Smoothing Parameter and Model Selection for General Smooth Models, J. Am. Stat. Assoc., 111, 1548–1563, https://doi.org/10.1080/01621459.2016.1180986, 2016.
Wu, J., Zheng, H., Zhe, F., Xie, W., and Song, J.: Study on the relationship between urbanization and fine particulate matter (PM2.5) concentration and its implication in China, J. Clean. Prod., 182, 872–882, https://doi.org/10.1016/j.jclepro.2018.02.060, 2018.
Wu, W. and Zhang, Y.: Effects of particulate matter (PM2.5) and associated acidity on ecosystem functioning: response of leaf litter breakdown, Environ. Sci. Pollut. R., 25, 30720–30727, https://doi.org/10.1007/s11356-018-2922-1, 2018.
Xue, T., Zheng, Y., Tong, D., Zheng, B., Li, X., Zhu, T., and Zhang, Q.: Spatiotemporal continuous estimates of PM2.5 concentrations in China, 2000–2016: A machine learning method with inputs from satellites, chemical transport model, and ground observations, Environ. Int., 123, 345–357, https://doi.org/10.1016/j.envint.2018.11.075, 2019.
Yang, X., Zhao, C., Yang, Y., Yan, X., and Fan, H.: Statistical aerosol properties associated with fire events from 2002 to 2019 and a case analysis in 2019 over Australia, Atmos. Chem. Phys., 21, 3833–3853, https://doi.org/10.5194/acp-21-3833-2021, 2021.
Zeng, Z., Gui, K., Wang, Z., Luo, M., Geng, H., Ge, E., An, J., Song, X., Ning, G., and Zhai, S.: Estimating hourly surface PM2.5 concentrations across China from high-density meteorological observations by machine learning, Atmos. Res., 254, 105516, https://doi.org/10.1016/j.atmosres.2021.105516, 2021.
Zhang, Q., Zheng, Y., Tong, D., Shao, M., Wang, S., Zhang, Y., Xu, X., Wang, J., He, H., Liu, W., Ding, Y., Lei, Y., Li, J., Wang, Z., Zhang, X., Wang, Y., Cheng, J., Liu, Y., Shi, Q., Yan, L., Geng, G., Hong, C., Li, M., Liu, F., Zheng, B., Cao, J., Ding, A., Gao, J., Fu, Q., Huo, J., Liu, B., Liu, Z., Yang, F., He, K., and Hao, J.: Drivers of improved PM2.5 air quality in China from 2013 to 2017, P. Natl. A. Sci. USA, 116, 24463–24469, https://doi.org/10.1073/pnas.1907956116, 2019.
Zhang, S., Wu, J., Fan, W., Yang, Q., and Zhao, D.: Review of aerosol optical depth retrieval using visibility data, Earth-Sci. Rev., 200, 102986, https://doi.org/10.1016/j.earscirev.2019.102986, 2020.
Zhang, Z., Wu, W., Wei, J., Song, Y., Yan, X., Zhu, L., and Wang, Q.: Aerosol optical depth retrieval from visibility in China during 1973–2014, Atmos. Environ., 171, 38–48, https://doi.org/10.1016/j.atmosenv.2017.09.004, 2017.
Zhao, B., Su, Y., He, S., Zhong, M., and Cui, G.: Evolution and comparative assessment of ambient air quality standards in China, J. Integr. Environ. Sci., 13, 85–102, https://doi.org/10.1080/1943815X.2016.1150301, 2016.
Zhao, S., Yu, Y., Yin, D., He, J., Liu, N., Qu, J., and Xiao, J.: Annual and diurnal variations of gaseous and particulate pollutants in 31 provincial capital cities based on in situ air quality monitoring data from China National Environmental Monitoring Center, Environ. Int., 86, 92–106, https://doi.org/10.1016/j.envint.2015.11.003, 2016.
Zhong, J., Zhang, X., Gui, K., Wang, Y., Che, H., Shen, X., Zhang, L., Zhang, Y., Sun, J., and Zhang, W.: Robust prediction of hourly PM2.5 from meteorological data using LightGBM, Natl. Sci. Rev., 8, nwaa307, https://doi.org/10.1093/nsr/nwaa307, 2021.
Zhong, J., Zhang, X., Gui, K., Liao, J., Fei, Y., Jiang, L., Guo, L., Liu, L., Che, H., Wang, Y., Wang, D., and Zhou, Z.: Reconstructing 6-hourly PM2.5 datasets from 1960 to 2020 in China, Earth Syst. Sci. Data, 14, 3197–3211, https://doi.org/10.5194/essd-14-3197-2022, 2022.
Short summary
In this study, daily PM2.5 concentrations are estimated from 1959 to 2022 using a machine learning method at more than 5000 terrestrial sites in the Northern Hemisphere based on hourly atmospheric visibility data, which are extracted from the Meteorological Terminal Aviation Routine Weather Report (METAR).
In this study, daily PM2.5 concentrations are estimated from 1959 to 2022 using a machine...
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