Alexei Lyapustin

25.8k total citations · 14 hit papers
213 papers, 16.9k citations indexed

About

Alexei Lyapustin is a scholar working on Global and Planetary Change, Atmospheric Science and Environmental Engineering. According to data from OpenAlex, Alexei Lyapustin has authored 213 papers receiving a total of 16.9k indexed citations (citations by other indexed papers that have themselves been cited), including 173 papers in Global and Planetary Change, 144 papers in Atmospheric Science and 57 papers in Environmental Engineering. Recurrent topics in Alexei Lyapustin's work include Atmospheric aerosols and clouds (133 papers), Atmospheric chemistry and aerosols (107 papers) and Atmospheric Ozone and Climate (68 papers). Alexei Lyapustin is often cited by papers focused on Atmospheric aerosols and clouds (133 papers), Atmospheric chemistry and aerosols (107 papers) and Atmospheric Ozone and Climate (68 papers). Alexei Lyapustin collaborates with scholars based in United States, Israel and China. Alexei Lyapustin's co-authors include Yujie Wang, Sergey Korkin, R. C. Levy, Itai Kloog, Ralph A. Kahn, Joel Schwartz, Thomas Hilker, Petros Koutrakis, A. M. Sayer and István László and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Alexei Lyapustin

208 papers receiving 16.6k citations

Hit Papers

Global Estimates of Fine Particulate Matter using a Combi... 2011 2026 2016 2021 2016 2019 2020 2018 2020 250 500 750

Peers

Alexei Lyapustin
Zhanqing Li United States
R. C. Levy United States
Ralph A. Kahn United States
Jun Wang United States
Sue Grimmond United Kingdom
Dar A. Roberts United States
Zhanqing Li United States
Alexei Lyapustin
Citations per year, relative to Alexei Lyapustin Alexei Lyapustin (= 1×) peers Zhanqing Li

Countries citing papers authored by Alexei Lyapustin

Since Specialization
Citations

This map shows the geographic impact of Alexei Lyapustin's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Alexei Lyapustin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexei Lyapustin more than expected).

Fields of papers citing papers by Alexei Lyapustin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alexei Lyapustin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Alexei Lyapustin. The network helps show where Alexei Lyapustin may publish in the future.

Co-authorship network of co-authors of Alexei Lyapustin

This figure shows the co-authorship network connecting the top 25 collaborators of Alexei Lyapustin. A scholar is included among the top collaborators of Alexei Lyapustin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Alexei Lyapustin. Alexei Lyapustin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Korkin, Sergey, A. M. Sayer, Amir Ibrahim, & Alexei Lyapustin. (2025). A practical guide to coding line-by-line trace gas absorption in Earth's atmosphere. Journal of Quantitative Spectroscopy and Radiative Transfer. 337. 109345–109345. 1 indexed citations
2.
Choi, Myungje, Alexei Lyapustin, Yujie Wang, et al.. (2024). Calibration of Maxar Constellation Over Libya-4 Site Using MAIAC Technique. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 17. 5460–5469. 1 indexed citations
3.
Ohneiser, Kevin, et al.. (2023). The impact of different aerosol layering conditions on the high-resolution MODIS/MAIAC AOD retrieval bias: The uncertainty analysis. Atmospheric Environment. 309. 119930–119930. 13 indexed citations
4.
Korkin, Sergey & Alexei Lyapustin. (2023). Radiative interaction of atmosphere and surface: Write-up with elements of code. Journal of Quantitative Spectroscopy and Radiative Transfer. 309. 108663–108663. 1 indexed citations
5.
Wei, Jing, Zhanqing Li, Xi Chen, et al.. (2023). Separating Daily 1 km PM2.5 Inorganic Chemical Composition in China since 2000 via Deep Learning Integrating Ground, Satellite, and Model Data. Environmental Science & Technology. 57(46). 18282–18295. 97 indexed citations breakdown →
6.
Wei, Jing, Zhanqing Li, Alexei Lyapustin, et al.. (2023). First close insight into global daily gapless 1 km PM2.5 pollution, variability, and health impact. Nature Communications. 14(1). 8349–8349. 85 indexed citations
7.
Mhawish, Alaa, Ali Omar, Meytar Sorek‐Hamer, et al.. (2022). Intercomparison of Aerosol Types Reported as Part of Aerosol Product Retrieval over Diverse Geographic Regions. Remote Sensing. 14(15). 3667–3667. 12 indexed citations
8.
Go, Sujung, Alexei Lyapustin, Gregory L. Schuster, et al.. (2022). Inferring iron-oxide species content in atmospheric mineral dust from DSCOVR EPIC observations. Atmospheric chemistry and physics. 22(2). 1395–1423. 22 indexed citations
9.
Korkin, Sergey, A. M. Sayer, Amir Ibrahim, & Alexei Lyapustin. (2021). A practical guide to writing a radiative transfer code. Computer Physics Communications. 271. 108198–108198. 6 indexed citations
11.
Ford, Bonne, et al.. (2020). The Relationship Between MAIAC Smoke Plume Heights and Surface PM. Geophysical Research Letters. 47(17). 11 indexed citations
12.
Yazdi, Mahdieh Danesh, Zheng Kuang, Konstantina Dimakopoulou, et al.. (2020). Predicting Fine Particulate Matter (PM2.5) in the Greater London Area: An Ensemble Approach using Machine Learning Methods. Remote Sensing. 12(6). 914–914. 91 indexed citations
13.
Ploton, Pierre, Frédéric Mortier, Maxime Réjou‐Méchain, et al.. (2020). Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nature Communications. 11(1). 4540–4540. 371 indexed citations breakdown →
14.
Marshak, Alexander, J. R. Herman, Simon Carn, et al.. (2018). Earth Observations from DSCOVR EPIC Instrument. Bulletin of the American Meteorological Society. 99(9). 1829–1850. 126 indexed citations
15.
Martins, Vitor S., Evlyn Márcia Leão de Moraes Novo, Alexei Lyapustin, et al.. (2018). Seasonal and interannual assessment of cloud cover and atmospheric constituents across the Amazon (2000–2015): Insights for remote sensing and climate analysis. ISPRS Journal of Photogrammetry and Remote Sensing. 145. 309–327. 74 indexed citations
16.
Sayer, A. M., N. Christina Hsu, Jaehwa Lee, et al.. (2018). Validation of SOAR VIIRS Over‐Water Aerosol Retrievals and Context Within the Global Satellite Aerosol Data Record. Journal of Geophysical Research Atmospheres. 123(23). 50 indexed citations
17.
Levy, R. C., S. Mattoo, Virginia Sawyer, et al.. (2018). Exploring systematic offsets between aerosol products from the two MODIS sensors. Atmospheric measurement techniques. 11(7). 4073–4092. 79 indexed citations
18.
Beloconi, Anton, Nektarios Chrysoulakis, Alexei Lyapustin, Jürg Utzinger, & Penelope Vounatsou. (2018). Bayesian geostatistical modelling of PM10 and PM2.5 surface level concentrations in Europe using high-resolution satellite-derived products. Environment International. 121(Pt 1). 57–70. 58 indexed citations
19.
Lyapustin, Alexei, et al.. (2013). Evaluation of High-Resolution MAIAC Aerosol Retrievals Using DRAGON Field Campaign Data. AGU Fall Meeting Abstracts. 2013. 1 indexed citations
20.
Lyapustin, Alexei, Yun Wang, Christina Hsu, et al.. (2011). ANALYSIS OF MAIAC DUST AEROSOL RETRIEVALS FROM MODIS OVER NORTH AFRICA. SHILAP Revista de lepidopterología. 89. 5 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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