Youhua Tang

7.8k total citations
97 papers, 4.4k citations indexed

About

Youhua Tang is a scholar working on Atmospheric Science, Global and Planetary Change and Health, Toxicology and Mutagenesis. According to data from OpenAlex, Youhua Tang has authored 97 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 80 papers in Atmospheric Science, 65 papers in Global and Planetary Change and 34 papers in Health, Toxicology and Mutagenesis. Recurrent topics in Youhua Tang's work include Atmospheric chemistry and aerosols (79 papers), Atmospheric aerosols and clouds (37 papers) and Air Quality and Health Impacts (34 papers). Youhua Tang is often cited by papers focused on Atmospheric chemistry and aerosols (79 papers), Atmospheric aerosols and clouds (37 papers) and Air Quality and Health Impacts (34 papers). Youhua Tang collaborates with scholars based in United States, China and Japan. Youhua Tang's co-authors include Gregory R. Carmichael, David G. Streets, Tianfeng Chai, Adrian Sandu, Jung‐Hun Woo, Daniel Tong, Itsushi Uno, Qiang Zhang, Li Pan and V. Ramanathan and has published in prestigious journals such as Science, Journal of Geophysical Research Atmospheres and Environmental Science & Technology.

In The Last Decade

Youhua Tang

92 papers receiving 4.2k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Youhua Tang United States 37 3.7k 2.6k 1.8k 732 346 97 4.4k
Steven E. Peckham United States 25 5.4k 1.5× 4.1k 1.6× 2.1k 1.2× 1.3k 1.7× 321 0.9× 40 6.0k
Brian K. Eder United States 21 3.6k 1.0× 2.5k 0.9× 2.0k 1.1× 1.1k 1.4× 363 1.0× 33 4.2k
C. Forster Germany 33 5.5k 1.5× 4.8k 1.8× 1.4k 0.8× 472 0.6× 193 0.6× 72 6.1k
Arjo Segers Netherlands 31 2.8k 0.8× 2.4k 0.9× 956 0.5× 681 0.9× 203 0.6× 110 3.6k
Daewon W. Byun United States 27 3.5k 1.0× 1.8k 0.7× 2.6k 1.5× 1.2k 1.6× 682 2.0× 57 4.3k
Gerhard Wotawa Austria 29 3.6k 1.0× 4.2k 1.6× 997 0.6× 518 0.7× 143 0.4× 57 5.2k
Johannes Flemming United Kingdom 33 3.1k 0.8× 2.8k 1.0× 1.1k 0.6× 596 0.8× 171 0.5× 93 3.8k
Robert Vautard France 38 3.8k 1.0× 2.6k 1.0× 2.1k 1.2× 951 1.3× 508 1.5× 86 4.9k
R. C. Easter United States 48 8.0k 2.2× 6.7k 2.5× 2.6k 1.5× 672 0.9× 276 0.8× 117 8.5k
Oriol Jorba Spain 36 2.0k 0.6× 1.6k 0.6× 1.3k 0.7× 774 1.1× 326 0.9× 112 2.9k

Countries citing papers authored by Youhua Tang

Since Specialization
Citations

This map shows the geographic impact of Youhua Tang'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 Youhua Tang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Youhua Tang more than expected).

Fields of papers citing papers by Youhua Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Youhua Tang. 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 Youhua Tang. The network helps show where Youhua Tang may publish in the future.

Co-authorship network of co-authors of Youhua Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Youhua Tang. A scholar is included among the top collaborators of Youhua Tang 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 Youhua Tang. Youhua Tang 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
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Li, Yunyao, Daniel Tong, Timothy DelSole, et al.. (2024). Multiagency Ensemble Forecast of Wildfire Air Quality in the United States: Toward Community Consensus of Early Warning. Bulletin of the American Meteorological Society. 105(6). E991–E1003. 2 indexed citations
3.
Campbell, Patrick, et al.. (2023). NOAA’s Global Forecast System Data in the Cloud for Community Air Quality Modeling. Atmosphere. 14(7). 1110–1110. 2 indexed citations
4.
Li, Yunyao, Daniel Tong, Siqi Ma, et al.. (2023). Impacts of estimated plume rise on PM 2.5 exceedance prediction during extreme wildfire events: a comparison of three schemes (Briggs, Freitas, and Sofiev). Atmospheric chemistry and physics. 23(5). 3083–3101. 17 indexed citations
5.
Campbell, Patrick, Youhua Tang, Pius Lee, et al.. (2022). Development and evaluation of an advanced National Air Quality Forecasting Capability using the NOAA Global Forecast System version 16. Geoscientific model development. 15(8). 3281–3313. 15 indexed citations
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Tang, Youhua, Huisheng Bian, Zhining Tao, et al.. (2021). Comparison of chemical lateral boundary conditions for air quality predictions over the contiguous United States during pollutant intrusion events. Atmospheric chemistry and physics. 21(4). 2527–2550. 6 indexed citations
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Tang, Youhua, Mariusz Pagowski, Tianfeng Chai, et al.. (2017). A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods. Geoscientific model development. 10(12). 4743–4758. 45 indexed citations
11.
Hong, Chaopeng, Qiang Zhang, Yang Zhang, et al.. (2017). Multi-year downscaling application of two-way coupled WRF v3.4 and CMAQ v5.0.2 over east Asia for regional climate and air quality modeling: model evaluation and aerosol direct effects. Geoscientific model development. 10(6). 2447–2470. 58 indexed citations
12.
Tang, Youhua, Mariusz Pagowski, Tianfeng Chai, et al.. (2017). 3D-Var versus Optimal Interpolation for Aerosol Assimilation: a Case Studyover the Contiguous United States. 4 indexed citations
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Huang, Min, Daniel Tong, Li Pan, et al.. (2015). Toward enhanced capability for detecting and predicting dust events in the western United States: the Arizona case study. Atmospheric chemistry and physics. 15(21). 12595–12610. 20 indexed citations
15.
Kim, Hyun Cheol, et al.. (2015). Evaluation of modeled surface ozone biases as a function of cloud cover fraction. Geoscientific model development. 8(9). 2959–2965. 11 indexed citations
16.
Cleary, Patricia, Laura Schulz, J. S. Schafer, et al.. (2015). Ozone distributions over southern Lake Michigan: comparisons between ferry-based observations, shoreline-based DOAS observations and model forecasts. Atmospheric chemistry and physics. 15(9). 5109–5122. 28 indexed citations
17.
Chai, Tianfeng, Hyun Cheol Kim, P. Lee, et al.. (2013). Evaluation of the United States National Air Quality Forecast Capability experimental real-time predictions in 2010 using Air Quality System ozone and NO 2 measurements. Geoscientific model development. 6(5). 1831–1850. 64 indexed citations
18.
Chung, C. E., V. Ramanathan, Gregory R. Carmichael, et al.. (2010). Anthropogenic aerosol radiative forcing in Asia derived from regional models with atmospheric and aerosol data assimilation. Atmospheric chemistry and physics. 10(13). 6007–6024. 67 indexed citations
19.
Huang, Min, Gregory R. Carmichael, B. Adhikary, et al.. (2010). Impacts of transported background ozone on California air quality during the ARCTAS-CARB period – a multi-scale modeling study. Atmospheric chemistry and physics. 10(14). 6947–6968. 43 indexed citations
20.
Mena‐Carrasco, Marcelo, Gregory R. Carmichael, J. Elliott Campbell, et al.. (2009). Assessing the regional impacts of Mexico City emissions on air quality and chemistry. Atmospheric chemistry and physics. 9(11). 3731–3743. 31 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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