Sean Raffuse

1.2k total citations
32 papers, 820 citations indexed

About

Sean Raffuse is a scholar working on Global and Planetary Change, Atmospheric Science and Health, Toxicology and Mutagenesis. According to data from OpenAlex, Sean Raffuse has authored 32 papers receiving a total of 820 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Global and Planetary Change, 22 papers in Atmospheric Science and 9 papers in Health, Toxicology and Mutagenesis. Recurrent topics in Sean Raffuse's work include Atmospheric chemistry and aerosols (22 papers), Fire effects on ecosystems (17 papers) and Atmospheric and Environmental Gas Dynamics (14 papers). Sean Raffuse is often cited by papers focused on Atmospheric chemistry and aerosols (22 papers), Fire effects on ecosystems (17 papers) and Atmospheric and Environmental Gas Dynamics (14 papers). Sean Raffuse collaborates with scholars based in United States, Canada and New Zealand. Sean Raffuse's co-authors include Narasimhan K. Larkin, Tara Strand, Susan O’Neill, Robert C. Solomon, Michael Jerrett, Ira B. Tager, John R. Balmes, Maya Petersen, Gabriele Pfister and Philip E. Morefield and has published in prestigious journals such as Journal of Geophysical Research Atmospheres, Environmental Science & Technology and Remote Sensing of Environment.

In The Last Decade

Sean Raffuse

28 papers receiving 793 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sean Raffuse United States 14 538 476 347 260 114 32 820
Ju Li China 15 487 0.9× 390 0.8× 240 0.7× 407 1.6× 46 0.4× 51 814
Yoojin Kang South Korea 8 285 0.5× 214 0.4× 143 0.4× 233 0.9× 92 0.8× 18 486
Roberto Bianconi Italy 15 338 0.6× 431 0.9× 181 0.5× 260 1.0× 25 0.2× 29 651
Andres Schmidt United States 13 370 0.7× 258 0.5× 123 0.4× 125 0.5× 10 0.1× 29 550
Carlos Román‐Cascón Spain 15 340 0.6× 438 0.9× 152 0.4× 355 1.4× 7 0.1× 38 646
Minghui Tao China 16 426 0.8× 432 0.9× 194 0.6× 141 0.5× 8 0.1× 40 627
Ming‐Tung Chuang Taiwan 19 528 1.0× 859 1.8× 697 2.0× 253 1.0× 9 0.1× 49 1.1k
Moon-Soo Park South Korea 16 486 0.9× 540 1.1× 254 0.7× 289 1.1× 6 0.1× 56 797
Guicai Ning China 22 918 1.7× 918 1.9× 721 2.1× 526 2.0× 5 0.0× 42 1.5k
Shuenn-Chin Chang Taiwan 19 407 0.8× 705 1.5× 640 1.8× 268 1.0× 12 0.1× 28 950

Countries citing papers authored by Sean Raffuse

Since Specialization
Citations

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

Fields of papers citing papers by Sean Raffuse

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sean Raffuse

This figure shows the co-authorship network connecting the top 25 collaborators of Sean Raffuse. A scholar is included among the top collaborators of Sean Raffuse 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 Sean Raffuse. Sean Raffuse 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.
Zhang, Zhichao, Irva Hertz‐Picciotto, Daniel J. Tancredi, et al.. (2025). Gestational exposure to particulate matter from urban wildfires is associated with changes in circulating oxylipins but not flame retardants 7 to 13 months post-exposure. Environment International. 200. 109468–109468. 1 indexed citations
2.
Raffuse, Sean, Susan O’Neill, & Rebecca J. Schmidt. (2024). A model for rapid PM 2.5 exposure estimates in wildfire conditions using routinely available data: rapidfire v0.1.3. Geoscientific model development. 17(1). 381–397. 3 indexed citations
3.
Hand, J. L., A. J. Prenni, Sean Raffuse, et al.. (2024). Spatial and Seasonal Variability of Remote and Urban Speciated Fine Particulate Matter in the United States. Journal of Geophysical Research Atmospheres. 129(23). e2024JD042579–e2024JD042579. 3 indexed citations
4.
O’Neill, Susan, Minghui Diao, Sean Raffuse, et al.. (2021). A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires. Journal of the Air & Waste Management Association. 71(7). 791–814. 31 indexed citations
5.
Zhang, Xiaolu, et al.. (2021). Intercomparison of thermal–optical carbon measurements by Sunset and Desert Research Institute (DRI) analyzers using the IMPROVE_A protocol. Atmospheric measurement techniques. 14(5). 3217–3231. 12 indexed citations
6.
7.
Gorham, K., Sean Raffuse, Nicole P. Hyslop, & W. H. White. (2020). Comparison of recent speciated PM2.5 data from collocated CSN and IMPROVE measurements. Atmospheric Environment. 244. 117977–117977. 17 indexed citations
8.
Yao, Jiayun, Michael Bräuer, Sean Raffuse, & Sarah B. Henderson. (2018). Machine Learning Approach To Estimate Hourly Exposure to Fine Particulate Matter for Urban, Rural, and Remote Populations during Wildfire Seasons. Environmental Science & Technology. 52(22). 13239–13249. 36 indexed citations
9.
Hyslop, Nicole P., et al.. (2018). Routine Speciated Particulate Monitoring in the United States: the CSN and IMPROVE Networks. AGU Fall Meeting Abstracts. 2018.
10.
Yao, Jiayun, Michael Bräuer, Sean Raffuse, & Sarah B. Henderson. (2018). A Machine Learning Approach to Estimate Hourly Exposure to Wildfire Smoke for Urban, Rural, and Remote Population. ISEE Conference Abstracts. 2018(1). 1 indexed citations
11.
Yao, Jiayun, Sean Raffuse, Michael Bräuer, et al.. (2017). Predicting the minimum height of forest fire smoke within the atmosphere using machine learning and data from the CALIPSO satellite. Remote Sensing of Environment. 206. 98–106. 54 indexed citations
12.
Raffuse, Sean, et al.. (2012). Modeling Regional Air Quality Impacts from Indonesian Biomass Burning. AGU Fall Meeting Abstracts. 2012. 1 indexed citations
13.
Raffuse, Sean, et al.. (2012). An Evaluation of Modeled Plume Injection Height with Satellite-Derived Observed Plume Height. Atmosphere. 3(1). 103–123. 37 indexed citations
14.
Raffuse, Sean, et al.. (2011). New Methods for Modeling and Monitoring Wildfires Using Multiple Data Sources: Smartfire v2. AGU Fall Meeting Abstracts. 2011. 1 indexed citations
15.
Raffuse, Sean, et al.. (2010). Developing an Improved Wildland Fire Emissions Inventory. AGU Fall Meeting Abstracts. 2010. 1 indexed citations
16.
Solomon, Robert C., et al.. (2009). Uncertainties in fuel loading and fire consumption calculations and the Smoke and Emissions Model Intercomparison Project. AGU Fall Meeting Abstracts. 2009. 1 indexed citations
17.
Larkin, Narasimhan K., Susan O’Neill, Robert C. Solomon, et al.. (2009). The BlueSky smoke modeling framework. International Journal of Wildland Fire. 18(8). 906–920. 176 indexed citations
18.
Strand, T., et al.. (2008). The BlueSky Smoke Modeling Framework. AGUFM. 2008. 1 indexed citations
19.
Raffuse, Sean, et al.. (2008). A Daily Wildland Fire Greenhouse Gas Emission Inventory for the Conterminous United States. AGU Fall Meeting Abstracts. 2008. 1 indexed citations
20.
Brown, Steve, et al.. (2007). Source Apportionment of Fine Particulate Matter in Phoenix, AZ, Using Positive Matrix Factorization. Journal of the Air & Waste Management Association. 57(6). 741–752. 40 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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