Soon-Bark Kwon

72 papers receiving 1.3k citations

Peers

Soon-Bark Kwon
Comparison fields: 5 of 113
  • Health, Toxicology and Mutagenesis 577
  • Automotive Engineering 423
  • Environmental Engineering 441
  • Atmospheric Science 197
  • Speech and Hearing 72
Replace Yu–Hsiang Cheng with:
Yu–Hsiang Cheng Taiwan
David Heist United States
Neyval Costa Reis Brazil
Mats Bohgard Sweden
Beatrice Pulvirenti Italy
Farhad Salimi Australia
Mauro Scungio Italy
Tracy L. Thatcher United States
C. Andrew Miller United States
Hossein Afshin Iran
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Citations per field
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Citations per year

Countries citing papers authored by Soon-Bark Kwon

Since Specialization
Citations

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

Fields of papers citing papers by Soon-Bark Kwon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Soon-Bark Kwon. 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 Soon-Bark Kwon. The network helps show where Soon-Bark Kwon may publish in the future.

Co-authors

The 25 scholars most cited alongside Soon-Bark Kwon, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Soon-Bark Kwon Line = papers co-authored together Soon-Bark Kwon links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 79 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2012161
2 2017139
3 201598
4 201681
5 201672
6 200359
7 200358
8 200456
9 200849
10 201337
11 201436
12 200534
13 201031
14 201630
15 201328
16 201926
17 200124
18 200224
19 201220
20 201617

About Soon-Bark Kwon

Soon-Bark Kwon is a scholar working on Automotive Engineering, Health, Toxicology and Mutagenesis, Electrical and Electronic Engineering, Computational Mechanics and Atmospheric Science, having authored 79 papers that have together received 1.4k indexed citations. Recurring topics across this work include Vehicle emissions and performance (27 papers), Air Quality and Health Impacts (24 papers), Aerosol Filtration and Electrostatic Precipitation (14 papers), Atmospheric chemistry and aerosols (12 papers), Education, Safety, and Science Studies (9 papers), Aerodynamics and Fluid Dynamics Research (9 papers), Cyclone Separators and Fluid Dynamics (8 papers) and Air Quality Monitoring and Forecasting (6 papers). The work is most often cited by research in Health, Toxicology and Mutagenesis (577 citations), Automotive Engineering (423 citations), Environmental Engineering (441 citations), Atmospheric Science (197 citations) and Speech and Hearing (72 citations). Soon-Bark Kwon has collaborated with scholars based in South Korea, Japan and Spain. Frequent co-authors include Duckshin Park, Youngmin Cho, Ki‐Tae Kim, Kyung Hwa Cho, Takafumi Seto, Sechan Park, K.W. Lee, Wootae Jeong, Chungyoon Chun and K.S. Lim. Their work appears in journals such as Journal of Aerosol Science, Aerosol Science and Technology, Environmental Science & Technology, Environmental Monitoring and Assessment and The Science of The Total Environment.

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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