Youn-Seo Koo

759 total citations
45 papers, 618 citations indexed

About

Youn-Seo Koo is a scholar working on Atmospheric Science, Health, Toxicology and Mutagenesis and Global and Planetary Change. According to data from OpenAlex, Youn-Seo Koo has authored 45 papers receiving a total of 618 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Atmospheric Science, 23 papers in Health, Toxicology and Mutagenesis and 14 papers in Global and Planetary Change. Recurrent topics in Youn-Seo Koo's work include Atmospheric chemistry and aerosols (25 papers), Air Quality and Health Impacts (21 papers) and Air Quality Monitoring and Forecasting (13 papers). Youn-Seo Koo is often cited by papers focused on Atmospheric chemistry and aerosols (25 papers), Air Quality and Health Impacts (21 papers) and Air Quality Monitoring and Forecasting (13 papers). Youn-Seo Koo collaborates with scholars based in South Korea, United States and Hong Kong. Youn-Seo Koo's co-authors include Ki‐Hyun Kim, Eui-Chan Jeon, Jin‐Seok Han, Sung‐Tae Kim, Jae-Bum Lee, Jong‐Hun Kim, Hae‐Kwan Cheong, Chang‐Hoi Ho, Danny D. Reible and Eui-Chan Jeon and has published in prestigious journals such as Atmospheric Environment, International Journal of Environmental Research and Public Health and Boundary-Layer Meteorology.

In The Last Decade

Youn-Seo Koo

40 papers receiving 536 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Youn-Seo Koo South Korea 14 358 341 221 150 91 45 618
Erin F. Katz United States 14 478 1.3× 224 0.7× 228 1.0× 53 0.4× 76 0.8× 23 645
Deepak Singh India 10 452 1.3× 259 0.8× 319 1.4× 92 0.6× 25 0.3× 32 626
Daocheng Gong China 13 544 1.5× 613 1.8× 305 1.4× 111 0.7× 30 0.3× 41 780
Eduardo Monteiro Martins Brazil 14 415 1.2× 280 0.8× 212 1.0× 43 0.3× 32 0.4× 42 649
Remzi Seyfioğlu Türkiye 10 416 1.2× 276 0.8× 206 0.9× 33 0.2× 51 0.6× 11 625
Kil-Choo Moon South Korea 8 539 1.5× 574 1.7× 251 1.1× 118 0.8× 22 0.2× 15 730
Madeleine Strum United States 14 615 1.7× 573 1.7× 315 1.4× 210 1.4× 17 0.2× 20 915
Sjaak Slanina China 9 720 2.0× 698 2.0× 284 1.3× 204 1.4× 29 0.3× 12 917
Michael R. Giordano United States 12 490 1.4× 566 1.7× 305 1.4× 274 1.8× 13 0.1× 28 845
Qing’e Sha China 14 439 1.2× 459 1.3× 215 1.0× 66 0.4× 21 0.2× 34 684

Countries citing papers authored by Youn-Seo Koo

Since Specialization
Citations

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

Fields of papers citing papers by Youn-Seo Koo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Youn-Seo Koo. 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 Youn-Seo Koo. The network helps show where Youn-Seo Koo may publish in the future.

Co-authorship network of co-authors of Youn-Seo Koo

This figure shows the co-authorship network connecting the top 25 collaborators of Youn-Seo Koo. A scholar is included among the top collaborators of Youn-Seo Koo 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 Youn-Seo Koo. Youn-Seo Koo 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.
Koo, Youn-Seo, et al.. (2024). Development of PM2.5 Forecast Model Combining ConvLSTM and DNN in Seoul. Atmosphere. 15(11). 1276–1276. 3 indexed citations
2.
Koo, Youn-Seo, Yunsoo Choi, & Chang‐Hoi Ho. (2023). Air Quality Forecasting Using Big Data and Machine Learning Algorithms. Asia-Pacific Journal of Atmospheric Sciences. 59(5). 529–530. 4 indexed citations
3.
Lee, Jae-Bum, et al.. (2022). Development of a deep neural network for predicting 6 h average PM 2.5 concentrations up to 2 subsequent days using various training data. Geoscientific model development. 15(9). 3797–3813. 11 indexed citations
4.
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7.
Koo, Youn-Seo, et al.. (2020). A Development of PM Concentration Reanalysis Method using CMAQ with Surface Data Assimilation and MAIAC AOD in Korea. Journal of Korean Society for Atmospheric Environment. 36(4). 558–573. 13 indexed citations
8.
Koo, Youn-Seo, et al.. (2016). The Effect of Dust Emissions on PM<sub>10</sub> Concentration in East Asia. Journal of Korean Society for Atmospheric Environment. 32(1). 32–45. 8 indexed citations
9.
Koo, Youn-Seo, et al.. (2013). A Review of the Estimation Methodology of Methane Emission in a Landfill using Inverse Modeling Technique. 12(3). 111–123. 2 indexed citations
10.
Koo, Youn-Seo, et al.. (2013). A Study on the Needs to Improve Effluent Quality Standard of Odor using Dispersion Model. 12(2). 83–90. 1 indexed citations
11.
Park, Sang-Jin, et al.. (2010). Odor Classification and Source Analysis using Pseudo Inverse. Journal of Korea Multimedia Society. 13(8). 1171–1182. 1 indexed citations
12.
Pal, Raktim, Ki‐Hyun Kim, Eui-Chan Jeon, et al.. (2008). Reduced sulfur compounds in ambient air surrounding an industrial region in Korea. Environmental Monitoring and Assessment. 148(1-4). 109–125. 20 indexed citations
13.
Baek, Sung‐Ok & Youn-Seo Koo. (2008). Critical Evaluation of and Suggestions for a Comprehensive Project Based on the Special Act on Seoul Metropolitan Air Quality Improvement. Journal of Korean Society for Atmospheric Environment. 24(1). 108–121. 6 indexed citations
14.
Koo, Youn-Seo, et al.. (2006). A Study on Examples Applicable to Numerical Land Cover Map Data for Atmospheric Environment Fields in the Metropolitan Area of Seoul - Real Time Calculation of Biogenic CO 2 Flux and VOC Emission Due to a Geographical Distribution of Vegetable and Analysis on Sensitivity of Air Temperature and Wind Field within MM5 -. Journal of Korean Society for Atmospheric Environment. 22(5). 661–678. 2 indexed citations
15.
Ahn, Dae‐Hee, et al.. (2006). Odor emission characteristics in livestock waste treatment facilities. Journal of Odor and Indoor Environment. 5(1). 1–9. 4 indexed citations
16.
Choi, Youn-Seok, et al.. (2006). An On-line GC Analysis of Odorous VOC and S Gas in Ambient Air from a Residential Area at Ansan City, Korea. Journal of Korean Society for Atmospheric Environment. 22(6). 929–939. 1 indexed citations
17.
Kim, Ki‐Hyun, et al.. (2006). Short-term Distributions of Reduced Sulfur Compounds in the Ambient Air Surrounding a Large Landfill Facility. Environmental Monitoring and Assessment. 121(1-3). 343–354. 23 indexed citations
18.
Kim, Ki‐Hyun, et al.. (2006). The emission characteristics and the related malodor intensities of gaseous reduced sulfur compounds (RSC) in a large industrial complex. Atmospheric Environment. 40(24). 4478–4490. 101 indexed citations
19.
Kim, Yoo-Keun, et al.. (2005). Comparison of the real-time MM5, RAMS and WRF over Seoul metropolitan area. 한국기상학회 학술대회 논문집. 318–319. 1 indexed citations
20.
Koo, Youn-Seo & Danny D. Reible. (1995). Flow and transport modeling in the sea-breeze. Part I: A modifiedE ? ? model with a non-equilibrium level 2.5 closure. Boundary-Layer Meteorology. 75(1-2). 109–140. 9 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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