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Journal of Hazardous Materials
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July 11, 2025
Bridging temporal gaps: AI-based temporal downscaling of biweekly NH<sub>3</sub> to daily scale with spatial transferability
Saman Malik, Eunjin Kang, Yoojin Kang, et al.
The Science of the Total Environment
|
April 17, 2025
Developing a novel Temporal Air-quality Risk Index using LSTM autoencoder: A case study with South Korean air quality data
Hyerim Park, Wonho Sohn, Eunjin Kang, et al.
International Journal of Environmental Research and Public Health
|
August 14, 2020
Beyond Strict Regulations to Achieve Environmental and Economic Health-An Optimal PM<sub>2.5</sub> Mitigation Policy for Korea
Kyungwon Park, Taeyeon Yoon, Changsub Shim, et al.
Iscience
|
October 25, 2023
Diurnal urban heat risk assessment using extreme air temperatures and real-time population data in Seoul
Cheolhee Yoo, Jungho Im, Qihao Weng, et al.
The Science of the Total Environment
|
September 25, 2025
Aerosol optical depth retrieval from Geostationary Environment Monitoring Spectrometer (GEMS): Advancing the first hyperspectral geostationary air quality mission using deep learning
Hyunyoung Choi, Seohui Park, Jungho Im, et al.
Scientific Reports
|
March 10, 2023
Feasibility of patch-type wireless 12-lead electrocardiogram in laypersons
Sunyoung Yoon, Taerim Kim, Eunjin Kang, et al.
International Journal of Medical Sciences
|
November 7, 2017
Gender-Specific Associations between Low Skeletal Muscle Mass and Albuminuria in the Middle-Aged and Elderly Population
Hye Eun Yoon, Yunju Nam, Eunjin Kang, et al.
Environmental Science & Technology
|
May 11, 2026
Correction to "Quantifying Multi-pollutant Co-exposure via Deep Learning-Based Simultaneous Prediction Using Geostationary Satellite Data"
Eunjin Kang, Sihun Jung, Jungho Im, et al.
Environmental Science & Technology
|
March 20, 2026
Quantifying Multi-pollutant Co-exposure via Deep Learning-Based Simultaneous Prediction Using Geostationary Satellite Data
Eunjin Kang, Sihun Jung, Jungho Im, et al.
Page
of 1
Search research articles
Search
Showing results (1-10 of 9) with videos related to
Sort By:
Page
of 1
Journal of Hazardous Materials
|
July 11, 2025
Bridging temporal gaps: AI-based temporal downscaling of biweekly NH<sub>3</sub> to daily scale with spatial transferability
Saman Malik, Eunjin Kang, Yoojin Kang, et al.
The Science of the Total Environment
|
April 17, 2025
Developing a novel Temporal Air-quality Risk Index using LSTM autoencoder: A case study with South Korean air quality data
Hyerim Park, Wonho Sohn, Eunjin Kang, et al.
International Journal of Environmental Research and Public Health
|
August 14, 2020
Beyond Strict Regulations to Achieve Environmental and Economic Health-An Optimal PM<sub>2.5</sub> Mitigation Policy for Korea
Kyungwon Park, Taeyeon Yoon, Changsub Shim, et al.
Iscience
|
October 25, 2023
Diurnal urban heat risk assessment using extreme air temperatures and real-time population data in Seoul
Cheolhee Yoo, Jungho Im, Qihao Weng, et al.
The Science of the Total Environment
|
September 25, 2025
Aerosol optical depth retrieval from Geostationary Environment Monitoring Spectrometer (GEMS): Advancing the first hyperspectral geostationary air quality mission using deep learning
Hyunyoung Choi, Seohui Park, Jungho Im, et al.
Scientific Reports
|
March 10, 2023
Feasibility of patch-type wireless 12-lead electrocardiogram in laypersons
Sunyoung Yoon, Taerim Kim, Eunjin Kang, et al.
International Journal of Medical Sciences
|
November 7, 2017
Gender-Specific Associations between Low Skeletal Muscle Mass and Albuminuria in the Middle-Aged and Elderly Population
Hye Eun Yoon, Yunju Nam, Eunjin Kang, et al.
Environmental Science & Technology
|
May 11, 2026
Correction to "Quantifying Multi-pollutant Co-exposure via Deep Learning-Based Simultaneous Prediction Using Geostationary Satellite Data"
Eunjin Kang, Sihun Jung, Jungho Im, et al.
Environmental Science & Technology
|
March 20, 2026
Quantifying Multi-pollutant Co-exposure via Deep Learning-Based Simultaneous Prediction Using Geostationary Satellite Data
Eunjin Kang, Sihun Jung, Jungho Im, et al.
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