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Environmental Science & Technology
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May 9, 2025
Assessing Event-Driven Dynamics of Pesticides and Transformation Products in an Agricultural Stream Using Comprehensive Target, Suspect, and Nontarget Analysis
Daeho Kang, Daeun Yun, Kyung Hwa Cho, et al.
Chemosphere
|
February 12, 2024
Profiling emerging micropollutants in urban stormwater runoff using suspect and non-target screening via high-resolution mass spectrometry
Daeho Kang, Daeun Yun, Kyung Hwa Cho, et al.
Water Research
|
March 19, 2023
Characterization of micropollutants in urban stormwater using high-resolution monitoring and machine learning
Daeun Yun, Daeho Kang, Kyung Hwa Cho, et al.
The Science of the Total Environment
|
October 16, 2021
Analysis of micropollutants in a marine outfall using network analysis and decision tree
Sang-Soo Baek, Daeun Yun, JongCheol Pyo, et al.
Water Research
|
October 19, 2023
Automatic classification of microplastics and natural organic matter mixtures using a deep learning model
Seunghyeon Lee, Heewon Jeong, Seok Min Hong, et al.
Water Research X
|
June 14, 2024
Spatiotemporal estimation of groundwater and surface water conditions by integrating deep learning and physics-based watershed models
Soobin Kim, Eunhee Lee, Hyoun-Tae Hwang, et al.
The Science of the Total Environment
|
July 3, 2021
Monitoring the vertical distribution of HABs using hyperspectral imagery and deep learning models
Seok Min Hong, Sang-Soo Baek, Daeun Yun, et al.
Water Research
|
February 3, 2022
A novel method for micropollutant quantification using deep learning and multi-objective optimization
Daeun Yun, Daeho Kang, Jiyi Jang, et al.
The Science of the Total Environment
|
September 19, 2024
Comparative efficiency of the SWAT model and a deep learning model in estimating nitrate loads at the Tuckahoe creek watershed, Maryland
Jiye Lee, Dongho Kim, Seokmin Hong, et al.
The Science of the Total Environment
|
November 12, 2024
Corrigendum to "Comparative efficiency of the SWAT model and a deep learning model in estimating nitrate loads at the Tuckahoe creek watershed, Maryland" [Sci. Total Environ. 954 (2024) 176256]
Jiye Lee, Dongho Kim, Seokmin Hong, et al.
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Search research articles
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Showing results (1-10 of 10) with videos related to
Sort By:
Page
of 1
Environmental Science & Technology
|
May 9, 2025
Assessing Event-Driven Dynamics of Pesticides and Transformation Products in an Agricultural Stream Using Comprehensive Target, Suspect, and Nontarget Analysis
Daeho Kang, Daeun Yun, Kyung Hwa Cho, et al.
Chemosphere
|
February 12, 2024
Profiling emerging micropollutants in urban stormwater runoff using suspect and non-target screening via high-resolution mass spectrometry
Daeho Kang, Daeun Yun, Kyung Hwa Cho, et al.
Water Research
|
March 19, 2023
Characterization of micropollutants in urban stormwater using high-resolution monitoring and machine learning
Daeun Yun, Daeho Kang, Kyung Hwa Cho, et al.
The Science of the Total Environment
|
October 16, 2021
Analysis of micropollutants in a marine outfall using network analysis and decision tree
Sang-Soo Baek, Daeun Yun, JongCheol Pyo, et al.
Water Research
|
October 19, 2023
Automatic classification of microplastics and natural organic matter mixtures using a deep learning model
Seunghyeon Lee, Heewon Jeong, Seok Min Hong, et al.
Water Research X
|
June 14, 2024
Spatiotemporal estimation of groundwater and surface water conditions by integrating deep learning and physics-based watershed models
Soobin Kim, Eunhee Lee, Hyoun-Tae Hwang, et al.
The Science of the Total Environment
|
July 3, 2021
Monitoring the vertical distribution of HABs using hyperspectral imagery and deep learning models
Seok Min Hong, Sang-Soo Baek, Daeun Yun, et al.
Water Research
|
February 3, 2022
A novel method for micropollutant quantification using deep learning and multi-objective optimization
Daeun Yun, Daeho Kang, Jiyi Jang, et al.
The Science of the Total Environment
|
September 19, 2024
Comparative efficiency of the SWAT model and a deep learning model in estimating nitrate loads at the Tuckahoe creek watershed, Maryland
Jiye Lee, Dongho Kim, Seokmin Hong, et al.
The Science of the Total Environment
|
November 12, 2024
Corrigendum to "Comparative efficiency of the SWAT model and a deep learning model in estimating nitrate loads at the Tuckahoe creek watershed, Maryland" [Sci. Total Environ. 954 (2024) 176256]
Jiye Lee, Dongho Kim, Seokmin Hong, et al.
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