Joo-Hyon Kang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Joo-Hyon Kang has a total of 57 co-authors. Here are the top co-authors by number of shared publications:
Dong Hoon Lee
Department of Civil and Environmental Engineering, Dongguk University-Seoul, Seoul, 04620, South Korea
12 shared publications- Joseph Albert Mendoza
School of Chemical, Biological, and Materials Engineering and Sciences, Mapua University, Intramuros, Manila, Philippines
6 shared publications Min Jeong Ban
Department of Civil and Environmental Engineering, Dongguk University, Seoul 04620, South Korea
5 shared publicationsJin Hwi Kim
4 shared publications- Jin Hwi Kim
Future and Fusion Lab of Architectural, Civil and Environmental Engineering, Korea University-Seoul, Seoul 02841, Republic of Korea
3 shared publications Keugtae Kim
Dongguk Univ Seoul, Dept Biol & Environm Sci, Seoul 10326, South Korea
3 shared publications
Latest Publications
- 4 February 2026
- Published by Elsevier in Journal of Hazardous Materials
- Vol. 504, 141381
Temporal fusion transformer-based forecasting of COVID-19 infection trends using environmental indicators
- 4 February 2026
- Published by Elsevier in Journal of Hazardous Materials
- Vol. 504, 141381
Temporal fusion transformer-based forecasting of COVID-19 infection trends using environmental indicators
- 23 February 2025
- Published by Elsevier in Water Research
- Vol. 278, 123373
Comparative assessment of sewer sampling methods for infectious disease surveillance: Insights from transport modeling and simulations of SARS-CoV-2 emissions
- 23 February 2025
- Published by Elsevier in Water Research
- Vol. 278, 123373
Comparative assessment of sewer sampling methods for infectious disease surveillance: Insights from transport modeling and simulations of SARS-CoV-2 emissions
- 31 December 2024
- Published by Elsevier in Chemosphere
- Vol. 370, 144041
Assessing the environmental determinants of micropollutant contamination in streams using explainable machine learning and network analysis
- 31 December 2024
- Published by Elsevier in Chemosphere
- Vol. 370, 144041
Assessing the environmental determinants of micropollutant contamination in streams using explainable machine learning and network analysis
- 16 July 2024
- Published by Elsevier in Science of The Total Environment
- Vol. 948, 174755
Machine learning approaches to identify spatial factors and their influential distances for heavy metal contamination in downstream sediment
- 16 July 2024
- Published by Elsevier in Science of The Total Environment
- Vol. 948, 174755
Machine learning approaches to identify spatial factors and their influential distances for heavy metal contamination in downstream sediment
- 2 September 2022
- Published by Elsevier in Environmental Pollution
- Vol. 312, 120086
Identifying the acute toxicity of contaminated sediments using machine learning models
- 2 September 2022
- Published by Elsevier in Environmental Pollution
- Vol. 312, 120086
Identifying the acute toxicity of contaminated sediments using machine learning models
- 1 August 2022
- Published by Elsevier in Journal of Environmental Management
- Vol. 320, 115806
Optimal allocation and operation of sewer monitoring sites for wastewater-based disease surveillance: A methodological proposal
- 1 August 2022
- Published by Elsevier in Journal of Environmental Management
- Vol. 320, 115806
Optimal allocation and operation of sewer monitoring sites for wastewater-based disease surveillance: A methodological proposal
- Open Access
- 10 November 2021
- Published by MDPI AG in Sustainability
- Vol. 13 (22) , 12413
HSPF-Based Assessment of Inland Nutrient Source Control Strategies to Reduce Algal Blooms in Streams in Response to Future Climate Changes
The HSPF model was modified to improve the growth-temperature formulation of phytoplankton and used to simulate Chl-a concentrations at the outlet of the Seom River watershed in Korea from 2025 to 2050 under four climate change scenarios: RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5. The mean and median Chl-a concentrations increased by 5–10% and 23–29%, respectively, and the number of algal outbreak cases per year (defined as a day with Chl-a concentration ≥100 µg/L) decreased by 31–88% relative to the current values (2011–2015). Among the climate change scenarios, RCP 2.6 (stringent) showed the largest number of algal outbreak cases, mainly because of the largest yearly variability of precipitation and TP load. For each climate change scenario, three nutrient load reduction scenarios were in the HSPF simulation, and their efficiencies in reducing algal blooms were determined. Nonpoint source reduction in TP and TN from urban land, agricultural land, and grassland by 50% (S1) and controlling the effluent TP concentration of wastewater treatment plants (WWTPs) to 0.1 mg/L (S2) decreased algal outbreaks by 20–58% and 44–100%, respectively. The combination of effluent TP control of WWTPs during summer and S1 was the most effective management scenario; it could almost completely prevent algal outbreaks. This study demonstrates the cost effectiveness of using a season-based pollutant management strategy for controlling algal blooms.
- 10 November 2021
- Published by MDPI AG in Sustainability
- Vol. 13 (22) , 12413
Open AccessHSPF-Based Assessment of Inland Nutrient Source Control Strategies to Reduce Algal Blooms in Streams in Response to Future Climate Changes
The HSPF model was modified to improve the growth-temperature formulation of phytoplankton and used to simulate Chl-a concentrations at the outlet of the Seom River watershed in Korea from 2025 to 2050 under four climate change scenarios: RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5. The mean and median Chl-a concentrations increased by 5–10% and 23–29%, respectively, and the number of algal outbreak cases per year (defined as a day with Chl-a concentration ≥100 µg/L) decreased by 31–88% relative to the current values (2011–2015). Among the climate change scenarios, RCP 2.6 (stringent) showed the largest number of algal outbreak cases, mainly because of the largest yearly variability of precipitation and TP load. For each climate change scenario, three nutrient load reduction scenarios were in the HSPF simulation, and their efficiencies in reducing algal blooms were determined. Nonpoint source reduction in TP and TN from urban land, agricultural land, and grassland by 50% (S1) and controlling the effluent TP concentration of wastewater treatment plants (WWTPs) to 0.1 mg/L (S2) decreased algal outbreaks by 20–58% and 44–100%, respectively. The combination of effluent TP control of WWTPs during summer and S1 was the most effective management scenario; it could almost completely prevent algal outbreaks. This study demonstrates the cost effectiveness of using a season-based pollutant management strategy for controlling algal blooms.
- 1 October 2020
- Published by Desalination Publications in Desalination and Water Treatment
- Vol. 200, 351-358
Comparative analysis of the photocatalytic removal of methylene blue and pyrene on TiO2 and WO3/TiO2 particles
- 1 October 2020
- Published by Desalination Publications in Desalination and Water Treatment
- Vol. 200, 351-358
Comparative analysis of the photocatalytic removal of methylene blue and pyrene on TiO2 and WO3/TiO2 particles
- 1 January 2020
- Published by Elsevier in Ecological Modelling
- Vol. 415
Automatic calibration and improvements on an instream chlorophyll a simulation in the HSPF model
- 1 January 2020
- Published by Elsevier in Ecological Modelling
- Vol. 415
Automatic calibration and improvements on an instream chlorophyll a simulation in the HSPF model