Estimating Operational Costs of Activated Carbon for Water Treatment Plants by Predicting the Rise of Harmful Algal Blooms Under Climate Change in Korea Using Machine Learning
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| Natura: | Artículo Open Access |
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Wiley
2026
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| _version_ | 1867019022195752960 |
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| author | Jayun Kim Himchan Park John J. Lenhart Jiyoung Lee Kendall Byrd Gayeon Jang Sangjun Kim Joonhong Park |
| author_facet | Jayun Kim Himchan Park John J. Lenhart Jiyoung Lee Kendall Byrd Gayeon Jang Sangjun Kim Joonhong Park Jayun Kim Himchan Park John J. Lenhart Jiyoung Lee Kendall Byrd Gayeon Jang Sangjun Kim Joonhong Park |
| collection | Wiley Open Access |
| contents | Estimating Operational Costs of Activated Carbon for Water Treatment Plants by Predicting the Rise of Harmful Algal Blooms Under Climate Change in Korea Using Machine Learning Jayun Kim Himchan Park John J. Lenhart Jiyoung Lee Kendall Byrd Gayeon Jang Sangjun Kim Joonhong Park Water Environment Research ABSTRACT The escalating frequency of harmful cyanobacterial blooms (HCBs), driven by climate change and eutrophication, poses risks to ecosystems, water resources, and public health. Given South Korea's heavy reliance on surface waters, increasingly affected by HCBs producing microcystins and taste and odor compounds (geosmin and 2‐methylisoborneol), this study used machine learning to predict cyanobacterial proliferation by 2100 under climate scenarios. It also estimates increases in treatment costs, assuming water treatment plants (WTPs) respond to increased bloom intensity solely by modifying their usage of powdered activated carbon (PAC). A random forest (RF) model trained on 28 years of Nakdong River data projected HCB occurrences under Shared Socioeconomic Pathway 5–8.5. The RF indicated significant increases in HCB magnitude and variability (cyanobacteria densities from 1.6 × 10 4 to 6.3 × 10 4 cells/mL; coefficient of variation from 1.60 to 1.77), corresponding to a 6.7°C increase in mean annual air temperature. Analysis of WTP operational data and prior studies revealed a correlation between PAC use and HCB events, suggesting the increase in HCBs necessitates significantly higher PAC doses to treat projected secondary metabolites, particularly microcystins. Under the worst‐case scenario, the projected cost burden for water treatment could triple from current levels, potentially reaching $22.1/month/household by 2100, supporting proactive implementation of advanced treatment facilities in high‐risk regions. These findings underscore the need for enhanced preparedness to address more complex HCB patterns under climate change, ensuring water safety, economic stability, and human health. Additionally, this study provides a methodological blueprint for other countries facing similar climatic and environmental challenges. 10.1002/wer.70310 http://creativecommons.org/licenses/by/4.0/ |
| doi_str_mv | 10.1002/wer.70310 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_wer_70310 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| publishDate | 2026 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Estimating Operational Costs of Activated Carbon for Water Treatment Plants by Predicting the Rise of Harmful Algal Blooms Under Climate Change in Korea Using Machine Learning Jayun Kim Himchan Park John J. Lenhart Jiyoung Lee Kendall Byrd Gayeon Jang Sangjun Kim Joonhong Park Water Environment Research Estimating Operational Costs of Activated Carbon for Water Treatment Plants by Predicting the Rise of Harmful Algal Blooms Under Climate Change in Korea Using Machine Learning Jayun Kim Himchan Park John J. Lenhart Jiyoung Lee Kendall Byrd Gayeon Jang Sangjun Kim Joonhong Park Water Environment Research ABSTRACT The escalating frequency of harmful cyanobacterial blooms (HCBs), driven by climate change and eutrophication, poses risks to ecosystems, water resources, and public health. Given South Korea's heavy reliance on surface waters, increasingly affected by HCBs producing microcystins and taste and odor compounds (geosmin and 2‐methylisoborneol), this study used machine learning to predict cyanobacterial proliferation by 2100 under climate scenarios. It also estimates increases in treatment costs, assuming water treatment plants (WTPs) respond to increased bloom intensity solely by modifying their usage of powdered activated carbon (PAC). A random forest (RF) model trained on 28 years of Nakdong River data projected HCB occurrences under Shared Socioeconomic Pathway 5–8.5. The RF indicated significant increases in HCB magnitude and variability (cyanobacteria densities from 1.6 × 10 4 to 6.3 × 10 4 cells/mL; coefficient of variation from 1.60 to 1.77), corresponding to a 6.7°C increase in mean annual air temperature. Analysis of WTP operational data and prior studies revealed a correlation between PAC use and HCB events, suggesting the increase in HCBs necessitates significantly higher PAC doses to treat projected secondary metabolites, particularly microcystins. Under the worst‐case scenario, the projected cost burden for water treatment could triple from current levels, potentially reaching $22.1/month/household by 2100, supporting proactive implementation of advanced treatment facilities in high‐risk regions. These findings underscore the need for enhanced preparedness to address more complex HCB patterns under climate change, ensuring water safety, economic stability, and human health. Additionally, this study provides a methodological blueprint for other countries facing similar climatic and environmental challenges. 10.1002/wer.70310 http://creativecommons.org/licenses/by/4.0/ |
| title | Estimating Operational Costs of Activated Carbon for Water Treatment Plants by Predicting the Rise of Harmful Algal Blooms Under Climate Change in Korea Using Machine Learning |
| topic | Water Environment Research |
| url | https://onlinelibrary.wiley.com/doi/10.1002/wer.70310 |