ArticleWater environment research : a research publication of the Water Environment Federation2026
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.
Article in Water environment research : a research publication of the Water Environment Federation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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
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