Algae blooms in lakes and reservoirs release trace chemicals that make tap water taste and smell earthy or musty. This review argues that purpose-built AI models can spot these compounds early and guide treatment from the reservoir to the faucet, replacing today's slow, reactive approach.
1
Three rapid detection methods are compared for trace-level algal odorants: portable gas chromatography-mass spectrometry, electronic noses, and biomarker fluorescence.
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Different dedicated models suit each stage: mechanism-constrained temporal models for source water, extracting-mapping models for treatment plants, and kinetic-hydraulic models for distribution pipes.
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Linking identification and treatment in a feedback loop enables adaptive treatment and proactive monitoring, though data scarcity, stage-isolated models, and limited online sensing remain obstacles.
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