Knowledge distillation enables prediction of ring-class polycyclic aromatic hydrocarbons concentration using underwater drone

Polycyclic aromatic hydrocarbons (PAHs) in urban estuaries exhibit sharp concentration shifts during rainfall events, yet their transient redistribution and compositional restructuring remain poorly resolved due to the mismatch between laboratory specificity and field-scale monitoring frequency. Conventional chromatography provides chemical resolution but lacks temporal coverage, whereas autonomous underwater drones deliver high-frequency measurements without molecular specificity. Here we bridge this monitoring gap by transferring laboratory-derived spectral information into sensor-based field models using knowledge distillation, enabling process-resolving PAHs assessment at scale. To overcome limited sample availability under rainfall conditions, a variational autoencoder expanded 142 observations thirtyfold, stabilizing model transfer. The integrated framework achieved an R² of 0.92 for ΣPAHs, improving predictive performance by 28%. Large-scale deployment across 59,392 drone measurements revealed rainfall-triggered surges dominated by high-molecular-weight PAHs and dynamic hotspot migration within the estuary. Interpretable analyses further indicate how spectral signatures reorganize along specific sensor pathways under hydrological perturbation. By coupling laboratory specificity with autonomous sensing, this approach establishes a scalable strategy for resolving pollutant dynamics in rainfall-impacted urban waters.