Electrochemical pH Mapping, CaCO₃ Plume Characterization, and GPR-Based Spatiotemporal Modeling via a Custom-Engineered

Overview

Ocean acidification threatens marine ecosystems at an accelerating rate, yet localized ocean alkalinity enhancement remains poorly characterized due to the absence of integrated sensing and delivery platforms. Existing approaches rely on broad-scale chemical intervention without real-time spatial feedback, leaving the spatiotemporal dynamics of targeted buffer deployment largely unstudied. This project engineered a fully custom submersible ROV to autonomously map pH fields, deploy acid-sensitive CaCO₃ capsules, and model neutralization plume dynamics using machine learning. The ROV system integrated an Atlas electrochemical pH probe, a camera system, and a servo-actuated trap-door dispensing mechanism, all controlled via a custom-designed PCB routing signals across an Arduino Mega, Raspberry Pi, and PC control station. Acid-sensitive CaCO₃ capsules coated with sodium alginate, cross-linked with calcium chloride and pectin, were designed to degrade only below pH 7.7. Trials were conducted in a controlled 50-gallon aquatic testbed at pH 6.7 across temperatures of 20°C and 25°C. Computer vision tracked plume spread while pH was logged spatiotemporally across a defined 4x4 grid. Gaussian Process Regression and Random Forest models were trained on collected ΔpH distributions. Deployment raised mean pH from 7.60 ± 0.02 to 7.82 ± 0.03 within 10 cm, with peak pH = 0.22 at 152 seconds. Higher temperature reduced time-to-peak by 24.3%. GPR substantially outperformed Random Forest (MAE = 0.035, R² = 0.88 vs. MAE = 0.062, R² = 0.61). These results validate that the ROV can be used to study localized OAE, and that even limited electrochemical sensor data can still produce accurate pH maps.

Competition history

• CSEF 2026 Environmental Engineering (Senior Division) · Entry S-11-14

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