Demonstrating Open Source and Low-Cost Sensors as Surrogates for River Water Quality Monitoring

High-frequency water-quality monitoring is essential to understand nutrient dynamics, contaminant “hot moments,” and ecohydrological feedback in rapidly changing river catchments. However, the cost and maintenance requirements of commercial sensors constrains their deployment, limiting use particularly in remote or logistically challenging environments. This study evaluates the feasibility of open source and lower-cost surrogate-based monitoring for nitrate and dissolved organic matter (DOM), UK. The scalability of open source sensors for broader applications, such as dense and smart sensor networks are also explored.Commercial field-deployed sondes continuously recorded optical and physicochemical variables—temperature (Tw), turbidity, dissolved oxygen (DO), electrical conductivity (EC), nitrate, and fluorescence (DOM fractions) —over seasonal cycles (sub-hourly data, i.e. 15 min frequency). Complementary low-cost sensors (e.g., EC, Tw and turbidity) captured in-situ hydrodynamic and water-quality variations. Empirical proxy models were developed to test whether low-cost parameters can represent DO, NO₃⁻ and fluorescent DOM (fDOM) dynamics, and whether turbidity, Tw, and EC enhance predictive power. Comparison with reference-grade instruments showed strong consistency, with coefficients of determination (R²) between 0.50 and 0.90 across flow regimes. Deviations during high-turbidity and runoff events highlight the need for adaptive calibration and uncertainty quantification.The results demonstrate that open source and low-cost sensor networks, when properly calibrated, can capture fine-scale variability in nutrient and DOM fluxes, offering a scalable and affordable alternative to commercial systems. With onboard telemetry the sensors allow for integrating real-time data assimilation and harmonised workflows that supports data-driven catchment management and strengthens environmental monitoring in resource-limited regions. The findings align with innovative and classical monitoring frameworks, promoting uncertainty reduction and advancing transferable methodologies for next-generation smart river monitoring.Keywords: Monitoring; Sensors; Dissolved Oxygen; Surrogates; Calibration; Water Quality