Open-Source, Low-Cost, In-Situ Turbidity Sensor for River Network Monitoring

Abstract

Fine sediment transport in rivers is important for catchment nutrient fluxes, global biogeochemical cycles, water quality and pollution in riverine, coastal and marine ecosystems. Monitoring of suspended sediment in rivers with current sensors is challenging and expensive and most monitoring setups are restricted to few single site measurements. To better understand the spatial heterogeneity of fine sediment sources and transport in river networks there is a need for new smart water turbidity sensing that is multi-site, accurate and affordable. In this work, we have created such a sensor, which detects scattered light from an LED source using two detectors in a control volume, and can be placed in a river. We compare several replicates of our sensor to different commercial turbidity probes in a mixing tank experiment using two sediment types over a wide range of typical concentrations observed in rivers. Our results show that we can achieve precise and reproducible turbidity measurements in the 0-4000 NTU or 0-16g/L range. Our sensor can also be used directly as a suspended sediment sensor and bypass an unnecessary calibration to Formazin. The developed turbidity sensor is much cheaper than existing options of comparable quality and is especially intended for distributed sensing across river networks.

Ötz-T: 3D-printed open-source turbidity sensor with Arduino shield for suspended sediment monitoring

“…The open-source, low-cost, in situ turbidity sensor for river network monitoring [12] which we developed in our group meets the criteria of sufficient range and accuracy, and suitability for river deployment. This sensor was calibrated for the full 0–4000 NTU range, in addition to 0–16 g/L range, and errors were quantified in laboratory tests.…”

“…These three points were chosen because we wanted to limit our exposure to formazin, which is a known carcinogen, and we expected the Ötztal Ache’s turbidity to be within this range since 90% of the time this river has an SSC of <5 mg/L [7] . In addition, our last open-source sensor [12] had 12 formazin calibration points and we wanted to see if we could obtain similarly accurate data with less calibration points. The form of the model that converts the digital IR light output to NTU is: where is the digital reading output by the IR detector’s ADC, and the calibration coefficients are listed in Table 2 .…”

“…The residual standard error of this model is 2.26 NTU ( ) and the P-value . This model was built using only the backscattering detector, other alternatives which combine detectors at different angles were compared in [12] .…”

“…The sensor we have presented here is an improvement from the previous open-source turbidity sensor [12] . This new version now incorporates a temperature and pressure sensor to monitor river stage and water temperature.…”

Short-term prediction of stream turbidity using surrogate data and a meta-model approach

“…illuminance, irradiance, solar exposure) and turbidity, despite its potential utility, may not be as straight forward as that between rainfall, water level and turbidity. To our knowledge, the predictive ability of such light-associated variables as potential covariates of turbidity is yet to be explored beyond their potential use in determining light-attenuation (Droujko & Molnar 2022), but is worth investigating given, for example, that low-cost 𝑖𝑛 − 𝑠𝑖𝑡𝑢 sensors that can measure both water temperature and illuminance are currently available (e.g. HOBO MX2202 data loggers; https://www.onsetcomp.com/).…”