Laboratory evaluation of open source and commercial electrical conductivity sensor precision and accuracy: How do they compare?

• Published in: PloS one Vol. 18; no. 5; p. e0285092
• Main Authors: Fulton, Stephanie G, Stegen, James C, Kaufman, Matthew H, Dowd, John, Thompson, Aaron
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 04.05.2023; Public Library of Science (PLoS)
• Subjects: Accuracy; Analysis; Anthropogenic factors; Calibration; Chemistry; Computer and Information Sciences; Computer software; Conductivity sensors; Configurations; Data collection; Data collection platforms; Data entry; Data management; Ecology and Environmental Sciences; Electric Conductivity; Electric properties; Electrical conductivity; Electrical resistivity; Engineering and Technology; ENVIRONMENTAL SCIENCES; Evaluation; Laboratories; Monitoring systems; Open source software; Parameter robustness; Performance evaluation; Physical Sciences; Public software; Remote sensing; Reproducibility of Results; Robotics; Salinization; Science Policy; Sensors; Software; Soils, Salts in; Surface water; Thermal conductivity; Time series; Water quality; Water quality measurements; United States; Long Island New York; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0285092

Variation in the electrical conductivity (EC) of water can reveal environmental disturbance and natural dynamics, including factors such as anthropogenic salinization. Broader application of open source (OS) EC sensors could provide an inexpensive method to measure water quality. While studies show that other water quality parameters can be robustly measured with sensors, a similar effort is needed to evaluate the performance of OS EC sensors. To address this need, we evaluated the accuracy (mean error, %) and precision (sample standard deviation) of OS EC sensors in the laboratory via comparison to EC calibration standards using three different OS and OS/commercial-hybrid (OS/C) EC sensors and data logger configurations and two commercial (C) EC sensors and data logger configurations. We also evaluated the effect of cable length (7.5 m and 30 m) and sensor calibration on OS sensor accuracy and precision. We found a significant difference between OS sensor mean accuracy (3.08%) and all other sensors combined (9.23%). Our study also found that EC sensor precision decreased across all sensor configurations with increasing calibration standard EC. There was also a significant difference between OS sensor mean precision (2.85 μS/cm) and the mean precision of all other sensors combined (9.12 μS/cm). Cable length did not affect OS sensor precision. Furthermore, our results suggest that future research should include evaluating how performance is impacted by combining OS sensors with commercial data loggers as this study found significantly decreased performance in OS/commercial-hybrid sensor configurations. To increase confidence in the reliability of OS sensor data, more studies such as ours are needed to further quantify OS sensor performance in terms of accuracy and precision across different settings and OS sensor and data collection platform configurations.