IOT Sensor Technologies to Manage Water Security and Provide High Integrity Carbon Credit Monitoring, Reporting and Verification
The Virridy Lume is a sensitive, continuous, in-situ, remotely reporting tryptophan-like fluorescence sensor for semi-quantification of fecal contamination as E. coli.
Water Quality Monitoring
Got Poop? Find out if you're drinking or swimming in contaminated water.
Virridy has developed the Lume for the measurement of fecal contamination risk in water. The Lume uses tryptophan-like fluorescence (TLF) combined with machine learning analytics to estimate E. coli contamination with remote reporting to alert water stakeholders.
This sensor technology is the first to demonstrate a fully integrated in-situ, autonomous, internet-connected fecal risk sensor. Compared to other technologies on the market, the Lume:
- Outputs quantification of E. coli levels and not simply relative fluorescent units;
- Does not requirer regular calibration and cleaning;
- Is designed continuous in-situ use;
- Retails for considerably lower cost.
Our innovative design approach includes:
- Applying our experience in IOT systems design to create a compact, rugged device designed for continuous, remote use;
- Using advanced LEDs and silicon photomultipliers to reduce cost, complexity and increase stability, and
- A quantification analytical system that compensates for background noise, biofouling and signal drift through fusing a network of sensor data with remote sensing and other parameters (streamflow, rainfall, landcover, etc.) in order to accurately estimate fecal contamination risk and change.
Get in touch to be a Lume beta tester!
Water Service Monitoring
Virridy's universal groundwater pump monitor combines a satellite-connected IOT gateway with sensors monitoring pump runtime and function.
Virridy’s IOT technologies support proactive environmental resource management with and among communities globally. We develop decision support information to enable early interventions and proactive water security management. Sensor data is transmitted to a cloud-based machine learning analysis platform for local and regional scale decision support.
Our gateway has been deployed across Ethiopia, Kenya, Nigeria and the United States, monitoring millions of people's water supplies and trigger operation and maintenance activities while providing the Digital Monitoring, Reporting and Verification data to support high integrity carbon credit generation.
Patents
- Thomas et al., US Patent-Pending, 2023, “DMRV Fusion Networks” Patent Family:
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- Drinking Water Treatment
- In-Stream Water Quality
- Wildfire Impact Prediction
- Water Quality Parameter Prediction
- Water Quality Variability Attribution
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- Bedell, E., Fankhauser, K., Sharpe, T., Wilson D., Thomas, E., Alarm Threshold Microbial Fluorimeter and Methods, US Patent 11,506,606 B2. Issued November 22, 2022.
- Wilson, D., Coyle, J., Thomas., E., Croshere, S., Machine Learning Techniques for Improved Water Service Delivery, US Patent 11,507,861 B2. Issued November 22, 2022.
- Fleming, M., Spiller, K., Thomas, E., System and Methods for Operating a Microcomputer in Sleep-Mode and Awake-Mode with Low Power Event Processing, United States Patent US 10,564,701. Issued Feb. 18, 2020.
- Thomas, E, Fleming, M., Distributed low-power monitoring system, United States Patent US 9,077,783 B2, Issued July 7, 2015.
Papers
Ecklu, J., Thomas, E., Digital Monitoring, Reporting, and Verification Technologies Supporting Carbon Credit-Generating Water Security Programs: State of the Art and Technology Roadmap Environmental Science and Technology Letters, 2025
Bedell, E., Harmon, O., Fankhauser, K., Shivers, Z., Thomas, E., A continuous, in-situ, near-time fluorescence sensor coupled with a machine learning model for detection of fecal contamination risk in drinking water: Design, characterization and field validation, Water Research, 2022
Fankhauser, K., Macharia D., et al. Estimating groundwater use and demand in arid Kenya through assimilation of satellite data and in-situ sensors with machine learning toward drought early action," Science of the Total Environment, 2022.
Thomas, E., Wilson, D., Kathuni, S., Libey A., Chintalpati, P., Coyle, J. “A contribution to drought resilience in East Africa through groundwater pump management informed by ensemble machine learning, in-situ instrumentation and remote sensing”, Science of The Total Environment, 2021.
Thomas, E., Brown, J., “Using Feedback to Improve Accountability in Global Environmental Health and Engineering, Environmental Science and Technology, 2020.
Thomas, E., et al., The Drought Resilience Impact Platform (DRIP): Improving Water Security Through Actionable Water Management Insights, Frontiers in Climate, V2. A6. 2020.
Bedell, E.; Sharpe, T.; Purvis, T.; Brown, J.; Thomas, E. Demonstration of Tryptophan-Like Fluorescence Sensor Concepts for Fecal Exposure Detection in Drinking Water in Remote and Resource Constrained Settings. Sustainability 2020, 12, 3768.
Evan Thomas, Elizabeth Jordan, Karl Linden, Beshah Mogesse, Tamene Hailu, Hussein Jirma, Patrick Thomson, Johanna Koehler, Greg Collins, Reducing drought emergencies in the Horn of Africa, Science of The Total Environment, Volume 727, 2020, 138772, ISSN 0048-9697,https://doi.org/10.1016/j.scitotenv.2020.138772.