Virridy has developed the Lume™ to remotely measure contamination risk in water. The Lume uses fluorescence combined with machine learning analytics to estimate contamination parameters, including fecal, algeae, nutrients and PFAS, with remote reporting to alert water stakeholders.

This sensor technology is the first to demonstrate a fully integrated in-situ, autonomous, internet-connected water contamination risk sensor. Compared to other technologies on the market, the Lume™:

  • Outputs quantification of water quality levels and not simply relative fluorescent units;

  • Does not require 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.

The Lume Family

Lume TLF for Fecal Contamination

Globally, over 4.4 billion people drink fecally contaminated water. In the United States, half of America’s rivers are impaired in part by fecal contamination and do not meet the Clean Water Act standards. Fecal contamination stems from treated and untreated wastewater, organic decomposition, and agricultural runoff. Socioeconomically marginalized communities are often the most negatively impacted by poor water quality. Monitoring of the source, concentration, risk and trends of fecal contamination exposure is a major limitation toward improved water quality management both in the United States and globally.

Fecal contamination in water sources is a significant public health concern, being a major contributor to waterborne diseases. Detection methods utilizing sensors, particularly those based on tryptophan-like fluorescence, offer real-time monitoring capabilities that allow for rapid identification of contamination events. Tryptophan fluorescence serves as a biomarker indicative of fecal pollution and is often linked to the presence of fecal indicator bacteria (FIB), such as E. coli and enterococci, which are associated with human and animal waste. These methods utilize the specific fluorescence signature emitted by tryptophan when exposed to certain wavelengths, enabling differentiation between contaminated and uncontaminated samples, even at low contamination levels. Moreover, real-time water quality sensors significantly enhance the monitoring of microbial contaminants, addressing challenges posed by traditional sampling methods. The integration of these technologies into a wireless sensor network framework allows for comprehensive evaluations of water quality. Continuous data streams from these sensors enable timely responses to detected contamination, facilitating public health interventions before disease spread occurs. Furthermore, emerging approaches that employ machine learning algorithms for analyzing sensor data show promise in enhancing predictive capabilities related to fecal contamination trends over time.

The Lume uses tryptophan-like fluorescence (TLF, 280 nm excitation, 340 nm detection) combined with machine learning analytics to estimate E. coli contamination with remote reporting to alert water stakeholders. The Lume sensitivity demonstrates performance in distinguishing between between DI and E. coli concentrations in a 5% wastewater effluent dilution which was enumerated with membrane filtration and contained 10 CFU/100mL, which signifies intermediate risk contamination (p-value <0.01, t = 4.2). The R2 between E. coli concentrations in the wastewater effluent dilutions and sensor output was 0.92. There was a drop in sensor output in the range of 1000 CFU/100mL. The drop in sensor output can be attributed partly to IFE, but could also be a result of light scatter from particles. The absorbance data collected on a benchtop spectrophotometer shows increasing absorbance as the concentrations increase. The calculated corrected fluorescence due to IFE increases the R2 between E. coli and sensor output to 0.95.

Sensor response from wastewater effluent dilutions graphed continuously. Boxes indicate the interquartile range and median, whiskers indicate maximum and minimum values except where outliers are indicated. The bar between 0 and 10 CFU/100mL indicate the significant differences between indicated concentrations calculated by a Students t-test.

Sensor response from wastewater effluent dilutions graphed continuously. Boxes indicate the interquartile range and median, whiskers indicate maximum and minimum values except where outliers are indicated. The bar between 0 and 10 CFU/100mL indicate the significant differences between indicated concentrations calculated by a Students t-test.

Lume FDOM for Nutrient Contamination

Virridy is presently developing a Lume sensor using florescent dissolved organic matter (FDOM 350 nm excitation, 450 nm emission) to predict total organic cabon (TOC) and total nitrogen (TN). This product is informed by a 2024 study conducted in parntership with In-Situ Inc. using In-Situ FDOM sondes.

Accurate, high-frequency monitoring of total organic carbon (TOC) and nitrogen concentrations in rivers is crucial for water quality management, but often challenging to implement. In a study conducted in partnership with In-Situ Inc. and Friends of the Yampa, Virridy demonstrated a novel approach to estimating TOC concentrations by combining in-situ fluorescent dissolved organic matter sensors with high-resolution land use land cover (LULC) data to predict TOC and total nitrogen (TN) concentrations in the Upper Yampa River, Colorado. We found that integrating fluorescing dissolved organic matter (FDOM) measurements with machine learning (ML) and downscaled LULC yields more accurate predictions than traditional grab sample monitoring methods. To provide fine-temporal resolution estimates of TOC, we developed a gradient boosting machine model on data collected from July 2023 to July 2024, incorporating FDOM readings and LULC data derived from 10 cm resolution imagery. With this model, we achieved root mean square error of < 0.7 mg/L for TOC with overall prediction errors below 8% with spatial-temporal cross-validation from withheld data. Very high-resolution LULC data significantly improved model performance compared to standard 30 m resolution data. Additionally, we successfully developed an ML pipeline that can use sensor inputs to accurately predict TN concentrations in relevant categories of low (TN < 0.1 mg/L), medium (0.1 mg/L < TN < 0.45 mg/L), and high (TN > 0.45 mg/L) with overall accuracy of 65%. This approach enables real-time water quality predictions, facilitating rapid response to changing conditions and enhancing compliance with Clean Water Act standards, and allows for more comprehensive river monitoring applicable to diverse watersheds.

GBM prediction of TOC from high frequency In-Situ Inc. FDOM data by month from July 2023 to April 2024. Including watershed-level LULC data (labeled as Ag. in Buffer) into the model improved RMSE to less than 0.7 mg/L for TOC, an average prediction error of 11%. The percentage of agricultural land at a watershed level (derived from NLCD data) was found to be a significant predictor of in-stream TOC.

Lab-measured TN across In-Situ Inc. sensor locations is plotted here across monitored time periods (data was not collected when the river was frozen from December 2023 to March 2024). Values are color-coded by monitoring location and marked by correct classification of low, medium, or high TN. Actual thresholds are marked with a dashed line at 0.1 mg/L (low) and 0.45 mg/L (high). While the threshold model performed well for low and medium TN values, it failed to accurately predict values of high TN.

Lume Chlorophyll for Algae Contamination

Virridy is presently developing a Lume sensor for Chlorophyll-a (Cl-a, 440 nm excitation; 650 nm emission).

Algae have become significant water contaminants primarily due to nutrient pollution, resulting in harmful algal blooms (HABs). Eutrophication, the process driven by excessive nutrients like nitrogen (N) and phosphorus (P), leads to rampant algal growth, which depletes oxygen in water bodies, creates toxins, and harms aquatic life. Studies have shown that increased nutrient inputs from anthropogenic sources significantly contribute to the prevalence of toxic blooms in freshwater and coastal waters, including those observed in Lake Erie and other locations globally. Such blooms not only threaten biodiversity but also pose risks to human and animal health. The monitoring and management of these blooms are critical in mitigating their negative impacts on water quality and ecosystem health. Chlorophyll-a (Chl-a) fluorescence has proven an effective method for measuring phytoplankton biomass and, by extension, the presence of algal blooms.

Lume PFAS for Per- and polyfluoroalkyl substances (PFAS) Contamination

Virridy is presently developing a Lume biosensor for PFAS using our TLF sensor combined with a fluorophore consumable.

Tryptophan-like fluorescence has been increasingly utilized in sensors for the detection of per- and polyfluoroalkyl substances (PFAS), including compounds such as perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS). The intrinsic properties of tryptophan fluorescence make it a sensitive tool for sensing these environmental pollutants. One notable advancement in PFAS detection involves the engineering of human liver fatty acid-binding protein (hLFABP), where tryptophan residues were strategically introduced to enhance its fluorescence properties. This biosensor configuration allows for intrinsic detection of PFAS through shifts in fluorescence emission spectra upon binding to these compounds, facilitating enhanced sensitivity compared to conventional sensors. The mutations within the binding pocket of hLFABP resulted in a notable ability to detect PFOA, PFOS, and perfluorohexanesulfonic acid (PFHxS) through measurable blue shifts in fluorescence.

Patents

  • Thomas et al., US Patent-Pending, 2023, “DMRV Fusion Networks” Patent Family:

      • Drinking Water Treatment

      • In-Stream Water Quality

      • Wildfire Impact Prediction

      • Water Quality Parameter Prediction

      • Water Quality Variability Attribution

  • 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

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.

Case Studies

Groundwater Pumps, Northern Kenya

The cyclical and intensifying droughts in northern Kenya, exacerbated by climate change, result in millions lacking reliable water access, with the government and international bodies providing only temporary, reactive emergency support.

Despite investment in borehole pumps, local communities and governments struggle with operation and maintenance due to a lack of funding, training, and viable service contracts, leading to high water point failure rates.

Launched by the Millennium Water Alliance, Virridy, and the University of Colorado Boulder, and primarily funded by USAID’s BHA, this program aims to create sustainable groundwater demand forecasts and incentivize efficient water system operations to prevent drought emergencies.

The Virridy Lume is being installed on all of the groundwater pumps and treatment systems, to directly monitor fecal contamination exposure after treatment, to estimate volumetric use, and to remotely trigger borehole repairs.

Drinking Water Filters, Rwanda

Virridy’s Amazi Meza program in Rwanda is deploying water treatment systems at primary and secondary schools for students who currently drink microbially contaminated drinking water. Diarrhea, associated with dirty drinking water, is still the leading cause of illness and death among school-aged children in Rwanda. Our program will reach about 600,000 students with clean drinking water services over the next several years and is expected to generate over 200,000 carbon credits by 2030.

The Virridy management team led the development and implementation of the first-ever United Nations Clean Development Mechanism and Gold Standard programs earning carbon credits for water treatment. Through these programs, tens of millions of dollars of private financing were leveraged to deliver household water filters to millions of people in Rwanda and Kenya, with revenue from carbon credits largely re-invested into education, repairs and replacements and resulting in significant health, economic and environmental benefits.

The Virridy Lume is installed in a sample of classroom water filters to directly monitor fecal contamination exposure both before and after treatment, and to estimate volumetric use of the filter.

Drip Irrigation and Soil Carbon Sequestration, Konya, Turkey

Orbia Advance Corporation’s Precision Agriculture business Netafim, the global leader in precision irrigation technology, announced today a new carbon credit program in Turkey in partnership with Virridy, a pioneer in environmental technology and a leader in water-focused carbon credit generation. The program will begin with more than 1,000 hectares of farmland in Turkey, focusing on alfalfa, corn and sugar beet crops, and has already been shown to reduce at least 3.5 CO₂e tons per hectare per year.

The program incentivizes farmers to adopt sustainable and regenerative agricultural practices, such as precision irrigation along with digital farming and automation, that reduce greenhouse gas emissions and enhance carbon sequestration. This method of utilizing carbon finance to deliver proven water and environmental benefits is a mechanism that is likely viable in other global irrigated regions as well.

With this partnership, Orbia Netafim is expanding its carbon programs by implementing precision irrigation on alfalfa, sugar beet and corn farms in Turkey. The carbon credits generated through precision irrigation adoption will help reduce greenhouse gas emissions, improve water efficiency and increase crop productivity.

The Lume technology supports high fidelity monitoring of water use and water quality, including organic carbon and nutrient runoff.

Boulder Creek

Over a third of wastewater treatment utilities in Colorado discharge into rivers that do not meet Clean Water Act nutrient or bacterial water quality standards. In many cases, built infrastructure could be substituted with green infrastructure to reduce non-point source contamination. However, the deployment of these approaches has been limited in part due to the lack of technologies capable of directly monitoring water quality and attributing any change to improved watershed management practices.

Continuous monitoring of nutrient and bacteria in the stream has been technologically limited. Currently, there are no specific technologies that can autonomously monitor water quality in situ and provide a direct enumeration of nutrient driven algae blooms or fecal bacterial contamination. To address this limitation, Virridy developed and validated the Lume - a sensitive, continuous, in-situ, remotely reporting fluorescence sensor suite and coupled it with a geospatially informed machine learning model to continuously estimate algae or fecal contamination in rivers. Other technologies on the market a.) output only relative fluorescence and not quantification of algae or fecal bacterial levels, b.) require regular calibration and cleaning, c.) are not designed for continuous in-situ use, and d.) are sold for between 5-10X our anticipated retail price. Our innovative design approach that accomplishes these advances includes 1.) Using advanced LEDs and silicon photomultipliers to reduce cost, complexity and increase stability, 2.) Applying our experience in IOT systems design to create a compact, rugged device designed for continuous, remote use, and 3.) An analytical system compensates for background noise, biofouling and signal drift through fusing a network of sensor data with remote sensing and other parameters (eg. streamflow, rainfall, landcover) in order to accurately and continuously estimate contamination.

We are currently monitoring fecal contamination in Boulder Creek to support stormwater permit compliance efforts associated with the E. coli Total Maximum Daily Load (TMDL) for Boulder Creek, one of the only such TMDLs in Colorado. Sensor data is paired with sampling and geospatially derived land management practices in a machine learning model to extend local sensor readings into spatially interpolated quantified contamination estimates. The City of Boulder and the University of Colorado Boulder are together regulated by the CDPHE to monitor and control e. Coli contamination in Boulder Creek. This presently manifests as weekly e. Coli sampling at six locations. Virridy will deploy sensors at each of these six sites, conduct 3-4 weekly grab samples, and provide real time web-based sensor and weekly lab data to the City, CU and the public. \textit{The City of Boulder has articulated using the Lume data to support continuous monitoring of water quality and public communication of safe and unsafe times to recreate in Boulder Creek. CU Boulder has articulated using the Lume data to support improved understanding of when and where e. Coli contamination events occur, and further to support monitoring of the effectiveness of their routine, scheduled stormwater sewer cleaning.

Four Lume TLF sensors are installed along Boulder Creek, Colorado.

At each sensor site, periodic water samples are collected and enumerated for E. coli.

Chicago River Waterway, Illinois

Current Announces H2NOW x Virridy Sensor Pilot in Calumet River for 2025 Season

Pilot of sensor technology supports H2NOW’s mission of bringing real-time water quality data to the public — and marks a key step toward more efficient detection of E. coli to protect public health

CHICAGO, IL, April 28, 2025 – Current, Chicago’s nonprofit water innovation hub, announces a partnership with Virridy to deploy Virridy’s Lume sensor in Current’s H2NOW Chicago open-water testbed in the Calumet River for the 2025 season.

The deployment is the first pilot of the Lume sensor in an urban river system, a key step toward commercialization of a lower-cost, more efficient solution for detection of E. coli than existing technologies, which will benefit public health. E. coli, which are bacteria found in the intestines of warm-blooded animals, are used as a risk indicator for fecal pollution in water.

Virridy’s Lume sensor is a compact, autonomous, internet-connected sensor that estimates E. coli levels using tryptophan-like fluorescence and machine learning. Lume’s advantages include continuous monitoring, no need for frequent calibration or cleaning, and quantitative results. If successful, this pilot can lead the way for future deployments in urban or rural waterways worldwide, contributing to cleaner, safer water systems.

Now entering its fifth season, H2NOW pioneered public-facing, real-time water quality monitoring in the Chicago and Calumet Rivers. As an open-water testbed, H2NOW offers startups, researchers and companies the opportunity to pilot emerging water technologies like the Lume sensor. Its real-time, accessible data demonstrates the power of cutting-edge water quality monitoring for public agencies, utilities and communities.

Current, which leads H2NOW, also anchors Great Lakes ReNEW, which in January 2024 was awarded up to $160 million over 10 years from the U.S. National Science Foundation to develop and grow a water-based regional innovation engine. The H2NOW x Virridy pilot exemplifies the innovation and tech translation that ReNEW champions, advancing commercialization of a technology that will contribute to clean water and healthy watersheds.

“Core to Current’s mission are partnerships like H2NOW x Virridy,” said Dr. Melissa Pierce, Technical Program Director of Current. “These enable us to accelerate water innovation and bring to market promising technologies for cleaner, safer waterways and water systems. We’re looking forward to a successful pilot with Lume this season.”

“We’re thrilled to pilot Lume in the Calumet River,” said Evan Thomas, Chief Executive Officer, Virridy. “Knowing whether you’re drinking or swimming in clean water is key to public health, and with Current’s H2NOW team, we’ll be able to apply our technical know-how to the real-world environment to bring Lume one step closer to commercialization.”

H2NOW’s 2025 season launches in late May and will run through late fall. For more information, visit h2nowchicago.org.

Manchester on the Sea, Massachusetts

With Blues Wireless, the Lume is installed in Manchester Bay, Massachusetts monitoring beach health.

Nutrient Runoff, Yampa River, Colorado

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The Yampa River watershed spans 250 miles through Routt County, Colorado, maintaining one of the last near-unimpeded rivers in the western U.S. This 45 km study reach extends from Bear River headwaters through Stagecoach Reservoir to Steamboat Springs. This segment was selected due to its thermal impairment status (303(d) listed since 2019), mixed land use gradient (62% agricultural, 28% forested, 10% urban) and hydrological controls (two minimally invasive reservoirs accounting for 15% of annual discharge. Field sensors were co-located with USGS’s long-term monitoring stations to leverage streamflow data as well as placed above and below points of potential contamination including historic ranching properties, reservoirs, water treatment facilities, and urban areas.

We deployed a network of ten in-situ sensors from July 2023-April 2024 at 1-7 km spacing, maintaining 15-minute logging intervals. This high temporal data resolution captured fine-scale variations in water quality parameters that may have been lost by only collecting grab samples. Sensors were removed from the river and monitoring was suspended from mid-November to mid-April to prevent equipment damage due to low temperatures while still capturing the entirety of the irrigation season and the snowmelt runoff. FDOM, turbidity, temperature, conductivity, and chlorophyll-a were continuously monitored, chosen as key indicators of water quality that are measured by in-situ sensors. FDOM acts as a proxy for total organic carbon loading, while chlorophyll-a indicates potential harmful algal blooms related to nutrient loading, both of which are critical for assessing the impacts of agricultural practices and land use changes on water quality. These sensors included thermistors for water temperature as well as optical sensors for turbidity and FDOM. The optical sensors were equipped with automatic wipers to reduce fouling and help to maintain accuracy. Field cleanings and calibration checks of the in-stream sensors followed standard USGS techniques and methods. FDOM sensors excite at 375 nm and detect emissions in the range of 460-530 nm. We paired this high-frequency sensor data with weekly grab samples that were lab-analyzed for quantification of nutrients including Total Nitrogen (TN), TOC, Total Kjeldahl Nitrogen (TKN), Nitrate/Nitrite as N, Total Phosphorus (TP), and Potassium.

Charles River, Boston

The Charles River Watershed Association (CRWA) has partnered with Virridy, a water quality monitoring and environmental data science company, to test Virridy’s Lume™ E.coli sensor in the Charles River as part of CRWA’s water quality monitoring program.

CRWA maintains one of the most robust water quality data sets in the nation that both the Environmental Protection Agency and Massachusetts Department of Environmental Protection rely on to set and enforce water quality standards. The program closely monitors water chemistry, tracks pollution, identifies cyanobacteria blooms, assesses streamflow, and identifies challenges for the Charles River ecosystem.

Data for the program has historically been collected by hundreds of dedicated community science volunteers, but through the pilot program, which began on May 2, Virridy’s Lume™ sensor will use tryptophan-like fluorescence (TLF) and machine learning analytics to estimate E. coli levels in surface waters.

“Restoring and protecting the Charles River and its watershed has been our mission since 1965 and we are excited to partner with Virridy to explore new ways to monitor the health of the river,” said Emily Norton, executive director of CRWA.

Seine and Marne Rivers, Paris, France

Working with the City of Paris and local universities, the Lume is installed along the Marne and Seine Rivers.