Cyanobacteria are naturally occurring microscopic organisms that function like algae in aquatic ecosystems, often forming blue-green blooms. Cyanobacterial harmful algal blooms (cyanoHABs) are an increasing global concern due to their threat to both human and aquatic ecosystem health, causing significant economic damage. Cyanotoxins produced by some cyanobacteria species can lead to acute and chronic illnesses in humans and affect aquatic life through low dissolved oxygen and altered food webs. In the U.S., cyanoHABs have been reported in at least 43 states, with 19 states issuing public health advisories in August 2016. These bacteria also produce taste-and-odor compounds that affect drinking water and aquaculture, and toxins that can cause allergic reactions, gastroenteritis, and seizures [1]. It is best to sample, study, and monitor HABs regionally [2] therefore a versatile wireless solution is crucial for the timely detection of HABs and mitigation strategies.
What’s our solution?
We have developed two novel wireless solutions for real-time monitoring and quick sample collection of water bodies. Together, our long-range (LoRa) systems can collect samples from the water, and analyze a few water quality parameters such as temperature, turbidity, total dissolved solids, pH, phycocyanin, and chlorophyll-a that are essential for water quality monitoring applications. In the event of HABs, the sample collectors and water specialists may need to collect water samples two to three times a day which is a labor-intensive and expensive process and requires expert knowledge and heavy equipment. Current systems typically do not integrate multiple sensors into a real-time system for practical applications. Our systems are novel in the sense that the Unmanned Aerial Vehicle (UAV) system (in Fig. 1 and Fig. 2) collects near-infrared (NIR) images as well as water samples and measures four crucial water quality parameters including turbidity, temperature, total dissolved solids, and pH in real-time. The wireless system includes a sensor node (drone) as a transmitter and a gateway as a receiver. This information is available on the cloud immediately, making timely detection faster and easier. This system has a range of more than 0.8 km (up to 1.6 km).
Fig. 2. Our two-drone sampling and imaging systems. Reprinted with permission from [Autonomous UAV-Mounted LoRaWAN System for Real-Time Monitoring of Harmful Algal Blooms (HABs) and Water Quality] referenced at [3].
The second optic-based low-cost (USD 367) system shown in Fig. 3 can measure temperature, turbidity, phycocyanin (PC), and chlorophyll-a (Chl-a) pigments in real time. It is based on the optical properties of phycocyanin and chlorophyll-a as they emit light at a specific wavelength (Chl-a emits between 670-690 nm and PC emits at around 650 nm) when excited by an external light source (Chl-a is excited between 400-500 nm and PC is excited around 600 nm) [4]. We pump the fluid (sample) into a cuvette and use light-emitting diodes (LEDs) to excite the pigments of Cyanobacteria and photodiodes (detectors) sense their response. This response generates a small electric current which is amplified and converted to voltage to be measured by the microcontroller. Like the previous system, this system can be deployed on a buoy to transmit the water quality data in real-time and inform us and the public about any potential cyanoHAB events.
Fig. 3. Our wireless optical fluorometer-nephelometer system. Reprinted with permission from [A Low-Cost LoRa Optical Fluorometer–Nephelometer for Wireless Monitoring of Water Quality Parameters in Real-Time] referenced at [5].
The accuracy of our systems
We have conducted comprehensive and accurate calibration for individual sensors and detectors using commercially available devices and test solutions. The drone system utilizes commercial sensors integrated into its custom circuit board. These sensors are calibrated and show good agreement against commercially available devices such as laboratory thermometers and sensors. The pH sensor shows a linear response with R² = 0.9719, the temperature sensor is linear with R² = 0.9999, total dissolved solids showed an R² = 0.9996, and turbidity is calibrated with R² = 0.9694 in the linear region.
Our optical fluorometer-nephelometer shows strong accuracy: temperature measurements with an average deviation of 0.75ºC and R² = 0.9995, phycocyanin detection with R² from 0.9638 to 0.9987 for 0.025-2.5 mg-PC/L, and chlorophyll-a detection with R² ≥ 0.9946 for 1-50 µg-Chl-a/L.
Future Work
We plan to fabricate and deploy many boxes like Fig. 3 in the field for large-scale monitoring of HABs and water quality in real time. Our initial tests indicate that both systems are reliable and can be utilized for rapid HAB detection with minimal maintenance required.
Summary
To conclude, we have designed, fabricated, and validated two powerful systems for water quality monitoring in real time. Both systems are accurate and versatile for water safety and quality monitoring.
Acknowledgments
The author acknowledges that the material herein is based on scientific evidence from IEEE Sensors Journal and this license is only valid for a single-time use in Smart Water Magazine.
References
[1] United States Geological Survey “Building knowledge to protect ecological and human health”, Accessed: July 2024, [Online.] Available: https://www.usgs.gov/news/featured-story/science-harmful-algal-blooms
[2] H. Dunleavy, “To Understand How Warming is Driving Harmful Algal Blooms, Look to Regional Patterns, Not Global Trends”, [Online.] Available: https://insideclimatenews.org/news/16062021/algal-blooms-global-regional-trends/?gad_source=1&gclid=Cj0KCQjw-5y1BhC-ARIsAAM_oKmeJS-Hm5w5e5GFfkexd1mE93jlFuGx__onT45IAQq2yppAxnjlFbcaAkVKEALw_wcB
[3] S. F. Hagh et al., “Autonomous UAV-Mounted LoRaWAN System for Real-Time Monitoring of Harmful Algal Blooms (HABs) and Water Quality,” IEEE Sens. J., vol. 24, no. 7, pp. 11414–11424, 2024, doi: 10.1109/JSEN.2024.3364142.
[4] A. Puiu, L. Fiorani, I. Menicucci, M. Pistilli, and A. Lai, “Submersible spectrofluorometer for real-time sensing of water quality,” Sensors (Switzerland), vol. 15, no. 6, pp. 14415–14434, 2015, doi: https://doi.org/10.3390/s150614415.
[5] S. F. Hagh et al., “A Low-Cost LoRa Optical Fluorometer-Nephelometer for Wireless Monitoring of Water Quality Parameters in Real-Time,” IEEE Sens. J., p. 1, 2024, doi: 10.1109/JSEN.2024.3403416.