Editorial Feature

Advancing Water Quality Monitoring with Photonic Sensing Technologies

As global environmental challenges threaten water quality, there is a growing demand for efficient monitoring solutions. Photonic sensing technologies emerge as promising real-time and precise water quality assessment tools, offering high sensitivity and selectivity in diverse aquatic environments.1

Advancing Water Quality Monitoring with Photonic Sensing Technologies

Image Credit: I. Noyan Yilmaz/Shutterstock.com

Principles of Photonic Sensing Technologies

Photonic sensing technologies use fundamental light-matter interactions, such as transmission and reflection, to identify containments or key water quality indicators like total suspended solids (TSS).

These sensors utilize light sources like LEDs or lasers to illuminate water, where the size and composition of impurities affect light interaction, causing changes in light intensity or wavelength.

These changes are then recorded using various detection methods, including photodiodes, phototransistors, or charge-coupled devices (CCDs), which measure the light's intensity after interacting with the contaminants. Optical fibers are often employed to direct the light to and from the water sample, allowing for remote or distributed sensing.2

In addition to measuring light transmission and reflection, some photonic sensors leverage specific optical phenomena to detect contaminants. For example, fluorescence sensors excite fluorescent molecules in the water with light of a particular wavelength and measure the intensity of the emitted fluorescence, which can be correlated with the concentration of specific contaminants.

Conversely, surface plasmon resonance (SPR) sensors monitor variations in the refractive index of a metal surface resulting from the binding of target molecules, providing a label-free and real-time detection method.3

Advantages of Photonic Sensing in Water Quality Monitoring

Photonic sensing technologies offer several key advantages for water quality monitoring:

  1. Precision and Accuracy: Photonic sensors can measure water quality parameters like chlorophyll concentration, turbidity, and dissolved oxygen with exceptional precision and accuracy, enabling reliable data-driven decision-making.
  2. Real-Time Data Access: Unlike grab sampling methods that require laboratory analysis, photonic sensors deliver real-time data access, allowing for rapid response to changes in water conditions and enabling proactive management strategies.
  3. Multi-parameter Monitoring: A remarkable strength of photonic sensors is their ability to simultaneously monitor multiple water quality parameters from a single device, providing a comprehensive view of the aquatic environment.
  4. Long-Term Stability and Minimal Drift: Photonic sensors typically experience minimal signal drift over extended periods, ensuring data reliability and reducing the need for frequent replacement or calibration.
  5. IoT Compatibility: The seamless integration of photonic sensors with Internet of Things (IoT) technology enables remote data collection, monitoring, and control, empowering decision-makers with real-time access to water quality conditions from virtually anywhere.4

Current Challenges and Recent Innovations

Development of Low-Cost, Robust Sensors

While photonic sensing offers transformative potential, several challenges have hindered its broader adoption for water quality monitoring. These include a limited range of detectable analytes, vulnerability to interferences, persistent bio-fouling affecting sensor accuracy, and high costs for development, deployment, and substantial power requirements.

A study published in Talanta designed a novel multi-wavelength optical sensor (OCS), offering a low-cost and durable option for monitoring aquatic environments. The OCS can operate autonomously or within a sensor network, measuring light transmission and side-scattering from multiple LEDs to detect water opacity and color changes. It is particularly effective in detecting pollution events, suspended solids, and algal blooms, which are critical for developing a high-resolution, real-time pollution alert system.5

Photonic Devices for Pathogen Detection

The lack of fast, sensitive, cost-effective methods to detect and measure indigenous microbial cells and pathogens has historically hindered water quality monitoring. Traditional cultivation-based methods, like heterotrophic plate counts (HPC), only capture a small fraction of the total microbial population, ignoring most non-cultivable bacterial cells.

The WaterSpy (European Union's Horizon 2020 funded) project addressed these limitations by developing a cost-effective, compact photonic device operating in the 6-10 μm spectral range, targeting Escherichia coli, Pseudomonas aeruginosa, and Salmonella enterica.

Using advanced quantum cascade lasers (QCLs) and novel higher operation temperature (HOT) photodetectors, the device employs attenuated total reflectance (ATR) spectroscopy to provide real-time, accurate water quality analysis.

Preliminary results show that the WaterSpy device can effectively detect pathogens, allowing for comprehensive and real-time monitoring of large water bodies in water distribution networks.6

Machine Learning for Algal Bloom Monitoring

Hypereutrophication and Eutrophication, caused by excessive nutrient input from agriculture, waste discharge, and energy and food production, lead to algal blooms (HABs). These blooms produce cyanotoxins that threaten human and animal health and impact the aquaculture industry.

Currently, satellite-mounted optical sensors like the ocean and land instrument (OLCI) monitor these blooms by measuring phytoplankton concentrations using chlorophyll-a. However, accurately retrieving chlorophyll-a across diverse global waters presents significant methodological challenges.

Another European Union's Horizon 2020 funded project proposed a novel method that bypasses chlorophyll-a retrieval and uses machine learning algorithms to directly estimate water health status from OLCI signals. The results are published in the ISPRS Journal of Photogrammetry and Remote Sensing.

This algorithm accurately estimates all trophic states on OLCI imagery across global water bodies, surpassing similar advanced methods by 5-12 % and eliminating the need to select a specific algorithm for water observation, achieving over 90 % accuracy in highly eutrophic and hypereutrophic waters.7

Future Outlook and Emerging Trends

Continued innovations in imaging, data analytics, and machine learning could revolutionize photonic water sensing. Highly sensitive optical biosensors may enable onsite, real-time microbial monitoring with minimal sample volumes.

Advanced spectroscopic techniques coupled with chemometrics and AI/ML could allow multi-parameter measurements and intelligent analysis from compact, fieldable instruments. Networking deployed sensors through IoT systems may provide high-resolution water quality mapping.

As these advancements continue, robust and scalable photonic sensing solutions are expected to become increasingly crucial in protecting global water resources. These solutions will allow for autonomous monitoring of pollutants, microbial loads, and nutrient levels in fresh and marine environments, which will be vital for enhancing environmental management and preserving water quality worldwide.

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References and Further Reading

  1. Koren, K., Zieger, SE. (2021). Optical Sensors for Water Monitoring. Sensors. https://www.mdpi.com/journal/sensors/special_issues/optical_sens_water_monitoring   
  2. Zainurin, SN., Wan Ismail, WZ., Mahamud, SNI., Ismail, I., Jamaludin, J., Ariffin, KNZ., Wan Ahmad Kamil, WM. (2022). Advancements in monitoring water quality based on various sensing methods: a systematic review. International Journal of Environmental Research and Public Health. doi.org/10.3390/ijerph192114080
  3. Kumar, M., Khamis, K., Stevens, R., Hannah, DM., Bradley, C. (2024). In-situ optical water quality monitoring sensors—applications, challenges, and future opportunities. Frontiers in Water. doi.org/10.3389/frwa.2024.1380133
  4. Desun Uniwill-Lily. (2023). Optical Water Quality Sensor Technology: At the Forefront of the Water Quality Monitoring Industry. [Online] Desun. Available at: https://disen-sensor.com/optical-water-quality-sensor-technology-at-the-forefront-of-the-water-quality-monitoring-industry/
  5. Murphy, K., et al. (2015). A low-cost autonomous optical sensor for water quality monitoring. Talanta. doi.org/10.1016/j.talanta.2014.09.045
  6. Doulamis, N., et al. (2018). Waterspy: A high sensitivity, portable photonic device for pervasive water quality analysis. Sensors. doi.org/10.3390/s19010033
  7. Werther, M., et al. (2021). Meta-classification of remote sensing reflectance to estimate trophic status of inland and nearshore waters. ISPRS Journal of Photogrammetry and Remote Sensing. doi.org/10.1016/j.isprsjprs.2021.04.003

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Owais Ali

Written by

Owais Ali

NEBOSH certified Mechanical Engineer with 3 years of experience as a technical writer and editor. Owais is interested in occupational health and safety, computer hardware, industrial and mobile robotics. During his academic career, Owais worked on several research projects regarding mobile robots, notably the Autonomous Fire Fighting Mobile Robot. The designed mobile robot could navigate, detect and extinguish fire autonomously. Arduino Uno was used as the microcontroller to control the flame sensors' input and output of the flame extinguisher. Apart from his professional life, Owais is an avid book reader and a huge computer technology enthusiast and likes to keep himself updated regarding developments in the computer industry.

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