Effective water quality monitoring is an essential part of public health strategies around the world. Waterborne diseases are still one of the leading causes of child mortality in the developing work, claiming nearly 3800 lives every day.1
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Many of these deaths are associated with the presence of pathogens in the water, but the World Health Organization (WHO) also identifies hazardous chemical contamination of drinking water, particularly with lead and arsenic, as another cause for concern in global health.2
There are numerous challenges to monitoring water quality. Often, the clarity of a water source is considered to be a good measure of its cleanliness and there are dedicated tests, such as Secchi disks, for evaluating this.3 However, simply measuring water clarity is by no means a comprehensive assessment of the quality and there are many chemical or biological contaminants that can be present and result in no obvious discoloration.
Overall, while it is clear that several measurement and analysis strategies must be employed to create a robust profile of water quality, there is no clear consensus on all the parameters and factors that should be considered.3
Optical Methods
One widely used method for assessing water quality is optical spectroscopy and optical sensors. The complexity and sophistication of optical instrumentation for water quality measurement can range from full spectrometers with a lamp or laser light sources with dispersive detection that can record full optical absorption profiles to low-cost instrumentation that can measure the transmission of the water at specific wavelengths and assess for ‘cloudiness’ or color changes.4
For many water quality applications, automated measurements are very important. Regular, automated measurements are a cost-effective way of providing monitoring data that can provide insights into whether there are any trends or correlations with particular events being detrimental to water quality.
To make automation of measurements feasible, it is very useful to use techniques that can make online measurements. What this means is that the measurement technique needs to be capable of measuring the water quality ‘in situ’, rather than requiring batch sampling and offline analysis. The challenge for such methods is that there cannot be any sample preparation or pre-concentration steps often required for many analytical methods.
Some examples of methods that can be successfully used for online water quality analysis include fluorescence detection.5 Fluorescence spectroscopy uses optical detectors to detect emission following the excitation of the sample at a particular wavelength. It is well-suited to online analysis and is particularly useful for identifying animal or human biological waste in samples. This is because one of the key amino acids, tryptophan, has a strong and somewhat distinctive fluorescent signal around 350 nm upon excitation at 280 nm.
With the correct use of standards and calibrations, it is possible to use optical spectroscopy methods not just for the identification of contaminant species in water but also for quantification.
Optical Sensors
Detectors for visible absorption spectroscopy and fluorescence tend to be multi-pixel CMOS or CCD devices that, when combined with a dispersive detector, record a full spectral lineshape as a function of energy. Some absorption spectrometers will use monochromator set up with photodiodes for single wavelength detection, which typically have a better sensitivity or limit of detection.
Particularly in the infrared region, spectral information can be highly beneficial for species identification as the information acts as a ‘chemical fingerprint’. However, while it can be challenging to identify specific species with other optical methods where the spectral features are less unique and specific to an individual species, there are other approaches that work well for a variety of contaminants.
For many chemical contaminants, it can be highly beneficial to combine measurement methods to confirm the presence of a particular species. For example, arsenic is one chemical contaminant that is present in many regions around the world and contamination of drinking water by arsenic is an issue that affects millions of people.6
The challenge for scientists trying to profile arsenic concentrations is that arsenic is not always present in the same form. There are organic and inorganic forms and, despite WHO guidelines stating only a trace amount (< 10 ppb) of arsenic is acceptable in drinking water, arsenic is not generally tested for as part of a water quality measurement.
Some uses of optical sensors for arsenic measurement include colorimetric, scattering measurements and fluorescence.7 Improving specificity in the identification of the arsenic compound i.e. is it some arsenic oxide from an organic source, can also be achieved by introducing auxiliary chemical compounds that selectively bind to only specific arsenic species and change the observed optical signal in response to the binding event.
Low-cost, portable and easy-to-use sensors are a crucial tool in preventing many of the deaths associated with water quality issues worldwide and online water analysis tools are essential in ensuring water treatment facilities are operating well and are reliable.
Monitoring Residential Water Filters in Real-Time
References and Further Reading
World Health Organisation, (2017), Protecting and Promoting Human Health, Available at: https://www.who.int/
World Health Organisation (2020) The public health impact of chemicals, Available at: https://www.who.int/publications/i/item/WHO-FWC-PHE-EPE-16-01
Behmel, S., Damour, M., Ludwig, R., & Rodriguez, M. J. (2016). Water quality monitoring strategies — A review and future perspectives. Science of the Total Environment, 571, pp. 1312–1329. https://www.sciencedirect.com/science/article/abs/pii/S0048969716314243?via%3Dihub
Murphy, K., Heery, B., Sullivan, T., Zhang, D., Paludetti, L., Tong, K., Diamond, D., Costa, E., Connor, N. O., & Regan, F. (2014). A low-cost autonomous optical sensor for water quality monitoring. Talanta, 132, pp. 520–527. https://www.sciencedirect.com/science/article/abs/pii/S0039914014008236?via%3Dihub
Carstea, E. M., Bridgeman, J., Baker, A., & Reynolds, D. M. (2016). Fluorescence spectroscopy for wastewater monitoring : A review. Water Research, 95, pp. 205–219. https://www.sciencedirect.com/science/article/abs/pii/S0043135416301488?via%3Dihub
Podgorski, J., & Berg, M. (2020). Global threat of arsenic in groundwater. Science, 850, pp. 845–850. https://www.science.org/doi/10.1126/science.aba1510
Zhang, L., Chen, X., Wen, S., Liang, R., & Qiu, J. (2019). Optical sensors for inorganic arsenic detection. Trends in Analytical Chemistry, 118, pp. 869–879. https://www.sciencedirect.com/science/article/abs/pii/S0165993619303620?via%3Dihub
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