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Out of the Lab and Into the Water: Monitoring Swimming Microorganisms to Measure Water Quality

A practical and automated bio-monitoring system that measures the swimming behaviour of microorganisms has been developed to assess water quality and detect the presence of pollutants, like heavy metals and antibiotics, in water samples.

Scientists from the Singapore University of Technology and Design (SUTD) have developed a new technology that can rapidly detect pollutants in the water by measuring their effects on swimming microorganisms. This chemical-free innovation can operate without complex laboratory equipment and primarily leverages a regular smartphone camera and the microorganism Paramecia present in water, making it a cheap and practical tool to assess water quality and drinkability in underdeveloped regions.

Environmental pollutants are usually measured by evaluating their impacts on a given population. While it may take several years to realise the true scale of environmental pollutants in larger animals, for smaller creatures, like microorganisms, the effects of pollutants can be observed in a matter of days.

With this knowledge, researchers led by Assistant Professor Javier Fernandez at the Fermart Lab, have developed a simple computer vision method to determine how pollutants affect the swimming behaviour of Paramecium aurelia, a fast-moving, single-celled organism.

“We chose Paramecia because they are ubiquitous in water bodies and large enough to be seen with a normal camera,” explained Fernandez. “They are also fast swimmers, so small differences in their swimming speed will translate to large changes, making them easy to measure.”

In their recent study published in Scientific Reports, the team used a simple microscope that can be set up on smartphone cameras, to investigate the relationship between Paramecia’s swimming speed and the presence of organic and inorganic contaminants at varying concentrations. They ran an object identification and tracking algorithm to automatically detect the presence of Paramecia in water samples and calculate their swimming speed and movement.

Their findings revealed that hazardous levels of pollutants immediately and significantly reduced the swimming speed of Paramecia. Even within normally permissible levels, the presence of pollutants such as heavy metals like zinc ion chloride and copper sulphate, as well as the common antibiotic erythromycin could affect the swimming speeds of Paramecia. When the concentration of heavy metals in water samples was only half of what is considered unsafe for drinking, the average swimming speed Paramecia dropped abruptly to nearly half (to 66 per cent for zinc chloride and 59 per cent for copper sulphate). Similar changes in swimming speed were also observed upon exposure to erythromycin, an antibiotic that is also commonly used as an indicator of pollution of urbanising global water cycles.

Based on the concept of “frugal engineering,” where advanced tasks can be performed with minimal resources, the system bears strong potential for widespread applicability. Dr. Fernandez explained that different tests are usually used to measure each pollutant, but Paramecia swimming can be used as a global indicator of water quality and pollution in specific environments.

Researchers believe that this straightforward and resourceless method can be used to quickly and accurately assess water quality globally or to substitute standardised methods of measuring toxicity that is usually conducted in laboratories.

“In the future, someone might try a different type of microorganism. Various pollutants would affect organisms differently, so pulling data together from multiple microorganisms would enable us to better understand the source of the pollutants,” concluded Dr. Fernandez. “What we’ve demonstrated proves that we can get information on water quality in a cheap and simple way, without any technical instruments and in no time at all.” [APBN]

Source: Shunmugam et al. (2021). Measurements of the swimming speeds of motile microorganisms using object tracking and their correlation with water pollution and rheology levels. Scientific reports, 11(1), 1-8.