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Detection of microplastics in surface water using a DSLR lens-based UV imaging system
Summary
Researchers developed a low-cost UV imaging system using a standard DSLR camera lens for detecting microplastics in surface water samples. The system demonstrated the ability to identify and distinguish fluorescent microplastic particles from natural debris without requiring expensive laboratory equipment. The study offers an accessible detection method that could support microplastic monitoring in resource-limited settings like the Philippines.
Abstract Microplastics are any synthetic solid particle or polymeric matrix, ranging from 1 μm to 5 mm in diameter. Due to their low density and size, microplastics can easily be transported through wind and water currents, contaminating the environment with the toxic compounds they contain, harming living organisms through microplastic consumption and exposure. Despite being the top global contributor of plastic waste in 2010, the Philippines still has very limited studies regarding microplastic contamination. There is a need, then, to develop an efficient and accessible means of identifying and quantifying microplastics. To do this, this study utilized a DSLR lens attached to a UV CCD camera to capture images of microplastics, which fluoresce under 395 nm illumination. An image processing algorithm was applied to the resulting images, including binarization, adaptive segmentation, and particle counting to quantify MPs as a fast and easily accessible testing method. Results from the study revealed that despite water samples having been taken within proximity of one another, the number of particles identified within the system still showed a wide spread in values. In addition, it was found that the size distribution of the microplastics obeys the power law. As a new application for UV-based imaging, our method showed that quantitative detection of microplastics is feasible for future in situ measurement of marine water samples. However, further refinement to the counting technique and comparison with standard testing for validation is still recommended for improvement.