Identifying microplastic pollutants in water is now a critical priority for environmental researchers as these tiny plastic particles pose growing threats to aquatic ecosystems and human health. Microplastics are plastic particles under 5 mm in size originate from a variety of sources including broken-down plastic containers, synthetic textiles, personal care products, and industrial pellets. Their persistence in water bodies and ability to absorb toxins make them particularly hazardous. Standard approaches frequently use destructive chemical treatment and infrared or Raman spectroscopy, which are labor-intensive and dependent on costly instruments. Imaging analysis offers a more accessible, scalable, and visually intuitive alternative for identifying and quantifying microplastics in water samples.
Detection starts with the acquisition of water samples. Liquid samples are passed through microporous membranes, typically with pore sizes ranging from 0.5 to 8 µm, depending on the desired particle diameter. Retained particles are carefully placed on a translucent medium, such as a membrane filter or transparent carrier plate, for imaging. To enhance contrast and distinguish plastics from organic matter, samples may be treated with fluorescent indicators such as Nile Red, which selectively binds to hydrocarbon-based materials and fluoresces under specific wavelengths of light. Fluorescent labeling markedly reduces misclassification rates.
Precision microscopic imaging setups, including microscopy systems coupled with robotic slide translation, are used to acquire comprehensive visual recordings of retained fragments. They are capable of surveying full filter areas, producing hundreds to thousands of frames per specimen. Computer-vision tools process the data to segment and label candidate particles based on morphological features, dimensions, surface patterns, and emission levels. Deep learning networks, fed with annotated image repositories of plastics and non-plastics, can achieve high classification accuracy, eliminating tedious human counting and lowering subjectivity.
One major advantage of imaging analysis is its ability to provide spatial and morphological data. Fibrous, fragmented, film-like, and bead-shaped microplastics exhibit unique geometries and textures amenable to automated measurement. This enables not just quantification but also source apportionment of contamination. An abundance of fibrous particles often indicates textile-derived pollution, while irregular fragments often result from weathering of macroplastic debris.
Validation is routinely performed using complementary analytical tools such as FTIR or 粒子径測定 micro-Raman analysis on a subset of detected particles. This hybrid approach combines the speed and scalability of imaging with the chemical specificity of spectroscopy, creating a robust workflow for large-scale monitoring.
Challenges remain, including the misidentification of natural particles such as cellulose fibers or mineral grains, especially in complex environmental samples. Natural coatings and particulate adhesion can blur diagnostic visual cues. Emerging techniques in digital filtering and boundary sharpening, along with the use of multi-spectral and polarized light imaging, are helping to overcome these limitations.
As global awareness of microplastic pollution increases, the demand for efficient, standardized detection methods grows. Imaging analysis provides a practical solution for environmental agencies, research institutions, and water treatment facilities to quantify exposure, map spread patterns, and test intervention efficacy. As robotic systems and machine learning models evolve, imaging-based methods are poised to become the cornerstone of routine microplastic surveillance in freshwater and marine environments.
