Efficient and Scalable Detection of Microplastics in Drinking Water Using Fluorescence High-Content Imaging
Abstract
The increasing production and inadequate disposal of plastics have led to widespread microplastic presence in the environment, posing potential health risks. However, existing microplastic detection techniques often face challenges in resolution, sensitivity, speed, and complexity of extraction and microplastic identification. In this study, a rapid, high‐throughput approach based on fluorescence microscopy is developed and validated to investigate the prevalence of microplastics in bottled drinking water. By utilizing Nile Red (NR) staining for fluorescence imaging, imaging the entire filtered area, machine learning for automated classification, and thermal treatment to remove false positives, microplastic concentrations up to 1.52 × 105 particles/liter and mineral microparticle concentrations up to 4.93 × 105 particles/liter are detected. Over 90% of the detected microplastics are within the smallest 1–5 µm size range, a size fraction overlooked by the Delegated Drinking Water Act (DDWA) 2024, which focuses on particles larger than 20 µm. With fewer than 1% of microplastic particles in bottled water exceeding 20 µm, these findings highlight the urgent need for more rigorous regulatory frameworks and advanced detection methods, such as the fluorescence‐based approach, to ensure the safety of drinking water.
Read the paper
Related studies
- Green Chemistry: A Framework for a Sustainable Future
- Nano/micro-plastic, an invisible threat getting into the brain
- Emerging contaminants antibiotic resistance genes and microplastics in the environment: Introduction to 21 review articles published in CREST during 2018–2022
- Water Resources and Sustainable Development
- Nano- and microplastics in the workplace
- Water Enclosure and World-Literature: New Perspectives on Hydro-Power and World-Ecology
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.