Lupato Silvia, Granetto Monica, Tiraferri Alberto, Sethi Rajandrea
Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy.
Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino 10129, Italy; Clean Water Center (CWC), Politecnico di Torino, Corso Duca degli Abruzzi 24, Turin 10129, Italy.
J Hazard Mater. 2025 Sep 5;495:138947. doi: 10.1016/j.jhazmat.2025.138947. Epub 2025 Jun 16.
Microplastics and microfibers are widespread environmental pollutants, with synthetic microfibers comprising 40-90 % of microplastics in aquatic systems. Accurate quantification is essential for assessing and mitigating their impact, but conventional methods face several challenges, including high costs, labor-intensive procedures, and limited accessibility. This study standardizes and validates a cost- and time-effective fluorescence-based method for the sensitive detection and automated quantification of polyester-based microfibers. Exploiting the fluorescence properties of polyester fibers under UV light, the method enables direct visualization and automated quantification of the fibers from images obtained using a simple camera setup through open-source software. The method was developed using synthetic, fluorescent microfiber suspensions with concentrations ranging from 1 µg/L to 125 µg/L and processed through glass-fiber filters. This procedure yielded a linear calibration curve (R² = 0.987) between the automatically computed fluorescent area on the filter surface and the mass of filtered microfibers weighted on an analytical balance. The limit of detection (LOD) and quantification (LOQ) were estimated to be 1 µg and 2.5 µg, respectively, demonstrating high sensitivity. Validation with washing machine wastewater samples showed excellent accuracy, with computed concentrations through fluorescence aligning closely with known suspension values. Additionally, the method quantified total fiber length and number, showing a strong correlation between fiber length and area. Morphological analysis revealed shorter fiber lengths in wash water samples compared to synthetic fibers with an average of 1.13 mm and 2.31 mm, respectively, likely due to fragmentation during laundering. Furthermore, washing machine wastewater samples consistently had higher fiber counts, indicating increased fragmentation. These findings highlight the method's robustness, adaptability, and potential for broad applications, allowing for both quantitative and morphological microfiber analysis in environmental and industrial settings, while also contributing to the development of future reference standards.
微塑料和微纤维是广泛存在的环境污染物，在水生系统中，合成微纤维占微塑料的40%-90%。准确量化对于评估和减轻它们的影响至关重要，但传统方法面临诸多挑战，包括成本高、程序繁琐以及可及性有限。本研究对一种基于荧光的成本效益高且省时的方法进行了标准化和验证，用于灵敏检测和自动量化聚酯基微纤维。该方法利用聚酯纤维在紫外光下的荧光特性，通过开源软件，能够从使用简单相机设置获取的图像中直接可视化并自动量化纤维。该方法使用浓度范围为1μg/L至125μg/L的合成荧光微纤维悬浮液进行开发，并通过玻璃纤维滤器进行处理。此程序在滤器表面自动计算的荧光面积与在分析天平上称重的过滤微纤维质量之间产生了一条线性校准曲线（R² = 0.987）。检测限（LOD）和定量限（LOQ）分别估计为1μg和2.5μg，显示出高灵敏度。用洗衣机废水样品进行验证显示出极佳的准确性，通过荧光计算的浓度与已知悬浮液值紧密相符。此外，该方法还能量化纤维的总长度和数量，显示出纤维长度与面积之间有很强的相关性。形态分析表明，洗涤水样品中的纤维长度比合成纤维短，平均分别为1.13mm和2.31mm，这可能是由于洗涤过程中的破碎所致。此外，洗衣机废水样品的纤维数量始终较高，表明破碎程度增加。这些发现突出了该方法的稳健性、适应性和广泛应用潜力，可在环境和工业环境中进行微纤维的定量和形态分析，同时也有助于未来参考标准的制定。
