Aquatic Ecology and Water Quality Management Group, Wageningen University, P.O. Box 47, 6700 DD, Wageningen, The Netherlands.
DICEA─Department of Civil, Constructional and Environmental Engineering, Sapienza University of Rome, Via Eudossiana, 18, 00184 Roma, Italy.
Environ Sci Technol. 2023 Sep 19;57(37):14015-14023. doi: 10.1021/acs.est.3c03620. Epub 2023 Sep 8.
The effects and risks of microplastics correlate with three-dimensional (3D) properties, such as the volume and surface area of the biologically accessible fraction of the diverse particle mixtures as they occur in nature. However, these 3D parameters are difficult to estimate because measurement methods for spectroscopic and visible light image analysis yield data in only two dimensions (2D). The best-existing 2D to 3D conversion models require calibration for each new set of particles, which is labor-intensive. Here we introduce a new model that does not require calibration and compare its performance with existing models, including calibration-based ones. For the evaluation, we developed a new method in which the volumes of environmentally relevant microplastic mixtures are estimated in one go instead of on a cumbersome particle-by-particle basis. With this, the new Barchiesi model can be seen as the most universal. The new model can be implemented in software used for the analysis of infrared spectroscopy and visual light image analysis data and is expected to increase the accuracy of risk assessments based on particle volumes and surface areas as toxicologically relevant metrics.
微塑料的影响和风险与三维（3D）特性相关，例如在自然界中存在的各种颗粒混合物中生物可及部分的体积和表面积。然而，这些 3D 参数很难估计，因为光谱和可见光图像分析的测量方法仅提供二维（2D）数据。现有的最佳 2D 到 3D 转换模型需要针对每组新的颗粒进行校准，这是一项劳动密集型的工作。在这里，我们引入了一种不需要校准的新模型，并将其性能与包括基于校准的模型在内的现有模型进行了比较。为了进行评估，我们开发了一种新方法，可以一次性估计环境相关的微塑料混合物的体积，而不是繁琐的逐个颗粒的基础。由此可见，新的 Barchiesi 模型是最通用的。新模型可以在用于分析红外光谱和可见光图像分析数据的软件中实现，并有望提高基于颗粒体积和表面积作为毒理学相关指标的风险评估的准确性。
