Jemec Kokalj Anita, Heinlaan Margit, Novak Sara, Drobne Damjana, Kühnel Dana
Biotechnical Faculty, University of Ljubljana, Večna pot 111, 1000 Ljubljana, Slovenia.
National Institute of Chemical Physics and Biophysics, Akadeemia tee 23, 12618 Tallinn, Estonia.
Nanomaterials (Basel). 2023 Jan 28;13(3):536. doi: 10.3390/nano13030536.
Polystyrene nanoparticles are the most investigated type of nanoplastics in environmental hazard studies. It remains unclear whether nanoplastic particles pose a hazard towards aquatic organisms. Thus, it was our aim to investigate whether the existing studies and data provided therein are reliable in terms of data completeness. We used the example of spp. studies for the purpose of polystyrene nanoplastic (nanoPS) hazard evaluation. First, a set of quality criteria recently proposed for nanoplastic ecotoxicity studies was applied. These rather general criteria for all types of nanoplastics and different test organisms were then, in the second step, tailored and refined specifically for spp. and nanoPS. Finally, a scoring system was established by setting mandatory (high importance) as well as desirable (medium importance) criteria and defining a threshold to pass the evaluation. Among the existing studies on nanoPS ecotoxicity for spp. ( = 38), only 18% passed the evaluation for usability in hazard evaluation. The few studies that passed the evaluation did not allow for conclusions on the hazard potential of nanoPS because there was no consensus among the studies. The greatest challenge we identified is in data reporting, as only a few studies presented complete data for hazard evaluation.
聚苯乙烯纳米颗粒是环境危害研究中研究最多的一类纳米塑料。纳米塑料颗粒是否对水生生物构成危害仍不清楚。因此，我们的目的是调查现有研究及其中提供的数据在数据完整性方面是否可靠。我们以 spp. 的研究为例进行聚苯乙烯纳米塑料（nanoPS）危害评估。首先，应用了最近针对纳米塑料生态毒性研究提出的一套质量标准。然后，在第二步中，针对 spp. 和 nanoPS 对这些适用于所有类型纳米塑料和不同测试生物的相当通用的标准进行了调整和细化。最后，通过设定强制性（高度重要）和期望性（中等重要）标准并定义通过评估的阈值，建立了一个评分系统。在现有的关于 spp. 的 nanoPS 生态毒性研究（ = 38）中，只有 18% 通过了危害评估可用性的评估。少数通过评估的研究无法得出关于 nanoPS 危害潜力的结论，因为各研究之间没有共识。我们发现最大的挑战在于数据报告，因为只有少数研究提供了用于危害评估的完整数据。
