Institute of Environmental Sciences (CML), Leiden University, Leiden2300 RA, The Netherlands.
Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing210044, People's Republic of China.
Environ Sci Technol. 2022 Nov 15;56(22):15238-15250. doi: 10.1021/acs.est.2c03333. Epub 2022 Oct 5.
The rapid development of nanomaterials (NMs) and the emergence of new multicomponent NMs will inevitably lead to simultaneous exposure of organisms to multiple engineered nanoparticles (ENPs) at varying exposure levels. Understanding the joint impacts of multiple ENPs and predicting the toxicity of mixtures of ENPs are therefore evidently of importance. We reviewed the toxicity of mixtures of ENPs to a variety of different species, covering algae, bacteria, daphnia, fish, fungi, insects, and plants. Most studies used the independent-action (IA)-based model to assess the type of joint effects. Using co-occurrence networks, it was revealed that 53% of the cases with specific joint response showed antagonistic, 25% synergistic, and 22% additive effects. The combination of nCuO and nZnO exhibited the strongest interactions in each type of joint interaction. Compared with other species, plants exposed to multiple ENPs were more likely to experience antagonistic effects. The main factors influencing the joint response type of the mixtures were (1) the chemical composition of individual components in mixtures, (2) the stability of suspensions of mixed ENPs, (3) the type and trophic level of the individual organisms tested, (4) the biological level of organization (population, communities, ecosystems), (5) the exposure concentrations and time, (6) the endpoint of toxicity, and (7) the abiotic field conditions (e.g., pH, ionic strength, natural organic matter). This knowledge is critical in developing efficient strategies for the assessment of the hazards induced by combined exposure to multiple ENPs in complex environments. In addition, this knowledge of the joint effects of multiple ENPs assists in the effective prediction of hybrid NMs.
纳米材料(NMs)的快速发展和新型多组分纳米材料的出现,将不可避免地导致生物体同时暴露于不同暴露水平的多种工程纳米颗粒(ENPs)下。因此,了解多种 ENPs 的联合影响并预测 ENPs 混合物的毒性显然非常重要。我们综述了多种 ENPs 混合物对各种不同物种的毒性,涵盖藻类、细菌、水蚤、鱼类、真菌、昆虫和植物。大多数研究使用基于独立作用(IA)的模型来评估联合作用的类型。通过共现网络分析,揭示了 53%具有特定联合反应的情况下表现出拮抗作用,25%协同作用,22%相加作用。nCuO 和 nZnO 的组合在每种联合相互作用中表现出最强的相互作用。与其他物种相比,暴露于多种 ENPs 的植物更可能经历拮抗作用。影响混合物联合反应类型的主要因素有:(1)混合物中各成分的化学成分;(2)混合 ENPs 悬浮液的稳定性;(3)测试的个体生物的类型和营养级;(4)个体的生物学组织水平(种群、群落、生态系统);(5)暴露浓度和时间;(6)毒性终点;(7)非生物场条件(例如,pH 值、离子强度、天然有机物)。这些知识对于开发评估复杂环境中多种 ENPs 联合暴露引起的危害的有效策略至关重要。此外,对多种 ENPs 联合作用的了解有助于有效预测混合纳米材料。