Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China.
Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China; State Key Laboratory of Livestock and Poultry Biotechnology Breeding, College of Biological Sciences, China Agricultural University, Beijing, China.
Ecotoxicol Environ Saf. 2024 Nov 1;286:117237. doi: 10.1016/j.ecoenv.2024.117237. Epub 2024 Oct 23.
Micro/nanoplastics (MNPs) and heavy metals (HMs) coexist worldwide. Existing studies have reported different or even contradictory toxic effects of co-exposure to MNPs and HMs on plants, which may be related to various influencing factors. In this study, existing publications were searched and analyzed using CiteSpace, meta-analysis, and machine learning. CiteSpace analysis showed that this research field was still in the nascent stage, and hotspots in this field included accumulation, cadmium (Cd), growth, and combined toxicity. Meta-analysis revealed the differential association of seven influencing factors (MNP size, pollutant treatment duration, cultivation media, plant species, MNP type, HM concentration, and MNP concentration) and 8 physiological parameters receiving the most attention. Co-exposure of the two contaminants had stronger toxic effects than HM treatment alone, and phytotoxicity was generally enhanced with increasing concentrations and longer exposure durations, especially when using nanoparticles, hydroponic medium, dicotyledons producing stronger toxic effects than microplastics, soil-based medium, and monocotyledons. Dry and fresh weight analysis showed that co-exposure to MNPs and Cd resulted in significant phytotoxicity in all classifications. Concerning the MNP types, polyolefins partially attenuated plant toxicity, but both modified polystyrene (PS) and biodegradable polymers exacerbated joint phytotoxicity. Finally, machine learning was used to fit and predict plant HM concentrations, showing five classifications with an accuracy over 80 %, implying that the polynomial regression model could be used to predict HM content in plants under complex pollution conditions. Overall, this study identifies current knowledge gaps and provides guidance for future research.
微/纳米塑料（MNPs）和重金属（HMs）在全球范围内共存。现有研究报告了 MNPs 和 HMs 共同暴露对植物的不同甚至相反的毒性作用，这可能与各种影响因素有关。本研究使用 CiteSpace、元分析和机器学习对现有文献进行了检索和分析。CiteSpace 分析表明，该研究领域仍处于起步阶段，该领域的热点包括积累、镉（Cd）、生长和联合毒性。元分析揭示了七个影响因素（MNP 尺寸、污染物处理时间、培养介质、植物种类、MNP 类型、HM 浓度和 MNP 浓度）和 8 个生理参数的差异关联，这些参数最受关注。两种污染物的共同暴露比单独处理 HM 具有更强的毒性作用，随着浓度和暴露时间的增加，植物毒性通常会增强，特别是使用纳米颗粒、水培介质、产生更强毒性的双子叶植物、土壤基介质和单子叶植物时。干重和鲜重分析表明，MNP 和 Cd 的共同暴露在所有分类中均表现出明显的植物毒性。关于 MNP 类型，聚烯烃部分减轻了植物毒性，但改性聚苯乙烯（PS）和可生物降解聚合物都加剧了联合植物毒性。最后，使用机器学习对植物 HM 浓度进行拟合和预测，有五类的准确率超过 80%，这意味着多项式回归模型可用于预测复杂污染条件下植物中的 HM 含量。总体而言，本研究确定了当前的知识空白，并为未来的研究提供了指导。
