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Effects of the co-exposure of microplastic/nanoplastic and heavy metal on plants: Using CiteSpace, meta-analysis, and machine learning
Summary
This meta-analysis found that co-exposure to micro/nanoplastics and heavy metals produces stronger toxic effects on plants than heavy metal exposure alone, with toxicity increasing at higher concentrations, longer durations, and with nanoparticles. Notably, polyolefin plastics partially reduced plant toxicity from heavy metals, while modified polystyrene and biodegradable polymers worsened it.
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.