Micro/nano plastics (M/NPs) and antibiotics, as widely coexisting pollutants in environment, pose serious threats to soil ecosystem. The purpose of this study was to systematically evaluate the ecological effects of the co-exposure of M/NPs and antibiotics on soil organisms through the meta-analysis and machine learning prediction. Totally, 1002 data set from 38 articles were studied. The co-exposure of M/NPs significantly promoted the abundance (62.68 %) and migration level (55.22 %) of antibiotic contamination in soil, and caused serious biotoxicity to plants (-12.31 %), animals (-12.03 %), and microorganisms (35.07 %). Using 10 variables, such as risk response categories, basic physicochemical properties, exposure objects, and exposure time of M/NPs, as data sources, Random Forests (RF) and eXtreme Gradient Boosting (XGBoost) models were developed to predict the impacts of M/NPs on the ecotoxicological effects of antibiotics in agricultural soil. The effective R2 values (0.58 and 0.60, respectively) indicated that both models can be used to predict the future ecological risk of M/NPs and antibiotics coexistence in soil. Particle size (13.54 %), concentration (5.02 %), and type (11.18 %) of M/NPs were the key characteristic parameters that affected the prediction results. The findings of this study indicate that the co-exposure of M/NPs and antibiotics in soil not only exacerbates antibiotic contamination levels but also causes severe toxic effects to soil organism. Furthermore, this study provides an effective approach for ecological risk assessment of the coexistence of M/NPs and antibiotics in environment.
Keywords: Antibiotic; Co-toxicity; Machine learning prediction; Meta-analysis; Micro/nano plastics.
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