Mechanistic Drivers of Nanoplastic-Induced Soil Enzymatic Suppression: A Synthesis Pairing Meta-Analysis and Explainable Machine Learning

X X Li, Yanxiang Chen, Ruirong Wang, Muzamil Abbas, Nadia Sarwar, Shan Hussain, Muhammad Jafir, Talha Nazir

Nanoplastics (NPs; <1000 nm) are persistent soil contaminants that suppress extracellular enzyme activity, the biochemical engine of terrestrial nutrient cycling. Despite a rapidly expanding primary literature, no comprehensive meta-analysis has systematically integrated quantitative effect-size synthesis with interpretable machine learning (ML) approaches to identify and rank the physicochemical drivers of NP-induced soil enzymatic toxicity. Following the PRISMA 2020 statement, we systematically searched four databases (Web of Science, Scopus, PubMed, Google Scholar) from database inception through December 2024 and extracted 413 effect sizes from 113 peer-reviewed studies. Hedges’ g was estimated using three-level random-effects models with restricted maximum likelihood (REML) estimation implemented in the metafor package. Three supervised ML algorithms—random forest (RF), gradient boosting machines (GBMs), and support vector regression (SVR)—were trained using 18 study-level predictors derived from the complete meta-analytic dataset, and SHapley Additive exPlanations (SHAP) were applied to quantify and rank the relative importance of individual predictors. The overall meta-analysis demonstrated a significant inhibitory effect of NPs on soil enzyme activity (Hedges’ g = −0.94; 95% CI: −1.14 to −0.73; k = 413; I2 = 78.4%; τ2 = 0.412). Among the evaluated enzymes, dehydrogenase activity exhibited the greatest inhibition (g = −1.12), whereas polystyrene nanoplastics produced the strongest adverse effects (g = −1.15). Particles smaller than 100 nm caused approximately 2.6-fold greater inhibition than particles larger than 500 nm, and dose–response meta-regression identified a nonlinear increase in toxicity at concentrations exceeding 200 mg kg−1. The RF model demonstrated the highest predictive performance, explaining 73% of the variance in an independent testing dataset (R2 = 0.73; test set n = 83). SHAP analysis identified particle diameter as the most influential predictor, revealing an approximate critical threshold of 150 nm, below which inhibitory effects increased markedly. Higher soil organic carbon concentrations partially mitigated enzymatic inhibition, likely through competitive adsorption and reduced nanoplastic bioavailability. Overall, our findings demonstrate that NP-induced inhibition of soil enzymatic activity is widespread and primarily governed by particle size, exposure concentration, and soil properties. The identified 150 nm threshold should be interpreted as a data-driven hypothesis requiring further validation under environmentally realistic exposure scenarios rather than as a universal regulatory limit. Nevertheless, the integration of three-level meta-analysis with interpretable machine learning (SHAP) provides a robust and reproducible framework for identifying key toxicity drivers and supports future ecological risk assessment and evidence-based regulatory decision-making for nanoplastics.