{
  "abstract": "Microplastics (MPs) increasingly accumulate in agricultural soils, yet their effects on soil enzymatic activity remain heterogeneous. Here, a literature-derived dataset was analyzed using an interpretable machine learning framework. eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) captured non-linear patterns in enzymatic responses to MP characteristics. Particle size showed the strongest model-based contribution, with a dataset-derived size-sensitive range of 200–600 μm associated with more pronounced predicted inhibition. Dosage showed a plateau-like pattern above approximately 100 g/kg, while exposure time exhibited non-linear variation across the observed duration. SHapley Additive exPlanations (SHAP) analysis further characterized the relative contributions of individual features to model predictions. Overall, the results provide quantitative, data-driven insights into heterogeneous MP-soil enzyme associations and highlight particle size as an important factor for further experimental investigation and risk assessment.",
  "abstract_shown": true,
  "affiliations": [
    "Hainan Province Key Laboratory of One Health, Collaborative Innovation Center of Life and Health, School of Life and Health Sciences, Hainan University, Haikou 570228, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Zifei Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Yitong Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Tianpeng Wang"
    },
    {
      "affs": [
        0
      ],
      "name": "Juanjuan Li"
    },
    {
      "affs": [
        0
      ],
      "name": "Xue Chi"
    },
    {
      "affs": [
        0
      ],
      "name": "Hong Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1bjqrdp0d992c.bib",
  "cited_by": 0,
  "doi": "10.3390/biology15201880",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13701847/?tool=EBI",
  "id": "1bjqrdp0d992c",
  "issns": [
    "2079-7737"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13701847",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QH301-705.5"
    ],
    [
      "openalex",
      "2310|Microplastics and Plastic Pollution"
    ]
  ],
  "openalex": "W7222972908",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13701847/pdf/?tool=EBI",
  "pmcid": "PMC13701847",
  "pmid": null,
  "published": "2026-10-01",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/1bjqrdp0d992c.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:40Z",
      "native_id": "PMC:PMC13701847",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13701847"
    },
    {
      "first_seen": "2026-10-31T14:21:22Z",
      "native_id": "020ffda35078475880df6f840cc0a3bc",
      "source": "doaj",
      "url": "https://doi.org/10.3390/biology15201880"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "openalex:2310|Microplastics and Plastic Pollution",
      "subfield": "environmental-science"
    }
  ],
  "title": "Unraveling the Context-Dependent Effects of Microplastics on Soil Enzymatic Activities via Interpretable Machine Learning",
  "title_text": "Unraveling the Context-Dependent Effects of Microplastics on Soil Enzymatic Activities via Interpretable Machine Learning",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T14:21:22Z",
  "url": "https://research.draconitic.com/paper/1bjqrdp0d992c",
  "venue": "Biology",
  "versions": []
}