Interpretable machine learning reveals transport of aged microplastics in porous media: multiple factors co-effect
Open accessAbstract
Microplastics (MPs) easily migrate into deeper soil layers, posing potential risks to subterranean habitats and groundwater. However, the mechanisms governing the vertical migration of MPs in soil, particularly aged MPs, remain unclear. In this study, we investigate the transport of MPs under varying MPs properties, soil texture and hydrology conditions. Under nearly all controlled conditions, aged MPs demonstrated a higher vertical mobility compared to virgin MPs. By employing interpretable machine learning models (IML), we not only identified the dominant role of individual parameters in the vertical migration of MPs but also discovered that the increased contribution of carbonyl index and O/C ratio in aged MPs, along with the enhanced interaction with other feature parameters, collectively promotes the elevated vertical mobility of aged MPs. The varying contributions of different feature parameters under individual control variables revealed the mechanisms of MPs vertical migration under different gradients and highlighted the dual constraints of physical obstruction and chemical retention between MPs and soil particles. The established machine learning model was also utilized to predict the differences in vertical mobilities of MPs with varying degrees of aging. The nonlinear increasing relationship between MPs vertical mobility and simulated aging time suggests that MPs can migrate to deeper soil layers shortly after entering the soil environment. The integration of laboratory experiment with IML elucidates the key drivers of vertical MP migration. It also provides a theoretical basis for the timely removal of MPs from soil and the assessment of their potential risks.
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The machine learning process consists of three key steps: feature input, model construction, and performance evaluation.
“The machine learning process consists of three key steps: feature input, model construction, and performance evaluation (Qian et al., 2024; Qiu et al., 2025).”
From A two-step structural equation modeling and explainable machine learning framework for understanding university students’ adoption of generative AI: balancing intrinsic motivations and perceived risks (2026) · cites this paper as Qiu et al., 2025
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- Authors
- Qiu, Y., Niu, J., Zhang, C., Chen, L., Su, B., & Zhou, S.
- Affiliations
- Ministry of Natural Resources (CN)
- Nanjing University (CN)
- Venue
- Water Research
- Publisher
- Elsevier BV
- Published
- 2025-04
- Volume
- 274
- Pages
- 123129
- ISSN
- 0043-1354 (ISSN-L)
- OpenAlex
- W4406288699
- Cited by
- 54
- References
- 47
- Citations by year
- 2026: 32 · 2025: 22
- FWCI
- 12.67
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 288 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Water Research (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (42371087)
- National Natural Science Foundation of China (42371087)
- Topics
- Physical Sciences › Environmental Science › Pollution › Microplastics and Plastic Pollution
- Physical Sciences › Environmental Science › Pollution › Pharmaceutical and Antibiotic Environmental Impacts
- Physical Sciences › Environmental Science › Water Science and Technology › Fecal contamination and water quality
- Keywords
- aged microplastics, microplastic transport, porous media, microplastic aging, carbonyl index, O/C ratio, soil texture, soil hydrology, groundwater contamination, model interpretability
- MeSH terms
- Machine Learning, Microplastics, Soil, Soil Pollutants, Water Pollutants, Chemical, Porosity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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