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Efficient quantification via statistical surrogate models for LNAPL migration in typical saturated intersecting

Yuntian Pang1, Cixiao Qu2, Mingyu Wang3

  • 1College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish Centre for Education and Research, University of Chinese Academy of Sciences, Beijing 100049, China.

Journal of Hazardous Materials
|February 20, 2026
PubMed
Summary

This study quantifies light non-aqueous phase liquid (LNAPL) migration in fractured bedrock. Statistical models accurately predict LNAPL movement, aiding environmental risk assessment in fractured aquifers.

Keywords:
Generalizable statistical surrogate modelsGroundwater contaminationLNAPL migrationPrimary controlling factorsSaturated intersecting fractures

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Area of Science:

  • Environmental Science
  • Hydrogeology
  • Geoscience

Background:

  • Light non-aqueous phase liquids (LNAPLs) contamination in fractured bedrock aquifers poses significant environmental risks.
  • Quantifying LNAPL migration in saturated intersecting fractures is crucial but challenging due to complex influencing factors.

Purpose of the Study:

  • To investigate and quantify the spatiotemporal migration characteristics of LNAPLs in saturated intersecting fractures.
  • To identify key factors controlling LNAPL migration and develop predictive statistical surrogate models.

Main Methods:

  • Utilized orthogonal experimental design and Latin hypercube sampling to generate representative scenarios.
  • Performed numerical simulations and validated model reliability against physical experiments.
  • Developed and validated statistical surrogate models for predicting LNAPL migration characteristics.

Main Results:

  • Identified primary controls on LNAPL migration in saturated intersecting fractures.
  • Developed generalizable statistical surrogate models with Mean Relative Errors (MREs) of 15-30% for migration predictions.
  • Demonstrated satisfactory predictive performance (REs<20%) when applying models to series of fractures.

Conclusions:

  • Statistical surrogate models provide reliable predictions for LNAPL migration characteristics in fractured aquifers.
  • The developed framework offers a feasible approach for quantifying LNAPL migration in various fracture scenarios.
  • This research aids in better understanding and managing LNAPL contamination in fractured bedrock environments.