Bridging the Gap between External and Internal Exposure: Challenges and a Machine Learning Approach Validated Using Benzene Ring-Containing Pollutants.

Authors

Xiao Zhang, Xiaolei Wang, Junze Ma, Sixian Wang, Xia Wang, Qingyuan Cao, Fengchang Wu, Xiaoli Zhao

Published in

Environmental science & technology. Jul 09, 2026. Epub Jul 09, 2026.

Abstract

Establishing quantitative linkages between external and internal exposures is essential for accurate exposure and health risk assessment, yet this goal remains hindered by multiple challenges. This Perspective systematically reviews major challenges limiting the quantitative linkage across three dimensions: (i) External exposure: complex multimedia and multipathway exposure, environmental factor variability, and substantial data gaps; (ii) Internal exposure: difficulties in biological sample collection, large-scale biomonitoring, and appropriate biomarker selection; and (iii) External-internal exposure linkage: interindividual variability, scarce toxicokinetic parameters, and methodological limitations. In this context, the Perspective proposes ML-EIExpLink (machine learning-based external-internal exposure linkage), a framework integrating multisource, multidimensional variables, including pollutant physicochemical properties, ADME parameters, exposure factors, environmental conditions, and socioeconomic indicators, to model complex external-internal exposure relationships. An exploratory case study on benzene ring-containing pollutants demonstrates the framework's feasibility and reliability. Using gradient boosting decision trees as the core predictive engine, the model achieved robust predictive performance, with an external validation Q_ext² of 0.784. Most key variables, including external air concentration, molecular planarity, and temperature, exhibited nonlinear and threshold effects on predicted blood concentrations. This Perspective introduces a novel ML-based framework for establishing robust, interpretable external-internal exposure linkages, thereby providing a useful basis for improved exposure assessment and health risk evaluation.

PMID:
42424524
Bibliographic data and abstract were imported from PubMed on 10 Jul 2026.

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