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Where Machine Learning Fails in Predicting Emerging Contaminant Adsorption: A Decision-Oriented Framework for Model

Yina Fan¹, Jun Li¹, Yi Ren¹

¹State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resources and Hydropower, Sichuan University, Chengdu 610065, China.

Environmental Science & Technology

|June 30, 2026

Summary

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Contaminants and Errors

Contaminants and Errors

Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...

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This review addresses challenges in machine learning (ML) for predicting emerging contaminant (EC) adsorption. It offers strategies to improve model reliability and transferability for better environmental remediation.

Area of Science:

Environmental Science
Computational Chemistry
Data Science

Background:

Emerging contaminants (ECs) pose environmental risks, making their adsorption behavior crucial for fate assessment and removal technologies.
Machine learning (ML) models are increasingly used for predicting adsorption, but their reliability is hampered by workflow challenges.
Existing research often overlooks systematic issues in ML model development, limiting practical application and transferability.

Purpose of the Study:

To systematically review and address key challenges in constructing reliable ML models for EC adsorption prediction.
To consolidate strategies for improving data quality, feature engineering, and model validation.
To explore future research directions for advancing EC adsorption prediction.

Main Methods:

Keywords:

adsorption prediction data bias emerging contaminants machine learning model construction model reliability

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Systematic literature review of ML applications in EC adsorption.
Identification and categorization of challenges in ML model construction (data scarcity, bias, feature representation, applicability).
Consolidation of improvement strategies including data quality assessment, automated feature extraction, cross-system modeling, and external validation.

Main Results:

Identified key challenges: data scarcity/bias, low sample-to-feature ratio (SFR), inadequate feature representativeness, and poor model applicability/credibility.
Proposed targeted solutions: rigorous data quality assessment, automated feature extraction, cross-system modeling, and external validation.
Highlighted future directions: generative data augmentation, physics-informed ML (PIML), and automated literature data extraction using LLMs.

Conclusions:

Addressing systematic challenges in the ML workflow is essential for robust and transferable EC adsorption prediction models.
Implementing proposed strategies can enhance model reliability and credibility.
Future research in areas like PIML and automated data extraction promises significant advancements in EC adsorption prediction and environmental applications.