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

Adsorption behavior of emerging contaminants (ECs) plays a central role in their environmental fate and the efficiency of artificial removal systems. Machine learning (ML) has been extensively leveraged for adsorption prediction. However, most existing research focuses primarily on model construction and application, with limited attention to systematic challenges and corresponding solutions across the modeling workflow, thereby constraining model reliability and transferability. This review systematically summarizes key challenges in model construction, including data scarcity and bias, a low sample-to-feature ratio (SFR), inadequate feature representativeness, and inadequate model applicability and credibility. To address these challenges, this work consolidates a set of targeted improvement strategies, encompassing rigorous data quality assessment, automated feature extraction, cross-system modeling approaches, and the incorporation of external validation. Furthermore, we explore future development directions such as generative data augmentation, physics-informed machine learning (PIML), and automated literature data extraction via large language models. This work provides guidance for prospective ML model developers to build more robust and reliable models, laying the foundation for transformative advances in EC adsorption prediction research and applications.