Abstract
Abstract Emerging contaminants (ECs), including pharmaceuticals, pesticides, endocrine-disrupting compounds, and personal care products, have become increasingly prevalent in agricultural water systems due to intensified agricultural activities and expanding chemical usage. Their persistence, bioaccumulation potential, and adverse ecological and human health effects have raised significant concerns regarding water quality and environmental sustainability. Conventional treatment technologies often exhibit limited effectiveness in completely removing these contaminants, necessitating the development of more advanced remediation approaches. This review critically evaluates recent advances in catalytic and computational strategies for EC removal from agricultural water systems. Catalytic approaches, including advanced oxidation processes, photocatalysis, Fenton-based systems, biochar-supported catalysts, and single-atom catalysts, demonstrate considerable potential for efficient contaminant degradation. Computational approaches such as machine learning, molecular modelling, density functional theory, and digital twin technologies have further enhanced treatment optimization, catalyst design, and process prediction capabilities. Evidence from recent studies suggests that integrated catalytic-computational frameworks can significantly improve treatment efficiency, operational performance, and environmental sustainability. Based on the reviewed literature, the integration of advanced catalytic materials with artificial intelligence-driven decision-support systems represents a promising direction for future agricultural water management. However, challenges related to scalability, data quality, catalyst stability, and field-scale implementation remain. Future research should focus on intelligent hybrid treatment systems capable of combining real-time monitoring, predictive modelling, and efficient contaminant degradation to support sustainable water resource management.