Advancing antimicrobial resistance mitigation through artificial intelligence: A critical review and new perspectives on integrated monitoring and smart quaternary treatments of urban wastewater

Urban wastewater treatment plants (UWWTPs) play a key role in protecting environmental quality and public health by reducing pollutant discharges into receiving ecosystems. In this context, antimicrobial resistance (AMR) mitigation is emerging as a priority to limit the environmental dissemination of antibiotic resistant bacteria (ARB), antibiotic resistance genes (ARGs), and mobile genetic elements (MGEs). However, conventional UWWTP configurations are not designed to specifically control these biological determinants. Recent regulatory frameworks, within a One Health perspective, require monitoring of micropollutants that threaten water quality and public health, along with the progressive implementation of advanced treatment steps ("quaternary treatments") to remove them. Compliance with more stringent requirements, combined with the need for sustainable treatment processes, poses new challenges while creating opportunities for technological innovation. To support progress in the quaternary treatment of wastewater, the integration of advanced technologies with robust monitoring, modeling, and control systems is essential. This review critically examines recent advances in the design, management, and optimization of quaternary treatment processes through artificial intelligence (AI) algorithms, with specific emphasis on AMR mitigation. Quaternary treatments, including advanced oxidation processes (AOPs), membrane filtration, adsorption, and hybrid treatments, are discussed as promising barriers against AMR dissemination. AI models are identified as powerful tools for developing smart, adaptive, and efficient design, monitoring, and control systems for these technologies. Nevertheless, further research is needed to optimize model performance and practical feasibility in UWWTPs specifically oriented toward AMR mitigation. Key strengths, limitations, and research priorities are highlighted to support the development of next-generation smart wastewater treatment systems.