1 Department of Building, Civil and Environmental Engineering, Concordia University Montreal QC H3G 1M8 Canada Jaehoon.hwang@concordia.ca.
2 NanoScience Technology Center, Department of Chemistry, University of Central Florida Florida USA lzhai@ucf.edu.

Per- and polyfluoroalkyl substances (PFAS) are highly persistent synthetic chemicals that pose severe environmental and health risks, prompting increasingly stringent regulations. The recent crises caused by PFAS contamination underscore the urgent need for rapid, sensitive, and on-site monitoring, along with effective removal and degradation from water sources. To address these challenges, a key future direction involves integrating detection with remediation, shifting from a singular focus to a comprehensive approach that facilitates both monitoring and elimination. This integration enhances cost-effectiveness, real-time process control, and treatment efficiency, ensuring proactive PFAS mitigation. Additionally, artificial intelligence (AI) and machine learning (ML) are emerging as powerful data-driven tools for optimizing detection sensitivity and treatment performance, offering new opportunities for improving integrated PFAS management systems. This perspective critically evaluates the advancements, challenges, and future potential of integrated detection-remediation strategies for scalable PFAS management in water systems.