Enhancing pollution management in watersheds: A critical review of total maximum daily load (TMDL) implementation

The total maximum daily load (TMDL) program is a regulatory tool to ensure that water bodies meet quality standards by calculating the maximum pollutant load that a water body can assimilate while meeting water quality criteria. Implementing the TMDL program poses significant challenges for water quality management, especially in the context of climate change. This review highlights the application of TMDL in water quality management through various case studies and field applications, demonstrating its practical implementation. TMDL programs have been extensively utilized for water quality management, from pollutant reduction to the adoption of best management practices. A comprehensive analysis of several models, covering watershed, economic, machine learning, and simple frameworks, is systematically discussed to examine their strengths, limitations, effectiveness, and adaptability within the TMDL framework. Criteria for model selection are emphasized, balancing factors such as data availability, model complexity and accuracy, as well as time and cost considerations. We demonstrate that emerging machine learning techniques, simplified modeling approaches, and margin of safety estimation methods can help address data limitations and mitigate uncertainties in water quality assessment during the total pollutant load control process. Additionally, we address current challenges and future research directions in TMDL, particularly regarding modeling complex pollutant interactions and enhancing regulatory frameworks. This comprehensive review serves as an invaluable resource for environmental scientists, water quality managers, and policymakers seeking to implement and apply the TMDL framework effectively. It provides a clear understanding of the TMDL process, its practical applications, and the selection of appropriate modeling tools for successful water quality management.