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<p>Water pollution&#xa0;presents a serious&#xa0;global challenge, necessitating the&#xa0;comprehensive evaluation of organic compounds in water bodies. Chemical Oxygen Demand (COD) is a crucial metric for assessing wastewater quality, providing insights into necessary treatment levels. This review critically examines both conventional and emerging COD detection methods, systematically categorized into six principal operational paradigms based on their underlying mechanisms, including optical, electrochemical, and AI-based approaches. Traditional techniques, such as the standard dichromate reflux method, offer regulatory reliability but involve hazardous reagents and prolonged analytical times. In contrast, emerging approaches- including spectrophotometry, chemiluminescence&#xa0;(CL), electrochemical sensing, flow injection analysis (FIA), UV/O<sub>3</sub> oxidation, photocatalysis, microfluidics, and Artificial Intelligence (AI)-driven models provide faster, more environmentally friendly alternatives with real-time monitoring capabilities. The integration of Machine Learning (ML) and Internet of Things (IoT) technologies further enhances predictive accuracy and&#xa0;system adaptability for across&#xa0;diverse water matrices. These innovative methods incorporate advanced oxidation processes&#xa0;(AOPs) and smart sensing technologies to&#xa0;increase sensitivity, reduce chemical usage, and support&#xa0;adaptive water-quality management. The proposed review article aims to provide a comprehensive understanding of COD detection techniques, facilitating the selection of appropriate methods for specific applications. The review highlights current challenges such as matrix interference, standardization, and limited scalability, and outlines that&#xa0;future research must prioritize the development of standardized protocols, integrated hybrid sensor networks, and scalable green technologies to advance sustainable COD monitoring in alignment with Sustainable Development Goal (SDG) 6, which aims to ensure the global water security.</p> Graphical Abstract <p></p>

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Emerging Chemical Oxygen Demand Detection Techniques for Wastewater: Advances in Sustainable Analytical Methods

  • 期刊论文

  • 发表日期：

    2025年10月2日

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  • Nishant Chavhan,
  • Gayatri Gawande

摘要

Water pollution presents a serious global challenge, necessitating the comprehensive evaluation of organic compounds in water bodies. Chemical Oxygen Demand (COD) is a crucial metric for assessing wastewater quality, providing insights into necessary treatment levels. This review critically examines both conventional and emerging COD detection methods, systematically categorized into six principal operational paradigms based on their underlying mechanisms, including optical, electrochemical, and AI-based approaches. Traditional techniques, such as the standard dichromate reflux method, offer regulatory reliability but involve hazardous reagents and prolonged analytical times. In contrast, emerging approaches- including spectrophotometry, chemiluminescence (CL), electrochemical sensing, flow injection analysis (FIA), UV/O3 oxidation, photocatalysis, microfluidics, and Artificial Intelligence (AI)-driven models provide faster, more environmentally friendly alternatives with real-time monitoring capabilities. The integration of Machine Learning (ML) and Internet of Things (IoT) technologies further enhances predictive accuracy and system adaptability for across diverse water matrices. These innovative methods incorporate advanced oxidation processes (AOPs) and smart sensing technologies to increase sensitivity, reduce chemical usage, and support adaptive water-quality management. The proposed review article aims to provide a comprehensive understanding of COD detection techniques, facilitating the selection of appropriate methods for specific applications. The review highlights current challenges such as matrix interference, standardization, and limited scalability, and outlines that future research must prioritize the development of standardized protocols, integrated hybrid sensor networks, and scalable green technologies to advance sustainable COD monitoring in alignment with Sustainable Development Goal (SDG) 6, which aims to ensure the global water security.

Graphical Abstract