Asis Mazumdar

Industries, agriculture, deforestation, urbanization, and waste disposal are major sources of river pollution, causing contamination and eutrophication. Although river water quality has been widely studied, sediment pollution remains a persistent and often overlooked threat due to the buildup of toxic substances. This review examines 22 years of global research on river sediment pollution, screening 554 Scopus articles in accordance with PRISMA guidelines. Of these, 179 papers focus on ten key parameters: polychlorinated biphenyls (PCBs), freely dissolved PCBs, pesticide concentration, particulate organic matter, total organic carbon, total organic nitrogen, total organic phosphorus, total suspended solids, ecotoxicity tests, and heavy metal concentration. A scientometric analysis using VOSviewer is done to evaluate keyword co-occurrence, publication trends, leading countries, influential journals, and research areas. This is the first review to integrate these ten parameters within a unified scientometric and qualitative framework, providing a comprehensive overview of the research landscape. The review highlights dominant pollutants such as PCBs and heavy metals, systematically identifies research gaps, and notes that environmental engineering is the leading subject area. It also identifies the most productive researchers, the most frequently cited articles, and the major contributing countries, including significant contributions from developing nations. Study sites, assessment methods, and key findings are summarized, along with lists of the most harmful contaminants and publication trends. The review recommends mitigation strategies and serves as a valuable reference for future research, risk assessment, and sediment management.

Freshwater essential for civilization faces risk from untreated effluents discharged by industries, agriculture, urban areas, and other sources. Increasing demand and abstraction of freshwater deteriorate the pollution scenario more. Hence, water quality analysis (WQA) is an important task for researchers and policymakers to maintain sustainability and public health. This study aims to gather and discuss the methods used for WQA by the researchers, focusing on their advantages and limitations. Simultaneously, this study compares different WQA methods, discussing their trends and future directions. Publications from the past decade on WQA are reviewed, and insights are explored to aggregate them in particular categories. Three major approaches, namely—water quality indexing, water quality modeling (WQM) and artificial intelligence‐based WQM, are recognized. Different methodologies adopted to execute these three approaches are presented in this study, which leads to formulate a comparative discussion. Using statistical operations and soft computing techniques have been done by researchers to combat the subjectivity error in indexing. To achieve better results, WQMs are being modified to incorporate the physical processes influencing water quality more robustly. The utilization of artificial intelligence was primarily restricted to conventional networks, but in the last 5 years, implications of deep learning have increased rapidly and exhibited good results with the hybridization of feature extracting and time series modeling. Overall, this study is a valuable resource for researchers dedicated to WQA.