A review of water quality models and monitoring methods for capabilities of pollutant source identification, classification, and transport simulation

Water quality monitoring and modeling are vital in improving the aquatic ecosystem's health and surroundings. The advancements in computer science and its integration with mathematics have resulted in the development of divergent algorithms and models for evaluating/predicting water quality and simulating the fate/transport of environmental contaminants. In this paper, four widely used statistical methods/algorithms, viz. (1) topological method, (2) multivariate statistics, (3) geostatistics, and (4) information entropy method, have been discussed and assessed. The assessment is based on its application merits and de-merits in recent environmental/water resources projects to advocate its suitability and flexibility in water quality analysis. The assessment parameters of suitability taken into account are pollutant source identification and classification. The reviewed methods argue for river water quality improvement through restoration and pollution control plans, simultaneously trying to minimize the number of sampling locations. Further, the five most widely used WQ models, viz. MIKE, AQUATOX, SWAT, IBER, and TELEMAC have been compared based on their mode of access (paid/freely available), input data requirement, output, and applicability for specific scenarios (e.g., oil spillage, contaminant transport, etc.). This paper is the first of its kind that compares and reviews IBER software and other water quality modeling/analysis software. The review is constructed to guide the reader in selecting a particular method and software/model in various scenarios. The study of the water quality models will also help in selecting the most accurate model to uncover the distribution of biochemical contaminants in a water body and its prediction to generate risk maps.

Graphical Abstract