Wasserstein distributional distance and linear programming convergence planning as monitoring network benchmarking tools: cross-continental surface water quality assessment across Brazilian and European river basins.

This framework helps improve and compare surface‑water quality monitoring networks in many different regions. It uses the Wasserstein distributional distance and linear‑programming convergence planning to check how well monitoring stations work in space and time. By looking at data from Brazilian and European river basins, the method systematically spots redundant stations and gaps in existing networks. Students are guided on how to handle historic water‑quality data—such as dissolved oxygen, pH, turbidity, and heavy‑metal concentrations—and how to calculate statistical distances between monitoring points. Linear‑programming models are then used to plan convergence, which optimizes the use of resources, the placement of sensors, and the frequency of sampling. The research supports the creation

of decision‑support tools for environmental engineers, allowing them to design monitoring networks that are both cost‑effective and high‑resolution. With computational simulations and data‑driven benchmarking, the framework provides thorough documentation for analyzing environmental data distributions. This ultimately improves watershed management, pollution control, and regulatory‑compliance monitoring under various international hydrological standards.

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