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Published on: September 11, 2016
A quality index-based methodological perspective to assess surface water geochemical evolution under global change
Lorenzo Chemeri1, Marco Taussi2, Jacopo Cabassi3
1Department of Pure and Applied Sciences, University of Urbino Carlo Bo, Via Ca' Le Suore 2/4, Urbino, 61029, Italy; Department of Earth Sciences, University of Florence, Via G. La Pira 4, 50121, Florence, Italy.
None:
Effective and sustainable water management is a major challenge under global change and increasing human pressures. Developing reliable and user-friendly tools is essential to evaluate water quality over time and space and to support decision-making. This study presents the application of the recently developed Chemical Water Quality Index (CWQI), a methodological framework designed to provide a simple, flexible, and widely applicable approach for quantifying water quality. The main objectives were to: (i) track the evolution of water chemistry along the river course, (ii) assess the contribution of different solutes to overall quality, (iii) detect contamination hotspots, and (iv) explore long-term trends in relation to environmental policies. The approach was tested on the Arno River Basin (Tuscany, Italy), one of the largest and most impacted catchments in central Italy, using published geochemical data from four periods (1988-1989, 1996-1997, 2002-2003 and 2017). Results indicate good to fair quality in upstream reaches, with clear deterioration downstream of Florence, primarily linked to chloride, sodium, and sulphate inputs from urban, industrial, and agricultural activities. Despite increasing anthropogenic pressures, water chemistry remained relatively stable over three decades, suggesting that regulatory measures helped to prevent further degradation. Beyond this case study, the CWQI represents an operational tool that can be readily applied in different contexts. Future developments should be aimed at overcoming some limitations and including integration with biological indicators and the use of longer, high-resolution datasets to capture seasonal variability, separate natural from anthropogenic drivers, and provide stronger support for sustainable river management.
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