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Monitoring network optimization and impact of fish farming upon water quality in the Três Marias Hydroelectric Reservoir, Brazil

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Abstract

Hydroelectric power is the main source of electrical energy in Brazil. Electrical energy providers have the duty to monitor water quality in reservoirs to preserve water quality and support best management practices that enable multiple water uses, including fish production. In this context, the objectives of this study were (i) to perform a historical evaluation of water quality in Três Marias Reservoir, (ii) to present an optimization of the water quality monitoring network, and (iii) to evaluate the evolution and impact of fish farming upon surface water quality by using secondary data measured in situ and remote sensing. A systematic approach was applied to analyze historical water quality data. Principal component analysis (PCA) and cluster analysis (CA) were applied to identify the most important parameters and monitoring points. Images obtained from Sentinel 2 were treated by contrast to quantify simple and weighted densities of fish farming activities in the region while regression analysis was performed to verify correlations between these densities and water quality parameters. Results showed that the pH and total suspended solids were the most important parameters for characterizing water quality, especially near tributaries, and that monitoring points could be grouped into three clusters (upstream, central, and downstream regions) with distinct water quality conditions. The PCA indicated that there is no redundance among parameters nor monitoring stations and that areas near tributaries must be prioritized for monitoring as these are important sources of suspended solids. Remote sensing images showed that the area occupied by fish farms has increased in the reservoir from 2016 to 2022 and the methodology used for this purpose in this study may be applied to other bodies of water. Chlorophyll-a showed a direct relationship with the density of fish farms indicating a possible influence of nutrient input to the reservoir by this activity. These results provide valuable information to support decision-making related to water management in the reservoir.

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Funding

The authors gratefully acknowledge the support provided by Companhia Energética de Minas Gerais (CEMIG), Agência Nacional de Energia Elétrica (ANEEL) and Universidade Federal de Minas Gerais (UFMG) for funding the project GT-0607 “Intelligent Water Quality Monitoring through the Development of Photooptical Algorithm.”

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Authors and Affiliations

  1. SIMOA-Intelligent Systems for Environmental Monitoring, Department of Sanitary and Environmental Engineering, Federal University of Minas Gerais, Av. Antônio Carlos, 6627, Belo Horizonte, Minas Gerais, 31270-901, Brazil

    Maria Clara V M Starling, Cristiano Christofaro, Luiz Eduardo Macedo-Reis, Phillippe Maillard & Camila C Amorim

  2. Federal University of the Jequitinhonha and Mucuri Valleys–UFVJM, Diamantina, MG, CEP 39100-000, Brazil

    Cristiano Christofaro

  3. Maram Projetos Ambientais, Rua Marquês de Pombal, 56A, Bom Retiro, Ipatinga, MG, Brazil

    Luiz Eduardo Macedo-Reis

  4. Department of Geography, Universidade Federal de Minas Gerais, 6627 Av. Antônio Carlos, Belo Horizonte, 31270-901, Brazil

    Phillippe Maillard

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MCVMS: conceptualization, methodology, investigation, writing original draft, writing, review, and editing. CC: conceptualization, methodology, investigation, writing original draft, writing, review, and editing. LEMR: conceptualization, methodology, investigation, and writing original draft. PM: conceptualization, methodology, investigation, writing original draft, writing, review, and editing. CA: conceptualization, writing, review, editing, project administration, funding acquisition, and supervision.

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Starling, M.C.V.M., Christofaro, C., Macedo-Reis, L.E. et al. Monitoring network optimization and impact of fish farming upon water quality in the Três Marias Hydroelectric Reservoir, Brazil. Environ Sci Pollut Res 31, 13455–13470 (2024). https://doi.org/10.1007/s11356-023-31761-5

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