Abstract
At the end of 2015, the Fundão dam belonging to the Samarco S.A. mining company was ruptured, releasing a flood of mud into the Gualaxo do Norte River, which advanced into the Doce River. The aim of the present study was to apply exploratory multivariate approaches to water quality data obtained during sampling campaigns at the Gualaxo do Norte River during the dry and rainy seasons, between July 2016 and June 2017. A total of 27 locations along the river were sampled, covering unaffected areas and regions influenced by the tailings waste from the dam. Determinations of chemical, physical, and microbiological water quality parameters were performed. Application of principal component analysis (PCA) resulted in the first two components together explaining 39.49% and 37.91% of the total variance for the dry and rainy season data, respectively. In both cases, the PCA groups were related to variables such as turbidity and total solids, which both presented higher values in regions affected by the mud flow. These results are in agreement with those obtained by the Kohonen neural network method, where two-dimensional maps confirmed the samples according to the affected and unaffected area by the disaster.
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References
Alexakis, D. E. (2020). Meta-evaluation of water quality indices. Application into Groundwater Resources. Water. https://doi.org/10.3390/w12071890
Alexakis, D. E., Kiskira, K., Gamvroula, D., et al. (2021). Evaluating toxic element contamination sources in groundwater bodies of two Mediterranean sites. Environmental Science and Pollution Research International. https://doi.org/10.1007/s11356-021-12957-z
American Water Work Association APHA. (2012). Standard methods for examination of water and wastewater. Water Environmental Federation.
Astel, A., Tsakovski, S., Barbieri, P., & Simeonov, V. (2007). Comparison of self-organizing maps classification approach with cluster and principal components analysis for large environmental data sets. Water Research. https://doi.org/10.1016/j.watres.2007.06.030
Azhar, S. C., Aris, A. Z., Yusoff, M. K., Ramli, M. F., & Juahir, H. (2015). Classification of river water quality using multivariate analysis. Procedia Environmental Sciences. https://doi.org/10.1016/j.proenv.2015.10.014
Ballabio, D. (2015). A MATLAB toolbox for principal component analysis and unsupervised exploration of data structure. Chemometrics and Intelligent Laboratory Systems. https://doi.org/10.1016/j.chemolab.2015.10.003
Ballabio, D., & Vasighi, M. (2012). A MATLAB toolbox for self organizing maps and supervised neural network learning strategies. Chemometrics and Intelligent Laboratory Systems. https://doi.org/10.1016/j.chemolab.2012.07.005
Barakat, A., El Baghdadi, M., Rais, J., Aghezzaf, B., & Slassi, M. (2016). Assessment of spatial and seasonal water quality variation of Oum Er Rbia River (Morocco) using multivariate statistical techniques. International Soil and Water Conservation Research. https://doi.org/10.1016/j.iswcr.2016.11.002
Bianchini, A. (2016). Avaliação do impacto da lama / pluma Samarco sobre os ambientes costeiros e marinhos (ES e BA) com ênfase nas Unidades de Conservação 1 a Expedição do Navio de Pesquisa Soloncy Moura do CEPSUL / ICMBio. Brasília.
Carmo, F. F., Kamino, L. H. Y., Junior, R. T., et al. (2017). Fundão tailings dam failures : the environment tragedy of the largest technological disaster of Brazilian mining in global context. Perspectives in Ecology and Conservation. https://doi.org/10.1016/j.pecon.2017.06.002
Çinar, Ö., & Merdun, H. (2009). Application of an unsupervised artificial neural network technique to multivariant surface water quality data. Ecological Research. https://doi.org/10.1007/s11284-008-0495-z
Costa, A. T. (2001). Geoquímica das águas e dos sedimentos da bacia do Rio Gualaxo do Norte, lestesudeste do Quadrilátero Ferrífero (MG): estudo de uma área afetada por atividades de extração mineral. Tese Universidade Federal de Ouro Preto.
da Silva, G. A. (2007). Utilização de Métodos Quimiométricos em Cromatografia Gasosa com Microextração em Fase Sólida 160. Tese Universidade Estadual de Campinas.
Fernandes, G. W., Goulart, F. F., Ranieri, B. D., et al. (2016). Deep into the mud: ecological and socio-economic of the dam breach, Brazil. Natureza & Conservação. https://doi.org/10.1016/j.ncon.2016.10.003
Freire, L. L., Costa, A. C., & Lima Neto, I. E. (2021). Spatio-temporal patterns of river water quality in the semiarid northeastern Brazil. Water, Air, and Soil Pollution. https://doi.org/10.1007/s11270-021-05406-7
Gamvroula, D., Alexakis, D., & Stamatis, G. (2013). Diagnosis of groundwater quality and assessment of contamination sources in the Megara basin (Attica, Greece). Arabian Journal of Geosciences. https://doi.org/10.1007/s12517-012-0533-6
Guedes, H. A. S., da Silva, D. D., Elesbon, A. A. A., Ribeiro, C. B. M., de Matos, A. T., & Soares, J. H. P. (2012). Application of multivariate statistical analysis in the study of water quality in the Pomba River (MG). Revista Brasileira de Engenharia Agrícola e Ambiental. https://doi.org/10.1590/S1415-43662012000500012
Haykin, S. (2001). Redes neurais - Princípios e prática. Porto Alegra: Bookman.
IGAM. (n.d.) Instituto Mineiro de Gestão das Águas - IGAM [WWW Document]. Accessed January 15, 2018, from http://www.igam.mg.gov.br/
Jung, K. Y., Lee, K. L., Im, T. H., Lee, I. J., Kim, S., Han, K. Y., & Ahn, M. (2016). Evaluation of water quality for the Nakdong River watershed using multivariate analysis. Environmental Technology & Innovation. https://doi.org/10.1016/j.eti.2015.12.001
Köppen, W. (1931). Climatology culture fund. Mexico: Economic Mexico.
Kowalkowski, T., Zbytniewski, R., Szpejna, J., & Buszewski, B. (2006). Application of chemometrics in river water classification. Water Research. https://doi.org/10.1016/j.watres.2005.11.042
Liu, C. W., Lin, K. H., & Kuo, Y. M. (2003). Application of factor analysis in the assessment of groundwater quality in a blackfoot disease area in Taiwan. Science of the Total Environment. https://doi.org/10.1016/S0048-9697(02)00683-6
Liu, L., Cao, T., Wang, X., et al. (2021). Spatio-temporal variability and water quality assessment of the Mudan River Watershed, Northern China: principal component analysis and water quality index. Desalination and Water Treat. https://doi.org/10.5004/dwt.2021.27758
Markad, A. T., Landge, A. T., Nayak, B. B., et al. (2021). A multivariate statistical approach for the evaluation of spatial and temporal dynamics of surface water quality from the small reservoir located in the drought-prone area of South-West India: a case study of Tiru reservoir (India). Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-020-12001-6
Matta, G., Kumar, A., Nayak, A., et al. (2022). Appraisal of spatial–temporal variation and pollution source estimation of Ganga River system through pollution indices and environmetrics in Upper Ganga basin. Applied Water Science. https://doi.org/10.1007/s13201-021-01552-9
Microsoft Corporation. (2018). Microsoft Excel.
Olkowska, E., Kudłak, B., Tsakovski, S., Ruman, M., Simeonov, V., & Polkowska, Z. (2014). Assessment of the water quality of Kłodnica River catchment using self-organizing maps. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2014.01.044
Ouyang, Y., Nkedi-Kizza, P., Wu, Q. T., Shinde, D., & Huang, C. H. (2006). Assessment of seasonal variations in surface water quality. Water Research. https://doi.org/10.1016/j.watres.2006.08.030
Phung, D., Huang, C., Rutherford, S., et al. (2015). Temporal and spatial assessment of river surface water quality using multivariate statistical techniques: a study in Can Tho City, a Mekong Delta area, Vietnam. Environmental Monitoring and Assessment. https://doi.org/10.1007/s10661-015-4474-x
Rakotondrabe, F., Ndam, N. J. R., Mfonka, Z., et al. (2018). Water quality assessment in the Bétaré-Oya gold mining area (East-Cameroon): Multivariate statistical analysis approach. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2017.08.080
Rhodes, V. P., de Lena, J. C., Santolin, C. V. A., et al. (2018). Speciation and quantification of Hg in sediments contaminated by artisanal gold mining in the Gualaxo do Norte River, Minas Gerais, SE, Brazil. Environmental monitoring and assessment. https://doi.org/10.1007/s10661-017-6394-4
Rudorf, N., Rudorf, C. M., Kampel, M., et al. (2018). Remote sensing monitoring of the impact of a major mining wastewater disaster on the turbidity of the Doce River plume of the eastern Brazilian coast. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2018.02.013
Sánchez, L. E., Alger, K., Alonso, L., et al. (2018). The impacts of the fundão dam rupture the path to sustainable and resilient mitigation. Gland. https://doi.org/10.2305/IUCN.CH.2018.18.pt
Simeonov, V., Stratis, J. A., Samara, C., et al. (2003). Assessment of the surface water quality in Northern Greece. Water Research. https://doi.org/10.1016/S0043-1354(03)00398-1
Singh, K. P., Malik, A., Mohan, D., & Sinha, S. (2004). Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India) - A case study. Water Research. https://doi.org/10.1016/j.watres.2004.06.011
Stamatis, G., Alexakis, D., Gamvroula, D., et al. (2011). Groundwater quality assessment in Oropos–Kalamos basin, Attica, Greece. Environmental Earth Sciences. https://doi.org/10.1007/s12665-011-0914-2
Telahigue, K., Rabeh, I., Chouba, L., et al. (2022). Assessment of the heavy metal levels and biomarker responses in the smooth scallop Flexopecten glaber from a heavily urbanized Mediterranean lagoon (Bizerte lagoon). Environmental Monitoring and Assessment. https://doi.org/10.1007/s10661-022-10071-2
The MathWorks Inc. (2018). MatLab 9.1, PLS Toolbox 8.2, Network Toolbox 9.1.
Funding
This work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—CAPES, Fundação de Amparo a Pesquisa do Estado de Minas Gerais—FAPEMIG, and Universidade Federal de Ouro Preto—UFOP, Brazil.
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All authors contributed to the study conception and design. Grazielle Rocha dos Santos and Deyse Almeida dos Reis performed the fieldwork. Grazielle Rocha dos Santos, Deyse Almeida dos Reis, Ingrid Couto Santos, Camila Soares Rocha, and Leandro Rodrigues Lima performed material preparation and the experimental. Paulo Bernardo Neves Castro and Anibal da Fonseca Santiago helped write the manuscript and design the figures. Grazielle Rocha dos Santos and Gilmare Antônia da Silva performed data treatment. Gilmare Antônia da Silva, Anibal da Fonseca Santiago, and Fabiana Aparecida Lobo were involved in planning and executing the work. Fabiana Aparecida Lobo provided the language revision. All authors discussed the results and commented on the manuscript.
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Santos, G., Reis, D., Santos, I. et al. Assessment of Gualaxo do Norte River water quality (Minas Gerais, Brazil) affected by the dam breach of Fundão utilizing exploratory multivariate techniques. Environ Monit Assess 195, 337 (2023). https://doi.org/10.1007/s10661-022-10907-x
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DOI: https://doi.org/10.1007/s10661-022-10907-x