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Integrated environmental impact assessment of the Sasa tailing dam failure using remote sensing techniques

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Failures of tailings dams represent a critical environmental hazard, releasing mining by-products that cause long-term damage to nearby ecosystems. This research presents a detailed analysis using remote sensing techniques of the 2003 Sasa tailing dam disaster in North Macedonia. By utilising Landsat 5 imagery and Google Earth Engine (GEE), multiple spectral indices — including the Normalised Difference Vegetation Index (NDVI), Normalised Difference Moisture Index (NDMI), Normalised Difference Water Index (NDWI), Modified NDWI (MNDWI), and a turbidity proxy — were integrated to examine the immediate and spatial impacts on vegetation health, soil moisture, water presence, and sediment levels. The findings indicated significant ecological fluctuations along the river path, including vegetation stress, changes in soil moisture, water pooling, and turbidity. These effects displayed spatial gradients, diminishing further from the contamination pathway and forming distinct zones of influence. Certain intermediate areas showed anomalous disturbances, where sediment and hydrological changes impeded vegetation recovery. Pixel-level, buffer-based, and zone-based analyses - combined with Z-scores and correlation studies — revealed a complex post-disaster landscape. Weak correlation with topographic features suggested that localised conditions, rather than large-scale gradients, governed short-term ecological recovery. The study provides a framework for integrated, multi-index and multiscale environmental impact assessment, contributing to improved remediation strategies, disaster response planning, and sustainable management of post-mining landscapes.
Rocznik
Strony
525--556
Opis fizyczny
Bibliogr. 44 poz., rys., tab., wykr.
Twórcy
  • Faculty of Natural and Technical Sciences, Goce Delcev University, Stip, North Macedonia
  • Department of Mining Engineering and Safety, VSB – TUO, Ostrava Poruba, Czech Republic
Bibliografia
  • [1] D. Cheng, Y. Cui, Z. Li, J. Iqbal, Watch Out for the Tailings Pond, a Sharp Edge Hanging over Our Heads: Lessons Learned and Perceptions from the Brumadinho Tailings Dam Failure Disaster. Remote Sens. 13 (9), 1775 (2021).DOI: https://doi.org/10.3390/rs13091775.
  • [2] D. Kossoff, W.E. Dubbin, M. Alfredsson, S.J. Edwards, M.G. Macklin, K.A. Hudson-Edwards, Mine tailings dams: Characteristics, failure, environmental impacts, and remediation. Appl. Geochem. 51, 229-45 (2014).DOI : https://doi.org/10.1016/j.apgeochem.2014.09.010.
  • [3] C.Y. Omachi, S.M.O. Siani, F.M. Chagas, M.L. Mascagni, M. Cordeiro, G.D. Garcia, et al., Atlantic Forest losscaused by the world’s largest tailing dam collapse (Fundão Dam, Mariana, Brazil). Remote Sens. Appl.: Soc.Environ. 12, 30-4 (2018). DOI: https://doi.org/10.1016/j.rsase.2018.08.003.
  • [4] N. Rudorff, C.M. Rudorff, M. Kampel, G. Ortiz, Remote sensing monitoring of the impact of a major mining waste water disaster on the turbidity of the Doce River plume off the eastern Brazilian coast. Int. Soc. Photogramme.145, 349-61 (2018). DOI: https://doi.org/10.1016/j.isprsjprs.2018.02.013.
  • [5] L.H. Silva Rotta, E. Alcântara, E. Park, R.G. Negri, Y.N. Lin, N. Bernardo, et al., The 2019 Brumadinho tailingsdam collapse: Possible cause and impacts of the worst human and environmental disaster in Brazil. Int. J. Appl. Earth. Obs. 90, 102119 (2020). DOI: https://doi.org/10.1016/j.jag.2020.102119.
  • [6] U.R.V. Aires, B.S.M. Santos, C.D. Coelho, D.D. Da Silva, M.L. Calijuri, Changes in land use and land coveras a result of the failure of a mining tailings dam in Mariana, MG, Brazil. Land. Use. Policy. 70, 63-70 (2018).DOI: https://doi.org/10.1016/j.landusepol.2017.10.026.
  • [7] D.B.D.S. Teixeira, M.F. Veloso, F.L.V. Ferreira, J.M. Gleriani, C.H. Do Amaral, Spectro-temporal analysis of the Paraopeba River water after the tailings dam burst of the Córrego do Feijão mine, in Brumadinho, Brazil. Environ. Monit. Assess. 193 (7), 435 (2021). DOI: https://doi.org/10.1007/s10661-021-09218-4.
  • [8] F. Thompson, B.C. De Oliveira, M.C. Cordeiro, B.P. Masi, T.P. Rangel, P. Paz, et al., Severe impacts of the Brumadinho dam failure (Minas Gerais, Brazil) on the water quality of the Paraopeba River. Sci. Total. Environ. 705,135914 (2020). DOI: https://doi.org/10.1016/j.scitotenv.2019.135914.
  • [9] C. Cacciuttolo, D. Cano, Spatial and Temporal Study of Supernatant Process Water Pond in Tailings Storage Facilities: Use of Remote Sensing Techniques for Preventing Mine Tailings Dam Failures. Sustainability-Basel.15 (6), 4984 (2023). DOI: https://doi.org/10.3390/su15064984.
  • [10] S.M. Ouellet, J. Dettmer, G. Olivier, T. DeWit, M. Lato, Advanced monitoring of tailings dam performance using seismic noise and stress models. Comm. Earth Environ. 3 (1), 301 (2022).DOI : https://doi.org/10.1038/s43247-022-00629-w.
  • [11] A.P.D. Souza, P.E. Teodoro, L.P.R. Teodoro, A.C. Taveira, J.F. De Oliveira-Júnior, J.L. Della-Silva, et al., Application of remote sensing in environmental impact assessment: a case study of dam rupture in Brumadinho, Minas Gerais, Brazil. Environ. Monit. Assess. 193 (9), 606 (2021). DOI: https://doi.org/10.1007/s10661-021-09417-z.
  • [12] M. Zare, F. Nasategay, J.A. Gomez, A. Moayedi Far, J. Sattarvand, A Review of Tailings Dam Safety Monitoring Guidelines and Systems. Mineral-Basel. 14 (6), 551 (2024). DOI: https://doi.org/10.3390/min14060551.
  • [13] M.M. Harb, F. Dell’Acqua, Remote Sensing in Multirisk Assessment: Improving disaster preparedness. IEEE. Geosci. Remote. M. 5 (1), 53-65 (2017). DOI: https://doi.org/10.1109/MGRS.2016.2625100.
  • [14] C . Huyck, E. Verrucci, J. Bevington, Remote Sensing for Disaster Response : A Rapid, Image-Based Perspective. Earthq. Hazard Risk Disasters. 1-24 (2014). DOI: https://doi.org/10.1016/B978-0-12-394848-9.00001-8.
  • [15] S. Kumari, S. Agarwal, N.K. Agrawal, A. Agarwal, M.C. Garg, A Comprehensive Review of Remote Sensing Technologies for Improved Geological Disaster Management. Geol. J. 60 (1), 223-35 (2025).DOI : https://doi.org/10.1002/gj.5072.
  • [16] P.S. Showalter, Remote sensing’s use in disaster research: a review. Disaster. Prev. Manag. 10 (1), 21-9 (2001).DOI: https://doi.org/10.1108/09653560110381796.
  • [17] L. Ammirati, R. Chirico, D. Di Martire, N. Mondillo, Application of Multispectral Remote Sensing for Mapping Flood-Affected Zones in the Brumadinho Mining District (Minas Gerais, Brasil). Remote Sens. 14 (6), 1501 (2022).DOI: https://doi.org/10.3390/rs14061501.
  • [18] S. Grebby, A. Sowter, J. Gluyas, D. Toll, D. Gee, A. Athab, et al., Advanced analysis of satellite data reveals ground deformation precursors to the Brumadinho Tailings Dam collapse. Commun. Earth Environ. 2 (1), 2 (2021).DOI: https://doi.org/10.1038/s43247-020-00079-2.
  • [19] N .M. Rana, K.B. Delaney, S.G. Evans, E. Deane, A. Small, D.A.M. Adria, et al., Application of Sentinel-1 In SAR to monitor tailings dams and predict geotechnical instability: practical considerations based on case study insights. B. Eng. Geol. Environ. 83 (5), 204 (2024). DOI: https://doi.org/10.1007/s10064-024-03680-3.
  • [20] P.L.B. Crioni, E.H. Teramoto, H.K. Chang, Monitoring river turbidity after a mine tailing dam failure using anempirical model derived from Sentinel-2 imagery. An. Acad. Bras. Cienc. 95 (1), e20220177 (2023).DOI : https://doi.org/10.1590/0001-3765202320220177.
  • [21] C .R.M. Filho, R.F. Do Valle Junior, M.M.A.P. De Melo Silva, R.G. Mendes, G. De Souza Rolim, T.C.T. Pissarra, et al., The Accuracy of Land Use and Cover Mapping across Time in Environmental Disaster Zones: The Case of the B1 Tailings Dam Rupture in Brumadinho, Brazil. Sustainability-Basel. 15 (8), 6949 (2023).DOI : https://doi.org/10.3390/su15086949.
  • [22] P. Vrhovnik, N. Rogan Šmuc, T. Dolenec, T. Serafimovsi, G. Tasev, M. Dolenec, Geochemical investigation of Sasa tailings dam material and its influence on the Lake Kalimanci surficial sediments (Republic of Macedonia)– preliminary study. Geologija 53 (2), 169-76 (2011). DOI: https://doi.org/10.5474/geologija.2011.013.
  • [23] P. Vrhovnik, N.R. Šmuc, T. Dolenec, T. Serafimovski, M. Dolenec, An evaluation of trace metal distribution and environmental risk in sediments from Lake Kalimanci (FYR Macedonia). Environ. Earth. Sci. 70 (2), 761-75(2013). DOI: https://doi.org/10.1007/s12665-012-2166-1.
  • [24] R. Sangeetham, N. Reddy S, A Review On Google Earth Engine: An Open Access Cloud Analysis Platform For Planetary Scale Satellite Data. E3S. Web. Conf. 591, 09011 (2024). DOI: https://doi.org/10.1051/e3sconf/202459109011.
  • [25] Q. Zhao, L. Yu, X. Li, D. Peng, Y. Zhang, P. Gong, Progress and Trends in the Application of Google Earth and Google Earth Engine. Remote Sens. 13 (18), 3778 (2021). DOI: https://doi.org/10.3390/rs13183778.
  • [26] M. Hemati, M. Hasanlou, M. Mahdianpari, F. Mohammadimanesh, A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth. Remote Sens. 13 (15), 2869 (2021).DOI : https://doi.org/10.3390/rs13152869.
  • [27] V. Kharat, S. Khatdeo, H. Kothe, R. Kshirsagar, M. Dixit, M.S. Balan, Atmospheric Image Correction and Removal of Cloud Cover for Satellite Images. 2023 Int. Conf. Sustain. Comput. Smart Syst. 1711-8 (2023).DOI : https://doi.org/10.1109/icscss57650.2023.10169542.
  • [28] G. Chander, B.L. Markham, J.A. Barsi, Revised Landsat-5 Thematic Mapper Radiometric Calibration. IEEE Geosci. Remote S. 4 (3), 490-4 (2007). DOI: https://doi.org/10.1109/lgrs.2007.898285.
  • [29] B. cai Gao, NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58 (3), 257-66 (1996). DOI: https://doi.org/10.1016/S0034-4257(96)00067-3.
  • [30] S.K. McFeeters, The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 17 (7), 1425-32 (1996). DOI: https://doi.org/10.1080/01431169608948714.
  • [31] S. Szabó, Z. Gácsi, B. Balázs, Specific features of NDVI, NDWI and MNDWI as reflected in land cover categories. Landsc. Environ. 10 (3-4), 194-202 (2016). DOI: https://doi.org/10.21120/LE/10/3-4/13.
  • [32] Y. Wang, F. Huang, Y. Wei, Water body extraction from LANDSAT ETM+ image using MNDWI and K-T transformation. 2013 21st Int. Conf. Geoinf. 1-5 (2013). DOI: https://doi.org/10.1109/geoinformatics.2013.6626162.
  • [33] X. Xiao, D. Hollinger, J. Aber, M. Goltz, E.A. Davidson, Q. Zhang, et al., Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sens. Environ. 89 (4), 519-34 (2004).DOI : https://doi.org/10.1016/j.rse.2003.11.008.
  • [34] D . Browning, C. Steele, Vegetation Index Differencing for Broad-Scale Assessment of Productivity Under Prolonged Drought and Sequential High Rainfall Conditions. Remote Sens. 5 (1), 327-41 (2013).DOI : https://doi.org/10.3390/rs5010327.
  • [35] T.L. Swetnam, S.R. Yool, S. Roy, D.A. Falk, On the Use of Standardized Multi-Temporal Indices for Monitoring Disturbance and Ecosystem Moisture Stress across Multiple Earth Observation Systems in the Google Earth Engine. Remote Sens. 13 (8), 1448 (2021). DOI: https://doi.org/10.3390/rs13081448.
  • [36] P. Lemenkova, O. Debeir, R Libraries for Remote Sensing Data Classification by K-Means Clustering and NDVI Computation in Congo River Basin, DRC. Appl. Sci-Basel. 12 (24), 12554 (2022).DOI : https://doi.org/10.3390/app122412554.
  • [37] A. Mimenbayeva, S. Artykbayev, R. Suleimenova, G. Abdygalikova, A. Naizagarayeva, A. Ismailova, Determination of the number of clusters of normalized vegetation indices using the k-means algorithm. East.-Eur. J. Enterp. Technol. 5 (2 (125)), 42-55 (2023). DOI: https://doi.org/10.15587/1729-4061.2023.290129.
  • [38] A. Nelson, T. Oberthür, S. Cook, Multi‐scale correlations between topography and vegetation in a hillside catchment of Honduras. Int. J. Geogr. Inf. Sci. 21 (2), 145-74 (2007). DOI: https://doi.org/10.1080/13658810600852263.
  • [39] N .A.S. Sampaio, F.C. Mazza, S.S.S.D. Siqueira, J.E. Miranda Junior, J.V.D.S. Moutinho, L.D.O. Pacífico, Applications of Correlation Analysis in Environmental Problems. Rev. Gest. Soc. Ambient. 18 (3), e04925 (2024).DOI: http://dx.doi.org/10.24857/rgsa.v18n3-085.
  • [40] D.W. Clow, L. Nanus, B. Huggett, Use of regression‐based models to map sensitivity of aquatic resources to atmospheric deposition in Yosemite National Park, USA. Water Resour. Res. 46 (9), 2009WR008316 (2010).DOI: https://doi.org/10.1029/2009WR008316.
  • [41] G. Pickup, V.H. Chewings, Correlations between dem-derived topographic indices and remotely-sensed vegetation cover in rangelands. Earth Surf. Proc. Land. 21 (6), 517-29 (1996).DOI : https://doi.org/10.1002/(SICI)1096-9837(199606)21:6<517::AID-ESP609>3.0.CO;2-N.
  • [42] B. Pratt, H. Chang, Effects of land cover, topography, and built structure on seasonal water quality at multiplespatial scales. J. Hazard. Mater. 209-210, 48-58 (2012). DOI: https://doi.org/10.1016/j.jhazmat.2011.12.068.
  • [43] G.C. Avena, C. Ricotta, F. Volpe, The influence of principal component analysis on the spatial structure of a multispectral dataset. Int. J. Remote Sens. 20 (17), 3367-76 (1999). DOI: https://doi.org/10.1080/014311699211381.
  • [44] J. Estornell, J.M. Martí-Gavliá, M.T. Sebastiá, J. Mengual, Principal component analysis applied to remote sensing. Model. Sci. Educ. Learn. 6, 2 (2013). DOI: https://doi.org/10.4995/msel.2013.1905.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a3079463-c268-4cde-8643-c6d2334fcacc
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