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Hydrochemical Contamination Profiling and Spatial-Temporal Mapping with the Support of Multivariate and Cluster Statistical Analysis

Authors: S. Barbosa, M. Pinto, J. A. Almeida, E. Carvalho, C. Diamantino

Abstract:

The aim of this work was to test a methodology able to generate spatial-temporal maps that can synthesize simultaneously the trends of distinct hydrochemical indicators in an old radium-uranium tailings dam deposit. Multidimensionality reduction derived from principal component analysis and subsequent data aggregation derived from clustering analysis allow to identify distinct hydrochemical behavioral profiles and generate synthetic evolutionary hydrochemical maps.

Keywords: Contamination plume migration, K-means of PCA scores, groundwater and mine water monitoring, spatial-temporal hydrochemical trends.

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References:


[1] Unified Soil Classification System (USCS) ASTM D2487-11 Directive: Standard Practice for Classification of Soils for Engineering Purposes.
[2] A. K. Howard, Soil Classification Handbook: Unified Soil Classification System. Denver, Colorado: Geotechnical Branch, Division of Research and Laboratory Services, Engineering and Research Center, Bureau of Reclamation, 1986.
[3] EDM, The legacy of abandoned mines – The context and the action in Portugal. EDM and DGEG (eds.), 2011. http://ec.europa.eu/environment/waste/mining/pdf/Appendix_III_to_Annex3.pdf.
[4] ISRM, International Society for Rock Mechanics, Rock Characterization, Testing and Monitoring - ISRM Suggested Methods, Pergamon Press, Oxford, UK, 1981.
[5] M. Pinto, Spatio-temporal evolution of hydrodynamic and hydrochemical conditions in a former tailings dam as a result of its environmental remediation (Evolução espácio-temporal das alterações hidrodinâmicas e hidroquímicas numa antiga barragem de rejeitados mineiros em resultado de obras de recuperação ambiental). MSc Dissertation, Faculty of Science and Technology, NOVA University of Lisbon, 2016.
[6] W. H. Chiang, W. Kinzelbach, “PMWIN Processing Modflow, A Simulation System for Modeling Groundwater Flow and Pollution”, Hamburg, Zürich, 334 p, 1998.
[7] R. M. Hirsch, J. R. Slack, R. A. Smith, Techniques of trend analysis for monthly water quality data. Water Resour. Res., 1982, 18-1, 107-121. https://doi.org/10.1029/2009WR008071.
[8] R.M. Hirsch, J.R. Slack, A Nonparametric Trend Test for Seasonal Data with Serial Dependence. Water Resources Research, 1984, 20, 727-732. https://doi.org/10.1029/WR020i006p00727.
[9] Z.W. Kundzewicz and A.J. Robson. Change Detection in Hydrological Records - A Review of the Methodology. Hydrological Sciences Journal, 2004, 49, 7-19.
[10] H. Boyacioglu and H. Boyacioglu, Investigation of Temporal Trends in Hydrochemical Quality of Surface Water in Western Turkey. Bull Environ Contam Toxicol, 2008, 80, 469–474. https:doi.org/10.1007/s00128-008-9439-0.
[11] K. Wahlin and A. Grimvall, Uncertainty in water quality data and its implications for trend detection: lessons from Swedish environmental data. Env. Science & Policy, 2008, 11, 2, 115-124. ISSN 1462-9011, https://doi.org/10.1016/j.envsci.2007.12.001.
[12] R. E. Chandler, E. M. Scott (eds.), Statistical Methods for Trend Detection and Analysis in the Environmental Sciences, John Wiley & Sons, Ltd, 2011. https:doi.org/10.1002/9781119991571.
[13] J. Mozejko, Detecting and Estimating Trends of Water Quality Parameters in Water Quality Monitoring and Assessment, Dr. Voudouris (Ed.), 2012. ISBN: 978-953-51-0486-5.
[14] D. Anghileri, F. Pianosi and R. Soncini-Sessa, Trend detection in seasonal data: from hydrology to water resources. Journal of Hydrology, 2014, 511, 171-179. ISSN 0022-1694. https://doi.org/10.1016/j.jhydrol.2014.01.022.
[15] D.T. Monteith, C.D. Evans, P.A. Henrys, G.L. Simpson, I.A. Malcolm, Trends in the hydrochemistry of acid-sensitive surface waters in the UK 1988–2008. Ecological Indicators, 2014, 37, Part B, 287-303. ISSN 1470-160X, https://doi.org/10.1016/j.ecolind.2012.08.013.
[16] R. J. Cooper, K. M. Hiscock, A. A. Lovett, S. J. Dugdale, G. Sünnenberg, E. Vrain, Temporal hydrochemical dynamics of the River Wensum, UK: Observations from long-term high-resolution monitoring (2011–2018), Science of The Total Environment, 2020, 724, 138253. ISSN 0048-9697, https://doi.org/10.1016/j.scitotenv.2020.138253.
[17] K. Voudouris, A.Panagopoulos, J. Koumantakis, Multivariate Statistical Analysis in the Assessment of Hydrochemistry of the Northern Korinthia Prefecture Alluvial Aquifer System (Peloponnese, Greece). Natural Resources Research, 2000, 9, 135–146. https://doi.org/10.1023/A:1010195410646
[18] C. Diamantino, E. Carvalho, R. Pinto, Water resources monitoring and mine water control in Portuguese old uranium mines. Proceeding of IMWA2016 Annual Conference, 2016, July 11–15, Leipzig, Germany.
[19] Q. Zhang, H. Wang, Y. Wang, M. Yang, L. Zhu, Groundwater quality assessment and pollution source apportionment in an intensely exploited region of northern China. Environ. Sci. Pollut. Res., 2017, 24, 16639–16650. https://doi.org/10.1007/s11356-017-9114-2.
[20] K. Y. Yeung, D. R. Haynor, W. L. Ruzzo, Validating clustering for gene expression data. Bioinformatics, 2001, Volume 17, Issue 4, 309–318. https://doi.org/10.1093/bioinformatics/17.4.309
[21] S. K. Swanson, J. M. Bahr, M. T. Schwar, K. W Potter, Two-way cluster analysis of geochemical data to constrain spring source waters. Chemical Geology, 2001, 179, 73–91.
[22] C. Ding, X. He, Cluster Structure of K-means Clustering via Principal Component Analysis. In: Dai H., Srikant R., Zhang C. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture Notes in Computer Science, vol 3056. Springer, Berlin, Heidelberg, 2004. https://doi.org/10.1007/978-3-540-24775-3_50.
[23] Koonce, J.E., Yu, Z., Farnham, I.M., Stetzenbach, K.J. (2006). Geochemical interpretation of groundwater flow in the southern Great Basin. Geosphere, 2 (2), 88–101. https://doi.org/10.1130/GES00031.1
[24] M. Templ, P. Filzmoser, C. Reimann, Cluster analysis applied to regional geochemical data: Problems and possibilities. Applied Geochemistry, 2008, 23, 2198–2213.
[25] G. Thyne, C. Güler, E. Poeter, Sequential Analysis of Hydrochemical Data for Watershed Characterization. Ground Water, 2008, Vol. 42, 5, 711–723. https://doi.org/10.1111/j.1745-6584.2004.tb02725x
[26] R. Ledesma-Ruiz, E. Pastén-Zapata, R. Parra, T. Harter, J. Mahlknecht, Investigation of the geochemical evolution of groundwater under agricultural land: A case study in Northeastern Mexico. Journal of Hydrology, 2015, 521, 410–423.
[27] D. Machiwala and K. J. Madan, Identifying sources of groundwater contamination in a hard-rock aquifer system using multivariate statistical analyses and GIS-based geostatistical modeling techniques. Journal of Hydrology: Regional Studies, 2015, 4, 80–110.
[28] P. Mandel, M. Maurel, D. Chenu, Better understanding of water quality evolution in water distribution networks using data clustering. Water Research, 2015, 87, 69-78.
[29] K. Peng, X. Li, Z. Wang, Hydrochemical characteristics of groundwater movement and evolution in the Xinli deposit of the Sanshandao gold mine using FCM and PCA methods. Environ. Earth Sci., 2015, 73,7873–7888. https://doi.org/10.1007/s12665-014-3938-6
[30] H. Li and Y. Gao, Multivariate statistical approaches to identify the major factors governing groundwater quality. Appl. Water Sci., 2018, 8, 215. https://doi.org/10.1007/s13201-018-0837-0
[31] G. Sotomayor, H. Hampel, R. F. Vázquez, Water quality assessment with emphasis in parameter optimisation using pattern recognition methods and genetic algorithm. Water Research, 2018, 130, 353-362.
[32] A.E. Marín Celestino, J.A. Ramos Leal, D.A. Martínez Cruz, J. Tuxpan Vargas, J. De Lara Bashulto, J. Morán Ramírez, Identification of the Hydrogeochemical Processes and Assessment of Groundwater Quality, Using Multivariate Statistical Approaches and Water Quality Index in a Wastewater Irrigated Region. Water, 2019, 11, 1702. https://doi.org/10.3390/w11081702.
[33] B. Helena, R. Pardo, M. Veja, E. Barrado, J. M. Fernandez, L. Fernandez, Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga River, Spain) by principal component analysis, 2000. Water Research, Vol. 34, Issue 3, 807-816. https://doi.org/10.1016/S0043-1354(99)00225-0
[34] ATSDR (Agency for Toxic Substances and Disease Regestry), Toxicological Profile for Uranium, in Toxic Substances Portal, Agency for Toxic Substances and Disease Registry, 339–357, 2011. https://www.atsdr.cdc.gov/toxprofiles/tp150-c7.pdf, Accessed 5 may 2021.
[35] L.S. Keith, O. M. Faroon, B. A. Fowler, Uranium. In G. F. Nordberg, B. A. Fowler, M. Nordberg (eds.), Handbook on the Toxicology of Metals, pp. 881–900, Academic Press, Fourth Edition, 2014.
[36] T. Hothorn, B. S. Everitt, A Handbook of Statistical Analyses using R. CRAN.R-project.org document, 2015. https://cran.r-project.org/web/packages/HSAUR3. Accessed 15 January 2017.
[37] T. Hothorn, B. S. Everitt, A Handbook of Statistical Analyses using R. Chapman & Hall/CRC Press, Third Edition, 2017.
[38] R Core Team, R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2013. http://www.R-project.org/.
[39] T. Rahlf, Data Visualisation with R – 100 Examples. Springer. e-Book ISBN: 978-3-319-49751-8, 2017.
[40] J. F. Santos, I. Pulido-Calvo, M. M. Portela, Spatial and temporal variability of droughts in Portugal. Water Resour. Res., 2010, 46, W03503.https://doi.org/10.1029/2009WR008071
[41] B. Zhang, X. Song, Y. Zhang, D. Han, C.Tang, Y. Yu, Y. Ma, Hydrochemical characteristics and water quality assessment of surface water and groundwater in Songnen plain, Northeast China, Water Resour. Res., 2012, 46, 2737-2748.
[42] P. Wuttichaikitcharoen and M. S. Babel, Principal Component and Multiple Regression Analyses for the Estimation of Suspended Sediment Yield in Ungauged Basins of Northern Thailand. Water, 2014, 6, 2412-2435; https://doi.org/10.3390/w6082412
[43] S. Selvakumar, N. Chandrasekar, G. Kumar, Hydrogeochemical characteristics and groundwater contamination in the rapid urban development areas of Coimbatore, India. Water Resources and Industry, 2017, 17, 26–33.
[44] K. Pearson, On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 1901, 2:559-572. In
[45] D. Kim and Se-K Kim, Comparing patterns of component loadings: Principal Component Analysis (PCA) versus Independent Component Analysis (ICA) in analysing multivariate non-normal data. Behav. Res., 2012, 44:1239–1243. https://doi.org/10.3758/s13428-012-0193-1.
[46] S. Le, J. Josse, F. Husson, FactoMineR: An R Package for Multivariate Analysis. Journal of Statistical Software, 2008, 25(1), 1-18. https://doi.org/10.18637/jss.v025.i01