Skip to main content
SpringerLink
Log in

Integrating Data from Suquía River Basin: Chemometrics and Other Concepts

  • Chapter
  • First Online:
The Suquía River Basin (Córdoba, Argentina)

Part of the book series: The Handbook of Environmental Chemistry ((HEC,volume 62))

  • 241 Accesses

Abstract

Assessing the water quality in a river basin seems to be an easy tool. However, some degree of expertise is required when planning and executing several tasks necessary to avoid either excessive or insufficient data. In this chapter we briefly describe some aspect of sampling and sample preparation, which have been discussed in deep in previous chapters. However, after sampling a river, it is necessary to analyse several parameters, arising from many monitoring stations, sampled at different time, etc. All of this generates an amazing database that needs to be carefully explored, looking to extract the most relevant information on changes in the water quality, probable pollution sources, temporal and spatial changes and so on. One simple approximation is the construction of water quality indices from both chemical and biological data, deep discussed in Bistoni et al. (Handb Environ Chem. https://doi.org/10.1007/698_2016_455, 2016) and AmÕ and Pesce (Handb Environ Chem. https://doi.org/10.1007/698_2015_434, 2015). The other way is using multivariate statistics (chemometrics), trying to evidence which changes are occurring, where and when. Here we discuss several chemometrics methods used to verify changes in the water quality of the Suquía River basin, starting with non-supervised methods, like cluster analysis (CA), factor analysis (FA, including principal components analysis – PCA) and going to supervised methods, namely, discriminant analysis (DA) and generalized procrustes analysis (GPA). Although PCA has become the most popular method of analysis for the evaluation of water quality, we think that PCA should be complemented with other methods that help to corroborate results from PCA. In this case, we used CA as a primary tool to evidence spatial differences in the water quality along the basin, confirming these results by PCA, which also added evidence on temporal differences. DA allowed further confirmation of both temporal and spatial changes, with an important data reduction, which is important for the survey of a river basin when the budget is restrictive. Finally, GPA brings further confirmation of other chemometrics methods, enabling a clear differentiation between water quality at diverse river sections, during both dry and rainy season. So far, we truly expect that this chapter helps readers to better design future surveys to evaluate changes of the water quality in other rivers worldwide.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hopke PK (2015) Chemometrics applied to environmental systems. Chemom Intel Lab Syst 149:205–214

    Article  CAS  Google Scholar 

  2. Nunes CA, Alvarenga V, de Souza Sant’Ana A, Sousa Santos J, Granato D (2015) The use of statistical software in food science and technology: advantages, limitations and misuses. Food Res Int 75:270–280

    Article  Google Scholar 

  3. Zuur AA, Ieno EL, Elphick CS (2010) A protocol for data exploration to avoid common statistical problems. Methods Ecol Evol 2010(1):3–14

    Article  Google Scholar 

  4. Quinn GP, Keough MJ (2002) Experimental design and data analysis for biologists. Cambridge University Press, New York, NY, 539 p

    Google Scholar 

  5. Daszykowski M, Walczak B (2006) Use and abuse of chemometrics in chromatography. Trends Anal Chem 25(11):1081–1096

    Article  CAS  Google Scholar 

  6. Pretsch E, Wilkins CL (2006) Use and abuse of chemometrics. Trends Anal Chem 25(11):1045

    Article  CAS  Google Scholar 

  7. Zuur AF, Ieno EN, Smith GM (2007) Analysing ecological data. Springer, New York, 672 p

    Book  Google Scholar 

  8. Leps J, Smilauer P (2003) Multivariate analysis of ecological data using CANOCO. Cambridge University Press, New York, NY, 283 p

    Google Scholar 

  9. Bilotta GS, Milner AM, Boyd IL (2015) How to increase the potential policy impact of environmental science research. Environ Sci Eur 27:9

    Article  Google Scholar 

  10. Pesce SF, Wunderlin DA (2000) Use of water quality indices to verify the impact of Córdoba City (Argentina) on Suquía River. Water Res 34:2915–2926

    Article  CAS  Google Scholar 

  11. Chapman D (1992) In: Chapman D (ed) Water quality assessment. Chapman & Hall, London, p 585. (On behalf of UNESCO, WHO and UNEP)

    Google Scholar 

  12. WHO (World and Health Organization) (1987) GEMS/WATER operational guide. World Health Organization, Geneva

    Google Scholar 

  13. Díaz É et al (2016) Hydrology and hydraulics of the Suquía River Basin. Handb Environ Chem. https://doi.org/10.1007/698_2016_466

  14. Bistoni MA et al (2016) Biota along the Suquía River Basin. Handb Environ Chem. https://doi.org/10.1007/698_2016_455

  15. Rodriguez MI, Ruiz M (2016) Limnology of the San Roque Reservoir. Handb Environ Chem. https://doi.org/10.1007/698_2016_467

  16. Amé MV, Pesce SF (2015) Spatial and temporal changes in water quality along the basin. Handb Environ Chem. https://doi.org/10.1007/698_2015_434

  17. Monferrán MV (2015) Metals and metalloids in water and sediment of the Suquía River Basin: spatial and temporal changes. Handb Environ Chem. https://doi.org/10.1007/698_2015_444

  18. Santiago AN et al (2015) Organic pollutants in the Suquía River Basin. Handb Environ Chem. https://doi.org/10.1007/698_2015_431

  19. Aboutalebi M, Bozorg-Haddad O, Loáiciga HA (2017) Multiobjective design of water-quality monitoring networks in river-reservoir systems. J Environ Eng 143(1), art. nr. 04016070

    Article  Google Scholar 

  20. McCarthy DT, Jovanovic D, Lintern A, Teakle I, Barnes M, Deletic A, Coleman R, Rooney G, Prosser T, Coutts S, Hipsey MR, Bruce LC, Henry R (2017) Source tracking using microbial community fingerprints: method comparison with hydrodynamic modelling. Water Res 109:253–265

    Article  CAS  Google Scholar 

  21. Stipaničev D, Dragun Z, Repec S, Rebok K, Jordanova M (2017) Broad spectrum screening of 463 organic contaminants in rivers in Macedonia. Ecotox Environ Safety 135:48–59

    Article  Google Scholar 

  22. Tousova Z, Oswald P, Slobodnik J, Blaha L, Muz M, Hu M, Brack W, Krauss M, Di Paolo C, Tarcai Z, Seiler T-B, Hollert H, Koprivica S, Ahel M, Schollée JE, Hollender J, Suter MJ-F, Hidasi AO, Schirmer K, Sonavane M, Ait-Aissa S, Creusot N, Brion F, Froment J, Almeida AC, Thomas K, Tollefsen KE, Tufi S, Ouyang X, Leonards P, Lamoree M, Torrens VO, Kolkman A, Schriks M, Spirhanzlova P, Tindall A, Schulze T (2017) European demonstration program on the effect-based and chemical identification and monitoring of organic pollutants in European surface waters. Sci Total Environ 601–602:1849–1868

    Article  Google Scholar 

  23. van der Oost R, Sileno G, Janse T, Nguyen MT, Besselink H, Simoni BA (2017) (Smart integrated monitoring) as a novel bioanalytical strategy for water quality assessment: part II-field feasibility survey. Environ Toxicol Chem 36:2400–2416

    Article  CAS  Google Scholar 

  24. Arnold JG, Fohrer N (2005) SWAT2000: current capabilities and research opportunities in applied watershed modelling. Hydrol Process 19(3):563–572

    Article  Google Scholar 

  25. Gassman PW, Reyes MR, Green CH, Arnold JG (2007) The soil and water assessment tool: historical development, applications, and future research directions. Transact ASABE 50(4):1211–1250

    Article  CAS  Google Scholar 

  26. Hawkins CP, Norris RH, Hogue JN, Feminella JW (2000) Development and evaluation of predictive models for measuring the biological integrity of streams. Ecol Appl 10(5):1456–1477

    Article  Google Scholar 

  27. Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transact ASABE 50(3):885–900

    Article  Google Scholar 

  28. Rauch W, Henze M, Koncsos L, Reichert P, Shanahan P, Somlyódy L, Vanrolleghem P (1998) River water quality modelling. I. State of the art. Water Sci Technol 38:237–244

    CAS  Google Scholar 

  29. Romagnoli M, Portapila M, Rigalli A, Maydana G, Burgués M, García CM (2017) Assessment of the SWAT model to simulate a watershed with limited available data in the Pampas region. Argentina Sci Total Environ 596–597:437–450

    Article  Google Scholar 

  30. Shanahan P, Henze M, Koncsos L, Rauch W, Reichert P, Somlyódy L, Vanrolleghem P (1998) River water quality modelling II. Problems of the art. Water Sci Technol 38:245–252

    CAS  Google Scholar 

  31. Somlyódy L, Henze M, Koncsos L, Rauch W, Reichert P, Shanaham P, Vanrolleghem P (1998) River water quality modelling III. Future of the art. Water Sci Technol 38:253–260

    Google Scholar 

  32. Zhai X, Xia J, Zhang Y (2017) Integrated approach of hydrological and water quality dynamic simulation for anthropogenic disturbance assessment in the Huai River Basin. China Sci Total Environ 598:749–764

    Article  CAS  Google Scholar 

  33. Hued AC, Bistoni MA (2005) Development and validation of a biotic index for evaluation of environmental quality in the central region of Argentina. Hydrobiologia 543:279–298

    Article  Google Scholar 

  34. Wunderlin DA, Díaz MP, Amé MV, Pesce SF, Hued AC, Bistoni MA (2001) Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. A case study: Suquía River basin (Córdoba-Argentina). Water Res 35(12):2881–2894

    Article  CAS  Google Scholar 

  35. Otto M (1998) Multivariate methods. In: Kellner R, Mermet JM, Otto M, Widmer HM (eds) Analytical chemistry, Wiley-VCH, Weinheim, Germany, 916 p

    Google Scholar 

  36. Adams MJ (1998) The principles of multivariate data analysis. In: Ashurst PR, Dennis MJ (eds) Analytical methods of food authentication. Blackie Academic & Professional, London, UK, 350 p

    Google Scholar 

  37. Graça MAS, Coimbra CN (1998) The elaboration of indices to assess biological water quality. A case study. Water Res 32:380–392

    Article  Google Scholar 

  38. Helena B, Pardo R, Vega M, Barrado E, Fernandez JM, Fernandez L (2000) Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga River, Spain) by principal component analysis. Water Res 34:807–816

    Article  CAS  Google Scholar 

  39. Johnson RA, Wichern DW (1992) Applied multivariate statistical analysis. 3rd edn. Prentice–Hall International, Englewood Cliffs, NJ, USA, 642 p

    Google Scholar 

  40. Vega M, Pardo R, Barrado E, Debn L (1998) Assessment of seasonal and polluting effects on the quality of river water by exploratory data analysis. Water Res 32:3581–3592

    Article  CAS  Google Scholar 

  41. 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 Res 41(19):4566–4578

    Article  CAS  Google Scholar 

  42. De Las Heras Jiménez A, Melgar Riol MJ (2005) Rivers water quality at Galicia-Cost River Basin during 1973-2002. Predictive analysis to 2010 [Calidad del agua de los ríos de la Cuenca Hidrográfica Galicia-Costa durante el período 1973-1995. Análisis predictivo a 2010]. Tecnologia del Agua 25(261):54–61

    Google Scholar 

  43. Kannel PR, Lee S, Kanel SR, Khan SP (2007) Chemometric application in classification and assessment of monitoring locations of an urban river system. Anal Chim Acta 582(2):390–399

    Article  CAS  Google Scholar 

  44. Khalil B, Ouarda TBMJ, St-Hilaire A (2011) A statistical approach for the assessment and redesign of the Nile Delta drainage system water-quality-monitoring locations. J Environ Monit 13(8):2190–2205

    Article  CAS  Google Scholar 

  45. Kowalkowski T, Zbytniewski R, Szpejna J, Buszewski B (2006) Application of chemometrics in river water classification. Water Res 40(4):744–752

    Article  CAS  Google Scholar 

  46. Shrestha S, Kazama F (2007) Assessment of surface water quality using multivariate statistical techniques: a case study of the Fuji river basin, Japan. Environ Model Softw 22(4):464–475

    Article  Google Scholar 

  47. Simeonov V, Stratis JA, Samara C, Zachariadis G, Voutsa D, Anthemidis A, Sofoniou M, Kouimtzis T (2003) Assessment of the surface water quality in Northern Greece. Water Res 37(17):4119–4124

    Article  CAS  Google Scholar 

  48. Singh KP, 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 Res 38(18):3980–3992

    Article  CAS  Google Scholar 

  49. Singh KP, Malik A, Sinha S (2005) Water quality assessment and apportionment of pollution sources of Gomti river (India) using multivariate statistical techniques – a case study. Anal Chim Acta 538(1–2):355–374

    Article  CAS  Google Scholar 

  50. Sundaray SK, Nayak BB, Lin S, Bhatta D (2011) Geochemical speciation and risk assessment of heavy metals in the river estuarine sediments-a case study: Mahanadi basin, India. J Hazard Mater 186(2–3):1837–1846

    Article  CAS  Google Scholar 

  51. Varol M (2011) Assessment of heavy metal contamination in sediments of the Tigris River (Turkey) using pollution indices and multivariate statistical techniques. J Hazard Mater 195:355–364

    Article  CAS  Google Scholar 

  52. Devic G, Djordjevic D, Sakan S (2014) Natural and anthropogenic factors affecting the groundwater quality in Serbia. Sci Total Environ 468-469:933–942

    Article  CAS  Google Scholar 

  53. Li G, Liu G, Zhou C, Chou C-L, Zheng L, Wang J (2012) Spatial distribution and multiple sources of heavy metals in the water of Chaohu Lake, Anhui, China. Environ Monit Assess 184(5):2763–2773

    Article  CAS  Google Scholar 

  54. Cao Y, Bark AW, Williams WP (1997) A comparison of clustering methods for river benthic community analysis. Hydrobiologia 347(1–3):25–40

    CAS  Google Scholar 

  55. Chabukdhara M, Nema AK (2012) Assessment of heavy metal contamination in Hindon River sediments: a chemometric and geochemical approach. Chemosphere 87(8):945–953

    Article  CAS  Google Scholar 

  56. Clarke RT, Wright JF, Furse MT (2003) RIVPACS models for predicting the expected macroinvertebrate fauna and assessing the ecological quality of rivers. Ecol Model 160(3):219–233

    Article  Google Scholar 

  57. Marchant R, Hirst A, Norris RH, Butcher R, Metzeling L, Tiller D (1997) Classification and prediction of macroinvertebrate assemblages from running waters in Victoria, Australia. J N Am Benthol Soc 16(3):664–681

    Article  Google Scholar 

  58. Monferrán MV, Galanti LN, Bonansea RI, Amé MV, Wunderlin DA (2011) Integrated survey of water pollution in the Suquía River basin (Córdoba, Argentina). J Environ Monit 13:398–409

    Article  Google Scholar 

  59. Tomic O, Berget I, Næs T (2015) A comparison of generalised procrustes analysis and multiple factor analysis for projective mapping data. Food Qual Prefer 43(2015):34–46

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ICYTAC: Instituto de Ciencia y Tecnología de Alimentos Córdoba, CONICET, Universidad Nacional de Córdoba, Ciudad Universitaria, Córdoba, 5000, Argentina

    María Verónica Baroni & Daniel A. Wunderlin

  2. Departamento de Química Orgánica, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Ciudad Universitaria, Córdoba, 5000, Argentina

    María Verónica Baroni & Daniel A. Wunderlin

Authors
  1. María Verónica Baroni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniel A. Wunderlin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel A. Wunderlin .

Editor information

Editors and Affiliations

  1. CONICET-Universidad Nacional Córdoba, Cordoba, Argentina

    Daniel A. Wunderlin

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Baroni, M.V., Wunderlin, D.A. (2017). Integrating Data from Suquía River Basin: Chemometrics and Other Concepts. In: Wunderlin, D.A. (eds) The Suquía River Basin (Córdoba, Argentina). The Handbook of Environmental Chemistry, vol 62. Springer, Cham. https://doi.org/10.1007/698_2017_202

Download citation

Publish with us

Policies and ethics

Search

Navigation