Skip to main content
SpringerLink
Log in

An assessment of selected hydrochemical parameter trend of the Nakdong River water in South Korea, using time series analyses and PCA

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Time series analyses (autocorrelation, spectral density, and cross-correlation) and principal component analysis (PCA) were used to understand the characteristics of the selected hydrochemical parameters pH, turbidity, alkalinity, Cl, hardness, total dissolved solids (TDS), and metals Fe and Mn in the Nakdong River, South Korea. Autocorrelation and spectral density for pH, alkalinity, hardness, and Cl were very similar to TDS, whereas Fe, Mn, and turbidity showed different trends from TDS. Cross-correlograms of pH, alkalinity, hardness, and Cl versus TDS were very similar to each other. Those of Fe and turbidity represented the opposite relations with other components. Cross-correlation coefficients had the highest values at zero lag, indicating that pH, alkalinity, hardness, and Cl are controlling factors for TDS. On the other hand, Fe and turbidity showed the highest values at 6-month lag and Mn at a month lag. PCA indicated that TDS had very close relation with hardness, pH, and Cl and very small relation with Mn. Turbidity and Fe had relatively opposite relations with TDS. It was concluded that the geostatistical methods were very useful for evaluating the hydrochemical characteristics of the Nakdong River water in South Korea.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Aflatooni, M., & Mardaneh, M. (2011). Time series analysis of ground water table fluctuations due to temperature and rainfall change in Shiraz plain. International Journal of Water Resources and Environmental Engineering, 3, 176–188.

    Google Scholar 

  • Alvarez-Mieles, G., Irvine, K., Griensven, A. V., Arias-Hidalgo, M., Torres, A., & Mynett, A. E. (2013). Relationships between aquatic biotic communities and water quality in a tropical river–wetland system (Ecuador). Environmental Science and Policy, 34, 115–127.

    Article  CAS  Google Scholar 

  • Angelini, P. (1997). Correlation and spectral analysis of two hydrogeological systems in Central Italy. Hydrological Science Journal, 42, 425–439.

    Article  CAS  Google Scholar 

  • Appelo, C. A. J., & Willemsen, A. (1987). Geochemical calculations and observations on salt water intrusions. I. A combined geochemical/mixing cell model. Journal of Hydrology, 94, 313–330.

    Article  CAS  Google Scholar 

  • Bouzourra, H., Bouhlila, R., Elango, L., Slama, F., & Ouslati, N. (2014). Characterization of mechanisms and processes of groundwater salinization in irrigated coastal area using statistics, GIS, and hydrogeochemical investigation. Environmental Science and Pollution Research. doi:10.1007/s11356-014-3428-0. 2014.

    Google Scholar 

  • Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1994). Time series analysis: forecasting and control (3rd ed.). Englewood Cliffs: Prentice-Hall. 598 p.

    Google Scholar 

  • Chun, K. C., Chang, R. W., Williams, G. P., Chang, Y. S., Tomasko, D., LaGory, K., Ditmars, J., Chung, H. D., & Lee, B. K. (2001). Water quality issues in the Nakdong River Basin in the Republic of Korea. Environmental Engineering Policy, 2, 131–143.

    Article  Google Scholar 

  • Diggle, P.J. (1992). Time series: a biostatistical introduction. Oxford, London, 257p.

  • Duffy, C. J., & Gelhar, L. W. (1986). A frequency domain analysis of groundwater quality fluctuations: interpretation of field data. Water Resource Research, 22, 1115–1128.

    Article  CAS  Google Scholar 

  • Erdogan, H., & Gulal, E. (2009). The application of time series analysis to describe the dynamic movements of suspension bridges. Nonlinear Analysis: Real World Applications, 10, 910–927.

    Article  Google Scholar 

  • Felmy, A. R., Bervin, D. C., Jenne, E. A. (1984). MINTEQ. A computer program for calculating aqueous geochemical equilibria. US EPA 84–032, Athens GA.

  • Helena, B., Pardo, R., Vega, M., Barrado, E., Fernandez, J. M., & Fernandez, L. (2000). Temporal evolution of groundwater composition in an alluvial (Pisuerga river, Spain) by principal component analysis. Water Research, 34, 807–816.

    Article  CAS  Google Scholar 

  • Helgeson, H. C., Brown, T. H., Nigrini, A., & Jones, T. A. (1970). Calculation of mass transfer in geochemical processes involving aqueous solutions. Geochimica et Cosmochimica Acta, 34, 569–592.

    Article  CAS  Google Scholar 

  • Hu, K. Z., Zhang, J. Z., & Xing, L. T. (2001). Study on dynamic characteristics of groundwater based on the time series analysis method. Water Science Engineering Technology, 5, 32–34.

    Google Scholar 

  • Jackson, J. E. (1991). A user’s guide to principal components. New York: Wiley.

    Book  Google Scholar 

  • Jain, C. K., Bhatia, K. K. S., & Seth, M. (1997). Characterization of waste disposals and their impact on the water quality of river Kali. Indian Journal of Environmental Protection, 17, 287–295.

    CAS  Google Scholar 

  • Jenkins, G. M., & Watts, D. G. (1968). Spectral analysis and its applications. San Francisco: Holden-Day Inc. 525 p.

    Google Scholar 

  • Kim, T., Lee, K. K., Ko, K. S., & Chang, H. W. (2000). Groundwater flow system inferred from hydraulic stresses and heads at an underground LPG storage cavern site. Journal of Hydrology, 236, 165–184.

    Article  CAS  Google Scholar 

  • Kim, J., Lee, J., Cheong, T., Kim, R. H., Koh, D. C., Ryu, J. S., & Chang, S. W. (2005). Use of time series analysis for the identification of tidal effect on groundwater in the coastal area of Kimje, Korea. Journal of Hydrology, 300, 188–198.

    Article  Google Scholar 

  • Kowalkowskia, T., Zbytniewskia, R., Szpejnab, J., & Buszewski, B. (2006). Application of chemometrics in river water classification. Environmental Chemistry, 40, 744–752.

    Google Scholar 

  • Kumari, M., Tripathi, S., Pathak, V., & Tripathi, B. D. (2013). Chemometric characterization of river water quality. Environmental Monitoring and Assessment, 185, 3081–3092.

    Article  CAS  Google Scholar 

  • Kura, N. U., Ramli, M. F., Ibrahim, S., Sulaiman, W. N. A., & Aris, A. Z. (2014). An integrated assessment of seawater intrusion in a small tropical island using geophysical, geochemical, and geostatistical techniques. Environmental Science and Pollution Research. doi:10.1007/s11356-014-2598-0.

    Google Scholar 

  • Laroque, M., Mangin, A., Razack, M., & Banton, O. (1998). Contribution of correlation and spectral analysis to the regional study of a large karst aquifer (Charente, France). Journal of Hydrology, 205, 217–231.

    Article  Google Scholar 

  • Lee, J. Y., & Lee, K. K. (2000). Use of hydrologic time series data for identification of recharge mechanism in a fractured bedrock aquifer system. Journal of Hydrology, 229, 190–201.

    Article  Google Scholar 

  • Liang, Y. H. (2011). Analyzing and forecasting the reliability for repairable systems using the time series decomposition method. International Journal Quality Reliability Management, 28, 317–327.

    Article  Google Scholar 

  • Lu, W. X., Zhao, Y., Chu, H. B., & Yang, L. L. (2013). The analysis of groundwater levels influenced by dual factors in western Jilin Province by using time series analysis method. Applied Water Science. doi:10.1007/s13201-013-0111-4.

    Google Scholar 

  • Mangin, A. (1984). Pour une meilleure connaissance des syste‘mes hydrologiques a’ partir des analyses correlatoire et sepctrale. Journal of Hydrology, 67, 25–43.

    Article  Google Scholar 

  • Nas, B., & Berktay, A. (2010). Groundwater quality mapping in urban groundwater using GIS. Environmental Monitoring and Assessment, 8, 215–227.

    Article  Google Scholar 

  • Oh, G. H. (1994). The paleoenvironment of the northern part of the Nakdong River delta. Korean Journal of Quaternary Research, 8, 33–42.

    Google Scholar 

  • Padilla, A., & Pulido-Bosch, A. (1995). Study of hydrographs of karstic aquifers by means of correlation and cross-spectral analysis. Journal of Hydrology, 168, 73–89.

    Article  Google Scholar 

  • Padilla, A., Pulido-Bosch, A., & Mangin, A. (1994). Relative importance of baseflow and quickflow from hydrographs of karst springs. Groundwater, 32, 267–277.

    Article  Google Scholar 

  • Parkhurst, D.L., Thorstenson, D.C., Plummer, LN. (1990). PHREEQE—a computer program for geochemical calculation. USGS water resources investigations report 80–96, 195p.

  • Plummer, L. N., Parkhurst, D. L., & Thorstenson, D. C. (1983). Development of reaction models for groundwater systems. Geochimica et Cosmochimica Acta, 47, 665–685.

    Article  CAS  Google Scholar 

  • Ryu, C. K., Kang, S., Chung, S. G., & Jeon, Y. M. (2011). Late Quaternary depositional environmental change in the northern marginal area of the Nakdong River delta, Korea. Journal of the Geological Society of Korea, 47, 213–233.

    Google Scholar 

  • Sankaran, S., Sonkamble, S., Krishnakumar, K., & Mondal, N. C. (2012). Integrated approach for demarcating subsurface pollution and saline water intrusion zones in SIPCOT area: a case study from Cuddalore in Southern India. Environmental Monitoring and Assessment. doi:10.1007/s10661-011-2327-9. 2012.

    Google Scholar 

  • Shannon, D. W., Morray, J. R., Smith, P. R. (1977). Use of a chemical equilibrium model computer code to analyze scale formation and corrosion in geothermal brines. Proceedings in Symposium on Oil Field and Geothermal Chemistry Conference 770609, pp. 21–36.

  • 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, 38, 3980–3992.

    Article  CAS  Google Scholar 

  • Spostigo, G., & Mattigod, S. V. (1979). GEOCHEM: a computer program for the calculation of chemical equilibria in soil solution and other natural water systems. Riverside: Department of Soil and Environmental Sciences, University of California.

    Google Scholar 

  • Triki, I., Trabelsi, N., Hentati, I., & Zairi, M. (2014). Groundwater levels time series sensitivity to pluviometry and air temperature: a geostatistical approach to Sfax region, Tunisia. Environmental Monitoring and Assessment, 186, 1593–1608.

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  • Venkatramanan, S., Chung, S. Y., Lee, S. Y., & Park, N. (2014a). Assessment of river water quality via environmentric multivariate statistical tools and water quality index: a case study of Nakdong River basin, Korea. Carpathian Journal of Earth and Environmental Sciences, 9, 125–132.

    Google Scholar 

  • Venkatramanan, S., Chung, S. Y., Park, N., Ramkumar, T., & Gnanachandrasamy, G. (2014b). A preliminary investigation of hydrogeochemistry, metals and saturation indices of minerals in Nakdong surface water and adjacent deltaic groundwater using WATEQ4F geochemical model. International Journal of Earth Sciences and Engineering, 7, 456–466.

    Google Scholar 

  • Venkatramanan, S., Chung, S. Y., Ramkumar, T., Gnanachandrasamy, G., Vasudevan, S., & Lee, S. Y. (2014c). Application of GIS and hydrogeochemisty of groundwater pollution status of Nagapattinam district of Tamil Nadu. India: Environmental Earth Sciences. doi:10.1007/s12665-014-3728-1.

    Google Scholar 

  • Venkatramanan, S., Chung, S. Y., Kim, T. H., Prasanna, M. V., & Hamm, S. Y. (2014d). Assessment and distribution of metals contamination in groundwater: a case study of Busan City. Korea: Water Quality Exposure and Health. doi:10.1007/s12403-014-0142-6.

    Google Scholar 

  • Wolery, T.J. (1979). Calculation of chemical equilibrium between aqueous solutions and minerals: the EQ3/EQ6 software package (41 pp.). Livermore, University of California, Lawrence Livermore Laboratory (UCRL 52658).

  • Wunderlin, D. A., Diaz, M. P., Ame, M. V., Pesce, S. F., Hued, A. C., & Bistoni, M. A. (2001). Pattern recognition techniques for the evaluation of spatial and temporal variations in water quality. A case study: Suquia river basin (Cordoba-Argentina). Water Research, 35, 2881–2894.

    Article  CAS  Google Scholar 

  • Yang, Z. P., Lu, W. X., & Long, Y. Q. (2009). Application and comparison of two prediction models for groundwater levels: a case study in Western Jilin Province, China. Journal of Arid Environment, 73, 487–492.

    Article  Google Scholar 

  • Zhao, J., Bian, Y. M., & Zhou, X. J. (2007). Application of time series analysis method in groundwater level dynamic forecast of Shenyang City. Water Resources Hydropower Northeast, 8, 31–34.

    Google Scholar 

  • Zhou, X. J., Lan, S. S., & Wang, B. (2007). Application of time series analysis in groundwater level forecast of Siping region. Water Science Engineering Technology, 8, 35–37.

    Google Scholar 

Download references

Acknowledgments

The authors are grateful to two anonymous referees for their constructive comments and suggestions which led to significant improvements to the manuscript. We wish to gratefully acknowledge the valuable suggestions given by Prof. Yu-Pin Lin, Associate Editor which greatly helped in the final presentation of this article. This research was supported by a grant (Code: 13AWMP-B066761-02) from AWMP Program funded by the Ministry of Land, Infrastructure and Transport of Korean government. The water quality data of the Nakdong River were supplied from the Busan Metropolitan City, South Korea, and many thanks are given to it.

Author information

Authors and Affiliations

  1. Department of Earth & Environmental Sciences, Institute of Environmental Geosciences, Pukyong National University, 599-1 Daeyeon-dong Nam-gu, Busan, 608-737, South Korea

    S. Y. Chung, S. Venkatramanan & R. Rajesh

  2. Department of Civil Engineering, Dong-A University, 840 Hadan-dong Saha-gu, Busan, 604-717, South Korea

    N. Park

  3. Department of Earth Sciences, Annamalai University, Annamalai Nagar, 608 502, Tamilnadu, India

    T. Ramkumar

  4. Radioactive Waste Technology Development Division, Korea Atomic Energy Research Institute, Yuseong-gu, Daejeon, 305-353, South Korea

    B. W. Kim

Authors
  1. S. Y. Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Venkatramanan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. N. Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. R. Rajesh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. T. Ramkumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. B. W. Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Venkatramanan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chung, S.Y., Venkatramanan, S., Park, N. et al. An assessment of selected hydrochemical parameter trend of the Nakdong River water in South Korea, using time series analyses and PCA. Environ Monit Assess 187, 4192 (2015). https://doi.org/10.1007/s10661-014-4192-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-014-4192-9

Keywords

Search

Navigation