Skip to main content
Log in

The Application of Water Quality Monitoring Data in a Reservoir Watershed Using AMOS Confirmatory Factor Analyses

  • Published:
Environmental Modeling & Assessment Aims and scope Submit manuscript

Abstract

This study investigates six water quality monitoring stations in the watershed of the Feitsui Reservoir. It uses nine parameters of water quality collected in an interval of two and half years for factor analyses, which was first conducted to determine four types of factors, respectively, those for organic pollution, eutrophication, seasonal influence, and sediment pollution. The analysis results effectively help to determine water quality in the watershed of the reservoir. The authors reutilize analysis of moment structures (AMOS) to acquire further results in order to confirm the goodness of fit of the previous factor analysis model. During the confirmation, we examine the hypothesized orthogonal results as well as utilize oblique rotation to explore the goodness of fit of the reflective indicators of the orthogonal rotation. As shown in the algorithm results, as long as the covariance curve is included in the four factors, no related issues are detected in the goodness of fit of reflective indicators and interior and external quality is reported with excellence. The orthogonal model, thus, stands. Additionally, when the analysis of structural equation modeling (SEM) is conducted, sample data mismatches the hypotheses of multivariate normality. Therefore, this study adopts the generalized least square (GLS) for an algorithm. Research results of this study have been submitted to the reservoir management authorities in Taiwan for the improvement of statistical application and strategic evaluation of water quality monitoring data in order to strengthen the managerial effectiveness of water quality in watersheds.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Arbuckle, J. L. (1994). Computer announcement: AMOS: analysis of moment structures. Psychometrika, 59, 135–141.

    Article  Google Scholar 

  2. Arbuckle, J. L., & Wothke, W. (1999). IBM AMOS 4.0 user's guide, Chicago, IL, USA.

  3. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Academy of Marketing Science, 16, 76–85.

    Article  Google Scholar 

  4. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.

    Google Scholar 

  5. Bollen, K. A. (1989). A new incremental fit index general structural equation models. Sociological Methods & Research, 17, 303–310.

    Article  Google Scholar 

  6. Burnham, K. P., & Anderson, D. R. (1998). Model select and inference: a practical information-theoretic approach. New York: Spring-Verlag.

    Book  Google Scholar 

  7. Charlton, A. J., Robb, P., Donarski, A. J., & Godward, J. (2008). Non-targeted detection of chemical contamination in carbonated soft drinks using NMR spectroscopy, variable selection and chemometrics. Analytica Chimica Acta, 618, 196–205.

    Article  CAS  Google Scholar 

  8. Chen, S. Y. (2005). Multivariate analysis (4th ed.). Taipei: Huatai Publisher. in Chinese.

    Google Scholar 

  9. Chen, Y. S., Huang, T. W., & Huang, F. M. G. M. (2006). Basic principles of structural equation modeling. Kaohsiung: Li-Wen Publisher. in Chinese.

    Google Scholar 

  10. Einax, J. W., Truckenbrodt, D., & Kampe, Ο. (1998). River pollution data interpreted by means of chemometric methods. Microchemical Journal, 58, 315–321.

    Article  CAS  Google Scholar 

  11. Helena, B., Pardo, R., Vega, M., Barrado, E., Ferna’ndez, J. M., & Ferna’ndez, L. (2000). Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga river, Spain) by principal component analysis. Water Research, 34, 807–815.

    Article  CAS  Google Scholar 

  12. Hsieh, P. H., Kuo, J. T., Wu, E. M.-Y., Ciou, S. K., & Liu, W. C. (2010). Optimal best management practice placement strategies for nonpoint source pollution management in the Fei-Tsui reservoir watershed. Environmental Engineering Science, 27, 441–447.

    Google Scholar 

  13. Lin, C., Wu, E. M.-Y., Lee, C. N., & Kuo, S. L. (2011). Applying multivariate statistical factor analyses on selecting an optimal method for recycling of food wastes. Environmental Engineering Science, 28, 349–356.

    Google Scholar 

  14. 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, 313, 77–85.

    Article  CAS  Google Scholar 

  15. MacGregor, J. F., & Kourti, T. (1995). Statistical process control of multivariate process. Control Engineering Practice, 3, 403–410.

    Article  Google Scholar 

  16. Nunnari, G., Dorling, S., Schlink, U., Cawley, G., Foxall, R., & Chatterton, T. (2004). Modelling SO2 concentration at a point with statistical approaches. Environmental Modelling and Software, 19, 887–897.

    Article  Google Scholar 

  17. Pires, J. C. M., Sousa, S. I. V., Pereira, M. C., Alvim-Ferraz, M. C. M., & Martins, F. G. (2008). Management of air quality monitoring using principal component and cluster analysis-part I: SO2 and PM10. Atmospheric Environment, 42, 1249–1255.

    Article  CAS  Google Scholar 

  18. Pires, J. C. M., Sousa, S. I. V., Pereira, M. C., Alvim-Ferraz, M. C. M., & Martins, F. G. (2008). Management of air quality monitoring using principal component and cluster analysis—part II: CO, NO2 and O3. Atmospheric Environment, 42, 1261–1268.

    Article  CAS  Google Scholar 

  19. Simeonov, V., Stratis, J. A., Samara, C., Zachariadis, G., Voutsa, D., Anthemidis, A., et al. (2003). Assessment of the surface water quality in Northern Greece. Water Research, 37, 4119–4125.

    Article  CAS  Google Scholar 

  20. Tajik, S., Ayoubi, S. S., & Nourbakhsh, F. S. (2012). Prediction of soil enzymes activity by digital terrain analysis: comparing artificial neural network and multiple linear regression models. Environmental Engineering Science, 29, 798–808.

    Article  CAS  Google Scholar 

  21. Theodora, K. (2006). The process analytical technology initiative and multivariate process analysis, monitoring and control. Analytical and Bioanalytical Chemistry, 384, 1043–1551.

    Article  Google Scholar 

  22. Ticlavilca, A., & McKee, M. (2011). Multivariate Bayesian regression approach to forecast releases from a system of multiple reservoirs. Water Resources Management, 25, 523–532.

    Article  Google Scholar 

  23. Varol, M., Gökot, B., Bekleyen, A., & Şen, B. (2012). Water quality assessment and apportionment of pollution sources of Tigris river (Turkey) using multivariate statistical techniques—a case study. River Research and Applications, 28, 1428–1436.

    Article  Google Scholar 

  24. Wu, E. M.-Y., & Kuo, S. L. (2012). Applying a multivariate statistical analysis model to evaluate the water quality of a watershed. Water Environment Research, 84, 2075–2085.

    Google Scholar 

  25. Wu, E. M.-Y., Kuo, S. L., & Liu, W. C. (2012). Generalized autoregressive conditional heteroskedastic model for water quality analyses and time series investigation in reservoir watersheds. Environmental Engineering Science, 29, 227–237.

    Google Scholar 

  26. 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, 2881–2882.

    Article  CAS  Google Scholar 

  27. Yang, Y. H., Zhou, F., Guo, H. C., Sheng, H., Liu, H., Dao, X., et al. (2010). Analysis of spatial and temporal water pollution patterns in Lake Dianchi using multivariate statistical methods. Environmental Monitoring and Assessment, 170, 407–416.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors sincerely appreciate financial supports for the project (NSC 100-2221-E214-011) from Taiwan’s National Science Council for the work.

The authors also deeply appreciate the anonymous reviewers for their insightful comments and suggestions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shu Lung Kuo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wu, E.MY., Tsai, C.C., Cheng, J.F. et al. The Application of Water Quality Monitoring Data in a Reservoir Watershed Using AMOS Confirmatory Factor Analyses. Environ Model Assess 19, 325–333 (2014). https://doi.org/10.1007/s10666-014-9407-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10666-014-9407-5

Keywords

Navigation