Identification of Water Quality Significant Parameter with Two Transformation/Standardization Methods on Principal Component Analysis and Scilab Software

https://doi.org/10.22146/jcef.26642

Jovan Putranda(1*), Sri Puji Saraswati(2)

(1) Researcher and Engineer
(2) Department of Civil and Environmental Engineering
(*) Corresponding Author

Abstract


Water quality monitoring is prone to encounter error on its recording or measuring process. The monitoring on river water quality not only aims to recognize the water quality dynamic, but also to evaluate the data to create river management policy and water pollution in order to maintain the continuity of human health or sanitation requirement, and biodiversity preservation. Evaluation on water quality monitoring needs to be started by identifying the important water quality parameter. This research objected to identify the significant parameters by using two transformation or standardization methods on water quality data, which are the river Water Quality Index, WQI (Indeks Kualitas Air, Sungai, IKAs) transformation or standardization method and transformation or standardization method with mean 0 and variance 1; so that the variability of water quality parameters could be aggregated with one another. Both of the methods were applied on the water quality monitoring data which its validity and reliability have been tested. The PCA, Principal Component Analysis (Analisa Komponen Utama, AKU), with the help of Scilab software, has been used to process the secondary data on water quality parameters of Gadjah Wong river in 2004-2013, with its validity and reliability has been tested. The Scilab result was cross examined with the result from the Excel-based Biplot Add In software. The research result showed that only 18 from total 35 water quality parameters that have passable data quality. The two transformation or standardization data methods gave different significant parameter type and amount result. On the transformation or standardization mean 0 variances 1, there were water quality significant parameter dynamic to mean concentration of each water quality parameters, which are TDS, SO4, EC, TSS, NO3N, COD, BOD5, Grease Oil and NH3N. On the river WQI transformation or standardization, the water quality significant parameter showed the level of Gadjah Wong River pollution, which are EC, DO, BOD5, COD, NH3N, Fecal Coliform, and Total Coliform. These seven parameters is the minimal amount of water quality parameters that has to be consistently measured on predetermined time and location, and also become the indicator of human health and environment health quality. The result of Scilab multivariate analysis was not different with the result from Biplot Add In multivariate analysis, in which the results of water quality significant parameter has been verified with bio-monitoring.

Keywords


Water quality monitoring, transformation/standardization, Scilab, Principal Component Analysis

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References

Annigeri, S. (2004). Scilab A Hands on Introduction. Bhoomaraddi: Department of Civil Engineering B.V., Bhoomaraddi College of Engineering and Technology.

Badan Lingkungan Hidup (BLH) DIY. (2010). Laporan Analisa Data Kualitas Air Sungai di Provinsi Daerah Istimewa Yogyakarta [Analysis of Water Quality Data of Rivers in Yogyakarta Province]. Yogyakarta: Badan Lingkungan Hidup (BLH) DIY.

Baudin, M. (2010). Introduction to Scilab. The Scilab Consortium.

Berthouex, P. M., & Brown, L. C. (2002). Statistics for Environmental Engineers. Washington: Lewis Publishers.

Cao, Y., Williams, D., & Williams, N. E. (1999). Data Transformation and Standardization in the Multivariate Analysis of River Water Quality. Journal Ecological Applications, 669-77.

Dillom, W. R., & Goldstein, M. (1984). Multivariate Analysis. UK: John Wiley and Sons.

Dillon, W. R. (1984). Multivariate Analysis Methods and Applications. UK: John Wiley and Sons.

Fataei, E. (2011). Assessment of Surface Water Quality Using Principle Component Analysis and Factor Analysis. World Journal of Fish and Marine Sciences, 159-66.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. (2009). Multivariate Data Analysis. Pearson Prentice Hall.

Karr, J. R. (1991). Biological Integrity: A Long-Neglected Aspect of Water Resources Management, Ecological Applications. 1(1), 66-84.

Legendre, L., & Legendre, P. (1998). Numerical Ecology. Amsterdam: Elsevier Scientific Publishing Company.

Lipkovich, L., & Smith, E. P. (2002). Biplot and Singular Decomposition Macros for Excel'. Blacksburg: Department of Statistics Virginia Tech.

Lumb, A., Halliwell, D., & Sharma, T. (2006). Application of CCME Water Quality Index to Monitor Water Quality: A Case of the Mackenzie River Basin Canada. Environment Monitoring and Assessment, 113, 411-429.

McBride, G. B. (2005). Using Statistical Methods for Water Quality Management. John Wiley & Sons.

Pemerintah Republik Indonesia. (2001). Peraturan Pemerintah Republik Indonesia Nomor 82 Tahun 2001 Tentang Pengelolaan Kualitas Air dan Pengendalian Pencemaran Air. Jakarta: Pemerintah Republik Indonesia.

Putranda, J. (2015). Kajian Kualitas Air Sungai Dengan 2 Teknik Standarisasi Menggunakan Scilab Dan Biplot Add In [Water Quality Study with 2 Standardization Techniques using Scilab and Biplot Add In]. Yogyakarta: Undergraduate Thesis, Department of Civil and Environmental Engineering, Universitas Gadjah Mada.

Resh, V. H., & Mc Elravy, E. P. (1993). Contemporary Quantitative Approaches to Biomonitoring using Benthic Macroinvertebrates. Pada D.M. Roosenberg and V.M. Resh. Freshwater Biomonitoring and Benthic Macroinvertebrates. New York: Chapman and Hall.

Saraswati, S. P. (2015). Pengembangan Metode Penentuan Kesehatan Perairan Sungai di Daerah Tropis Berbasis Ekohidraulik [Development of Water Health Determination Method in Tropical Area Ecohydraulic Based]. Yogyakarta: Doctoral Dissertation, Department of Civil and Environmental Enginering, Universitas Gadjah Mada.

Smith, L. I. (2002). A Tutorial on Principle Component Analysis.

Suryanto, S. (2015). Analisa Data Kualitas Air Sungai Menggunakan Metode Statistik Multivariate Principle Component Analysis dengan 2 Teknik Standarisasi [Water Quality Data Analysis using Multivariate Principle Component Analysis with 2 Standardization Techniques]. Yogyakarta: Undergraduate Thesis, Department of Civil and Environmental Engineering Department, Universitas Gadjah Mada.

Zhang, Q., Li, Z., Zeng, G., Li, J., Fang, Y., & Yuan. (2009). Assessment of Surface Water Quality using Multivariate Statistical Techniques in Red Soil Hilly Region: A Case Study of Xiangjiang Watershed, China. Environment Monitoring Assesment, 123–131.

Zhou, F., Li, Y., & Guo, H. (2006). Application of Multivariate Statistical Methods to Water Quality Assessment of the Water Courses in Northwestern New Territories, Hongkong. Environmental Monitoring Assessment, Springer Science, published online.



DOI: https://doi.org/10.22146/jcef.26642

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