Assessment of Spatial Water Quality Observation of Citarum River Bandung Regency Using Multivariate Statistical Methods

Ahmad Musnansyah, Anton Abdulbasah Kamil, Linda Marliana, Endang Widayati, - Zulfakriza


Citarum River is one of the most important rivers in Indonesia. Around 16 million people interrelate with this river, covers 12,000 Km2 of the watershed, supplies water for irrigation of 420,000 hectares of rice fields, provides 80% of water need for the city of Jakarta- the capital of Indonesia. Unfortunately, Citarum was also known as one of the most polluted rivers in the world. Although there is much attention to this river nowadays, there is still no analysis to determine the latent contributing factors of water quality cluster distribution. This study aims to provide spatial water quality on the Citarum River Bandung Regency. This study can help the government decide on how to manage the water quality of Citarum and all socio-cultural factors involved in polluting the river. Open Data can also use the data and result for further research. Assessment of Citarum water quality is done through the application of multivariate statistical approaches. The data set comprises one-month observation data from 75 stations positioned in Citarum Bandung Regency and its tributaries. Factor Analysis with PCA as the extraction method gives two factors while CA showed three clusters suggesting the different physicochemical characteristics and pollution levels of the Citarum water systems. BOD, COD and DO, together with total P and Fecal Coliform are identified as two underlying factors on water quality in Citarum and its tributaries in Bandung Regency. Descriptive Statistic values confirm the quality of Citarum Bandung Regency low water quality.


Citarum; water quality; multivariate statistics; cluster analysis; latent factors; coliform.

Full Text:



National Planning Agency of Indonesia, “Physical and Spatial Condition.† (accessed Oct. 11, 2018).

“Cita Citarum Infographic - ‘Citarum Now.’† (accessed Oct. 11, 2018).

S. Sembiring, “water quality in three reservoirs on the Citarum river, Indonesia,†p. 7.

Satgas Citarum Sektor 7 Lokalisir Sarang Siluman Limbah Di Anak Sungai Cisuminta | Citarum Harum.

Temuan Warna Air Merah Pekat dari Limbah Pabrik Citarum. .

“Walhi: Pollution in Citarum River Reach Alarming State.† (accessed Nov. 11, 2018).

K. C. Media, “Setiap Hari, Ada 1.500 Ton Sampah Dibuang di Sungai Citarum,† (accessed May 04, 2020).

“Sungai Citarum Masih Tercemar Sampah Rumah Tangga,†, Jan. 22, 2019. (accessed May 04, 2020).

G. H. Cahyana, “Restorasi Citarum, dari Sungai Menuju Gelas,†Jul. 2018, doi: 10.31219/

I. Mohamed, F. Othman, A. I. N. Ibrahim, M. E. Alaa-Eldin, and R. M. Yunus, “Assessment of water quality parameters using multivariate analysis for Klang River basin, Malaysia,†Environ Monit Assess, vol. 187, no. 1, p. 4182, Nov. 2014, doi: 10.1007/s10661-014-4182-y.

K. P. Singh, A. Malik, D. Mohan, and S. Sinha, “Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)—a case study,†Water Research, vol. 38, no. 18, pp. 3980–3992, Nov. 2004, doi: 10.1016/j.watres.2004.06.011.

A. Barakat, M. El Baghdadi, J. Rais, B. Aghezzaf, and M. Slassi, “Assessment of spatial and seasonal water quality variation of Oum Er Rbia River (Morocco) using multivariate statistical techniques,†International Soil and Water Conservation Research, vol. 4, no. 4, pp. 284–292, Dec. 2016, doi: 10.1016/j.iswcr.2016.11.002.

S. C. Azhar, A. Z. Aris, M. K. Yusoff, M. F. Ramli, and H. Juahir, “Classification of River Water Quality Using Multivariate Analysis,†Procedia Environmental Sciences, vol. 30, pp. 79–84, Jan. 2015, doi: 10.1016/j.proenv.2015.10.014.

D. Phung et al., “Temporal and spatial assessment of river surface water quality using multivariate statistical techniques: a study in Can Tho City, a Mekong Delta area, Vietnam,†Environ Monit Assess, vol. 187, no. 5, p. 229, Apr. 2015, doi: 10.1007/s10661-015-4474-x.

Y. Wang et al., “Assessment of surface water quality via multivariate statistical techniques: A case study of the Songhua River Harbin region, China,†Journal of Hydro-environment Research, vol. 7, no. 1, pp. 30–40, Mar. 2013, doi: 10.1016/j.jher.2012.10.003.

S. Shrestha and F. Kazama, “Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan,†Environmental Modelling & Software, vol. 22, no. 4, pp. 464–475, Apr. 2007, doi: 10.1016/j.envsoft.2006.02.001.

M. Sharma, A. Kansal, S. Jain, and P. Sharma, “Application of Multivariate Statistical Techniques in Determining the Spatial Temporal Water Quality Variation of Ganga and Yamuna Rivers Present in Uttarakhand State, India,†Water Qual Expo Health, vol. 7, no. 4, pp. 567–581, Dec. 2015, doi: 10.1007/s12403-015-0173-7.

Q. Zhang et al., “Assessment of surface water quality using multivariate statistical techniques in red soil hilly region: a case study of Xiangjiang watershed, China,†Environ Monit Assess, vol. 152, no. 1, p. 123, Jun. 2008, doi: 10.1007/s10661-008-0301-y.

Agency for The Assessment and Application of Technology (BPPT), “Peraturan Pemerintah Republik Indonesia.†

F. H. Chapelle, P. M. Bradley, P. B. McMahon, and B. D. Lindsey, “What Does ‘Water Quality’ Mean?†Ground Water, vol. 47, no. 6, pp. 752–754, Nov. 2009, doi: 10.1111/j.1745-6584.2009.00569. x.

O. US EPA, “Dissolved Oxygen,†US EPA, Nov. 04, 2015. (accessed May 12, 2020).

S. Jouanneau et al., “Methods for assessing biochemical oxygen demand (BOD): A review,†Water Research, vol. 49, pp. 62–82, Feb. 2014, doi: 10.1016/j.watres.2013.10.066.

N.-Y. Kwok, S. Dong, W. Lo, and K.-Y. Wong, “An optical biosensor for multi-sample determination of biochemical oxygen demand (BOD),†Sensors and Actuators B: Chemical, vol. 110, no. 2, pp. 289–298, Oct. 2005, doi: 10.1016/j.snb.2005.02.007.

C. N. Sawyer, P. L. McCarty, and Parkin, Gene F., Chemistry for Environmental Engineering and Science, 5th ed. New York: McGraw-Hill, 2003.

R. Helmer, I. Hespanhol, United Nations Environment Programme, Water Supply and Sanitation Collaborative Council, and World Health Organization, Eds., Water pollution control: a guide to the use of water quality management principles, 1st. ed. London; New York: E & FN Spon, 1997.

Q. Zheng et al., “Self-Organized TiO2 Nanotube Array Sensor for the Determination of Chemical Oxygen Demand,†Advanced Materials, vol. 20, no. 5, pp. 1044–1049, Mar. 2008, doi: 10.1002/adma.200701619.

A. M. Jirka and M. J. Carter, “Micro semiautomated analysis of surface and waste waters for chemical oxygen demand,†Anal. Chem., vol. 47, no. 8, pp. 1397–1402, Jul. 1975, doi: 10.1021/ac60358a004.

Clean Water and Waste Water Management Technology Group, “Attachment 2, Indonesian Government Regulations No 82-year 2001 on Water Quality and Water Polution Management.† (accessed Nov. 25, 2018).

“Coliform Bacteria in Drinking Water Supplies.† (accessed Nov. 24, 2018).

Minister of State for the Environment of the Republic of Indonesia, “Keputusan Menteri Negara Lingkungan Hidup nomor 115 tahun 2003, tentang Pedoman Penentuan Status Mutu Air,†Oct. 07, 2003. (accessed Nov. 26, 2018).

D. K. Nasional, “Buku Indeks Kualitas Lingkungan Hidup Indonesia 2017.† (accessed Dec. 03, 2018).

O. US EPA, “Learn More Topic: Definitions of Key Terms,†US EPA, Dec. 02, 2014. (accessed May 12, 2020).

I. Jolliffe, “Principal Component Analysis,†in International Encyclopedia of Statistical Science, M. Lovric, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 1094–1096.

D. D. Suhr, “Principal Component Analysis vs. Exploratory Factor Analysis,†p. 11.



  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development