Malaysian Journal of Analytical Sciences
Vol 19 No 5 (2015): 1019 - 1031
ANALYSIS
OF SURFACE WATER POLLUTION IN THE KINTA RIVER USING MULTIVARIATE TECHNIQUE
(Penilaian Pencemaran Air Permukaan di Sungai Kinta
Menggunakan Teknik Multivariat)
Hamza Ahmad Isiyaka and Hafizan Juahir*
East
Coast Environmental Research Institute (ESERI),
University
Sultan Zainal Abidin,
Gong
Badak Campus, 21300 Kuala Terengganu, Terengganu. Malaysia.
*Corresponding author: hafizanjuahir@unisza.edu.my
Received:
14 April 2015; Accepted: 9 July 2015
Abstract
This study aims
to investigate the spatial variation in the characteristics of water quality
monitoring sites, identify the most significant parameters and the major
possible sources of pollution, and apportion the source category in the Kinta
River. 31 parameters collected from eight monitoring sites for eight years
(2006-2013) were employed. The eight monitoring stations were spatially grouped
into three independent clusters in a dendrogram. A drastic reduction in the
number of monitored parameters from 31 to eight and nine significant parameters
(P<0.05) was achieved using the forward stepwise and backward stepwise
discriminate analysis (DA). Principal component analysis (PCA) accounted for
more than 76% in the total variance and attributes the source of pollution to
anthropogenic and natural processes. The source apportionment using a combined
multiple linear regression and principal component scores indicates that 41% of
the total pollution load is from rock weathering and untreated waste water, 26%
from waste discharge, 24% from surface runoff and 7% from faecal waste. This
study proposes a reduction in the number of monitoring stations and parameters
for a cost effective and time management in the monitoring processes and
multivariate technique can provide a simple representation of complex and
dynamic water quality characteristics.
Keywords: multivariate
techniques, source apportionment, multiple linear regression, principal
component analysis; Kinta river; water pollution
Abstrak
Kajian ini bertujuan untuk menyiasat variasi bagi ruang
dalam mengenalpasti ciri-ciri di stesen
pemantauan kualiti air,
mengenalpasti parameter yang paling penting
dan sumber utama kemungkinan
terhadap pencemaran dan membahagi
sumber kategori di
Sungai Kinta. 31 parameter yang dikumpul dari lapan stesen pemantauan
selama lapan tahun (2006-2013) telah digunapakai dalam kajian ini. Lapan
stesen pengawasan telah telah dibahagikan kepada tiga kelompok bebas dalam bentuk dendrogram. Pengurangan mendadak bilangan
parameter yang dipantau dari
31 parameter kepada lapan dan sembilan parameter penting
(P <0.05) telah dicapai dengan
menggunakan kaedah langkah demi langkah ke hadapan dan langkah demi langkah ke
belakang melalui analisis pembezalayan (AP). Analisis
komponen utama (AKU)
menyumbang lebih daripada 76%
dalam jumlah varians dan sifat-sifat
punca pencemaran kepada proses antropogenik dan
semula jadi. Pembahagian sumber menggunakan
pelbagai regresi linear
gabungan dan skor komponen utama menunjukkan
bahawa 41% daripada jumlah beban
pencemaran adalah daripada luluhawa batu dan air
sisa yang tidak dirawat, 26% daripada pelepasan sisa,
24% daripada air
larian permukaan dan 7% daripada sisa najis.
Kajian
ini mencadangkan pengurangan dalam bilangan stesen pemonitoran dan parameter
untuk pengurusan kos yang berkesan dan masa dalam proses pemantauan dan teknik
multivariat boleh menyediakan perwakilan yang mudah untuk ciri-ciri kualiti air
yang kompleks dan dinamik.
Kata
kunci:
teknik multivariate, sumber pembahagian; regresi linear, analisis komponen utama; Sungai Kinta;
pencemaran air
References
1.
Mustapha,
A., Aris, A. Z., Ramli, M. F. and Juahir, H. (2012). Spatial-temporal variation
of surface water quality in the downstream region of the Jakara River,
north-western Nigeria: A statistical approach. Journal of Environmental
Science and Health Part A 47(11): 1551-1560.
2.
Khalil,
B., Ouarda, T. B. M. J. and St-Hilaire, A. (2011). Estimation of water quality
characteristics at ungauged sites using artificial neural networks and
canonical correlation analysis. Journal of Hydrology 405(3): 277-287.
3.
Zhang,
Q., Li, Z., Zeng, G., Li, J., Fang, Y., Yuan, Q. and Ye, F. (2009). Assessment
of surface water quality using multivariate statistical techniques in red soil
hilly region: a case study of Xiangjiang watershed, China. Environmental
Monitoring and Assessment 152(1-4): 123-131.
4.
Satheeshkumar,
P. and Khan, A. B. (2012). Identification of mangrove water quality by
multivariate statistical analysis methods in Pondicherry coast, India. Environmental
Monitoring and Assessment 184(6): 3761-3774.
5.
Vieira,
J., Fonseca, A., Vilar, V. J. P., Boaventura, R. A. R. and Botelho, C. M. S.
(2012). Water quality in Lis River. Portugal. Environmental Monitoring and
Assessment 184(12): 7125-7140.
6.
Ogwueleka,
T. C. (2015). Use of multivariate statistical techniques for the evaluation of
temporal and spatial variations in water quality of the Kaduna River, Nigeria. Environmental
Monitoring and Assessment 187(3): 1-17.
7.
Usman,
U. N., Toriman, M. E., Juahir, H., Abdullahi, M. G., Rabiu, A. A. and Isiyaka,
H. (2014). Assessment of Groundwater Quality Using Multivariate Statistical
Techniques in Terengganu. Science and Technology 4(3): 42-49.
8.
Mustapha,
A., Aris, A. Z., Juahir, H., Ramli, M. F. and Kura, N. U. (2013). River water
quality assessment using environmentric techniques: case study of Jakara River
Basin. Environmental Science and Pollution Research 20(8): 5630-5644.
9.
Wang,
Y. B., Liu, C. W., Liao, P. Y. and Lee, J. J. (2014). Spatial pattern
assessment of river water quality: implications of reducing the number of
monitoring stations and chemical parameters. Environmental Monitoring and
Assessment, 186(3): 1781-1792.
10.
Mustapha,
A., Aris, A. Z., Juahir, H., Ramli, M. F. and Kura, N. U. (2013). River water
quality assessment using environmentric techniques: case study of Jakara River
Basin. Environmental Science and Pollution Research 20(8): 5630-5644.
11.
Zhang, Q., Li, Z., Zeng, G., Li, J., Fang, Y., Yuan, Q. and
Ye, F. (2009). Assessment of surface water quality using multivariate
statistical techniques in red soil hilly region: a case study of Xiangjiang
watershed, China. Environmental Monitoring and Assessment 152(1-4): 123-131.
12.
Mustapha, A., Aris, A. Z., Ramli, M. F. and Juahir, H.
(2012). Spatial-temporal variation of surface water quality in the downstream
region of the Jakara River, north-western Nigeria: A statistical approach. Journal of Environmental Science
and Health Part A, 47(11): 1551-1560.
13.
Mustapha,
A., Aris, A. Z., Yusoff, F. M., Zakaria, M. P., Ramli, M. F., Abdullah, A. M.
and Narany, T. S. (2014). Statistical Approach in Determining the Spatial
Changes of Surface Water Quality at the Upper Course of Kano River, Nigeria. Water
Quality, Exposure and Health 6(3): 127-142.
14.
Department
of Environment Malaysia (DOE) (2009) Malaysia Environmental Quality Report,
Ministryof Science, Technology and Environment, Kuala Lumpur.
15.
Shrestha,
S. and Kazama, F. (2007). Assessment of surface water quality using
multivariate statistical techniques: A case study of the Fuji river basin,
Japan. Environmental Modelling & Software 22(4): 464-475.
16.
Fan,
X., Cui, B., Zhao, H., Zhang, Z. and Zhang, H. (2010). Assessment of river
water quality in Pearl River Delta using multivariate statistical techniques. Procedia
Environmental Sciences 2: 1220-1234.
17.
Tobiszewski,
M., Tsakovski, S., Simeonov, V. and Namieśnik, J. (2010). Surface water quality
assessment by the use of combination of multivariate statistical classification
and expert information. Chemosphere 80(7):
740-746.
18.
Al-Odaini,
N. A., Zakaria, M. P., Zali, M. A., Juahir, H., Yaziz, M. I. and Surif, S.
(2012). Application of chemometrics in understanding the spatial distribution
of human pharmaceuticals in surface water. Environmental Monitoring and
Assessment 184(11): 6735-6748.
19.
Juahir,
H., Zain, S. M., Yusoff, M. K., Hanidza, T. T., Armi, A. M., Toriman, M. E. and
Mokhtar, M. (2011). Spatial water quality assessment of Langat River Basin
(Malaysia) using environmetric techniques. Environmental Monitoring and
Assessment 173(1-4): 625-641.
20.
Deepulal,
P. M., Sujatha, C. H. and George, R. (2012). Chemometric study on the trace
metal accumulation in the sediments of the Cochin Estuary―Southwest coast of
India. Environmental Monitoring and Assessment 184(10): 6261-6279.
21.
Wahid,
N. B. A., Latif, M. T. and Suratman, S. (2013). Composition and source
apportionment of surfactants in atmospheric aerosols of urban and semi-urban
areas in Malaysia. Chemosphere 91(11):
1508-1516.
22.
Aris,
A. Z., Praveena, S. M., Isa, N. M., Lim, W.Y., Juahir, H., Yusoff, M. K. and
Mustapha, A. (2013). Application of environmetricmethods to surface water
quality assessment of Langkawi Geopark (Malaysia). Environmental Forensics 14(3): 230–239.
23.
Oyeyiola,
A. O., Davidson, C. M., Olayinka, K. O., Oluseyi, T. O. and Alo, B. I. (2013).
Multivariate analysis of potentially toxic metals in sediments of a tropical
coastal lagoon. Environmental Monitoring and Assessment 185(3): 2167-2177.
24.
Isiyaka,
H., Juahir, H., Toriman, M. E., Gasim, B. M., Azid, A., Amri, M. K., Ibrahim,
A., Usman, U. N., Rano, A. R. A. and Garba, M. A. (2014). Spatial Assessment of
Air Pollution Index Using Environ Metric Modeling Techniques. Advances in
Environmental Biology 8(24): 244-256.
25.
Pati,
S., Dash, M. K., Mukherjee, C. K., Dash, B. and Pokhrel, S. (2014). Assessment
of water quality using multivariate statistical techniques in the coastal
region of Visakhapatnam, India. Environmental Monitoring and Assessment
186(10): 6385-6402.
26.
Latif,
M. T., Dominick, D., Ahamad, F., Khan, M. F., Juneng, L., Hamzah, F. M. and
Nadzir, M. S. M. (2014). Long term assessment of air quality from a background
station on the Malaysian Peninsula. Science of The Total Environment 482: 336-348.
27.
Mustaffa,
N. I. H., Latif, M. T., Ali, M. M. and Khan, M. F. (2014). Source apportionment
of surfactants in marine aerosols at different locations along the Malacca
Straits. Environmental Science and Pollution Research 21(10): 6590-6602.
28.
Jaafar,
S. A., Latif, M. T., Chian, C. W., Han, W. S., Wahid, N. B. A., Razak, I. S.,
Khan, M. F. and Tahir, N. M. (2014). Surfactants in the sea-surface microlayer
and atmospheric aerosol around the southern region of Peninsular Malaysia. Marine
Pollution Bulletin 84(1): 35-43.
29.
Ghani,
A., Zakaria, N. A., Kiat, C.C., Ariffin, J., Abu Hasan, Z., Abdul Gaffar, A.
B., 2007. Revised equations for manning’s coefficient for sand-bed rivers.
International Journal of River Basin Management 5 (4): 329–346.
30. Zali, M. A.,
Retnam, A., Juahir, H., Zain, S. M., Kasim, M. F., Abdullah, B. and Saadudin,
S. B. (2011). Sensitivity analysis for water quality index (WQI) prediction for
Kinta River, Malaysia. World Applied
Sciences Journal: 60 – 65.
31.
Lau,
J., Hung, W.T. and Cheung, C.S. (2009). Interpretation of air quality in
relation to monitoring station’s surrounding. Atmospheric Environmetric
43: 769-777.
32.
Singh,
K. P., Malik, A., Mohan, D. and 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(18): 3980-3992.
33.
Farmaki,
E. G., Thomaidis, N. S., Simeonov, V. and Efstathiou, C. E. (2012). A
comparative chemometric study for water quality expertise of the Athenian water
reservoirs. Environmental Monitoring and Assessment 184(12):
7635-7652.
34.
Azid,
A., Juahir, H., Toriman, M. E., Kamarudin, M. K. A., Saudi, A. S. M., Hasnam,
C. N. C. and Yamin, M. (2014). Prediction of the Level of Air Pollution Using
Principal Component Analysis and Artificial Neural Network Techniques: a Case
Study in Malaysia. Water, Air, & Soil Pollution 225(8): 2063 – 2077.
35.
Zhang, Q., Li, Z., Zeng, G., Li, J., Fang, Y., Yuan, Q. and
Ye, F. (2009). Assessment of surface water quality using multivariate
statistical techniques in red soil hilly region: a case study of Xiangjiang
watershed, China. Environmental Monitoring and Assessment 152(1-4): 123-131.
36.
Brūmelis,
G., Lapiņa, L., Nikodemus, O. and Tabors, G. (2000). Use of an artificial model
of monitoring data to aid interpretation of principal component analysis. Environmental
Modelling & Software 15(8): 755-763.
37.
Love,
D., Hallbauer, D., Amos, A. and Hranova, R. (2004). Factor analysis as a tool
in groundwater quality management: two southern African case studies. Physics
and Chemistry of the Earth, Parts A/B/C 29(15):
1135-1143.
38.
Liu,
C.W., Lin, K. H. and Kuo, Y. M. (2003). Application of factor analysis in the
assessment of groundwater quality in a black foot disease area in Taiwan. Science of Total Environment 313: 77–89.
39.
Chatterjee,
S. and Price, B. (1999) Regression Analysis by Example. third ed. Wiley,
Chichester.
40.
Petrie,
A. and Sabin, C. (2000). Medical Statistics at a Glance. Blackwell Science,
Oxford.
41.
Kovač-Andrić,
E., Brana, J. and Gvozdić, V. (2009). Impact of meteorological factors on ozone
concentrations modelled by time series analysis and multivariate statistical
methods. Ecological Informatics 4(2):
117-122.
42.
Gazzaz,
N. M., Yusoff, M. K., Aris, A. Z., Juahir, H. and Ramli, M. F. (2012).
Artificial neural network modeling of the water quality index for Kinta River
(Malaysia) using water quality variables as predictors. Marine Pollution
Bulletin 64(11): 2409-2420.
43.
Pastor-Bárcenas,
O., Soria-Olivas, E., Martín-Guerrero, J. D., Camps-Valls, G.,
Carrasco-Rodríguez, J. L. and del Valle-Tascón, S. (2005). Unbiased sensitivity
analysis and pruning techniques in neural networks for surface ozone modelling.
Ecological Modelling 182(2): 149-158.
44.
Napacho,
Z. A. and Manyele, S. V. (2010). Quality assessment of drinking water in Temeke
District (part II): Characterization of chemical parameters. African Journal
of Environmental Science and Technology 4(11): 775-789.
45.
Kanmani,
S. and Gandhimathi, R. (2013). Investigation of physicochemical characteristics
and heavy metal distribution profile in groundwater system around the open dump
site. Applied Water Science 3(2):
387-399.
46.
WHO. (2011). Hardness in
drinking water: background document for preparation of WHO guidelines for
drinking water quality. World Health Organization, Geneva.
47.
Busse, M. (2013). Sign and symptoms of too much magnesium. http:// www.livestrong.com/article/379016-signs-and-symptoms-of-toomuch-
magnesium
48.
Seth,
R., Mohan, M., Dobhal, R., Gupta, V. K., Singh, P., Singh, R. and Gupta, S.
(2014). Application of Chemometric Techniques in the Assessment of Groundwater
Quality of Udham Singh Nagar, Uttarakhand, India. Water Quality, Exposure
and Health 6(4): 199-216.
49.
Chen,
H., Teng, Y., Yue, W. and Song, L. (2013). Characterization and source
apportionment of water pollution in Jinjiang River, China. Environmental
Monitoring and Assessment 185(11): 9639-9650.
50.
Myers, S. A., Nield, A. and Myers, M. (2012). Zinc
transporters, mechanisms of action and therapeutic utility: implications for
type 2 diabetes mellitus. Journal of
Nutrition and Metabolism: 1-14.
51.
Minnesota
Pollution Control Agency. (2007).
Phosphorus: Sources, Forms, Impact on Water Quality
52.
Kumar,
A., Bisht, B. S., Joshi, V. D., Singh, A. K. and Talwar, A. (2010). Physical,
chemical and bacteriological study of water from rivers of Uttarakhand. Journal
of Human Ecology 32(3): 169-173.
53.
Yang,
L., Linyu, X. U. and Shun, L. (2009). Water quality analysis of the Songhua
River Basin using multivariate techniques. Journal of Water Resource and
Protection 1(02): 110-121.
54.
McFarland,
A. M., & Hauck, S. L. (1999). Relating agricultural land uses to in-stream
stormwater quality. Journal of Environmental Quality 28(2): 836–844.