Application of Multivariate Statistical Analysis in Evaluation of Surface River Water Quality of a Tropical River

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Application of Multivariate Statistical Analysis in Evaluation of Surface River Water Quality of a Tropical River Hindawi Journal of Chemistry Volume 2017, Article ID 5737452, 13 pages https://doi.org/10.1155/2017/5737452 Research Article Application of Multivariate Statistical Analysis in Evaluation of Surface River Water Quality of a Tropical River Teck-Yee Ling,1 Chen-Lin Soo,1 Jing-Jing Liew,1 Lee Nyanti,2 Siong-Fong Sim,1 and Jongkar Grinang3 1 Department of Chemistry, Faculty of Resource Science and Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia 2Department of Aquatic Science, Faculty of Resource Science and Technology, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia 3Institute of Biodiversity and Environmental Conservation, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia Correspondence should be addressed to Teck-Yee Ling; [email protected] Received 18 November 2016; Accepted 26 December 2016; Published 19 January 2017 Academic Editor: Athanasios Katsoyiannis Copyright © 2017 Teck-Yee Ling et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The present study evaluated the spatial variations of surface water quality in a tropical river using multivariate statistical techniques, including cluster analysis (CA) and principal component analysis (PCA). Twenty physicochemical parameters were measured at 30 stations along the Batang Baram and its tributaries. The water quality of the Batang Baram was categorized as “slightly polluted” where the chemical oxygen demand and total suspended solids were the most deteriorated parameters. The CA grouped the 30 stations into four clusters which shared similar characteristics within the same cluster, representing the upstream, middle, and downstream regions of the main river and the tributaries from the middle to downstream regions of the river. The PCA has determined a reduced number of six principal components that explained 83.6% of the data set variance. The first PC indicated that the total suspended solids, turbidity, and hydrogen sulphide were the dominant polluting factors which is attributed to the logging activities, followed by the five-day biochemical oxygen demand, total phosphorus, organic nitrogen, and nitrate-nitrogen in the second PC which are related to the discharges from domestic wastewater. The components also imply that logging activities are the major anthropogenic activities responsible for water quality variations in the Batang Baram when compared to the domestic wastewater discharge. 1. Introduction logged forest was then converted to commercial oil palm and acacia plantations [1]. The Batang Baram (“batang” denotes big river) (coordinates: ∘ ∘ Although development continues to grow in this area, 4 35 5.28 N and 113 58 44.256 E)islocatedonthenorthern the study on the water quality of the river is relatively scarce part of Sarawak where it flows 400 km westwards, mostly despite the river serving as an important source for drinking through primary and secondary forest to the South China water for the rural community. The discharges of domestic Sea. The river is the second longest river in Sarawak and the sewage and agricultural runoff can lead to eutrophication [2– third longest river in Malaysia. The Baram area was once a 4] while deforestation can cause sedimentation and nutrient pristine area but it has undergone profound changes associ- enrichment in the river [5–8]. In the year 1995, sampling was ated with the population growth and development. Increasing conducted in the uppermost catchment of the Baram River residential area, numerous longhouses, and swidden agricul- basin. The study revealed that the overall water quality of the ture are found along the river. Commercial logging has also river was relatively good at that time but was subjected to high been carried out actively in the area for decades where the suspended solids which came from soil erosions due to land 2 Journal of Chemistry Downstream region of Batang Baram St 26 N St 25 St 30 South China Sea Sabah St 24 St 29 St 27 Peninsular St 28 St 23 Malaysia St 21 St 22 Sarawak St 20 St 18 St 19 St 17 St 16 St 15 St 14 St 13 2 km St 2 St 1 St 3 Upstream region of Batang Baram St 12 St 11 St 4 St 5 St 8 St 6 St 7 St 10 St 9 Sampling station Figure 1: The study area in Sarawak state and location of the 30 sampling stations along the Batang Baram and its tributaries in the present study. clearing and timber harvesting [9]. The author also pointed whole study area was subjected to logging activities. Numer- out that elevated ammonia was found near to the domestic ous longhouses and plantation activities were included in and animal waste discharges. Table 1. In situ parameters including temperature, pH, Water quality assessment and monitoring on large river conductivity, oxygen saturation (DOsat), dissolved oxygen basin like the Batang Baram potentially generate a large (DO), and turbidity were measured using a multiparameter data set. Numerous studies have shown that multivariate water quality sonde (YSI6920 V2-2). Transparency, depth, statistical analysis is useful for the assessment of the spatial and flow velocity were measured using a Secchi disc with water quality variations in a river [10–16]. Cluster analysis a measuring tape, a depth sounder (PS-7, Hondex), and a could reveal similarities among the large number of sampling stream flow meter (Geopacks), respectively. Total discharge, stations in a river while principal component analysis assists mean velocity, and mean depth were calculated according in identifying important factors accounting for most of the to [17]. The water samples were taken for the analyses of variances in water quality of a river. Hence, the aim of the chlorophyll a (chl a), total suspended solids (TSS), five- present study was to apply the multivariate statistical analysis day biochemical oxygen demand (BOD5), chemical oxygen in the interpretation of the physicochemical characteristics demand (COD), total phosphorus (TP), total ammonia − of the Batang Baram and its tributaries. The analysis output nitrogen (TAN), nitrite-nitrogen (NO2 -N), nitrate-nitrogen − would provide valuable information for the decision making (NO3 -N), organic nitrogen (Org-N), and total sulphide in the river basin management. (TS). All sampling bottles were acid-washed, cleaned, and dried before use. Analyses of chl a, TSS, and BOD5 began in − − thefieldimmediatelyaftersamplingwhileNO2 -N, NO3 -N, 2. Materials and Methods and TS analyses were completed in the field after sampling. Water samples were acidified to pH < 2 for COD, TP, TAN, 2.1. Field Collection. In situ and ex situ parameters were and Org-N analyses. The samples were placed in an ice box collected at 30 sampling stations located along the Batang and transported to the laboratory for further analysis [18]. Baram and its tributaries covering a distance of approximately of 172 km (Figure 1). Table 1 shows the details of the samplings from upstream to downstream directions that were carried 2.2. Laboratory Analysis. All the analyses were conducted out in the year 2015. The water level of river was high according to the standard methods [16, 17]. Chl was during samplings due to the rain before each sampling. The determined from samples filtered through a 0.7 mglass Journal of Chemistry 3 Table 1: The details of the sampling regime and sampling locations surveyed in the present study. Sampling Station Location Remark Batang Baram St 1 ∘ ∘ LioMatolonghouse N0310 10.8 E 115 11 47.6 Sungai Selungo St 2 ∘ ∘ N0310 15.0 E 115 11 46.7 Batang Baram St 3 ∘ ∘ Long Tungan longhouse N0309 34.0 E 115 10 51.0 Sungai Temendan St 4 ∘ ∘ N0307 16.7 E 115 09 15.2 Batang Baram St 5 ∘ ∘ 29-30 July 2015 N0307 21.8 E 115 09 10.5 Batang Baram Long Semiyang St 6 ∘ ∘ N0306 54.0 E 115 07 21.8 longhouse Sungai Sebatu St 7 ∘ ∘ N0305 50.6 E 115 05 46.3 Sungai Sela’an St 8 ∘ ∘ N0305 50.5 E 115 04 52.7 Sungai Moh St 9 ∘ ∘ N0302 34.1 E 115 04 35.9 Batang Baram Long Moh longhouse St 10 ∘ ∘ N0303 37.5 E 115 03 23.3 Replantation Batang Baram St 11 ∘ ∘ Long Apu longhouse N0308 23.6 E11448 52.7 Sungai Lasa St 12 ∘ ∘ Residential area N0308 40.8 E11448 49.2 Sungai Menuang St 13 ∘ ∘ N0316 19.1 E11449 22.4 Batang Baram St 14 ∘ ∘ N0316 56.6 E11447 53.1 Batang Baram St 15 ∘ ∘ Long San longhouse 3-4 September 2015 N0317 49.6 E11447 08.1 Sungai Akah St 16 ∘ ∘ Residential area N0319 11.3 E11447 32.7 Sungai Keluang St 17 ∘ ∘ N0319 58.6 E11442 21.8 Batang Baram St 18 ∘ ∘ Long Sangah longhouse N0321 19.0 E11441 31.5 Batang Baram St 19 ∘ ∘ Long Naha’a longhouse N0319 50.8 E114 35 43.1 Sungai Patah St 20 ∘ ∘ N0321 02.9 E114 36 22.3 Sungai Kahah St 21 ∘ ∘ N0323 12.9 E11434 05.7 Batang Baram St 22 ∘ ∘ N0323 08.4 E11433 35.8 Sungai Piping St 23 ∘ ∘ N0324 47.4 E11433 39.0 Batang Baram St 24 ∘ ∘ N0325 45.9 E11433 00.5 Sungai Jertang St 25 ∘ ∘ 21-22 January 2015 N0326 19.3 E114 32 28.3 4 Journal of Chemistry Table 1: Continued. Sampling Station Location Remark Sungai Kesseh St 26 ∘ ∘ Long Kesseh longhouse N0327 26.2 E11430 44.5 Batang Baram St 27 ∘ ∘ Long Kesseh longhouse N0327 11.3 E11430 29.4 Sungai Nakan Long Nakan longhouse St 28 ∘ ∘ N0326 29.6 E114 29 20.6 Oil palm plantation Sungai Liyans St 29 ∘ ∘ N0327 07.8 E11429 15.3 Sungai Kemenyeh St 30 ∘ ∘ N0327 46.7 E11428 29.4 microfibre filter (Whatman GF/F) and extracted for 24h AN, and pH was calculated according to the following using 90% (v/v) acetone.
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