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 𝑎 was during samplings due to the rain before each sampling. The determined from samples filtered through a 0.7 𝜇mglass 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. For TSS, filtration of an adequate equation: 𝜇 sample through a 1.0 mglassmicrofibrefilter(Whatman = 0.22 ∗ + 0.19 ∗ + 0.16 ∗ GF/B) was carried out in the field and drying of the filter WQI SIDO SIBOD SICOD ∘ (2) was conducted in the laboratory in an oven at 105 Cuntil + 0.15 ∗ SIAN + 0.16 ∗ SISS +0.12∗SIpH, a constant weight was obtained. It was then determined by calculating the difference between the initial and final where SIDO is the subindex for DO (% saturation), SIBOD weight of the sample and expressed as milligram per liter is the subindex for BOD (mg/L), SICOD is the subindex for of sample. For BOD5, it was determined as the difference COD (mg/L), SIAN is the subindex for AN (mg/L), SISS is the between the initial and final DO content, after a five-day subindex for SS (mg/L), and SIpH is the subindex for pH [19]. period of incubation of the sample. The initial DO content was measured in the field. Whenever the in situ DO value 2.4. Statistical Analysis. Comparison of physicochemical was deemed too low, it was raised by vigorous aeration. COD parameters between the stations in the Batang Baram was was determined by the closed reflux method followed by the conducted using one-way ANOVA and Tukey’s pairwise titrimetric method. For TP analysis, persulfate digestion of comparisons with 5% significance level. The independent samples was conducted followed by the ascorbic acid method. − − samples t-test was used to compare the physicochemical TAN, NO2 -N, and NO3 -N were determined by Nessler’s parameters between the main river and tributary stations. method, diazotization method (low range), and cadmium − Pearson’s correlation analysis was performed to determine reduction method, respectively. Before the analyses of NO2 - − the relationship among all the parameters. Cluster analysis NandNO3 -N, the water sample was filtered through a (CA) was used to investigate the grouping of the sampling 𝜇 0.7 m glass microfibre filter (Whatman GF/F). Org-N was stations by using the physicochemical parameters collected determined by the Macro-Kjeldahl Method where ammonia in the river. 𝑍-score standardization of the variables and was removed from the water sample before digestion and Ward’s method using Euclidean distances as a measure of distillation. Subsequently, ammonia was analyzed by using similarity were used. The cluster was considered statistically Nessler’s method. TS was analyzed using the methylene significant at a linkage distance of <60% and the number blue method. H2Swascalculatedaccordingto[18]withthe of clusters was decided by the practicality of the outputs following equation: [12]. Principal component analysis (PCA) was conducted to characterize the loadings of all physicochemical parameters for each of the PCs obtained having eigenvectors higher than one (Kaiser criterion). The component has significant = TS , H2S + (1) loadingonavariablewhentheloadingisgreaterthan0.4 (1 + 𝐾 1/ [H ]) [20]. The data were square-rooted and standardized prior to the analysis. The quality of data for PCA was confirmed with Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy where H2S is the unionized hydrogen sulphide, TS is the total test and Bartlett’s test of sphericity. All the statistical analyses + sulphide, 𝐾 is the conditional ionization constant, and [H ] were carried out by using the Statistical Software for Social is the hydrogen ion concentration. Sciences (SPSS Version 22, SPSS Inc., 1995). A calibration curve was constructed for each chemical analysis. The blank and standard solutions were treated in the 3. Results and Discussion same way as the sample. 3.1. The Physicochemical Characteristics of the Batang Baram and Its Tributaries. Figures2and3showthemeanvaluesof 2.3. Water Quality Index (WQI). Water quality index (WQI) the physicochemical parameters of the Batang Baram and its which combines the six variables of DO, BOD, COD, TSS, tributariesfromupstreamtodownstreamregions.Duringthe Journal of Chemistry 5
3000.0 160.0 2.0 2.0 140.0 1.8 1.8 /s) 2500.0 /s) 3 3 1.6 1.6 120.0 2000.0 1.4 1.4 100.0 1.2 1.2 1500.0 80.0 1.0 1.0 60.0 0.8 0.8 1000.0 0.6 0.6 40.0 0.4 0.4
500.0 (m/s) velocity Mean (m/s) velocity Mean Total discharge (m discharge Total 20.0 (m discharge Total 0.2 0.2 0.0 0.0 0.0 0.0 1 5 9 13 17 21 25 29 1 5 9 1317212529 Station Station
Main river Main river Tributary Tributary (a) (b) 18.0 7.0 28.0 34.0 16.0 6.0 27.0 R M 32.0 L 14.0 C) KL Q K C) 5.0 ∘ ∘ 12.0 26.0 IJ IJ I P J J 30.0 G 10.0 4.0 O 25.0 N 28.0 8.0 C 3.0 B H H H A H 6.0 24.0 F 26.0 2.0 D EF D E C B B Mean depth (m) depth Mean 4.0 (m) depth Mean Temperature ( Temperature 23.0 24.0 ( Temperature 2.0 1.0 0.0 0.0 22.0 22.0 1 5 9 1317212529 1 5 9 1317212529 Station Station
Main river Main river Tributary Tributary (c) (d) 8.0 8.0 60.0 90.0 S S V 7.8 R 7.8 Q U 80.0 Q 50.0 P P 7.6 O O 7.6 T 70.0 N M M S/cm) L S/cm) 7.4 M L 7.4 K 𝜇 𝜇 I KL I JK J 40.0 S 60.0 7.2 7.2 J R Q P F G FG F G H I H O 50.0 E E F F N 7.0 D 7.0 30.0 L M pH pH E C AB B K A J C 40.0 6.8 6.8 G H 6.6 6.6 20.0 E D 30.0 20.0 6.4 6.4 10.0 B 6.2 6.2 ( Conductivity A 10.0 ( Conductivity 6.0 6.0 0.0 0.0 1 5 9 13 17 21 25 29 1 5 9 1317212529 Station Station
Main river Main river Tributary Tributary (e) (f) 120.0 120.0 8.0 8.0 W V 110.0 V 110.0 7.5 U 7.5 T U 100.0 100.0 7.0 S T R T 7.0 Q R S Q Q
P OP Q NO P MN MN LM P I J KL K GH I HI FG D FG E F 90.0 O N 90.0 6.5 C 6.5 L MM B HIJ K GHI J GHIJ FG I GH E F D C B E E A A
DOsat (%) DOsat 80.0 80.0 (%) DOsat 6.0 6.0 DO (mg/L) DO (mg/L) 70.0 70.0 5.5 5.5 60.0 60.0 5.0 5.0 1 5 9 1317212529 1 5 9 1317212529 Station Station
Main river Main river Tributary Tributary (g) (h)
Figure 2: Continued. 6 Journal of Chemistry
600.0 600.0 0.10 1.2 HH H N N A A 500.0 500.0 0.08 1.0 A 400.0 400.0 A 0.8 M 0.06 A A L A A A G A AA A LM L L 300.0 L 300.0 G G 0.6 F K K K 0.04 F JK E 200.0 IJ 200.0 0.4 HI D GH CD FGH Turbidity (NTU) Turbidity Turbidity (NTU) Turbidity EFG BC Transparency (m)Transparency (m) Transparency DEF CDEF DEF 0.02
BCDE B
100.0 BCD 100.0 0.2
AB ABC A AB A A A A A A A A 0.0 0.0 0.00 0.0 1 5 9 13 17 21 25 29 1 5 9 13 17 21 25 29 Station Station
Main river Main river Tributary Tributary (i) (j)
Figure 2: In situ parameters of (a) total discharge, (b) mean velocity, (c), mean depth, (d) temperature, (e) pH, (f) conductivity, (g) DOsat,(h) DO, (i) turbidity, and (j) transparency measured at the 30 sampling stations located along the Batang Baram (left axis) and its tributaries (right axis) (different letters indicate significant difference at 𝑝 value ≤ 0.05). sampling, total discharge of the Batang Baram ranged from to 3.5 𝜇S/cm at station 27. The highest values of BOD5 (5.7 ± 3 3 3 3 126.3 m /s to 2711.8 m /s and from 0.5 m /s to 133.6 m /s in 0.2 mg/L) and Org-N (2.74 ± 0.01 mg/L) were observed at stations 1 and 4, respectively, while the highest values of main river and tributaries, respectively. Figure 2 illustrates − that total discharge of main river showed an increasing TP (2.2 ± 0.1 mg/L) and NO3 -N (0.07 ± 0.01 mg/L) were trend towards downstream regions whereas the highest total observed at station 6. The conductivity value (82 𝜇S/cm– discharge in tributaries was observed at station 8 followed by 133 𝜇S/cm) in the uppermost part of the Baram River basin station 16. Mean velocity of the river was relatively consistent reported by [9] was relatively higher than the present study in main river with a mean value of 1.2 m/s. High mean velocity which agrees with the present result that conductivity value (>1 m/s) was also observed in some of the tributaries located was higher in the upper part of the river. However, the author also reported the concentrations of the BOD5 (0.7 mg/L to upstreambutmostofthetributarieswereslowflowing − (≈0.2 m/s). Mean depth of the Batang Baram ranged from 2.0 mg/L) and NO3 -N (0.01 mg/L–0.02 mg/L) which were 0.9 m to 16.5 m and from 0.2 m to 6.1 m in main river and lower than the present study. tributaries, respectively. Both main river and tributaries were Significantly higher COD value (p value ≤ 0.05) was observed in the middle section of the river (110.1 mg/L– relatively deeper downstream compared to upstream. − TheresultsofANOVAshowedthatalloftheparameters 181.8 mg/L) whereas NO2 -N (0.001 mg/L–0.002 mg/L) and demonstrated significant variationsp ( value ≤ 0.05) from one TAN (0.12 mg/L–0.33 mg/L) values were significantly lower sampling station to another. The physicochemical parameters (p value ≤ 0.05) there. Significantly higherp ( value ≤ 0.05) showed different distribution patterns along the main river. TAN was observed at stations 21 (1.57 ± 0.07 mg/L) and 22 2 (1.49 ± 0.20 mg/L) while significantly higherp ( value ≤ 0.05) The turbidity, TSS, and H Svaluesincreasedsignificantly(p − ≤ NO2 -N was observed at station 20 (0.055 ± 0.001 mg/L). value 0.05) towards downstream with the highest values of − turbidity (468.8 ± 45.4 NTU) and TSS (320.0 ± 19.3 mg/L) Similar to BOD5 and NO3 -N, the NH3-N concentration in which were both observed at station 20 while the highest the uppermost part of the Baram River basin which ranged value of H2S(0.83 ± 0.01 mg/L) was observed at station 22. from 0.7 mg/L to 2.0 mg/L [9] was lower than the present The high turbidity and TSS downstream indicate the accu- study. The author attributed the high ammonia concentration mulation of sediment in the river. Reference [21] reported that in his study to the sewage discharge from the longhouse a spit was formed in the Baram River mouth and continued and animal waste. The higher nutrients concentration in the to expand due to the erosion associated with deforestation present study indicated the deterioration of water quality over and land use changes in the upstream region. The similar time due to the increase in population and land development distribution pattern and significant positive correlationp ( in the area. ≤ 2 Table 3 shows that the river temperature, pH, conductiv- value 0.05) between H S, turbidity, and TSS (Table 2) − indicated that H2S was associated with suspended solids in ity, transparency, chl a,andNO2 -N were significantly higher the river. (p value ≤ 0.05) in tributaries than in the main river. The − 5 3 high water temperature in tributaries particularly at stations On the other hand, the conductivity, BOD ,TP,NO -N, ∘ and Org-N showed higher values at the upper part of the 12, 13, and 16 (>29 C) indicated that direct solar radiation river and decreased significantly (p value ≤ 0.05) towards duetotheforestcanopyexposureafterlogginghadincreased downstream region. In contrary, [22] demonstrated that therivertemperatureinthosetributaries[6].TheBaram TP, TN, and NH3-N concentrations tend to increase from River basin contained high dissolved ions which gave the upstream to downstream regions in the Qiantang River, East high conductivity values in the river [9]. Besides, significant China. In the present study, the highest conductivity value positive correlation (p value ≤ 0.05) between temperature was observed at station 1 (50.0 𝜇S/cm) and steadily decreased and conductivity indicated that the high temperature in Journal of Chemistry 7 S 2 .361 .403 .454 .566 .596 − − − − .547 .362 − -N Org-N H − 3 .392 .554 .390 − − − -N NO − 2 .528 − .456 .467 − − .392 .540 .515 .493 .399 COD TP TAN NO − − 5 .544 .444 .478 − − .605 .608 TSS BOD − − a .361 .596 .582 .915 .714 .389 .555 .491 .454 .605 − − − − .493 .650 .413 .480 − − − .547 .523 .564 .515 .456 .550 .540 .564 .544 .550 .545 .523 − − − 0.05) of the in situ and ex situ parameters collected from the 30 sampling stations. ≤ .539 .582 − − value 𝑝 .511 .381 .554 .545 .454 .714 .362 .399 .467 .444 − − − − − .391 .539 .650 .527 .366 .566 .546 .601 .706 .389 .608 .915 .366 − − − − − − .528 .390 .403 .480 − − − − Table 2: Correlation matrix ( .511 .610 .549 .391 .658 .549 .389 .413 .555 − − − .546 − Discharge Velocity Depth Temp pH Cond DOsat DO Turb Trans Chl -N -N .454 5 − − a 2 3 S 2 BOD DOsat Chl TSS .389 DO .601 .641 .527 TurbTrans .706 .381 .513 Cond Org-N H TAN NO DischargeVelocity .392 .392 .834 DepthTemppH .834 .658 .610 .641 .513 NO COD .478 TP .491 8 Journal of Chemistry
16.0 35.0 400.0 400.0 14.0 H 30.0 350.0 L L 350.0 K JK ) ) 12.0 J 300.0 K K 300.0 3 3 25.0 10.0 250.0 J JK IJ 250.0 I 20.0 FG
8.0 FG 200.0 HIH 200.0 (mg/m (mg/m GH EFG FGH a a DEF H 15.0 6.0 DEF H 150.0 150.0 EFG TSS (mg/L) TSS (mg/L) EF CDE E DE G DE Chl
Chl 10.0 4.0 100.0 CDE 100.0 DEFG DEF BCD AB ABC ABC ABC BCD AB AB AB AB 5.0 AB AB
2.0 50.0 AB 50.0
A ABC AB AB AB AB A A A A A A A 0.0 A 0.0 0.0 0.0 1 5 9 1317212529 1 5 9 1317212529 Station Station Main river Main river Tributary Tributary (a) (b) 200.0 300.0 7.0 6.0 L M R 180.0
6.0 PQ Q 250.0 PQ 5.0 160.0 JK IJK
5.0 MN 140.0 KL OP
KLM 4.0 200.0 NO MN JKL 120.0 HI HI KLM 4.0 LM IJK HI
(mg/L) (mg/L) IJ IJ
FGH 100.0 150.0
GH 3.0 HI EFG 5 5 CDEF EFG CDEF GH 3.0 GH DEFG FGH 80.0 BCD FGH BC FGH BCDE FGH FGH FGH B B B BCDE 100.0 2.0 BCDE ABCD DEF COD (mg/L) COD 60.0 (mg/L) COD BOD 2.0 A BOD AB AB ABCD ABCD AB ABCD 40.0 ABC A 1.0 1.0 AB A 50.0 20.0 0.0 0.0 0.0 0.0 1 5 9 13 17 21 25 29 1 5 9 1317212529 Station Station Main river Main river Tributary Tributary (c) (d) 3.0 2.5 2.0 2.0 G 1.8 M 1.8 2.5 M G 2.0 1.6 1.6 F 2.0 1.4 1.4 EF EF 1.5 1.2 1.2 L
1.5 KL 1.0 IJK 1.0 JK HIJK HIJK GHIJ DE FGHI 1.0 FGHI FGH 0.8 FGH 0.8 EFG
TP (mg/L) CD TP (mg/L) CD 1.0 DEF CD DE CD (mg/L) TAN 0.6 D 0.6 (mg/L) TAN BC BC
0.5 C BC
AB 0.4 0.4 ABC
0.5 ABC AB AB AB AB AB AB AB A AB A AAB ABA 0.2 A A A A A 0.2 A A A A A A A 0.0 0.0 0.0 0.0 1 5 9 13 17 21 25 29 1 5 9 1317212529 Station Station Main river Main river Tributary Tributary (e) (f)
0.020 P 0.060 0.12 0.12 F 0.018 LM F 0.016 0.050 0.10 0.10
KLM F JKLM 0.014 O 0.040 0.08 EF 0.08
0.012 HIJKL
0.010 N FGHIJ 0.030 0.06 0.06 EFGHIJ FGHIJ DEFGH -N (mg/L) -N (mg/L) -N (mg/L) -N (mg/L) DE 2 2
0.008 3 3 M CD BCD BCDEFGH
0.020 BCD 0.04 ABCD 0.04 ABCD
0.006 ABCD ABCD ABCD ABCD NO NO NO NO ABCD ABCD IJKL ABCD GHIJKL ABCD GHIJK FGHIJ 0.004 ABC ABC AB DEFGHI DEFGH CDEFGH EFGHIJ 0.010 ABC ABC ABC
ABCDEFG 0.02 0.02 ABC ABCDEF ABCDE ABC ABC ABCD AB AB 0.002 A AB AB A A 0.000 0.000 0.00 A 0.00 1 5 9 13 17 21 25 29 1 5 9 1317212529 Station Station Main river Main river Tributary Tributary (g) (h)
Figure 3: Continued. Journal of Chemistry 9
3.0 3.0 P 0.9 Q 0.4 0.8 N 2.5 O 2.5 0.3 N N 0.7 M M 0.3 2.0 L L 2.0 0.6 K K K P J 0.5 0.2 1.5 IJ I 1.5 L H 0.4 O G S (mg/L) 0.2 S (mg/L) JK
G 2 2 HIJ GHIJ 1.0 F F 1.0 0.3 HIJ H M M H DE EF EFGH M Org-N (mg/L) Org-N (mg/L) Org-N D D EFG 0.1 C 0.2 L DEF KL JK BCD BCD IJ BCD
0.5 0.5 FGHI B B B ABC 0.1 DEF CDE AB A 0.1 AB A A A A 0.0 0.0 0.0 0.0 1 5 9 13 17 21 25 29 1 5 9 13 17 21 25 29 Station Station Main river Main river Tributary Tributary (i) (j)
− − Figure 3: Ex situ water quality of (a) chl a, (b) TSS, (c), BOD5, (d) COD, (e) TP, (f) TAN, (g) NO2 -N, (h) NO3 -N, (i) Org-N, and (j) H2S measured at the 30 sampling stations located along the Batang Baram (left axis) and its tributaries (right axis) (different letters indicate significant difference at 𝑝 value ≤ 0.05).
Table 3: Mean difference of in situ and ex situ water quality 25 parameters between the main river of the Batang Baram and its tributaries. 20 1 2 3 4 Parameter Mean difference 𝑝 value ∘ Temperature, C −0.7 0.050 15 pH −0.2 0.001 Cluster Cluster Cluster Cluster Conductivity, 𝜇S/cm −16.2 0.000 10 In situ DOsat, % −0.2 0.915 DO, mg/L +0.2 0.000 5 Turbidity, NTU +132.0 0.000 −0.4 0.000 0
Transparency, m combination Rescaled cluster distance 3 8 9 4 6 3 5 1 2 7 18 19 11 15 14 29 20 12 13 25 17 26 16 21 28 23 24 27 22 10 Chl a,mg/m −2.6 0.048 30 Case TSS, mg/L +120.7 0.000 Case Case Case Case Case Case Case Case Case Case Case Case Case Case Case Case Case Case Case Case Case Case Case Case Case Case Case Case Case BOD5,mg/L +0.5 0.026 Station COD, mg/L −8.3 0.474 TP, mg/L +0.4 0.002 Figure 4: Clustering of the 30 sampling stations along the Batang Ex situ Baram and its tributaries. TAN, mg/L +0.1 0.497 − NO2 -N, mg/L −0.006 0.006 − Table 4 shows that most of the sampling stations were NO3 -N, mg/L +0.007 0.169 classified as Class III and categorized as “slightly polluted” Org-N, mg/L +0.3 0.093 according to the water quality index (WQI). Among the 30 H2S, mg/L +0.1 0.000 stations along the Batang Baram and its tributaries, only two Positive value of mean difference indicates parameter studied was higher in stations which were located at tributaries of Sungai Kesseh the main river of Batang Baram whereas negative value indicates parameter studied was higher in the tributary. The significant difference at 𝑝 value ≤ andSungaiNakanwerecategorizedas“clean.”ThepHand 0.05 was indicated in bold. DO were classified as Class I and/or Class II indicating good condition whereas the COD was the worst parameter where tributaries increased the ionic mobility and solubility of most of the stations were classified as Classes III, IV,and/or V. minerals which is reflected in high conductivity in tributaries. The river also possesses pollution risk by suspended solids as Also, the high photosynthesis rate as indicated by the high TSS was classified as Class III and/or Class IV at most of the chl 𝑎 in tributaries had increased the pH values in tributaries. stations and was classified as Class V at stations 19 and 20. The On the other hand, the main river contained significantly results revealed a deteriorating water quality of the Batang higher (p value ≤ 0.05) DO, turbidity, TSS, BOD5,TP,and Baram and its tributaries when compared to the uppermost H2S (Table 3). Most of these parameters were significantly part of the Baram River basin reported by [9] where the river and positively correlated (p value ≤ 0.05) with total discharge was grouped as a Class II river. andmeanvelocityoftheriver(Table2).Hence,wecan assume that the fast flowing main river had increased the DO 3.2. Cluster Analysis (CA). Cluster analysis was used to content due to more rapid aeration and had introduced more detect similarities among the sampling stations in the study pollutants into the river via surface runoff. Nevertheless, area. The dendogram shows that sampling stations in the tributaries of the Batang Baram were also well aerated as all present study can be grouped into four significant clusters of the stations were recorded with DO content of more than as illustrated by Figure 4. The clustering pattern shows that 5 mg/L and DOsat more than 80%. physicochemical characteristics of the Batang Baram changed 10 Journal of Chemistry
Table 4: Classification of water quality of the Batang Baram from upstream to downstream regions according to WQI.
Class Station Status AN BOD5 CODDOpHTSSWQI 1 III III IV II I III III Slightly polluted 2 III III III II II II III Slightly polluted 3 III III III II I III III Slightly polluted 4 III III III II II IV III Slightly polluted 5 III III IV II II III III Slightly polluted 6 III II III II I IV III Slightly polluted 7 III III IV II I III III Slightly polluted 8 III III III II II II III Slightly polluted 9 III III III II II III III Slightly polluted 10 III III III II I III III Slightly polluted 11 II II V II I IV III Slightly polluted 12 II II V II I I II Slightly polluted 13 II III V II I I III Slightly polluted 14 II III V II I IV III Slightly polluted 15 III III V II I IV III Slightly polluted 16 II II V II I II II Slightly polluted 17 II II IV II I II II Slightly polluted 18 II III V II I IV III Slightly polluted 19 II II V II I V III Slightly polluted 20 II II IV II I V III Slightly polluted 21 IV II IV II I I III Slightly polluted 22 IV II IV II II IV III Slightly polluted 23 III II IV II I II II Slightly polluted 24 IV II III I I IV III Slightly polluted 25 IV II V II I III III Slightly polluted 26 III II II II I I II Clean 27 III II II I II IV III Slightly polluted 28 III II III II I I II Clean 29 II II IV II I IV III Slightly polluted 30 III II V II I I III Slightly polluted from upstream to downstream regions as demonstrated by 3.3. Principal Component Analysis (PCA). The PCA was the grouping of upstream stations, middle stations, and used to explore the most important factors determining downstream stations into different clusters. Cluster 1 consists the spatial variations in physicochemical parameters of the of stations that were located mostly in the middle section Batang Baram. A total of six principal components (PCs) were of the river (stations 11, 14, 15, 18, 19, and 20) except station obtained with eigenvalues more than one which accounted 29 which was located downstream. Tributaries located from foraround83.6%ofthetotalvarianceinthe20physico- middle to downstream regions of the river shared similar chemical parameters of the Batang Baram (Table 5). The first characteristics as demonstrated by the grouping of stations component (PC1), accounting for 30.0% of the total variance 12,13,16,17,21,23,25,26,28,and30incluster2.The inthedatasetsoftheriverwater,hassignificantpositive main river located downstream (stations 22, 24, and 27) loadings on turbidity, TSS, and H2S and negative loadings also showed similarity and grouped together as cluster 3. on conductivity and transparency. These factors imply that These two clusters show that main river and tributaries of soil erosion occurred in the present study area, and a high the Batang Baram which were located downstream shared loading of turbidity and H2S is associated with the presence nosimilarity.Finally,cluster4consistsofstationsthatwere of suspended solids [23]. Similarly, strong positive loadings located upstream of the river including stations that were on turbidity and suspended solids were also observed in a located at the main river and tributaries (station 1 to station Mekong Delta area of Vietnam [11] which is a result of soil 10).Thisanalysissuggeststhatareducednumberofsampling erosion from disturbed land. The Sarawak forest is subjected stations in each cluster may serve as a rapid assessment of the to high timber harvesting pressure rendering sedimentation water quality of the Batang Baram and leads to a more cost- problem in its forest streams [1, 6, 24–26]. The present effective monitoring study in the future. study shows that the Batang Baram in Sarawak state is no Journal of Chemistry 11
Table 5: Loadings of the physicochemical parameters on the first six varimax-rotated PCs (eigenvalue > 1) along the Batang Baram and its tributaries. a Rotated Component Matrix Component Parameter 123456 Total discharge +0.900 Mean velocity +0.674 +0.640 Mean depth +0.712 Temperature −0.904 pH −0.451 −0.607 +0.444 Conductivity −0.605 −0.476 DOsat −0.750 +0.488 DO +0.678 Turbidity +0.948 Transparency −0.791 Chl a −0.906 TSS +0.956
BOD5 +0.854 COD −0.423 −0.692 TP +0.850 TAN +0.780 NO2 +0.784 NO3 +0.612 +0.563 Org-N +0.716
H2S +0.759 +0.427 Initial eigenvalue 6.0 3.7 2.7 1.7 1.4 1.2 % of variance 30.1 18.6 13.5 8.7 6.8 5.9 Cumulative % 30.1 48.7 62.2 70.9 77.7 83.6 a Rotation converged in 11 iterations. exception. Logging activities in the surrounding area have associated with high volume of river water and high dissolved caused sedimentation and increased suspended solids level oxygen level. The high COD/BOD5 ratio in the Batang Baram in the river. The PC1 has the largest proportion of the indicates a large nonbiodegradable fraction of organic matter total variance indicating that logging activities are the major in the river. source of river water contamination in the Batang Baram. The PC4 (8.7% of the total variance) has significant − The PC2 accounting for 18.6% of the total variance has positive loadings on transparency, TAN, NO3 -N, and H2S significant positive loadings on mean velocity, BOD5,TP, andnegativeloadingsontemperature,pH,andconductivity. − NO3 -N, and Org-N and negative loadings on pH and DOsat. In an anaerobic condition, the high loading of organic These factors indicate an inflow of effluent from longhouses matter in river can lead to the formation of ammonia and and residential area largely consisting of organic pollutants; organic acids which coupled with the production of hydrogen and a negative loading of pH and DOsat is attributed to the sulphide and carbon dioxide during decomposition [27, 28] process of decomposition of the organic matter. Similarly, the can cause acidification of water. By employing the PCA for analysisofPCAwasappliedintheQiantangRiverwhich the data interpretation, [10] also revealed that parameters − indicated that TN, NO3 ,NH3-N, and TP were the dominant related to organic pollutants and temperature were the most pollution factors in the river [22]. The authors attributed the important parameters contributing to water quality variation pollutions to the domestic sewage, discharge of poultry and intheSavaRiver,Croatia.ThePC5(6.8%ofthetotalvariance) animal feces, and fertilizer that were flushed into the river. is significantly and negatively loaded on COD but positively − Also, an “organic” factor that positively loaded with COD, loaded on NO2 -N. Again, the high loading of organic matter 3− − BOD5,TON,TP,andPO4 wasreportedinamainriver in the river likely led to the build-up of NO2 -N in the system in northern Greece which represented the influence of water. Similar to PC2, both PC4 and PC5 can be explained municipal and industrial effluents [16]. The PC3 accounting as influences from domestic discharges which contained high for13.6%oftotalvariancehassignificantpositiveloadingson nutrients and organic matter. As PC2 has a larger proportion total discharge, mean velocity, mean depth, DOsat, and DO of the total variance than PC4 and PC5, we can assume that and negative loadings on conductivity and COD suggesting organic pollution in the Batang Baram is more severe than the a dilution of chemically oxidizable material in the river inorganic pollution. Reference [29] also reported that organic 12 Journal of Chemistry pollutants, followed by nutrients and salt concentration, were [6] T.-Y. Ling, C.-L. Soo, J.-R. Sivalingam, L. Nyanti, S.-F. Sim, the most important parameters contributing to water quality and J. 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