Assessment of Surface Water Quality Using the Water Quality Index ...

1H. PhamofetWater al. / Journal of Water Sustainability (2017) 225-244 Journal Sustainability, Volume 7, Issue 4, 4December 2017, 225-245 © University of Technology Sydney & Xi’an University of Architecture and Technology

Assessment of Surface Water Quality Using the Water Quality Index and Multivariate Statistical Techniques – A Case Study: The Upper Part of Dong Nai River Basin, Vietnam Hung Pham1,4,*, Md. Mostafizur Rahman2, Nguyen Cong Nguyen3, Phu Le Vo4, Trung Le Van4, HuuHao Ngo5 1

Department of Natural Resources and Environment Lam Dong Province, Da Lat 670000, Vietnam 2

Graduate School of Environmental Science, Hokkaido University, Sapporo, Japan Faculty of Environment and Natural Resources, Da Lat University, Da Lat City 670000, Vietnam 4 Faculty of Environment and Natural Resources, Ho Chi Minh City University of Technology, HCM 700000, Vietnam 5 School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University 3

of Technology Sydney, Broadway, NSW 2007, Australia

ABSTRACT Surface water resource in the Upper Part of Dong Nai (UPDN) River Basin has an important role for socio-economic development of provinces in the Southeast region of Vietnam. The purpose of this paper is to determine the influence of anthropogenic sources of pollutants on water quality parameters by using the integration solution between the Water Quality Index (WQI) and multivariate statistical techniques. In this study, eight physico-chemical parameters of surface water samples were collected from 42 monitoring sites in the UPDN for assessing spatial and temporal water quality variations during the period of 2012-2016. WQI and the multivariate statistical techniques comprising hierarchical cluster analysis (HCA), One-way analysis of variance (ANOVA), and Spearman correlation analysis (SCA) were used to evaluate characteristics of surface water quality. The results revealed that surface water of the river was moderately polluted and the water quality highly varied between monitoring sites and seasons. The study also found that the concentrations of Total Suspended Solids (TSS) were the most prominent parameter affecting water quality in the rainy season (r = -0.85). The proposed solution demonstrated integrated tools rather than the conventional techniques in assessing the river water quality. This integration technique could be an efficient approach to communicate information on water quality for the sustainable practices of watershed management in the Upper Part of Dong Nai River Basin. Keywords: The Upper Part of Dong Nai river; water quality index (WQI); multivariate statistical techniques

1.

INTRODUCTION

The quality of surface water is highly affected by both natural and anthropogenic factors (Pejman et al., 2009). Polluted water carries a significant load of pollutants in dissolved and *Corresponding to: [email protected] DOI: 10.11912/jws.2017.7.4. 225-245

particulate phases, that reflects the major influences on the system, including: the lithology of the basin, anthropogenic inputs, atmospheric inputs and climatic conditions (Markicha and Browna, 1998; Shrestha and Kazama, 2007).

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H. Pham et al. / Journal of Water Sustainability 4 (2017) 225-245

Human activities are the major factors determining the quality of the surface water through effluent discharges, the use of agricultural chemicals and land use change (Niemi et al., 1990), whereas natural forces such as stormwater runoff events cause soil erosion which is a seasonal phenomenon, and is largely affected by climate, land cover, land slope, and soil resilience (Wischmeier and Smith, 1978). The natural processes and anthropogenic sources are multivariate and complex so that the assessment and management of surface water quality require a fundamental understanding of spatial and temporal variations of water characteristics, including hydrological regime, chemical and biological parameters (Phung et al., 2015). If the surface water is contaminated, its quality cannot be restored by preventing pollutants from these sources. Hence, a monitoring program, which can provide reliable estimation of water quality parameters, is a necessary scheme to protect water quality (Mullai et al., 2013;Phung et al., 2015; Yisa and Jimoh, 2010). Along with the development of various statistical software on Windows (Statistical Package for Social Sciences SPSS, R-analysis software and Excel), the multivariate statistical techniques, such as Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), Factor Analysis (FA) and Discriminant Analysis (DA) have widely been applied for characterizing and evaluating surface water quality in many previous studies (Islam et al., 2017; Shammi et al., 2017). These statistical techniques are useful for verifying temporal and spatial variations caused by natural and anthropogenic factors linked to seasonality (Hanh et al., 2010; Pejman et al., 2009;Phung et al., 2015; Shammi et al., 2016; Shrestha and Kazama, 2007;Varol et al., 2012; Wang et al., 2012). Additionally, One-way Analysis of Variance (ANOVA) was used to examine the significance of the mean differences of groups of monitoring sites and seasonal factors (Pham,

2017). Spearman correlation analysis (SCA) was used to assess the relationships among dependent and independent variables (physicochemical parameters and WQI) (Eddlemon and Boopathy, 2013). In Vietnam, the QCVN 08-MT: 2015/BTNMT is currently being used as the national technical regulation on surface water quality (Pham, 2017). The monitoring program requires a large number of parameters to be measured, analyzed and interpreted using multivariate methods. In another way, the Water Quality Index (WQI), a single number expressing water quality by integrating measurement values of many physico-chemical parameters, can be used to indicate the overall status of surface water quality (Cude, 2001; MONRE, 2011; Said et al., 2004). Worldwide, several national water quality indices have been developed to classify the relative quality of surface water such Canada, USA, and Asian countries. The WQI approach was developed by the US National Sanitation Foundation (NSF) in 1970, which is known as NSF-WQI. The NSF-WQI was firstly proposed by Horton, using the weighted arithmetic mean function based on 10 most commonly used water quality variables. This WQI is calculated by multiplying the value sub-index with the weight of each quality parameter (Hasan et al., 2015; Ichwana et al., 2016;Tyagi et al., 2013). Later, Canadian Council of Ministers of the Environment (CCME) also endorsed its WQI, the so-called CCME-WQI which provides a comprehensive way of expressing complex data on water quality. The CCME-WQI combines three aspects to evaluate the quality of water, including: scope, frequency and amplitude which is easily understood by the public, managers and policy makers (Lumbet al., 2006). Ray et al. (2015) applied the CCME WQI method for assessing water quality of Goalichara canal in Bangladesh. Further, the Bharvaga model was applied for developing WQI for surface water and groundwater sources with multiple water use practices such


as drinking and industrial purposes in India (Jerome and Pius, 2010; Parmar and Parmar, 2010; Ramarishnaiah et al., 2009). The number of selected water quality parameters is different from country to country, but in general, WQI approach is based on the weighted arithmetic method and the most common steps, including: parameter selection, determination of quality function (curve) of each parameter and sub-index aggregation with mathematical expression. The selection of physico-chemical and biological parameters is carried out by the judgment of professional experts or government agencies that is determined in the legislative area. It is claimed that the water quality parameters should be selected from 5 commonly recognized impairment categories, comprising (1) oxygen status and demand, (2) physical characteristics, (3) eutrophication, (4) health aspect, and (5) solid and dissolved substances (Nguyenet al., 2013; Tyagi et al., 2013). In fact, nine parameters, including temperature, pH, turbidity, faecal coliform, DO, COD, total phosphates (TP), NO3--N and TSS were selected to calculate WQI in many river basins in the world (Tyagi et al., 2013). This approach is also widely exercised in Asian countries. Fourteen (14) parameters were selected to evaluate water quality of Perennial River, Tamirabarani, bay of Bengal (MophinKani and Murugesan, 2011). Liou et al. (2004) selected thirteen (13) to generalize water quality in Keya River, Taiwan. Nguyen et al. (2013) also assessed the quality of coastal waters in Ha Long Bay through nine parameters. Eight parameters were selected to determine the quality of Ciambulawung River, Indonesia (Effendi et al., 2015). India and Malaysia determined seven (7) and six (6) parameters to calculate WQI in KankariaLake at Ahmedabad (Kumar and Sharma, 2014) and Pelus River (Hasan et al., 2015), respectively. In generally, no water quality index is universally accepted so that water distributors, managers and policy

227

makers in different countries may use and adopt it with little modifications (Tyagi et al., 2013). Recently, the combination of WQI and multivariate statistical techniques has showed some certain advantages in the overall assessment of surface water quality (Mophin-Kani and Murugesan, 2011). In Vietnam, Ministry of Natural Resources and Environment (MONRE) developed the WQI method, issued in the Decision No. 879/QD-TCMT, to create a benchmark of the surface water quality for the protection and management of water resources. This WQI method was developed by the combination of weighted arithmetic WQI with river status index (RSI) to suit the conditions in Vietnam (MONRE, 2011). Accordingly, nine (9) water quality parameters, including: DO, BOD5, COD, NH4+-N, PO43--P; TSS, pH, temperature and total coliforms, were selected to calculate WQI. The WQI number ranges from 0 to 100, its value range corresponding to color signifies better water quality when it is higher (MONRE, 2011). The Dong Nai river basin originates in the Central Highland region of Vietnam. The Upper Part of Dong Nai (UPDN) river basin, an environmentally sensitive mountain area, has a crucial role in protecting water resources in the downstream areas. The Dong Nai river basin is the largest national river basin and is the most dynamic economic development region in Vietnam. The basin includes 10 provinces and Ho Chi Minh City forming the southern focal economic zone, which is a highly developed and populous region, with a relatively high income per capita compared with other regions in Vietnam. Water source of the Dong Nai river basin is an dispensable resource for the development of 60 industrial parks and export processing zones at the downstream part (MONRE, 2006;Ringler and Huy, 2004). However, this river basin is facing with water issues in terms of quality and quantity which is posed by human activities.

228


Water quality monitoring and assessment is an essential practice to propose solutions for sustainable management of the basin. Both central government and provincial agencies put much effort to management practices over the past decade. Nevertheless, a few studies either related or inadequate on water quality have done in this river basin. Therefore, the purpose of this paper is to identify spatial and temporal variations in surface water quality, and to determine the influence of anthropogenic sources on water quality of the UPDN river basin by integrating WQI and multivariate statistical techniques (HCA, One-way ANOVA, and SCA). 2. MATERIALS AND METHODS 2.1 Study Area Dong Nai river is the largest inland river in Vietnam, flowing through 11 provinces in the Southeast of Vietnam. The UPDN river basin

Figure 1

belongs to the administrative boundary of Lam Dong province, which plays an important role in socio-economic development of this province (Fig. 1). The topography of the study area is clearly stratification from North to South. The northeast of the UPDN river basin seats on Langbiang plateau, which has high mountains with an elevation over 1,500 m above sea level. The middle part of the river basin, the low mountains, belongs to Di Linh plateau having an elevation from 700 m to 1000 m above sea level. The transition zone between highlands and plains is 200 m - 700 m above sea level that locates in the southwest of the river basin (Fig. 2). This area has been affected by tropical monsoon climate regimes. There are two distinct seasons, the rainy season lasts from May to November and the dry season is from December to April of the following year. The mean annual temperature was 22oC, annual precipitation and humidity were 2,500 mm and 83%, respectively over the past 33 years, from 1981-2014 (StaB, 2015).

Location and land use map of the Study (DONRE, 2013)


229

Langbiang plateau Di Linh plateau Transition zone

Boundary of the Study Area Figure 2

Digital Elevation Model (DEM) of the Study Area (Hung et al., 2017)

The study area covers 775,500 ha, accounting about 80% of the total provincial area. The UPDN river basin was divided into 5 subbasins (Da Nhim, Da Dang, La Nga, Da Huoai, and Cat Tien) in terms of river flow, topology, cascade hydropower plants, and water infrastructure in the main flow of the river. The Da Nhim sub-basin originates in Biduop Nui Ba National Park, and covers an area of 186,605 ha. The Da Dang sub-basin, a right tributary of the main river, flowing through Centre of Da Lat city, covers an area of 157,104 ha. The Cat Tien sub-basin is located downstream of Da Nhim and Da Dang hydropower reservoirs covering an area of 220,130 ha. Other tributaries of the UPDN river basin are La Nga and Da Huoaisub-basins occupying an area of 132,233 ha and 74,158 ha, respectively (DONRE, 2016). Land use pattern in every river sub-basin varies from each other (Table 1), therefore its impact on surface water resources may also be different. Most of these sub-basins are high slope lands in which more than 20% of each sub-basin has a land slope is greater than 25 degree (Table 2). Fig. 1 and Table 1 show that the area of forest land in the UPDN river basin was the

largest area with occupying 49.93% (387,189 ha) of the total basin area. The second largest area was the perennial cultures land with major crops such as coffee, tea gardens and accounts for 20.14% (156,218 ha) area of the entire basin. The residential land (urban and rural) occupied 5.57% (43,178 ha) of the basin area. The area of annual crops such as tomatoes, carrots, vegetables covered with 4.78% (16.571 ha) of the basin area and the rest was other land types. However, the distribution of land use types in the five sub-basins was not similar. In particular, Da Dang river sub-basin with Da Lat city and Lien Nghia, Dinh Van, Nam Ban towns had the largest residential land (urban and rural), while the La Nga sub-basin had the largest annual crops land that accounted for 55.34% of the total area of this basin. The types of land use are different in the sub-basins so that the impact on surface water quality may be different. Fig. 2 and Table 2 show that 76.68% area of the total basin has the angle of slope in degrees over 8o, while the slope is between 8o-15o, 15o-25o and more than 25o, accounting for 21.54%, 25.95% and 29.19%, respectively.

230


Table 1 Characteristics of land use in the river basin (DONRE, 2013) Catchment (Sub-basin) Da Nhim Da Dang La Nga

Land use

Da Huoai Cat Tien

The UPDN river

Area (%) Area (%) Area (%) Area (%) Area (%) Area (%) Area (ha)

Aquaculture Forest Industrial Zone Fruit plants Infrastructure Barren land* Pasture Perennial cultures Public facilities** Residential, Rural Residential, Urban Rice Annual crops Water bodies*** Total

0.73 71.14 0.05 0.13 0.39 1.53 0.37 5.66 0.56 2.11

0.00 35.37 0.13 0.06 4.19 10.06 0.00 12.40 16.17 3.27

0.52 30.14 0.18 0.00 0.17 1.35 0.03 55.34 0.22 6.05

0.00 63.64 0.00 0.70 0.01 1.21 0.00 25.68 0.69 3.72

0.80 2.64 10.91 2.98 100

7.53 0.24 6.65 3.93 100

1.32 0.96 0.15 3.57 100

1.01 0.49 1.13 1.72 100

0.02 70.55 0.00 0.03 0.00 0.74 0.02 15.06 0.17 3.01

0.27 49.93 6.15 0.12 0.97 2.97 0.10 20.14 3.56 3.43

0.33

2.14

4.23 2.31 3.51

2.13 4.78 3.31 100

100

2,107 387,189 47,732 906 7,555 23,014 771 156,218 27,633 26,607 16,571 16,482 37,064 25,651 775,500

Note: *Mountainous land; **Commercial, industrial and public facilities; ***Streams, rivers, lakes

Table 2 Spatial distribution of slope in the river basin based on DEM (Hung et al., 2017) Land slop (degrees)

0-5 5-8 8-15 15-25 >25 Total 2.2

Catchment (Sub-basin) Da Nhim Da Dang La Nga

The UPDN river Da Huoai Cat Tien

Area (%) Area (%) Area (%) Area (%) Area (%) Area (%) Area (ha)

13.23 7.98 19.31 27.67 31.80 100

16.89 11.36 23.11 24.46 24.19 100

15.92 12.69 25.82 23.99 21.58 100

Acquisition of monitoring data

The data on 8 physical and chemical parameters of surface water quality were collected at 42 monitoring sites in the UPDN river basin during the period of 2012-2016 from Centre for

10.82 8.68 19.96 24.25 36.29 100

11.77 8.00 20.40 27.36 32.47 100

13.79 9.53 21.54 25.95 29.19 100

106,934 73,905 167,014 201,244 226,403 775,500

Monitoring Natural Resources and Environment-DONRE of Lam Dong Province (Fig. 3 and Table 3). The physico-chemical parameters include Dissolved Oxygen (DO, mg/L), Biochemical Oxygen Demand (BOD, mg/L), Chemical Oxygen Demand (COD, mg/L),


Ammonium (NH4+-N, mg/L), Phosphate (PO43--P, mg/L), Total Suspended Solids (TSS, mg/L), and pH. Then, the data were structured in MS Excel and SPSS software program for statistical analysis. Mean and standard deviation (SD) values of the physico-chemical parameters at sampling sites of the UPDN river sub-basin are shown in Table 3. 2.3

Water quality index (WQI)

The Decision No. 879/QD-TCMT on WQI in Vietnam is used to determine the level of water quality based on 9 parameters including: DO, BOD5, COD, NH4+-N, PO43--P; TSS, pH, temperature and total coliforms. In this study, 8 parameters was applied to determine WQI without total coliforms because the local monitoring program had inadequate data of this parameter, see Eq. 1 (Linh et al., 2016;

231

MONRE, 2011; Pham, 2017). WQI 

1/ 2 WQI pH  1 5 1 2  (1)  WQI WQI  a 2 b 100  5 a 1 b 1 

Where, WQIa (denotation the sub-index values for the ‘organics’) is calculated through six parameters: DO, BOD5, COD, NH4+-N, and PO43--P; WQIb(representation the sub-index values for the ‘particulates’) is calculated with TSS parameter; WQIpHis calculated with pH parameter and temperature; The WQI number ranges from 0 to 100, its value range corresponding to color signifies better water quality when it is higher. For WQI method, the ratings of water quality have been categorized by using the classification in Table 4.

Figure 3 Surface water monitoring sites of the UPDN river basin (DONRE, 2016)

232


Table 3 Mean and standard deviation (SD) values of water quality parameters at different sampling sites of the UPDN (2012-2016) pH

SD

Sub-basin Code Monitoring site

DO

SD

(mg/L

TSS

SD

(mg/L)

COD

SD

(mg/L)

BOD

SD

(mg/L)

)

Da Nhim (11 sites)

Da Dang (15 sites)

NH4+-

SD

PO43--

N

P

(mg/L)

(mg/L)

SD

Temp. SD (oC)

N1

Da Sar bridge

6.9

0.45

6.6

0.75

28.0

10.70

16.1

4.87

6.5

3.16

0.35

0.23

0.02

0.01

24.3

1.09

N2

LiengTru bridge

6.9

0.54

6.7

0.65

10.9

8.25

16.6

6.26

5.8

2.93

0.17

0.07

0.01

0.01

21.7

2.08

N3

NT Bo Sua bridge

6.8

0.56

6.5

0.66

48.8

45.75

16.3

2.78

6.1

3.41

0.46

0.34

0.04

0.03

21.5

2.64

N4

Bong Lai bridge

7.0

0.74

6.3

1.00

114.3

167.70 20.4

8.40

9.4

7.43

0.58

0.43

0.04

0.02

25.3

1.79

N5

Prenn bridge

7.4

0.46

6.0

1.07

31.7

23.33

24.1

9.98

10.5

6.23

1.16

1.56

0.10

0.09

24.5

1.43

N6

Phu Hoi bridge

7.0

0.47

6.3

1.07

55.7

48.98

16.2

3.34

7.3

5.27

0.59

0.35

0.04

0.02

24.0

2.28

N7

Bao Dai Ta Hine bridge

6.9

0.53

6.1

0.91

87.5

80.45

17.4

5.96

8.1

5.24

0.60

0.41

0.05

0.06

25.7

2.02

N8

Ka Do bridge

6.8

0.45

5.5

0.64

108.7

166.29 20.8

8.40

7.1

5.37

0.78

0.82

0.06

0.05

26.6

1.11

N9

Tuyen Lam reservoir 1

7.2

0.39

6.1

1.21

17.0

10.39

6.43

10.6

8.07

0.63

0.44

0.03

0.04

23.6

1.03

N10

Tuyen Lam reservoir 2

6.8

0.46

5.6

0.92

115.1

101.57 25.9

11.49 12.2

9.29

0.90

0.78

0.06

0.04

22.3

2.12

N11

Da Ron reservoir 1

6.9

0.58

6.5

0.51

115.1

112.43 22.2

10.58 10.4

8.45

1.41

1.65

0.09

0.08

24.3

2.36

D1

Cam Do bridge

7.1

0.45

5.2

1.21

35.1

17.89

34.5

4.05

14.7

4.97

3.30

2.17

0.23

0.24

24.3

1.36

D2

Thai Phien bridge

6.8

0.41

6.4

0.61

12.5

8.74

16.3

6.24

6.0

3.27

0.21

0.10

0.02

0.02

24.8

1.82

D3

Cam Ly bridge

6.9

0.70

6.0

0.70

40.4

25.99

21.6

8.68

9.3

7.26

1.09

0.67

0.08

0.03

23.8

1.40

D4

Cam Ly waterfall

7.1

0.69

5.8

0.90

29.1

19.58

27.0

10.77 13.3

8.44

1.29

0.98

0.11

0.09

23.7

1.41

D5

Hoa Lac bridge

6.7

0.39

5.8

1.02

73.6

41.24

17.2

5.57

8.8

6.68

0.51

0.46

0.06

0.08

24.7

1.63

D6

Xuan Huong lake 1

6.9

0.48

6.5

0.39

32.6

17.86

14.8

5.28

6.0

3.64

0.31

0.17

0.02

0.02

24.5

1.64

D7

Xuan Huong lake 2

6.9

0.71

6.5

0.41

37.4

26.95

15.7

5.57

7.8

5.05

0.25

0.18

0.02

0.02

24.2

1.60

D8

Xuan Huong lake 3

5.7

0.17

5.3

0.63

35.8

13.09

13.9

5.79

6.1

4.74

0.48

0.43

0.02

0.02

20.3

1.60

D9

Xuan Huong lake 4

7.6

0.77

6.7

1.10

17.2

7.31

21.7

6.89

9.6

5.99

0.76

0.52

0.03

0.03

22.8

1.25

D10

Xuan Huong lake 5

7.1

0.47

5.4

1.28

44.0

45.10

26.3

11.18 9.8

7.04

2.53

1.94

0.11

0.07

22.2

2.19

D11

Chien Thang lake

8.4

0.84

7.8

2.29

47.9

19.47

44.7

15.21 15.6

9.32

1.54

0.92

0.07

0.09

22.6

1.41

17.4

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H. Pham et al. / Journal of Water Sustainability 4 (2017) 225-245 Sub-basin

Code D12 D13

Da Dang

D14

(15 sites)

Monitoring

pH

SD

site

DO

SD

(mg/L)

PhuocThanh 7.4 bridge 7.9 Dan Kia Da Don 7.3 bridge

TSS

SD

(mg/L)

COD

SD

(mg/L)

BOD

SD

(mg/L)

NH4+-N

SD

(mg/L)

PO43--P

SD

Temp.

SD

o

(mg/L)

( C)

0.61

4.9

2.00

45.6

25.25

47.6

18.71

21.0

12.83

5.11

4.01

0.21

0.15

21.9

1.52

0.67

5.8

2.30

42.4

26.17

57.3

24.94

21.0

8.89

4.33

3.94

0.30

0.32

22.3

1.48

0.27

6.6

1.31

42.2

23.23

55.5

18.41

22.9

15.33

7.32

5.26

0.33

0.28

22.7

1.03

Dankia lake 7.1

0.49

4.9

1.32

43.2

30.22

55.1

35.98

29.7

27.65

5.79

4.19

0.44

0.53

22.3

1.73

Dai Binh bridge Dai Nga bridge Minh Rong bridge Bobla waterfall Ho Tay reservoir Nam Phuong reservoir Tan Rai reservoir Loc Thang reservoir

7.2

0.27

5.0

1.03

126.7

117.13

57.3

19.94

29.4

16.80

7.80

4.49

0.53

0.25

23.8

1.81

6.8

0.44

6.5

0.72

37.3

37.35

23.4

13.31

8.9

7.35

0.38

0.25

0.02

0.02

23.7

2.47

6.9

0.44

6.6

1.40

19.7

7.96

32.1

6.01

13.2

8.16

3.17

2.97

0.29

0.29

21.4

0.91

7.0

0.53

5.5

0.58

35.4

13.74

61.4

12.00

27.1

20.36

2.92

3.30

0.31

0.19

23.3

1.29

6.9

0.51

6.0

0.69

25.6

8.40

23.9

5.19

9.8

6.15

0.54

0.38

0.02

0.01

25.2

1.68

6.9

0.43

6.5

0.74

26.0

26.88

16.7

6.82

5.7

2.94

0.55

0.73

0.03

0.02

25.0

1.45

7.1

0.53

6.3

0.71

9.8

2.94

17.2

6.96

6.4

4.22

0.17

0.14

0.01

0.01

25.4

1.07

7.0

0.51

6.4

0.62

16.1

8.25

20.6

6.49

9.2

5.14

0.38

0.30

0.09

0.16

24.8

0.96

6.9

0.31

6.4

0.97

27.5

21.40

19.6

10.73

9.8

7.15

0.31

0.29

0.02

0.02

23.8

1.20

D15

L1 L2 L3 La Nga (8 sites)

L4 L5 L6 L7 L8 H1

Da Huoai (4 sites)

Madagui bridge

H2

DaHuoai 2

6.8

0.38

6.2

0.81

34.6

35.31

15.0

6.30

7.3

6.57

0.27

0.18

0.02

0.01

24.3

1.54

H3

DaHuoai 3

7.0

0.42

6.3

0.81

25.8

19.02

18.4

4.54

8.2

5.96

0.28

0.25

0.01

0.01

25.2

1.97

H4

KDL Dambri 6.9

0.38

6.1

0.53

7.3

4.85

13.7

3.95

6.7

4.30

0.16

0.12

0.02

0.01

25.5

1.59

234 Sub-basin

H. Phamet al. / Journal of Water Sustainability 4 (2017) 225-245 Code T1

Cat Tien (4 sites)

T2 T3 T4

Monitoring

pH

SD

site CauT re bridge Phuoc Cat bridge Da Mi bridge Da Teh reservoir

National guidelines*

DO

SD

(mg/L)

TSS

SD

(mg/L)

COD

SD

(mg/L)

BOD

SD

(mg/L)

NH4+-N

SD

(mg/L)

PO43--P

SD

Temp.

SD

o

(mg/L)

( C)

7.7

1.21

6.6

1.89

45.6

21.17

25.5

8.20

11.1

6.41

0.32

0.19

0.03

0.02

26.9

2.31

7.1

0.46

6.3

0.76

64.9

27.07

17.1

8.33

7.3

5.78

0.32

0.34

0.02

0.01

26.0

1.87

5.8

0.46

5.0

0.93

75.6

139.60

19.3

12.31

11.2

12.60

0.32

0.27

0.02

0.02

21.2

1.42

7.2

0.48

6.2

0.93

62.3

67.53

16.1

5.30

7.9

5.40

0.56

0.71

0.05

0.06

24.3

1.39

5.5-9

-

≥4

-

15.06% >12.4%> 5.66%, respectively. The percentage of annual cultures land in these sub-basins was ranked Da Nhim>Da Dang >Cat Tien>Da Huoai>La Nga, corresponding to 10.91% >6.65% >2.3% >1.13%>0.15%, respectively. This explained why in the rainy season, there is a negative correlation between WQI and PO43--P concentration at sub-basins in the following order: La Nga>Da Nhim>Da Dang >Da Huoai>Cat Tien, with r = -0.63, -0.50, -0.43, -0.34, and -0.26, respectively. This was also evidenced that annual crops practices contributing PO43--P (from runoff) to surface water higher than perennial cultures activities. This result is similar to previous studies (Canada, 2004; Hanh et al., 2010), that the use of fertilizer releasing PO43--P into the watershed during the rainy season was insignificant because residual phosphate was formed relatively insoluble forms with many cations. The percentage of residential land (rural and urban) in the Da Dang sub-basin was higher than other sub-basins. It was ranked Da Dang > La Nga > Da Huoai > Cat Tien > Da Nhim, corresponding to 10.8% >7.37% >4.73% >3.34% >2.91%, respectively. This explained why, there was a significantly negatively correlation between WQI and COD, BOD, NH4+-N concentration in the dry season, respectively, r = -0.68, -0.63, -0.66 in the Da Dang sub-basin. However, there was slightly correlated or uncorrelated between WQI and

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H. Phamet al. / Journal of Water Sustainability 4 (2017) 225-245

COD, BOD, NH4+-N concentration in the rainy season. The results implied dilution effects during the rainy season resulted in a significant benefit in terms of improved water quality in this river sub-basin (Hanh et al., 2010). As seen in Table 6, DO and pH parameters did not

much correlate with WQI because these parameters were quite good quality and meet the permissible level (National technical regulation on surface water quality, QCVN 08MT: 2015/BTNMT). Hence, the effect of DO and pH on WQI was not significant.

Table 6 Summarized correlation between WQI and seven (7) physico-chemical parameters Da Nhim Parameters Dry Rainy season season (%) (%) pH 0.03 -0.05 * DO 0.17 -0.06 TSS -0.71** -0.88** COD -0.31** -0.35** BOD -0.22** 0.01 + NH4 -N -0.17* -0.58** PO43--P -0.24** -0.50**

Catchments (Sub-basins) Da Dang La Nga Da Huoai Cat Tien Dry Rainy Dry Rainy Dry Rainy Dry Rainy season season season season season season season season (%) (%) (%) (%) (%) (%) (%) (%) ** -0.52 0.06 -0.12 -0.09 -0.09 0.25 0.19 0.28 * 0.15 0.25 0.09 0.10 0.27 -0.10 0.05 -0.11 -0.76** -0.73** -0.02 -0.69** 0.60** -0.58** -0.67** -0.96** -0.68** -0.32** -0.25 -0.22 -0.18 -0.08 -0.45 -0.21 -0.63** -0.35** -0.27 -0.27 -0.04 -0.08 -0.64** -0.03 -0.66** -0.22 0.24 -0.19 -0.54* -.49* -0.20 -0.29 -0.48** -0.43** 0.05 -0.63** 0.11 -0.34 -0.02 -0.26

The UPDN river Dry Rainy season season (%) (%) ** -0.25 -0.03 0.17* 0.06 -0.62** -0.85** -0.64** -0.35** -0.54** -0.21** -0.51** -0.46** -0.43** -0.53**

Note: (**): Correlation is significant at the 0.01 level (2-tailed);(*): Correlation is significant at the 0.05 level (2tailed)

CONCLUSIONS

This study demonstrated that the need of integrating Water Quality Index (WQI) and multivariate statistical techniques (HCA, CA, ANOVA) for the water quality assessment, the pollution sources identification, the pollution factors apportionment as well as an understanding of the spatial and temporal variations in water quality. It is an effective and useful tool to communicate information on the water quality to citizens and policy makers in many ways in terms of water quality management. Using WQI, the quality of surface water in sub-basins of the UPDN river basin was ranked as WQI (Da Huoai) >WQI (La Nga) >WQI (Dang Dang>WQI (Cat Tien). This could be attributed to anthropogenic activities such as domestic, agricultural practices and seasonal

change. The concentration of TSS, which represents a result of soil erosion, was the most significant parameter contributing to water quality variations for the entire upper part and for each cluster. According to acquired information of cluster analysis, it is noted that sampling sites of the monitoring plan can be revised and optimized in a reasonable manner.

ACKNOWLEDGMENTS

The authors would like to thank you DONRE of Lam Dong province for its support this study by providing early access to statistical data and reports of local authorities. We also greatly appreciate Ho Chi Minh City University of Technology-VNU-HCM for assistance in providing grants for this research (Grant Number: TNCSMTTN-20161-6).


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