Limnology

, Volume 9, Issue 1, pp 19–26

Relationship between river water quality and land use in a small river basin running through the urbanizing area of Central Japan

Authors

    • Department of Natural Environmental Studies, Institute of Environmental Studies, Graduate School of Frontier SciencesThe University of Tokyo
  • Hiroo Ohmori
    • Department of Natural Environmental Studies, Institute of Environmental Studies, Graduate School of Frontier SciencesThe University of Tokyo
  • Masumi Yamamuro
    • Department of Natural Environmental Studies, Institute of Environmental Studies, Graduate School of Frontier SciencesThe University of Tokyo
Research paper

DOI: 10.1007/s10201-007-0227-z

Cite this article as:
Bahar, M.M., Ohmori, H. & Yamamuro, M. Limnology (2008) 9: 19. doi:10.1007/s10201-007-0227-z

Abstract

In this study, the relationship between water quality (as represented by major inorganic ion concentrations) and land use characteristics is examined for a small river basin which runs through the urbanizing area of central Japan. Water samples were taken from 24 sites at base flow and analyzed, and the proportions of the various land uses associated with the respective drainage basins were calculated using a digital land-use map (scale: 1:25000). The electrical conductivity (EC) of the water ranged from 84.5 to 600 μS cm−1. Ca2+ and Na+ were the major cations, accounting for 77% of all cations. Among the anions, HCO3 was dominant (56%), followed by Cl (24%), SO42− (13%) and NO3 (7%). Applying principal component analysis to land use in the drainage basin yielded three principal components. The first principal component expressed the degree of occupation by residential areas, the second indicated the degree of urban developing area (i.e., fast-developing and industrial areas), and the third showed the degree of coverage with farmland and green space. The residential area showed significant positive correlations with K+, Mg2+, Ca2+, NO3, HCO3, EC and TMI (total major ions). Urban developing area showed significant positive correlations with Ca2+, Cl, HCO3, EC and TMI as well as weak negative correlations with NO3 and SO42−. Industrial area showed weak positive correlations with Na+ and Cl and a moderate negative correlation with NO3. Farmland showed significant positive correlations with NO3 and SO42−; these ions are present due to fertilizers and the biological activity of plants. Forest area is inversely related to almost all ions, indicating the need for this form of land use in order to maintain river water quality.

Keywords

River waterInorganic ionLand useUrbanizationGIS

Introduction

River water chemistry is controlled by many natural and anthropogenic factors. These factors can be either spatially diffused or concentrated. Calculating the inputs from point sources is a relatively simple task, as direct measurements can be made at these sources, but analyzing stream water chemistry due to non-point sources is much more difficult (Baker 2003). Since the river water quality reflects biogeochemical processes in the watershed, the effects of land use may be assessed by exploring the water quality, which could provide a suitable indicator for monitoring (Hakamata et al. 1992). Water quality “reflects the composition of water as affected by natural processes and by humans’ cultural activities, expressed in terms of measurable quantities and related to intended water use” (Novotny and Chesters 1981). Anthropogenic factors tend to increase the loading of nutrients into streams, potentially causing a degradation of water quality (Novotny and Olem 1994). Bolstad and Swank (1997) observed that consistent changes in water quality variables were concomitant with land use changes. Similarly, Tong (1990) found that urban development in the watershed caused substantial modifications of flood runoff and water quality. Changing land use and land management practices are therefore regarded as some of the main factors that can alter the hydrological system as well as the quality of receiving water (Changnon and Demissie 1996).

Watershed management and catchment scale studies have become increasingly important for determining the impact of human activity on water quality, both within the watershed and in receiving waters. Effective analytical tools, such as geographical information systems (GIS), high-resolution digital land-use data and multivariate statistics, are able to deal with spatial data and complex interactions, and are entering into common usage in watershed management. Although there have been some studies on the impacts of land use on water quality, the complex intrinsic relationships between land use and water quality are yet to be elucidated.

While water quality has improved remarkably over the past few decades, Japanese rivers are still heavily impacted by canalization, loss of most dynamic flood plains, flow regulation, invasion by exotic species, and intensive urbanization (Yoshimura et al. 2005). The deterioration in the water quality of rivers in the valleys that dissect the urbanized uplands in and around big cities like Tokyo is one of the most serious environmental issues we face. In a study by Terazono (2003) in this upland area, it was found that the basic water quality of spring water is dictated by the dissolution of carbonate minerals in layers during groundwater flow, while the other loads are controlled by land-use development in drainage basins. It was also found that the spatial variation in the additional loads is larger than that of the basic loads, and there were clear relationships between the additional loads and land use. Land use changes in the uplands due to urbanization have resulted in a degradation in the water quality of rivers. Many studies have pointed out that the land-use pattern in a drainage basin affects the quality of the river water (Woli et al. 2004; Herlihy et al. 1998; Tufford et al. 2003; Hakamta et al. 1992; Sliva and Williams 2001). The land use–water quality relationship is complex and is likely to be site- or region-specific (Baker 2003). There have been relatively few studies on the relationship between river water quality (in terms of major inorganic ions) and land use (gauged using high-resolution digital maps). Therefore, the purpose of this study was to examine the relationship between river water quality and land use characteristics by analyzing the concentrations of major inorganic ions at base flow conditions.

Materials and methods

Study area

The study area is the O-hori river basin, which is located in the northwestern part of the Shimousa Upland, in the eastern part of the Tokyo Metropolitan Area (Fig. 1). The landforms consist of upland surfaces at altitudes of 15–30 m and alluvial lowland (2–9 m). The Shimousa upland was formed during the Last Interglacial Age, and consists of many layers of marine, brackish and alluvial sand clay beds, each of which contains almost horizontal stratum. These are covered with volcanic ash layers to a maximum thickness of about 5 m.
https://static-content.springer.com/image/art%3A10.1007%2Fs10201-007-0227-z/MediaObjects/10201_2007_227_Fig1_HTML.gif
Fig. 1

Map of study area showing sampling locations

There are two major rivers in the basin, O-hori and Jigane-hori. The O-hori River begins at Aota-shinden, Kashiwa City, Chiba Prefecture, passes through Nagareyama City and Kashiwa City, and drains into the Tega Lake. The river’s length is 6.9 km. The 6-km-long Jigane-hori River begins at two natural ponds near Kashiwanoha Park and drains into the O-hori River near Yabatsuka Bridge. The two rivers have many tributaries which divide up the upland in the basin. The total area of the drainage basin is 31 km2. The urbanized ratio of this drainage basin is currently higher than 70%; it is a typical city river (FRIR 2000).

Water collection and analysis

Grab samples were collected from 24 sites along the main river and its tributaries (Fig. 1) on four occasions from May 2006 to January 2007 during base flow conditions, at least one week after heavy rainfall events, if any. There was no significant variation in the discharge measured at sampling site 15 among sampling events. The temperature, pH and EC (electrical conductivity) were measured in the field using a digital pH meter and an EC meter (Horiba D-54, Tokyo, Japan). Alkalinity, expressed as HCO3, was quantified with a digital titrator (Hach, Loveland, CO, USA) with 0.16 N HCl, and Bromcresol Green–Methyl Red was used as an indicator. Water samples (100 ml each) were collected separately at each sampling site using polyethylene bottles. The pre-washed bottles were rinsed with sample water thrice on site before collecting the sample water. The water samples were then brought to the laboratory and stored in a dark and cool room (at 4 °C) until the analyses were completed. In the laboratory, the concentrations of Na+, K+, Mg2+, Ca2+, Cl, NO3 and SO42− were determined by ion chromatography (Shimadzu SCL-10Asp, Kyoto, Japan). TMI (total major ions) was calculated by adding together the concentrations of the ions determined by ion chromatography as well as that of HCO3, measured in the field.

GIS analysis

ArcGIS 9.1 Desktop GIS software was used to determine the land use characteristics within the O-hori River sub-watersheds. A raster image of the watershed area and the drainage divisions of the sub-watersheds was collected from the local city hall. This raster image was then edited and digitized with the Ground Control Point technique using a 1:25000-scaled topographic map. The resulting polygon data was then overlaid onto the digital 10-m mesh land-use map published in 1994 by the Geographical Survey Institute of Japan. Using the ArcView topology toolbar, the types and proprtions of land uses of the drainage basins of each sampling site were estimated. The drainage basin of the lower reach streams was calculated for each sampling site by including the drainage basins of all upper streams and tributaries. GIS tools were used to calculate the area of each land-use type within each sub-watershed, which was subsequently divided by the watershed area to derive the percentage of the watershed covered by each land-use type (Fig. 2).
https://static-content.springer.com/image/art%3A10.1007%2Fs10201-007-0227-z/MediaObjects/10201_2007_227_Fig2_HTML.gif
Fig. 2

Land-use characteristic gradients for each of the 24 sub-basins used in the study

Statistical analysis

Principal component analysis (PCA) was used to reduce the land-use variables to a small number of principal components that reflect the underlying processes, i.e., the correlation among variables. The principal component is expressed by the following linear equation:
$$ z_{{aj}} = a_{{i1}} x_{{1j}} + a_{{i2}} x_{{2j}} + a_{{i3}} x_{{3j}} + \cdots + a_{{im}} x_{{mj}} , $$
where z is the component score, a is the component’s loading, x is the measured value, i is the component number, j is the sample number, and m is the total number of variables. Pearson’s product moment correlation analysis was used to find out the relationship between ion concentration and land use characteristics. The significance of the correlation was assigned based in the 95% confidence level, and is classified as positive and negative according to the gradient of the regression relationship.

Results and discussion

Major ion chemistry

This study analyzes the samples collected in August 2006. Analyses of other time samples yield similar results. The chemical compositions of the water samples collected from O-hori River and its tributaries are shown in Table 1. The table also shows the mean, the standard deviation, and the coefficient of variation for each parameter. The charge balance between cations and anions, which is calculated by the formula (TZ+ − TZ)/(TZ+ + TZ) × 100 was within acceptable limits (less than 10%), confirming the reliability of the analytical results (Datta and Subramanian 1997; Singh and Hasnain 1998). TMI and EC are related by the equation TMI (meq l) = 0.02EC (μS cm−1) − 0.11, with a correlation coefficient of 0.99, and total cations and anions are related by the equation TZ+ (meq l) = 0.91TZ (meq l) − 0.02, with a correlation coefficient of 0.97 (n = 24).
Table 1

Data matrix for 24 water sample concentrations [in meq l1, except for EC (μS cm1) and pH]

Site no.

EC

pH

Na+

K+

Mg2+

Ca2+

Cl

NO3

SO42−

HCO3

TMI

1

314

7.15

0.92

0.11

0.57

1.36

0.68

0.33

0.51

1.44

5.98

2

364

7.22

1.31

0.14

0.56

1.29

0.90

0.44

0.54

1.50

6.70

3

334

7.18

1.04

0.12

0.57

1.33

0.74

0.36

0.50

1.58

6.28

4

336

7.25

1.01

0.11

0.61

1.47

0.75

0.29

0.56

1.65

6.46

5

355

7.28

1.11

0.13

0.63

1.52

0.82

0.31

0.63

1.79

6.96

6

368

7.89

1.38

0.16

0.54

1.81

0.87

0.23

0.38

2.45

7.84

7

355

7.42

1.14

0.13

0.64

1.56

0.82

0.28

0.60

2.03

7.23

8

489

7.59

2.59

0.11

0.65

1.46

0.80

0.55

1.69

2.13

10.02

9

353

7.69

1.25

0.37

0.49

1.62

0.50

0.34

0.53

2.50

7.69

10

365

7.52

1.22

0.14

0.68

1.69

0.76

0.26

0.73

2.27

7.76

11

251

7.45

0.70

0.04

0.66

0.86

0.54

0.55

0.38

0.94

4.69

12

364

7.75

0.77

0.08

0.95

2.05

0.49

0.32

0.53

2.66

7.87

13

372

8.34

1.13

0.12

0.73

1.83

0.82

0.26

0.72

2.39

8.02

14

600

7.88

3.46

0.21

0.76

1.40

2.94

0.18

0.39

2.71

12.09

15

479

7.42

2.15

0.15

0.79

1.72

1.69

0.24

0.62

2.66

10.07

16

386

7.39

0.99

0.07

0.94

1.96

0.82

0.31

0.52

2.24

7.87

17

373

7.41

1.50

0.15

0.70

1.43

1.10

0.23

0.43

2.09

7.66

18

347

7.39

1.27

0.14

0.63

1.42

0.89

0.19

0.39

1.81

6.76

19

321

7.66

1.15

0.12

0.60

1.37

0.83

0.13

0.36

1.77

6.37

20

379

6.91

1.55

0.09

0.63

1.39

1.65

0.05

0.22

1.92

7.53

21

489

7.22

2.32

0.14

0.63

1.74

2.28

0.04

0.12

2.57

9.87

22

212

6.51

0.56

0.04

0.54

0.92

0.49

0.07

0.23

1.30

4.16

23

142.3

5.77

0.39

0.01

0.47

0.46

0.27

0.07

0.31

0.69

2.68

24

84.5

5.71

0.30

0.00

0.24

0.17

0.33

0.10

0.06

0.24

1.44

Mean

351.3

7.29

1.30

0.12

0.63

1.41

0.95

0.26

0.50

1.89

7.08

SDa

107.7

0.60

0.71

0.07

0.15

0.44

0.62

0.14

0.31

0.65

2.31

CVb

0.31

0.08

0.55

0.60

0.23

0.31

0.65

0.55

0.62

0.34

0.33

aStandard deviation

bCoefficient of variation

The O-hori river water is slightly acidic to mildly alkaline. The pH ranges between 5.71 and 8.34. The mean pH value along the O-hori river system conforms to typical river water values (4.5–8.5), as presented by McCutcheon et al. (1992). The EC ranges between 84.5 and 600 μS cm−1. Tributaries have high EC values compared to values measured along the main stream. Like many other major river studies (Cameron et al. 1995; Karim and Veizer 2000; Douglas et al. 2002), EC in the O-hori river basin increases from the headwaters to the river mouth. This is because the number of tributaries and the intensity of anthropogenic activity increase in the downstream direction. HCO3 is the most dominant ion, followed by Ca2+, Na+, Cl, Mg2+, SO42−, NO3 and K+. The average HCO3 concentration accounts for about 61% of the TMI and about 56% of the total anions in equivalent units. The other anions, such as Cl, SO42− and NO3, account for 24, 13 and 7% respectively. Among the cations, Ca2+ is the major constituent, which accounts for about 41% of the total cations in equivalent units. Na+ and Mg2+ are second and third in cationic abundance, accounting for 36 and 20% of the total cations. K+ is the ion with the lowest concentration in the water of the O-hori river basin.

Land use characteristics and PCA

GIS analysis classified the land use in the O-hori river basin into eight distinct patterns based on the percentage of land use. These include forest (13%), farmland (12%), urban developing area (13%), industrial area (5%), low-rise residential area (24%), high-rise residential area (3%), commercial area (8%), and park (4%). The proportions of the different land uses for the sub-basins, as defined by the locations of the sampling sites, are shown in Fig. 2. Principal component analysis (PCA) was used to identify groupings of land-use characteristics in drainage basins for all sampling sites. Three principal components were identified with eigenvalues larger than 1, explaining about 72% of the total variance. The eigenvectors of individual land-use items are shown in Fig. 3. Table 2 presents the eigenvalues, the variance and the cumulative variance. The first component, which explains 38% of the total variance within the dataset, is characterized by high positive loadings for low-rise residential areas, high-rise residential areas, and others, and high negative loadings for forests. The second component, which explains a further 20% of the variance, is characterized by high positive loadings for developing areas, industrial areas, and parks. The third component, which explains a further 14% of the variance within the dataset, is characterized by high positive loadings for farmlands and parks. Based on these results, the principal components of the land-use characteristics were found to be residential areas (PC 1), urban developing areas (PC 2), and farmlands and green areas (PC 3). The groupings of land-use items based on the factor loadings of PCA are shown in Fig. 4.
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Fig. 3

Eigenvectors of individual land-use items in the O-hori river basin

Table 2

Eigenvalues, variance and cumulative variance of the principal components of land use in the O-hori river basin

 

Principal component

1

2

3

Eigenvalues

3.38

1.84

1.30

Variance (%)

37.52

20.42

14.44

Cumulative variance (%)

37.52

57.94

72.38

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Fig. 4

Grouping of land-use items based on the factor loadings from principal component analysis

Relationship between major ion concentrations and land-use characteristics

The correlation coefficients between the major inorganic ion concentrations and the individual land-use characteristics are shown in Table 3. Forest area shows negative correlations with all major ions, EC and TMI, but the correlations are significant with EC, Na+, K+, Mg2+, Ca2+, HCO3 and TMI at the 5% level. Farmland shows significant positive correlations with K+, Ca2+, NO3 and SO42−. Developing and commercial area shows positive significant correlations with Mg2+, Ca2+ and HCO3. Industrial area shows weak positive correlations with Na+ and Cl, with correlation coefficients of 0.21 and 0.32, respectively, but a significant negative correlation with NO3. Low-rise residential area shows significant positive correlations with Mg2+, Ca2+, NO3 and SO42−, and high-rise residential area with EC, K+ and HCO3. High-rise residential area also shows weak positive correlations with Na+, Mg2+, Ca2+, Cl and NO3.

Table 4 presents the correlation coefficients between the principal components of land use and the ion concentrations. PC 1 shows significant positive correlations with EC, K+, Mg2+, Ca2+, NO3, HCO3 and TMI. PC 2 shows significant positive correlations with EC, Ca2+, Cl, HCO3 and TMI. Both PC 1 and PC 2 show weak positive correlations with Na+. PC 3 only shows positive significant correlations with NO3 and SO42−. The grouping of ions based on the correlation coefficients between the principal components and the ion concentrations is shown in Fig. 5.
Table 3

Correlation coefficients between individual land use characteristics and ion concentrations in the O-hori river basin

 

EC

Na+

K+

Mg2+

Ca2+

Cl

NO3

SO42−

HCO3

TMI

PC 1

PC 2

PC 3

Forest

−0.71

−0.41

−0.55

−0.64

−0.85

0.33

0.38

0.34

−0.77

−0.72

−0.80

−0.50

0.15

Farmland

0.37

0.28

0.50

0.15

0.40

0.05

0.61

0.54

0.39

0.39

0.34

0.04

0.72

Developing area

0.26

0.10

0.01

0.42

0.43

0.25

0.32

0.14

0.40

0.28

0.06

0.86

0.20

Industrial area

0.02

0.21

0.14

0.27

0.16

0.32

0.60

0.31

0.02

0.13

−0.62

0.46

0.20

Low-rise residential area

0.35

0.08

0.35

0.47

0.50

0.05

0.71

0.40

0.35

0.34

0.85

0.29

0.25

High-rise residential area

0.46

0.35

0.70

0.28

0.39

0.31

0.23

0.14

0.50

0.48

0.83

−0.12

0.14

Commercial area

0.35

0.16

0.34

0.42

0.63

0.12

0.01

0.09

0.64

0.43

0.55

0.04

−0.44

Park

0.34

0.31

0.06

0.06

0.19

0.31

−0.01

0.13

0.12

0.27

−0.09

0.68

0.50

Others

0.27

0.04

0.23

0.50

0.41

0.11

0.15

0.06

0.32

0.27

0.73

0.29

−0.38

Bold values are significant at the 5% level

PC 1: first principal component, PC 2: second principal component, and PC 3: third principal component

Table 4

Correlation coefficients between the principal components of land use and ion concentrations in the O-hori river basin

 

EC

Na+

K+

Mg2+

Ca2+

Cl

NO3

SO42−

HCO3

TMI

PC 1

0.51

0.22

0.57

0.59

0.69

0.12

0.52

0.35

0.61

0.54

PC 2

0.42

0.30

0.09

0.32

0.44

0.41

−0.30

−0.06

0.42

0.40

PC 3

0.20

0.18

0.19

−0.07

0.08

−0.07

0.58

0.47

0.01

0.16

Bold values are significant at the 5% level

PC 1: first principal component, PC 2: second principal component, and PC 3: third principal component

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Fig. 5

Grouping of ions basis on the correlation coefficients between the principal components and the ion concentrations. Groups correspond to land-use groups

Possible sources of major inorganic ions

An examination of the relationships between the major inorganic ion concentrations and land-use characteristics revealed multiple influences on ion concentrations related to sources. Na+ does not show significant positive correlations with land-use characteristics, only weak positive correlations with residential area (PC 1) and urban developing area (PC 2). In the O-hori river basin, Na+ likely comes from domestic effluents and industrial activity. Residential area and farmland are positively correlated with K+. The possible sources of K+ are domestic effluents and fertilizers used on agricultural land. Mg2+ and Ca2+ often come from carbonate minerals in surface and groundwaters. PC 1 shows positive correlations with both of these, and PC 2 only with Ca2+. So, the anthropogenic sources of these two cations are domestic wastewater, housing, and industrial spillages.

Anions like Cl, NO3 and SO42− are major inorganic components that deteriorate the water quality and are likely to originate from sources of pollution. Cl may be derived from domestic effluents, roads and industries. It shows weak positive correlations with industrial and residential areas. Cl concentration seems to be a general indicator of any unforested land, and it could be used as a good surrogate indicator for general human disturbance in the watershed (Herlihy et al. 1998). The natural source of Na+ and Cl is the dissolution of the mineral halite. The correlation between the proportion of farmland in drainage basins and NO3–N is significant in this study, in agreement with other previous studies (Smart et al. 1985; Neill 1989; Sauer et al. 2001; Woli et al. 2004). NO3 also shows a high positive correlation with residential area. It likely comes from fertilizers used on agricultural land and residential gardens, the biological activity of plants, and domestic effluents. Sources of SO42− include sulfuric salts in domestic wastewater and fertilizer.

Natural processes such as the dissolution of carbonate minerals and the dissolution of atmospheric and soil CO2 gas could be the mechanisms that supply HCO3 to surface and groundwaters. In the present study, HCO3 shows significant correlations with residential area and urban developing area. Therefore, the potential anthropogenic sources of CO2 are (1) CO2 gas originating from municipal wastes, and (2) CO2 gas arising from the oxidation of organic materials leaking from sewage systems.

Conclusions

This study has shown which land-use types have the greatest influence on water quality in the O-hori river basin. The highest concentrations of major ions were found in areas associated with human activities. Forested areas had lower levels of inorganic ions and were found to maintain the water quality, as it was inversely related to almost all ion concentrations. Farmland coverage has a significant influence on the concentrations of both NO3 and SO42−, and urban coverage has an influence on the concentrations of K+, Mg2+, Ca2+, Cl, NO3 and HCO3.

The levels of contaminants will change again according to future changes in land-use patterns. Hence future land development and management should be considered with care. With better land-use planning, we may be able to curtail some of the current water quality problems. Since water quality is maintained or improved by increasing the forested area in the drainage basin, the protection of this form of land use should be encouraged.

Acknowledgments

The authors would like to thank the Environment Section of Kashiwa City Hall, Chiba Prefecture, Japan for providing the drainage area map of O-hori River and its tributaries. The authors are grateful to Dr. Yasushi Agata for his help during the field surveys and GIS analysis. Financial support from ADB-JSP during this study is gratefully acknowledged.

Copyright information

© The Japanese Society of Limnology 2008