Relationships between land use patterns and water quality in the Taizi River basin, China

Relationships between land use patterns and water quality in the Taizi River basin, China

Ecological Indicators 41 (2014) 187–197 Contents lists available at ScienceDirect Ecological Indicators journal homepage: www.elsevier.com/locate/ec...

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Ecological Indicators 41 (2014) 187–197

Contents lists available at ScienceDirect

Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind

Relationships between land use patterns and water quality in the Taizi River basin, China Hongmei Bu a,∗ , Wei Meng b,c,∗∗ , Yuan Zhang b,c , Jun Wan b,c a Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China b Laboratory of Riverine Ecological Conservation and Technology, Chinese Research Academy of Environmental Sciences, Beijing 100012, China c State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China

a r t i c l e

i n f o

Article history: Received 24 September 2013 Received in revised form 29 January 2014 Accepted 2 February 2014 Keywords: Land use Landscape metrics Water quality Correlation coefficient Multiple linear regression Factor analysis

a b s t r a c t Using land use types and landscape metrics, as well as statistical and spatial analysis, we determined the relationships between land use patterns and river water quality in the Taizi River basin, China, during dry and rainy seasons in 2009. Correlation and multiple linear regressions indicated that vegetated areas had a positive contribution to river water quality and predicted total nitrogen during the rainy season. Builtup land use strongly influenced nitrogen and phosphorus parameters in river water. Agricultural land use was associated with most physicochemical variables and nitrogen during the rainy season. Landscape metrics during both seasons were significantly associated with river water quality. Shannon’s diversity index was the primary predictor for chloride and ammoniacal nitrogen. Mean Euclidean nearest neighbor index defined ammoniacal nitrogen, orthophosphate, and total phosphorus during the dry season. Biological oxygen demand and permanganate index were expressed by Contagion during the rainy season. Factor analysis indicated that the river suffered organic, phosphorus, and nitrogen pollution and a zone dominated by agricultural and built-up land uses in the river basin tended to have worse water quality than other areas. The results provide a useful approach that uses landscape patterns to estimate water quality in rivers for water pollution control and land use management. © 2014 Elsevier Ltd. All rights reserved.

1. Introduction Land use patterns have important effects on river water quality and aquatic ecosystems within a watershed (Lee et al., 2009; Tran et al., 2010; Rothwell et al., 2010). Numerous problems related to water quality are caused by inappropriate land use and practices in a river basin, such as population increase, urbanization, and industrial and agricultural activities (Ngoye and Machiwa, 2004) because these anthropogenic activities are directly reflected in the land use characteristics of river basins (Kang et al., 2010). The relationships between land use patterns and water quality at a watershed scale explained variations of river water quality in

∗ Corresponding author at: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Datun Road, A 11, Beijing 100101, China. Tel.: +86 10 64889367; fax: +86 10 64889367. ∗∗ Corresponding author at: Laboratory of Riverine Ecological Conservation and Technology, Chinese Research Academy of Environmental Sciences, Beijing 100012, China. E-mail addresses: [email protected] (H. Bu), [email protected] (W. Meng). http://dx.doi.org/10.1016/j.ecolind.2014.02.003 1470-160X/© 2014 Elsevier Ltd. All rights reserved.

water resource conservation and watershed ecosystem management (Woli et al., 2004; Li et al., 2009). Advanced spatial tools (e.g., geographical information systems) combined with water quality assessment techniques make these studies convenient (Griffith, 2002; Ierodiaconou et al., 2005; Rothwell et al., 2010). Generally, agricultural land use has strong influence on nutrient parameters in river water, such as nitrogen and phosphorus contents (Pieterse et al., 2003; Ngoye and Machiwa, 2004; Woli et al., 2004). Industrial and urban land uses are associated with organic pollution, as well as heavy metals and nutrients (Ferrier et al., 2001; Li et al., 2009; Kang et al., 2010). Schoonover and Lockaby (2006) established regression models to develop the relationships among land cover and water quality at watershed scale in western Georgia, USA, and indicated that nutrient and fecal coliform concentrations within the watershed were often higher than those in non-urban watersheds during both base and storm flows. Kang et al. (2010) found that industrial and urban land uses were major contributors to the stream concentrations of Escherichia coli and Enterococci bacteria during wet and dry weather conditions in the Yeongsan River basin, Korea, whereas agricultural, industrial, and mining areas were significant sources of numerous heavy metal

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Fig. 1. The Taizi River basin showing (A) the water sampling sites, the digital elevation model (DEM), the drainage system, and 11 zones, and (B) land use patterns.

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species. Lee et al. (2009) indicated that water quality of reservoirs in South Korea was closely associated with both the proportions of land use and the spatial configurations of urban, agricultural, and forest areas. The Taizi River, a major tributary of the Liao River in Northeast China, has played a critical role in local economic development over the past two decades. The river water has become seriously polluted because of urbanization and industrialization in the river basin. Understanding the relationships between land use patterns and river water quality in the Taizi River basin will provide useful information for water pollution control and land use management at watershed scale. The main objectives of this study are to (1) reflect the relationships between river water quality and land use types, (2) detect the correlations between water quality and spatial configuration metrics, and (3) identify major sources of the pollutants at subwatershed scale in the Taizi River basin during dry and rainy seasons. 2. Materials and methods 2.1. Study area The Taizi River (40◦ 29 –41◦ 39 N, 122◦ 25 –124◦ 55 E) is one of the two largest tributaries of the Liao River in Northeast China, originating from Hongshilazi Hill in the north and the foot of Caomaozi Mountain in the south and finally discharging into the Bohai Sea. The river covers a total area of 13,202 km2 and has a total length of 413 km. The annual average discharge is 3.77 km3 . The Taizi River basin lies within a warm temperate monsoon climate zone, and the temperature and rainfall show large variations during the year, with the annual average approximately 6.2 ◦ C and 778.1 mm, respectively. The population in the river basin in 2009 was six million, centralized in Anshan, Benxi, Liaoyang, and Haicheng City (Fig. 1A). The river basin is an important agricultural and industrial area in Northeast China. Agricultural land covers 30.1% of the total drainage area (Fig. 1B), and the main agricultural

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products are maize and rice planted along the river during the annual growing season. Industries mainly include petrochemical, metallurgy, and equipment manufacturing. High economic output is distributed in the urban belt, mines, farms, and crops areas. The wastewater discharges mainly from urban and industrial areas of the river basin are centralized in the middle and lower reaches of the river with highly populated tributaries (Fig. 1A). 2.2. Water sampling and analytical methods Water samples were collected in two field surveys along the Taizi River during the dry (May) and rainy (August) seasons in 2009. Sixty-nine sampling sites were separated at approximately 5-km intervals along the mainstream of the Taizi River and its tributaries in each survey, showing a good spatial distribution (Fig. 1A). Fifteen representative parameters were chosen to measure, including pH, dissolved oxygen (DO), electrical conductivity (EC), total dissolved solids (TDS), chloride (Cl), sulfate (SO4 ), biological oxygen demand (BOD5 ), permanganate index (CODMn ), silicate (SiO4 ), ammoniacal nitrogen (NH3 -N), total nitrogen (TN), nitrite nitrogen (NO2 -N), nitrate nitrogen (NO3 -N), orthophosphate (PO4 ), and total phosphorus (TP). The values of pH, DO, EC, and TDS were directly determined in situ using a multiparameter water quality monitoring instrument (YSI Incorporated, Yellow Springs, Ohio, USA). Calibration of sensors was performed before measurement. The pretreatment and determination for the other parameters in the laboratory followed the national standard methods (NEPB, 2002). These standard methods for examining water and wastewater are accepted worldwide (Bu et al., 2010a,b). 2.3. Spatial analysis Digital elevation model (DEM) data and land use maps interpreted from 2007 Landsat TM images (30 m resolution) were used

Table 1 Landscape metrics used in the study (Uuemaa et al., 2005; Lee et al., 2009). Landscape metric (abbreviation)

Description

Calculation

Shannon’s diversity index (SHDI)

A popular measure of diversity in community ecology, indicates the patch diversity in landscape

SHDI = −

Patch density (PD)

Number of patches per unit area (number per 100 ha)

Mean shape index (SHMN)

Mean patch perimeter divided by the minimum perimeter of the corresponding land use area

m 

Contagion (CONTAG)

Tendency of land use types to be aggregated (%)

Largest patch index (LPI)

Percentage of total landscape area in the largest patch (%) Total length of all edge segments per unit area for the considered landscape (m/ha)

Edge density (ED)

(pi Inpi ) i=1

SHMN =

pij / min pij n

m n   m

 CONTAG =

i−1

1+

Distance to the nearest neighboring patch of the same land use type based on the edge-to-edge distance (m)

ENNMN =

Cohesion index (COHE)

Measures the physical connectedness of the corresponding patch type

COHE =

 1−

(Pi )

gik /

k=1

gik



2 In(m)

m n

Mean Euclidean nearest neighbor index (ENNMN)

j−1

i=1

j=1

 m

In(pi )

gik /

k=1

gik

 × 100%

hij

n

m  1 −1 pij m j=1 √ 1− √ × 100 A j=1

pij

aij

pi is the proportion of the landscape occupied by land use type i; pij is the perimeter of patch j of land use type i; gik is the number of adjacencies between pixels of land use types i and k based on the double-count method; m is the number of patch types present in the landscape; n is the number of patches; hij is the distance from patch j of land use type i; aij is the area of patch j of land use type i; A is the total number of cells in the landscape.

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to delineate watershed boundaries and measure the land use composition in the Taizi River basin using ArcGIS version 9.2. Multi-temporal data sets were applied to increase the classification accuracy and avoid the influence of great temporal variability of individual plots and limitations by solar illumination and cloud cover. The Landsat images were also orthorectified to avoid misclassification using a DEM and field surveys. The total accuracy and Kappa coefficient for the classification were equal to 81% and 0.76, respectively. Unsupervised classification techniques were used to identify 11 land use types in the land use maps (Fig. 1B). In this study, land use types were reclassified into eight categories: (1)

vegetated areas (VEG), including forest land and grassland; (2) dry farmland (DRY), mostly planted with maize; (3) paddy land (PAD), mostly planted with rice; (4) waters (WAT), including reservoir, river, and pond; (5) built-up land (BUI), including residential and industrial areas; (6) washland (WAS); (7) wetland (WET); and (8) unused land (UNU), including gravel, bare ground, and bare rock. Based on the DEM and stream networks of the watershed, 11 subwatersheds (indicated as 11 zones in Fig. 1) were delineated to compare the relationships between land use patterns and water quality. The proportions of land use types in the 11 zones were also computed using ArcGIS.

Fig. 2. Values (mean ± S.E.) of physicochemical parameters (including pH, DO, EC, TDS, Cl, SO4 , BOD5 , CODMn , and SiO4 ) and nutrient variables (including NH3 -N, TN, NO2 -N, NO3 -N, PO4 , and TP) during dry and rainy seasons in different zones of the Taizi River basin, China.

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To investigate the effects of landscape configuration on river water quality, the landscape metrics in 11 zones were also calculated using land use data with the computer program FRAGSTATS 3.3. The following landscape metrics were used (Table 1): Shannon’s diversity index (SHDI), patch density (PD), mean shape index (SHMN), Contagion (CONTAG), largest patch index (LPI), edge density (ED), mean Euclidean nearest neighbor index (ENNMN), and cohesion index (COHE). 2.4. Statistical analysis One-way analysis of variance (ANOVA) with the post hoc test was used to compare water quality variations under different zones and seasons at significance level of p < 0.05. Correlations between land use patterns and water quality chemistry were tested using Pearson’s correlation coefficients with statistical significances at p < 0.01 and p < 0.05 levels (2-tailed), respectively. We conducted multiple linear regressions, an effective approach to identify significant land use patterns to explain water quality variation in a watershed. A stepwise regression approach based on the p values was used to eliminate insignificant independent variables from the models (Kang et al., 2010). For the correlation and regression analysis, the one-sample Kolmogorov–Smirnov test was used in testing the normal distribution for all variables (Mirabella, 2006). The results showed Asymp. Sig. (2-tailed) values (p values) ranging from 0.207 (WAT) to 0.994 (TDS) and greater than 0.05, suggesting normal distribution. Factor analysis (FA) was used to identify pollution factors that affected water quality among 11 zones during dry and rainy seasons. Before FA was performed, the water quality data were initially standardized by z-scale transformation to avoid misclassification because of the wide differences in data units and dimensionality (Bu et al., 2010b). Kaiser–Meyer–Olkin (KMO) and Bartlett’s sphericity tests were conducted to measure the adequacy of the sampling data for FA. All statistics were finished by using SPSS 13.0. 3. Results 3.1. Characteristics of water quality The physicochemical and nutrient parameters of water quality during dry and rainy seasons in each zone of the Taizi River basin are presented in Fig. 2. All variables show significant spatial differences (p < 0.05) among the 11 zones. The highest values of pH and CODMn are observed in zone 6. The low concentration of DO and maximum amounts of EC, TDS, Cl, SO4 , NH3 -N, NO2 -N, PO4 , and TP are recorded in zone 10. The maximum of SiO4 occurs in zone 9, and the highest concentrations of BOD5 , TN, and NO3 -N, as well as high TDS, CODMn , and NH3 -N are found in zone 11. The ANOVA test also indicates that all variables are significantly different (p ≤ 0.05) between dry and rainy seasons except pH, Cl, BOD5 , CODMn , SiO4 , PO4 , and TP. Dissolved oxygen, EC, TDS, SO4 , and NH3 -N have large values during the rainy season, whereas high concentrations of TN, NO2 -N, and NO3 -N occur during the dry season. The BOD5 values, whether during the dry or the rainy season, reach maximum in zone 11. 3.2. Land use patterns in different zones The compositions of land use types are significantly different in 11 zones (Fig. 3). Large compositions of vegetated areas, over 80% of their land areas, are distributed in zones 1, 2, and 4. Agricultural lands are mainly centralized in zones 9–11 in the middle and lower reaches of the Taizi River and its tributaries, ranging from 29.2% (zone 11) to 40.7%

Fig. 3. Composition (%) of land use types in 11 different zones in the Taizi River basin, China.

(zone 10) for dry farmland, and from 7.5% (zone 11) to 19.0% (zone 9) for paddy land. Built-up land also has higher compositions in zones 9–11, ranging from 10.3% (zone 11) to 22.5% (zone 10) of their respective drainage areas. Water areas have extensive distributions from 0.8% (zone 9) to 9.3% (zone 8). The values of landscape metrics also show large variations in the 11 zones (Fig. 4). The highest values of SHDI (1.5), PD (0.6/100 ha), and ED (34.9 m/ha) and the lowest values of SHMN (2.0) and CONTAG (2.5%) are recorded in zone 9. The highest values of LPI and COHE occur in zone 5, whereas their lowest values are observed in zone 8. The ENNMN values are in the range of 508.5 m (zone 10) to 758.7 m (zone 3). 3.3. Linkage between land use types and water quality Correlation analysis shows significant relationships between land use types and water quality during the dry and rainy seasons in the Taizi River basin (Table 2). During the dry season, the proportions of vegetated areas in different zones are positively correlated with pH and DO (p < 0.01), and negatively related with EC, Cl, SO4 , NH3 -N, and TP (p < 0.01) and NO2 -N and PO4 (p < 0.05). Dry farmland, paddy, and built-up land uses display significant negative correlations with pH and DO, and positive correlations with EC, Cl, SO4 , and nutrient variables (p < 0.01 or p < 0.05). During the rainy season, vegetated areas and dry farmland are also significantly related to most physicochemical variables and nutrients (p < 0.01 or p < 0.05) and built-up land use except pH and paddy land use except TDS, BOD5 , and CODMn . The TP concentrations show significant negative and positive correlations with vegetated areas and built-up land uses (p < 0.05) during the rainy season, respectively. Stepwise multiple regression models create a “goodness of fit” (R2 values > 0.50). EC, BOD5 , SiO4 , NH3 -N, and TP during the dry and rainy seasons are predicted based on the proportions of dry farmland, wetland, paddy, and built-up land uses (Table 3). During the dry season, nutrients (NH3 -N, NO2 -N, PO4 , and TP) are defined by built-up land use. During the rainy season, pH, DO, and SiO4 are estimated based on paddy land, whereas EC, TDS, and SO4 are estimated

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Fig. 4. Landscape pattern metrics in 11 different zones in the Taizi River basin, China.

based on dry farmland. Specifically, Cl, CODMn , and NO2 -N are explained by more than one land use type during the rainy season. 3.4. Linkage between landscape metrics and water quality The correlations between the landscape metrics and water quality are also significant during both dry and rainy seasons (Table 4). The composition and configuration indices, such as SHDI, PD, CONTAG, and ED are correlated with more physicochemical and

nutrient variables (p < 0.01 or p < 0.05) during the rainy season than during the dry season, thereby showing more sensitivity to seasonal changes. The regression analysis results suggest that most landscape metrics are superior in predicting water quality variables during either season (Table 5). These landscape metrics account for 40% (for TP) to 92% (for EC) of the variances of water quality variables during the dry season and 46% (for pH) to 91% (for Cl) during the rainy season.

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Table 2 Pearson’s correlation coefficients between land use types and water quality chemistry during dry and rainy seasons in the Taizi River basin, China. Vegetated areas

Dry farmland

Paddy land

Waters

Built-up land

Washland

Wetland

Dry season pH DO EC TDS Cl SO4 BOD5 CODMn SiO4 NH3 -N TN NO2 -N NO3 -N PO4 TP

0.921** 0.768** −0.893** −0.514 −0.892** −0.781** −0.415 0.016 −0.514 −0.870** −0.418 −0.782* −0.158 −0.704* −0.839**

−0.907** −0.769** 0.906** 0.533 0.855** 0.735* 0.457 0.011 0.499 0.847** 0.388 0.762** 0.160 0.688* 0.803**

−0.811** −0.755** 0.836** 0.632* 0.800** 0.778** 0.198 −0.004 0.708* 0.821** 0.396 0.595 0.122 0.702* 0.838**

−0.188 0.154 −0.153 −0.177 −0.103 −0.105 0.107 −0.234 −0.641* −0.184 −0.322 −0.040 −0.341 −0.406 −0.342

−0.861** −0.729* 0.843** 0.356 0.961** 0.781** 0.404 0.008 0.535 0.890** 0.510 0.894** 0.246 0.801** 0.930**

−0.502 −0.244 0.370 0.592 0.157 0.548 0.109 −0.265 0.200 0.520 −0.015 0.096 −0.102 −0.014 0.181

−0.173 −0.306 0.563 0.045 0.463 0.301 0.800** 0.318 0.078 0.410 0.788** 0.583 0.781** 0.398 0.295

Rainy season pH DO EC TDS Cl SO4 BOD5 CODMn SiO4 NH3 -N TN NO2 -N NO3 -N PO4 TP

0.665* 0.766** −0.877** −0.772** −0.947** −0.883** −0.663* −0.696* −0.767** −0.932** −0.897** −0.565 −0.073 −0.378 −0.620*

−0.678* −0.716* 0.893** 0.778** 0.943** 0.894** 0.676* 0.704* 0.776** 0.918** 0.896** 0.607* 0.088 0.349 0.598

−0.729* −0.835** 0.776** 0.583 0.861** 0.762** 0.440 0.540 0.846** 0.864** 0.792** 0.318 0.016 0.282 0.505

0.008 0.405 −0.131 0.097 −0.153 0.045 0.233 −0.065 −0.337 −0.193 −0.102 0.698* 0.164 −0.046 −0.035

−0.500 −0.815** 0.859** 0.771** 0.948** 0.817** 0.637* 0.728* 0.685* 0.968** 0.890** 0.365 0.002 0.478 0.688*

−0.413 −0.395 0.321 0.361 0.258 0.500 0.057 0.066 0.223 0.190 0.492 0.797** 0.731* 0.497 0.519

−0.065 −0.437 0.584 0.649* 0.641* 0.554 0.816** 0.845** 0.499 0.483 0.567 0.119 −0.099 0.061 0.195

*

p < 0.05 (2-tailed). p < 0.01 (2-tailed). Unused land, not significantly correlated with any water quality parameters at the above two levels, is not shown here. **

Table 3 Stepwise multiple regression models for water quality parameters and land use types during dry and rainy seasons in the Taizi River basin, China. Parameter

Independent variable

Regression equations

R2

Adjusted R2

Sig.

Dry season pH DO EC Cl SO4 BOD5 SiO4 NH3 -N TN NO2 -N NO3 -N PO4 TP

Vegetated areas Dry farmland Dry farmland Built-up land Built-up land Wetland Paddy land Built-up land Wetland Built-up land Wetland Built-up land Built-up land

7.507 − 0.011VEG 7.308 − 0.068DRY 25.556 + 12.780DRY 6.959 + 2.696BUI 43.291 + 4.301BUI 5.004 + 2.494WET 9.249 + 0.237PAD 1.136 + 0.087BUI 3.693 + 2.466WET −0.029 + 0.022BUI 2.372 + 1.200WET 0.025 + 0.008BUI −0.001 + 0.034BUI

0.848 0.592 0.821 0.924 0.610 0.641 0.502 0.793 0.621 0.800 0.610 0.642 0.864

0.831 0.547 0.801 0.915 0.567 0.601 0.446 0.770 0.579 0.777 0.567 0.602 0.849

<0.001 0.006 <0.001 <0.001 0.005 0.003 0.015 <0.001 0.004 <0.001 0.005 0.003 <0.001

Rainy season pH DO EC TDS Cl SO4 BOD5 CODMn SiO4 NH3 -N TN NO2 -N NO3 -N TP

Paddy land Paddy land Dry farmland Dry farmland Built-up land, wetland, dry farmland Dry farmland Wetland Wetland, built-up land Paddy land Built-up land Vegetated areas Washland, waters, dry farmland Washland Built-up land

8.230 − 0.032PAD 7.155 − 0.117PAD 17.989 + 16.127DRY 118.879 + 10.449DRY −8.950 + 1.709BUI + 9.125WET + 1.003DRY 13.591 + 3.996DRY 4.875 + 4.306WET 2.640 + 2.400WET + 0.154BUI 9.531 + 0.337PAD −0.132 + 0.174BUI 6.966 − 0.060VEG −0.058 + 0.078WAS + 0.014WAT + 0.002DRY 1.131 + 1.325WAS 0.091 + 0.015BUI

0.531 0.698 0.798 0.605 0.988 0.799 0.666 0.924 0.716 0.936 0.805 0.936 0.535 0.473

0.479 0.664 0.775 0.561 0.975 0.777 0.629 0.853 0.684 0.929 0.783 0.908 0.483 0.415

0.011 0.001 <0.001 0.005 <0.001 <0.001 0.002 <0.001 0.001 <0.001 <0.001 <0.001 0.011 0.019

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Table 4 Pearson’s correlation coefficients between landscape metrics and water quality chemistry during dry and rainy seasons in the Taizi River basin, China. SHDIa

PDb

SHMNc

CONTAGd

LPIe

EDf

ENNMNg

COHEh

Dry season pH DO EC TDS Cl SO4 BOD5 CODMn SiO4 NH3 -N TN NO2 -N NO3 -N PO4 TP

−0.862** −0.643* 0.849** 0.536 0.757** 0.713* 0.535 −0.033 0.296 0.768** 0.429 0.674* 0.207 0.511 0.631*

−0.826** −0.703* 0.715* 0.525 0.676* 0.595 0.393 −0.116 0.266 0.609* 0.307 0.519 0.059 0.352 0.468

0.702* 0.709* −0.407 −0.325 −0.681* −0.373 −0.085 0.329 −0.427 −0.458 −0.133 −0.466 0.192 −0.295 −0.474

0.839** 0.617* −0.838** −0.539 −0.728* −0.721* −0.548 0.023 −0.262 −0.759** −0.458 −0.652* −0.247 −0.480 −0.593

0.668* 0.536 −0.567 −0.200 −0.534 −0.401 −0.103 −0.243 −0.030 −0.317 −0.109 −0.408 0.041 −0.475 −0.431

−0.715* −0.514 0.873** 0.652* 0.569 0.642* 0.567 0.072 0.265 0.655* 0.420 0.487 0.287 0.467 0.518

0.260 0.713* −0.530 −0.158 −0.582 −0.439 −0.205 −0.322 −0.769** −0.609* −0.538 −0.560 −0.472 −0.683* −0.632*

0.532 0.467 −0.310 0.003 −0.328 −0.231 −0.134 −0.086 0.158 −0.192 0.123 −0.283 0.226 −0.098 −0.100

Rainy season pH DO EC TDS Cl SO4 BOD5 CODMn SiO4 NH3 -N TN NO2 -N NO3 -N PO4 TP

−0.655* −0.647* 0.822** 0.774** 0.878** 0.893** 0.746** 0.699* 0.717* 0.799** 0.847** 0.727* 0.135 0.274 0.508

−0.678* −0.453 0.669* 0.581 0.746** 0.730* 0.726* 0.533 0.642* 0.664* 0.680* 0.752** 0.069 0.099 0.328

0.665* 0.333 −0.384 −0.191 −0.577 −0.302 −0.472 −0.193 −0.525 −0.599 −0.426 −0.335 0.229 0.000 −0.168

0.616* 0.643* −0.809** −0.780** −0.857** −0.896** −0.754** −0.706* −0.689* −0.762** −0.843** −0.749** −0.181 −0.284 −0.509

0.334 0.215 −0.539 −0.516 −0.532 −0.611* −0.595 −0.532 −0.378 −0.526 −0.403 −0.511 0.144 0.065 −0.186

−0.610* −0.553 0.826** 0.713* 0.795** 0.908** 0.625* 0.658* 0.721* 0.695* 0.771** 0.661* 0.149 0.161 0.389

0.145 0.667* −0.535 −0.450 −0.595 −0.381 −0.396 −0.579 −0.556 −0.575 −0.581 0.139 −0.066 −0.345 −0.452

0.289 0.009 −0.290 −0.478 −0.301 −0.431 −0.606* −0.386 −0.162 −0.250 −0.284 −0.808** −0.131 −0.004 −0.186

a b c d e f g h * **

Shannon’s diversity index. Patch density. Mean shape index. Contagion. Largest patch index. Edge density. Mean euclidean nearest neighbor index. Cohesion index. p < 0.05 (2-tailed). p < 0.01 (2-tailed).

3.5. Impact factors on water quality in different zones In FA, the KMO and Bartlett’s sphericity test results are 0.771 and 980.5 (df = 105, p < 0.001), respectively. The first three rotated factors with eigenvalue of 1 or greater are extracted using Varimax with Kaiser Normalization, explaining 75.0% of the total variance in the water quality data set (Table 6). The score of each factor for all data in different zones are reported in Fig. 5. Among these zones, 10, 11, and 9 have significantly higher total factor scores than the other zones. 4. Discussion 4.1. Influence of land use types on river water quality Numerous studies have reported that vegetated areas have a positive contribution to water quality (Tong and Chen, 2002; Li et al., 2009; Lee et al., 2009), whereas agricultural and built-up land uses have negative contribution to water quality in watersheds (Baker, 2003; White and Greer, 2006; Lee et al., 2009; Walker et al., 2009). The results of this study are consistent with such previous findings. Vegetated areas are associated with most water quality variables during both dry and rainy seasons in the watershed. Additionally, the proportion of vegetated area is the primary

predictor for pH values during the dry season and TN concentrations during the rainy season, which suggests the fixation and absorption effects of forest land and grassland for pollutants in river water (Nakagawa and Iwatsubo, 2000; Piatek et al., 2009). During the dry season, dry farmland and paddy land uses are strongly correlated with most physicochemical and nutrient variables, which suggests that agricultural land uses have negative effects on river water quality because of intensive fertilization during the farming season (Ngoye and Machiwa, 2004; Lee et al., 2009). However, agricultural land use predicts fewer water quality variables than built-up land use during the dry season, indicating that built-up land use is the primary contributor to degraded water quality rather than agricultural land use (Baker, 2003; Schoonover and Lockaby, 2006). Nutrient variables during the dry season cannot be assessed by agricultural land uses despite fertilizer use during this period. Thus, river water quality during the dry season is contaminated by possible point sources (Woli et al., 2004) mainly from domestic and industrial discharges since these pollution sources mainly distribute in built-up land areas (Fig. 1A) where population is dense and industrial activities are intensive. During the rainy season, agricultural land uses predict more water quality variables than during the dry season because of agricultural runoff from soil erosion (Li et al., 2009; Tran et al., 2010), as expected. In this period large amounts of rainfall cause the fertilizer

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Table 5 Stepwise multiple regression models for water quality parameters and landscape metrics during dry and rainy seasons in the Taizi River basin, China. Parameter

Independent variable

Regression equations

R2

Adjusted R2

Sig.

Dry season pH DO EC TDS Cl SO4 SiO4 NH3 -N NO2 -N PO4 TP

SHDIa ENNMNb , COHEc EDd , ENNMN ED SHDI CONTAGe ENNMN SHDI, ENNMN SHDI ENNMN ENNMN

8.829 − 0.651SHDI −348.278 + 0.007ENNMN + 3.506COHE 114.314 + 21.390ED − 0.556ENNMN 50.230 + 7.750ED −14.454 + 39.140SHDI 319.555 − 3.357CONTAG 20.648 − 0.016ENNMN 1.240 + 1.176SHDI − 0.003ENNMN −0.189 + 0.303SHDI 0.353 + 4.188 × 10−4 ENNMN 1.132 − 0.001ENNMN

0.744 0.847 0.920 0.425 0.573 0.520 0.592 0.766 0.455 0.467 0.400

0.715 0.808 0.900 0.361 0.525 0.467 0.546 0.708 0.394 0.407 0.333

0.001 0.001 <0.001 0.030 0.007 0.012 0.006 0.003 0.023 0.021 0.037

Rainy season pH DO EC TDS Cl SO4 BOD5 CODMn SiO4 NH3 -N TN NO2 -N

PDf ENNMN, CONTAG ED, ENNMN CONTAG SHDI, ENNMN ED CONTAG CONTAG ED, ENNMN SHDI, CONTAG SHDI, ENNMN COHE

8.686 − 1.504PD −0.954 + 0.005ENNMN + 0.061CONTAG 184.051 + 25.731ED − 0.735ENNMN 1410.543 − 14.380CONTAG 33.226 + 54.633SHDI − 0.090ENNMN −97.789 + 7.603ED 31.122 − 0.330CONTAG 20.725 − 0.217CONTAG 10.480 + 0.301ED − 0.011ENNMN −71.352 + 18.516SHDI + 0.730CONTAG 3.575 + 3.244SHDI − 0.005ENNMN 33.637 − 0.337COHE

0.459 0.691 0.850 0.608 0.912 0.825 0.569 0.498 0.720 0.803 0.855 0.652

0.399 0.614 0.813 0.565 0.890 0.806 0.521 0.442 0.650 0.754 0.818 0.614

0.022 0.009 0.001 0.005 <0.001 <0.001 0.007 0.015 0.006 0.002 <0.001 0.003

a b c d e f

Shannon’s diversity index. Mean euclidean nearest neighbor index. Cohesion index. Edge density. Contagion. Patch density.

used in the crops to overflow into the river reaches, thereby leading to the deterioration of river water quality. Nevertheless, built-up land use remains significantly related to most variables, which suggests that point source pollution also positively contributes to the nutrient content and organic matter in the river water (Kazi et al., 2009). Land use itself does not affect water quality. However, human activities on land use changes could influence the types and degree of pollution. Therefore, measuring the proportions of certain land use types in a watershed might enable us to conveniently predict water quality. Table 6 Factor loadings of the 15 variables on rotated component matrix for all water quality data during dry and rainy seasons in the Taizi River, China. Variables

Factor 1

2

3

pH DO EC TDS Cl SO4 BOD5 CODMn SiO4 NH3 -N TN NO2 -N NO3 -N PO4 TP Eigenvalue % of variance Cumulative %

−0.164 −0.580 0.696 0.830 0.628 0.796 0.787 0.717 0.408 0.453 0.232 −0.151 −0.148 0.116 0.175 4.746 31.64 31.64

−0.196 −0.488 0.601 0.126 0.586 0.261 0.188 0.155 0.626 0.316 0.442 −0.049 −0.190 0.860 0.880 3.198 24.54 56.18

−0.856 −0.212 0.320 0.378 0.103 0.313 −0.131 −0.095 0.003 0.660 0.812 0.867 0.879 −0.063 0.179 2.143 18.82 75.00

Bold values are strong loadings (>0.500).

4.2. Influence of landscape metrics on river water quality Through the use of spatial tools, landscape pattern metrics have been developed to quantify the land use patterns and understand spatial heterogeneity and landscape structure (Griffith, 2002; Virtanen et al., 2002). In this study, SHDI predicts Cl and NH3 N during either season, indicating that the relationships between variables of Cl and NH3 -N and SHDI are consistent over seasons (Lee et al., 2009). Results imply possible point pollution sources, such as sewage discharge and industrial wastes. CONTAG reflects the aggregated degree of land use types, which is high when low levels of dispersion of land use types are observed. The correlations between CONTAG and water quality variables are negative during two seasons, which suggest that degraded water quality usually occurs in highly fragmented and low dispersion landscapes (Uuemaa et al., 2005). SHMN, as a measure of the shape complexity of land uses within a watershed (Lee et al., 2009), is related to positive DO and negative Cl during the dry season, indicating that more complex land use shapes may lead to better water quality. As indicators of landscape fragmentation, PD and ED are positively correlated with most water quality variables during the rainy season, and ED predicts EC, SO4 , and SiO4 during this period, reflecting non-point pollution problems (Griffith, 2002), such as soil erosion and surface runoff. ENNMN is an effective indicator of landscape structure for water quality variables of DO, EC, SiO4 , NH3 -N, PO4 , and TP during the dry season. LPI provides a measure of the size of the largest patch within a watershed but displays no significant correlation with water quality variables except DO during the dry season and SO4 during the rainy season. COHE, which reflects the physical connectedness of land uses within watersheds, predicts DO with ENNMN during the dry season and NO2 -N during the rainy season, indicating the effects of landscape dispersion on some water quality variables (Xiao and Ji, 2007).

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The higher the factor scores are, the higher is the influence of the factor and the worse is the water quality (Bu et al., 2010a). Zones 8–11 with high proportions of built-up land uses are polluted by organic matter mostly because of domestic sewage inputs, particularly in residential areas. Zones 5 and 6 located in mountain areas with fewer people suffer organic pollution because livestock production is prevalent there. The largest amount of organic wastes from livestock excrement are directly discharged into the river and cause depletion of DO in receiving waters combined with high water temperature resulting from oxygen consumption from the decomposition of organic materials in the water (Kannel et al., 2008), a condition that leads to higher BOD5 and CODMn . Zones 9–11 are almost simultaneously polluted by phosphorous and nitrogen, indicating that the water pollution for these areas may be from the same sources, for example, agricultural and built-up land uses. Zone 5 becomes a nitrogen pollution area, which reflects the water pollution from surface runoff because of soil erosion along the river and sand extraction in the river channel. Finally, the total factor scores recognize zones 10, 11, and 9 as highly polluted regions because of mixed pollution resulting from anthropogenic activities. Thus, a zone dominated by agricultural and built-up land uses tends to have worse water quality in the Taizi River basin. 5. Conclusions The results of this study demonstrate the relationships between land use patterns and river water quality during dry and rainy seasons in 11 zones of the Taizi River basin. During the dry season, built-up land use rather than agricultural land uses significantly influences the concentrations of Cl, SO4 , and more nutrient variables, which indicates possible point source pollution. During the rainy season, agricultural and built-up land uses have significant effects on most water quality variables, showing mixed pollution of agricultural, domestic, and industrial sources. Additionally, the degraded water quality during both seasons is positively associated with landscape metrics of SHDI, PD, and ED, and negatively related to SHMN, CONTAG, LPI, ENNMN, and COHE. Finally, factor scores in FA recognize zones 9–11 as highly polluted regions associated with point and non-point pollution sources, such as agricultural runoff, domestic waste, and industrial discharge. Acknowledgements Fig. 5. Factor scores for all data during dry and rainy seasons in different zones of the Taizi River basin, China (1–11 denote 11 zones, respectively). (a) Defined by the first two factor scores, (b) defined by factor scores 1 and 3, and (c) defined by factor score 1 and total factor score.

Land use patterns actually affect water quality through changing hydrological and chemical runoff processes in a watershed. When runoff carries pollutants from upland areas into a river system, the spatial patterns of the watershed modify the land use effect on the water quality of adjacent aquatic systems (Lee et al., 2009). Therefore, a number of landscape metrics could be used to define certain water quality characteristics.

4.3. Source identification of pollution in different zones In FA, factor 1 reflects the organic pollution level of river water because of high loadings on BOD5 and CODMn . Factor 2 displays phosphorus pollution in river water as high loadings on PO4 and TP. Factor 3 represents nitrogen pollution of river water with high loadings on positive NH3 -N, TN, NO2 -N, and NO3 -N.

This research was supported by the Major Science and Technology Program for Water Pollution Control and Treatment in China (Grant No. 2008ZX07526-001) and the National Natural Science Foundation of China (Grant No. 41103069). The authors express sincere gratitude to Xiaodong Qu and Libin Chen for their assistance during the fieldwork and anonymous reviewers for their valuable comments. References Baker, A., 2003. Land use and water quality. Hydrol. Process. 17, 2499–2501. Bu, H., Tan, X., Li, S., Zhang, Q., 2010a. Temporal and spatial variations of water quality in the Jinshui River of the South Qinling Mts, China. Ecotox. Environ. Safe. 73, 907–913. Bu, H., Tan, X., Li, S., Zhang, Q., 2010b. Water quality assessment of the Jinshui River (China) using multivariate statistical techniques. Environ. Earth Sci. 60, 1631–1639. Ferrier, R.C., Edwards, A.C., Hirst, D., Littlewood, I.G., Watts, C.D., Morris, R., 2001. Water quality of Scottish rivers: spatial and temporal-trends. Sci. Total Environ. 265, 327–342. Griffith, J.A., 2002. Geographic techniques and recent applications of remote sensing to landscape-water quality studies. Water Air Soil Poll. 138, 181–197. Ierodiaconou, D., Laurenson, L., Leblanc, M., Stagnitti, F., Duff, G., Salzman, S., Versace, V., 2005. The consequences of land use change on nutrient exports: a

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