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Spatial determinants of hazardous chemicals in surface water of Qiantang River, China
Ecological Indicators 24 (2013) 375–381
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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Spatial determinants of hazardous chemicals in surface water of Qiantang River, China Shiliang Su a,b , Rui Xiao a , Xiaoya Mi a , Xiangyang Xu a , Zhonghao Zhang a , Jiaping Wu a,∗ a b
College of Environment and Natural Resources, Zhejiang University, Hangzhou, China Institute for Resource Information Sciences, Cornell University, Ithaca, USA
a r t i c l e
i n f o
Article history: Received 21 June 2011 Accepted 21 July 2012 Keywords: River hazardous chemicals Spatial determinants Spatial regression Scale effects
a b s t r a c t Spatial regression, incorporating spatial error or lag dependency, was performed to interpret determinants of hazardous chemicals at full sub-basin scale and at 500 m riparian buffer scale in Qiantang River, eastern coastal China. Monitoring data from 41 monitoring stations were collected between 1996 and 2003 and pretreated for 7 variables—petroleum, hexavalent chromium, total cadmium, total lead, total mercury, total cyanide, and volatile phenol. Results showed that primary predictors and the predictive ability of spatial regression differed with variables and scales. Topology, distance to river source, land use/land cover (LULC), population density, and gross domestic product (GDP) can be primary predictors for the pattern of certain hazardous chemical variables in 1996 and 2003. LULC types were good predictors for changes of cyanide and heavy metals, while GDP and population density contributed to petroleum dynamics between 1996 and 2003. This study demonstrated that spatial regression is a promising tool for generating indicators to tackle with hazardous chemical pollution. We also advocate applying multi-scale approaches to uncover the dynamics of hazardous chemicals. © 2012 Elsevier Ltd. All rights reserved.
1. Introduction Hazardous chemicals, including petroleum products, heavy metals, medical wastes and various other toxic materials, are indispensable materials for daily life and production (Duan et al., 2011). Large quantities of uncontrolled hazardous chemicals have been released into rivers worldwide due to global rapid population growth and intensive domestic activities, as well as expanding industrial and agricultural production (Kacar, 2011; Srebotnjak et al., 2012; Su et al., 2011a). Hazardous chemical pollution not only deteriorates river water quality and imbalances aquatic ecosystems, but also impedes socio-economic development and threatens human health (De and Ramaiah, 2007). It is therefore imperative to identify the determinants of hazardous chemical pollution in rivers for effective management within both national and international contexts. However, limited efforts have been made on this specific issue, since most previous studies merely focused on their spatiotemporal variations. Usually, one sampling site presents more similar water quality to neighboring sites than to those far away within the same river
∗ Corresponding author at: No. 866 Yuhangtang Road, Hangzhou, Zhejiang Province, China. Tel.: +86 571 88982813; fax: +86 571 86971359. E-mail addresses: [email protected] (S. Su), xr [email protected] (R. Xiao), [email protected] (X. Mi), [email protected] (X. Xu), [email protected] (Z. Zhang), [email protected] (J. Wu). 1470-160X/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ecolind.2012.07.015
basin, since the adjacent sites are dominated by similar ecosystems and experience comparable human pressure (Chang, 2008; Tu, 2011; Su et al., 2012). Therefore, patterns of river hazardous chemicals should exhibit spatial autocorrelation to some extent at certain scales. However, studies regarding the spatial determinants of river hazardous chemicals are limited, and seldom incorporate spatial autocorrelation into analysis. In addition, spatial determinants of hazardous chemicals could be scale-dependent, given that the characteristics of some determinants vary between scales (Smucker and Vis, 2011; Uuemaa et al., 2005; Zhou et al., 2012). For example, land use and land cover (LULC) presents high variability at different scales (e.g., catchment, watershed, and riparian zone), due to the distinct composition of different LULC types. The impact of LULC on river water quality consequently varies between scales (Buck et al., 2004; Monteagudo et al., 2012; Zhou et al., 2012). However, most previous studies have employed a static approach and failed to uncover the changes of determinants over time, and the corresponding effects at different scales. With this background in mind, this study tries to tackle the above concerns by examining the Qiantang River in China as a case study. The objectives are: (1) to characterize the natural (topography, distance to river source), socio-economic (gross domestic product and population density), and anthropogenic (LULC and management) determinants of hazardous chemicals patterns in 1996 and 2003; (2) to analyze the spatial dynamics of hazardous chemicals associated with determinant changes between 1996 and 2003; (3) to incorporate spatial autocorrelation into analysis; and
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Table 1 Data statistics for hazardous chemicals between 1996 and 2003 in Qiantang River, China (n = 3936; unit: mg/L).
Petroleum Cr6+ TCd TPb THg TCN V-ArOH
Mean
Maximum
Minimum
Standard deviation
.14 .004 .0007 .007 .0001 .003 .004
3.03 .035 .008 .084 .0003 .029 .323
.01 .002 .0001 .0003 .00001 .001 .001
.29 .003 .001 .012 .00002 .003 .019
(4) to examine scale effects on hazardous chemicals dynamics over time. 2. Materials and methods 2.1. Study site Qiantang River is located in eastern coastal China (Fig. 1), and contributes significantly to the sustainable development of the Yangtze River Delta, given its multiple supportive services and functions (Su et al., 2011b). As a rapidly growing, spatially expansive urbanized area, the Qiantang River basin has 20 million inhabitants and covers 40,000 km2 . This area underwent rapid socio-economic development after China’s market transition in 1994. Various heavy industries operate in Qiantang River basin, including chemical, dyeing, electroplating, and food plants. Over a period of eight years, this area has nearly doubled its gross domestic product (GDP), increasing from about 190 billion in 1996 to 360 billion dollars in 2003. This basin exemplifies some of the direct water pollution problems associated with urbanization facing developing countries. The case study of Qiantang River therefore provides us a good opportunity to examine the spatial determinates of hazardous chemicals. 2.2. Data sources and processing Monthly data were measured at 41 monitoring stations in the study area between 1996 and 2003 (Fig. 1). All data were acquired from the Environmental Monitoring Center of Zhejiang Province (3936 samples in total). Hazardous chemical variables included petroleum, hexavalent chromium (Cr6+ ), total cadmium (TCd), total mercury (THg), total cyanide (TCN), total lead (TPb), and volatile phenol (V-ArOH). These seven parameters were officially selected and monitored according to the national documents on river water quality monitoring, as well as the local water quality characteristics. Two missing TCN values were replaced by data smoothing (for details, see Su et al., 2011a). Because the distribution of original data was skewed, it was transformed by the normal score approach (for details, see Blom, 1958). Finally, the data for the seven parameters were standardized. General data statistics are displayed in Table 1. All chemical analysis followed national quality standards for surface waters in China (GB3838-2002; http://english.mep. gov.cn/standards reports/standards/water environment/quality standard/200710/t20071024 111792.htm). The specific method used for measuring water samples is briefly described as follows: petroleum, infrared spectrophotometry; Cr6+ , diphenylcarbohydrazide spectrophotometric method; TCN, pyridine – barbituric acid colorimetry (isonicotinic acid – pyrazolone colorimetric method); THg, cold-vapor atomic absorption spectrophotometry; TCd and TPb, atomic absorption spectrophotometry (chelating extraction); and V-ArOH, 4-AAP spectrophotometric method after distillation.
The drainage area (sub-basin) for each monitoring site was delineated from a 30 m × 30 m resolution digital elevation model (DEM) data using ArcGIS 9.2 (ESRI Inc., Redlands, CA). The DEM was also used to calculate surface elevation, standard deviation of slope and average slope. Mean elevation, slope gradient, and distance to river source for the full sub-basin and 500 m buffers were then calculated. The 500 m buffer scale was selected, because it is suitable for assessing localized determinants (Benson et al., 2006). This specific width can keep the general information and reduce the noise of LULC, given that certain LULC type may become too homogeneous when the width is too small. Data for GDP and total population were officially obtained at the rural community level. After intersecting sub-basin/buffer with population density/GDP data, corresponding values for each subbasin/buffer in 1996 and 2003 were calculated based on their area, assuming that population density and GDP were evenly distributed at rural community level. Landsat Thematic Mapper (TM) images in 1996 and 2003 were first geometrically registered and then classified by spectral mixture analysis (for more information, see Su et al., 2011c). The final LULC maps included four types, namely water, build-up, forest, and farmland (Fig. 2). Water includes both natural (e.g., river and lake) and artificial (e.g., reservoir and ponds) water bodies. Build-up refers to all the category of constructed areas, such as human settlements, commercial and industrial facilities, roads and shopping centers, and so on. Forest includes natural forest species and artificial plantation. Farmland represents all the area used for growing crops. This classification scheme was chosen because these four LULC types could represent the dominant ecosystems in the study area. Visual image interpretation was then applied to correct the misclassified pixels by SMA for each subbasin and 500 m riparian buffer. LULC patterns for each monitoring site were calculated at two scales (full sub-basin and 500 m riparian buffer). 2.3. Spatial regression Spatial regression was employed to analyze the relationships between hazardous chemical parameters and likely influential factors. Spatial error regression and spatial lag regression, incorporating spatial error or lag dependency, were specifically applied. The spatial lag regression is given as: yi =
wij yi + xi + i + εi
(1)
where i represents monitoring sites and is the spatial autoregressive coefficient; wij is the spatial weights matrix that describes the relationships among the sites; yi represents the dependent variable of i; xi is the vector of observed parameters of site i; is the matrix of explanatory variables; εi is a random error; and i denotes a spatial specific effect. The spatial error regression is expressed as follows: i =
wij i + εi
yi = xi + i + i
(2) (3)
where i represents the spatially auto-correlated error term and is the spatial autocorrelation coefficient. Spatial regression was performed using GeoDa 0.9.5-i (Beta) (Anselin et al., 2006). Lagrange Multiplier diagnostics was referred to in order to select the suitable spatial regression model, either error or lag (Anselin, 2005). Specifically, spatial regression was first performed for the year 1996 and then for 2003 in order to explore the relationships between annual means of hazardous chemicals and the input variables at 500 m buffer and sub-basin scales. After that, the changes of the hazardous chemical variables and contributing variables between 1996 and 2003, calculated by Eq. (4),
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Fig. 1. Location of Qiantang River basin, in China, monitoring stations and major tributaries with names indicated by letters.
Fig. 2. Land use/cover of Qiantang River basin in 1996 and 2003.
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were used to generate spatial regression models, depicting the relationships between the determinant changes and hazardous chemical dynamics over time. C=
R2 − R1 × 100% R1
(4)
where C is the percent change of the variable; R1 is the value of the variable in 1996; and R2 is the value of the variable in 2003. We selected these two years for analysis because they were normal hydrological years. In addition, the TM images of these two years were available to generate LULC maps. The input variables used in the spatial regression models include the following: percentage of percentage of water (water%), percentage of forest (forest%), farmland (farmland%), percentage of build-up (build%), population density (POP), gross domestic product (GDP), deviation of slope gradient (slope std), average slope (slope mean), mean elevation (elevation), and distance to river source (distance). Selection of these predictors was based on the following criteria: (1) comparability to previous studies; (2) availability of data; and (3) actual ecological conditions of the study area. Additionally, independent variables selected from stepwise regression were used in spatial regression, due to the multicollinearity issue (For similar issues, see Chang, 2008). Therefore, the same independent variables obtained from stepwise regression models acted as independent variables in spatial regression. 2.4. Statistical analysis Water management in China can be divided into three categories of development zones from a spatial perspective – urban, suburban and rural. One-way analysis of variance (ANOVA), Bonferroni Post Hoc multiple comparisons in particular, was applied to characterize the influential management practices on hazardous chemicals. All statistics were performed by the SPSS 16.0 (SPSS Inc., Chicago, IL) at 95% confidence level. 3. Results and discussion 3.1. Determinants of hazardous chemical patterns in 1996 and 2003 3.1.1. Natural determinants 3.1.1.1. Topography. Spatial determinants of hazardous chemicals in 1996 and 2003 explored by spatial regression are shown in Table 2. Slope gradient and elevation are both negative for heavy metal variables, indicating that the existence of fast-moving water flowing through high-elevation and sloped areas may reduce heavy metal depositions. Our results were inconsistent with the argument that the standard deviation of slope, rather than the average slope, was a better predictor for water quality (Sliva and Williams, 2001; Chang, 2008), since the standard deviation of slope was not incorporated in any regression model. This may be attributed to the diverse and steep terrain across Qiantang River basin. 3.1.1.2. Distance to river source. Another important natural factor affecting hazardous chemical patterns was distance from the river source. Water flowing from the river source was usually of high quality, and thus Distance had positive relationships with heavy metal variables (Table 2). However, Distance exhibited positive coefficients for petroleum (Table 2). It is thus implied that being closer to the river source could also result in more petroleum concentrations. Higher petroleum concentrations near the river source could be attributable to sand mining and shipping activities in the vicinity of some sources (Su et al., 2011a). This specific association suggests that the major factors associated with the spatial pattern
of hazardous chemicals could have a wider regional impact than just the area immediately surrounding the monitoring site. 3.1.2. Socio-economic determinants 3.1.2.1. Gross domestic product. As shown in Table 2, GDP was a positive predictor for heavy metals and petroleum. These results indicate the potential occurrence of hazardous chemical pollution under rapid economic development. Compared to 1996, as demonstrated in THg and TCd levels, the significance of GDP decreased in 2003. GDP had positive influence on Hg and Cd concentrations in 1996 but exerted no significant impact on these two parameters. Such results may be attributable to the national industrial structure. In China, the growth of GDP relied heavily on industrial production in 1990s. However, service and tourism have gradually become the main contributors to GDP increases in 2000s. 3.1.2.2. Population density. POP was positively correlated with most heavy metal variables except Cr6+ (Table 2). Such results supported the observation of more heavy metals in urbanizing areas with high population density (Caeiro et al., 2005; Buzier et al., 2011; Kang et al., 2010; Su et al., 2011a). It should be noted that POP was negatively correlated with petroleum and Cr6+ . These results could be connected with sand mining activities and some small chemical workshops in less populated rural areas (Su et al., 2011b). 3.1.3. Anthropogenic determinants 3.1.3.1. Land use and land cover. Built-up and farmland played important roles in predicting heavy metal and V-ArOH concentrations. V-ArOH, THg, TPb, TCd, and petroleum were all correlated with these two predictors to some extent (Table 2). These findings further supported the view that the presence of farmland and built-ups were dominant predictors for decreased water quality (Jung et al., 2008; Tran et al., 2010; Carey et al., 2011). Electronic industries, small workshops, and tannery and chemical plants were distributed throughout this region, discharging industrial wastewaters containing heavy metals and V-ArOH (Huang et al., 2010; Su et al., 2011a). Forest cover generally had a positive correlation with heavy metals (Table 2), inconsistent with the previous assumption that forest possesses the ecological function of filtering pollutants (Ngoye and Machiwa, 2004; Tu, 2011). Being spatially close to forest, polluted wastewater from livestock farms may be discharged into adjacent forest (Jung et al., 2008). Additionally, consideration should be given to forest species and management practices in the identified relationships. High-cash forests, such as nurseries, chestnut trees, and bamboo, occupied a large proportion of the total forest areas. They usually locate on lower hills close to river systems. Forest ecosystems could become highly dysfunctional as a result of excessive fertilization by farm owners seeking high profits. The presence of water bodies had positive relationships with V-ArOH, TPb and TCd (Table 2). Two possible explanations can be inferred from these results. First, industrial factories are usually located near water bodies, due to the low cost of using water for production. Therefore, water bodies may be, to some extent, spatially correlated with the number of industrial factories discharging hazardous chemicals. Second, a number of aquaculture ponds were distributed across Qiantang basin. Receiving great quantities of waste from domestic, industrial and agricultural activities, these ponds may become pollution sources for Qiantang River through surface runoff and underground water exchanges. All these results suggest that the spatially varying LULC patterns and land use practices should be taken into consideration. Without taking this information into account, results from the simple statistics may be misleading.
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Table 2 Spatial regression models for hazardous chemicals in 1996 and 2003 at different scales (n = 41).a Parameter V-ArOH
TCN THg
TPb
TCd
Cr6+
Petroleum
Year 1996 full 1996 buffer 2003 fullc 2003 bufferd 1996 fulld 1996 bufferc 2003 fulld 2003 bufferd 1996 fulld 1996 bufferd 2003 fullc 2003 bufferd 1996 fulld 1996 bufferd 2003 fullc 2003 bufferd 1996 full 1996 bufferd 2003 fullc 2003 bufferd 1996 fulld 1996 bufferd 2003 fullc 2003 bufferd
R2
Spatial regression models b
NS NSb 0.014 × farmland% + 0.003 × build% − 0.002 × distance − 0.029 × WY − 0.001 0.005 × water% − 0.007 × elevation − 0.003 × distance + 0.005 (lambda = 0.068) NSb 0.023 × farmland% − 0.148 × water% − 0.241 × elevation + 0.228 × GDP + 0.48 (lambda = 0.704) 0.367 × GDP − 0.278 × elevation + 0.657 × WY + 0.31 0.208 × farmland% − 0.174 × water% + 0.083 (lambda = 0.167) 0.277 × forest% + 0.065 × build% + 0.054 × distance − 0.031 (lambda = 0.071) 0.916 × build% − 0.395 × elevation + 0.612 × GDP + 0.156 × distance − 0.294 (lambda = 0.016) 0.121 × build% + 0.07 × distance + 0.201 (lambda = 0.209) 0.135 × forest% + 0.248 × water% − 0.14 × elevation + 0.174 × distance + 0.536 × WY − 0.081 0.477 × forest% + 0.427 × GDP + 0.537 × POP + 0.207 × distance + 0.174 (lambda = 0.565) 0.884 × build% − 0.34 × elevation + 0.77 × GDP + 0.435 × distance − 0.239 (lambda = 0.454) 0.257 × build% + 0.3 × GDP + 0.321 (lambda = 0.505) 0.092 × build% − 0.237 × elevation + 0.033 × distance + 0.248 × WY + 0.206 0.096 × farmland% − 0.201 × elevation + 0.104 × water% + 0.082 (lambda = 0.203) NSb 0.179 × GDP − 0.051 × elevation + 0.326 × distance + 0.339 (lambda = 0.015) 0.537 × GDP − 0.138 × elevation + 0.217 × distance + 0.227 × WY + 0.242 −0.156 × elevation − 0.291 × distance + 0.293 (lambda = 0.328) 0.083 × build% − 0.212 × slope mean − 0.192 × water% − 0.152 × distance + 0.255 (lambda = 0.325) 0.158 × build% − 0.185 × distance − 0.241 × slope mean + 0.279 (lambda = 0.325) 1.275 × GDP − 0.51 × slope mean − 1.195 × POP + 0.797 × WY + 0.094 0.477 × GDP − 0.515 × POP + 0.179 (lambda = 0.808)
.58** .51** .65** .55** .56** .61** .73** .64** .61** .59** .57** .53** .65** .61** .61** .53** .44** .57** .74** .65** .70**
a Abbreviation: full sub-basin scale (full), 500 m riparian buffer scale (buffer), percentage of build-up (build%), percentage of water (water%), percentage of forest (forest%), percentage of farmland (farmland%), population density (POP), gross domestic product (GDP), mean elevation (elevation), deviation of slope gradient (slope std), average slope (slope mean), distance to river source (distance). b NS: No significant relationships were identified by spatial regression. c Spatial lag models; WY = weighted mean of water quality for adjacent stations. d Spatial error models. ** p < 0.01.
3.1.3.2. Management. The results of multiple comparisons are shown in Table 3. Urban areas exhibited more obvious Cd pollution than rural areas. Urban areas also experienced more serious Hg pollution than suburban areas. These results indicated that less urbanized or industrialized rural/suburban areas presented less hazardous chemical pollution than their urban counterparts. Contrarily, petroleum concentrations in urban and suburban areas were lower than those in rural area. Similarly, rural areas were characterized by higher Cr6+ values compared to suburban areas. Such discovery was concurrent with negative relationships between POP and petroleum and Cr6+ in Table 2. Evaluations for local government officials are mainly associated with economic growth. Industry is a major sector of the labor market and also a major contributor to local economic development. Officials often ignore the environmental consequences of industrial activities in order to pursue more economic growth. Such a system thus indirectly results in failure to control hazardous chemicals in rural areas.
The significant positive relationships suggest that increases in GDP and population density are good predictors of more petroleum and heavy metal concentrations over time (Table 4). Conversely, a lower percentage of riparian forests usually correlates with higher petroleum and TPb concentrations (Table 4). This may be linked to the ecological functions of forest ecosystems, as forests can filter pollutants and thus maintain river water quality (Ngoye and Machiwa, 2004; Tu, 2011). Changes of water area were incorporated in the regression models for TCN and THg (Table 4), indicating that increases in water bodies can result in more TCN and THg. In China, economic crop production efficiency is much higher than that of food crops. For example, economic benefits of aquaculture ponds were about twice as high as those of arable land. Since it was a clear comparative disadvantage, some farmers were reluctant to grow grain and a lot of aquaculture ponds were constructed across the basin. In order to pursue economic gains, feeds and pesticides were intensely abused, converting these ponds into pollution sources.
3.2. Spatial dynamics of hazardous chemicals associated with determinant changes between 1996 and 2003
3.3. Incorporating spatial autocorrelation into analysis
Dynamics of hazardous chemicals associated with the changes of determinants between 1996 and 2003 are displayed in Table 4. Anthropological determinants were the contributing factors, since natural factors (topology and distance to river source) remained constant over this short period. Changes of LULC correlated significantly with changes of socio-economic indicators in most cases (Su et al., 2011c). Therefore, the number of anthropological variables that were incorporated in the regression models was generally below three (Table 4). For example, population growth and economic development spur settlement sprawl at the cost of farmland (Su et al., 2011c). As a result, the changes of the four indicators (POP, GDP, Build-up%, and Farmland%) did not always simultaneously act as independent variables in one regression model.
Spatial lag regression and spatial error regression were employed to interpret the spatial determinants of river hazardous chemicals. Spatial lag models were suited for most hazardous chemical variables at both scales. This suggests that river hazardous chemical patterns depended both on the neighboring patterns and on a set of local independent parameters. To approve this statement, we further apply the Global Moran’s I index (Moran, 1948) to characterize the spatial autocorrelation of hazardous chemical patterns. As shown in Table 5, most hazardous chemical variables presented moderate spatial autocorrelation. This should be attributed to the water mixing and exchanges motivated by river flows. If the spatial autocorrelation is not incorporated into analysis, the covariates effects will be exaggerated and generate
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Table 3 Bonferroni Post Hoc multiple comparisons of water quality variables values among different development zones.a Parameters
V-ArOH TCN THg TPb TCd Cr6+ Petroleum a *
Urban Suburban
Urban Rural
Suburban Rural
Mean difference
Sig.
Mean difference
Sig.
Mean difference
Sig.
−.003 .000 .00001* .002 .0004* .000 .011
.620 1.000 .030 .859 .028 1.000 .786
.001 −.000 .00001 .003 .0005* −.001 −.089*
.867 1.000 .136 .643 .002 .127 .050
.004 −.000 .00000 .001 .0001 −.002* −.096*
.216 1.000 1.000 1.000 .980 .034 .012
The number of samples is 1056 for urban zone, 1536 for suburban zone, and 1344 for rural zone. The mean difference is significant at p < 0.05 level.
Table 4 Determinants of hazardous chemicals dynamics between 1996 and 2003 at different scales (n = 41).a R2
Parameter
Scale
Spatial regression models
Changes of V-ArOH
Full Buffer Fullc Buffer Full Bufferc Fullc Bufferc Full Buffer Fulld Bufferc Fullc Bufferc
NSb NSb 0.25 × water% c + 0.221 × farmland% c − 0.125 × WY − 0.433 NSb NSb 0.011 × build% c + 0.193 × water% c + 0.306 × WY + 0.175 1.364 × GDP c − 5.096 × forest% c + 0.624 × WY + 6.517 0.462 × build% c − 5.336 × forest% c + 0.607 × WY + 5.047 NSd NSd 0.088 × build% c + 0.021 (lambda = 0.193) 0.147 × farmland% c + 0.093 × build% c + 0.187 × WY − 0.028 0.044 × GDP c + 0.282 × WY − 0.14 0.073 × POP c − 0.063 × forest% c + 0.254 × WY − 0.22
Changes of TCN Changes of THg Changes of TPb Changes of TCd Changes of Cr6+ Changes of petroleum
.41**
.39** .45** .47**
.33** .42** .37** .44**
a Abbreviation: full sub-basin scale (full), 500 m riparian buffer scale (buffer), change of percentage of build-up (build% c), change of percentage of water (water% c), change of percentage of forest (forest% c), change of percentage of farmland (farmland% c), change of population density (POP c), change of gross domestic product (GDP c). b NS: no significant relationships were identified by spatial regression. c Spatial lag models; WY = weighted mean of water quality for adjacent stations. d Spatial error models. ** p < 0.01.
incorrect estimations as well as misleading determinants. Spatial error also demonstrated relatively strong predictive ability for several hazardous chemical variables. This indicated that the unobserved factors, such as rainfall, temperature, soil texture and hydrological processes, should follow a spatial pattern for these variables. These spatially auto-correlated factors can influence hazardous chemical dynamics through various physical and biogeochemical processes. All these results demonstrate the need to incorporate spatial analysis when interpreting the spatial determinants of hazardous chemicals. 3.4. Scale effects on hazardous chemical dynamics over time No widely accepted conclusion has been reached on which scale should be selected when analyzing determinants of hazardous chemicals. Regarding the spatial patterns of hazardous chemicals,
our results signified that variations of V-ArOH and heavy metals were more correctly predicted at the full sub-basin scale. Contrarily, spatial determinants of petroleum patterns were better predicted at the 500 m buffer scale. Regarding the spatial dynamics of hazardous chemicals, the coefficient of determination (R2 ) is generally higher at the buffer scale than at the sub-basin scale, indicating that the influence of spatial determinants operated more significantly at the buffer scale. Patterns of hazardous chemicals are associated with the temporal dimension over which they are measured. Hydrologic and biogeochemical processes occur at different temporal scales (Chang, 2008; Zhou et al., 2012). Moreover, changes in LULC and socio-economics were affected by the measurement interval. Temporal scale should therefore be another important influential factor taken into account. The significant predictors identified for hazardous chemicals in 1996 were distinct from those in 2003.
Table 5 Moran’s I values for hazardous chemical variables between 1996 and 2003 in the Qiantang River.a , .b
V-ArOH TCN THg TPb TCd Cr6+ Petroleum a
1996
1997
1998
1999
2000
2001
2002
2003
2004
−.072 .053 .546 .190 .353 .045 .005
.068 .247 .420 .051 .317 −.077 . 047
−.139 .119 .412 .271 .306 .108 .027
−.047 .012 .426 .320 .473 .112 .283
−.111 .170 .307 .605 .457 .042 .226
−.067 .029 .147 .435 .482 −.006 .275
.025 .061 .386 .451 .581 .063 .315
−.022 .074 .300 .565 .546 .095 .351
.051 .086 .026 .322 .132 .114 .622
All the Moran’s I statistics were calculated at a 95% confidence interval. Range of Moran’s I values concentrates in the interval [−1,1]. 1 and −1 respectively denote the strongest positive spatial autocorrelation and strongest negative spatial autocorrelation. b
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Specifically, no significant spatial determinants were identified for V-ArOH and Cr6+ (1996) and TCN. It is possible that the small percentage and spatially uneven distribution may have led to the ineffective interpretation of their responses to these factors. These findings illustrate the importance of considering multiple spatiotemporal scale approaches in order to advance the understanding of the determinants of hazardous chemicals. 3.5. Limitations Despite the insights into river hazardous chemical dynamics from this study, some methodological limitations need to be addressed. First, annual mean values of hazardous chemical variables were used in spatial regression, and seasonal variations were therefore not addressed. Further study related to these variations will be conducted. Second, due to the low resolution of TM images, only four dominant LULC types were identified. Some high-resolution and multi-temporal satellite images can be used in future research. Third, the population and GDP data was at the administrative scale instead of the buffer/sub-basin scale. We simply calculated the corresponding value for each buffer/sub-basin according to its area. This process should generate some level of error due to the mismatch of scales. Fourth, we only characterize the determinants at two scales. More detailed comparison should be made among different spatiotemporal scales. Finally, spatial regression models for the dynamics of hazardous chemicals exhibited relatively limited predictive ability, with R2 no greater than 0.50 (Table 4). This implies that other factors not included in the model made important contributions to the total variations of hazardous chemical dynamics. Climate, geology, soil and other socio-economic indicators may account for the unexplained effects. Further research, when relevant data is available, will be carried out to analyze the impact of these factors. 4. Conclusions This study investigated spatial determinants of seven hazardous chemicals in Qiantang River between 1996 and 2003. Results showed that primary predictors and the predictive ability of spatial regression differed with variables and scales. Topology, distance to river source, LULC, population density, and GDP were found to be primary predictors for the pattern of certain hazardous chemical variables in 1996 and 2003. Generally, natural factors were included in regression models for 1996, while population and GDP were the main influential factors in 2003. As for the determinants of dynamics, LULC types were good predictors for changes in cyanide and heavy metals, while GDP and population density contributed to petroleum dynamics between 1996 and 2003. Results of this study demonstrated that spatial regression is a promising tool for interpreting spatial determinants of hazardous chemicals in rivers. We also advocate applying multi-scale approaches to uncover the dynamics of hazardous chemicals. Since it is applicable to other river basins, this approach provides an operational basis for generating indicators of hazardous chemical pollution. Acknowledgements We thank Editor-in-Chief Felix Müller and two reviewers for providing constructive comments and suggestions. Professor Stephen DeGloria at Cornell University is greatly appreciated for revising the original manuscript. Part financial sources include
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Contents lists available at SciVerse ScienceDirect
Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind
Spatial determinants of hazardous chemicals in surface water of Qiantang River, China Shiliang Su a,b , Rui Xiao a , Xiaoya Mi a , Xiangyang Xu a , Zhonghao Zhang a , Jiaping Wu a,∗ a b
College of Environment and Natural Resources, Zhejiang University, Hangzhou, China Institute for Resource Information Sciences, Cornell University, Ithaca, USA
a r t i c l e
i n f o
Article history: Received 21 June 2011 Accepted 21 July 2012 Keywords: River hazardous chemicals Spatial determinants Spatial regression Scale effects
a b s t r a c t Spatial regression, incorporating spatial error or lag dependency, was performed to interpret determinants of hazardous chemicals at full sub-basin scale and at 500 m riparian buffer scale in Qiantang River, eastern coastal China. Monitoring data from 41 monitoring stations were collected between 1996 and 2003 and pretreated for 7 variables—petroleum, hexavalent chromium, total cadmium, total lead, total mercury, total cyanide, and volatile phenol. Results showed that primary predictors and the predictive ability of spatial regression differed with variables and scales. Topology, distance to river source, land use/land cover (LULC), population density, and gross domestic product (GDP) can be primary predictors for the pattern of certain hazardous chemical variables in 1996 and 2003. LULC types were good predictors for changes of cyanide and heavy metals, while GDP and population density contributed to petroleum dynamics between 1996 and 2003. This study demonstrated that spatial regression is a promising tool for generating indicators to tackle with hazardous chemical pollution. We also advocate applying multi-scale approaches to uncover the dynamics of hazardous chemicals. © 2012 Elsevier Ltd. All rights reserved.
1. Introduction Hazardous chemicals, including petroleum products, heavy metals, medical wastes and various other toxic materials, are indispensable materials for daily life and production (Duan et al., 2011). Large quantities of uncontrolled hazardous chemicals have been released into rivers worldwide due to global rapid population growth and intensive domestic activities, as well as expanding industrial and agricultural production (Kacar, 2011; Srebotnjak et al., 2012; Su et al., 2011a). Hazardous chemical pollution not only deteriorates river water quality and imbalances aquatic ecosystems, but also impedes socio-economic development and threatens human health (De and Ramaiah, 2007). It is therefore imperative to identify the determinants of hazardous chemical pollution in rivers for effective management within both national and international contexts. However, limited efforts have been made on this specific issue, since most previous studies merely focused on their spatiotemporal variations. Usually, one sampling site presents more similar water quality to neighboring sites than to those far away within the same river
∗ Corresponding author at: No. 866 Yuhangtang Road, Hangzhou, Zhejiang Province, China. Tel.: +86 571 88982813; fax: +86 571 86971359. E-mail addresses: [email protected] (S. Su), xr [email protected] (R. Xiao), [email protected] (X. Mi), [email protected] (X. Xu), [email protected] (Z. Zhang), [email protected] (J. Wu). 1470-160X/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ecolind.2012.07.015
basin, since the adjacent sites are dominated by similar ecosystems and experience comparable human pressure (Chang, 2008; Tu, 2011; Su et al., 2012). Therefore, patterns of river hazardous chemicals should exhibit spatial autocorrelation to some extent at certain scales. However, studies regarding the spatial determinants of river hazardous chemicals are limited, and seldom incorporate spatial autocorrelation into analysis. In addition, spatial determinants of hazardous chemicals could be scale-dependent, given that the characteristics of some determinants vary between scales (Smucker and Vis, 2011; Uuemaa et al., 2005; Zhou et al., 2012). For example, land use and land cover (LULC) presents high variability at different scales (e.g., catchment, watershed, and riparian zone), due to the distinct composition of different LULC types. The impact of LULC on river water quality consequently varies between scales (Buck et al., 2004; Monteagudo et al., 2012; Zhou et al., 2012). However, most previous studies have employed a static approach and failed to uncover the changes of determinants over time, and the corresponding effects at different scales. With this background in mind, this study tries to tackle the above concerns by examining the Qiantang River in China as a case study. The objectives are: (1) to characterize the natural (topography, distance to river source), socio-economic (gross domestic product and population density), and anthropogenic (LULC and management) determinants of hazardous chemicals patterns in 1996 and 2003; (2) to analyze the spatial dynamics of hazardous chemicals associated with determinant changes between 1996 and 2003; (3) to incorporate spatial autocorrelation into analysis; and
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Table 1 Data statistics for hazardous chemicals between 1996 and 2003 in Qiantang River, China (n = 3936; unit: mg/L).
Petroleum Cr6+ TCd TPb THg TCN V-ArOH
Mean
Maximum
Minimum
Standard deviation
.14 .004 .0007 .007 .0001 .003 .004
3.03 .035 .008 .084 .0003 .029 .323
.01 .002 .0001 .0003 .00001 .001 .001
.29 .003 .001 .012 .00002 .003 .019
(4) to examine scale effects on hazardous chemicals dynamics over time. 2. Materials and methods 2.1. Study site Qiantang River is located in eastern coastal China (Fig. 1), and contributes significantly to the sustainable development of the Yangtze River Delta, given its multiple supportive services and functions (Su et al., 2011b). As a rapidly growing, spatially expansive urbanized area, the Qiantang River basin has 20 million inhabitants and covers 40,000 km2 . This area underwent rapid socio-economic development after China’s market transition in 1994. Various heavy industries operate in Qiantang River basin, including chemical, dyeing, electroplating, and food plants. Over a period of eight years, this area has nearly doubled its gross domestic product (GDP), increasing from about 190 billion in 1996 to 360 billion dollars in 2003. This basin exemplifies some of the direct water pollution problems associated with urbanization facing developing countries. The case study of Qiantang River therefore provides us a good opportunity to examine the spatial determinates of hazardous chemicals. 2.2. Data sources and processing Monthly data were measured at 41 monitoring stations in the study area between 1996 and 2003 (Fig. 1). All data were acquired from the Environmental Monitoring Center of Zhejiang Province (3936 samples in total). Hazardous chemical variables included petroleum, hexavalent chromium (Cr6+ ), total cadmium (TCd), total mercury (THg), total cyanide (TCN), total lead (TPb), and volatile phenol (V-ArOH). These seven parameters were officially selected and monitored according to the national documents on river water quality monitoring, as well as the local water quality characteristics. Two missing TCN values were replaced by data smoothing (for details, see Su et al., 2011a). Because the distribution of original data was skewed, it was transformed by the normal score approach (for details, see Blom, 1958). Finally, the data for the seven parameters were standardized. General data statistics are displayed in Table 1. All chemical analysis followed national quality standards for surface waters in China (GB3838-2002; http://english.mep. gov.cn/standards reports/standards/water environment/quality standard/200710/t20071024 111792.htm). The specific method used for measuring water samples is briefly described as follows: petroleum, infrared spectrophotometry; Cr6+ , diphenylcarbohydrazide spectrophotometric method; TCN, pyridine – barbituric acid colorimetry (isonicotinic acid – pyrazolone colorimetric method); THg, cold-vapor atomic absorption spectrophotometry; TCd and TPb, atomic absorption spectrophotometry (chelating extraction); and V-ArOH, 4-AAP spectrophotometric method after distillation.
The drainage area (sub-basin) for each monitoring site was delineated from a 30 m × 30 m resolution digital elevation model (DEM) data using ArcGIS 9.2 (ESRI Inc., Redlands, CA). The DEM was also used to calculate surface elevation, standard deviation of slope and average slope. Mean elevation, slope gradient, and distance to river source for the full sub-basin and 500 m buffers were then calculated. The 500 m buffer scale was selected, because it is suitable for assessing localized determinants (Benson et al., 2006). This specific width can keep the general information and reduce the noise of LULC, given that certain LULC type may become too homogeneous when the width is too small. Data for GDP and total population were officially obtained at the rural community level. After intersecting sub-basin/buffer with population density/GDP data, corresponding values for each subbasin/buffer in 1996 and 2003 were calculated based on their area, assuming that population density and GDP were evenly distributed at rural community level. Landsat Thematic Mapper (TM) images in 1996 and 2003 were first geometrically registered and then classified by spectral mixture analysis (for more information, see Su et al., 2011c). The final LULC maps included four types, namely water, build-up, forest, and farmland (Fig. 2). Water includes both natural (e.g., river and lake) and artificial (e.g., reservoir and ponds) water bodies. Build-up refers to all the category of constructed areas, such as human settlements, commercial and industrial facilities, roads and shopping centers, and so on. Forest includes natural forest species and artificial plantation. Farmland represents all the area used for growing crops. This classification scheme was chosen because these four LULC types could represent the dominant ecosystems in the study area. Visual image interpretation was then applied to correct the misclassified pixels by SMA for each subbasin and 500 m riparian buffer. LULC patterns for each monitoring site were calculated at two scales (full sub-basin and 500 m riparian buffer). 2.3. Spatial regression Spatial regression was employed to analyze the relationships between hazardous chemical parameters and likely influential factors. Spatial error regression and spatial lag regression, incorporating spatial error or lag dependency, were specifically applied. The spatial lag regression is given as: yi =
wij yi + xi + i + εi
(1)
where i represents monitoring sites and is the spatial autoregressive coefficient; wij is the spatial weights matrix that describes the relationships among the sites; yi represents the dependent variable of i; xi is the vector of observed parameters of site i; is the matrix of explanatory variables; εi is a random error; and i denotes a spatial specific effect. The spatial error regression is expressed as follows: i =
wij i + εi
yi = xi + i + i
(2) (3)
where i represents the spatially auto-correlated error term and is the spatial autocorrelation coefficient. Spatial regression was performed using GeoDa 0.9.5-i (Beta) (Anselin et al., 2006). Lagrange Multiplier diagnostics was referred to in order to select the suitable spatial regression model, either error or lag (Anselin, 2005). Specifically, spatial regression was first performed for the year 1996 and then for 2003 in order to explore the relationships between annual means of hazardous chemicals and the input variables at 500 m buffer and sub-basin scales. After that, the changes of the hazardous chemical variables and contributing variables between 1996 and 2003, calculated by Eq. (4),
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Fig. 1. Location of Qiantang River basin, in China, monitoring stations and major tributaries with names indicated by letters.
Fig. 2. Land use/cover of Qiantang River basin in 1996 and 2003.
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were used to generate spatial regression models, depicting the relationships between the determinant changes and hazardous chemical dynamics over time. C=
R2 − R1 × 100% R1
(4)
where C is the percent change of the variable; R1 is the value of the variable in 1996; and R2 is the value of the variable in 2003. We selected these two years for analysis because they were normal hydrological years. In addition, the TM images of these two years were available to generate LULC maps. The input variables used in the spatial regression models include the following: percentage of percentage of water (water%), percentage of forest (forest%), farmland (farmland%), percentage of build-up (build%), population density (POP), gross domestic product (GDP), deviation of slope gradient (slope std), average slope (slope mean), mean elevation (elevation), and distance to river source (distance). Selection of these predictors was based on the following criteria: (1) comparability to previous studies; (2) availability of data; and (3) actual ecological conditions of the study area. Additionally, independent variables selected from stepwise regression were used in spatial regression, due to the multicollinearity issue (For similar issues, see Chang, 2008). Therefore, the same independent variables obtained from stepwise regression models acted as independent variables in spatial regression. 2.4. Statistical analysis Water management in China can be divided into three categories of development zones from a spatial perspective – urban, suburban and rural. One-way analysis of variance (ANOVA), Bonferroni Post Hoc multiple comparisons in particular, was applied to characterize the influential management practices on hazardous chemicals. All statistics were performed by the SPSS 16.0 (SPSS Inc., Chicago, IL) at 95% confidence level. 3. Results and discussion 3.1. Determinants of hazardous chemical patterns in 1996 and 2003 3.1.1. Natural determinants 3.1.1.1. Topography. Spatial determinants of hazardous chemicals in 1996 and 2003 explored by spatial regression are shown in Table 2. Slope gradient and elevation are both negative for heavy metal variables, indicating that the existence of fast-moving water flowing through high-elevation and sloped areas may reduce heavy metal depositions. Our results were inconsistent with the argument that the standard deviation of slope, rather than the average slope, was a better predictor for water quality (Sliva and Williams, 2001; Chang, 2008), since the standard deviation of slope was not incorporated in any regression model. This may be attributed to the diverse and steep terrain across Qiantang River basin. 3.1.1.2. Distance to river source. Another important natural factor affecting hazardous chemical patterns was distance from the river source. Water flowing from the river source was usually of high quality, and thus Distance had positive relationships with heavy metal variables (Table 2). However, Distance exhibited positive coefficients for petroleum (Table 2). It is thus implied that being closer to the river source could also result in more petroleum concentrations. Higher petroleum concentrations near the river source could be attributable to sand mining and shipping activities in the vicinity of some sources (Su et al., 2011a). This specific association suggests that the major factors associated with the spatial pattern
of hazardous chemicals could have a wider regional impact than just the area immediately surrounding the monitoring site. 3.1.2. Socio-economic determinants 3.1.2.1. Gross domestic product. As shown in Table 2, GDP was a positive predictor for heavy metals and petroleum. These results indicate the potential occurrence of hazardous chemical pollution under rapid economic development. Compared to 1996, as demonstrated in THg and TCd levels, the significance of GDP decreased in 2003. GDP had positive influence on Hg and Cd concentrations in 1996 but exerted no significant impact on these two parameters. Such results may be attributable to the national industrial structure. In China, the growth of GDP relied heavily on industrial production in 1990s. However, service and tourism have gradually become the main contributors to GDP increases in 2000s. 3.1.2.2. Population density. POP was positively correlated with most heavy metal variables except Cr6+ (Table 2). Such results supported the observation of more heavy metals in urbanizing areas with high population density (Caeiro et al., 2005; Buzier et al., 2011; Kang et al., 2010; Su et al., 2011a). It should be noted that POP was negatively correlated with petroleum and Cr6+ . These results could be connected with sand mining activities and some small chemical workshops in less populated rural areas (Su et al., 2011b). 3.1.3. Anthropogenic determinants 3.1.3.1. Land use and land cover. Built-up and farmland played important roles in predicting heavy metal and V-ArOH concentrations. V-ArOH, THg, TPb, TCd, and petroleum were all correlated with these two predictors to some extent (Table 2). These findings further supported the view that the presence of farmland and built-ups were dominant predictors for decreased water quality (Jung et al., 2008; Tran et al., 2010; Carey et al., 2011). Electronic industries, small workshops, and tannery and chemical plants were distributed throughout this region, discharging industrial wastewaters containing heavy metals and V-ArOH (Huang et al., 2010; Su et al., 2011a). Forest cover generally had a positive correlation with heavy metals (Table 2), inconsistent with the previous assumption that forest possesses the ecological function of filtering pollutants (Ngoye and Machiwa, 2004; Tu, 2011). Being spatially close to forest, polluted wastewater from livestock farms may be discharged into adjacent forest (Jung et al., 2008). Additionally, consideration should be given to forest species and management practices in the identified relationships. High-cash forests, such as nurseries, chestnut trees, and bamboo, occupied a large proportion of the total forest areas. They usually locate on lower hills close to river systems. Forest ecosystems could become highly dysfunctional as a result of excessive fertilization by farm owners seeking high profits. The presence of water bodies had positive relationships with V-ArOH, TPb and TCd (Table 2). Two possible explanations can be inferred from these results. First, industrial factories are usually located near water bodies, due to the low cost of using water for production. Therefore, water bodies may be, to some extent, spatially correlated with the number of industrial factories discharging hazardous chemicals. Second, a number of aquaculture ponds were distributed across Qiantang basin. Receiving great quantities of waste from domestic, industrial and agricultural activities, these ponds may become pollution sources for Qiantang River through surface runoff and underground water exchanges. All these results suggest that the spatially varying LULC patterns and land use practices should be taken into consideration. Without taking this information into account, results from the simple statistics may be misleading.
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Table 2 Spatial regression models for hazardous chemicals in 1996 and 2003 at different scales (n = 41).a Parameter V-ArOH
TCN THg
TPb
TCd
Cr6+
Petroleum
Year 1996 full 1996 buffer 2003 fullc 2003 bufferd 1996 fulld 1996 bufferc 2003 fulld 2003 bufferd 1996 fulld 1996 bufferd 2003 fullc 2003 bufferd 1996 fulld 1996 bufferd 2003 fullc 2003 bufferd 1996 full 1996 bufferd 2003 fullc 2003 bufferd 1996 fulld 1996 bufferd 2003 fullc 2003 bufferd
R2
Spatial regression models b
NS NSb 0.014 × farmland% + 0.003 × build% − 0.002 × distance − 0.029 × WY − 0.001 0.005 × water% − 0.007 × elevation − 0.003 × distance + 0.005 (lambda = 0.068) NSb 0.023 × farmland% − 0.148 × water% − 0.241 × elevation + 0.228 × GDP + 0.48 (lambda = 0.704) 0.367 × GDP − 0.278 × elevation + 0.657 × WY + 0.31 0.208 × farmland% − 0.174 × water% + 0.083 (lambda = 0.167) 0.277 × forest% + 0.065 × build% + 0.054 × distance − 0.031 (lambda = 0.071) 0.916 × build% − 0.395 × elevation + 0.612 × GDP + 0.156 × distance − 0.294 (lambda = 0.016) 0.121 × build% + 0.07 × distance + 0.201 (lambda = 0.209) 0.135 × forest% + 0.248 × water% − 0.14 × elevation + 0.174 × distance + 0.536 × WY − 0.081 0.477 × forest% + 0.427 × GDP + 0.537 × POP + 0.207 × distance + 0.174 (lambda = 0.565) 0.884 × build% − 0.34 × elevation + 0.77 × GDP + 0.435 × distance − 0.239 (lambda = 0.454) 0.257 × build% + 0.3 × GDP + 0.321 (lambda = 0.505) 0.092 × build% − 0.237 × elevation + 0.033 × distance + 0.248 × WY + 0.206 0.096 × farmland% − 0.201 × elevation + 0.104 × water% + 0.082 (lambda = 0.203) NSb 0.179 × GDP − 0.051 × elevation + 0.326 × distance + 0.339 (lambda = 0.015) 0.537 × GDP − 0.138 × elevation + 0.217 × distance + 0.227 × WY + 0.242 −0.156 × elevation − 0.291 × distance + 0.293 (lambda = 0.328) 0.083 × build% − 0.212 × slope mean − 0.192 × water% − 0.152 × distance + 0.255 (lambda = 0.325) 0.158 × build% − 0.185 × distance − 0.241 × slope mean + 0.279 (lambda = 0.325) 1.275 × GDP − 0.51 × slope mean − 1.195 × POP + 0.797 × WY + 0.094 0.477 × GDP − 0.515 × POP + 0.179 (lambda = 0.808)
.58** .51** .65** .55** .56** .61** .73** .64** .61** .59** .57** .53** .65** .61** .61** .53** .44** .57** .74** .65** .70**
a Abbreviation: full sub-basin scale (full), 500 m riparian buffer scale (buffer), percentage of build-up (build%), percentage of water (water%), percentage of forest (forest%), percentage of farmland (farmland%), population density (POP), gross domestic product (GDP), mean elevation (elevation), deviation of slope gradient (slope std), average slope (slope mean), distance to river source (distance). b NS: No significant relationships were identified by spatial regression. c Spatial lag models; WY = weighted mean of water quality for adjacent stations. d Spatial error models. ** p < 0.01.
3.1.3.2. Management. The results of multiple comparisons are shown in Table 3. Urban areas exhibited more obvious Cd pollution than rural areas. Urban areas also experienced more serious Hg pollution than suburban areas. These results indicated that less urbanized or industrialized rural/suburban areas presented less hazardous chemical pollution than their urban counterparts. Contrarily, petroleum concentrations in urban and suburban areas were lower than those in rural area. Similarly, rural areas were characterized by higher Cr6+ values compared to suburban areas. Such discovery was concurrent with negative relationships between POP and petroleum and Cr6+ in Table 2. Evaluations for local government officials are mainly associated with economic growth. Industry is a major sector of the labor market and also a major contributor to local economic development. Officials often ignore the environmental consequences of industrial activities in order to pursue more economic growth. Such a system thus indirectly results in failure to control hazardous chemicals in rural areas.
The significant positive relationships suggest that increases in GDP and population density are good predictors of more petroleum and heavy metal concentrations over time (Table 4). Conversely, a lower percentage of riparian forests usually correlates with higher petroleum and TPb concentrations (Table 4). This may be linked to the ecological functions of forest ecosystems, as forests can filter pollutants and thus maintain river water quality (Ngoye and Machiwa, 2004; Tu, 2011). Changes of water area were incorporated in the regression models for TCN and THg (Table 4), indicating that increases in water bodies can result in more TCN and THg. In China, economic crop production efficiency is much higher than that of food crops. For example, economic benefits of aquaculture ponds were about twice as high as those of arable land. Since it was a clear comparative disadvantage, some farmers were reluctant to grow grain and a lot of aquaculture ponds were constructed across the basin. In order to pursue economic gains, feeds and pesticides were intensely abused, converting these ponds into pollution sources.
3.2. Spatial dynamics of hazardous chemicals associated with determinant changes between 1996 and 2003
3.3. Incorporating spatial autocorrelation into analysis
Dynamics of hazardous chemicals associated with the changes of determinants between 1996 and 2003 are displayed in Table 4. Anthropological determinants were the contributing factors, since natural factors (topology and distance to river source) remained constant over this short period. Changes of LULC correlated significantly with changes of socio-economic indicators in most cases (Su et al., 2011c). Therefore, the number of anthropological variables that were incorporated in the regression models was generally below three (Table 4). For example, population growth and economic development spur settlement sprawl at the cost of farmland (Su et al., 2011c). As a result, the changes of the four indicators (POP, GDP, Build-up%, and Farmland%) did not always simultaneously act as independent variables in one regression model.
Spatial lag regression and spatial error regression were employed to interpret the spatial determinants of river hazardous chemicals. Spatial lag models were suited for most hazardous chemical variables at both scales. This suggests that river hazardous chemical patterns depended both on the neighboring patterns and on a set of local independent parameters. To approve this statement, we further apply the Global Moran’s I index (Moran, 1948) to characterize the spatial autocorrelation of hazardous chemical patterns. As shown in Table 5, most hazardous chemical variables presented moderate spatial autocorrelation. This should be attributed to the water mixing and exchanges motivated by river flows. If the spatial autocorrelation is not incorporated into analysis, the covariates effects will be exaggerated and generate
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Table 3 Bonferroni Post Hoc multiple comparisons of water quality variables values among different development zones.a Parameters
V-ArOH TCN THg TPb TCd Cr6+ Petroleum a *
Urban Suburban
Urban Rural
Suburban Rural
Mean difference
Sig.
Mean difference
Sig.
Mean difference
Sig.
−.003 .000 .00001* .002 .0004* .000 .011
.620 1.000 .030 .859 .028 1.000 .786
.001 −.000 .00001 .003 .0005* −.001 −.089*
.867 1.000 .136 .643 .002 .127 .050
.004 −.000 .00000 .001 .0001 −.002* −.096*
.216 1.000 1.000 1.000 .980 .034 .012
The number of samples is 1056 for urban zone, 1536 for suburban zone, and 1344 for rural zone. The mean difference is significant at p < 0.05 level.
Table 4 Determinants of hazardous chemicals dynamics between 1996 and 2003 at different scales (n = 41).a R2
Parameter
Scale
Spatial regression models
Changes of V-ArOH
Full Buffer Fullc Buffer Full Bufferc Fullc Bufferc Full Buffer Fulld Bufferc Fullc Bufferc
NSb NSb 0.25 × water% c + 0.221 × farmland% c − 0.125 × WY − 0.433 NSb NSb 0.011 × build% c + 0.193 × water% c + 0.306 × WY + 0.175 1.364 × GDP c − 5.096 × forest% c + 0.624 × WY + 6.517 0.462 × build% c − 5.336 × forest% c + 0.607 × WY + 5.047 NSd NSd 0.088 × build% c + 0.021 (lambda = 0.193) 0.147 × farmland% c + 0.093 × build% c + 0.187 × WY − 0.028 0.044 × GDP c + 0.282 × WY − 0.14 0.073 × POP c − 0.063 × forest% c + 0.254 × WY − 0.22
Changes of TCN Changes of THg Changes of TPb Changes of TCd Changes of Cr6+ Changes of petroleum
.41**
.39** .45** .47**
.33** .42** .37** .44**
a Abbreviation: full sub-basin scale (full), 500 m riparian buffer scale (buffer), change of percentage of build-up (build% c), change of percentage of water (water% c), change of percentage of forest (forest% c), change of percentage of farmland (farmland% c), change of population density (POP c), change of gross domestic product (GDP c). b NS: no significant relationships were identified by spatial regression. c Spatial lag models; WY = weighted mean of water quality for adjacent stations. d Spatial error models. ** p < 0.01.
incorrect estimations as well as misleading determinants. Spatial error also demonstrated relatively strong predictive ability for several hazardous chemical variables. This indicated that the unobserved factors, such as rainfall, temperature, soil texture and hydrological processes, should follow a spatial pattern for these variables. These spatially auto-correlated factors can influence hazardous chemical dynamics through various physical and biogeochemical processes. All these results demonstrate the need to incorporate spatial analysis when interpreting the spatial determinants of hazardous chemicals. 3.4. Scale effects on hazardous chemical dynamics over time No widely accepted conclusion has been reached on which scale should be selected when analyzing determinants of hazardous chemicals. Regarding the spatial patterns of hazardous chemicals,
our results signified that variations of V-ArOH and heavy metals were more correctly predicted at the full sub-basin scale. Contrarily, spatial determinants of petroleum patterns were better predicted at the 500 m buffer scale. Regarding the spatial dynamics of hazardous chemicals, the coefficient of determination (R2 ) is generally higher at the buffer scale than at the sub-basin scale, indicating that the influence of spatial determinants operated more significantly at the buffer scale. Patterns of hazardous chemicals are associated with the temporal dimension over which they are measured. Hydrologic and biogeochemical processes occur at different temporal scales (Chang, 2008; Zhou et al., 2012). Moreover, changes in LULC and socio-economics were affected by the measurement interval. Temporal scale should therefore be another important influential factor taken into account. The significant predictors identified for hazardous chemicals in 1996 were distinct from those in 2003.
Table 5 Moran’s I values for hazardous chemical variables between 1996 and 2003 in the Qiantang River.a , .b
V-ArOH TCN THg TPb TCd Cr6+ Petroleum a
1996
1997
1998
1999
2000
2001
2002
2003
2004
−.072 .053 .546 .190 .353 .045 .005
.068 .247 .420 .051 .317 −.077 . 047
−.139 .119 .412 .271 .306 .108 .027
−.047 .012 .426 .320 .473 .112 .283
−.111 .170 .307 .605 .457 .042 .226
−.067 .029 .147 .435 .482 −.006 .275
.025 .061 .386 .451 .581 .063 .315
−.022 .074 .300 .565 .546 .095 .351
.051 .086 .026 .322 .132 .114 .622
All the Moran’s I statistics were calculated at a 95% confidence interval. Range of Moran’s I values concentrates in the interval [−1,1]. 1 and −1 respectively denote the strongest positive spatial autocorrelation and strongest negative spatial autocorrelation. b
S. Su et al. / Ecological Indicators 24 (2013) 375–381
Specifically, no significant spatial determinants were identified for V-ArOH and Cr6+ (1996) and TCN. It is possible that the small percentage and spatially uneven distribution may have led to the ineffective interpretation of their responses to these factors. These findings illustrate the importance of considering multiple spatiotemporal scale approaches in order to advance the understanding of the determinants of hazardous chemicals. 3.5. Limitations Despite the insights into river hazardous chemical dynamics from this study, some methodological limitations need to be addressed. First, annual mean values of hazardous chemical variables were used in spatial regression, and seasonal variations were therefore not addressed. Further study related to these variations will be conducted. Second, due to the low resolution of TM images, only four dominant LULC types were identified. Some high-resolution and multi-temporal satellite images can be used in future research. Third, the population and GDP data was at the administrative scale instead of the buffer/sub-basin scale. We simply calculated the corresponding value for each buffer/sub-basin according to its area. This process should generate some level of error due to the mismatch of scales. Fourth, we only characterize the determinants at two scales. More detailed comparison should be made among different spatiotemporal scales. Finally, spatial regression models for the dynamics of hazardous chemicals exhibited relatively limited predictive ability, with R2 no greater than 0.50 (Table 4). This implies that other factors not included in the model made important contributions to the total variations of hazardous chemical dynamics. Climate, geology, soil and other socio-economic indicators may account for the unexplained effects. Further research, when relevant data is available, will be carried out to analyze the impact of these factors. 4. Conclusions This study investigated spatial determinants of seven hazardous chemicals in Qiantang River between 1996 and 2003. Results showed that primary predictors and the predictive ability of spatial regression differed with variables and scales. Topology, distance to river source, LULC, population density, and GDP were found to be primary predictors for the pattern of certain hazardous chemical variables in 1996 and 2003. Generally, natural factors were included in regression models for 1996, while population and GDP were the main influential factors in 2003. As for the determinants of dynamics, LULC types were good predictors for changes in cyanide and heavy metals, while GDP and population density contributed to petroleum dynamics between 1996 and 2003. Results of this study demonstrated that spatial regression is a promising tool for interpreting spatial determinants of hazardous chemicals in rivers. We also advocate applying multi-scale approaches to uncover the dynamics of hazardous chemicals. Since it is applicable to other river basins, this approach provides an operational basis for generating indicators of hazardous chemical pollution. Acknowledgements We thank Editor-in-Chief Felix Müller and two reviewers for providing constructive comments and suggestions. Professor Stephen DeGloria at Cornell University is greatly appreciated for revising the original manuscript. Part financial sources include
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