Geographically weighted regression to measure spatial variations in correlations between water pollution versus land use in a coastal watershed
Introduction
River water pollution in watersheds is strongly related to increasing anthropological influences, such as urbanization, agriculture, industry and sewage (Li et al., 2009, Xu et al., 2009, Ye et al., 2009). These anthropological influences are part of the larger process of watershed land use and land cover change that can affect the water pollution of rivers, lakes, and downstream estuarine and coastal waters (Baker, 2003, Roberts and Prince, 2010). Watershed land use impacts water pollution through non-point sources, which are major contributors of contaminants to the catchment-coast continuum (Swaney et al., 2012, Huang et al., 2013b). Therefore, exploring the linkages between land use and water pollution is commonly recognized as an imperative step to forecast river water pollution in watersheds and to control land-based pollution in coastal bays.
Many studies address the general relationships between land use and water pollution. Generally, built-up land and agricultural land have significant positive correlations with water pollution, which are associated with point or non-point source pollution (Johnson et al., 1997, Sliva and Williams, 2001, Fedorko et al., 2005, Mehaffey et al., 2005, Stutter et al., 2007, Tu et al., 2007, Bahar et al., 2008, Tran et al., 2010, Pratt and Chang, 2012, Yang, 2012). Woodland is significantly negatively correlated with nutrients, due to the general understanding that forests can absorb nutrients (Osborne and Kovacic, 1993, Novotny, 2002, Galbraith and Burns, 2007, Bahar et al., 2008, Lopez et al., 2008).
However, the relationships between land use and water pollution can be inconsistent across time and space. Inconsistency is well documented in terms of seasonal or inter-annual variations (Sliva and Williams, 2001, Kaushal et al., 2008, Lee et al., 2009, Huang et al., 2013a), spatial scale effects (e.g. buffer versus entire watersheds) (Sliva and Williams, 2001, Uuemaa et al., 2005, Guo et al., 2010, Tran et al., 2010, Pratt and Chang, 2012) and watershed characteristics represented by various dominant land uses such as woodland, built-up, and mining (Mehaffey et al., 2005, Xiao and Ji, 2007, Bahar et al., 2008). The methods used in most of the above-mentioned studies are conventional global statistical methods, such as Pearson correlation analysis (Tong and Chen, 2002, Galbraith and Burns, 2007, Bahar et al., 2008, Lee et al., 2009, Sun et al., 2011b) and multiple regression (Sliva and Williams, 2001, Fedorko et al., 2005, Huang et al., 2013a, Yang, 2012). These global statistics are commonly used to analyze the overall association for the entire study area, and may hide some local relationships, especially among watersheds that are dominated by different uses, such as urban, forest or agriculture (Tu and Xia, 2008, Tu, 2011). Global statistics are not designed to explore spatial variations in relationships between land use and water pollution.
Some researchers have applied a statistical method named geographically weighted regression (GWR) to examine the spatially varying relationships between land use and water pollution (Brunsdon et al., 1998, Tu, 2011, Pratt and Chang, 2012). However, few studies have examined spatial variations in relationships between land use and water pollution in the watersheds of China. Such examinations are critical for China's watershed management, due to the fact that freshwater pollution is a prime concern, especially in the relatively developed regions such as the Eastern coastal areas of China (Huang et al., 2013a). More attempts need to be made to investigate relationships in these areas of China between land use and water pollution in the coastal watersheds with intensive human activities, great spatial variability of land use, and subsequent water pollution.
Our previous study used global multiple linear regression to show that land use, especially the percentage of built-up land, can be one of the most important predictors of water pollution in the Jiulong River Watershed (JRW), which is a typical medium-sized subtropical coastal watershed in China (Huang et al., 2013a). However, a better understanding concerning how land use impacts water pollution is critical for developing watershed management practices in such a coastal watershed. The objectives of this study are to explore the spatial variations in the relationships between land use and water pollution and to develop potential insights for watershed management in the JRW.
Section snippets
Study area
The JRW covers about 14,700 sq km in the eastern coastal area of China (from 116°46′55″E to 118°02′17″E, and from 24°23′53″ to 25°53′38″N) and consists mainly of eight counties/districts: Zhangzhou, Xinlou, Zhangping, Hua’an, Changtai, Pinghe, Longhai and Nangjing (Fig. 1). The JRW is situated in a subtropical zone with a monsoon climate: annual average temperature is 19–21 °C, and annual precipitation averages 1400–1800 mm, of which 70% occurs between April and September. The watershed
Water pollutants
Surface water samples of base flow were collected from 21 sampling sites at twelve time points. Each time point was during a particular season of a particular year. There were four years from 2010 to 2013, and three seasons per year. The flood season is in August, the dry season is in November, and the transition season is in March. The samples were kept at 4 °C and transported to the laboratory for analysis. Seven pollutants were analyzed following standard methods (SEPAC, 2002) and completed
Spatial variation in land use classes
Fig. 2 shows the land use composition for each of the 21 sub-watersheds along with the three sub-watershed groups. The group of agricultural sub-watersheds with IDs from A1 to A12 have more than 10% cropland; A1, A8, A11 and A12 have over 20% cropland. The sub-watersheds with IDs U1, U2, and U3 are in the urban group, and have 27%, 9% and 14% Built-up. The natural group of sub-watersheds with IDs from N1 to N6 have over 80% forest.
Global correlation between land use and water pollution
Table 1 shows the Pearson correlations between water pollution
Relationships between land use types and water pollution indicators
The relationships between water pollutants and land use revealed multiple influences on water pollution sources. The percentage of cropland was significantly positively correlated with NO3−–N in this study (Table 1), which is understandable because the NO3−–N is likely to come from fertilizers used on the agricultural land (Cao et al., 2006, Shen et al., 2011). Others have reported that salinity tends to increase in the non-forest areas, especially the urban areas (de Souza et al., 2013). We
Conclusions
Globally, the association between water pollution was stronger with built-up percent than with cropland percent or forest percent. Cropland percent had a weak association, forest percent had a negative association, while built-up percent had a positive association with water pollution. Local R2 values are all less 0.4 for cropland percent. All the sub-watersheds with a high built-up percent have high pollution concentrations; and an increase in built-up percent is associated with a noticeable
Acknowledgments
This study was supported by the Natural National Science Foundation of China (Grant No. 40901100 and Grant No. 41471154) and the National Science and Technology Support Program (Grant No. 2013BAC06B01). Anonymous reviewers supplied constructive feedback that helped to improve this paper.
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