Quantitative analysis of the factors influencing spatial distribution of soil heavy metals based on geographical detector
Graphical abstract
Introduction
With the rapid development of modern industry and agriculture, a large number of heavy metal elements now enter the soil through surface runoff and other ways that introduce serious soil pollution risk. Soil pollution with heavy metals not only reduces the quality of the soil (Yang et al., 2008) but also threatens the health of crops and the human body (Song et al., 2009). In addition, each heavy metal species in the soil has different mobility and availability, and water-soluble heavy metals pose significant potential risk to crops and the human body (Brümmer, 1986). Therefore, to reduce the environmental risk and ensure the health of the human body, attention should be given to both total heavy metals and water-soluble heavy metals.
Heavy metal in the soil results from a combination of natural and human factors (Huo et al., 2010; Li et al., 2017a; Ma et al., 2014; Nanos and Rodríguez Martín, 2012; Reimann and de Caritat, 2005). Natural factors include soil type (Fritsch et al., 2010; Xu et al., 2013), terrain (elevation and slope) (Ding et al., 2017; Wang et al., 2007), and distance from rivers (Ding et al., 2017). Human factors include land use (Kuusisto-Hjort and Hjort, 2013; Li et al., 2017b; Lv et al., 2013), traffic activities (Lough et al., 2005; Rozanski et al., 2017; Yang et al., 2015), inhabitants (Karim et al., 2015; Mamat et al., 2014), emission sources (Dragovic et al., 2014), and river water irrigation. Human factors probably contributed to the high nugget effect and strong spatial variability of heavy metal in the soil (Wang et al., 2014). To reduce soil pollution and environmental risk, four research questions need to be answered: (1) Among these influencing factors, which are responsible for the pollution? (2) What is the degree of influence of each of these factors? (3) Do these influencing factors operate independently or are they interconnected? (4) What is the geographical domain of the pollution risk? By answering these questions, we can address the factors primarily responsible for the pollution and the task of soil remediation in the geographic domain at risk (Tang et al., 2015). An effective method is necessary to answer these research questions.
Multivariate analysis methods, including correlation analysis, principle component analysis, and cluster analysis (Bourliva et al., 2017; Fu and Wei, 2013; Mikkonen et al., 2018; Wu et al., 2017; Yuan et al., 2014), and geostatistical techniques, including mapping the spatial distribution of heavy metal in the soil and hot-spot analysis (Lv et al., 2014; Xiao et al., 2013), are useful techniques to identify the sources of heavy metals in soils. Multivariate analysis is based only on the distribution characteristics of heavy metals elements and can be used to speculate on the possible influencing factors, but it cannot be combined to determine the spatial distribution characteristics of these influencing factors (Chandrasekaran et al., 2015; Karim et al., 2015; Rastmanesh et al., 2017; Zhou et al., 2016). The range and the ratio of nugget to sill (RNS) in variograms of geostatistical techniques could be used to evaluate these influencing factors. A short range and high value of the RNS reflects weak spatial dependence and anthropogenic origin. Conversely, a long range and low value of the RNS reflects strong spatial dependence and natural origin (Zawadzki and Fabijańczyk, 2012). Although distribution maps that show the probability of soil pollution based on geostatistical techniques can be used to identify zones with high pollution risk (Fabijańczyk et al., 2017), they cannot be used to quantitatively calculate the degree of influence of each of the specific factors (Li et al., 2015). In addition, they require a relatively high number of samples (at least 100 samples) for variogram calculation and modeling (Webster and Oliver, 1992). Correlation analysis could be used to determine the quantitative relationship between the spatial distribution of heavy metal in the soil and influencing factors using cross-correlograms (Zhao et al., 2010b), but it cannot obtain the interactions influencing of factors on the spatial distribution of heavy metal in the soil.
Given the shortcomings of these methods and the purpose of the study, we considered the geographical detector, developed by Wang et al. (2010), to be a useful tool to answer the four research questions that have raised. On the basis of the formation of the spatial distribution of pollution, the spatial variability of pollutants, and their concentration in particular soil horizons varied according to the influencing factors (Ma et al., 2014; Qishlaqi et al., 2010; Wang et al., 2014; Zawadzki and Fabijańczyk, 2012). Geographical detector theory is based primarily on the spatial variability of pollutants. If a particular influencing factor contributed to soil pollution, that pollutant would exhibit a spatial distribution and spatial variability similar to that of the influencing factor (Gao and Wang, 2019; Zhou et al., 2018). Similarly, if the spatial variability of a pollutant was larger in a specific area where an influencing factor existed, this area would be identified as the geographical domain of the pollution risk. Therefore, in theory, a geographical detector could answer the four research questions.
The research objects of this work were total concentration and water-soluble concentration of arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), plumbum (Pb), and zinc (Zn) in the soil in Huanjiang County, China. The research purposes were as follows: (1) to quantitatively calculate the degree of influence of each factor on the spatial distribution of total and water-soluble heavy metals in soil, (2) to identify the main factors influencing the spatial distribution of total and water-soluble heavy metals, (3) to analyze the interaction effect of factors on the spatial distribution of total and water-soluble heavy metals, and (4) to identify risk zones with a higher total or water-soluble concentration of heavy metals.
Section snippets
Study area
The study area is located in the middle and upper reaches of the Pearl River Basin, which is the fourth-largest watershed with the second-largest discharge volume in southern China. The study area, which is a small watershed, is about 273 km2, including southern Luoyang, western Changmei, all of Da'an, and northern Si'en (Fig. 1). The population of the study area is relatively dense, and the soils in this region have been polluted by heavy metals (Qiao et al., 2017).
The Digital Elevation Model
The effect of 10 factors on the spatial distribution of total heavy metals
The main influencing factors for the spatial distribution of As were the mine area and the river, and the secondary influencing factors were soil pH value, soil type, and DEM. For Cd, the main influencing factor was the mine area, and the secondary influencing factors were the river, DEM, and pH value. For Cr, the main influencing factors were pH value and the mine area, and the secondary influencing factors were DEM, land use type, and river. For Cu, the main influencing factor was the mine
Statistical contamination of total heavy metals
The statistical results of the total content of heavy metals are given in Table 4. Since there is no standard for water-soluble heavy metal concentration, this part analyzed only the contamination level of the total heavy metals in the soil. Contamination level of total heavy metals referred to standard values, natural background values, and geochemical background values. The standard values referred to the environmental quality standard for agricultural land (GB15618-2018), which is a national
Conclusion
With the rapid development of modern industry and agriculture, heavy metals in the soil have introduced the serious risk of soil pollution. As such, attention must be given to the concentration of both total heavy metals and water-soluble heavy metals. To reduce soil pollution and environmental risk, this study addressed four research questions (What is the influence degree? Which are the main factors? Do factors operate independently or are they interconnected? Which regions have high
Acknowledgments
This work was supported by the Beijing Postdoctoral Research Foundation.
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