Spatial Variation and Influence Factor Analysis of Soil Heavy Metal As Based on Geodetector

Stochastic Environmental Research and Risk Assessment

https://doi.org/10.1007/s00477-021-01976-4 (0123456789().,-volV)(0123456789().,- volV)

ORIGINAL PAPER

Spatial variation and influence factor analysis of soil heavy metal As based on geoDetector

1 1 1 1 Xvlu Wang • Yingjun Sun • Liping Zhang • Yuang Mei

Accepted: 13 January 2021 Ó The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021

Abstract Heavy metals in soil are closely related to our production and life. As heavy metal has relatively high toxicity, it is necessary to clarify its existence. This study took Liaocheng City as the study area. Sampling was carried out by the 10 km 9 10 km grid center method and the concentration of heavy metal arsenic (As) at the sampling points was extracted. Based on the national secondary standard and Shandong background value, the single factor index was used to evaluate the status of heavy metal As in the soil of Liaocheng City, and eight factors related to heavy metal As were selected. This study used geographic detectors to identify spatial relationships among the factors. By the statistical description of the heavy metal As in the soil of Liaocheng City and the evaluation of the single factor index, we found that there was light pollution in most areas of Liaocheng City. By analyzing the results of the GeoDetector, it was found that the soil organic matter, soil subcategory, distance to river, and GDP were the dominant factors that affected the concentration and spatial variation of heavy metal As. The interaction results showed that the interaction between GDP and other inﬂuencing factors signiﬁcantly increased the explanatory power of As.

Keywords Soil heavy metal As Á GeoDetector Á Inﬂuence factors Á Spatial distribution

1 Introduction was contaminated by As, it took about 100 years to remove it in its natural state (Allaway 1968). As pollution affected The National Soil Pollution Survey Bulletin published by soil microorganisms, reduced soil enzyme activity, affected the Ministry of Environmental Protection and the Ministry crop growth, and ultimately harmed human health (Zheng of Land and Resources in 2014 pointed out that the state of 2007). the soil environment in the country was not optimistic The method of spatial analysis was an important method (National Survey Communique on Soil Pollution Status to study heavy metal pollution. At present, spatial auto- 2014). The area where a smelter exists with agricultural correlation analysis, multiple linear regression, geographi- production processes would cause soil pollution, and heavy cally weighted regression, principal component analysis, metals in agricultural products would exceed the standard spatial interpolation, and spectral decomposition were in different degree (Du et al. 2020; Zhuang 2015). Soil mainly used (Hu et al. 2017). Among them, Yang et al. heavy metal pollution was an important issue in China, (2020a, b) used categorical regression analysis to identify because it would not only cause damage to the soil, but also the sources of soil heavy metal pollution. Li et al. (2019) be detrimental to plant growth and people’s health (Zhang used the modified receptor model as source analysis tool. 2018). As was one of the eight heavy metals, once the soil Wang et al. (2020a, b, c) used the principal component analysis/absolute principal component scores (PCA/APCS) receptor model to detect heavy metal sources in soil. These & Yingjun Sun [email protected] methods considered the location and relevance of the data. They analyzed the spatial characteristics of heavy metals. Xvlu Wang [email protected] However, there was a lack of research on the influencing factors of heavy metals and the interaction of various 1 College of Surveying and Mapping Geo-Informatics, factors. The geographical detectors could be used to detect Shandong Jianzhu University, Jinan, Shandong, China 123 Stochastic Environmental Research and Risk Assessment the relationship between heavy metals and influencing sampling and collecting 0–20 cm surface soil mixture to factors. obtain samples. Samples were sent back to the laboratory The geographical detectors are a set of statistical for drying treatment. After the soil sample was dissolved in methods developed by researcher Jinfeng Wang to detect acid, the content of lead (Pb), copper (Cu), and chromium spatial differentiation and reveal the driving forces of the (Cr) in the sample was measured by inductively coupled differentitation (Wang and Xv 2017), which has been plasma mass spectrometry (ICP-MS), and the content of As applied in many fields. In the study of soil heavy metals, and mercury (Hg) was measured by atomic fluorescence geodetectors were mainly used to detect the influencing spectroscopy, in which the meter is measured (Zhang factors and pollution sources of heavy metals in farmland 2018). The spatial distribution of sampling points in the instead of typical cities (Wang et al. 2020a, b, c; Li et al. study area is shown in Fig. 1. 2017). Therefore, this study selected Liaocheng as the Referring to the research of Zhang et al. (2019) and Li research area and used geographic detectors to study the et al. (2017), the influencing factors selected in this study influencing factors of As. In addition, the geographical were soil organic matter (OrgC), soil pH, soil subcategory, detectors were widely used in vegetation (Guo et al. 2020; Normalized Difference Vegetation Index (NDVI), distance Duan and Tan 2020; Meng et al. 2020), soil pollution from roads, distance to rivers, elevation, and Gross (Jiang et al. 2020), hydrology (Yang et al. 2020a, b; Erfu Domestic Product (GDP). Among them, soil pH, soil and Wang 2020), humanities (Wang et al. 2020a, b, c;Wu organic matter, and soil subcategory were point data, which et al. 2020), and other fields. had the same source and one-to-one correspondence with Therefore, it was necessary to understand the concen- soil sampling points. NDVI, river data, elevation data, tration and spatial variation of heavy metal As in soil. The 2015 GDP data, and the 2015–2016 Shandong Statistical influencing factors of soil heavy metal As was analyzed Yearbook were all derived from Resource Environmental and an attribution analysis was conducted. In this paper, the Science and Data Center, Chinese Academy of Sciences GeoDetector was used to study the correlation between (http://www.resdc.cn/). The statistical yearbook data and heavy metal As and selected factors in Liaocheng City, and 2015 GDP data were used to calculate the initial 2013 its influence on the concentration and spatial distribution of Liaocheng GDP data. The Euclidean distance was calcu- heavy metal As was analyzed. This study detected the lated using the original road and water flow data sets, and dominant factors affecting soil heavy metal pollution and analyzed the source of heavy metals, and it laid the foundation for further soil heavy metal pollution control in this area.

2 Materials and methods

2.1 Study area

Liaocheng is located in the western part of Shandong Province, which is part of the Yellow Huaihai Plain, located at 35° 470–37° 020 N, 115° 160–116° 320 E (Cao et al. 2010), with a total area of 8415 km2. Belonging to the temperate monsoon climate zone, Liaocheng has mild climate and abundant water resources (Zhang et al. 2007). Liaocheng had a total resident population of 6.04 million, and the annual gross product was 266.362 billion yuan, an increase of 8.8% over the previous year (http://www.liao cheng.gov.cn/). The study area includes Guan County, Shen County, Yanggu, Dong’e, Chiping District, Gaotang, Dongchangfu District, and Linqing City.

2.2 Materials

Soil samples were obtained, based on 10 km 9 10 km grid center, using GPS positioning (Wei 2018) for point Fig. 1 Spatial distribution of sampling sites in the study area 123 Stochastic Environmental Research and Risk Assessment the corresponding values were extracted through sampling SSW q ¼ 1 À ð2Þ points to obtain the distance to the road and the distance to SST the water flow. XL 2 SSW ¼ Nhrh ð3Þ 2.3 Methods h¼1 SST ¼ Nr2 ð4Þ 2.3.1 Evaluation of heavy metal where h = 1,…, L is the stratification of variable Y or The single factor pollution index is one of the most used factor X, that is, classification or partition; Nh and N are the methods to evaluate heavy metal pollution in China (Yang number of units in the layer and the whole area, respec- et al. 2016). This study adopted the single factor pollution tively; SSW and SST are the variances within the layer, index method to evaluate the heavy metal pollution of the sum and the total variance of the whole area, respectively. soil in the study area. The calculation formula is as follows: Interactions occur between different factors. To evaluate whether the explanatory power of the dependent variable P ¼ C=S ð1Þ increases when the influencing factors work together, it is where P is the single factor pollution index of a certain necessary to select the dominant factors. The interaction heavy metal, C is the actual measured concentration of a detection can identify interactions between different certain heavy metal (mg/kg), and S is the soil environ- influencing factors (Chen et al. 2019), as determined by the mental quality standard (this article used the soil back- following table. ground value of Shandong Province), which divides the Achievement of the geographic detector used the pollution level according to single factor pollution index GeoDetector software (http://geodetector.cn/). The surface (see Table 1). In addition, the potential ecological risk data needed to be separated and decentralized for the cal- index (Hakanson 1980) and the geological accumulation culation due to the different spatial dimensions of the index (Muller 1969) were used as supplementary surface data and heavy metal data. ArcGIS software was validation. used to extract the values of surface data, such as NDVI, elevation data, and GDP of the sampling points to calculate 2.3.2 Geographical detector the Euclidean distance between the sampling points and the roads and rivers, and to match distance data with the heavy The geographical detector (GeoDetector) is mainly used to metal data of the sampling points. analyze the correlation of heavy metal As with the eight selected impact factors and multiple impact factor interactions. Mainly because the GeoDetector q values have a 3 Results and discussion clear physical meaning with no assumption of linearity, objectively detecting that the dependent variable explains 3.1 Statistical analysis 100 9 q% (Wang et al. 2010, 2016; Wang et al. 2019). This study applied factor detectors and interaction detec- The statistics of soil heavy metal As in the study are shown tors in GeoDetectors to analyze the correlations more in Table 2. The mean value of As did not exceed the comprehensively between heavy metals and selected national secondary standard. However, it has been shown impact factors (Hu et al. 2011). The formula for the value that, except for black and terracotta soils, the As content of of q is: crop stems and leaves in China’s dryland soils could exceed the national food hygiene standard when the As content was 25–40 mg/kg (Zheng 2007). The As content of the soil in the study area was 1.9–42 mg/kg, which could cause potential health hazards to animals and plants as well Table 1 Single factor pollution index rating criteria as human being. Hierarchy Pollution index P Pollution of pollution In this paper, Kriging Interpolation method was used to 1PB 1 Uncontaminated estimate the spatial distribution of heavy metal As (Fig. 2). 21\ P B 2 Slightly contaminated The areas with high heavy metal As concentration were 32\ P B 3 Lightly contaminated mainly distributed in Dongchangfu and Dong’e. According to the land use map in 2013 (Fig. 3), large number of urban 43\ P B 5 Moderately contaminated lands were distributed in Dongchangfu, and the pollutants 5P[ 5 Extremely contaminated discharged from urban production and life accumulate in the soil, which caused As to accumulate. The As

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Table 2 Statistical description Statistical description Kriging interpolation parameters and Kriging parameters of As Mean value (mg/kg) 10.75 Transformation type Log Minimal value (mg/kg) 1.9 Remove trend Once

Maximum value (mg/kg) 42 Nugget C0 0.0099

Background values for Shandong province 9.3 Sill C0 ? C 0.0223 (mg/kg)

National level II standards 30 Nugget effect C0/(C0 ? C) 44.4% (mg/kg) Root mean standard error 1.29

Fig. 2 Map of land use agglomeration area in Dong’e County was distributed along the river canal. It might be that ﬂow of water diffused pollutants into the coastal soil. Second, there are heavy Fig. 3 Different pollution Index (From left to right: a single factor metal As accumulations in Shen County and Linqing City. pollution; b Potential Ecological Pollution; c Geological In addition to the impact of urban land and factories, there accumulation) were large areas of farmland where heavy metal As accumulated. The accumulation of As may be caused by the maximum value was at light pollution, indicating that a few inﬂuence of agriculture and parent material. spots in Liaocheng were lightly polluted. Comparing the calculation results of the single factor 3.2 Analysis of pollution index pollution index with the results of the above two pollution indexes, the calculated results of the three pollution This paper used single factor index, potential ecological indexes were almost the same. According to the compar- risk index, and geological accumulation index to evaluate ison in Fig. 4, the single factor index was more sensitive to As in the soil of Liaocheng City. The results are shown in the slight pollution; therefore, the single factor pollution Table 3 and Fig. 4. The average value of the three pollution index was used to evaluate the pollution situation in the indexes did not reach light pollution. However, the study area. The results are shown in Fig. 4a. Combined

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Table 3 Statistical analysis of Model type Range Mean values Indicators of light pollution pollution indices Single factor index 0.6–1.33 0.96 [ 1 Potential ecological risk index 2.04–45.16 11.56 ^ 40 (low risk area)

Geological accumulation index (Igeo) - 2.88–1.59 - 0.4 0 ^ Igeo ^ 1

Fig. 4 Different pollution Index (From left to right: a single factor pollution; b Potential Ecological Pollution; c Geological accumulation) with the calculation results of the three pollution indexes, the quality of the classification. The final optimal classifi- the results showed that a few areas of Liaocheng City were cation results were shown in Table 4. slightly polluted by heavy metal As, and the polluted area were mainly concentrated in Dongchangfu District, Lin- 3.4 Factor detector qing County, Chiping County, Dong’e County, and Shen County. Considering the analysis of heavy metal statistical The spatial distribution of each impact factor selected in description, the slight pollution of heavy metal As might be this paper after classification was shown in the caused by domestic production emissions, the use of pes- figure below. ticides and fertilizers, and soil parent material. The factor detector was used to identify the interpreta- tion ability of the eight influencing factors on heavy metal 3.3 Analysis of geoDetector As. The results are shown in Fig. 5. According to q value, the descending order were OrgC (0.129) [ soil subcate- Before performing the geodetection analysis, it was nec- gory (0.105) [ GDP (0.049) [ distance flow Distance essary to convert continuous data of influencing factors (0.043) [ Elevation (0.029) [ NDVI (0.021) [ pH into type data. Different classification methods and the (0.015) [ distance from road (0.010). In order to express number of classifications could affect the results of the relationship between As and influencing factors, the geodetection (Cao et al. 2013). After experimental com- Pearson correlation coefficient was used for calculation, parison, K-means was identified as the most appropriate and the result is shown in Fig. 6. method for this study, and the q value was used to measure

Table 4 Spatial distribution of inﬂuence factors OrgC Soil pH Elevation NDVI Distance from road Distance to river GDP

Optimal classiﬁcation 6 9 8 7 8 6 8

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Fig. 5 Factor explanatory power

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Fig. 5 continued

Fig. 6 Spatial distribution of heavy metal As

Fig. 7 Pearson correlation analysis

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Table 5 Interaction of the effect factors on heavy metal As OrgC Soil pH Soil subcategory Elevation NDVI Distance from road Distance to river GDP

OrgC 0129 Soil pH 0.158 0.015 Soil subcategory 0.219* 0.133* 0.105 Elevation 0.194 0.082 0.141 0.029 NDVI 0.155 0.100 0.147 0.090 0.021 Distance from road 0.145 0.046 0.131 0.072 0.061 0.010 Distance to river 0.172 0.070 0.142 0.079 0.079 0.075* 0.043 GDP 0.200 0.097 0.186* 0.136* 0.199* 0.073 0.115* 0.049 *Indicates factors that have the strongest interaction

Table 6 Factor interaction type Interactions Judgments

Nonlinear weakening q(X1 \ X2) \ Min(q(X1),q(X2)) Single factor nonlinear attenuation Min(q(X1),q(X2)) \ q(X1 \ X2) \ Max(q(X1),q(X2)) Double factor enhancement q(X1 \ X2) [ Max(q(X1,q(X2)) Independent q(X1 \ X2) = q(X1) ? q(X2) Nonlinear enhancement q(X1 \ X2) [ q(X1) ? q(X2)

So, OrgC and soil subcategory had the strongest ability 3.5 Interaction detector to explain heavy metal As in soil. According to the spatial distribution map of OrgC and soil subcategory in Fig. 7a The spatial distribution of heavy metal As in soil was not and c, it can be seen that the location of As aggregation caused by just one factor, but by a combination of multiple roughly coincided with the high OrgC content and the factors. In this paper, the interaction of various influencing fluvo-aquic soil. Organic matter might adsorb As with factors on heavy metal As was measured by the interaction negative ions in the soil, while damp soils were more of GeoDetectors. The results were shown in Table 5. viscous and tended to adsorb heavy metals, leading to As Judging from Table 6, except for the interaction type of accumulation. The distance to the river and GDP had a ‘‘soil subcategory \ OrgC’’, which was two-factor strong explanatory power for the heavy metal As in the enhancement, the interaction of other factors was non-lin- soil. The GDP level reflect the degree of industrialization ear enhancement. This indicated that the interaction of in this area to a certain extent. Industrial production would multiple factors was stronger than that of single factor. Soil emit pollutants and cause light pollution of heavy metal As subcategory, elevation, NDVI, and distance from the river in the soil. Rivers were known to be pollutant carriers, so interacted more strongly with GDP than with other factors, the distance from the river reflected the degree of pollutant and GDP could enhance the influence of natural factors on diffusion. Elevation, NDVI, and pH were also natural heavy metal As. This indicated that the spatial distribution factors; these three influencing factors had little explana- of heavy metal As in soil was not only influenced by tory power for the heavy metal As in the soil. Although the environmental factors and soil texture, but also by the distance from the road had the smallest explanatory power, degree of industrialization. it also reflected that the vehicle exhaust had a certain impact on the heavy metal As in the soil. According to the 3.6 Discussion above analysis, the heavy metal As in the soil of Liaocheng City was mainly affected by the parent material of the soil Through descriptive statistics, the As content was between and the geographical environment. In areas with more 1.9 and 42 mg/kg, which would cause potential harm to the developed urbanization, the soil was greatly disturbed by environment in the study area. From the evaluation of human being, causing light pollution. pollution index, As in some areas was slightly polluted. The slightly polluted places were mainly concentrated in Dongchangfu District, Linqing City, Chiping County, Dong’e County and Shen County.

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The factor detection of As and the impact factors shows While Linqing City and Shen County were dominated by that OrgC (q = 0.129) and soil subcategory (q = 0.105) had agriculture, there was improper use of pesticides and fer- the strongest explanatory power for soil heavy metal As. In tilizers, and high content of organic matter adsorbed heavy the spatial distribution, the location where As accumulated metal As, which maked the soil slightly polluted with had a higher content of OrgC, and the soil type was mainly heavy metal As. fluvo-aquic soil. The results indicated that the two natural Through interaction analysis, the spatial distribution of factors, OrgC and soil subcategory, were the main factors heavy metal As was found to be related to the interaction of affecting the As content and spatial distribution. In addi- various influencing factors. Among them, four influencing tion, The impact factor of GDP (q = 0.049) had a relatively factors including soil subcategory, elevation, NDVI, and strong explanatory power for soil heavy metal As, which distance to river had a stronger interaction with GDP than something come off that human activities also had a certain with other factors. GDP could enhance the explanatory impact on As. power of natural factors on soil heavy metal As, indicating Through the detection of the interaction between As and that the degree of industrialization affected the spatial the influence factors, it was shown that the interaction of distribution of heavy metal As in the soil. In conclusion, As influencing factors had stronger influence on As than a in the study area was mainly affected by OrgC and soil single factor. The interaction between soil subcategory and subcategory, but also affected by human activities. The OrgC had the greatest impact on As. In addition, natural research results could provide reference for the treatment factors (Soil subcategory, elevation, NDVI, and distance to of heavy metal As in Liaocheng to select key treatment rivers) interacted more strongly with GDP than with other areas and reduce the impact of related factors. factors. It was revealed that the content and spatial distri- In addition, this study had some limitations. Firstly, the bution of heavy metal As were controlled by a variety of human factors used in this paper were GDP and the dis- influencing factors. tance from road, without considering the influence of other The heavy metal As in the study area was mainly human factors such as industrial structure. There were affected by natural factors (OrgC and soil subcategory). In deficiencies in the selection of human factors. Secondly, areas with relatively high GDP, As was also affected by there were certain defects in the selection of natural factors. human activities. Through analyzing the influencing factors The selected factors had little influence on As except for of soil heavy metal pollution in the country, Qi et al. found OrgC and soil subcategory. In follow-up research, it was that As has a strong correlation with climate zone type and necessary to refine the selection of factors that had a greater soil type (Qi et al. 2019); Yang et al. found that soil parent impact on As. materials, soil types, land use types and industrial activities were the main influencing factors of heavy metals in Bei- Acknowledgements This work was supported by the National Natural Science Foundation of China (Nos. 41301509). jing (Yang et al. 2020a, b). Therefore, the main influence on As was natural factors, and it was also affected by Compliance with ethical standards human activities. Compared with other studies, the natural factors considered in this study were more comprehensive. Conflict of interest The authors declare no conflict of interest. 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