Spatial Variation and Influence Factor Analysis of Soil Heavy Metal As Based on Geodetector
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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 foun- dation 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 cli- mate 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.