Identifying the influencing factors controlling the spatial variation of heavy metals in suburban soil using spatial regression models

Science of the Total Environment 717 (2020) 137212

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Identifying the influencing factors controlling the spatial variation of heavy metals in suburban soil using spatial regression models Zihao Wu a, Yiyun Chen a,b,c, Yiran Han a, Tan Ke a, Yaolin Liu a,b,c,⁎ a b c

School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China Key Laboratory of Geographic Information System of Ministry of Education, Wuhan University, Wuhan 430079, China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Heavy metals in topsoil are spatially autocorrelated. • Spatial regression models effectively reveal the influencing factors of heavy metals. • The spatial variation of Pb depends on the distance from industrial enterprises. • The spatial variation of Cd depends on pH, SOM, and the topographic wetness index.

a r t i c l e

i n f o

Article history: Received 2 September 2019 Received in revised form 29 January 2020 Accepted 7 February 2020 Available online 08 February 2020 Editor: Paulo Pereira Keywords: Heavy metal contamination Spatial variation Spatial lag model Soil intrinsic factor Environmental variables

a b s t r a c t Determining the factors that control the spatial variation of heavy metals in suburban soil is important in identifying and preventing pollution sources. Soil intrinsic factors combined with environmental variables can effectively explain the spatial distribution of heavy metals. Compared with classical statistical methods, such as multiple linear regression (MLR) models, spatial regression models that can cope with the spatial dependence of heavy metals have greater potential in establishing an accurate relationship between influencing factors and heavy metals. This study aims to identify the factors that influence the spatial variation of lead (Pb) and cadmium (Cd) in 138 topsoil samples from the suburbs of Wuhan City, China, by using spatial regression models with MLR as the reference. Moran's I values reveal the spatial autocorrelation of Pb and Cd. The spatial lag model (SLM) outperforms MLR and has higher R2 and lower spatial dependence of residuals. The significant coefficients of the spatial lag term in SLMs indicate that the spatial variation of Pb and Cd depends on their surrounding observations. SLM results show that Pb content depends on the distance from the nearest industrial enterprises and suggest that industrial pollution is the main source of Pb. Cd content depends on pH, soil organic matter, and the topographic wetness index, indicating that intrinsic and topographical factors contribute to the spatial variation of Cd. Parent materials and application of phosphorus fertilizer are the most likely sources of Cd. The findings highlight the spatial autocorrelation of heavy metals and the effects of intrinsic factors and environmental variables on the spatial variation of such metals. Moreover, this study reveals the effectiveness of spatial regression models in identifying the influencing factors of heavy metals. © 2020 Elsevier B.V. All rights reserved.

⁎ Corresponding author at: School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China E-mail address: [email protected] (Y. Liu).

https://doi.org/10.1016/j.scitotenv.2020.137212 0048-9697/© 2020 Elsevier B.V. All rights reserved.

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Z. Wu et al. / Science of the Total Environment 717 (2020) 137212

1. Introduction Among all kinds of soil pollution, heavy metal contamination has elicited the most attention because of its adverse impacts on ecosystems and human health (Cheng et al., 2019; Khan et al., 2017; Pan et al., 2016). Suburban areas are urban–rural transition zones, which are a combination of construction lands, farmlands, and natural cover. Protecting suburban soil from heavy metal pollution is important in safeguarding food security and protecting the ecological environment (Shi et al., 2018; Yang et al., 2009). However, heavy metal concentrations accumulate rapidly in suburban soils because of long-term cultivation and rapid urbanization and industrialization (Ahmad et al., 2019; Hu et al., 2018; Li et al., 2017; Wu et al., 2018). Heavy metal pollution has been reported in the suburban soil of several cities worldwide, such as Los Angeles (Clarke et al., 2015), Cleveland, and Columbus in the United States (Sharma et al., 2015); Sheffield in England (Weber et al., 2019); Trieste in Italy (Giglio et al., 2017); Tokyo in Japan (Hossain et al., 2009); and Beijing (Hu et al., 2018) and Shanghai (Bi et al., 2018) in China. These reports suggest that the concentration of heavy metals in suburban areas exceeds local natural background values and thus poses immense health risks to residents. Therefore, investigating the spatial distribution of heavy metals in suburban soil and determining their influencing factors can help identify and prevent potential pollution sources, protect the ecological environment, and reduce health risks to residents. Complex soil formation processes and intense human activities affect the spatial variation of heavy metals. Common influencing factors primarily include natural conditions (e.g., climate, vegetation, parent materials, and topography) (Cao et al., 2017; Chen et al., 2012; Ding et al., 2017; Liu et al., 2018b; Shi et al., 2018), agriculture (e.g., sewage sludge, fertilizers and pesticides, and livestock manure) (Ahmad et al., 2019; Hu et al., 2018; Wang et al., 2015), and industrialization and urbanization (De Silva et al., 2016; Du et al., 2019; Li et al., 2017; Taghipour and Jalali, 2019; Wang et al., 2015; Yesilonis et al., 2008). These studies have indicated that the spatial variability of heavy metals in topsoil is generally associated with the variation in environmental factors. Aside from these extrinsic factors, soil intrinsic factors (e.g., pH and soil organic matter or SOM) and their effects on heavy metal accumulation in topsoil have been investigated. pH and SOM control the mobility and bioavailability of heavy metals and have a significant correlation with heavy metal contents in topsoil (Boechat et al., 2016; Caporale and Violante, 2016; Liu et al., 2018d; Xiao et al., 2019). However, whether pH and SOM affect the spatial variation of heavy metals or not remains unclear. Soil intrinsic factors combined with environmental variables may satisfactorily explain the spatial variation of heavy metals. Multivariate statistical analysis methods, such as principal component/factor/clustering analysis (Cai et al., 2015; Lu et al., 2012; Sun et al., 2013), one-way/multivariate analysis of variance (Navas and Machin, 2002; Xu et al., 2016; Yesilonis et al., 2008), multiple linear regression (MLR) (Cao et al., 2017; Maas et al., 2010), and ensemble learning approach (e.g., stochastic gradient boosting and random forest) (Wang et al., 2015), have been utilized to identify the factors that affect heavy metal accumulation in soil. However, heavy metals are spatially dependent, and this feature violates the classical statistical assumption that samples are independent of one other (Huo et al., 2010; Li et al., 2017). Therefore, the results of classical statistical methods are not always reliable. Spatial regression models, namely, spatial lag model (SLM) and spatial error model (SEM), are reliable in fields where dependent variables are spatially autocorrelated. These fields include economics (Baltagi et al., 2014; Liu et al., 2018a), public health (Chiang et al., 2010; GarciaVargas et al., 2014; Wei et al., 2016), urbanization (Chen et al., 2016; Tian, 2017), ecology (Cienciala et al., 2017; Miralha and Kim, 2018), and agricultural production (Dall'erba and Dominguez, 2016; Kunimitsu et al., 2016). Spatial regression models can accurately determine the relationship between target variables and influencing factors by delineating spatial dependence as a lag/error term (Huo et al., 2010; Li et al., 2017).

Therefore, spatial regression models have considerable potential in investigating the influencing factors of heavy metals. To address these knowledge gaps, this study aims to identify the factors that control the spatial variation of heavy metals by using spatial regression models (i.e., SLM and SEM). The objectives are as follows: (i) to explore the spatial distribution of Pb and Cd via ordinary kriging (OK); (ii) to evaluate the performance of SLM and SEM; and (iii) to determine the influencing factors of Pb and Cd. MLR is used as the reference of the spatial regression models. 2. Materials and methods 2.1 Study area. One of the six suburbs of northern Wuhan City, Huangpi District (30.87°N, 114.37°E), has a total area of 2256.70 km2 and an estimated population of 0.911 million at the end of 2014. The area is characterized by a typical subtropical humid monsoon, with an annual average temperature of 16.7 °C and annual average rainfall of 1200 mm (Pan and Chen, 2015). The northern part of Huangpi is the foot of Dabie Mountains, and its southern section is adjacent to Yangtze River. The area's terrain is high in the north and low in the south, with an elevation of 16–874 m. The main soil types in the region are fluvisols, anthrosols, luvisols, cambisols, and planosols. The region is dominated by farmlands, construction lands, forests, grasslands, and water areas. Huangpi is the most important agricultural base in Wuhan City, and its grain crop and vegetable outputs accounted for 36.27% and 31.16%, respectively, of the city's total output in 2014 (Pan and Chen, 2015). In recent years, heavy metal contaminations caused by long-term cultivation and rapid urbanization and industrialization have considerably affected the region's local soil ecological environment (Guo et al., 2017; Yu et al., 2014). 2.1. Soil sampling and chemical analysis A total of 138 samples (0–20 cm) were collected in 2014 to ensure full coverage of Huangpi District (Fig. 1). The land use types included paddy field, irrigated land, forest, and grassland, from which 28, 78, 28, and 10 samples were obtained, respectively. The samples were air-dried for 14 days in a laboratory (20 °C–25 °C) then gently crushed in a porcelain mortar and made to pass through a 2 mm stainless steel sieve. The total fractions of two key heavy metals, namely, Pb and Cd, were measured. Exactly 0.5 g of each prepared soil sample was treated with aqua regia digestion using HNO3 and HCl. The total concentrations of Pb and Cd in the digested solution were measured with a graphite furnace atomic absorption spectrophotometer (Xu et al., 2016). SOM content was measured with the potassium dichromate method (Nelson and Sommers, 1974), and pH was measured in a 1:2.5 soil-to-water suspension. Available nitrogen (N), phosphorus (P), and potassium (K) were measured with the alkali solution diffusion method, sodium bicarbonate leaching for molybdenum–antimony colorimetric method, and ammonium acetate exchange-flame photometry, respectively. The last four measurement methods were based on the work of Sheng et al. (2015). 2.2. Influencing factors and data preprocessing Considering the multiple sources of heavy metals in Huangpi District, we selected nine possible influencing factors, as follows: (i) natural factors, namely, soil type, pH, SOM content, topographic wetness index (TWI), and distance to rivers (Yangtze River and its tributaries), and (ii) anthropogenic factors, namely, land use types, distance to the nearest industrial enterprises (Dis_IE), road density, and population density. According to the soil map surveyed by Harmonized World Soil Database, the soil types of the samples included fluvisols, anthrosols, luvisols, cambisols, and planosols (Fig. 2a) (FAO/IIASA/ISRIC/ISSCAS/ JRC, 2012). The land use types and distance to rivers were based on the 2014 land use map (30 m resolution) of Huangpi District provided by the Hubei Provincial Department of Land and Resources (Fig. 2b).

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Fig. 1. Topography and rivers in the study area and sampling locations.

The spatial distributions of roads (highways and railways), industrial enterprises, and community-scale population sizes were surveyed by the Wuhan Geomatics Institute in 2014 (Fig. 2c and d). The distance of each sample to the nearest industrial enterprises (Dis_IE) was measured. We built a buffer zone with a 500 m radius for each sampling point and identified the enclosed total road length and population. We assumed that the population was evenly distributed in the communities. Then, the road and population densities were obtained. TWI is an effective topographical indicator and recognized as an accurate delineation of topographic changes and their influence on surface runoff (Beven and Kirkby, 1979). A high TWI value indicates a decrease in slope and/or an increase in catchment area, thereby exhibiting remarkable potential for confluence and sediment deposition (Cao et al., 2017; Duan et al., 2015). TWI was calculated using the catchment area per unit contour length (a) and slope gradient (β), both of which were derived from digital elevation model (DEM) data (30 m resolution) (CAS, 2015).  TWI ¼ ln

a tanβ



Fig. 2e presents the TWI spatial distribution, and Table 1 shows the data sources of possible influencing variables. Indicator calculation was completed on the ArcGIS software platform (version 10.2.2).

2.3. Statistical analysis and geostatistics Descriptive statistical analysis of heavy metals and other soil properties was conducted. The analyzed properties included maximum, minimum, mean, standard error, coefficient of variation (CV), skewness, and kurtosis. The difference in Pb and Cd contents among various land use and soil types was determined through one-way analysis of variance (ANOVA) with the least significant difference (LSD) test at a significance level of 0.05. The descriptive statistical analysis and ANOVA were conducted with SPSS software (version 19.0). OK is the most commonly used geostatistical method because of its cost effectiveness, unbiased estimation, and rapid mapping (Liu et al., 2018c; Shahbeik et al., 2014). OK identifies optimal weighting factors via a semivariogram of the principles of unbiased prediction and minimum variance. Furthermore, the values in unsampled areas are equal to the linear weighted sum of the surrounding observed values. OK is effective for the spatial estimation of soil properties (Metahni et al., 2019; Reza et al., 2016). Spatial distribution maps of heavy metals derived from OK can be used for overlay analyses with independent variables (Fig. 2). In this work, OK was utilized to interpolate pH, SOM, Pb, Cd, and the regression residuals of Pb and Cd. Prior to interpolation, the datasets passed through the Kolmogorov–Smirnov (K\\S) test (p N 0.05), which revealed the datasets' normal distribution and possibility for direct use in interpolation. The best semivariogram and relevant parameters based on the

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Z. Wu et al. / Science of the Total Environment 717 (2020) 137212

Fig. 2. Maps of the possible influencing factors, including soil types (a), land use types (b), industrial enterprises, roads, and rivers (c), population (d), topographic wetness index (TWI) (e), soil organic matter (SOM) content (f), and pH (g).

principle of the highest R2 and the lowest residual sum of squares of functions were determined with GS+ software (version 9.0), as shown in Table S1. The spatial distribution maps (30 m resolution) were obtained with the ArcGIS software platform (version 10.2.2). 2.4. Spatial regression models The assumptions of MLR require all observations to be independent and identically distributed (Cliff and Ord, 1972; Miralha and Kim, 2018). The results of a non-spatial model would be biased if observations are spatially autocorrelated. Therefore, two frequently used global spatial regression models, namely, SLM and SEM, were developed to determine the spatial dependence of variables accurately (Anselin, 1988; Ward and Gleditsch, 2008b).

2.4.1. SLM SLM posits that an independent variable is influenced by independent variables in the adjacent region. Therefore, spatial lag term, which quantifies the influence of surrounding independent variables, was used as a new explanatory variable in the current regression. The spatial lag term's coefficient reveals the direction and degree of spatial dependence. The formula is as follows: yi ¼ βxi þ ρwi y j þ ϵi ;

ð1Þ

where yi and yj are dependent variables at locations i and j, respectively; xi is a vector of independent variables; β is a vector of regression coefficients; ρ is the spatial autoregressive coefficient; wi is a vector of spatial connectivity; and ϵi is a random error term.

Table 1 Possible influencing factors of heavy metals. Possible influencing factors

Data source

Resolution

Links

Soil type Topographic wetness index Land-use type Distance to river Distance to nearest industrial enterprise Road density Population density pH Soil organic matter

Harmonized World Soil Databasea ASTER DEMb Hubei Provincial Department of Land and Resources

1 km 30 m 30 m

http://www.fao.org/home/en/ http://www.gscloud.cn Not open to the public

Wuhan Geomatics Institute

Vector data

a b

Source: FAO/IIASA/ISRIC/ISSCAS/JRC (2012). Source: CAS (2015).

Site measurement

Z. Wu et al. / Science of the Total Environment 717 (2020) 137212

2.4.2. SEM SEM assumes that the spatial dependence of MLR residuals is derived from the error, which may be due to the model ignoring independent variables with spatial dependence. Therefore, the residuals of MLR are usually decomposed into spatial components of the error term and a random error term; the latter satisfies the independence assumption. The formulas are as follows: yi ¼ βxi þ ui ;

ð2Þ

ui ¼ λwi u j þ ϵi ;

ð3Þ

5

Table 2 Descriptive statistics of soil properties. Variables

Max

Min

Mean ± Se

CV/(%)

Skewness

Kurtosis

Pb (mg/kg) Cd (mg/kg) SOM (g/kg) pH N (mg/kg) P (mg/kg) K (mg/kg)

80.12 1.42 39.79 8.23 218.23 302.70 720.42

1.42 0.06 3.97 4.08 10.24 0.01 21.19

20.52 ± 0.95 0.39 ± 0.02 20.30 ± 0.61 5.71 ± 0.08 81.61 ± 2.66 33.72 ± 4.87 180.04 ± 10.21

54.23 61.76 35.07 14.47 38.29 169.76 68.58

1.62 1.26 0.29 0.71 0.72 3.32 1.84

6.20 2.43 0.09 −0.35 2.29 10.32 4.67

CV: coefficient of variation, Se: standard error.

3.2. Spatial distribution of Pb and Cd where ui and uj are the error terms at locations i and j, respectively, and λ is the coefficient of spatial component errors. The other variables are similar to those in Eq. (1). 2.4.3. Model selection and evaluation Lagrange multiplier (LM) and robust LM tests were performed to determine quantitatively which spatial regression models should be used. Four LM statistics, namely, LM-Lag, robust LM-Lag, LM-Error, and robust LM-Error, were calculated. Model selection was based on the significance of the four statistics (Anselin et al., 2004), as shown in the flowchart in Fig. S1. Log-likelihood (LIK), R2, Akaike information criterion (AIC), Schwartz criterion (SC), and Moran's I value of the residuals (Ir) of regressions were used to evaluate model performance. Moran's I is a commonly used indicator to measure the overall degree of spatial dependence (Cliff and Ord, 1981; Moran, 1950). The Moran's I value ranges from −1 to 1. A value of 1 indicates perfect positive spatial dependence, a value of −1 indicates perfect positive spatial dependence, and a value of 0 suggests spatial randomness of target variables. Moreover, a low absolute Moran's I value of regression residuals indicates that the regression effectively explains the spatial dependence of the dependent variable. Therefore, a well-fitted regression model has high values of LIK and R2, low values of AIC and SC, and a low absolute value of Ir (Anselin, 1988; Chen et al., 2016; Miralha and Kim, 2018). MLR, SLM, and SEM were fitted using Geoda software (version 1.6.7). As categorical variables, the soil and land use types of the samples were separately converted to dummy variables before fitting the regressions (Table S2). 3. Results 3.1. Descriptive statistics Table 2 presents the results of the descriptive statistics. The Pb and Cd contents ranged from 1.42 to 80.12 mg/kg and from 0.06 to 1.42 mg/kg, respectively, and had mean values of 20.52 and 0.39 mg/kg, respectively. The mean values of SOM, N, P, and K were 20.30 g/kg, 81.61 mg/kg, 33.72 mg/kg, and 180.04 mg/kg, respectively. The mean value of pH was 5.71. The skewness values of these soil properties were low, except for that of P. The CV values of these soil properties revealed that pH had the least variability; the value for P was 169.76%, which was the largest among the soil properties. Table 3 lists the mean Pb and Cd contents among the different land use and soil types. The mean Cd content of grassland was 0.28 mg/kg, which was higher than the background value (0.20 mg/kg). The Cd contents of forests, paddy fields, and irrigated lands were higher than that of grasslands. Forests had the highest Pb content, followed by grasslands, irrigated lands, and paddy fields. However, the Pb and Cd contents among the different land use types had no significant difference according to the LSD test. Table 3 also suggests that the difference in the mean Pb and Cd contents among several soil types was significant. The Pb content in fluvisols, planosols, and anthrosols was significantly higher than that in cambisols. The Cd content in fluvisols and cambisols was significantly higher than that in luvisols and planosols.

Fig. 3 shows the spatial distributions of Pb and Cd. The Pb content exhibited a gradual decrease from the southern to the northern part of the study area. Combining the positions of industrial enterprises (Fig. 2c) showed that proximity to industrial enterprises corresponded to a high Pb concentration. Specifically, the highest value of Pb appeared on the western side of central Huangpi District. This area is located on the southwest side of Lohan Temple Industrial Park. The Wuhan Zhonghe Energy Saving Technology Company (shown in Fig. 2c) is an important representative factory in this industrial park. Moreover, the Pb level was elevated in the southern part of Huangpi District and the northern bank of Yangtze River. These areas have a large number of industrial enterprises, and the high density of industrial enterprises resulted in a high Pb concentration. The spatial distribution of Cd was high in the northern and southeastern parts of the study area but low in the central area. Fig. 2 shows that TWI was high in the northeast, SOM was high in the northwest, and pH was high in the north. Moreover, the three indicators were high in the southeastern Huangpi District. Figs. 2 and 3 show that the spatial pattern of Cd was similar to those of pH, SOM, and TWI. 3.3. Determining the influencing factors of the spatial distribution of Pb and Cd MLR was used to explore the factors that affect the variation in Pb and Cd in Huangpi District. The R2 results showed that the nine predictors explained 32% and 34% of Pb and Cd variations, respectively (Table 4). Pb was related to Dis_IE, and Cd was related to pH, SOM, and TWI. However, the Moran's I values of Pb and Cd were significant at the 0.001 level, which violates the independence assumption of MLR. Moreover, the results of the LM and robust LM tests suggested that the MLR findings are unreliable, and spatial regression models are required (Table 4). Therefore, SLM is the best choice for Pb and Cd. The SLM results showed that after considering the spatial lag term, the explanatory power for the spatial variations in Pb and Cd increased to 37% and 49%, respectively (Table 5). ρ was positive and significant in the SLMs of Pb and Cd, indicating that Pb and Cd were spatially dependent and received positive feedback from surrounding observations. The Pb content was negatively related to Dis_IE, indicating that Table 3 Least significant difference test in ANOVA with mean values of lead (Pb) and cadmium (Cd) among different land-use and soil types. Land-use types Mean and significance test

Soil types

Mean and significance test

Fluvisols Anthrosols Luvisols Cambisols Planosols

27.18a 20.11a 18.77ab 7.72b 25.13a

Pb (mg/kg) Cd (mg/kg) Paddy field Irrigated land Forest Grassland

19.04a 20.05a 22.93a 20.58a

0.39a 0.39a 0.41a 0.28a

Pb (mg/kg) Cd (mg/kg) 0.60a 0.39b 0.35b 0.45ab 0.30b

Values suffixed with the same superscript letters along the columns are not significantly different (p N 0.05).

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Fig. 3. Spatial interpolation maps of Pb and Cd.

proximity to industrial enterprises corresponded to a high Pb content. The Cd content was positively related to pH, SOM, and TWI. The Cd contents of paddy fields and irrigated lands were 0.09 and 0.07 mg/kg

Table 4 Results of multiple linear regression, Lagrange multiplier (LM), and robust LM tests. Variables

Pb

Cd

Constant Dis_IE Dis_River RD PD pH SOM TWI LU1 LU2 LU3 ST1 ST2 ST3 ST4 R2 Log-likelihood (LIK) Akaike info criterion (AIC) Schwarz criterion (SC) Moran's I Moran's I of residuals LM-Lag LM-Error Robust LM-Lag Robust LM-Error

17.62⁎ −0.36⁎⁎⁎ 0.79 −0.36 0.01 −0.25 0.18 0.04 −0.55 −0.17 5.96 3.98 −0.10 −0.56 −5.58 0.32 −501.3 1032.6 1076.5 0.39⁎⁎ 0.11⁎ 7.73⁎⁎ 3.55 16.25⁎⁎⁎ 12.07⁎⁎

−0.32 0.01 −0.01 −0.01 0.00 0.07⁎⁎ 0.01⁎ 0.01⁎ 0.10 0.12 0.16 0.07 0.01 −0.01 0.02 0.34 31.7 −33.2 10.4 0.46⁎⁎ 0.24⁎⁎ 34.40⁎⁎⁎ 20.71⁎⁎⁎ 20.78⁎⁎⁎ 4.97⁎

Dis_IE: distance to the nearest industrial enterprises; Dis_River: distance to rivers; RD: road density; PD: population density; SOM: soil organic matter; TWI: topographic wetness index; LU1, LU2, LU3: dummy variables of land use types; ST1, ST2, ST3, ST4: dummy variables of soil types. ⁎ Denotes p b 0.05 (two-tailed). ⁎⁎ Denotes p b 0.01 (two-tailed). ⁎⁎⁎ Denotes p b 0.001 (two-tailed).

higher than that of grasslands, respectively. The differences in Cd contents among the different soil types were negligible and not significant. The model evaluation results showed that SLM had higher R2 and LIK and lower AIC and SC than MLR (Tables 4 and 5). The Moran's I values of the MLR residuals of Pb (RESPb_MLR), SLM residuals of Pb (RESPb_SLM), MLR residuals of Cd (RESCd_MLR), and SLM residuals of Cd (RESCd_SLM) were also evaluated. The results of Moran's I revealed that RESPb_MLR and RESCd_MLR were spatially autocorrelated (p b 0.05), whereas RESPb_SLM and RESCd_SLM did not show spatial dependence (p N 0.05). 3.4. Residual analysis Fig. 4 presents the spatial distribution of the four regression residuals. For Pb and Cd, the spatial distributions of SLM residuals were relatively similar to those of MLR residuals. The positive values denoted underestimation and were mainly distributed in areas with high Pb and Cd contents. The positive residuals of Pb were located in the midwestern and southeastern parts of the district, whereas the positive residuals of Cd were located in the northern and southern parts of the area. The negative values indicated overestimation and were mainly distributed in areas with low Pb and Cd contents. The negative residuals of Pb were distributed in the northeastern and southwestern sections, whereas the negative residuals of Cd were distributed in the central region. Despite having similarities, the Pb and Cd patterns of SLM and MLR residuals varied in terms of local details. The spatial distribution of MLR residuals showed a spatial clustering trend. The SLMs considered the spatial dependence of Pb and Cd; thus, the spatial distribution of their residuals exhibited randomness rather than spatial dependence. Moreover, the absolute values of SLM residuals were lower than those of MLR residuals. 4. Discussion 4.1. Heavy metal pollution and model suitability According to the Chinese Environmental Quality Standard for Soils (GB 15618–1995), the mean value of Pb (20.52 mg/kg) is less than the

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4.2. Influencing factors of the spatial variation of Pb and Cd

Table 5 Results of spatial lag models. Variables

Pb

Constant ρ (wi ∗ yi) Dis_IE Dis_River RD PD pH SOM TWI LU1 LU2 LU3 ST1 ST2 ST3 ST4 R2 Log-likelihood (LIK) Akaike info criterion (AIC) Schwarz criterion (SC) Moran's I of residuals

10.74 0.32 −0.24⁎ 0.57 −0.30 0.01 −0.33 0.17 0.01 −1.13 −0.31 5.27 3.53 0.99 1.07 −3.42 0.37 −497.6 1027.2 1074.0 −0.03

Cd −0.34 0.48 0.01 −0.01 −0.02 0.01 0.06⁎⁎ 0.01⁎ 0.01⁎ 0.09 0.07 0.11 −0.01 0.01 −0.01 0.02 0.49 45.3 −56.6 −11.7 −0.09⁎⁎⁎

Dis_IE: distance to the nearest industrial enterprises; Dis_River: distance to rivers; RD: road density; PD: population density; SOM: soil organic matter; TWI: topographic wetness index; LU1, LU2, LU3: dummy variables of land use types; ST1, ST2, ST3, ST4: dummy variables of soil types. CAS. ASTER DEM, Geospatial Data Cloud, 2015. FAO/IIASA/ISRIC/ISSCAS/JRC. Harmonized World Soil Database (version 1.2). FAO, Rome, Italy and IIASA, Laxenburg, Austria, 2012. ⁎ Denotes p b 0.05 (two-tailed). ⁎⁎ Denotes p b 0.01 (two-tailed). ⁎⁎⁎ Denotes p b 0.001 (two-tailed).

background value (35 mg/kg). Only 7.24% of the current samples exceeded the background value, and none of them exceeded the Chinese national second-level pollution standard (250 mg/kg). This finding suggests that Pb pollution poses minimal potential harm to human health. However, the mean Cd value (0.39 mg/kg) was nearly twice the national background value (0.20 mg/kg). Among the samples, 76.09% had Cd contents exceeding the background value, and 60.14% had contents exceeding the second-level standard (0.30 mg/kg). Therefore, Cd poses a high risk of contamination to Huangpi District. The factors that influenced the spatial distribution of heavy metal contents were determined using MLR and SLM. Soil heavy metal contents (e.g., Pb and Cd) are typical regionalized variables with strong spatial dependence (Benamghar and Jaime Gomez-Hernandez, 2014), which was confirmed in the present study by the calculated Moran's I values (Table 4). This finding violates the assumption of MLR that all observations are independent and identically distributed (Cliff and Ord, 1972; Miralha and Kim, 2018), thus rendering the results of MLR unreliable. Most of the absolute coefficient values of the variables in SLMs were lower than those in MLRs. This finding indicates that MLR overestimates the effect of each variable on Pb and Cd by ignoring the spatial dependence of heavy metals. By contrast, SLM considers the spatial autocorrelation of the dependent variable and regards the spatial lag term as an independent variable (Ward and Gleditsch, 2008a). As a result, the relationship between heavy metals and independent variables obtained by SLM is more reliable than that derived by MLR. The results of a series of model evaluation indicators, including R2, LIK, AIC, SC, and Ir, also prove that SLM is more accurate than MLR. Similar findings have been obtained in previous studies. For example, Huo et al. (2010) explored the relationship among Cr, Ni, Pb, Hg, and environmental variables and concluded that SEM has higher R2 and LIK and lower Ir than MLR; thus, it is better than MLR. Li et al. (2017) also found that SLM has a better fit of the heavy metal pollution load index than MLR. Therefore, we conclude that SLM and SEM are feasible and reliable in identifying the influencing factors of heavy metals.

We discovered that Pb content depends on the distance from the nearest industrial enterprises, suggesting that industrial pollution is the main source of Pb. The highest value of Pb appeared at the western side of central Huangpi District. This area is near Lohan Temple Industrial Park, which is well-known for its steel products, machinery and equipment manufacturing, and building materials. Another region with a high Pb content was the southern part of Huangpi District and the northern bank of Yangtze River. Many industrial enterprises, including chemical, metallurgical, and food processing plants, are built along Yangtze River because of its rich water resources, convenient transportation, and low-cost industrial waste treatment. These plants produce heavy metal wastes during production and pollute the surrounding soil through the discharge of industrial wastes and atmospheric deposition from industrial emissions (Qu et al., 2014; Zhang et al., 2015). As a result, the two areas have a high Pb content, and the high value distribution of Pb corresponds to the proximity to industrial enterprises. A similar phenomenon was observed on the southern bank of Yangtze River by Yang et al. (2016), who reported that Pb content is the highest at Wuhan Petrochemical Plant and decreases with distance from the plant. The spatial variation in Cd depends on pH, SOM, and TWI. pH and SOM are important indicators of the soil environment and associated with heavy metal contents (Caporale and Violante, 2016; Xiao et al., 2019). Heavy metals in a low-pH environment exhibit high bioavailability and mobility (McLaren et al., 2005; Sprynskyy et al., 2011), resulting in high plant uptake (Ran et al., 2016) and strong leaching (Fang et al., 2016). Therefore, pH is expected to have a positive relation with heavy metals in topsoil. SOM can absorb heavy metals and reduce their mobility and bioavailability (Liu et al., 2019; Mingkui et al., 2007; Zhang et al., 2016). However, dissolved organic matter tends to complex with heavy metals and thus increases the solubility and mobility of heavy metals (Ashworth and Alloway, 2008; Li et al., 2013). Therefore, the relationship between SOM and heavy metal contents depends on the actual soil environment (Zhang et al., 2014). In the present study, pH and SOM showed a positive correlation with Cd, a result that is consistent with the findings of Fu-yan et al. (2009) and Maas et al. (2010). Topographical factors may affect heavy metal accumulation by influencing runoff intensity and sediment transport. A region with a high value of TWI has a gentle slope and large catchment area, which is conducive to sediment deposition and heavy metal accumulation. Thus, heavy metal contents are expected to have a positive relation with TWI. Our study revealed a significant positive relationship between TWI and Cd, which means that TWI is an effective predictor of the spatial distribution of heavy metals. Similar findings were obtained by Cao et al. (2017) and Duan et al. (2015). Therefore, intrinsic and topographical factors account for the spatial variation of Cd. Land use type, which can be considered a comprehensive expression of nature cover and human activities, could be another factor that affects Cd accumulation in topsoil. Land use type can reveal the spatial distribution of heavy metal accumulation in soil (Luo et al., 2007; Thinh Nguyen et al., 2016). In the present study, the difference in Cd content among the various land use types reflects the effects of natural background and fertilization on the spatial distribution of heavy metals. Given that human activities do not influence the Cd content of grasslands in the study area, the Cd in grasslands was obtained from parent materials. This inference is consistent with those of Zhang et al. (2015) and Ma et al. (2005), who concluded that soil parent materials mainly control the Cd level in Wuhan. Moreover, the mean Cd content of grasslands (0.28 mg/kg) is higher than the Chinese background value (0.20 mg/kg) (GB 15618–1995); this situation indicates the relatively high background value of local Cd. The Cd content of paddy fields and irrigated lands is higher than that of grasslands probably because of the high Cd content in P fertilizer in farmlands (Sun et al., 2013; Yang et al., 2017). In the study area, the average P content of irrigated lands (45.64 mg/kg) and paddy fields (35.93 mg/kg) is significantly higher

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Fig. 4. Spatial distribution of RESPb_MLR (a), RESPb_SLM (b), RESCd_MLR (c), and RESCd_SLM (d). RESPb_MLR: the residuals of multiple linear regression (MLR) of lead (Pb); RESPb_SLM: the residuals of spatial lag model (SLM) of Pb; RESCd_MLR: the residuals of MLR of cadmium (Cd); RESCd_SLM: the residuals of SLM of Cd.

than that of grasslands (4.29 mg/kg), which confirms the use of large amounts of P fertilizer. Therefore, the difference in Cd content between farmlands and grasslands can be attributed to the application of P fertilizer. Although the Cd in farmlands is higher than that in grasslands, such a difference is not significant. This condition is possibly due to the pH in farmlands being significantly lower than that in grasslands, indicating that high plant uptake and strong leaching decrease the topsoil Cd content in farmlands (Salmanzadeh et al., 2017). This inference is consistent with the findings of Yu et al. (2014), who revealed that the Cd

pollution in the soil, vegetables, and groundwater of a suburban farmland in Wuhan City increase the health risk of local residents. Consequently, land use type may be another factor of Cd content, and parent materials and P fertilizer application are the most probable sources of Cd. Soil types are associated with various soil properties, such as solid– liquid equilibrium constants, pH, soil texture, SOM content, and cation exchange capacity; thus, they affect the adsorption capacity of soil for heavy metals and control their spatial distribution (Hou et al., 2017;

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McDowell et al., 2013; Salmanzadeh et al., 2017). Soil types are related to heavy metal contents (Itanna et al., 2004; Maas et al., 2010). In this study, significant differences were observed in Pb and Cd contents among the different soil types. The difference in Pb content among the various soil types may be due to their different distances from industrial plants. The fluvisols with the highest Pb were located in the south of Huangpi District where numerous factories are located, whereas the cambisols with the lowest Pb were far from industrial factories. Meanwhile, the difference in Cd content among the different soil types may be due to their different soil environments and topographic conditions. The fluvisols with the highest Cd content had high pH and SOM and low TWI values. Meanwhile, the planosols with the lowest Cd had low pH and SOM and high TWI values. However, after controlling other factors (e.g., Dis_IE, pH, SOM, and TWI) in SLM, the difference in Pb and Cd among the different soil types became not significant. This condition confirms that soil types indirectly affect heavy metal contents. Road and population densities are important indicators of heavy metal pollution from urbanization sources. Vehicle exhaust emissions, tire wear, and improper disposal of domestic waste cause heavy metals to accumulate in topsoil (Kummer et al., 2009; Li et al., 2017; Wang et al., 2015). A significant correlation among population density, road density, and Pb content in the urban area of Wuhan was observed in a previous work (Zhang et al., 2015). However, these factors are not associated with Pb and Cd in our study area because Huangpi District is a suburban area with a small population and low road densities.

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industrial enterprises are the main source of Pb. Cd content depends on pH, SOM, and TWI, indicating that intrinsic and topographical factors contribute to the spatial variation of Cd. Parent materials and the application of P fertilizer are the most likely sources of Cd. These findings highlight the spatial autocorrelation of heavy metals and the effects of intrinsic factors and environmental variables on the spatial variation of heavy metals. The effectiveness of the spatial regression model in identifying the influencing factors of heavy metals was also confirmed.

Declaration of competing interest No conflict of interest exists in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed. Acknowledgments This study was supported by the National Key Research and Development Program of China (Grant No. 2017YFB0503505). We extend our gratitude to the Wuhan Geomatics Institute for providing data. Appendix A. Supplementary data

4.3. Limitations and prospects This study identified the influencing factors of Pb and Cd and inferred their sources. However, the SLM model used here does not consider several possible predictors, such as soil texture, parent materials, and certain agricultural activities, due to the lack of data. Soil texture is an important indicator of soil components. Fine-grained soil contains soil particles with large surface reactivities and surface areas and exhibits enhanced adsorption properties compared with coarse-grained soil (Jing et al., 2018). Heavy metal contents are associated with soil texture (Davis et al., n.d.; Xu et al., 2013). However, the relationship between heavy metals and soil texture in Huangpi District remains unexplored. Parent materials are the natural source of heavy metals and associated with their natural background values (Yang et al., 2019). In this study, we inferred that the parent material is a source of Cd in Huangpi District, and the local Cd background value is higher than the national background value. Future research could confirm this inference by acquiring local parent material data. Several agricultural activities, such as the use of pesticides (Wang et al., 2015), livestock manure (Provolo et al., 2018), plastic film (Yu et al., 2013), and sewage sludge (Huang et al., 2015), are reported to cause heavy metal pollution in farmlands and the surrounding soil. Whether or not these activities control the spatial variation of heavy metals in Huangpi District requires further investigation. 5. Conclusions This study used SLM and MLR to explore the influencing factors that control the spatial variation of Pb and Cd in Huangpi District. Nine intrinsic factors and environmental variables were utilized as possible factors. We found that the 7.24% Pb content and 76.09% Cd content of the samples exceeded the background values. The Moran's I values revealed the spatial autocorrelation of Pb and Cd. The results of a series of model evaluation indices, including R2, LIK, AIC, SC, and Ir, suggest that SLM outperformed MLR. The SLM findings indicate that the nine variables explained 37% and 49% of the spatial variations of Pb and Cd, respectively. The significant coefficients of the spatial lag term in the SLMs indicated that the spatial variations of Pb and Cd depend on their surrounding observations. Pb content depends on the distance from the nearest industrial enterprises, suggesting that emissions from

Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2020.137212.

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