Distribution of selenium and zinc in soil-crop system and their relationship with environmental factors

Journal Pre-proof Distribution of selenium and zinc in soil-crop system and their relationship with environmental factors

Yuefeng Xu, Zhe Hao, Yonghua Li, Hairong Li, Li Wang, Zhenfeng Zang, Xiaoyong Liao, Ru Zhang PII:

S0045-6535(19)32529-9

DOI:

https://doi.org/10.1016/j.chemosphere.2019.125289

Reference:

CHEM 125289

To appear in:

Chemosphere

Received Date:

21 August 2019

Accepted Date:

01 November 2019

Please cite this article as: Yuefeng Xu, Zhe Hao, Yonghua Li, Hairong Li, Li Wang, Zhenfeng Zang, Xiaoyong Liao, Ru Zhang, Distribution of selenium and zinc in soil-crop system and their relationship with environmental factors, Chemosphere (2019), https://doi.org/10.1016/j. chemosphere.2019.125289

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Journal Pre-proof Distribution of selenium and zinc in soil-crop system and their relationship with environmental factors Yuefeng Xua, b, Zhe Haoc, d, Yonghua Lia, *, Hairong Lia, Li Wanga, Zhenfeng Zanga, b, Xiaoyong Liaoa, *, Ru Zhanga, b

Affiliations: a

Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and

Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China b University

c

of Chinese Academy of Sciences, Beijing 100049, China

Key Laboratory of Engineering Oceanography, Second Institute of Oceanography, Ministry of

Natural Resources, Hangzhou 310012, China d

Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing

210093, China Corresponding authors: Yonghua Li Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11 A Datun Road, Beijing 100101, China. Tel.: +86 10 64889198; E-mail address: [email protected]. OR Xiaoyong Liao Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11 A Datun Road, Beijing 100101, China. Tel.: +86 10 64889848; E-mail address: [email protected] 1

Journal Pre-proof Graphical abstract

Journal Pre-proof Abstract Selenium (Se) and zinc (Zn) are essential microelements for humans with crucial biological functions. In this study, we determined Se and Zn concentrations in soils and rice grains on Hainan Island and investigated how their spatial distributions are related to soil mineral elements, topography, and vegetation coverage. Overall, the concentrations of Se and Zn in soils were higher than the background values for Chinese soil; the Se concentrations in rice grains were higher than the threshold value for Se deficiency in grains, but Zn concentrations were lower than the proposed critical concentration. Both Spearman’s correlation and stepwise regression analysis showed that the concentrations of soil Fe and Ca significantly affected soil Se and Zn: a difference of 1 g kg-1 in soil Fe changed soil Se by 2.820 μg kg-1 and soil Zn by 0.785 mg kg-1, respectively, while a difference of 1 g kg-1 in soil Ca changed soil Se by 3.249 μg kg-1 and soil Zn by 0.356 mg kg-1, respectively. For rice grains, Se and Zn concentrations decreased with increasing elevation; every 100 m increase in elevation could decrease Se by 0.022 mg kg-1 and Zn by 0.912 mg kg-1. Moreover, the impact of Fe and Ca on soil Zn was relatively strong in the northeast region, while the influence of elevation on rice grain Se was more significant in the central region. The findings contribute to a better understanding of factors driving the distribution of Se and Zn in soils and crops. Keywords Selenium; Zinc; Soil mineral elements; Topography; Vegetation coverage

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Journal Pre-proof 1. Introduction Selenium (Se) and zinc (Zn) are essential micronutrients in the human body and play an important role in human growth, development, and maintenance of the immune system. Se is incorporated into selenoproteins, which have a wide range of pleiotropic effects, ranging from antioxidant and anti-inflammatory effects to the production of active thyroid hormone (Rayman, 2012). As the cofactor to more than 300 enzymes, Zn has catalytic, structural, and regulatory functions in organisms (Lothar and Philip, 2000). Therefore, it is essential to understand the geochemical cycle of Se and Zn in soil–crop systems. In natural environments, the main source of Se and Zn in the soil is the geological parent material. Higher contents of Se have been found in soils originated from Precambrian carbonates, Cretaceous shales, and coals than in soils derived from limestones and sandstones (Sharma et al., 2015). Zn contents in soils formed on clays, shales, and mafic igneous rocks are typically higher than in soils developed on limestones and sandstones (Alloway, 2011). The Se content in soil generally ranges between 0.01 and 2 mg kg-1 with a world average value of 0.4 mg kg-1 (Natasha et al., 2018); for Zn, the typical range is 10–300 mg kg-1, with a world average value of 50 mg kg-1 (Kiekens, 1995). The accumulations of Se and Zn in crops primarily depend on the availabilities of Se and Zn in soil. Numerous studies have demonstrated that the availabilities of Se and Zn in soil not only depend on the total Se and Zn content in soil, but also the influence of soil physicochemical properties (Antoniadis et al., 2018; Eich-Greatorex et al., 2007; Soltani et al., 2015; Tolu et al., 2014). It has been recognized that the 3

Journal Pre-proof availability of Se in soil generally increases with increasing pH (Xu et al., 2018). In acidic soil, selenite is the major inorganic Se species, while Se mainly exists in the selenate state in alkaline soil (Wang et al., 2013). Compared with selenate, selenite is less available as it is more easily absorbed by iron and manganese oxides. Organic matter (OM) also plays an important role in Se availability by forming OM-bound Se associations or releasing Se from OM-bound Se complexes to maintain the dynamic equilibrium of available Se in soil (Li et al., 2017; Qin et al., 2013). Selenium can also be reduced to elemental Se (0) and metallic Se (-II) under anaerobic conditions; these are

water

insoluble

forms

and

cannot

be

directly

utilized

by

plants

(Fernández-Martínez and Charlet, 2009). With regard to Zn, Singh et al. (2008) reported that desorption of both native and added Zn decreased continuously with increasing soil pH. Because Zn is present in the +II oxidation state in soil, the activity of Zn2+ in soil is related to negatively charged adsorptive surfaces, such as OM, clay, and iron and manganese hydrous oxides. Moreover, the reductive dissolution of Fe oxides can mobilize Zn (Chuan et al., 1996). More studies have focused on soil pH, OM, clay, Fe oxides, while fewer studies have considered the relationship between other mineral elements in soil and Se or Zn in soils and crops. In addition to soil physicochemical properties, topography and vegetation are fundamental factors affecting soil formation and soil properties. The effect of vegetation is manifested in many aspects, including the production function, regulation of climatic conditions, the influence on soil development, and the redistribution processes of water and sediment resources. Considerable research has 4

Journal Pre-proof been carried out to study the effect of topography and vegetation cover on soil properties such as soil moisture (Ali et al., 2010; Özkan and Gökbulak, 2017; Qiu et al., 2001), soil nitrogen (Griffiths et al., 2009; Zhang et al., 2011), soil pH (Karaca et al., 2018; Manandhar and Odeh, 2014), and soil OM (Fissore et al., 2017; Phil-Eze, 2010; Zhong and Xu, 2009). However, little is known about the influence of topography and vegetation coverage on Se and Zn concentrations and distributions in soils and crops. Hainan Island, as the second largest island in China (Hao et al., 2019) and an important agriculture production base, was chosen as our study area. In this study, 173 sets of cultivated topsoil samples and rice grain samples were collected from Hainan Island and multiple environmental factors were analyzed, including soil mineral elements (Fe, Mn, K, Na, Ca, Mg, and P), topography (elevation, slope and topographic wetness index), and vegetation coverage (normalized-difference vegetation index). The main objectives were to (1) determine the concentrations of Se and Zn in soils and rice grains; (2) analyze the spatial distributions of Se and Zn; and (3) quantify the influence of environmental factors on Se and Zn concentrations and distributions. This work provides the experimental basis for interpreting the geochemical cycle of Se and Zn in the environment and contributes to understanding the influence mechanism of multiple environmental factors on the spatial distributions of Se and Zn in soils and crops.

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Journal Pre-proof 2. Materials and methods 2.1. Study area The study area was Hainan Island (18°10′-20°10′N, 108°37′-111°05′E), which has an area of 35,400 km2, and is located at the southern tip of China (Fig. 1). It is characterized by a tropical oceanic monsoon climate, with a mean annual temperature and precipitation of 23.8 °C and 1685 mm, respectively (Hao et al., 2019). On Hainan Island, rice is widely cultivated, accounting for 80% of the island’s planting area of grain crops in 2016 (HSY, 2017). The island’s elevation ranges from 0 to 1795 m above sea level. It is more mountainous in the middle and flatter along the periphery (Fig. 1).

Fig. 1 Location of Hainan Island and spatial distributions of sampling sites (JFL, Jianfengling Mountain; BWL, Bawangling Mountain; YGL, Yinggeling Mountain; WZS, Wuzhishan Mountain; DLS, Diaoluoshan Mountain).

2.2. Sampling and analyses 2.2.1. Sampling and pretreatment In October 2016, we collected 173 cultivated topsoil samples (0–20 cm) and the corresponding rice grain samples from the study area. Figure 1 shows the distributions of sampling sites; the elevation of which ranged from 2 m to 540 m above sea level. In order to reduce the influence of differences in land-use type on the research results, 6

Journal Pre-proof soil samples were not collected from forest-covered areas. Sampling sites were located at a distance of at least 200 m from industries, roads, and residential areas. Around each sampling site, rice grain samples and topsoil samples under the sampled rice plants (surrounding the root) were collected in quintuplicate using stainless steel scissors and a wooden spade separately, and then pooled to form a representative rice grain and topsoil sample. Collected samples were placed in numbered plastic bags. The geographic coordinate information of the sampling points was recorded using a Trimble GeoXT global positioning system (GPS). In the laboratory, soil samples were mixed evenly while discarding the stones and other impurities, air-dried, and passed through 100-mesh sieves to determine the soil element contents. Rice grain samples were shelled, washed with distilled water, dried at 60 °C to a constant weight, and then crushed using a stainless-steel vegetation disintegrator (FW-100, Taisite Instrument Co., Ltd., Tianjin, China) for a short time and stored for determination. 2.2.2. Chemical analysis The rice grain samples were digested using HNO3, while the soil samples were acid-digested using HNO3, HClO4, and HF. In brief, rice grain samples weighing 0.2 g were digested in a 50 mL beaker using 3 mL concentrated HNO3. Soil samples weighing 0.1 g were placed in a Teflon crucible, and then a 17 mL mixture of concentrated HNO3, HClO4, and HF (V1:V2:V3 = 8:1:8) was added. For both rice grain and soil samples, the digestive solution was kept overnight at room temperature (25 °C) and subsequently digested in a hot plate at 170 °C until the solution became 7

Journal Pre-proof clear (Ali et al., 2017). The rice grain and soil digestion solution were, respectively, diluted to 15 mL and 25 mL in test tubes with 1% concentrated HNO3. The concentrations of Zn, Fe, Mn, K, Na, Ca, Mg, and P were determined using OPTIMA 5300DV inductively coupled plasma optical emission spectrometry (ICP-OES) (PerkinElmer, Waltham, MA, USA). The content of Se was analyzed by Elan-DRC-e inductively coupled plasma mass spectrometry (ICP-MS) (PerkinElmer). The accuracy and precision of the method were tested using reagent blanks, duplicated samples, and national standard reference materials (GBW10010 for rice grain and GBW07410 for soil; obtained from the National Standard Sample Study Center in Beijing, China). Ten percent of the samples in each batch were randomly selected as replicates. Good agreement was achieved between the measured values and the certified values for GBW10010 and GBW07410 with the recoveries between 89% and 108%. The relative standard deviation in duplicated samples was under 5%. 2.3. Data sources and processing 2.3.1. Topography parameters Digital Elevation Model (DEM) data with 30 m resolution was acquired from the China Geospatial Data Cloud. The topography parameters, including the slope and topographic wetness index (TWI), were quantified based on the 30 m DEM data. The slope map was generated using the Surface Toolset in ArcGIS. TWI was calculated as the natural logarithm of the ratio of the upslope drainage area (α) to the local slope angle (tanβ) for each unit area (30 m × 30 m cell) on the landscape (Beven and Kirkby, 8

Journal Pre-proof 1979; Buchanan et al., 2014; Xu et al., 2018). In this study, the single-direction flow algorithm was used to calculate α. The algorithm assumed that the flow area from one cell was routed into the steepest of its eight neighboring cells, and thus the steeper slopes routed more water than shallower slopes (Jenson and Domingue, 1988). The TWI map based on the single-direction flow algorithm was created using the Hydrology Toolset in ArcGIS. This index can be used to predict the spatial distribution of soil moisture and reflect soil erosion and deposition processes to a certain degree (Welsch et al., 2001). The smaller the TWI, the lower the soil moisture content is. For each sampling site, the elevation, slope, and TWI were then extracted using the Extraction Toolset in ArcGIS. 2.3.2. Vegetation coverage index The normalized-difference vegetation index (NDVI) is one of the most widely used vegetation coverage indices to monitor changes in vegetation coverage (Guindin-Garcia et al., 2012; Matsushita et al., 2007; Rouse et al., 1974). Moderate-resolution imaging spectroradiometer (MODIS) products offer high-quality data at consistent spatial resolutions and a temporal resolution of 16 days. The 16 day composite 250 m MODIS vegetation index products (MOD13Q1) are widely used in surveys of vegetation dynamics (Boschetti et al., 2009; Rankine et al., 2017; Song et al., 2013). In this study, a time series of MODIS MOD13Q1 products (Vegetation Indices 16 Day L3 Global 250 m) were downloaded from the National Aeronautics and Space 9

Journal Pre-proof Administration (NASA, 2019) for the study area from 2001 to 2015 (15 years, 225 images). Then, we calculated the mean value of NDVI from 2001 to 2015, following which we extracted the NDVI values of the sampling sites for each pixel through the Extraction Toolset to reflect the state of general vegetation coverage. 2.4. Data analysis 2.4.1. Spatial autocorrelation Spatial autocorrelation is a method used to determine the internal spatial association of a variable and can describe the variable distribution in the whole region (Zhang et al., 2016). Moran’s I index is a widely used spatial statistic for detecting global spatial pattern, the values of which range from -1 to 1. The threshold value of Moran’s I index is 0, indicating no spatial autocorrelation. If I value is >0, there is a positive spatial autocorrelation. If I value is <0, there is a negative spatial autocorrelation. The statistical significance for the spatial autocorrelation relationship is determined by standardizing the statistic Z value. At a confidence level of 0.05, |Z| = 1.96; at a confidence level of 0.01, |Z| = 2.54. |Z| > 1.96 or |Z| > 2.54 indicates that the global spatial autocorrelation is significant or highly significant. 2.4.2. Geographic weighted regression To investigate how soil mineral elements, topography, and vegetation coverage affect Se and Zn in soil and rice grain, we ran two types of regression models: stepwise regression (SR) and geographic weighted regression (GWR). SR is a method of fitting linear regression models in which the choice of predictive variables is 10

Journal Pre-proof carried out by an automatic procedure. After we conducted SR, we used the variables included by SR for GWR. GWR is a locally linear regression that reflects the spatial heterogeneity of the relationship between Se and Zn and the selected explanatory variables. The GWR model can be expressed as follows: n

yi = β0(ui,vi) +

∑ β (u ,v )x k

i i

ik

+ εi

k=1

where yi denotes the dependent variables, such as the Se and Zn concentrations in soil and rice grain at location i; (ui, vi) denotes the coordinates at location i; β0 (ui, vi) denotes the intercept at location i; βk (ui, vi) is the regression coefficient for independent variable k at location i; xik is the value of independent variable k at location i; and εi is the specific error term at location i. The regression coefficients calculated by using weighted least squares can be estimated with the following weighting function (Song et al., 2016): -1

β(ui,vi) = (XTW(ui,vi)X) XTW(ui,vi)Y where β (ui, vi) denotes the local regression coefficient at location i; X is the matrix of the independent variables; W (ui, vi) denotes an n by n matrix in which the diagonal elements denote the geographical weights of observed samples at location i; and Y is the vector of the dependent variable. The geographical weighting structure is established through the Gaussian Kernel function, which can be written as follows: Wij = exp ( - dij2/b2) where dij is the distance between locations i and j; and b is the kernel bandwidth. 11

Journal Pre-proof When the distance between the locations is greater than the kernel bandwidth, the weight rapidly approaches zero. The data in this study were analyzed using SPSS 18.0 and ArcGIS 10.0. Correlation and SR analyses were performed using SPSS 18.0. Spatial autocorrelation, spatial interpolation, and GWR analyses were conducted using ArcGIS 10.2. 3. Results and discussion 3.1. Concentrations of Se and Zn in soils and rice grains

Table 1 Concentrations of Se and Zn in soils and rice grains (mg kg-1)

Table 1 summarizes the Se and Zn concentrations measured in soils and rice grains from the study area. The Se concentrations in soil samples ranged from 0.02 to 2.44 mg kg-1, and the Zn concentrations ranged from 3.20 to 279.33 mg kg-1. The average values of Se and Zn concentrations (0.43 ± 0.38 mg kg-1 for Se; 101.06 ± 56.04 mg kg-1 for Zn) are higher than the corresponding background values for Chinese soil (0.29 ± 0.26 mg kg-1 for Se; 74.20 ± 32.78 mg kg-1 for Zn; Wei et al., 1991) and lower than the values prescribed in the Canadian Agriculture Soil Quality Guidelines for the Protection of Environmental Health (1.00 mg kg-1 for Se; 250.00 mg kg-1 for Zn; CCME, 2009; CCME, 2018), indicating that there is no potential risk of Se and Zn pollution in the soil. At present, there is no uniform criterion for the classification of Se and Zn 12

Journal Pre-proof concentrations in soil. Enrichment factor (EF) is often used to assess the enrichment degree of elements in soil, which can be calculated by the following equation (Wang et al., 2019): 𝐸𝐹 =

(Ci/Cref)sample (Bi/Bref)background

where (Ci/Cref)sample is the ratio of Se or Zn and the reference element in the soil samples and (Bi/Bref)background represents the ratio of Se or Zn and the reference element in the natural background environment. In this study, Fe is adopted as the reference element, which is one stable element in the crust and is present at relatively high concentrations (Qiao et al., 2019). The calculation of (Bi/Bref)background refers to the background values for Chinese soil, which for Se, Zn and Fe are 0.29 mg kg-1, 74.2 mg kg-1 and 2.94% respectively. Generally, the soils can be classified as no enrichment (EF<1), slight enrichment (140; Sutherland, 2000). The average EF values of Se and Zn in soils were 1.48 and 1.25 respectively, suggesting that overall the soil Se and Zn on Hainan Island are slightly enriched. The Se concentrations in rice grains ranged from 0.01 to 0.43 mg kg-1, with an average of 0.08 ± 0.08 mg kg-1. According to the classification standard for Se concentration in grains in China (Tan, 1989), a Se concentration lower than 0.025 mg kg-1 is considered deficient. The Se concentrations in 86% of rice grain samples were higher than 0.025 mg kg-1. Yang et al. (2009) administered 4282 questionnaires on 13

Journal Pre-proof Hainan Island and found the daily diet of residents generally contained 463.4 g of cereals. According to the daily cereal consumption per capita and the average Se concentration in rice grains (the main staple food of residents on Hainan Island), the estimated daily Se intake per capita from rice is 37.104 µg day-1. On the basis of a daily dietary rice intake of 300–500 g, as suggested by the China Nutrition Society, the daily average Se intake from rice in China is 7.5–12.5 µg day-1 (Chen et al., 2002), which is far below that on Hainan Island. The Zn concentrations in rice grains ranged from 5.13 to 36.65 mg kg-1 (Table 1), with an average of 16.40 ± 4.95 mg kg-1, which is low compared with the HarvestPlus Program’s target value of 40 mg kg-1 for Zn in grains (Ortiz-Monasterio et al., 2007). Although the threshold value for Zn deficiency in mature grains is not straightforward to establish, a critical concentration range of 20–24 mg kg-1 has been proposed (Rafique et al., 2006). On Hainan Island, 82% and 93% of the rice grain samples have Zn concentrations < 20 and < 24 mg kg-1, respectively. These results reveal that Se concentrations in rice grains are relatively high, while Zn concentrations are relatively low. We suggest that Zn concentrations in rice grains should be increased by adopting a few effective methods, such as plant breeding (genetic biofortification) and fertilization (agronomic biofortification). It is worth noting that the Se and Zn concentrations in soils have no significant correlations with the Se and Zn concentrations in rice grains (Table 2). Previous studies have pointed out that the total Se and Zn concentrations in soils are not reliable indices for the capacity of soil to supply Se and Zn for plant uptake (Navarro-Alarcon and Cabrera-Vique, 2008; Noulas et al., 2018; Ryser et al., 2006). 14

Journal Pre-proof The available Se and Zn concentrations in soils are what can be absorbed and utilized by plants, and they are the key restricting factors for the Se and Zn concentrations of crops (Banjoko and McGrath, 1991; Ma and Rao, 1997; Ryser et al., 2006; Zhao et al., 2005). Nonetheless, there was a significant positive correlation between soil Se and soil Zn (r = 0.154, P = 0.044; Table 2); there was also a positive correlation between rice grain Se and rice grain Zn (r = 0.207, P = 0.006). These correlations may be associated with the composition of soil parent materials and pedogenic processes (Lu et al., 2012; Sun et al., 2013). 3.2. Spatial distributions of Se and Zn in soils and rice grains 3.2.1 Spatial autocorrelation Global spatial autocorrelation analyses of soil Se, soil Zn, rice grain Se, and rice grain Zn were conducted, and the Moran’s I index values were found to be 0.065, 0.309, 0.588, and 0.060, respectively. The Moran’s I index values are greater than zero, which indicates that the distributions of Se and Zn in soils and rice grains have positive correlations. The Z score, which is a standardized statistic, for soil Se, soil Zn, rice grain Se, and rice grain Zn were 1.450 (P = 0.147), 6.265 (P < 0.001), 11.927 (P < 0.001), and 1.308 (P = 0.191), respectively. According to the Z score and P value, the global spatial autocorrelations of soil Zn and rice grain Se present significant spatial clustering, while the soil Se and rice grain Zn exhibit no obvious agglomeration. 3.2.2 Spatial distribution The inverse-distance weighting method was used to explore the spatial 15

Journal Pre-proof distribution characteristics of Se and Zn concentrations in soils and rice grains (Fig. 2). As shown in Fig. 2a, the soil Se in the eastern regions of Hainan Island was obviously higher than that in the western region. The regions with a lower soil Se (< 0.175 mg kg-1) were mainly clustered in three areas, Dongfang, Ledong, and Sanya; a higher soil Se (> 0.400 mg kg-1) was found in Lingao, Haikou, Wenchang, Qionghai, Dingan, Wanning, and Baoting. The results are consistent with previous studies, which showed that the areas with high concentration of soil Se were mainly located in the northeast of Hainan Island (Hao et al., 2018; Liao et al., 2018). Overall, the content of soil Zn in the eastern region was also higher than that in the western region (Fig. 2b). This finding was also reported by Liao et al. (2018). The regions with a higher soil Zn (> 165 mg kg-1) were distributed in Lingao, Haikou and part of Dingan. Fig. 2c shows a different distribution pattern for rice grain Se. Most of the regions with a lower rice grain Se (< 0.040 mg kg-1) were located in the central area, while the western and eastern parts had a higher Se concentration (> 0.070 mg kg-1). The Se concentrations in rice grains were low in Changjiang, Danzhou, Baisha, Wuzhishan, and Qiongzhong; they were high in Chengmai, Haikou, Wenchang, and Wanning. Fig. 2d confirms that rice grain Zn varied substantially across the study area, with a noticeably lower value in Changjiang and Danzou. The regions with a higher rice grain Zn (> 18.5 mg kg-1) were Chengmai, Tunchang, Qionghai, Wanning, and most of Wenchang.

Fig. 2 Spatial distributions of Se and Zn in soils and rice grains in the study area 16

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3.3. Effect of environmental factors on Se and Zn in soils and rice grains 3.3.1. Correlation analysis We quantified the Spearman’s correlation coefficients to evaluate the relationships between the Se and Zn concentrations in soils and rice grains and soil mineral elements, topography parameters, and NDVI (Table 2).

Table 2 Spearman’s correlation coefficients for Se/Zn, soil mineral elements, topography parameters, and NDVI in the study area

The results show that soil Se was positively and significantly correlated with soil Fe. This agrees with results reported by Matos et al. (2016). Chan (2009) revealed that, at pH 5.0, selenite on Fe oxide surface could form inner-sphere complexes, including bidentate and monodentate inner-sphere complexes, and selenate formed inner-sphere monodentate complexes. Liao et al. (2018) collected 8713 soil samples (0–20 cm) on Hainan Island, and reported that the mean value of soil pH was 5.52 ± 0.74, which was conducive to Se adsorption by Fe oxide. As for Fe oxide, its effect is mainly related to the high surface area, which can form stable complexes with soil Se in acidic soil and protect adsorbed Se from leaching. Meanwhile, positive and significant correlations were found for soil Zn with soil Fe and Mn. In acidic soil, Fe and Mn oxides are expected to have a positive surface 17

Journal Pre-proof charge. Although this means that anions are mainly retained, it has repeatedly been reported that increasing oxide concentrations in soil are also linked to enhanced retention capacity for cationic metals (Mahdavi et al., 2012; Zhang et al., 2017b). This is probably because of the colloidal dimensions of the oxides which render them chemically reactive, and because there is still some negative-charge sites onto oxide surfaces when the soil pH is lower than the point of zero charge (Antoniadis and Golia, 2015). To study the importance of oxides on Zn sorption in soil from the Mediterranean, Antoniadis et al. (2018) removed Al, Fe, Mn, and Si oxides from the soils, and found that in acidic soils with low oxide concentrations, the addition of Fe and Al amorphous oxides might reduce the transport of Zn as a result of enhanced sorption. The results of our study showed that soil Se has statistically significant negative correlations with soil K, Na, Ca and Mg, whereas statistically significant positive correlations were found between soil Zn and Ca and Mg. This may be mainly related to the mineral composition of parent material. Furthermore, soil Se and Zn had obviously positive correlations with soil P, as has also been reported in other studies (Prasad et al., 2016; Tian et al., 2016). One early study reported that P could markedly reduce the uptake of Se in wheat and sunflower (Singh and Singh, 1978). Some recent studies have also shown that P could reduce the uptake and accumulation of Se in wheat (Lee et al., 2011; Liu et al., 2018). This is probably because phosphate and selenite have similar physical and chemical properties and compete for absorption into plant roots, and phosphate is more readily taken up by plants while Se is retained in 18

Journal Pre-proof the soil. Owing to phosphate ion carrying negative charges on its surface, Zn can be adsorbed or get precipitated in soil, which reduces its availability and absorption by plants. Studies have found that an increase in P supply depresses the Zn concentrations in plants (Zhang et al., 2017a; Zhu et al., 2001). However, conflicting results about the effect of P application on the uptake of Se and Zn have been reported in plants; more research is needed to explore the specific mechanisms. For rice grains, the results show that Se had statistically significant negative correlations with soil K, Na, and Mg. This is related to the relationships between soil Se and soil K, Na, and Mg. Furthermore, there were statistically significant negative correlations for rice grain Se with elevation and NDVI, and the correlation between Zn and elevation was negative. The close relationship between elevation and soil-available Se was found in a previous study, which demonstrated that available Se content in topsoil significantly decreases as elevation increases (Wang et al., 2013). Peng and Wang (1995) also showed a significant increase of residual Se content with increasing elevation, which reduced Se uptake by plants. Elevation and NDVI were significantly positively correlated with soil Fe (Table 2). With the increase of elevation and NDVI, the concentration of soil Fe increases, which can result in a reduction of soil available Se and Zn, and then inhibit rice uptake. As the elevation increases, the air temperature generally decreases, and TWI also declines (Table 2); this can drive declines in soil respiration rate and organic matter decomposition rate (Kirschbaum, 1995; Zhou et al., 2013). This means that organic matter-bound Se and Zn are less converted to inorganic Se and Zn, and thus the uptake of Se and Zn by rice 19

Journal Pre-proof decreases accordingly. Under the cropping system of three harvests a year on Hainan Island, when the uptake rate of Se by rice is greater than the production and replenishment rate of available Se, the Se content in rice grains may decrease with the increase of NDVI. 3.3.2. SR modeling Table 3 summarizes the results of applying the SR models with Se (in mg kg-1) and Zn (in mg kg-1) in soils and rice grains as dependent variables, and the environmental factors, including Fe (in %), Mn (in mg kg-1), K (in %), Na (in %), Ca (in %), Mg (in %), P (in mg kg-1), elevation (in 100 m), slope (in degrees), TWI, and NDVI, as independent variables. We can determine whether the model offers a good fit for the data based on the F-test and its associated significance level. The significance (Sig.) figure is 0.05 or below showing a statistically significant relationship between independent variables and the dependent variable (Torres-Reyna, 2007). The results from the SR models showed that the main influencing factors for soil Se were soil Fe, Ca, and Na; the main influencing factors for soil Zn were soil Fe, P, and Ca; and the main influencing factor for rice Se and Zn was elevation. The "B" values are the beta coefficients for each independent variable in the regression model. The adjusted R2 value is a modified version of R2 value that has been adjusted for the number of predictors in the model. It reflects the explanatory power of the regression model (Harrison and Sayogo, 2014).

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Journal Pre-proof Table 3 Summary of SR results

The adjusted R2 values for soil Se and Zn associated with the main influencing factors are 0.119 and 0.428, respectively, which indicates that the models separately explain 11.9% and 42.8% of the variations in soil Se and Zn, respectively. On the basis of the regression models, every 1 g kg-1 increase in Fe concentration can increase soil Se by 2.820 μg kg-1 and soil Zn by 0.785 mg kg-1. Every 1 g kg-1 increase in Ca concentration can decrease soil Se by 3.249 μg kg-1 and increase soil Zn by 0.356 mg kg-1. In addition, every 1 g kg-1 increase in Na concentration can decrease soil Se by 5.205 μg kg-1, and every 1 g kg-1 increase in P concentration can increase soil Zn by 0.002 mg kg-1. The variations in rice grain Se and Zn are affected by elevation. With every 100 m increase in elevation, rice grain Se and Zn decrease by 0.022 mg kg-1 and 0.912 mg kg-1, respectively. The adjusted R2 values indicate that the regression models separately explained 6.8% and 2.3% of the variations in rice grain Se and Zn. 3.3.3. GWR modeling Because the SR model cannot reflect the geographic variations of the relationships between Se and Zn and the main influencing factors, we used the GWR model to present the spatially varying relationships (Tu and Xia, 2008). Fig. 3 shows the spatial distributions of local R2 values obtained from the GWR models for soil Se, soil Zn, rice grain Se, and rice grain Zn. The higher the local R2 value, the stronger the 21

Journal Pre-proof spatial correlation found. As shown in Fig. 3a, the local R2 values for soil Se presented a zonal distribution, gradually increasing from the southwest to the northeast. In addition to the soil samples in Dongfang, the local R2 values for soil Zn had a similar distribution. Compared with soil Se, soil Zn was more sensitive to the variations of Fe, P, and Ca, as demonstrated by higher local R2 values (Fig. 3b). In terms of the local R2 value, a stronger spatial association between soil Zn and Fe, P, and Ca was found in the northeast area than in other areas. For rice grain Se, lower R2 values were mainly concentrated in the coastal region of Hainan Island. At higher elevations, the local R2 values were above 0.2 (Fig. 3c), which may be due to the large variation in elevation. The variation resulted in the formation of a complicated natural ecological environment and staggered distribution of the geographical environment, which could influence soil weathering and pedogenic processes. Moreover, Fig. 3d shows the spatial distribution of the local R2 values for rice grain Zn, which is higher in the south than in the north.

Fig. 3 Spatial distributions of local R2 values on the basis of the GWR model: (a) the association of Fe, Ca, and Na concentrations with soil Se, (b) the association of Fe, P, and Ca concentrations with soil Zn, (c) the association of elevation with rice grain Se, and (d) the association of elevation with rice grain Zn.

22

Journal Pre-proof 4. Conclusions The present study analyzed the concentrations and spatial distributions of Se and Zn in soils and rice grains on Hainan Island, and explored the spatial correlations of Se and Zn concentrations with multiple environmental factors, including soil mineral elements (Fe, Mn, K, Na, Ca, Mg, and P), topography parameters (elevation, slope, and TWI), and vegetation coverage (NDVI). The following conclusions can be drawn. First, the concentrations of soil Se and Zn were higher than the background values of Chinese soil. The Se concentrations in rice grains were relatively high, but Zn concentrations were relatively low. Second, the results of Spearman’s correlation and stepwise regression analysis showed that the concentrations of soil Fe, Ca, and Na significantly affected soil Se, and the concentrations of soil Fe, P, and Ca significantly affected soil Zn. For rice grains, elevation had a significant negative impact on Se and Zn concentrations. Third, geographically weighted regression analysis indicated that the influence of Fe, P, and Ca on soil Zn appeared to be relatively strong in the northeast region of Hainan Island, while the influence of elevation on rice grain Se was relatively strong in the central region of Hainan Island. Overall, these results provide evidence that the concentrations of Se and Zn in soils and rice grains can be explained and mapped in terms of environmental factors and predicted at the regional scale. However, there are many other factors that affect the distributions of Se and Zn in soils and rice grains, such as soil bacterial community, water availability, microclimate, and agricultural activity. Further research concerning these factors is needed to clarify the mechanism of Se and Zn distribution and migration in 23

Journal Pre-proof agro-ecosystems. Conflict of interest The authors declare no conflict of interest. Acknowledgments This research was financially supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA19040303), the National Natural Science Foundation of China (No. 41671499), and the Second Tibetan Plateau Scientific Expedition and Research Program of Ministry of Science and Technology of the People's Republic of China (No. 2019QZKK0607). References Ali, F., Peng, Q., Wang, D., Cui, Z.W., Huang, J., Fu, D.D., Liang, D.L., 2017. Effects of selenite and selenate application on distribution and transformation of selenium fractions in soil and its bioavailability for wheat (Triticum aestivum L.). Environmental Science and Pollution Research. 24, 8315-8325. Ali, G.A., Roy, A.G., Legendre, P., 2010. Spatial relationships between soil moisture patterns and topographic variables at multiple scales in a humid temperate forested catchment. Water Resources Research. 46, 2290-2296. Alloway, B.J., 2011. Zinc in Soils and Crop Nutrition (2nd edn.). International Zinc Association and the International Fertilizer Industry Association, Brussels, Belgium and Paris, France, pp 16-17. 24

Journal Pre-proof Antoniadis, V., Golia, E.E., 2015. Sorption of Cu and Zn in low organic matter-soils as influenced by soil properties and by the degree of soil weathering. Chemosphere. 138, 364-369. Antoniadis, V., Shaheen, S.M., Tsadilas, C.D., Selim, M.H., Rinklebe, J., 2018. Zinc sorption by different soils as affected by selective removal of carbonates and hydrous oxides. Applied Geochemistry. 88, 49-58. Banjoko, V.A., McGrath, S.P., 1991. Studies of the distribution and bioavailability of soil zinc fractions. Journal of the Science of Food and Agriculture. 57, 325-334. Beven, K.J., Kirkby, M.J., 1979. A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Bulletin. 24, 43–69. Boschetti, M., Stroppiana, D., Brivio, P.A., Bocchi, S., 2009. Multi-year monitoring of rice crop phenology through time series analysis of MODIS images. International Journal of Remote Sensing. 30, 4643-4662. Buchanan, B.P., Fleming, M., Schneider, R.L., Richards, B.K., Archibald, J., Qiu, Z., Walter, M.T., 2014. Evaluating topographic wetness indices across central New York agricultural landscapes. Hydrology and Earth System Sciences. 18, 3279-3299. CCME, Canadian Council of Ministers of the Environment. 2009. Canadian soil quality guidelines for the protection of environmental and human health: Selenium (2009). In: Canadian environmental quality guidelines, 1999, Canadian Council of Ministers of the Environment, Winnipeg. CCME, Canadian Council of Ministers of the Environment. 2018. Canadian soil 25

Journal Pre-proof quality guidelines for the protection of environmental and human health: zinc (2018). In: Canadian environmental quality guidelines, 1999, Canadian Council of Ministers of the Environment, Winnipeg. Chan, Y.T., Kuan, W.H., Chen, T.Y., Wang, M.K., 2009. Adsorption mechanism of selenate and selenite on the binary oxide systems. Water Research. 43, 4412–4420. Chen, L.C., Yang, F.M., Xu, J., Hu, Y., Hu, Q.H., Zhang, Y.L., Pan, G.X., 2002. Determination of selenium concentration of rice in China and effect of fertilization of selenite and selenate on selenium content of rice. Journal of Agricultural and Food Chemistry. 50, 5128–5130. Chuan, M.C., Shu, G.Y., Liu, J.C., 1996. Solubility of heavy metals in a contaminated soil: Effects of redox potential and pH. Water, Air and Soil Pollution. 90, 543–556. Eich-Greatorex, S., Sogn, T.A., Øgaard, A.F., Aasen, I., 2007. Plant availability of inorganic and organic selenium fertiliser as influenced by soil organic matter content and pH. Nutrient Cycling in Agroecosystems. 79, 221–231. Fernández-Martínez, A., Charlet, L., 2009. Selenium environmental cycling and bioavailability: a structural chemist point of view. Reviews in Environmental Science and Biotechnology. 8, 81–110. Fissore, C., Dalzell, B.J., Berhe, A.A., Voegtle, M., Evans, M., Wu, A., 2017. Influence of topography on soil organic carbon dynamics in a Southern California grassland. Catena. 149, 140-149. 26

Journal Pre-proof Griffiths, R.P., Madritch, M.D., Swanson, A.K., 2009. The effects of topography on forest soil characteristics in the Oregon Cascade Mountains (USA): Implications for the effects of climate change on soil properties. Forest Ecology and Management. 257, 1–7. Guindin-Garcia, N., Gitelson, A.A., Arkebauer, T.J., Shanahan, J., Weiss, A., 2012. An evaluation of MODIS 8- and 16-day composite products for monitoring maize green leaf area index. Agricultural and Forest Meteorology. 161, 15-25. Hao, Z., Chen, L.H., Wang, C.L., Zou, X.Q., Zheng, F.Q., Feng, W.H., Zhang, D.R., Peng, L., 2019. Heavy metal distribution and bioaccumulation ability in marine organisms from coastal regions of Hainan and Zhoushan, China. Chemosphere. 226, 340-350. Hao, Z., Li, Y.H., Zou, X.Q., Chen, L.H., 2018. Distribution and transfer of Se in soil of Hainan Province. IOP Conference Series: Earth and Environmental Science. 199, 022004. Harrison, T.M., Sayogo, D.S., 2014. Transparency, participation, and accountability practices in open government: A comparative study. Government Information Quarterly. 31, 513-525. HSY, Hainan Statistical Yearbook. 2017. Statistical Bureau of Hainan Province and Survey Office of National Bureau of Statistics in Hainan. China Statistical Press, Beijing. Jenson, S.K., Domingue, J.O., 1988. Extracting topographic structure from digital elevation data for geographic information system analysis. Photogrammetric 27

Journal Pre-proof Engineering and Remote Sensing. 54, 1593–1600. Karaca, S., Gülser, F., Selçuk, R., 2018. Relationships between soil properties, topography and land use in the Van Lake Basin, Turkey. Eurasian Journal of Soil Science. 7, 115-120. Kiekens, L., 1995. Zinc, in Alloway, B.J. (ed.) Heavy Metals in Soils (2nd edn.). Blackie Academic and Professional, London, pp 284-305. Kirschbaum, M.U.F., 1995. The temperature dependence of soil organic matter decomposition, and the effect of global warming on soil organic C storage. Soil Biology and Biochemistry.27: 753-760. Lee, S., Woodard, H.J., Doolittle, J.J., 2011. Effect of phosphate and sulfate fertilizers on selenium uptake by wheat (Triticum aestivum). Soil Science and Plant Nutrition. 57(5), 696–704. Li, Z., Liang, D.L., Peng, Q., Cui, Z.W., Huang, J., Lin, Z.Q., 2017. Interaction between selenium and soil organic matter and its impact on soil selenium bioavailability: a review. Geoderma. 295, 69–79. Liao, X.J., Zhang, C., Sun, G.Y., Li, Z.G., Shang, L.H., Fu, Y.R., He, Y.S., Yang, Y., 2018. Assessment of metalloid and metal contamination in soils from Hainan, China. International Journal of Environmental Research and Public Health. 15,454. Liu, H.E., Shi, Z.W., Li, J.F., Zhao, P., Qin, S.Y., Nie, Z.J., 2018. The impact of phosphorus supply on selenium uptake during hydroponics experiment of winter wheat (Triticum aestivum) in China. Frontiers in Plant Science. 9, 373. 28

Journal Pre-proof Lothar, R., Philip, G., 2000. Zinc and the immune system. Proceedings of the Nutrition Society, 59, 541-552. Lu, A.X., Wang, J.H., Qin, X.Y., Wang, K.Y., Han, P., Zhang, S.Z., 2012. Multivariate and geostatistical analyses of the spatial distribution and origin of heavy metals in the agricultural soils in Shunyi, Beijing, China. Science of the Total Environment. 425, 66-74. Ma, L.Q., Rao, G.N., 1997. Chemical fractionation of cadmium, copper, nickel, and zinc in contaminated soils. Journal of Environmental Quality. 13, 372–376. Mahdavi, S., Jalali, M., Afkhami, A., 2012. Removal of heavy metals from aqueous solutions using Fe3O4, ZnO, and CuO nanoparticles. Journal of Nanoparticle Research. 14, 846. Manandhar, R., Odeh, I.O.A., 2014. Interrelationships of land use/cover change and topography with soil acidity and salinity as indicators of land degradation. Land. 3, 282-299. Matos, R.P., Lima, V.M.P., Windmöller, C.C., Nascentes, C.C., 2016. Correlation between the natural levels of selenium and soil physicochemical characteristics from the Jequitinhonha Valley (MG), Brazil. Journal of Geochemical Exploration. 172, 195-202. Matsushita, B., Yang, W., Chen, J., Onda, Y., Qiu, G., 2007. Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-Density Cypress Forest. Sensors. 7, 2636-2651. 29

Journal Pre-proof NASA,

National

Aeronautics

and

Space

Administration.

2019.

EARTHDATA-NASA's Earth Observing System Data and Information System. https://earthdata.nasa.gov/ (accessed 13 March 2019). Natasha, Shahid, M., Niazi, N.K., Khalid, S., Murtaza, B., Bibi, I., Rashid, M.I., 2018. A critical review of selenium biogeochemical behavior in soil-plant system with an inference to human health. Environmental Pollution. 234, 915-934. Navarro-Alarcon, M., Cabrera-Vique, C., 2008. Selenium in food and the human body: a review. Science of the Total Environment. 400, 114–141. Noulas, C., Tziouvalekas, M., Karyotis, T., 2018. Zinc in soils, water and food crops. Journal of Trace Elements in Medicine and Biology. 49, 252-260. Ortiz-Monasterio, J.I., Palacios-Rojas, N., Meng, E., Pixley, K., Trethowan, R., Pena, R.J., 2007. Enhancing the mineral and vitamin content of wheat and maize through plant breeding. Journal of Cereal Science. 46, 293–307. Özkan, U., Gökbulak, F., 2017. Effect of vegetation change from forest to herbaceous vegetation cover on soil moisture and temperature regimes and soil water chemistry. Catena. 149, 158-166. Peng, A., Wang, Z., 1995. Selenium environmental bioinorganic chemistry. China Environmental Science Press, Beijing. Phil-Eze, P.O., 2010. Variability of soil properties related to vegetation cover in a tropical rainforest landscape. Journal of Geography and Regional Planning. 3, 177-184. Prasad R., Shivay, Y.S., Kumar, D., 2016. Interactions of Zinc with Other Nutrients in 30

Journal Pre-proof Soils and Plants - A Review. Indian Journal of Fertilisers. 12, 16-26. Qiao, P.W., Yang, S.C., Lei, M., Chen, T.B., Dong, N., 2019. Quantitative analysis of the factors influencing spatial distribution of soil heavy metals based on geographical detector. The Science of the Total Environment. 664, 392-413. Qin, H.B., Zhu, J.M., Liang, L., Wang, M.S., Su, H., 2013. The bioavailability of selenium and risk assessment for human selenium poisoning in high-Se areas, China. Environment International. 52, 66–74. Qiu, Y., Fu, B.J., Wang, J., Chen, L.D., 2001. Soil moisture variation in relation to topography and land use in a hillslope catchment of the Loess Plateau, China. Journal of Hydrology. 240, 243-263. Rafique, E., Rashid, A., Ryan, J., Bhatti, A.U., 2006. Zinc Deficiency in Rainfed Wheat in Pakistan: Magnitude, Spatial Variability, Management, and Plant Analysis Diagnostic Norms. Communications in Soil Science and Plant Analysis. 37, 181-197. Rankine, C., Sánchez-Azofeifa, G.A., Guzmán, J.A., Espirito-Santo, M.M., Sharp, I., 2017. Comparing MODIS and near-surface vegetation indexes for monitoring tropical dry forest phenology along a successional gradient using optical phenology towers. Environmental Research Letters. 12, 105007. Rayman, M.P., 2012. Selenium and human health. Lancet. 379. 1256–1268. Rouse, J.W.J., Haas, R.H., Schell, J.A., Deering, D.W., 1974. Monitoring vegetation systems in the Great Plains with ERTS. Nasa Special Publication. 351, 309–317. Ryser, A.L., Strawn, D.G., Marcus, M.A., Fakra, S., Johnson-Maynard, J.L., Möller, 31

Journal Pre-proof G., 2006. Microscopically focused synchrotron X-ray investigation of selenium speciation in soils developing on reclaimed mine lands. Environmental Science and Technology. 40, 462–467. Sharma, V.K., McDonald, T.J., Sohn, M., Anquandah, G.A.K., Pettine, M., Zboril, R., 2015. Biogeochemistry of selenium. A review. Environmental Chemistry Letters. 13, 49-58. Singh, D., McLaren, R.G., Cameron, K.C., 2008. Effect of pH on Zinc Sorption–Desorption by Soils. Communications in Soil Science and Plant Analysis. 39, 2971-2984. Singh, M., Singh, N., 1978. Selenium toxicity in plants and its detoxication by phosphorus. Soil Science. 126, 255–262. Soltani, S.M., Hanafi, M.M., Wahid, S.A., Kharidah, S.M.S., 2015. Zinc fractionation of tropical paddy soils and their relationships with selected soil properties. Chemical Speciation and Bioavailability. 27, 53-61. Song, W.J., Li, Y.H., Hao, Z., Li, H.R., Wang, W.Y., 2016. Public health in China: An environmental and socio-economic perspective. Atmospheric Environment. 129, 9-17. Song, Y., Njoroge, J.B., Morimoto, Y., 2013. Drought impact assessment from monitoring the seasonality of vegetation condition using long-term time-series satellite images: a case study of Mt. Kenya region. Environmental Monitoring and Assessment. 185, 4117- 4124. Sun, C.Y., Liu, J.S., Wang, Y., Sun, L.Q., Yu, H.W., 2013. Multivariate and 32

Journal Pre-proof geostatistical analyses of the spatial distribution and sources of heavy metals in agricultural soil in Dehui, Northeast China. Chemosphere. 92, 517–523. Sutherland, R.A., 2000. Bed sediment-associated trace metals in an urban stream, Oahu, Hawaii. Environmental Geology. 39, 611–627. Tan, J.A., 1989. The Atlas of Endemic Diseases and their Environments in the People's Republic of China. Environmental Life Elements and Health. Science Press, Beijing, p. 193. Tian, H., Ma, Z.Z., Chen, X.L., Zhang, H.Y., Bao, Z.Y., Wei, C.H., Xie, S.Y., Wu, S.T., 2016. Geochemical characteristics of selenium and its correlation to other elements and minerals in selenium-enriched rocks in Ziyang County, Shaanxi Province, China. Journal of Earth Science. 27, 763-776. Tolu, J., Thiry, Y., Bueno, M., Jolivet, C., Potin-Gautier, M., Le Hécho, I., 2014. Distribution and speciation of ambient selenium in contrasted soils, from mineral to organic rich. Science of the Total Environment. 479, 93–101. Torres-Reyna, O., 2007. Linear regression using Stata. Princeton University. (http://dss.princeton.edu/training/) Tu, J., Xia, Z.G., 2008. Examining spatially varying relationships between land use and water quality using geographically weighted regression I: model design and evaluation. Science of the Total Environment. 407, 358–378. Wang, J., Li, H.R., Li, Y.H., Yu, J.P., Yang, L.S., Feng, F.J., Chen, Z., 2013. Speciation, distribution and bioavailability of soil selenium in the Tibetan plateau Kashin-Beck disease area-a case study in Songpan County, Sichuan Province, 33

Journal Pre-proof China. Biological Trace Element Research. 156, 367–375. Wang, S., Cai, L.M., Wen, H.H., Luo, J., Wang, Q.S., Liu, X., 2019. Spatial distribution and source apportionment of heavy metals in soil from a typical county-level city of Guangdong Province, China. Science of the Total Environment. 655, 92-101. Wei, F.S., Yang, G.Z., Jiang, D.Z., Liu, Z.H., Sun, B.M., 1991. Basic statistics and its characteristics of the background values for elements of Chinese Soils. Environmental Monitoring in China. 7, 1-6 (in Chinese). Welsch, D.L., Kroll, C.N., McDonnell, J.J., Burns, D.A., 2001. Topographic controls on the chemistry of subsurface stormflow. Hydrological Processes. 15, 1925-1938. Xu, Y.F., Li, Y.H., Li, H.R., Wang, L., Liao, X.Y., Wang, J., Kong, C., 2018. Effects of topography and soil properties on soil selenium distribution and bioavailability (phosphate extraction): A case study in Yongjia County, China. Science of the Total Environment. 633, 240-248. Yang, B., Pan, X.H., Li, Y.Z., Chen, Y.Q., Zhai, F.Y., 2009. Survey of dietary patterns and nutrients intake status of residents in Hainan. China Tropical Medicine. 9, 1673-1692 (in Chinese). Zhang, S.L., Zhang, X.Y., Huffman, T., Liu, X.B., Yang, J.Y., 2011. Influence of topography and land management on soil nutrients variability in Northeast China. Nutrient Cycling in Agroecosystems. 89, 427-438. Zhang, W., Chen, X. X., Liu, Y.M., Liu, D.Y., Chen, X.P., Zou, C.Q., 2017a. Zinc uptake by roots and accumulation in maize plants as affected by phosphorus 34

Journal Pre-proof application and arbuscular mycorrhizal colonization. Plant and Soil. 413, 59-71. Zhang, W., Ge, M., Liu, X.L., Cen, M.Y., 2016. Optimal weighted combinatorial forecasting model of QT dispersion of ECGs in Chinese adults. International Journal of Biometeorology. 60, 1089–1098. Zhang, W., Huang, X., Jia, Y., Rees, F., Tsang, D.C.W., Qiu, R., Wang, H., 2017b. Metal immobilization by sludge-derived biochar: roles of mineral oxides and carbonized organic compartment. Environmental geochemistry and health. 39, 379-389. Zhao, S.H., Yu, W.T., Zhang, L., Shen, S.M., Ma, Q., 2005. Biogeochemical cycling of selenium, nutrition adjustment and differentiation cause in environment. Chinese Journal of Ecology. 24, 1197–1203 (in Chinese). Zhong, B., Xu, Y.J., 2009. Topographic Effects on Soil Organic Carbon in Louisiana Watersheds. Environmental Management. 43, 662-672. Zhou, Z., Jiang, L., Du, E.Z., Hu, H.F., Li, Y.D., Chen, D.X., Fang, J.Y., 2013. Temperature and substrate availability regulate soil respiration in the tropical mountain rainforests, Hainan Island, China. Journal of Plant Ecology, 6, 325-334. Zhu, Y.G., Smith, S.E., Smith, F.A., 2001. Zinc (Zn)-phosphorus (P) interactions in two cultivars of spring wheat (Triticum aestivum L.) differing in P uptake efficiency. Annals of Botany. 5, 941-945.

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Declaration of interests  The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

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Fig. 1 Location of Hainan Island and spatial distributions of sampling sites (JFL, Jianfengling Mountain; BWL, Bawangling Mountain; YGL, Yinggeling Mountain; WZS, Wuzhishan Mountain; DLS, Diaoluoshan Mountain).

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Fig. 2 Spatial distributions of Se and Zn in soils and rice grains in the study area

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Fig. 3 Spatial distributions of local R2 values on the basis of the GWR model: (a) the association of Fe, Ca, and Na concentrations with soil Se, (b) the association of Fe, P, and Ca concentrations with soil Zn, (c) the association of elevation with rice grain Se, and (d) the association of elevation with rice grain Zn.

Journal Pre-proof Figure captions:

Fig. 1 Location of Hainan Island and spatial distributions of sampling sites (JFL, Jianfengling Mountain; BWL, Bawangling Mountain; YGL, Yinggeling Mountain; WZS, Wuzhishan Mountain; DLS, Diaoluoshan Mountain).

Fig. 2 Spatial distributions of Se and Zn in soils and rice grains in the study area

Fig. 3 Spatial distributions of local R2 values on the basis of the GWR model: (a) the association of Fe, Ca, and Na concentrations with soil Se, (b) the association of Fe, P, and Ca concentrations with soil Zn, (c) the association of elevation with rice grain Se, and (d) the association of elevation with rice grain Zn.

Journal Pre-proof Highlights 

Effect of soil chemistry, topography and vegetation on soil–rice Se/Zn was studied.



Hainan rice grains were relatively high in Se but low in Zn.



Soil mineral composition had a significant impact on Se and Zn contents in soils.



Soil-Fe difference of 1 g/kg changed soil Se by 2.820 μg/kg and Zn by 0.785 mg/kg.



Se and Zn contents in rice grains decreased with increasing elevation.

Journal Pre-proof Table 1 Concentrations of Se and Zn in soils and rice grains (mg kg-1) Soil

Rice grain

Range

GM

AM

SD

Range

GM

AM

SD

Se

0.02–2.44

0.31

0.43

0.38

0.01–0.43

0.05

0.08

0.08

Zn

3.20–279.33

84.83

101.06

56.04

5.13–36.65

15.68

16.40

4.95

a GM:

geometric mean, AM: arithmetic mean, SD: standard deviation

Table 2 Spearman’s correlation coefficients for Se/Zn, soil mineral elements, topography parameters, and NDVI in the study area

Fe

Mn

K

Na

Ca

Mg

P

Elevation

Slope

TWI

Fe

1.000

Mn

0.661**

1.000

K

-0.066

-0.177*

1.000

Na

0.029

0.104

0.132

1.000

Ca

0.109

0.352**

0.042

0.098

1.000

Mg

0.427**

0.444**

0.144

0.177*

0.632**

1.000

P

0.417**

0.529**

-0.020

-0.063

0.198**

0.314**

1.000

Elevation

0.175*

0.038

0.228**

0.033

0.234**

0.216**

0.093

1.000

Slope

0.113

-0.015

0.076

-0.041

0.028

0.082

-0.002

0.582**

1.000

TWI

0.014

0.001

0.011

0.139

-0.165*

-0.033

0.045

-0.375**

-0.607**

1.000

NDVI

0.190*

0.071

0.072

-0.059

0.198**

0.167*

0.056

0.721**

0.511**

-0.409**

NDVI

1.000

Soil Se

Soil Zn

grain Se

grain Zn

Soil Se

0.238**

0.026

-0.237**

-0.274**

-0.224**

-0.171*

0.163*

-0.015

0.016

0.004

0.036

1.000

Soil Zn

0.567**

0.581**

-0.102

0.042

0.189*

0.341**

0.537**

0.078

0.103

0.013

0.014

0.154*

1.000

grain Se

-0.109

-0.124

-0.187*

-0.296**

-0.144

-0.184*

-0.125

-0.265**

-0.094

-0.004

-0.214**

0.119

-0.128

1.000

grain Zn

-0.007

0.061

0.066

0.015

0.111

0.073

0.043

-0.143

-0.103

0.133

-0.113

-0.029

0.085

0.207**

a

*and ** indicate that the correlations are significant at p<0.05 and p<0.01, respectively.

1.000

Journal Pre-proof Table 3 Summary of SR results Dependent variable

Independent variables

B

Soil Se

Fe, Ca, Na

0.028, -0.032, -0.052

Soil Zn

Fe, P, Ca

7.849, 0.019, 3.562

Grain Se

Elevation

Grain Zn

Elevation

Intercept

R2

Adjusted R2

F

Sig.

0.422

0.135

0.119

8.778

0.000

40.084

0.438

0.428

43.958

0.000

-0.022

0.095

0.073

0.068

13.498

0.000

-0.912

17.079

0.029

0.023

5.112

0.025