Geostatistical analysis of the horizontal distribution of soil zinc in Guangdong, China

Chunlin Yang1*, Ruiping Guo2, Rongzeng Liu1, Qingling Yue1, Xiujuan Ren1, Zhifeng.Wu3* 1 Henan Institute of Science and Technology, Xinxiang, 453003, China

benjami@163.com 2 Nuclear and Radiation Safety Center, Ministry of Environmental Protection, Beijing 100082, China

guorp2011@163.com 3 Guangdong Institute of Eco-environmental and Soil Sciences, Guangzhou 510650, China

zfwu@soil.cn

Abstract—Spatial distributions of soil Zn concentration in three horizontal soils in Guangdong, China, were surveyed and analyzed using geostatisitic and GIS. A total of 260 soil profiles data followed an approximately lognormal distribution. The Zn geometric mean concentration of 38.4 mg/kg in surface soils is higher than that in global soils. From A- to C-horizon Zn geometric mean concentrations had an increasing tendency of 38.4, 41.9 to 45.9 mg/kg. The ordinary kriging estimates of Zn concentration were mapped. It showed higher local concentration around big city and historical mining area. The soil Zn distribution was mainly dependent on bedrock properties. The anthropogenic impact is distinguished in local areas such as mining areas，where the Zn concentration is higher than their guide value. The results showed a strong gradient of stock of Zn around the mining area.

Soil Zn; Soil distribution; Spatial analysis; (key words)

I. INTRODUCTION Zn is a potentially toxic element to humans at high

concentrations. Even though zinc is a very essential requirement for a healthy body, excess zinc can be harmful, and cause zinc toxicity(Fosmire 1990).There is evidence of induced copper deficiency at low intakes of 100–300 mg /kg. The USDA RDA is 15 mg /kg. Even lower levels, closer to the RDA, may interfere with the utilization of copper and iron or to adversely affect cholesterol.Because of the potential threaten to human health, much work have been done on elevated quantities of Zn in soils (Carlosena et al., 1998).Soil is an important pathway of human Zn exposure in soil and dust is now widely accepted (Mielke and Reagan, 1998) and Zn in soil is a lesser-known but equally critical cause of Zn poisoning in children. Levels of zinc in rivers flowing through industrial or mining areas can be as high as 20 ppm (Emsley,2001). Effective sewage treatment greatly reduces this; treatment along the Rhine, for example, has decreased zinc levels to 50 ppb. Concentrations of zinc as low as 2 ppm adversely affects the amount of oxygen that fish can carry in their blood (Heath,1995). Soils contaminated with zinc through the mining of zinc-containing ores, refining, or where zinc-containing sludge is used as fertilizer, can contain several grams of zinc per kilogram of dry soil. Levels of zinc in excess of 500 ppm in soil interfere with the ability of plants to absorb other essential metals, such as iron and manganese. Zinc levels of 2000 ppm to 180,000 ppm (18%) have been recorded in some soil

samples.Background concentrations of Zn that occur naturally in soils in the world average 64 mg/kg with a range of 5 to 770 mg/kg (Kabata-Pendia and Dudka, 1991). In China, the average Zn concentration in soils is 35mg/kg. The baseline values for Zn in agricultural soils in European Mediterranean region was 28 mg/kg (Micó et al., 2007).In order to identify patterns in the spatial distribution of soil Zn concentration, it is essential to present soil survey data in the form of map. The background value was defined as the natural concentration of heavy metals in soils without human influence (Salminen and Gregorauskiene, 2000). In fact, totally unpolluted soils are almost impossible to find in Guangdong because of the long distance transport of trace pollutants and human influences. The baseline values can be used to expect the range of concentrations for heavy metals, including the diffuse input of these elements to soils (Tack et al., 1997).

II. METHODS

A. Description of the study area Guangdong province is located in the southern part of the

south China (Fig. 1A), between latitude 20° 10′–25°31′(N) and longitude 109°41′–117°17′(E). The area influences by a subtropical monsoon climate with an average annual precipitation of 1336 mm and averages annual evaporation 1100 mm. Its average annual temperature is 17–27 °C and averages 1828 h of sunshine annually. From north to south the altitude of landform decreases and coteau, platform, and plain alternate. The coteau area is up to about 60% of the total area. Regional faults with NNE- or NE-treading are the main geological character, and always cut through the regional sandstone and sandshale areas. Granite is the most extensive parent rock of soil forming, accounted for more than 40% of Guangdong area. Moreover, the larger area of limestone and sandshale are cropped out in study area; and some basalt parent rock is in the Leizhou Peninsula (Fig. 1B).

Guangdong soil profile is the typical Al-enriched weathering profile and is the product of the latest stage of weathering. It had been developed on a variety of rocks, such as, granite, sandshale, limestone, and basalt. Because of the intensive eluviation and illuviation in the hydrothermal condition of study area, the soil profiles are deficient in soluble salt, alkali metal, and alkali-earth metal, but rich in Fe

and Al oxides and H+ (Lan et al., 2003). Therefore, the average pH value in Guangdong soil profiles is acidic in nature.

Figure 1. Location of Guangdong Province in China (A), geological sketch

indicating the distribution of limestone and sandstone (B), and sampling locations (C) (number 1 is Beijiang River, 2 is Xijiang River, 3 is Dongjiang

River, 4 is Huanjiang River, and 5 is Jianjiang.

B. Sampling and chemical analysis Soil samples used in this study were collected from

locations shown in (Fig. 1C). The sampling points in each area were selected on the flat terrain and far from major roads based on the map of bedrock distribution. Soil samples from the 260 soil profiles with 1.5 m length, 0.8 m width, and 1.2 m depth, were collected for investigating the distribution of Soil organic matter in different soil horizons. All soil samples were air-dried, and crushed and then divided into two portions. One portion was sieved through a 20 mesh nylon screen (about 1 mm aperture size) for analysis of soil properties and stored at room temperature (25oC). The second portion was passed through a 200-mesh nylon sieve prior to the acid digest procedure. Then, soils samples were characterized for organic carbon content determined by the Walkley-Black method.

C. Data analysis All soil organic matter contents were presented on a dry

matter basis. These values were transformed to logarithms (base 10) because they had positive-skewed frequency distributions (Table 1). Because the data fit a lognormal distribution (Fig. 2), the central tendency and variation of the data were expressed as the geometric mean (GM) and geometric standard deviations (GSD), respectively.

D. Geostatistical analysis

Techniques of geostatistics include the use of semivariograms, kriging and multivariate analysis. Soil organic matter concentration measured in soil samples, like almost all total trace element concentrations measured in soil, displays an asymmetric, skewed distribution of data (Webster, 1994). Skewed distribution can be related to outliers due to sampling or measurement error, or data values that are realistic but underrepresented in the sampling. Different geostatistical procedures were used for dealing with skewed

data and the results were compared in order to choose the most relevant one (Lark, 2000).

Thus, three horizons geostatistical procedure were performed by lognormal kriging (LogK). For these three procedures, we first calculated the experimental variogram, and fitted it using spherical models. Then, we calculated the kriging maps.

There are some methods now available to make local estimates of spatial distributions. The classical estimator of the variogram was first used,

[ ]2)()(21)( hxZxZEh ii +−=γ

(1)

where γ(h) is the semi-variogram, Z(xi) is the Soil organic matter Concentration at location xi, h is the lag distance . We usually used the robust location estimator of the variogram, the equation is:

(2)

where γ(h) is the semi-variogram, Z(xi) is the data of Soil organic matter concentration at location xi, h is the lag distance and N(h) is the number of pairs of points which are h distance apart .

If the data are truly lognormal, a better solution for skewed data is first to transform data by taking logarithms, then, proceeding as ordinary kriging. For an estimate of the original scale of the data, back transform the estimator. The general form of this back transformation is:

])(21)(exp[)( 02

LK00LK^^

ϕσ −+= ΥΖ xxx (3)

where )(

21

02LK xσ

is the kriging variance, and ϕ is the Lagrange multiplier in the ordinary kriging.

Table 1. Summary statistics of Soil organic matter concentration (/%) for A, B, C Horizons

Horizon Number Geometric

Mean Mean

G.S.D

A 260 2.19 2.76 1.70 B 258 0.76 0.99 1.80 C 259 0.43 0.69 1.86

III. RESULTS AND DISCUSSION

A. Descriptive parameters and probability distribution The concentration of Zn in soil samples from A-horizon

ranged 5.6-378mg/kg, with an arithmetic mean of 49.8mg/kg. In soils from B-horizon, the concentration range was from 4.3

[( ) ]2

1)()(

)(21)(ˆ hxZxZ

hNh i

hN

ii +−∑=

=γ

to 330 mg/kg with a mean of 37.8 mg/kg. The C-horizon soil Zn concentrations between 40.9 and 520 mg/kg. The higher concentration was observed in soils from C-horizon.

The histogram of Zn (Fig. 2) shows clearly that the statistical distributions of the raw data of three horizons are all positively skewed, but the log-transformed data are near normal.

Figure 2. Distribution frequency of soil Zn concentration in A-, B-and C-

horizon soils in Guangdong (left: origin data; right: log10 transformed)

B. Spatial Variations of soil organic matter All experimental variograms show large nugget effect. Fig.

3 shows omnidirectional experimental variograms and fitted variogram models for the three geostatistical procedures. In this figure, all experimental variograms level off to reach a constant sill. They have rather large nugget values. For all experimental variograms, the nugget/sill ratios are about 0.25 to 0.44 (Table II).

To illustrate the spatial distribution features, a map for Zn concentrations is shown in Fig. 4. The map clearly showed a strong gradient on the northern part of the area. Clear spatial patterns can be identified, showing high concentrations in the limestone and sandshale areas (Fig. 4). And the history mining area was also a reason of high soil concentrations of Zn (Fig. 4). It’s reported that paddy soils around the Lechang, (mining area) Pb/Zn Mine, in Guangdong, were heavily contaminated by Zn. The reason was that the irrigation of wastewater discharged from the mine waste dumps (Yang et al., 2004).

Figure 3. Omnidirectional semivariograms for three horizons

Table II. Parameters of the fitted variogram models

Horizon Model

Nugget Sill

Range (km)

A Spherical 0.31 0.17 120 B Spherical 0.25 0.99 120 C Spherical 0.43 0.69 120

Figure 4. Soil Zn concentrations spatial distribution maps of A-, B-, C-Horizon

C. Vetical variation In the present study, Means of A-, B-, and C-horizontal

soils had a mean concentration of 38.4mg/kg, 41.9 mg/kg to 45.9 mg/kg from A- to C-horizontal soils (Table.I), and contours of concentration displayed similar spatial distribution patterns (Fig.4). These similar distributional patterns were that the soil samples with high concentration of Zn mainly located in the limestone and sandshale areas, through these parent rocks also varied in their Zn content, from small amounts to up to 428 mg/kg. It was further confirmed that the distribution of Zn was controlled by properties of regional parent rocks.

IV. CONCLUSIONS

The datasets for Zn in Guangdong were log-normally distributed in three horizons. It’s observed that a good variogram structure of Zn in three horizons, revealing that there

are clear spatial patterns of Zn on the distribution maps. The kriging interpolated maps have shown areas with outliers of Zn concentrations in history mining areas and in Guangzhou. This study can reflect the natural distribution pattern of soil Zn in large scale and show the higher concentrations areas were influenced by human activity in Guangdong.

ACKNOWLEDGMENT This work was funded by the National Natural Science Fund (41001353) and National Social Science Fund (11BJT001) in China.

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