ARTICLE

J. Cent. South Univ. (2019) 26: 410-421

DOI: https://doi.org/10.1007/s11771-019-4013-y

Dynamic change and diagnosis of physical, chemical and biological properties in bauxite residue disposal areas

GUO Ying(郭颖)¹, ZHU Feng(朱锋)¹, WU Chuan(吴川)¹, TIAN Tao(田桃)¹,RICHARD J. Haynes², XUE Sheng-guo(薛生国)¹

1. School of Metallurgy and Environment, Central South University, Changsha 410083, China;

2. School of Agriculture and Food Sciences, the University of Queensland, Brisbane, Australia

Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Abstract: Vegetation encroachment occurred in bauxite residue disposal area (BRDA) following natural weathering processes, whilst the typical indicators of soil formation are still uncertain. Residue samples were collected from the BRDA in Central China, and related physical, chemical and biological indicators of bauxite residue with different storage years were determined. The indicators of soil formation in bauxite residue were selected using principal component analysis, factor analysis, and comprehensive evaluation to establish soil quality diagnostic index model on disposal areas. Following natural weathering processes, the texture of bauxite residue changed from silty loam to sandy loam. The pH and EC decreased, whilst porosity, nutrient element content and microbial biomass increased. The identified minimum data set (MDS) included available phosphorus (AP), moisture content (MC), C/N, sand content, total nitrogen (TN), microbial biomass carbon (MBC), and pH. The soil quality index of bauxite residue increased, and the relative soil quality index decreased from 1.89 to 0.15, which indicated that natural weathering had a significant effect on improveing the quality of bauxite residue and forming a new soil-like matrix. The diagnostic model of bauxite residue was established to provide data support for the regeneration on disposal area.

Key words: bauxite residue disposal area; soil properties; minimum data set; diagnostic indices; natural weathering; soil formation in bauxite residue

Cite this article as: GUO Ying, ZHU Feng, WU Chuan, TIAN Tao, RICHARD J. Haynes, XUE Sheng-guo. Dynamic change and diagnosis of physical, chemical and biological properties in bauxite residue disposal areas [J]. Journal of Central South University, 2019, 26(2): 410–421. DOI: https://doi.org/10.1007/s11771-019-4013-y.

1 Introduction

Bauxite residue is a by-product of alumina production, which is stored in bauxite residue disposal areas (BRDAs). Over time, the properties of the residue change as it is weathered and leached and after 10 years, or more, natural colonization with herbaceous plants can occur [1, 2]. Therefore, it is important to evaluate how the chemical, physical and biological properties (i.e. soil quality) of bauxite residue change over time and how these changes allow plants to establish and grow in the material. Soil quality is defined as the ability of soil to maintain plant productivity, protect environmental quality and promote the health of animals and plants within an ecosystem [3] and is evaluated based on measurement of key physical, chemical and biological quality of the soil medium [4–7]. The choice of appropriate indicators that will reflect changes in “soil quality” over time requires an understanding of the properties of bauxite residue and the changes that will most likely occur with weathering.

Bauxite residues contain residual NaOH and therefore initially have a very high pH (11 to 13) [8–11] and are highly saline (i.e. have a high EC). Over time, as the material is leached, soluble salts and soluble alkalinity are leached from the material and the pH and EC decrease [12]. Leaching of Na down the profile and the concomitant dissolution of calcite and, sometimes other minerals that release K and Mg, result in a decrease in exchangeable Na and increases in exchangeable Ca (and exchangeable K and Mg). As a result, the exchangeable sodium percentage (ESP) (the percentage of exchangeable bases present as Na) decreases. The material is often deficient in other nutrients such as P and it has an initially low organic matter (OM) content (organic C and total N (TN)). Over time as plants begin to grow in the residue, C and N inputs arise via deposition of above and below-ground plant litter and the organic matter content increases [13]. Bauxite residue is effectively a chemically and heat-treated inorganic material so that initially it supports a very small and inactive microbial community [14]. As organic matter accumulates in the residue, the size and activity of the microbial community increase. The production of organic acids by microbial fermentative metabolisms may assist in decreasing residue pH by reaction of H⁺ with various sources of alkalinity in both the bauxite residue pore water and solids [15]. The size of the microbial community can be quantified as microbial biomass C, N and P (MBC, MBN, MBP) (i.e., the C, N and P held in microbial cells) using fumigation- extraction techniques.

When bauxite residue is first deposited, it forms a pasty mass which then solidifies and the material initially has a high bulk density, low total porosity and low water permeability [16]. Over time, under the influence of weathering and drying and rewetting cycles the material becomes less dense and more porous and the physical properties become more similar to that of a fertile soil [17]. In addition, drying causes irreversible changes in the nature of residue (changing from a mud-like substance to a solid filed mass) and the size of the primary particles present (i.e., clay, silt and sand sized particles) can increase over time [18]. Such changes may also influence water holding capacity and/or the volume of water held at field capacity.

The aim of this study was to quantify the changes in key chemical (pH, EC, exchangeable cations, ESP, extractable P, total N, organic C), physical (bulk density (BD), total porosity, particle size analysis, and moisture content (MC)) and biological (microbial biomass C, N and P) indicators of “soil” quality in bauxite residue over a 20-year period at a site in central China. The data were then analyzed using principal component analysis, factor analysis and comprehensive evaluation in order to establish soil quality diagnostic index model and a soil quality index for residue disposal areas. Such a model will help in evaluating soil quality regeneration at other bauxite residue disposal sites

2 Materials and methods

2.1 Experimental site and sampling

The experimental site was located in a BRDA in the Central China. The climate was temperate continental monsoon, and the annual average temperature was 12.8–15.5 °C, annual average sunshine percentage was 45%–60%. There were two bauxite residue disposal areas in this area, not far apart. In one of the bauxite residue disposal area, bauxite residue was stacked on top of each other in different years. From top to bottom, bauxite residue was 1-year-old, 4-year-old and 7-year-old. Another BRDA had 10-year-old of bauxite residue and 20-year-old of bauxite residue. We chose 1-year-old, 4-year-old, 7-year-old, 10-year-old, 20-year-old, and 20-year-old with plants growth of bauxite residue (20P) as the experimental samples. Soil samples around the BRDA were selected as controls (CK). Samples were collected at depths of 20 cm with the ringsampler and mixed evenly. Some samples were air dried in the laboratory, and sieved (<2 mm) prior to analysis.

2.2 Sampling analyses

The particle size distribution of residue samples was determined after dispersion in Calgon using a particle size analyzer (Malvern Mastersizer 2000) and the percentages of sand (2–0.02 mm), silt (0.02–0.002 mm) and clay sized particles (<0.002 mm) were calculated. The moisture content (MC) of field-moist samples was calculated gravimetrically after oven-drying at 105 °C [19]. The bulk density (BD) was determined by the bulk density loop method and particle density was determined by the pycnometer method. Porosity is the percentage of bauxite residue pores to the total volume of bauxite residue, as shown in Eq. (1),

          (1)

The pH was measured using a pH meter (pHS- 3C, Shanghai Lei Magnetic). Conductivity (EC) was measured using a lightning magnetic DDS-307 conductivity meter. Exchangeable Ca²⁺, Mg²⁺, K⁺ and Na⁺ were determined after extraction by ammonium acetate method [20]. Exchangeable K⁺ was used as an index of available K⁺ (AK). The effective cation exchange capacity ECEC and the exchangeable sodium percentage ESP in the bauxite residue were calculated by Eqs. (2) and (3), respectively.

          (2)

 (3)

Bauxite residue organic matter (OM) content and organic C were determined by potassium dichromate oxidation method and a factor of 1.724 was used to convert organic C to organic matter content. Total nitrogen (TN) bauxite residue was determined by semi-micro-Kelvin method. Available potassium (AK) was extracted by ammonium acetate. Available phosphorus (AP) was determined by colorimetric method after extraction with sodium bicarbonate. The content of microbial biomass carbon (MBC), microbial biomass nitrogen (MBN) and microbial biomass phosphorus (MBP) in the bauxite residue was determined by the chloroform fumigation/K₂SO₄ extraction method [21].

2.3 Statistical analyses

SPSS was used to conduct descriptive statistical analysis of the data and explore the correlation between the data and perform sensitivity analysis. The sensitivity response of soil properties was found to differ with time. The variation coefficient of the physical, chemical and microbial indexes of soil quality was therefore used to indicate the sensitivity of the index [22]. The greater the variation coefficient is, the more sensitive the index is to the differential response of different storage years. According to the size of the variation coefficient, the differential response of index factors was divided into four sensitivity levels, including extreme sensitivity (CV>100%), high sensitivity (CV=40%–100%), moderate sensitivity (CV=10%–40%) and low sensitivity (CV<10%).

Correlation analysis refers to the internal relationship between two or more variables, and measures the degree of correlation between two or more variables. Spearman correlation analysis was used to examine the relation-ships among these indicators to reduce redundancy. In statistics, principal component analysis (PCA) is a technique to simplify factor data sets by using the concept of dimensionality reduction [23]. The Kaiser-Meyer- Olkin (KMO) and Bartlett’s test were used to determine the correlation between these soil attributes. KMO is the criterion of variable bias correlation. The partial correlation coefficient is greater than the simple correlation coefficient. When the value of KMO is less than 0.6, the effect is poor, which is basically not suitable for factor analysis. Factor analysis can be carried out under the KMO value greater than 0.7, and the effect is optimal when it is greater than 0.9. Bartlett’s test is used to detect the independence of factors. If Bartlett’s test value is 0, there is a certain relationship between variables. The data were then subjected to PCA. The first step was to standardize the data. The principle of principal component analysis was the projection of data in the matrix in a certain coordinate system. The diagonalization of the matrix will produce feature roots and feature vectors, and the projection in the direction of feature vectors in the coordinate system will be the feature value.

2.4 Soil quality index (SQI)

The PCA indicator factors were analyzed by principal components to screen the minimum data set factors (MDS) [24]. MDS is a set of parameters that can reflect the least soil quality. Norm value is the length of vector norm of a certain index in multi-dimensional space composed of multiple principal components [25]. Vector norm to some extent represents the information dimension of the total factor loading of multiple principal components. Norm’s calculation is:

                     (4)

where N_ik is the factor load of the ith variable on the first k principal components of the eigenvalue>1; U_ik is the load on the ith variable on the kth principal component; U_ik is the eigenvalue of the kth principal component.

If the factor load of an indicator factor is greater than 0.5 in the two principal components, the correlation of the indicator factor is taken into account and it is classified into the part with low correlation. After the first grouping, if the correlation between indicator factors and other factors is relatively low, then this factor can be grouped separately. After grouping norm values, factor loading is selected as the index within the range of the maximum norm value of 10%. If this index has a high correlation with other indexes, which is greater than 0.5, this high score factor is selected as the factor of the minimum data set. If the correlation is low, all become the minimum data set factor. The correlation between the selected factors was tested, and the final factor data set was determined by taking into account the other factors.

Soil quality (SQ) index was determined in three separate steps by (a) selecting a minimum data set (MDS) of indicators that best represented soil functions and processes, (b) scoring the MDS indicators based on their performance and then (c) integrating the indicator scores into a comparative index of SQ [26].

The factors with high eigenvalues (>1) and soil variables with high factor loadings that explained at least 5% of the variation in the dataset were assumed to be indicators. However, only highly loaded attributes having absolute values within 10% were retained for the MDS. If the highly weighted variables were uncorrelated, each was considered important and was considered for the MDS. The final step associated with SQ assessment was combining the selected indicators into an overall SQI using comprehensive scoring. The formula of relative soil quality index (RSQI) can analyze the change of soil quality. The difference value can represent the rate of change [27].

                         (5)

                    (6)

3 Results and discussion

3.1 Physical properties of bauxite residue

With increasing storage years, the texture of bauxite residue gradually changed with decreases in the percentages of clay and silt-sized particles present accompanied by increases in the percentage of sand sized particles (Figure 1). If the medium was a soil, this would equate to a change from a silty loam a sandy loam. This change reflects the linkage of clay and silt-sized particles together to form larger sand sized particles [28]. Variations in particle size distribution of bauxite residue may also have been partly related to bauxite source. There was also a concomitant decrease in bulk density and increase in total porosity with increasing years [29]. Other workers have observed the same trend with fine particles aggregating together to form larger particles and hence the development of larger pores [30]. This binding of particles together is promoted by pozzolanic materials present in the residue [31]. The increased porosity results in a decrease in bulk density. The increase in organic matter content with time is also likely to promote increased bulk density since bulk density in soils is often negatively correlated with organic matter content [32].

The soil water content at sampling time decreased between 1 year and 4 years and then progressively increased with time, while some variation in water content may have been related to differences in water input by rainfall and runoff. The major factor is likely to be the changes in residue properties with time. Residue is initially deposited in a pasty mud form and has a very high water content [33]. During the process of drying the physical properties of the residue change since it contains pozzolanic materials which binds particles together to form a solidified material. Evidently this material held less water than the material that was initially deposited. Over time, under the influence of drying and wetting cycles and natural weathering the porosity and water holding capacity of the residue increased so water content also increased.

3.2 Chemical properties of bauxite residue

With increasing storage year, the pH gradually decreases (from 11.15 to 9.97) (Figure 2) as soluble alkalinity (OH^–, CO₃^2–/HCO^3– and Al(OH)₄^–) was leached down the profile [34]. The major cation present (Na⁺) have been the major balancing cation leached and as a result there were decreases in EC and exchangeable Na (Figure 2) and ESP (data not shown) with increasing time. In comparison with the 20-year site the 20-year site with plants had a lower pH and EC. Thus, both natural weathering/leaching and plant growth reduce residue alkalinity and salinity.

Figure 1 Change of bauxite residue physical characteristics with different storage years

With increasing stacking time, organic carbon content increased from 5.65 to 10.73 g/kg and total nitrogen content increased from 0.004% to 0.033%. This increase in organic matter content over time is a characteristic of pedogenisis and as organic matter accumulates, microbial activity also increases [35], as do the CEC and aggregation and aggregate stability [34]. The increase in total N means that there is an increase in potentially mineralizable N present in the residue and the mineral N that is released can be used by plants. Indeed, plant productivity is often positively correlated with the size of soil nitrogen reserves [36].

Organic matter can also improve soil chemistry and change the way of adsorbing trace elements [37]. It can improve nutrient circulation in soil and support the growth of microorganisms. With increasing stacking time, organic carbon content increased from 5.65 to 10.73 g/kg. Soil organic matter content can indicate the degree of pedogenesis. Over time there was a marked increase in Olsen extractable P. This suggests that some P-containing materials are dissolving over time, releasing P. While no P-containing crystalline minerals were identified in the residue, its mineralogy was composed of 56%–63% amorphous material and P-containing compounds were likely to have been present within this amorphous fraction.

With increasing stacking time, total nitrogen content increased from 0.004% to 0.033% which is essential element for plants, while total carbon increased from 1.3% to 5.4%. The soil total nitrogen content is closely related to plant growth which is as one of the important nutritional indicators. Since P is often a limiting factor for plant growth in bauxite residues, this release of P is of great significance to plant establishment and growth in the residue. Similarly, there was a increase in exchangeable K, Mg and Ca over time as these nutrients were released from amorphous and crystalline minerals. The release of Ca was marked and is attributable to weathering of calcite present in the residue. Available nitrogen is a form of nitrogen, which can be directly used by plants. In the process of pedogenesis, organic carbon content increased with the increase of organic matter and the increase of time. The plants growth is based on the available nitrogen whilst, particularly in the substance extremely absence of parent material such as bauxite residue, and also for another nutrient of phosphorus which mainly affects the nucleus. The initial calcite content of this residue was about 10%. This dissolution of calcite and increase in exchangeable Ca is important since it decreases the ESP and also promotes leaching of Na (by cation exchange between the released Ca and exchangeable Na) [38], thus further decreasing ESP. Increased availability of K and Mg will be of importance for plant growth in the residue. The potassium as another essential nutrient for plant growth is potassium and it is mainly used for weathering during soil development. With increasing stacking time, available potassium content increased, and reached the highest level in 20 years.

Figure 2 Change of bauxite residue chemical characteristics with different storage years

3.3 Biological properties of bauxite residue

With an increase of bauxite residue storage time, the microbial biomass carbon (MBC), microbial biomass nitrogen (MBN) and microbial biomass phosphorus (MBP) content of bauxite residue increased (Figure 3). It is a sensitive response to the changeability of soil properties by MBC, MBN and MBP as an active component of soil, which are leading indicators to reflect changes in soil organic matter.

Microbial biomass nitrogen is one of the important reservoirs of soil nitrogen and it plays an important role in the nitrogen cycle [39]. The turnover rate of microbial biomass nitrogen is quintuple faster than that of soil organic nitrogen. It is the most suitable for the growth of microorganisms at the carbon to nitrogen ratio of 25. As the storage time increased, there was no significant change in the carbon to nitrogen ratio on the bauxite residue without plant growth. In areas where plants are grown, the carbon to nitrogen ratio is reduced, and around 25 is suitable for the growth of microorganisms. The microbial biomass C/N ratio was above 30 in the residue at all sampling times other than for the 20P site. A C/N ratio above 25 is an indicator of a shortage of N [40] and the lower microbial C/N ratio in the 20P site may be related to greater N inputs. The bauxite residue has an increasing MBC/SOC ratio with increasing storage time (Figure 3), indicating that the soil quality of bauxite residue is rising.

Figure 3 Change of bauxite residue biological characteristics with different storage years

3.4 Sensitivity analysis of evaluation indexes

The results of sensitivity of soil index vary with the change of environment. With the change of time, the response of different soil indexes to land using pattern and land changing process is different. Therefore, the variation coefficient of the physical, chemical and microorganism indexes of soil quality was used to indicate the sensitivity of the index [41]. In terms of value, the greater the variation coefficient of index, the more sensitive the index to the differential response of different storage years. The results of sensitivity analysis of the soil quality diagnostic indices for bauxite residue are presented in Table 1. According to the size of variation coefficient, the differential response of index factors was divided into four sensitivity levels. These were: extreme sensitivity (CV>100%), high sensitivity (CV=40%–100%), moderate sensitivity (CV= 40%–10%) and low sensitivity (CV<10%). The results of sensitivity grade are shown in Table 1 by calculating the variation coefficient of 19 kinds of bauxite residues.

Table 1 Sensitivity grades of soil quality integration index in bauxite residue disposal

The low sensitivity index of bauxite residue quality diagnosis was pH, and the extremely sensitive indexes were TN and MBC. Highly sensitive index factors are clay content, EC, TC, OM, available potassium, available phosphorus, MBN, MBP, MBC/SOC, which were main indexes for soil quality evaluation. Sand, silt, bulk density, porosity, moisture content, ESP, C/N were moderately sensitive indexes.

The sensitivity analysis showed that soil chemistry and microbial characteristics were more sensitive to the difference of environmental changes. Soil physical indicators were basically moderately sensitive and soil physical indicators were basically moderately sensitive, indicating that soil physical traits had potential influence (Table 1). Thus in general, soil chemical and biological indicators were more sensitive to changes brought about by increasing storage time than soil physical properties (which were generally moderately sensitive).

3.5 Indicator selection for MDS

KMO was equal to 0.831, greater than 0.7, and Bartlett’s P was 0, less than 0.05, which was suitable for principal component analysis. The variance of common factors indicated that the extraction rate of each factor information was higher, which was suitable for principal component analysis. The cumulative variance contribution rate of the first three principal components, PC1, PC2, PC3, reaches 89.05% (>80%), with high utilization rate and small loss, which can be used to reflect the degree of variation of the properties of bauxite residue (Table 2).

Table 2 Eigenvalues and percent of variance explained

Principal component analysis separated MBC/SOC, available phosphorus, total nitrogen, MBP, MBN, organic matter, total carbon, bulk density, ESP, EC, pH, porosity, available potassium, MBC, sand content, C/N entered into the first principal component (Table 3). Sand content and silt content entered into the second principal component while moisture content and C/N entered into the third principal component. The factor loading of the first principal component with a total of 15 indicators was greater than 0.5, which entered into MDS. We ranked these 15 soil indicators as group 1. The highest norm of these 15 factors was the content of rapidly available phosphorus, and the indicators within the range of 10% were MBC/SOC, rapidly available phosphorus, total nitrogen, MBP, MBN, organic matter, total carbon, bulk density, ESP, EC, pH and clay content. The correlation analysis of these indicators showed that the correlation between available phosphorus, MBC/SOC, ESP, EC, pH, total nitrogen, bulk density, MBP and the remaining indicators was significant. According to the principle of the MDS method [42], the available phosphorus was selected to enter the final MDS. The loading value of sand content and silt content was greater than 0.5 in the first and second principal components, and it was less in the first group than that in the second group by correlation analysis. The norm value of sand content was higher than silt content, so sand content entered into the MDS. The factor loading of moisture content and C/N was greater than 0.5 in the third principal component, meanwhile the factor loading of C/N was greater than 0.5 in the first principal component. The correlation coefficient of C/N in group 1 was lower than that in group 3 according to the correlation analysis, so C/N was included in group 1. The correlation between C/N and other indicators in group 1 was low, and entered into group 4 and finally MDS. The MDS determined by BRDA includes available phosphorus, moisture content, C/N, sand content, total N, MBC. The pH was significantly correlated with most factors, and was an extremely important influencing factor for bauxite residue, according to the typical correlation.

Table 3 Principal component loading matrix and calculated norm values

The final MDS for the comprehensive diagnosis of BRDA included available phosphorus, moisture content, C/N, sand content, total N, MBC and pH.

3.6 Diagnosis of bauxite residue quality

Principal component values F₁, F₂ and F₃, and the corresponding weights E₁, E₂ and E₃ were calculated. The comprehensive score calculation of BRDA was calculated using the following equation (Table 4):

                   (7)

where the weights E₁, E₂ and E₃ are the percentage of the contribution rate of rotary variance to the total variance.

With increasing storage year, the soil quality index in the BRDA was in a state of increase (–1.02–0.73). The soil quality index in BRDA where plants grow is better than where no plants grow, but is significantly lower than that in surrounding areas (Table 4). Based on the MDS, the equation of soil quality in the BRDA is obtained, which provides a basis for the diagnosis of the degree and process of bauxite residue soil transformation.

Table 4 Integrative value of principal and rank

The variance of the principal component was calculated using the following equation:

                (8)

The calculation equations of the three principal components were obtained by analyzing the three principal components and combining the soil evaluation factors selected.

  (9)

                          (10)

                 (11)

where x is the content of physical, chemical or biological characteristics of bauxite residue.

The SQI of bauxite residue was calculated using the following equation:

          (12)

where x is the content of physical, chemical or biological characteristics of bauxite residue.

The RSQI of bauxite residue increased gradually with increasing storage year, and the quality of bauxite residue of growing plants was obviously better than that of non-growing plants. ΔRSQI showed that initial change rate of bauxite residue in natural weathering process is faster and the late change rate was slow (Table 5).

Table 5 Quality change of bauxite residue

4 Conclusions

Nineteen physical, chemical, and biological properties of bauxite residue showed significant differences among the different storage time. The texture of bauxite residue changes from silty loam to sandy loam, and porosity increases. The pH and EC decreased, whilst nutrient element content and microbial biomass increased. The diagnostic index of bauxite residue quality was selected, and a diagnosis model of bauxite residue quality was established. Correlation analysis, typical correlation analysis and sensitivity analysis were used to select the typical indicators of soil formation in bauxite residue.TN and MBC are highly sensitive to soil quality evolution. The determined MDS included available phosphorus (AP), moisture content (MC), C/N, sand content, total nitrogen (TN), microbial biomass carbon (MBC), and pH. The soil quality index of bauxite residue increased, and the relative soil quality index decreased. The results show that natural weathering can improve the quality of bauxite residue and form a new kind of soil matrix. The diagnostic model of bauxite residue was established to validate if natural weathering processes can ameliorate BRDAs over time and provide data support for the regeneration on disposal area.

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(Edited by YANG Hua)

中文导读

赤泥堆场土壤性状的动态变化及诊断研究

摘要：以华中某赤泥堆场为研究对象，分析不同堆存年限的赤泥物理、化学、生物学特性，运用主成分分析、综合评价法等方法建立赤泥堆场土壤质量诊断模型。结果表明：在自然风化过程中，赤泥质地由类粉质壤土转变为类砂质壤土，孔隙度增加，pH、EC降低，养分含量增加，微生物量碳（MBC）升高；确定的最小数据集（MDS）为：速效磷(AP)、含水率(MC)、C/N、砂粒含量、全氮（TN）、MBC、pH；建立了赤泥堆场土壤化诊断模型，随着赤泥堆存年限的延长，综合质量指数上升，相对质量指数从1.89下降到0.15，逐渐形成一种新的类土基质。赤泥堆场土壤化诊断模型的建立，为赤泥生态化处置及堆场生态修复提供理论依据。

关键词：赤泥堆场；土壤性状；最小数据集；诊断指标；自然风化；赤泥土壤化

Foundation item: Projects(41877551, 41842020) supported by the National Natural Science Foundation of China

Received date: 2018-11-04; Accepted date: 2018-11-27

Corresponding author: XUE Sheng-guo, PhD, Professor; Tel: +86-13787148441; E-mail: sgxue70@hotmail.com, sgxue@csu.edu.cn; ORCID: 0000-0002-4163-9383