Biocrusts significantly affect the bioavailability and ecological risk of heavy metals in gold mine tailings

doi:10.21203/rs.3.rs-2751264/v1

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Biocrusts significantly affect the bioavailability and ecological risk of heavy metals in gold mine tailings

https://doi.org/10.21203/rs.3.rs-2751264/v1

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published 17 Aug, 2023

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Aims Biocrust is an important living cover in ecologically fragile regions and an indicator of multiple heavy metal contamination. Could it be a new pathway for remediating heavy metal pollution in mine, and what are the possible repair effects and mechanisms? This study investigated the role of biocrusts in enriching or degrading heavy metals in gold mine tailings and analysed the migration, transformation mechanisms and potential toxic effects of heavy metals in underlying biocrusts.

Methods The speciation of heavy metals in the underlying layer of biocrusts (moss crusts, mixed crusts (moss + algal) and algal crusts) were analysed using the BCR sequential extraction procedure. The risk assessment code (RAC) and potential ecological risk index (Er) were used to evaluate the effects of biocrusts on the ecological risk assessment of heavy metals in soil.

Results Our results showed that 1) well-developed biocrusts had a strong ability to enrich heavy metals and heavy metals accumulate on the surface; 2) biocrust growth promoted the transfer of heavy metal speciation from an inert to an active form in the underlying layer, improving their bioavailability; 3) Spearman correlation and redundancy analysis (RDA) showed that the total amount of heavy metals was the driving factor of soil heavy metal translocation, with varying influences of soil pH, cationic exchange capacity (CEC) and organic matter content (SOM); 4) the comprehensive potential ecological risk index suggested that the heavy metals in gold tailings at the strong risk levels, mainly As seriously exceeded the standard (about seven times of the background value), and biocrusts could reduce the Er of heavy metals in the underlying layer. The RAC results indicated low mobility and bioavailability of heavy metals, associated with low ecological risk.

ConclusionsAs the ‘skin’ of soil, biocrusts could protect soil from heavy metal contamination. However, biocrust development enhanced the bioavailability of heavy metals and increased ecological risk. With low risk levels in gold tailings as the heavy metals were mainly found in the residual fraction, were stable and not easily transported. Further exploration of the influencing mechanism of biocrusts on heavy metals will help validate their use in mine restoration processes.

Biocrust

Heavy metal speciation

Bioavailability

Ecological risk

Gold mine tailings

Metal tailings are the legacy of mine operations, causing surface fragmentation, nutrient losses and excessive heavy metals (Buch et al. 2021, Khan et al. 2008), slowing natural vegetation colonisation, which is not conducive to reconstructing ecosystems and restoring ecosystem function. In recent years, scholars have used different methods and technologies to repair heavy metal pollution in mine tailings, such as the soil change method (Douay et al. 2008), leaching method (Kuhlman and Greenfield 1999), phytoremediation method (McGrath et al. 2001), biochar remediation method (da Silva Medeiros et al. 2021). However, these methods can have high costs, low remediation efficiencies and cause secondary pollution. Therefore, we must find more environmentally friendly and effective repair materials.

Biocrusts are complex organic aggregations—cemented by soil particles and microorganisms, algae and mosses—that are ecologically adaptable and highly tolerant of toxic environments (Bu et al. 2013, Belnap et al. 2014). As a pioneer colonising organism and dominant ground cover in the revegetation process of mine tailings, biocrusts could promote soil particle refinements, enrich the topsoil organic matter and nutrient contents and improve soil structure by adsorbing and trapping atmospheric dustfall and minerals dissolved in precipitation, impacting mineral circulation and nutrient redistribution in fragile ecosystems (Evans and Belnap 1999, Li et al. 2010, Xu et al. 2013). Algae and mosses can tolerate heavy metals (Venter et al. 2018) and are good indicators of heavy metal contamination. For example, Orlekowsky et al. (2013) investigated algae species and 11 heavy metals, including Hg and As, in gold mine tailings in South Africa, the results showed that the tailings had significantly more algal species and higher heavy metal contents (except Cr and U) after 15 years of repair than unrepaired tailings. Mao et al. (2022) used moss bags to monitor atmospheric heavy metal pollution in Xichang and showed that T. taxirameum were able to accumulate heavy metals and was a good biomonitor of air pollution. However, biocrusts also become enriched with large amounts of heavy metal pollutants, resulting in multi-point excessive heavy metals (Wu et al. 2011) and posing an ecological risk to mine tailings and surrounding farmland (Di Palma et al. 2017, Sonter et al. 2018).

Previous studies have mainly focused on the total amounts of heavy metals, but their impact on the soil environment also depends on their bioavailability (Lin et al. 2014, Lin et al. 2022, Xian 1989). Therefore, analysing heavy metal forms is essential to more accurately and effectively reflect the degree of heavy metal pollution and their toxicity, mobility and bioavailability in the environment (Hu et al. 2022, Siahcheshm et al. 2022, Zhang et al. 2017). Nevertheless, little is known about the distribution patterns of biocrusts on metalliferous substrates, the mechanisms of heavy metal degradation, or heavy metal elimination by biocrusts. Moreover, factors influencing the migration and transformation of heavy metals on small spatial scales are not well-documented.

As the research object, we used three typical biocrusts (moss crusts, algal crusts and moss-algal mixed crusts) from the Ma’anqiao gold tailings in the Qinling Mountains. We hypothesised that different biocrusts to enrich heavy metals differs, affecting the morphological distributions of heavy metals in the underlying layer. Our research aims to answer the following questions: (1) Does biocrust growth affect heavy metal enrichment and transformation in soil? (2) Which key chemical factors drive the migration of heavy metal speciation in soil? (3) How does biocrust succession affect heavy metal bioavailability and ecological risk? This study offers a new pathway and reference for restoring heavy metal pollution in degraded ecosystems.

2.1 Study site

In September 2021, we undertook research on the Ma’anqiao gold mine tailings in Zhouzhi County, Xi’an City, Shaanxi Province, China (107°53′12″–108°11′39″ E，33°44 ′49″–33° 53′ 50″ N, Fig. 1a). The experimental site, located in the northern foot of the Qinling Mountains, is characterised by a temperate continental-monsoon climate, with an annual average temperature of 13.2°C and average annual precipitation of 699.98 mm. Multiple heavy metals exceeded the background concentration at numerous plots in the tailings (Wu et al. 2011), with severe heavy metal pollution resulting in low soil fertility, sparse vegetation and poor plant growth. However, a large area of biocrusts developed (Fig. 2a), with algal crust, dark in colour, continuously distributed under natural conditions (Fig. 2b) and moss crust, dark when dry and green when wet, mostly patchy and mixed with algal crust (Figure 2c, d).

2.2 Sample collection

Based on the biocrust development at the gold mine tailings, we established five representative biocrust plots (40×40 m, at least 50 m apart) (Figure 1B), with each plot containing algal crusts, algal–moss crusts and moss crusts. Five samples of each biocrust were randomly collected from each plot using a sterile cutting ring, with same biocrust mixed to form one composite sample. The same method was used to collect soil from 0–5 cm underlying biocrusts. Five composite samples were collected for each biocrust type. Thirty composite samples were collected (15 in the biocrust layer, 15 in the underlying layer). Adjacent bare soil was selected as the control. The composite samples were divided into two parts for further analysis. The first part was air-dried to analyze soil heavy metal forms and the second part was stored at 4 ◦C to analyze soil properties.

Soil pH was measured in the supernatant of a 1:2.5 (soil:water ratio) extract using a pH meter (pH S-25, Leici, Shanghai, China). Cation exchange capacity (CEC) was determined by flame photometric method. Redox potential (Eh) was determined using the depolarisation method. Soil organic carbon was determined using the potassium dichromate oxidation and external heating method.

Total Cr and As contents were determined by an inductively coupled plasma emission spectrometer (ICP-OES, Optima 8000, PerkinElmer, USA) after decomposing the samples with a microwave digester (Multiwave PRO, Antonpa, Austria). Cr was digested with HNO₃·3HCl-HClO₄ and As with HNO₃-HClO₄-H₂SO₄. The chemical fractions of heavy metals in samples were analysed using the BCR sequential extraction procedure, dividing the metal distribution into acid soluble (F1), reducible or Fe-Mn oxides (F2), oxidisable or organic matter-bound (F3) and residual (F4) fractions. Details of the extraction processes are in Rauret et al. (1999). ICP-OES was used to analyse the heavy metals contents in the extracted solutions.

2.3 Ecological risk assessment of heavy metals

Potential ecological risk index

The potential ecological risk assessment method, proposed by Hakanson (1980), reﬂects the pollution level of a single heavy metal and the comprehensive ecological impact of various heavy metals. It is calculated using the following formulas:

where Cⁱ is the measured value of heavy metal i (total content), is the reference value of heavy metal i selected from the background value of soils in Shaanxi Province (CNEMC 1990, Cr = 62 mg/kg, As = 11.1 mg/kg), is the toxicity coefficient of heavy metal i (Cr = 2, As = 10), is the potential ecological risk coefﬁcient of heavy metal i, and RI is all heavy metal potential risk for a region. Table 1 shows the potential ecological risk classiﬁcation of heavy metals.

Limited by the number and nature of heavy metals, direct application of Hakanson grading criteria may result in poor accuracy (Li et al. 2016). Therefore, we synthesised the literature (Hakanson 1980, Fernandez and Carballeira 2001, Ma et al. 2020) to adjust the grading standard for the single potential ecological risk index (Er) and the comprehensive potential ecological risk index (RI):

The first upper limit of Er value was obtained by multiplying the non-polluting pollution factor by the maximum toxicity factor among the pollutants, while the upper limit values of other risk levels were obtained by multiplying the risk value of the previous level by 2. Because the T value of As in this study was the largest (10), the upper limit of the first level (slight ecological risk) was adjusted to 10.

The RI value in the grading scale was adjusted by calculating the unit toxicity coefficient (1.13) for the pollutants by dividing Hakanson’s first upper limit value (150) by the sum of the toxicity factors (133) for the eight pollutants, multiplying the sum of the toxicity factors (12) for the specific metals (As, Cr) evaluated in this study, and then taking ten integers to get the first level upper limit value (20) for RI. The values for each of the other levels was double the previous level’s threshold value.

Risk assessment code

The RAC ecological risk assessment method determines the risk level based on the proportion of the total bioavailable amount (Sundaray et al. 2011), calculated as follows:

where A_i is the concentration of metal i in the acid-soluble fraction and T_i is the total concentration of metal i in the four fractions. The RAC index was divided into ﬁve levels: <1% (no risk), 1–10% (low risk), 11–30% (medium risk), 31–50% (high risk) and >50% (very high risk).

2.4 Statistical analysis

The Shapiro-Wilk test was used to check for normal distribution. One-way ANOVA (analysis of variance) was used to examine diﬀerences between treatments. SPSS 26 (IBM Corporation, NY, USA) was used for the descriptive data analysis. Spearman correlation analysis identified the relationships between the total heavy metal contents and the main chemical factors. Redundancy analysis (RDA) was conducted to identify the relationships between chemical fractions of heavy metals and environmental factors. Figures were constructed using Origin 2021 (OriginLab, Northampton, MA, USA) and Canoco5.0 (Microcomputer Power, Ithaca, NY, USA).

3.1 Accumulation and its distribution of heavy metals in the biocrust layer and underlying layer

The total Cr contents in the tested samples ranged from 21.54–36.49 mg/kg (Table 2), below the average background concentrations in Shannxi (62 mg/kg). The moss biocrust had the highest Cr content (36.49 mg/kg), significantly higher than the lowest value in algal biocrust (32.67 mg/kg). In contrast, Cr enrichment in the underlying layer ranked algal crust > mixed crust > moss crust, with lower values than the biocrust layer (P<0.05).

The As concentrations ranged from 76.06–97.54 mg/kg (average 79.37 mg/kg), up to 7.15 times the background value. As accumulation in the biocrust layer and underlying layer followed a similar trend to Cr, but with no significant differences between layers (Table 2). Moreover, unlike Cr, the algal crusts and mixed crusts had 9.59 and 1.08 mg/kg higher average As contents in the underlying layer than the biocrust layer, respectively.

3.2 Heavy metal fractions and bioavailability in the underlying layer

The Cr fraction F4 significantly decreased in the underlying biocrusts compared to bare soil, with Ms, Ts and Cs decreasing by 8.04%, 5.24% and 4.04%, respectively (Fig. 3a). However, all other fractions increased. The greatest proportion of non-residual Cr increased in Ms, with F1, F2 and F3 increasing by 1.05%, 6.68% and 0.31%, respectively.

The As fraction F4 also significantly decreased in the underlying biocrusts compared to bare soil (Fig. 3b), with Ms, Ts and Cs decreasing by 5.81%, 7.07% and 6.21%, respectively. However, the largest percentage increases for Ts and Cs occurred in F2 and F3, respectively.

Heavy metal bioavailability can be evaluated by the bioactivity coefficient (Xu et al. 2022, Zhang et al. 2019), usually divided into available K1 (acid soluble), potentially available K2 (reducible and oxidisable) and inert K3 (residual) based on their chemical fractions. In the underlying layer, K1 and K2 for Cr and As decreased in the order moss crust > mixed crust > algal crust > bare soil, with K3 the opposite (Fig. 3a, b). K1 for Cr and K2 for As were significantly higher than bare soil, whereas K3 was significantly lower than bare soil (P<0.05) (Appendix Information, Table S1).

3.3 Influencing factors of total amount and speciation of heavy metals in underlying biocrusts

3.3.1 Total content

With the developmental succession of biocrusts, soil pH and Eh gradually decreased, while CEC and SOM gradually increased in the underlying layer (Table 3). In particular, the underlying moss crust had significantly lower soil pH and Eh but significantly higher CEC and SOM than the bare soil (ANOVA, P<0.05).

According to the Spearman correlation analysis (Table 4), total Cr in the underlying layer positively correlated with pH (P=0.007). However, total As in the underlying layer positively correlated with As in the biocrust layer(P=0.000) and SOM (P=0.042) but negatively correlated with CEC (P=0.039).

3.3.2 Chemical fraction

The RDA indicated that soil environmental factors explained 39.57% and 9.54% of the variation in the chemical fractions of Cr in the underlying layer in Axis 1 and Axis 2, respectively (Fig. 4a). F4 were the most strongly correlated with total Cr (P<0.01). F3 was the most strongly correlated with CEC. F1 and F2 was the most strongly correlated with SOM (P<0.01) (Appendix Information, Table S2). The migration in Cr by each factor was as follows: total Cr (37.52%) > pH (30.5%) > CEC (15.5%) > SOM (13.0%) > Eh (4.1%) . Moreover, total Cr (P=0.002) and pH (P=0.004) significantly affected Cr migration and transformation (Table 5).

Soil environmental factors explained 39.34% and 22.95% of the variation in the chemical fractions of As in the underlying layer in Axis 1 and Axis 2, respectively (Fig. 4b). F2 and F4 was the most strongly correlated with total As (P<0.01). F3 was the most strongly correlated with CEC (P<0.05). F1 was the most strongly correlated with pH (P<0.01) (Appendix Information, Table S2). The migration in As by each factor was as follows: total As (35.5%) > pH (21.8%) > CEC (20.3%) > SOM (19.0%)> Eh (7.1%). Moreover, total As (P=0.002), pH (P=0.034), CEC (P=0.036) and SOM (P=0.04 ) significantly affected As migration and transformation (Table 5).

3.4 Effect of biocrusts on the ecological risk of heavy metals in the underlying layer

The highest Er value for As in bare soil was 87.87, indicating a very strong ecological risk level (Fig. 5a). Biocrusts could lessen the potential ecological risk of heavy metals in the underlying layer, with the Er value of As (in descending order): algal crusts (77.94) > mixed crusts (75.11) > moss crusts (73.62). The ecological risk level of Cr was low in the study area (Er values < 10). The comprehensive potential ecological risk in the main tailings was similar to that of As (Fig. 5b), with the highest RI in bare soil (88.69, very strong), higher than the underlying layer (75.56, strong).

The RAC results revealed no risk of Cr and As in the bare soil. However, the risks of Cr and As in the underlying layer increased with biocrust succession, with RAC-Cr always higher than RAC-As, indicating that biocrusts facilitate the migration of heavy metals from the inactive to active form, increasing the ecological risk (Fig. 6). Although the tailings had much higher total As than Cr, Cr had a higher bioavailability ratio than As.

4.1 Effect of biocrusts on heavy metal enrichment

Biocrusts, as the ‘skin’ of the soil, improve soil texture by adsorbing and trapping wind- and water-eroded materials and atmospheric dustfall, but also enrich a large number of heavy metals, which affect physicochemical soil properties such as pH and organic matter in the 2–5 cm underlying biocrust (Gao et al. 2018, Guo et al. 2022). We showed that highly developed biocrusts had better ability to enrich heavy metals, consistent with others (Fan et al. 2021, Hu et al. 2022, Xu et al. 2013), due to different adsorption mechanisms of heavy metals in the two biocrusts, with algal crusts mainly adsorbing more heavy metals by secreting extracellular polymers (Mota et al. 2016). Mosses have a high CEC and a unique hairy branching structure that can absorb heavy metal ions directly from the leaf surface through ion exchange and particle capture (Fernandez et al. 2002, Gallego-Cartagena et al. 2021). Xu et al. (2013) reported that biocrust development can reduce heavy metal contamination in the underlying layer, with the reducing ability ranked moss crust > mixed crust > algal crust. Our results indicated that the underlying layer had 25–40% lower Cr contents than the biocrust layer because Cr(Ⅲ) mainly exists in alkaline soil, is rapidly adsorbed and fixed by soil colloids, and is not easily mobile (Dhal et al. 2013, Guo et al. 2021, Hu et al. 2016). Unlike Cr, most minerals have a weak capacity to adsorb As in an alkaline environment (Chen et al. 2004, Schmitt et al. 2002). Yao et al. (2016) determined that pH and Eh influenced the damage degree of As, with increasing pH decreasing As sorption by soil particles. In this study, the total As of algal crusts and mixed crusts in the underlying layer was 9.6 mg/kg and 1.1 mg/kg higher than the biocrust layer, respectively, possibly due to the alkaline soil in the tailings pond (pH = 8.9) preventing As from being quickly immobilised in the biocrust layer. Besides, abundant summer precipitation in the Qinling area results in secondary aggregation in subsurface by As downward leached and migrated under rainwater (Zhou et al. 2010).

4.2 Effect of biocrusts on the migration and transformation of heavy metals in the underlying layer

Many hydroxyl and carboxylic acid groups distributed on the surface of biocrusts (Gardea-Torresdey et al. 1990) could complex heavy metal ions in the soil, increasing their solubility in soil solution and enhancing heavy metal migration (Christensen et al. 1996). Our results indicated that biocrusts affect heavy metal forms in the underlying layer, compared with bare soil, with the proportion of F4 (by 4.01–8.04%) decreasing and the proportion of F2, F3 and F1 increasing, more so in F2 (3.54–6.69%). This could be because: (1) biocrust succession improves soil texture in degraded ecosystems, changing the proportion of metals bound to Fe/Mn oxides and the heavy metal distribution (Bartoli et al. 2012, Chamizo et al. 2012, Yıldırım and Tokalıoğlu 2016), (2) Fe/Mn oxides in the soil environment immobilise heavy metals through adsorption and co-precipitation, and the metal could react with Mn or Fe to form relatively stable compounds (Burachevskaya et al. 2021, Krupadam, 2007, Zhang et al. 2017).

The speciation distribution of heavy metals reflects the migration rules and circular processes of heavy metals in soil and their bioavailability status (Sungur et al. 2015), with the degree of bioavailability of metal forms ranked F1 > F2 > F3 > F4 (Adebiyi and Ayeni 2022, Fu et al. 2019, Rauret et al. 1999). In this study, heavy metal mobility and bioavailability varied by biocrust type and heavy metal properties. The availability and potential availability of heavy metals significantly increased in the underlying layer, with moss crusts > mixed crusts > algal crusts (Fig. 3). The available As and Cr increased most in the underlying mixed crust and moss crust, respectively, due to the different As and Cr solubility in the underlying of different biocrust (Burachevskaya et al. 2021, Zakir et al. 2008).

4.3 Influencing factors of heavy metal migration and transformation

In this study, total Cr positively correlated with its residual fraction (F4) in the underlying layer (R=0.945, P=0.000), which could be due to the inherently low Cr content in the four components of mine tailings (Appendix Information, Table S3). In addition, the residual fraction mainly occurs in secondary minerals and silicate lattice (Nemati et al. 2009), reducing soil compactness and increasing the adsorption of other forms of Cr by soil colloids. However, total As positively correlated with its reducible fractions (R=0.659, P=0.003), reflecting that As adsorption by soils depends on the release of reducible metals adsorbed on Fe/Mn oxides. In alkaline soils, the dissociation and release of Fe/Mn oxide colloids could promote the accumulation and precipitation of available As in subsurface soils (Cheng et al. 2022).

Soil pH not only affects the total amount of heavy metals in soil but, more importantly, changes the distribution pattern of geochemical components of heavy metals, thus affecting their bioavailability (Bai et al. 2012, Krupadam et al. 2007). The RDA showed that pH restricted heavy metal bioavailability (Cr, P=0.002, As, P=0.034), in particular, the positive correlation between pH and residual fraction indicated that increasing pH favoured the enrichment of less mobile forms (Egbenda et al. 2015, Park et al. 2013). The tailings bare soil had a high pH ( pH = 9.02), but biocrust components, such as moss, secrete acidic substances and reduce the acidity of the moss substrate (Zhang et al. 2022). Therefore, biocrusts may regulate the pH of alkaline soil. With the development and succession of biocrusts, pH gradually decreased (Table 3), with organic matter and exchangeable cations in soil solution adsorbed to soil colloids and clay mineral surfaces (Burachevskaya et al. 2021, Tyopine et al. 2018, Orlekowsky et al. 2013), facilitating the conversion of heavy metal from inactive to active forms. In this study, pH and Eh significantly decreased with biocrust development, while CEC and SOM significantly increased in the underlying layer (Table 2). The correlation showed that pH and Eh negatively correlated with F1 and F2 but positively correlated with F4, while SOM and CEC positively correlated with F2 and F3 but negatively correlated with F4 (Fig. 4a), indicating that biocrusts increased the availability of metal Cr and As by adjusting pH and Eh and enriching organic matter and cationic sorption sites (Li et al. 2009), with moss crust > mixed crust > algal crust (Fig. 3a), consistent with Xu et al. (2013) in the Kubuqi Desert.

Heavy metal properties influence the effect of organic matter on heavy metal speciation. The significant positive correlation between As in two layers and organic matter (Table 4, P=0.028, 0.046) confirmed this metal affinity against organic compounds (Sungur et al. 2015, Siahcheshm et al. 2022, Wang et al. 2011). The complexation reaction between heavy metals and organic matter determines, to a certain extent, the morphology, bioavailability and, thus, transfer efficiency of metals (Peng et al. 2009, Hu et al. 2018). Li and Yang (2004) showed that dissolved organic matter reduced the adsorption of metal Cr in soil, enhancing Cr bioactivity and mobility. Yamaguchi et al. (2011) observed that As mainly existed as anions in soil, as pH rises, the negative charge increases on the surface of soil colloids and fights with As^－ for positive charge, desorbing As from soil colloids and further increasing the soluble As content. We also found the organic matter significantly positive correlated with matal acid-soluble fractions (Cr, R=0.789, P=0.000, As, R=0.586, P=0.011). This may be because the study area was located in typical gold mine tailings in the Qinling Mountains, where the biocrusts were enriched with large amounts of organic-rich dustfall. The strong complexation between organic matter and As significantly altered the microbial activities associated with soil As metabolism (Jeong et al. 2019), indirectly affecting As availability.

4.4 Effect of biocrusts on the ecological risk of heavy metals in the underlying layer

The RI results suggest that soil pollution in the gold mine tailings was a strong ecological risk, mainly caused by the strong ecological risk level of As. The underlying biocrusts had significantly lower Er and RI values than the bare soil, indicating that the biocrusts, as the ‘skin’ of the soil ecosystem, could resist the impact of heavy metal pollutants on the soil environment, consistent with others (Orlekowsky et al. 2013, Xu et al. 2012, 2013). F1 has stronger mobility, while F2 and F3 are unstable fractions that can be released under reducing and weak oxidising conditions, respectively. These fractions are considered potentially toxic and present a potential risk for living organisms (Anju and Banerjee 2010, Lin et al. 2014, Yang et al. 2019a). The RAC evaluation showed that Cr and As were risk-free in bare soil. However, with biocrust succession, the RAC values of Cr and As gradually increased in the underlying layer, indicating that biocrust development increased the metal fraction F1, directly affecting the degree of hazard to organisms and, thus, increasing the ecological risk of heavy metal pollutants. This is essentially due to changes in physicochemical parameters such as particle size and pH (Belnap 2003, Sun et al. 2004). Therefore, controlling moss crust development is important for the ecological restoration of gold mine tailings under some conditions (Chen et al. 2009).

As biocrust evolve, the ability to adsorb heavy metals, and in terrestrial ecosystems moss crusts are a good material for remediating heavy metal pollution. But we should also be aware of their negative effects. Specifically, moss crusts have a higher available form of heavy metals than algal crusts, which makes them a higher ecological risk. Yang et al. (2019b) reported the potential risk of heavy metal As in soils by Er and RAC method in the Xiaoqing River sewage irrigation area, with a slight–medium risk Er value while low-risk RAC. Xiao et al. (2022) reported Er value for As as a serious ecological risk, while the RAC was low-risk in the Antimony tailings of Qinglong, Guizhou. Unlike the Er method, which focuses on the total amount of heavy metals, the RAC method focuses on the pollution degree of the directly usable form of heavy metals. Therefore, the two evaluation methods should complement each other to improve the evaluation results.

Biocrusts have enriched the heavy metals in gold tailings ponds and increased the surface accumulation of heavy metals. Heavy metals were predominantly in the residual fraction of the mine tailings. Biocrusts increased the metal availability in the underlying layer by adjusting pH and redox potential and enriching organic matter and cationic sorption sites. While the gold mine tailings had much higher total As concentration than Cr accumulation, Cr had a higher bioavailable proportion than As. Biocrust succession promoted the migration and transformation of heavy metals but also increased its ecological risk. We cannot determine whether continuous growth and enrichment of heavy metals will cause the RAC index to exceed the low-risk threshold. Long-term experimental research on the migration of heavy metals in biocrusts and their interaction mechanisms with environmental factors should be strengthened to reveal the ecological functions and response mechanisms for biocrust remediation of contaminated soils.

Acknowledgments

This research was funded by the National Scientific Foundation of China (41971131), the National Key Research and Development Program of China (2016YFE0203400, 2017YFC0504703) and the Qinchuangyuan West (Xianyang) Science and Technology Venture Bay (2022-747).

Author Contribution

Chongfeng Bu conceived the ideas and designed the methodology, Jin Fan, Yangzhou Qi, Wenjuan Zhou, Chun Wang, and Yingxin Wei collected the data, Jin Fan analyzed the data and wrote the manuscript, Kadambot H.M. Siddique contributed critically to the manuscript. All authors gave final approval for publication.

Declaration of competing interest

All the authors declare no conflicts of interest.

Appendix A. Supplementary material

Supplementary materials include Appendix Tables S1-S3.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Adebiyi FM, Ayeni DA (2022) Chemical speciation, bioavailability and risk assessment of potentially toxic metals in soils around petroleum product marketing company as environmental degradation indicators. Pet Res 7: 286-296.
Anju M, Banerjee DK (2010) Comparison of two sequential extraction procedures for heavy metal partitioning in mine tailings. Chemosphere 78: 1393-1402.
Bai JH, Xiao R, Zhang KJ, Gao HF (2012) Arsenic and heavy metal pollution in wetland soils from tidal freshwater and salt marshes before and after the flow-sediment regulation regime in the Yellow River Delta, China. J Hydrol 450: 244-253.
Bartoli G, Papa S, Sagnella E, Fioretto A (2012) Heavy metal content in sediments along the Calore river: relationships with physical-chemical characteristics. J Environ Manage 95: S9-S14.
Belnap J (2003) The world at your feet: desert biological soil crusts. Front Ecol Environ 1: 181-189.
Belnap J, Miller DM, Bedford DR, Phillips SL (2014) Pedological and geological relationships with soil lichen and moss distribution in the eastern Mojave Desert, CA, USA. J Arid Environ 106: 45-57.
Bu CF, Wu SF, Xie YS, Zhang XC (2013) The study of biological soil crusts: Hotspots and prospects. Clean-Soil, Air, Water 41: 899-906.
Buch AC, Niemeyer JC, Marques ED, Silva-Filho EV (2021) Ecological risk assessment of trace metals in soils affected by mine tailings. J Hazard Mater 403: 123852.
Burachevskaya M, Minkina T, Fedorenko A, Fedorenko G, Chernikova N, Rajput VD, Mandzhieva S, Bauer T (2021) Accumulation, translocation, and toxicity of arsenic in barley grown in contaminated soil. Plant Soil 467: 91-106.
Chamizo S, Cantón Y, Miralles I, Domingo F (2012) Biological soil crust development affects physicochemical characteristics of soil surface in semiarid ecosystems. Soil Biol Biochem 49: 96-105.
Chen Z, Yang DG, Sun QY (2009) Effects of biocrust on soil microbial biomass and enzyme activities in copper mine tailings Chin J Appl Eco 20: 2193-2198 (In Chinese with English abstract).
Cheng JW, Cai SW, Huang MQ, Yan X (2022) Heavy metal speciation migrationtransformation and risk assessement in plant rhizosphere soil of Zunyi manganese mineland, Guizhou. Environ Chem 41: 2833-2841 (In Chinese with English abstract).
Christensen JB, Jensen DL, Christensen TH (1996) Effect of dissolved organic carbon on the mobility of cadmium, nickel and zinc in leachate polluted groundwater. Water Res 30: 3037-3049.
CNEMC (1990) (China National Environmental Monitoring Centre) Background values of soil elements in China. China Environment Publishing Group.
da Silva Medeiros DCC, Nzediegwu C, Benally C, Messele SA, Kwak JH, Naeth MA, Ok YS, Chang SX, El-Din MG (2021) Pristine and engineered biochar for the removal of contaminants co-existing in several types of industrial wastewaters: A critical review. Sci Total Environ 809: 151120.
Dhal B, Thatoi H, Das N, Pandey B (2013) Chemical and microbial remediation of hexavalent chromium from contaminated soil and mining/metallurgical solid waste: a review. J Hazard Mater 250: 272-291.
Di Palma A, Capozzi F, Spagnuolo V, Giordano S, Adamo P (2017) Atmospheric particulate matter intercepted by moss-bags: Relations to moss trace element uptake and land use. Chemosphere 176: 361-368.
Douay F, Roussel H, Pruvot C, Loriette A, Fourrier H (2008) Assessment of a remediation technique using the replacement of contaminated soils in kitchen gardens nearby a former lead smelter in Northern France. Sci Total Environ 401: 29-38.
Egbenda PO, Thullah F, Kamara I (2015) A physico-chemical analysis of soil and selected fruits in one rehabilitated mined out site in the Sierra Rutile environs for the presence of heavy metals: Lead, Copper, Zinc, Chromium and Arsenic. Afr J Pure Appl Chem 9: 27-32.
Evans RD, Belnap J (1999) Long-term consequences of disturbance on nitrogen dynamics in an arid ecosystem. Ecol 80: 150-160.
Fan J, Li SY, Du YX, Wang RR, Yu HL, Huang JY (2021) Differences of bacterial communities in different biological soil crusts around thermal power plant and their influencing factors. Chin J Appl Ecol 32: 4107-4118 (In Chinese with English abstract).
Fernandez JA, Carballeira A (2001) Evaluation of contamination, by different elements, in terrestrial mosses. Arch Environ Contam Toxicol 40: 461-468.
Fernandez JA, Ederra A, Nunez E, Martinez-Abaigar J, Infante M, Heras P, Elias MJ, Mazimpaka V, Carballeira A (2002) Biomonitoring of metal deposition in northern Spain by moss analysis. Sci Total Environ 300: 115-127.
Fu JT, Yu DM, Chen X, Su Y, Li Cai H, Wei YP (2019) Recent research progress in geochemical properties and restoration of heavy metals in contaminated soil by phytoremediation. J Mt Sci 16: 2079-2095.
Gallego-Cartagena E, Morillas H, Carrero JA, Madariaga JM, Maguregui M (2021) Naturally growing grimmiaceae family mosses as passive biomonitors of heavy metals pollution in urban-industrial atmospheres from the Bilbao Metropolitan area. Chemosphere 263: 128190.
Gao LQ, Zhao YG, Xu MX, Sun H, Yang QY (2018) The effects of biological soil crust succession on soil ecological stoichiometry characteristics. Acta Ecol Sin 38: 678-688 (In Chinese with English absrtract).
Gardea-Torresdey JL, Becker-Hapak MK, Hosea JM, Darnall DW (1990) Effect of chemical modification of algal carboxyl groups on metal ion binding. Environ Sci Technol 24: 1372-1378.
Guo Q, Bu CF, Li YP, Ju MC, Li YH, Wei YX (2022) The spatial distribution characteristics of soil nutrients underlying biological crusts at regional scale: A case study of Mu Us sandy land. Acta Pedol Sin 59: 699-708 (In Chinese with English absrtract).
Guo SY, Xiao CQ, Zhou N, Chi R (2021) Speciation, toxicity, microbial remediation and phytoremediation of soil chromium contamination. Environ Chem Lett 19: 1413-1431.
Hakanson L (1980) An ecological risk index for aquatic pollution control. A sedimentological approach. Water Res 14: 975-1001.
Hu CY, Yang XL, Dong JY, Zhang XM (2018) Heavy metal concentrations and chemical fractions in sediment from Swan Lagoon, China: Their relation to the physiochemical properties of sediment. Chemosphere 209: 848-856.
Hu LG, Cai Y, Jiang GB (2016) Occurrence and speciation of polymeric chromium(III), monomeric chromium(III) and chromium(VI) in environmental samples. Chemosphere 156: 14-20.
Hu Y, Peng CR, Li D (2022) Types and species composition of biological soil crust in metal tailings ponds. Acta Hydrobiol Sin 40: 622-630 (In Chinese with English abstract).
Jeong S, Hong JK, Jho EH, Nam K (2019) Interaction among soil physicochemical properties, bacterial community structure, and arsenic contamination: Clay-induced change in long-term arsenic contaminated soils. J Hazard Mater 378: 120729.
Khan S, Cao Q, Zheng YM, Huang YZ, Zhu YG (2008) Health risks of heavy metals in contaminated soils and food crops irrigated with wastewater in Beijing, China. Environ Pollut 152: 686-692.
Krupadam RJ, Ahuja R, Wate SR (2007) Heavy metal binding fractions in the sediments of the Godavari estuary, East Coast of India. Environ Model Assess 12: 145-155.
Kuhlman MI, Greenfield TM (1999) Simplified soil washing processes for a variety of soils. J Hazard Mater 66: 31-45.
Li JL, He M, Han W, Gu YF (2009) Availability and mobility of heavy metal fractions related to the characteristics of the coastal soils developed from alluvial deposits. Environ Monit Assess 158: 459-469.
Li L, Cui JJ, Liu JH, Gao JJ, Bai YZ, Shi XF (2016) Extensive study of potential harmful elements (Ag, As, Hg, Sb, and Se) in surface sediments of the Bohai Sea, China: Sources and environmental risks. Environ Pollut 219: 432-439.
Li TQ, Yang XE (2004) Soil dissolved organic matter and its effect on chemical and biological behaviors of soil heavy metals. Chin J Appl Ecol 15: 1083-1087 (In Chinese with English abstract).
Li XR, Tian F, Jia RL, Zhang ZS, Liu LC (2010) Do biological soil crusts determine vegetation changes in sandy deserts? Implications for managing artificial vegetation. Hydrol Processes 24: 3621-3630.
Lin C, Liu Y, Li WQ, Sun XW, Ji WD (2014) Speciation, distribution, and potential ecological risk assessment of heavy metals in Xiamen Bay surface sediment. Acta Oceanol Sin 33: 13-21.
Lin SX, Liu XL, Zhang ZL, Zhang QH (2022) Speciation characteristics and ecological risk assessment of heavy metals in sediments from Caohai Lake, Guizhou Province. Stoch Environ Res Risk Assess 36: 3929–3944.
Ma JH, Han CX, Jiang YL (2020) Some problems in the application of potential ecological risk index. Geogr Res 39: 1233-1241 (In Chinese with English abstract).
Mao HT, Wang XM, Wu N, Chen LX, Yuan M, Hu JC, Chen YE (2022) Temporal and spatial biomonitoring of atmospheric heavy metal pollution using moss bags in Xichang. Ecotoxicol Environ Saf 239: 113688.
McGrath SP, Zhao FJ, Lombi E (2001) Plant and rhizosphere processes involved in phytoremediation of metal-contaminated soils. Plant Soil 232: 207-214.
Mota R, Rossi F, Andrenelli L, Pereira SB, De Philippis R, Tamagnini P (2016) Released polysaccharides (RPS) from Cyanothece sp. CCY 0110 as biosorbent for heavy metals bioremediation: interactions between metals and RPS binding sites. Appl Microbiol Biotechnol 100: 7765-7775.
Nemati K, Bakar NKA, Sobhanzadeh E, Abas MR (2009) A modification of the BCR sequential extraction procedure to investigate the potential mobility of copper and zinc in shrimp aquaculture sludge. Microchem J 92: 165-169.
Orlekowsky T, Venter A, van Wyk F, Levanets A (2013) Cyanobacteria and algae of gold mine tailings in the Northwest Province of South Africa. Nova Hedwigia 97: 281-294.
Park S, Kim KS, Kang D, Yoon H, Sung K (2013) Effects of humic acid on heavy metal uptake by herbaceous plants in soils simultaneously contaminated by petroleum hydrocarbons. Environ Earth Sci 68: 2375-2384.
Peng JF, Song YH, Yuan P, Cui XY, Qiu GL (2009) The remediation of heavy metals contaminated sediment. J Hazard Mater 161: 633-640.
Rauret G, Lopez-Sanchez JF, Sahuquillo A, Rubio R, Davidson C, Ure A, Quevauviller P (1999) Improvement of the BCR three step sequential extraction procedure prior to the certification of new sediment and soil reference materials. J Environ Monit 1: 57-61.
Schmitt D, Taylor HE, Aiken GR, Roth DA, Frimmel FH (2002) Influence of natural organic matter on the adsorption of metal ions onto clay minerals. Environ Sci Technol 36: 2932–2938.
Siahcheshm K, Orberger B, Wagner C (2022) Bioavailability and heavy metals speciation assessment in the contaminated soils of Doustbaglu mineralised area, NW Iran. Environ Earth Sci 81: 1-11.
Sonter LJ, Ali SH, Watson JEM (2018) Mining and biodiversity: key issues and research needs in conservation science. Proc Biol Sci 285: 20181926.
Sun QY, An SQ, Yang LZ, Wang ZS (2004) Chemical properties of the upper tailings beneath biotic crusts. Ecol Eng 23: 47-53.
Sundaray SK, Nayak BB, Lin S, Bhatta D (2011) Geochemical speciation and risk assessment of heavy metals in the river estuarine sediments--a case study: Mahanadi basin, India. J Hazard Mater 186: 1837-1846.
Sungur A, Soylak M, Yilmaz E, Yilmaz S, Ozcan H (2015) Characterisation of heavy metal fractions in agricultural soils by sequential extraction procedure: the relationship between soil properties and heavy metal fractions. Soil Sediment Contam 24: 1-15.
Tyopine AA, Jayeoye TJ, Okoye CO (2018) Geoaccumulation assessment of heavy metal pollution in Ikwo soils, eastern Nigeria. Environ Monit Assess 190: 1-11.
Venter A, Siebert S, Rajakaruna N, Barnard S, Levanets A, Ismail A, Allam M, Peterson B, Sanko T (2018) Biological crusts of serpentine and non‐serpentine soils from the Barberton Greenstone Belt of South Africa. Ecol Res 33: 629-640.
Wang Y, Yang ZF, Shen ZY, Tang ZW, Niu JF, Gao F (2011) Assessment of heavy metals in sediments from a typical catchment of the Yangtze River, China. Environ Monit Assess 172: 407-417.
Wu YG, Xu YN, Zhang JH, Hu SH, Liu K (2011) Heavy metals pollution and the identification of their sources in soil over Xiaoqinling gold-mining region, Shaanxi, China. Environ Earth Sci 64: 1585-1592.
Xian X (1989) Effect of chemical forms of cadmium, zinc, and lead in polluted soils on their uptake by cabbage plants. Plant Soil 113: 257-264.
Xiao H, Han ZW, Xiong J, Luo GF, Tiao YT, Yang M (2022) Bioavailability and ecological risk assessment of Sb and As in tailings of Qinglong Antimony Mine in Guizhou. Environ Eng 40: 123-132 (In Chinese with English abstract).
Xu J, Ao YQ, Zhang JX (2013) Spatial contaminant of heavy metal and nutrition elements in artificial biological soil crusts in sand-land. J Arid Land Resour Environ 27: 7402-7410 (In Chinese with English abstract).
Xu J, Ao YQ, Zhang JX, Yao YP, Gao TY, Luo H (2012) Heavy metal contaminant in development process of artificial biological soil crusts in sand-land. Acta Ecologica Sinica 32: 7402-7410 (In Chinese with English abstract).
Xu JC, Wang SQ, Yao T, She XS, Gan ZT (2022) Vertical distributions and bioavailabilities of heavy metals in soil in an-tea plantations in Qimen County, China. Processes 10: 664.
Yamaguchi N, Nakamura T, Dong D, Takahashi Y, Amachi S, Makino T (2011) Arsenic release from flooded paddy soils is influenced by speciation, Eh, pH, and iron dissolution. Chemosphere 83: 925-932.
Yang B, Ren J, Wang M, Luo HL, Cao YZ (2019a) Concentrations and chemical fractions of Cu, Zn, Cd, and Pb at ten metallurgical sites in China. Environ Sci Pollut Res 26: 3603-3611.
Yang XM, Zhuang T, Han L, Liu GF (2019b) Fraction distribution and ecological risk assessment of soil heavy metals in the farmland soil from the sewage irrigated area of Xiaoqing River. Environ Chem 38: 644-652 (In Chinese with English abstract).
Yao RJ, Yang JS, Xie WP, Wu DH, Yu SP, Zhang X (2016) Accumulation and potential ecological risk assessment of heavy metals in greenhouse soils from coastal area of Jiangsu Province. J Agro-Environ Sci 35: 1498-1506 (In Chinese with English abstract).
Yıldırım G, Tokalıoğlu Ş (2016) Heavy metal speciation in various grain sizes of industrially contaminated street dust using multivariate statistical analysis. Ecotoxicol Environ Saf 124: 369-376.
Zakir HM, Shikazono N, Otomo K (2008) Geochemical distribution of trace metals and assessment of anthropogenic pollution in sediments of Old Nakagawa River, Tokyo, Japan. Am J Environ Sci 4: 654.
Zhang R, Wang ZR, Huang HY, Song JJ, Wu BH, Wang ML, Xu H (2022) Assessment about bioindicator capacity of acrocarpous moss Campylopus schmidii exposed to abandoned pyritic tailings. J Environ Manage 317: 115471.
Zhang X, Yang HH, Cui ZJ (2017) Migration and speciation of heavy metal in salinised mine tailings affected by iron mining. Water Sci Technol 76: 1867-1874.
Zhang YJ, Zhang YL, Zhang Y, Meng LY, Shi MW (2019) Study on speciation and distribution characteristics of heavy metals in soil around typical Coal Gangue in Fengfeng Mining Area. IOP Conf Ser: Earth Environ Sci 371: 032081.
Zhou YJ, Yue SC, Zhou TF (2010) Migration of heavy metals in Yangshanchong tailings impoundment in Tongling, Anhui Province. Res Environ Sci 23: 497-503 (In Chinese with English abstract).

Table1 Potential ecological risk index and classification of heavy metal pollution

Table 2 Heavy metal content in biocrust layer and underlying layer

Types	Cr (mg/kg)		As (mg/kg)
Types	Biocrust layer	Underlying layer	Biocrust layer	Underlying layer
Moss crust	36.49 ± 0.66 Aa	21.54 ± 0.54 Ba	82.80 ± 5.32 Aa	80.95 ± 2.69 Aa
Mixed crust	34.80 ± 0.54 Aab	23.28 ± 0.78 Bab	81.45 ± 5.85 Aa	82.53 ± 2.54 Aa
Algae crust	32.67 ± 1.31 Ab	24.21 ± 0.21 Bb	76.06 ± 6.55 Aa	85.65 ± 5.23 Aa
Bare soil		25.32 ± 0.36 b		97.54 ± 0.92 b

Different uppercase and lowercase letters indicate significant differences among different soil layers in the same crust type and different crust types in the same layer, respectively, at P<0.05.

Table 3 Physical and chemical properties in the underlying layer of biocrust

Underlying layer	pH	Eh (mv)	CEC (cmol/kg)	SOM (g/kg)
Moss crust	8.90 ± 0.01 a	89.41 ± 1.55 a	3.06 ± 0.19 a	18.26 ± 0.11 a
Mixed crust	8.96 ± 0.01 b	91.03 ± 0.78 a	2.28 ± 0.09 b	18.08 ± 0.20 a
Algae crust	9.02 ± 0.02 b	91.36 ± 2.73 ab	2.33 ± 0.20 ab	17.51 ± 0.17 b
Bare soil	9.02 ± 0.07 b	101.00 ± 1.53 b	1.86 ± 0.04 b	16.26 ± 0.18 c

Different letters within the same column indicate significant differences at P<0.05.

Table 4 Spearman correlation analysis among heavy metal contents and physicochemical properties

Spearman	pH	Eh	CEC	SOM	Cr (biocrust layer)	As (biocrust layer)	Cr (underlying layer)
Eh	0.141	1
CEC	–0.587*	–0.079	1
SOM	–0.569**	–0.023	0.290	1
Cr (biocrust layer)	–0.683**	–0.436	0.496	0.565*	1
As (biocrust layer)	0.009	–0.191	–0.116	0.545**	0.229	1
Cr (underlying layer)	0.664**	0.076	–0.343	–0.466	–0.207	–0.073	1
As (underlying layer)	0.114	–0.107	–0.537*	0.517*	0.071	0.813**	–0.079

* P<0.05, ** P<0.01.

Table 5 Significance of environmental factors in redundancy analysis

Item	Cr					As
Item	Cr	pH	CEC	SOM	Eh	As	pH	CEC	SOM	Eh
Explain (%)	37.5	30.5	15.5	13.0	4.1	35.5	21.8	20.3	19.0	7.1
F	7.8	5.7	2.4	1.90	0.6	7.2	3.6	3.3	3.1	1.0
P	0.002	0.004	0.078	0.130	0.682	0.002	0.034	0.036	0.040	0.412

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Biocrusts significantly affect the bioavailability and ecological risk of heavy metals in gold mine tailings

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Abstract

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1. Introduction

2. Materials And Methods

2.1 Study site

2.2 Sample collection

2.3 Ecological risk assessment of heavy metals

Potential ecological risk index

Risk assessment code

2.4 Statistical analysis

3. Results

3.1 Accumulation and its distribution of heavy metals in the biocrust layer and underlying layer

3.2 Heavy metal fractions and bioavailability in the underlying layer

3.3 Influencing factors of total amount and speciation of heavy metals in underlying biocrusts

3.3.1 Total content

3.3.2 Chemical fraction

3.4 Effect of biocrusts on the ecological risk of heavy metals in the underlying layer

4. Discussion

4.1 Effect of biocrusts on heavy metal enrichment

4.2 Effect of biocrusts on the migration and transformation of heavy metals in the underlying layer

4.3 Influencing factors of heavy metal migration and transformation

4.4 Effect of biocrusts on the ecological risk of heavy metals in the underlying layer

5. Conclusions

Declarations

References

Tables

Supplementary Files

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