A method for constructing a combined remediation system of plant-microbial communities in coal mine restoration areas

Abstract

The present invention belongs to the technical field of the restoration of coal mine restoration areas. In order to understand the effects of heavy metals and PAHs on the diversity of restored plants and soil microbial communities under different utilization modes, a method for constructing a combined plant-microbial community restoration system in coal mine restoration areas is provided. The quadrat survey method is used to investigate the composition and structural characteristics of the vegetation community in the gangue restoration area; the Miseq high-throughput sequencing technology is used to detect the composition and diversity of the bacterial community in the soil of the gangue area, analyze the composition, distribution and diversity characteristics of the microbial community at different stages of mining area accumulation and restoration and in different seasons of the restoration area, and analyze their differences, and use the co-evolution strategy to screen the plant rhizosphere-microbial ecological community that can efficiently degrade pollutants; construct a soil-plant-microbial efficient degradation system. The plants that can enrich heavy metals and have a strong nitrogen fixation effect are screened out, and the dominant bacterial genera that are tolerant to heavy metals or can degrade PAHs are found, which helps the further restoration of the gangue mountain in this area.

Description

Technical Field

[0001] The present invention belongs to the technical field of coal mine restoration area repair, and particularly relates to a method for constructing a combined plant-microbial community restoration system in a coal mine restoration area. Background Art

[0002] Currently, the mainstream soil remediation technologies are mainly divided into physical remediation (solidification / stabilization, thermal desorption, and electrokinetic remediation), chemical remediation (soil washing, oxidation-reduction), and biological remediation (plant, animal, and microbial remediation). Among them, as an efficient, low-cost, and environmentally friendly remediation technology, biological remediation technology has overcome the disadvantages of high cost, ineffective results, and easy generation of secondary pollution in physical and chemical remediation, and has gradually become a research hotspot. By statistically analyzing the literature on gangue remediation from 2010 to 2020, more than 60% of them are biological remediation. The plant-microbial remediation technology is a pollution control technology that uses the composite system composed of soil-plant-microorganisms to directly or indirectly absorb and degrade pollutants in the soil. The plant-microbial combined remediation technology is based on the plant remediation technology and includes four processes: phytostabilization, phytoextraction, phytovolatilization, and phytodegradation ( Figure 1 ). Heavy metals are mainly removed by phytoextraction, and the plant remediation effect can be enhanced by adding microorganisms. For soil PAHs, the contribution of phytoextraction is usually negligible (<0.4%), and the main process is rhizosphere degradation, that is, the activity of rhizosphere microorganisms and fungi is enhanced by plant roots to indirectly degrade polycyclic aromatic hydrocarbons.

[0003] In the gangue area, the plant-microbial remediation technology has been widely applied, and a large number of literatures have screened plants (such as ryegrass, alfalfa, tall fescue, etc.) and microorganisms ( Magnaporthe oryzae, Burkholderia sp ) suitable for the remediation of a certain mining area, and the effect is good. However, the main pollutants in the later stage of the remediation of the complex pollution mining area are not clear, and there are few comparisons of the ecological risks and health risks before and after the remediation. The soil plants and microorganisms in the polluted area are different from the natural ecosystem. The restoration of the coal mine reclamation area often involves artificial intervention, control, and management of the ecosystem, and finally a highly productive and stable plant community is established, ultimately achieving the two goals of protection and sustainable utilization.

[0004] The diversity of soil microbial communities is a key factor determining soil quality. It is often disturbed by factors such as pollutant types, climate change, and nitrogen deposition, which change the diversity, density, and pest and disease loads of pathogens and beneficial organisms, ultimately affecting the functions of ecosystems and human health. In 2015, a literature published in Nature constructed a conceptual framework, indicating that land use and management decisions would change soil biodiversity and thus affect human health. There are differences in physical and chemical properties and pollutant contents after gangue stacking and restoration, and the microbial community structure is continuously adjusted to adapt to different habitats. A large number of studies have shown that compared with uncontaminated soils, there are significant changes in the microbial diversity and community in mining area soils, and organisms with strong heavy metal tolerance dominate. However, there are few investigations on potential pathogenic bacteria in mining area soils, and the association between pollutants, microorganisms, and human health is not clear. Summary of the Invention

[0005] In order to understand the effects of heavy metals and PAHs on the diversity of restored plants and soil microbial communities under different utilization modes, the present invention provides a method for constructing a joint remediation system of plant-microbial communities in coal mine restoration areas, screening out plants that can enrich heavy metals and have strong nitrogen fixation effects, and finding out dominant bacterial genera that are tolerant to heavy metals or can degrade PAHs, thereby contributing to the further restoration of gangue mountains in this area.

[0006] The present invention is realized by the following technical solutions: a method for constructing a joint remediation system of plant-microbial communities in coal mine restoration areas, using the quadrat survey method to investigate the composition and structural characteristics of the vegetation community in the gangue restoration area; using the Miseq high-throughput sequencing technology to detect the composition and diversity of bacterial communities in the gangue area soil, analyze the composition, distribution, and diversity characteristics of microbial communities at different stages of mining area stacking and restoration and different seasons in the restoration area, and analyze their differences, and using the co-evolution strategy to screen the plant rhizosphere-microbial ecological communities that can efficiently degrade pollutants; finally, constructing a high-efficiency degradation system of soil-plant-microorganisms.

[0007] Using the Miseq high-throughput sequencing technology to detect the composition and diversity of bacterial communities in the gangue area soil, the specific method is: using the universal primers 338F (5’-ACTCCTACGGGAGGCAGCAG- 3’) and 806R (5’-GGACTACHVGGGTWTCTAAT- 3’) to extract the genomic DNA of soil microorganisms, perform 16S rDNA PCR amplification, then construct a library and perform Miseq high-throughput sequencing. The PE reads obtained by Miseq sequencing are first spliced according to the overlap relationship, and at the same time, the sequence quality is controlled and filtered. After distinguishing the samples, OTU clustering analysis and species taxonomic analysis are carried out;

[0008] Microbial diversity analysis is as follows: α-diversity: Sobs is the actual observed value of community richness; Shannon and Simpson indices reflect species evenness. The Shannon index describes the disorder and uncertainty of individual occurrences, and the Simpson index is the probability that two randomly sampled OUTs belong to different species. The larger the value of the Shannon index and the smaller the value of the Simpson index, the higher the evenness of community species distribution; Chao and Ace indices reflect community richness. These two indices estimate the number of OUTs in the community through different algorithms, and the larger the value, the more species there are. β-diversity: It reflects the difference in community composition between different samples, and is measured by the sample similarity distance value; NMDS is unconstrained ordination analysis, which simplifies the research objects in multi-dimensional space to low-dimensional space for location analysis and classification, reflecting the similarity and difference of microbial communities; the β-diversity of samples is measured by NMDS, using the weighted_normalized_unifrac distance, considering the evolutionary relationship and species abundance between samples;

[0009] Microbial community structure: Annotate and classify bacterial OTUs, and analyze through a bacterial community bar chart. Analyze at the phylum taxonomic level, and classify phyla with a relative abundance < 1% as others. Analyze at the genus taxonomic level, and classify genera with an average abundance less than 1.5% as others to obtain the microbial community structure;

[0010] Differential analysis: LEfSe analysis is used to distinguish two or more biological conditions or groups, find the groups with significant differences in abundance, and estimate the magnitude of the impact of group or species abundance on the difference using linear discriminant analysis LDA. Study the species with an LDA threshold higher than 2 from the phylum to family level to obtain the differences between soil microbiomes in different regions. Conduct a significance test for inter-group differences at the genus level through the Wilcox rank-sum test and correct it through fdr multiple testing;

[0011] A co-evolution strategy was adopted to screen the plant rhizosphere-microbial ecological community with high pollutant degradation efficiency. The specific method is as follows: Multiple regression analysis was used to understand the impact of heavy metal and PAHs content in the soil on the microbial α-diversity index. The Shannon index was selected to represent microbial evenness, and the Chao index was used to explain microbial richness. The stepwise backward elimination method of multiple regression analysis was used to analyze the relationship between pollutant content and diversity index, and the formulas were obtained: Shannon = 3.648 + 0.054Pb; Chao = 2560.7 + 40.7As + 13.1 Nap - 260.0Ace + 236 Flu; The results showed that the higher the Pb content, the smaller the difference in the relative abundance of each microorganism, and it was difficult to form a dominant microbial community; As, Nap, Ace, and Flu had an impact on microbial richness;

[0012] Determine OTUs in different abundance intervals, the top 30 genera in relative abundance, pathogenic bacteria and probiotic bacteria to understand the impact on the microbial community structure: The Spearman correlation between microorganisms and heavy metals, PAHs content reflects their impact on the microbial community structure. Select OTUs with a relative abundance higher than 0.1%, and divide them into: >1% is high abundance, 0.5%-1% is medium abundance, and 0.1%-0.5% is low abundance, and analyze the Spearman correlation between OTUs in different abundance intervals and pollutants;

[0013] Further analyze the toxic effects of heavy metals on different types of microorganisms. Select the top 30 microorganisms in relative abundance at the genus level and conduct a correlation analysis with pollutants; then select the top 30 species in total abundance at the phylum taxonomic level and heavy metals, PAHs, and calculate the Spearman rank correlation coefficient between species to reflect the correlation between species; use Spearman correlation to analyze the impact of pollutants on pathogenic bacteria and probiotic bacteria;

[0014] Predict the functions of microorganisms: Select the main functions and their hierarchical clustering of the KEGG pathways at the second level of Tax4Fun function prediction, and use BugBase phenotype prediction to determine the high-level phenotypes present in the microbial samples. Normalize the OTUs by the predicted 16S copy number, and then predict the microbial phenotypes; FAPROTAX function prediction analyzes the metabolic and ecological functions of prokaryotes, and maps prokaryotic taxa to ecological functions related to chemoheterotrophy, aerobic_chemoheterotrophy, and fermentation;

[0015] Detect the responses of plants under the stress of the main control pollutants. Select soil samples at different distances of 0-800 m in the dry season of the restoration area, and understand the plant responses through the toxicity indicators of the model plant barley. The specific method is: through soil cultivation, observe the growth indicators of barley seedlings, namely root length, shoot length, biomass, chlorophyll, oxidative stress indicators, namely MDA, CAT, POD, SOD, GSH, and genotoxicity indicators, namely the changes in mitotic index and micronuclei; conduct a correlation analysis between the plant toxicity indicators and the soil heavy metal content indicators to obtain the soil-plant toxicity assessment.

[0016] The specific method for constructing an efficient soil-plant-microorganism degradation system is as follows: For the gangue accumulation area to be repaired, with high contents of Pb, As, Cu, and PAHs and poor physical and chemical properties, adopt the leguminous plant + Massilia genus restoration mode; for the gangue restoration area polluted by Pb and PAHs, adopt Pb hyperaccumulating plants + cash crops + Pseudomonas.

[0017] The Pb hyperaccumulating plants mentioned above are Phytolacca acinosa Roxb., Thysanolaena latifolia orMimosa pudica 。

[0018] Based on the current situation of soil pollution in the coal mine restoration areas of Shanxi Province and the new requirements put forward by the state for soil pollution investigation, soil environmental safety and environmental risks (GB15618 - 2018 and GB36600 - 2018), native plants with certain remediation potential for the main control pollutants are selected, and a co - evolution strategy is adopted to screen the plant rhizosphere - microbial ecological communities that can efficiently degrade pollutants; finally, a highly efficient degradation system of soil - plant - microorganism is constructed to reveal its important role and ecological chemical processes in the soil remediation of coal mine restoration areas, and to achieve rapid, economical, effective and safe in - situ remediation applications of contaminated soil. The purpose of the present invention is to fill the technical gap in the risk control of typical industrial polluted sites and provide support for the development of emerging resource - recycling industries. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the combined plant - microorganism remediation of HMs - PAHs; Figure 2 It is a technical roadmap for the analysis of plant and microbial diversity; Figure 3 It is the beta - diversity analysis of bacterial communities based on NMSD; Figure 4 : (A) rainy season in the stacking area (B) rainy season in the restoration area and (C) dry season in the restoration area, analysis of dominant bacterial groups at the phylum level; Figure 5 It is the Lefse multi - level species difference discriminant analysis. Note: Different colored nodes represent microbial groups that are significantly enriched in the corresponding groups and have a significant impact on the differences between groups; light yellow nodes represent microbial groups that have no significant differences in different groups or have no significant impact on the differences between groups; Figure 6 It is the significance test of the differences between the soil microbiomes in the stacking area and the restoration area; Figure 7 It is a heat map of the correlation between the top 30 genera in relative abundance and pollutants; Figure 8 It is a network diagram of the correlation between soil pollutants and microorganisms in the mining area. Note: The size of the nodes in the figure represents the species abundance, and different colors represent different species; the connecting lines represent Spearman correlations, red for positive correlations and green for negative correlations, and the thickness of the lines represents the magnitude of the correlation coefficient. The thicker the line, the higher the correlation between the species; the more lines, the closer the connection between the nodes; Figure 9 It is a heat map of the correlation between pathogenic bacteria, probiotics and pollutants. Note: Black fonts are pathogenic bacteria and blue fonts are probiotics; Figure 10 It is a heat map of the functional derivation at the Tax4Fun level 2 and hierarchical clustering comparison; Figure 11 : (A) BugBase phenotype prediction and (B) FAPROTAX function prediction; Figure 12 It is the effect of the soil in the gangue restoration area on the (A) bud length, (B) root length, (C) bud weight and (D) root weight of barley seedlings; Figure 13Effect of the soil in the restoration area on the contents of (A) chlorophyll a, (B) chlorophyll b, and (C) total chlorophyll in barley leaves; Figure 14 Effect of the soil in the restoration area on the malondialdehyde content in barley leaves; Figure 15 Effect of the soil in the restoration area on the activities of (A) SOD, (B) CAT, (C) POD, and (D) GSH in barley leaves; Figure 16 Effect of the soil in the restoration area on the micronucleus rate of barley root tip cells. Specific implementation mode

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this invention belongs. All materials cited herein and the materials they cite will be incorporated by reference. Equivalent technologies of the specific implementation modes described that can be understood by those of ordinary skill in the art through conventional experiments will be included in this application.

[0022] I. Analysis of the diversity of restoration plants in the coal mine restoration area: In temperate climates, grass and legume species are most suitable for the restoration of degraded sites. In tropical regions, grasses, legumes, and even trees can be planted. These several types of plants each have their own advantages. Grass has a wide growth range and strong adaptability; leguminous plants can fix nitrogen through rhizobia and supply more nutrients to the plants; the greatest advantage of trees is to fix the soil and reduce soil erosion. The main restoration plants in the restoration area are shown in Table 1, with a total of 13 species belonging to 13 genera in 8 families, and leguminous plants accounting for 50% of the total number of species. They are mainly divided into three categories: trees, shrubs, and ground cover plants.

[0023] Table 1: Main restoration plants in the restoration area

[0024]

[0025] II. Analysis of the microbial diversity in the coal mine restoration area

[0026] 1. Extraction of soil microbial genomic DNA and 16S rDNA PCR amplification

[0027] Take out the pre-stored soil samples from an -80°C refrigerator, extract genomic DNA, detect the DNA concentration and purity (OD260 / OD280) in the extract using a NanoDrop2000 ultra-micro spectrophotometer, and detect the DNA integrity by agarose gel electrophoresis. Using the extracted DNA as a template, amplify the V3-V4 region of bacterial 16S rDNA. The universal primers are 338F (5’-ACTCCTACGGGAGGCAGCAG- 3’) and 806R (5’-GGACTACHVGGGTWTCTAAT- 3’). The PCR test uses TransGen AP221-02: TransStart Fastpfu DNA Polymerase, and the PCR instrument is an ABI GeneAmp 9700 type. The reaction conditions are: 95°C for 3 min, 95°C for 30 s, 72°C for 45 s, 72°C for 10 min, for a total of 27 cycles. Detect the PCR products by agarose gel electrophoresis, cut out the bands with the correct size and appropriate concentration, and purify and recover them. Referring to the preliminary quantification results of electrophoresis, detect and quantify the PCR products using a QuantiFluor -ST blue fluorescence quantification system (Promega), and then mix them in the corresponding proportions according to the sequencing amount requirements of each sample for subsequent experiments.

[0028] 2. Library construction and sequencing on the machine: Add the Illumina official adapter sequence to the outer end of the target region through PCR, use a gel recovery kit to cut and recover the PCR products, elute with Tris-HCl buffer, detect by 2% agarose electrophoresis, add sodium hydroxide for denaturation to generate single-stranded DNA fragments, complete library construction, and perform Miseq sequencing.

[0029] 3. Bioinformatics analysis: The PE reads obtained from Miseq sequencing are first spliced according to the overlap relationship, and at the same time, the sequence quality is controlled and filtered. After distinguishing the samples, OTU clustering analysis and species taxonomic analysis are performed.

[0030] 4. Microbial diversity index analysis: Through Illumina MiSeq sequencing of the bacterial 16S rRNA gene in soil samples, 776,548 optimized sequences are obtained. Species annotation results in 38 phyla, 132 classes, 316 orders, 501 families, 984 genera, 1,992 species, and 5,775 OTUs. The Good's coverage rate is above 97.5%, and the sequencing has covered all species in the samples. The sequencing results of this time can represent the true situation of the samples.

[0031] Alpha diversity can reflect the diversity within a microbial community and is often characterized by some indices. Sobs is the actual observed value of community richness; the Shannon and Simpson indices reflect species evenness. The Shannon index describes the disorder and uncertainty of the occurrence of individuals, and the Simpson index is the probability that two randomly sampled OUTs belong to different species. The larger the value of the Shannon index and the smaller the value of the Simpson index, the higher the evenness of species distribution in the community. The Chao and Ace indices reflect community richness, and these two indices estimate the number of OUTs in the community through different algorithms. The larger the value, the more species there are. Table 2 shows the results of bacterial alpha diversity.

[0032] Table 2: Alpha diversity of soil bacterial communities

[0033]

[0034] In different regions (D rainy season, H rainy season), the sobs value in the stacking area is higher than that in the restoration area, and the actual number of species is larger. There is no significant statistical difference in the bacterial evenness and richness between the two regions. In different seasons (H rainy season, H dry season), the microbial evenness and richness are higher in the rainy season. The selected waste rock stacking area in the present invention has a large human disturbance. On the one hand, it is less polluted compared to other waste rock mountains, and the growth of most microorganisms is not restricted. On the other hand, this stacking area is prepared for soil covering and restoration, and the alpha diversity may increase at the initial stage of restoration. Therefore, there is no significant difference in the microbial diversity and richness between the two areas.

[0035] The present invention measures the beta diversity of samples through NMDS and uses the weighted_normalized_unifrac distance, considering the evolutionary relationship and species abundance between samples. Among the samples in different regions, the samples in the stacking area have larger differences, while the samples in the restoration area have smaller differences. The microorganisms in D3 and D4 in the stacking area are similar to those in the restoration area. Among different seasons, the samples in the rainy season have smaller differences, while those in the dry season have larger differences.

[0036] 5. Analysis of microbial community structure: To understand the composition of the bacterial community, the bacterial OTUs are annotated and classified, and the analysis is carried out through the bacterial community bar chart.

[0037] Analysis is carried out at the phylum taxonomic level. The phyla with a relative abundance < 1% are classified as others, and a total of 14 major bacteria are obtained. Such as Figure 4, the bacteria with relatively high relative abundances in the soil at the sampling points were Actinobacteria, Proteobacteria, Chloroflexi, Acidobacteria, Firmicutes, Bacteroidetes, Gemmatimonadota, Myxobacteria, Patesclbacteria, Cyanobacteria, Deinococcota, Planctomycetota, and Methyloirabilota, WPS-2 .

[0038] Generally speaking, in different regions, there are 14 phyla of microorganisms in the gangue accumulation area and 10 in the gangue restoration area. The two dominant bacterial groups, Actinobacteria and Proteobacteria, account for about 0.6 in the accumulation area and more than 0.7 in the restoration area. The microbial community in the restoration area is stable. In different seasons, the dominant bacteria account for a higher proportion in the rainy season. We speculate that after years of competition and symbiosis, the microorganisms in the restoration area have formed a microbial community with a stable structure, and some microorganisms at a disadvantage have been eliminated.

[0039] Analyzing at the genus taxonomic level, genera with an average abundance of less than 1.5% were classified as others, and a total of 26 genera were obtained. Genera with relatively high relative abundances and clearly named include Arthrobacter ( Arthrobacter ), Gemmiger ( Blastococcus ), Massilia ( Massilia ), Nocardioides ( Nocardioides ), Microvirgula ( Microvirga ), Micrococcus roseus ( Rubellimicrobium ), Bacillus ( Bacillus ), Streptomyces ( Treptomyces ), Rhodobacter ( Solirubrobacte ), Streptomyces ( Streptomyces ), Pseudomonas ( Pseudomonas ), Sphingomonas ( Sphingomonas ). Similar to the phylum level, at the genus level in different regions, the dominant bacterial groups in the restoration area also account for a relatively high proportion. The dominant genera in the restoration area in both the dry and rainy seasons account for 0.4 - 0.6, with a small difference. At different distances, in both the accumulation area and the restoration area (rainy season), the relative abundance increases with the increase in distance. Novosphingobium ( Noviherbaspirillum ) has an increasing relative abundance with the increase in distance in the accumulation area, while the opposite is true in the restoration area (rainy season).

[0040] 6. Comparison of differences between samples: LEfSe analysis can be used to distinguish between two or more biological conditions (or taxa), find taxa with significant differences in abundance, and estimate the magnitude of the impact of group (species) abundance on the differences using linear discriminant analysis (LDA). In this study, we selected species with an LDA threshold higher than 2 at the phylum to family level. The results are as follows Figure 5 . There are 22 species of microorganisms enriched in the rainy season in the stacking area, 9 species in the rainy season in the restoration area, and 12 species in the dry season in the restoration area.

[0041] To further understand the microbial differences between the stacking area and the restoration area, a Wilcoxon rank-sum test was used to perform a significance test for differences between groups at the genus level, and a fdr multiple test correction was performed. The results are as follows Figure 6 shown, showing that the genus Massilia ( Massilia ) and the genus Microvirgula ( Microvirga ) are lower in the stacking area. Massilia is a eutrophic bacterium that is easy to grow when nutrients such as nitrogen, phosphorus, and organic matter are sufficient, which is beneficial to improving the disease resistance of plants. For example, it can produce chitinase to inhibit Ralstonia solanacearum, thereby improving the disease resistance of sugar beet seedlings. Microvirgula is a rhizobium in the phylum Alphaproteobacteria, which is a symbiotic rhizobium that can nodulate and fix nitrogen.

[0042] The microbial diversity community and compositional diversity of the soil were studied by 16S rRNA sequencing. The results showed that there were 13 species in 13 genera of 8 families in the vegetation of the waste rock restoration area, and leguminous plants accounted for 50% of the total number of species. The vegetation was mainly divided into three categories: trees, shrubs, and ground cover plants. The microbial α-diversity showed that the actual number of species in the stacking area was higher than that in the restoration area, but there was no significant difference in microbial evenness and richness; the number of microbial species in the rainy season in the restoration area was higher than that in the dry season, and the microbial evenness and richness were higher in the rainy season. In the microbial community of the waste rock area and the surrounding soil, at the phylum level, there were mainly Actinobacteria, Proteobacteria, Chloroflexi, Acidobacteria, Gemmatimonadetes, and Firmicutes; at the genus level, there were mainly Arthrobacter, Pseudomonas, Sphingomonas, and Gemmobacter. The relative abundance of the dominant bacterial groups in the stacking area was relatively low. The contents of Pseudomonas and Sphingomonas were higher near the waste rock mountain and at high levels of heavy metal pollution in the surrounding area. In the comparison of differences between samples, LEfSe analysis showed that more characteristic microorganisms were enriched in the stacking area. Compared with the stacking area, the genera Massilia and Microvirgula were significantly enriched in the restoration area.

[0043] III. Microbial responses under the stress of the main control pollutants

[0044] 1. Effects of the main control pollutants on microbial diversity

[0045] A. Effects of pollutants on microbial α-diversity: To understand the effects of heavy metal and PAH contents in soil on microbial α-diversity, the pollutant contents and diversity in this gangue area (including the rainy season in the stacking area, the rainy season in the restoration area, and the dry season in the restoration area) were analyzed. The Shannon index was selected to represent microbial evenness, and the Chao index was used to explain microbial richness. The stepwise backward elimination method of multiple regression analysis was used to analyze the relationship between pollutant contents and diversity indices. The following equations were obtained: Shannon = 3.648 + 0.054Pb; Chao = 2560.7 + 40.7As + 13.1 Nap - 260.0Ace + 236 Flu. This shows the effects of heavy metals and PAHs on the microbial Shannon index. The results show that the microbial evenness in this gangue area is affected by Pb, that is, the higher the Pb content, the smaller the difference in the relative abundances of various microorganisms, and it is difficult to form a dominant bacterial community. The effects of heavy metals and PAHs on the microbial Chao index show that As, Nap, Ace, and Flu have an impact on microbial richness.

[0046] B. Effects of pollutants on microbial community structure

[0047] Effects of pollutants on OTUs in different abundance intervals: The Spearman correlation between microorganisms and heavy metal and PAH contents can reflect their effects on microbial community structure. 374 OTUs with a relative abundance higher than 0.1% were selected and divided into high abundance (>1%), medium abundance (0.5% - 1%), and low abundance (0.1% - 0.5%) to analyze the Spearman correlation between OTUs in different abundance intervals and pollutants. After statistics, there are 14, 39, and 321 OTUs in high, medium, and low abundance levels respectively. Generally speaking, the number of OTUs significantly correlated with heavy metals is 12 - 37, and the order of heavy metal influence is As > Zn > Cd > Cu > Pb > Cr. The number of negative correlations of As, Cd, Cu, and Zn is 2.7 - 6.4 times that of positive correlations, while the number of positive and negative correlations of Cr and Pb is the same. Cd and Pb have a greater impact on high-abundance microorganisms, and it is mainly negative correlation; Cd, Cu, and Zn have a greater impact on medium-abundance microorganisms, and the proportion of significantly correlated OTUs is the same, and both Cd and Cu are negative correlations; among low-abundance microorganisms, As and Zn have a greater impact. Compared with heavy metals, the number of OTUs significantly correlated with PAHs is smaller, ranging from 7 - 29. Flt, Pyr, and BaP have a greater impact, accounting for more than 20%. The impact of Nap is the smallest, with 6, followed by Flu with 8. High-abundance microorganisms are mainly affected by Phe and Chy, medium-abundance microorganisms are mainly affected by BbF and Chy, and low-abundance microorganisms are mainly affected by Acy. Generally speaking, microorganisms with a relative abundance value higher than 0.1% in this area are more affected by heavy metals.

[0048] Table 3: Percentage of significant correlations between OTUs in different abundance intervals and pollutants

[0049]

[0050] Effect of pollutants on the top 30 genera in relative abundance: To further analyze the toxic effects of heavy metals on different categories of microorganisms, we selected the microorganisms with the top 30 relative abundances at the genus level and performed a correlation analysis with pollutants ( Figure 7 ). There was a negative correlation between heavy metals and the microorganisms with the top 30 relative abundances. The ranking of the number of significantly correlated heavy metals was Pb (4) > Cd, Cu (3) > Zn, As, Cr (2). Nap in PAHs was not correlated with the relative abundance of microorganisms. Starting from Phe, the correlation results of LMW, MMW, and HMW with microorganisms were similar. Massilia was significantly negatively correlated with Cd, Cr, Pb, and most PAHs. Novosphingobium (Noviherbaspirillum was significantly negatively correlated with Pb and multiple PAHs. In addition, Adhaeribacter ( Adhaeribacter ) and Arthrobacter ( Arthrobacter ) were also significantly negatively correlated with multiple PAHs.

[0051] The species correlation network diagram mainly reflects the species correlations at each taxonomic level under a certain environmental condition. Select the species with the top 30 total abundances at the phylum taxonomic level and heavy metals, PAHs, and calculate the Spearman rank correlation coefficient between species to reflect the correlations between species. For example, Figure 8 , among the microorganisms with known classifications, Pseudomonas ( Pseudomonas ) was negatively correlated with most pollutants. Pseudomonas can degrade hydrocarbons such as phenanthrene, benzo a ]anthracene, and benzo a ]pyrene, and is also coupled with the reduction of iron in surface and subsurface sediments in the carbon cycle. Mycobacterium can metabolize multiple PAHs, such as naphthalene, anthracene, fluoranthene, phenanthrene, and benzo[ a ]anthracene. Secondly, Streptomyces ( Streptomyces ) and Cellulomonas ( Celluomonas ) also showed negative correlations with pollutants.

[0052] 2. Effect of pollutants on pathogenic bacteria and probiotics: To clarify the relationship between the contents of heavy metals and PAHs in the soil of the gangue restoration area and its surrounding areas and opportunistic pathogenic bacteria and probiotics, Spearman correlation was used for analysis.

[0053] The correlation analysis between pathogenic bacteria, probiotics and pollutants showed ( Figure 9 ) that the correlation between pollutants and pathogenic bacteria was stronger. Pb was positively correlated with two pathogenic bacteria, and 7 PAHs such as BkF and BaP were significantly positively correlated with 1 pathogenic bacteria. Here Legionella ( Legionella) is significantly positively correlated with heavy metal Pb and most PAHs. Legionella is a Gram-negative bacterium that widely exists in aquatic systems and soil and has been found in swimming pools, industrial cooling fluids, and wastewater treatment plants. It enters the human body through the ingestion of contaminated water sources. The outbreak source is usually a poorly maintained cooling tower. The common pathogenic microorganism of this genus is Legionella pneumophila, which can cause pneumonia-type diseases. Another microorganism significantly positively correlated with Pb is Corynebacterium ( Corynebacterium ), and its typical pathogen is Corynebacterium diphtheriae, which spreads through contact with open wounds, droplets, and contaminated surfaces, enters the nasal cavity, and has an adverse impact on the human respiratory system.

[0054] Studies have shown that low concentrations of heavy metals can promote the growth of microorganisms. Generally, the growth of soil pathogens follows the survival-pathogenicity trade-off rule, that is, when the stress increases, pathogenic bacteria must enhance their stress resistance ability to ensure their own survival, which is often accompanied by a decrease in resource intake ability, loss of virulence genes, reduction in the accuracy of virulence gene expression, and increase in harmful mutations due to changes in cell structure, that is, the survival cost increases. In this way, the resources allocated to pathogenic-related characteristics (such as growth, reproduction, virulence factors, etc.) are relatively reduced, that is, the pathogenic ability decreases. Legionella is significantly positively correlated with various pollutants. When the stress decreases, the survival cost decreases, and the pathogenic ability may increase, posing a health risk to the surrounding residents.

[0055] IV. Microbial function prediction

[0056] 1. Prediction method: The 16S rRNA gene can analyze phylogenetic marker genes but cannot provide direct evidence of improving the community function ability. Therefore, microbial function prediction is used to analyze the community function. Microbial function prediction methods include PICRUSt function prediction (PICRUSt1 and PICRUSt2), Tax4Fun function prediction, BugBase phenotype prediction, FAPROTAX function prediction, etc.

[0057] Among them, both PICRUSt functional prediction and Tax4Fun functional prediction can obtain KO, Pathway, and EC information according to the information in the KEGG database. The study compared the performance of three commonly used microbial community functional prediction tools and found that, compared with metagenomics, there will be relatively large errors when the prediction tools are used for environmental samples such as soil. However, there is no essential difference in the accuracy of the results of each prediction software. It is found that the difference between the two is small, and one can be selected for analysis. In addition, Tax4Fun functional prediction is based on the SILVA database, and the last update was in 2015, while PICRUSt functional prediction is based on GreenGene and was updated in 2013. Therefore, Tax4Fun functional prediction was selected in this invention. BugBase phenotype prediction can determine the high-level phenotypes present in microbial samples. The process is to normalize the OTUs by the predicted 16S copy number and then use the provided pre-computed files to predict microbial phenotypes. FAPROTAX functional prediction mainly analyzes the metabolic and ecological functions of prokaryotes. It is based on representative literature of artificial cultivation, constructs a database manually, and maps prokaryotic taxa (such as genera or species) to metabolic or other ecologically relevant functions (such as nitrification and denitrification, sulfur, nitrogen, hydrogen, and carbon cycles, pathogenicity, etc.).

[0058] 2. Results of microbial function prediction: Based on Tax4Fun functional prediction, the main functions at the second level of the KEGG pathway and their hierarchical clustering results are as Figure 10 shown. The clustering analysis results show that soil samples can be divided into two groups according to community function, one group is the rainy season, and the other group is the dry season. The abundance of infectious disease viruses at the gangue mountain in the restoration area is extremely high. Affected by high levels of pollutants, the abundances of the excretory system and immune system of farmland microorganisms in the stacking area are relatively high.

[0059] The relative abundances of phenotypes involved in BugBase phenotype prediction are sorted from high to low as forming biofilm, stress oxidative tolerant, containing mobile element, gram positive, gram negative, pathogenic, oxygen utilizing, and seven major categories.

[0060] The results of BugBase phenotype prediction show that ( Figure 11A), there are more anaerobic processes in the restoration area, and the relative abundance of potentially pathogenic bacteria in the accumulation area is higher.

[0061] The FAPROTAX functional prediction microbial annotation was assigned to 53 ecological function groups, and the top 12 functions with a proportion higher than 1% were selected for analysis ( Figure 11 B). The chemoheterotrophy and aerobic chemoheterotrophy in the restoration area are significantly higher than those in the accumulation area, while the fermentation in the accumulation area is significantly higher than that in the restoration area.

[0062] The multiple regression analysis of pollutants and microbial α-diversity index showed that the Shannon index was affected by Pb, and the Chao index was affected by As, Nap, Ace, and Flu. Microorganisms with a relative abundance value higher than 0.1% in this area were greatly affected by heavy metals. Cd and Pb had a greater impact on high-abundance microorganisms, Cd, Cu, and Zn had a greater impact on medium-abundance microorganisms, As and Zn mainly affected low-abundance microorganisms. Among PAHs, high-abundance microorganisms were greatly affected by Phe and Chy, medium-abundance microorganisms were affected by BbF and Chy, and low-abundance microorganisms were mainly affected by Acy. Among the top 30 genera in relative abundance, Massilia, Novosphingobium, Adhaeribacter, and Arthrobacter were significantly negatively correlated with Pb and multiple PAHs. The abundance of infectious disease viruses at the gangue hill in the restoration area is extremely high, and the abundances of the microbial excretion system and immune system in the farmland in the accumulation area are relatively high. Combining with the relevant excitation of pathogenic bacteria, Legionella and Corynebacterium may be the main potential pathogenic bacteria in this area.

[0063] V. Plant responses under the stress of main control pollutants: To understand the effects of soil pollutants in the gangue area on plants, we selected soil samples at different distances of 0 - 800 m in the dry season in the restoration area, and used the toxicity indicators of the model plant barley to understand the plant response status.

[0064] 1. Soil sample collection: Samples were collected at 0 m, 100 m, 200 m, 400 m, 600 m, and 800 m downstream of the gangue hill. The control soil was collected from an uncontaminated site 5 km away from the mining area. After removing the surface soil at each sampling point, 3 - 5 sub-samples were collected by the plum blossom sampling method, and the sampling depth was 0 - 20 cm. The soil was thoroughly mixed by the quartering method, and after removing stones and plant residues, all collected samples were stored in sealed polyethylene bags and transferred to the laboratory under natural drying conditions.

[0065] 2. Treatment of barley seeds: Before germination, barley seeds were surface-sterilized with 3% (v / v) H2O2 solution for 30 min and then rinsed thoroughly with deionized water. Subsequently, the seeds were soaked in distilled water for 4 - 6 hours. Then, seeds with plump grains were selected and transferred to moist filter paper for germination. After 36 - 48 h, barley seeds with root lengths of about 1.5 - 2 cm were transplanted into round plastic flowerpots (upper diameter 9 cm, lower diameter 7 cm, height 7 cm). 10 seeds were placed in each pot, and 200 g of soil samples at different distances from the coal waste pile were weighed into each pot. All experimental groups were grown in a constant temperature incubator at 25 ± 1 °C under a light:dark cycle of 12 h:12 h. Distilled water was added regularly every day during the experiment.

[0066] 3. Barley seedling growth experiment: Seven days after planting the barley seeds, the seedlings of each treatment group were collected and their shoot lengths and root lengths were measured. In addition, all the seedlings in each group were washed with distilled water and dried, and then the shoots and roots were cut and weighed (fresh weight).

[0067] 4. Determination of chlorophyll content: Seven days after the barley seedlings were exposed, fresh leaves (0.1 g) were collected, shredded, and immersed in 10 mL of extraction solution (95% ethanol:80% acetone = 1:1). Then, the extraction was carried out at room temperature in the dark for 18 h. The absorbance of the extracted supernatant was measured at 645 nm and 663 nm using a microplate reader. The calculation formulas for chlorophyll a, chlorophyll b, and total chlorophyll contents are as follows: Chlorophyll a (mg / g) = (12.7 A 663 - 2.69 A 645 ) × V / W / 1000; Chlorophyll b (mg / g) = (22.9 A 645 - 4.68 A 663 ) × V / W / 1000; Total chlorophyll (mg / g) = Chlorophyll a + Chlorophyll b.

[0068] 5. Determination of oxidative stress response: Seven days after exposure, fresh leaves weighing 0.1 g were ground into a homogenate in a mortar with pre-cooled phosphate buffer (0.1 mol / L, pH = 7.4). Subsequently, the homogenate was centrifuged at 3000 rpm for 10 min at 4 °C to extract the supernatant for determination. The activities of MDA, SOD, POD, CAT, and GSH in barley leaves were measured using a kit. In addition, the protein content in the supernatant was measured using the Bradford method, and the results were expressed as U / mg protein.

[0069] 6. Determination of the mitotic index and micronuclei in barley root tips: After the barley seedlings were exposed for 2 days, their roots were removed from the soil and thoroughly rinsed with tap water to remove the soil adhering to the roots. Then, young roots with a length of 1 - 1.5 cm were cut from the tip of the root and fixed with Carnoy's fixative (ethanol: acetic acid = 3:1) for 24 hours, and then transferred to 70% ethanol for storage. After the fixed roots were rinsed with distilled water, an appropriate amount of 1 mol / L hydrochloric acid was added, and they were placed in a water bath at 60°C for hydrolysis for 8 minutes. After staining with Schiff reagent, the root tip meristem was cut and dropped with 45% acetic acid, covered with a cover slip, and conventionally squashed. The mitotic index (MI) is expressed as the percentage of mitotic cells in the total number of root tip cells (1000 cells), and the micronucleus rate (MN) is expressed as the number of cells containing micronuclei per 1000 cells. Approximately 5000 root tip cells were observed for each treatment. And they were observed and recorded with an optical microscope with a built-in digital camera at a magnification of 1000 times.

[0070] 7. Data statistics and analysis: The Origin 8.0 statistical software was used to analyze the data, and the results were described as the mean ± standard deviation (SD) of three independent experiments. One-way ANOVA was used to analyze the significant differences between treatment groups, and then Fisher's least significant difference (LSD) test was performed. To determine the relationship between heavy metals and phytotoxicity, Pearson correlation test was used. * p <0.05 represents significant difference.

[0071] VI. Assessment of phytotoxicity of the soil around the restoration area

[0072] 1. Effect of the soil around the restoration area on the growth of barley seedlings: To better evaluate the harm of soil pollution in the waste rock mountain restoration area and its surrounding areas to plant growth, a phytotoxicity assessment method was used to comprehensively predict the phytotoxicity of the surveyed area.

[0073] Figure 12 The effects of soils at different distances downstream of the waste rock mountain restoration area on the shoot length, root length, shoot weight, and root weight of barley seedlings. Generally speaking, the four growth indicators showed a trend of first decreasing, then increasing, and then decreasing with the increase of the distance from the waste rock mountain. All soil exposure groups had significant differences compared with the control group. Under the exposure of the soil at 100 m, the shoot length of barley seedlings reached the lowest, which was 36% less than that of the control group. The root length of barley reached the lowest value under the exposure of the soil at 800 m, which was 27% less than that of the control group. Under the exposure of soils at different distances in the waste rock mountain restoration area, the shoot weight of barley seedlings decreased significantly ( p(<0.001), reaching the lowest value under the treatment of 200 m of soil, which was 39% of the shoot weight of the control group. At the same time, under the exposure of soil at 0 m from the waste rock mountain, the root weight reached the lowest value, which was significantly reduced by about 34% compared with the control group.

[0074] 2. Effects of the soil around the restoration area on the chlorophyll content of barley seedlings: After 7 days of treatment with soil samples at different distances, the changes in the contents of chlorophyll a, chlorophyll b, and total chlorophyll in barley leaves are as Figure 13 shown. Under the exposure of soil at 0 m, 100 m, 400 m, and 600 m, the content of chlorophyll a in barley decreased significantly, and decreased by 19%, 12%, 20%, and 17% respectively compared with the control group, reaching the lowest value at 400 m. However, there was no significant change in the content of chlorophyll b in different soil treatment groups compared with the control group, indicating that chlorophyll b was not sensitive to the pollutants in the soil. The total chlorophyll content of barley seedlings also decreased significantly under the exposure of soil at 0 m, 400 m, and 600 m ( p (<0.05), decreasing by 16%, 12%, and 11% respectively compared with the control group.

[0075] 3. Effects of the soil around the restoration area on the malondialdehyde content of barley seedlings: Figure 14 It shows the changes in the malondialdehyde content of barley seedlings after 7 days of exposure to soil at different distances downstream of the waste rock mountain. As the distance from the waste rock mountain increased, the malondialdehyde content of barley seedlings gradually increased, and under the exposure of 200 m of soil, it reached 2.06 times that of the control group soil. However, there was no significant change in the content of MDA under the exposure of soil at 400 m, 600 m, and 800 m.

[0076] 4. Effects of the soil around the restoration area on the enzyme and non-enzyme systems in the antioxidant defense system of barley seedlings: In order to resist the damage of plants caused by the external polluted environment, the plant's own protection system will produce a series of antioxidant defense enzymes and non-enzymes to resist the stress of pollutants, and their activity levels can be used as significant indicators of plant growth under adversity and being damaged. Therefore, the activities of SOD, CAT, POD, and GSH in barley seedlings were measured next. As Figure 15 shown, the activity of POD increased significantly under the exposure of soil at 200 m and 800 m ( p (<0.01), increasing by about 30% and 35% respectively compared with the control group. The activity of SOD only showed a significant upward trend under the exposure of 200 m of soil ( p (<0.01), increasing by about 33% compared with the control group. Under the exposure of all soil treatment groups, the activity of CAT in barley seedlings decreased significantly ( p<0.001), the CAT activities at 0 m, 100 m, 200 m, 400 m, 600 m, and 800 m were decreased by approximately 46%, 43%, 36%, 48%, 34%, and 44% compared with the control group. The activity of non-enzymatic GSH generally showed a trend of first increasing and then decreasing with the increase of the distance from the waste rock mountain, which was basically consistent with the change trend of POD activity. Under the exposure of the soil at 200 m and 800 m, the activity of GSH increased significantly by approximately 1.81 times and 1.16 times compared with the control.

[0077] 5. Effects of the soil around the restoration area on the mitotic index of barley root tips: Table 4 shows the effects of the soil at different distances downstream of the waste rock mountain on the 48-hour mitotic index of barley root tip cells. Only under the exposure of the soil at 0 m and 200 m, the mitotic index was significantly decreased compared with the control group, by 21% and 24% respectively. In the distribution of mitotic phases, the prophase index in the soil exposure groups at different distances was significantly decreased compared with the control group, and showed a trend of first decreasing and then increasing with the increase of the distance, which was similar to the change of the growth index in the previous text. Under the exposure of the soil at 0 m, 100 m, 200 m, 400 m, 600 m, and 800 m, the prophase index was decreased by 46%, 71%, 77%, 64%, 51%, and 65% respectively. There were no significant changes in the metaphase, anaphase, and telophase indices compared with the control group.

[0078] Table 4: Effects of the soil at different distances downstream of the waste rock mountain on the 48-hour mitotic index of barley root tip cells

[0079]

[0080] 6. Effects of the soil around the restoration area on micronuclei in barley root tip cells: The heavy metal-polluted soil around the waste rock mountain can not only inhibit the mitotic index of barley root tips, but also induce the occurrence of micronuclei in cells. As Figure 16 shown, the soil at different distances downstream of the waste rock mountain can significantly induce the production of micronuclei in barley root tips. The micronuclei in the soil at 0 m, 100 m, 200 m, 400 m, 600 m, and 800 m increased by approximately 8.6, 7.4, 12.2, 7.4, 11, and 6 times compared with the control respectively. This result indicates that the incidence of micronuclei in cytogenetic toxicity is a sensitive monitoring tool for plants suffering from external polluted environmental stress.

[0081] 7. Correlation between the heavy metal content in the soil around the restoration area and phytotoxicity: In order to clarify the relationship between heavy metals and phytotoxicity in the polluted soil of the restoration area, the Pearson correlation coefficient was used to explain their relationship next. As shown in Table 5, there was no significant correlation between the shoot length and root length of barley seedlings and the six heavy metals, while the shoot weight was significantly positively correlated with the Cr element ( p(<0.01), the root weight was positively correlated with the As element ( p (<0.05). At the same time, it was also observed that the Cu element was significantly negatively correlated with the chlorophyll b and total chlorophyll contents in barley seedlings, indicating that the presence of the Cu element in the soil could affect the photosynthesis of barley leaves. In addition, there were significant negative and positive correlations between Pb and Zn and the incidence of micronuclei in root tips, respectively. Moreover, the contents of Pb and Zn in the soil of this study were significantly higher than the soil background values in Shanxi Province and had a certain potential ecological risk. Therefore, these results indicated that the micronucleus test was a sensitive index for detecting heavy metal-contaminated soil.

[0082] Table 5: Correlation between heavy metal contents in the soil of the restoration area and phytotoxicity

[0083]

[0084] The results were as follows: Soils at different distances from the gangue hill all inhibited the growth of barley seedlings, and the root length, root weight, shoot length, and shoot weight all decreased significantly, reaching the lowest value under the exposure of the 200 m soil; after exposure to the soils at 0 m, 400 m, and 600 m, the total chlorophyll content in barley leaves decreased significantly; after exposure to the soils at 100 m and 200 m, the MDA content in barley seedlings increased significantly; under the exposure of soils at different distances, the activities of SOD, POD, and GSH in barley leaves were significantly induced, while the activity of CAT decreased significantly; the mitotic index of barley root tips exposed to the contaminated soils at 0 m and 200 m decreased significantly, and after exposure to the soils at different distances, the micronucleus rates of all treatment groups increased significantly; correlation analysis showed that the shoot weight of barley was significantly positively correlated with the Cr element, the root weight was positively correlated with the As element, the Cu element was significantly negatively correlated with the chlorophyll b and total chlorophyll contents, and there were significant negative and positive correlations between Pb and Zn and the incidence of micronuclei in root tips, respectively.

[0085] VII. Late ecological restoration of the coal mine restoration area: The damage to the environment caused by mining in the mining area is irreversible, threatening the ecology and human health, and its ecological restoration is long. The plant-microbial restoration in the mining area can not only improve the soil structure, enhance the soil quality, and reduce the soil pollutant content, but also play an important role in improving the local biodiversity and enhancing the stability of the local ecosystem.

[0086] Based on the results of the ecological risk and health risk of the main control pollutants, the key to the later restoration of the gangue reclamation area is Pb and PAHs. These pollutants also have relatively high contents in the farmland around the gangue area. The Pb content is more than twice the soil background value of Shanxi Province. The contents of low-ring and mid-ring PAHs exceed the negligible risk value, and some high-ring PAHs (such as BbF and BaP) even pose a carcinogenic risk. If targeted restoration cannot be carried out in the later work, the degradation or removal efficiency of these main control pollutants is extremely low, which will prolong the ecological restoration time and pose a serious threat to the health of surrounding residents. Therefore, we need to further make good use of the plant-microbe combined remediation mode to accelerate the restoration and reconstruction of the ecological system in the gangue area.

[0087] 1. Plant configuration and selection: In recent years, a large number of scholars have introduced highly enriched plants based on pot experiments and tested the remediation effects through field experiments. However, in the long run, native species have greater remediation potential. In temperate climates, grass and legume species are most suitable for the restoration of degraded sites. In tropical regions, grasses, legumes and even trees can be planted. These several types of plants have their own advantages. Grass has a wide growth range and strong adaptability; leguminous plants can fix nitrogen through rhizobia and supply more nutrients to the plants; the greatest advantage of trees is to fix the soil and reduce soil erosion. The commonly used remediation plants in Chinese mining areas are shown in Table 6.

[0088] Table 6: Commonly used plant species for mine restoration

[0089]

[0090] 2. Microbe configuration and selection: Microbes usually include plant rhizobia, mycorrhizal fungi, rhizosphere microbes isolated from the original soil, and non-symbiotic microbes. A large number of studies have isolated microbes for remediation, as shown in Table 7. Rhizobia are a type of Gram-negative bacteria widely distributed in the soil. It grows slowly in vitro and has no nitrogen-fixing ability by itself. After infecting the roots of leguminous plants, rhizobia can develop from single bacteria into nodules containing 10 8The root nodules of individual bacteria, with about 100 root nodules per plant, can fix molecular nitrogen in the air to form ammonia, providing nitrogen nutrition for plants and improving the soil. Rhizobia can improve the resistance of symbiotic plants to heavy metals and the plant extraction ability. However, the impact of heavy metals on the nodulation rate of rhizobia is much greater than that on plant growth. When the concentration of heavy metals is too high, it directly strongly inhibits the nodulation of rhizobia, making it difficult to form a combined remediation system. Mycorrhizal fungi are widely distributed in the soil and have been found in ecosystems such as forests, farmlands, grasslands, deserts, saline-alkali lands, and industrial polluted areas. Mycorrhizal fungi are widely used, but their remediation effects vary depending on the soil environment, symbiotic plant species, types and concentrations of pollutants. The number of indigenous rhizosphere microorganisms depends on the composition of root exudates, plant species, root types, plant age, and soil types. Many rhizosphere microorganisms belong to plant growth-promoting bacteria (PGPB). They settle in different ecological niches of plant roots and can degrade pollutants more effectively than single species / strains, but they do not have wide applicability. However, most soil microorganisms (>99%) have not been cultured, but they may play an important role in phytoremediation.

[0091] Table 7: Microbial species for mine area remediation

[0092]

[0093] 3. Remediation measures for waste rock mountains at different reclamation stages: For the waste rock accumulation area to be remediated, adopt leguminous plants + Massilia.

[0094] The vegetation succession sequence is from low shrubs to mixed shrubs, conifers, and deciduous tree species. Annual herbaceous plants are preferentially selected for vegetation reclamation in the mining area. There are many leguminous plants in this area and they grow well. Microbial diversity shows that Massilia is negatively correlated with pollutants. This bacterium has heavy metal resistance and PAHs degradation ability, and its content increases significantly in the restoration area. Compared with single microorganisms, multi-microbial agents have stronger applicability. Therefore, in the later stage of this mining area, subsequent remediation can be carried out by combining leguminous plants (such as alfalfa) with microbial agents containing Massilia.

[0095] For the waste rock restoration area polluted by Pb and PAHs, adopt Pb hyperaccumulating plants + cash crops + Pseudomonas.

[0096] Hyperaccumulating plants Pteris vittata L. and the economic plant castor bean ( Ricinus community L. ) When planted together, it can not only improve the extraction rate of heavy metals As and Cd, but also increase the Pteris vittata L yield. Therefore, we can consider co-planting Pb hyperaccumulating plants with cash crops such as castor bean and alfalfa (forage). The Pb hyperaccumulating herbaceous plant Phytolacca acinosa ( Phytolacca Acinosa RoxbThe accumulation of Pb can increase with the increase in the concentration of PbCl2; when the PbCl2 content reaches the highest concentration of 200 mg / kg, the highest Pb content in the above-ground part is 1.763 mg / g DW, and it can be combined with Phytolacca acinosa Roxb Thysanolaena latifolia and Mimosa pudica other Pb hyperaccumulator plants. Pseudomonas aeruginosa showed the ability to dissolve phosphate and immobilize lead. In addition, Pseudomonas also showed high degradation ability to PAHs.

[0097] Therefore, in the later stage of the restored area of this mining area, subsequent restoration can be carried out through Pb hyperaccumulator plants + cash crops + Pseudomonas.

[0098] Based on the current situation of soil pollution in the coal mine restoration area of Shanxi Province, this invention analyzes the responses of plant and microbial diversity under the stress of main control pollutants, and screens and constructs a plant-microbial community restoration system for efficiently degrading the main control pollutants. The main conclusions are as follows:

[0099] The analysis of plant and microbial diversity shows that there are 13 species in 13 genera of 8 families in the vegetation of the gangue restoration area, and leguminous plants account for 50% of the total number of species. In the microbial communities of the soil in the gangue area and its surrounding areas, at the phylum level, there are mainly Actinobacteria, Proteobacteria, Chloroflexi, Acidobacteria, Gemmatimonadetes, and Firmicutes; at the genus level, there are mainly Arthrobacter, Pseudomonas, Sphingomonas, and Gemmobacter. The relative abundance of the dominant flora in the stacking area is relatively low. The contents of Pseudomonas and Sphingomonas are relatively high near the gangue mountain and at the high places of heavy metal pollution in the surrounding areas. There are more characteristic microorganisms enriched in the stacking area. Compared with the stacking area, Massilia and Microvirga are significantly enriched in the restored area.

[0100] Under the stress of pollutants, the shannon index of microbial α-diversity is affected by Pb, and chao is affected by As, Nap, Ace, and Flu. Microorganisms with a relative abundance value higher than 0.1% in the microbial structure are more affected by heavy metals. Cd and Pb have a greater impact on high-abundance microorganisms, Cd, Cu, and Zn have a greater impact on medium-abundance microorganisms, As and Zn mainly affect low-abundance microorganisms, and high-abundance microorganisms in PAHs are more affected by Phe and Chy, medium-abundance microorganisms are affected by BbF and Chy, and low-abundance microorganisms are mainly affected by Acy. Among the top 30 genera in relative abundance, Massilia, Novosphingobium, Adhaeribacter, and Arthrobacter are significantly negatively correlated with Pb and various PAHs. The abundance of infectious disease viruses at the gangue mountain in the restored area is extremely high, and the abundances of the microbial excretion system and immune system in the stacking area farmland are relatively high. Combining the correlation analysis of pathogenic bacteria, Legionella and Corynebacterium may be the main potential pathogenic bacteria in this area.

[0101] Phytotoxicity experiments showed that the soils at different distances from the gangue hill all inhibited the growth of barley seedlings, with significant decreases in root length, root weight, shoot length, and shoot weight. The total chlorophyll content in barley leaves decreased significantly. The activities of SOD, POD, and GSH in barley leaves were significantly induced, while the activity of CAT decreased significantly. When exposed to the contaminated soils at 0 m and 200 m, the MDA content in barley seedlings increased significantly, and the mitotic index decreased significantly. Moreover, after exposure to the soils at different distances, the micronucleus rates in all treatment groups increased significantly. Correlation analysis showed that there was a significant positive correlation between the shoot weight of barley and the Cr element, a positive correlation between the root weight and the As element, a significant negative correlation between the Cu element and the chlorophyll b and total chlorophyll contents, and significant negative and positive correlations respectively between Pb and Zn and the incidence rate of root tip micronuclei.

[0102] Based on the results of the present invention, the following suggestions are put forward: further utilize the plant-microorganism combined remediation mode to accelerate the restoration and reconstruction of the ecosystem in the gangue area. For the gangue accumulation area to be remediated, where the contents of Pb, As, Cu, and PAHs are relatively high and the physical and chemical properties are poor, the leguminous plant + Massilia remediation mode can be adopted; for the gangue restoration area contaminated with Pb and PAHs, the Pb hyperaccumulating plant + cash crop + Pseudomonas can be adopted.

[0103] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a combined remediation system of plant-microbial communities in a coal mine restoration area, characterized in that: The quadrat survey method was adopted to investigate the composition and structural characteristics of the vegetation community in the gangue restoration area; the Miseq high-throughput sequencing technology was used to detect the composition and diversity of the bacterial community in the gangue area soil, analyze the composition, distribution and diversity characteristics of the microbial community at different stages of mining area accumulation and restoration and in different seasons of the restoration area, and analyze their differences. The co-evolution strategy was used to screen the plant rhizosphere-microbial ecological community with high efficiency in degrading pollutants; finally, a soil-plant-microbial high-efficiency degradation system was constructed; The specific method for constructing the soil-plant-microbial high-efficiency degradation system is as follows: for the gangue accumulation area to be restored, with high contents of Pb, As, Cu and PAHs and poor physical and chemical properties, the leguminous plant + Massilia restoration mode is adopted, and the leguminous plant is selected from Medicago sativa; for the gangue restoration area polluted by Pb and PAHs, the Pb hyperaccumulating plant + cash crop + Pseudomonas restoration mode is adopted, and the cash crop is selected from Ricinus communis; The Pb hyperaccumulating plant is Phytolacca acinosa Roxb., Thysanolaena latifolia or Mimosa pudica .

2. The method for constructing a plant-microbial community combined remediation system in a coal mine restoration area according to claim 1, characterized in that: The Miseq high-throughput sequencing technology was used to detect the composition and diversity of the bacterial community in the gangue area soil. The specific method is as follows: the universal primers 338F: 5’-ACTCCTACGGGAGGCAGCAG-3’ and 806R: 5’-GGACTACHVGGGTWTCTAAT-3’ were used to extract the genomic DNA of soil microorganisms, 16S rDNA PCR amplification was carried out, then the library was constructed and Miseq high-throughput sequencing was performed. The PE reads obtained by Miseq sequencing were first spliced according to the overlap relationship, and at the same time, the sequence quality was controlled and filtered. After distinguishing the samples, OTU clustering analysis and species taxonomic analysis were carried out; The microbial diversity analysis is as follows: α diversity: sobs is the actual observed value of community richness; the Shannon and Simpson indices reflect species evenness. The Shannon index describes the disorder and uncertainty of the appearance of individuals, and the Simpson index is the probability that two randomly sampled OTUs belong to different species. The larger the value of the Shannon index and the smaller the value of the Simpson index, the higher the evenness of the community species distribution; the chao and Ace indices reflect community richness, and the two indices estimate the number of OTUs contained in the community through different algorithms, and the larger the value, the more species; β diversity: it reflects the difference in community composition between different samples and is measured by the sample similarity distance value; NMDS is non-constrained ordination analysis, which simplifies the research objects in the multi-dimensional space to the low-dimensional space for positioning analysis and classification, reflecting the similarity and difference of the bacterial flora; the β diversity of the samples is measured by NMDS, and the weighted_normalized_unifrac distance is used, considering the evolutionary relationship and species abundance between samples; Microbial community structure: Bacterial OTUs were annotated and classified, and analyzed through a bar chart of the bacterial community. Analysis was carried out at the phylum taxonomic level, and phyla with a relative abundance < 1% were grouped into others. Analysis was also carried out at the genus taxonomic level, and genera with an average abundance less than 1.5% were grouped into others, to obtain the microbial community structure; Differential analysis: LEfSe analysis was used to distinguish between two or more biological conditions or groups, to find groups with significant differences in abundance, and linear discriminant analysis (LDA) was used to estimate the magnitude of the impact of group or species abundance on the differences. From the phylum to the family level, species with an LDA threshold higher than 2 were studied to obtain the differences between the soil microbiomes in different regions. Wilcox rank sum test was used to conduct a significance test of the differences between groups at the genus level before, and fdr multiple test correction was performed.

3. A method for constructing a combined remediation system of plant-microbial communities in a coal mine restoration area according to claim 1, characterized in that: A co-evolution strategy was adopted to screen for plant rhizosphere-microbial ecological communities with high efficiency in degrading pollutants. The specific method was as follows: Multiple regression analysis was used to understand the effects of heavy metal and PAHs contents in the soil on the microbial α-diversity index. The Shannon index was selected to represent microbial evenness, and the Chao index was used to explain microbial richness. Stepwise backward elimination method of multiple regression analysis was used to analyze the relationship between pollutant content and diversity index, and the formulas were obtained: Shannon = 3.648 + 0.054Pb; Chao = 2560.7 + 40.7As + 13.1 Nap - 260.0Ace + 236 Flu; The results showed that: The higher the Pb content, the smaller the differences in the relative abundances of various microorganisms, and it was difficult to form dominant bacterial communities; As, Nap, Ace, and Flu had effects on microbial richness; Determination of the effects of OTUs in different abundance intervals, the top 30 genera in relative abundance, pathogenic bacteria, and probiotic bacteria on the microbial community structure: The Spearman correlation between microorganisms and heavy metal and PAHs contents reflected their effects on the microbial community structure. OTUs with a relative abundance higher than 0.1% were selected and divided into: >1% as high abundance, 0.5%-1% as medium abundance, and 0.1%-0.5% as low abundance, and the Spearman correlation between OTUs in different abundance intervals and pollutants was analyzed; Further analysis of the toxic effects of heavy metals on different categories of microorganisms. The top 30 microorganisms in relative abundance were selected at the genus level and their correlation with pollutants was analyzed; Then, the top 30 species in total abundance at the phylum taxonomic level were selected and their correlations with heavy metals and PAHs were calculated, and the Spearman rank correlation coefficient between species was calculated to reflect the correlations between species; Spearman correlation was used to analyze the effects of pollutants on pathogenic bacteria and probiotic bacteria; Predict microbial functions: Select the main functions of the Tax4Fun functional prediction KEGG pathways at the second level and their hierarchical clustering. The BugBase phenotype prediction determines the high-level phenotypes present in the microbial samples. Normalize the OTUs by the predicted 16S copy number and then predict the microbial phenotypes; The FAPROTAX functional prediction analyzes the metabolic and ecological functions of prokaryotes and maps prokaryotic taxa to chemoheterotrophy, aerobic_chemoheterotrophy, fermentation and other ecologically relevant functions.

Images