Rhizosphere Soil Microbial Properties on Tetraena Mongolica in the Arid and Semi-Arid Regions, China

International Journal of Environmental Research and Public Health

Article Rhizosphere Soil Microbial Properties on Tetraena mongolica in the Arid and Semi-Arid Regions, China

Mengying Ruan 1, Yuxiu Zhang 1,* and Tuanyao Chai 2

1 School of Chemical and Environmental Engineering, China University of Mining & Technology-Beijing, Beijing 100083, China; [email protected] 2 College of Life Science, University of Chinese Academy of Sciences, Beijing 100049, China; [email protected] * Correspondence: [email protected]; Tel.: +86-010-62331792

Received: 26 June 2020; Accepted: 13 July 2020; Published: 16 July 2020

Abstract: Tetraena mongolica is a rare and endangered species unique to China. The total number and density of Tetraena mongolica shrubs in desertification areas have experienced a sharp decrease with increases in coal mining activities. However, available information on the T. mongolica rhizosphere soil quality and microbial properties is scarce. Here, we investigated the effect of coal mining on the soil bacterial community and its response to the soil environment in the T. mongolica region. The results showed that the closer to the coal mining area, the lower the vegetation coverage and species diversity. The electrical conductivity (EC) in the contaminated area increased, while the total nitrogen (TN), available phosphorus (AP), available potassium (AK), and soil organic carbon (SOC) decreased. The activity of NAG, sucrose, β-glucosidase, and alkaline phosphatase further decreased. In addition, the mining area could alter the soil’s bacterial abundance and diversity. The organic pollutant degradation bacteria such as Sphingomonas, Gemmatimonas, Nocardioides, and Gaiella were enriched in the soil, and the carbon-nitrogen cycle was changed. Canonical correspondence analysis (CCA) and Pearson’s correlation coefficients showed that the change in the bacterial community structure was mainly caused by environmental factors such as water content (SWC) and EC. Taken together, these results suggested that open pit mining led to the salinization of the soil, reduction the soil nutrient content and enzyme activity, shifting the rhizosphere soil microbial community structure, and altering the carbon-nitrogen cycle, and the soil quality declined and the growth of T. mongolica was affected in the end. Therefore, the development of green coal mining technology is of great significance to protect the growth of T. mongolica.

Keywords: Tetraena mongolica; open pit mining; soil nutrients; enzyme activities; soil microbial communities

1. Introduction T. mongolica is a rare and endangered species unique to the western Ordos Plateau in North-Central and Wuhai City, China [1]. China Red Data Book lists this rare species as the second highest priority for conservation. It is a common deciduous shrub inhabiting arid and semi-arid regions. T. mongolica is a xerophyte with a well-developed root system that is particularly suited to its habitat and able to cope with drought and other harsh natural conditions. It serves a role in stabilizing the sand in the region by promoting soil and water conservation and windbreaking. T. mongolica has been in decline in recent years. During the last 28 years, it has decreased 19.36% and covers less than 13.40 km2 a 1 [2]. · − This could be caused by the climate change but is attributed mainly to human activities, especially the coal mining [3]. The distribution of coal in Wuhai City is similar to the distribution of T. mongolica. Wuhai City mainly focuses on open pit mining. Coal mining gave rise to the destruction of the T. mongolica

Int. J. Environ. Res. Public Health 2020, 17, 5142; doi:10.3390/ijerph17145142 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2020, 17, 5142 2 of 16 environment, where contaminants such as Cu, Pb, Mo, and As were highly enriched in the soil [4]. Furthermore, the combustion of coal particles and coal produces airborne compounds that may contain heavy metals and Polycyclic Aromatic Hydrocarbons (PAHs), which settle or wash out of the atmosphere and contaminate the soil. The normal function of the soil in mining areas is changed due to the influence of these heavy metals and polycyclic aromatic hydrocarbons [5]. Coal mining activities affect the soil’s physical quality and interfere with the diversity of soil bacterial community. Coal mining activities affect the physical quality of the soil: high bulk density and low water holding capacity, porosity, moisture content, pH, and soil nutrients (total nitrogen and available phosphorus) [6]. Coal mining activities interfered with the soil bacterial community structure, which led to a significant reduction in soil enzyme activity, with the dominant bacteria comprising Proteobacteria, Actinomycetes, and Acidobacteria [7]. Plant growth depends on nutrient cycling in the soil releasing nutrients throughout [8]. Soil nutrient determines the growth of vegetation and plays an important role in the development of ecosystem, especially the vegetation community. The diversity of soil bacterial community structure is closely related to soil function [9]. Soil bacterial community structure is the foundation of soil function. Bacteria are responsible for nutrient conversion cycle, organic matter decomposition, humus formation, and system stabilization in soil ecological functions [10]. Soil bacteria participate in soil nutrient cycling and energy flow. Rhizosphere bacterial community composition depended on plant diversity. The root activity significantly affected the biological properties of soil [11]. Until recently, research on T. mongolica has focused on the biological characteristics, insect-related and pollination characteristics, landscape fragmentation process, and cell and genetic structure [12,13]. However, available information on the T. mongolica rhizosphere soil microbial properties is scarce, especially the coal mining. In this study, the soil nutrients and bacterial community structure of soil around T. mongolica were measured to evaluate the change of soil quality. The objectives of the study were (1) to understand the changes of plant community structure in the T. mongolica regions, (2) to evaluate the variation of soil quality in T. mongolica soil under open pit mining conditions, (3) to investigate the effect of soil bacterial diversity near T. mongolica and its response to open pit mining.

2. Materials and Methods

2.1. Study Area

The study area is located in Wuhai City (39◦02030”–39◦54055” N, 106◦36025”–107◦08005” E), a new industrial city in the west of the Inner Mongolian autonomous region of Northwest China. There is a continental climate in the study area. The average temperature is 9.3 ◦C, the average annual rainfall is 162 mm, and the average sunshine exposure is 3138.6 h [14]. The vegetation coverage in the study area is 25%, and the main vegetation type is desert grassland, including such species as T. mongolica, Ammopiptanthus mongolicus, and Zygophyllum xanthoxylum. In August 2018, based on the distribution of coal resources and T. mongolica, soil was collected from T. mongolica area disturbed by coal mining (P1, P2, P3), soil was less polluted from the Nature Reserve of T. mongolica (H1, H2), an adjacent unexploited area (CK). The distribution and characteristics of the samples are described in Figure1. The soil closely bound to the roots collected within the space used by the roots is considered to be the rhizosphere [15]. In each plot, three quadrants (5 5 m per × quadrant) were established as three soil cores. Using community ecology techniques, we investigated plant assemblages in the T. mongolica regions. In each quadrant, ﬁve samples were randomly collected and mixed to form a composite soil sample at soil depth (0–15 cm, 15–30 cm). Each soil sample was divided into two parts. Each part was naturally air-dried and ﬁltered through a 2 mm mesh to test the soil properties. This portion was stored at 80 ◦C for subsequent high-throughput Illumina sequencing analysis. Int. J. Environ. Res. Public Health 2020, 17, 5142 3 of 16 Int. J. Environ. Res. Public Health 2020, 17, x FOR PEER REVIEW 3 of 16

Figure 1. Tetraena mongolica distribution area and sample location. Figure 1. Tetraena mongolica distribution area and sample location. 2.2. Analysis of the Properties of Soil Samples 2.2. AnalysisThe dried of soilthe P wasroperties passed of throughSoil Samples a 1mm sieve to determine the particle fractions. A conductivity meterThe was dried used tosoil measure was passed the soil through electrical a conductivity 1 mm sieve (EC) to determine of the air-dried the particle samples. fractions. Soil water A contentconductivity (SWC) meter was determinedwas used to by measure drying soil the samples soil electrical at 105 ◦ conductivityC for 24 h. The (EC) total of nitrogen the air-dried (TN) wassamples. determined Soil water by thecontent Kjeldahl (SWC) method was determined [16]. The level by ofdrying available soil phosphorussamples at 105 (AP) °C in for the 24 soil h. wasThe determinedtotal nitrogen by (TN) the molybdenum-blue was determined by method. the Kjeldahl The available method [16] potassium. The level (AK) of was available extracted, phosphorus and the level(AP) wasin the determined soil was determined using the methods by the describedmolybdenum by Bao-blue [17 method.]. The soil The organic available carbon potassium (SOC) content (AK) was determined extracted, and using the dichromate level was determined oxidation [18 using]. Mp-Nitrophenyl-N-acetyl- the methods described byβ -D-glucosaminide, Bao [17]. The soil disodiumorganic carbonphenyl phosphate (SOC) content colorimetry, was and determined indophenol-blue using colorimetry dichromate were used oxidation to measure [18]. N-acetyl-Mp-Nitrophenylβ-d-glucosidase-N-acetyl (EC-β-D 3.2.1.52,-glucosaminide, NAG), alkaline disodium phosphatase phenyl (EC 3.1.3.1), phosphate and sucrase colorimetry (EC 3.2.1.26), and activities,indophenol respectively-blue colorimetry [19]. The were soil usedβ-glucosidase to measure (EC3.2.1.21) N-acetyl-β- leveld-glucosidase was determined (EC 3.2.1.52 by culture， NAG and） spectrophotometry., alkaline phosphatase Three (EC repeated 3.1.3.1), experiments and sucrase were (EC carried 3.2.1.26) out activities, for the above respectively indicators. [19]. The soil β-glucosidase (EC3.2.1.21) level was determined by culture and spectrophotometry. Three repeated 2.3. Soil Microbial Community Analysis experiments were carried out for the above indicators. According to the manufacturer instructions, 500 mg of soil genomic DNA was extracted from each2.3. Soil sample Microbial using Community the E.Z.N.A. Analysis soil DNA kit (Omega Biotek, GA 30071, USA). The extracted DNA was purified and quantified by a NanoDrop 2000 spectrophotometer (Thermo, MA 02451, USA). According to the manufacturer instructions, 500 mg of soil genomic DNA was extracted from Each extracted 16S rRNA v3-v4 region was amplified by PCR using the following procedure: at 95 C for each sample using the E.Z.N.A. soil DNA kit (Omega Biotek, GA 30071, USA). The extracted ◦DNA 2 min, followed by 25 cycles of denaturation for 30 s at 95 C, annealing for 30 s at 55 C, and extension for was purified and quantified by a NanoDrop 2000 spectrophotometer◦ (Thermo,◦ MA 02451, USA). 45 s at 72 C, and then 10 min at 72 C. The primers 341F (5’-barcode-CCTACGGGNGGCWGCAG-3’) Each extracted◦ 16S rRNA v3-v4 region◦ was amplified by PCR using the following procedure: at 95 and°C for 805R 2 min, (5’-GACTACHVGGGTATCTAATCC-3’) followed by 25 cycles of denaturation for were 30 useds at 95 [20 °C,]. annealing The bar code for 30 for s at each 55 °C, primer and corresponds to the unique eight-base sequence for each sample. The PCR mixture contained 5 µL extension for 45 s at 72 °C, and then 10 min at 72 °C. The primers 341F of 10 x PCR buffer solution, 0.5 µL of 10 mm nucleotides, 10 ng of template DNA, 0.5 µL of each (5’-barcode-CCTACGGGNGGCWGCAG-3’) and 805R (5’-GACTACHVGGGTATCTAATCC-3’) primer (50 mm), and 50 µL of ddH O in total. The PCR products were determined by 2% agarose gel were used [20]. The bar code for each2 primer corresponds to the unique eight-base sequence for each electrophoresis.sample. The PCR The mixture amplicon contain extracteded 5 μL from of 10 2% x PCR agarose buffer gel solution, was purified 0.5 μL using of 10 a PCRmm nucleotides, purification kit (Beckman, Indiana 46268, USA) and quantified using a Qubit® 2.0 fluorimeter (Invitrogen, CA 10 ng of template DNA, 0.5 μL of each primer (50 mm), and 50 μL of ddH2O in total. The PCR products were determined by 2% agarose gel electrophoresis. The amplicon extracted from 2% agarose gel was purified using a PCR purification kit (Beckman, Indiana 46268, USA) and quantified

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92008, USA) according to the standard protocol. Paired sequencing was performed on the Illumina MiSeq platform (Shanghai Sangon Biotechnology Co., LTD., Shanghai, China) in accordance with the standard protocol (2300). The operational taxonomic units (OTUs) table was input into PICRUSt for metagenome prediction using the KEGG (Kyoto Encyclopedia of Genes and Genomes) database.

2.4. Data Analysis Simpson Index, Shannon–Wiener Index, and Pielou’s Evenness Index can measure the species diversity of the plant community. Community richness index (Chao1 and ACE estimator) and community diversity index (Shannon and Simpson index) were calculated in Mothur using the high-throughput sequencing data. The Shannon and Simpson indices were employed to estimate soil microbial diversity. The raw data were collated with Excel 2016 and visualized with Origin 2017. SPSS 23.0 was applied for statistical analysis. The ﬁgures were produced with Origin 2017. Redundancy analysis (RDA) and canonical correspondence analysis (CCA) were used to determine the correlations between soil indicators among the samples using Canoco 5.0.

3. Results

3.1. Effect of Open Pit Mining on Plant Community Structure Field investigations were conducted on the vegetation community structure in the area where Tetraena mongolica was distributed in Table1. The vegetation coverage and species diversity indicators in polluted areas close to the mining area is low. The vegetation coverage and species diversity of Tetraena mongolica: Nature reserve is lower than that of the control area. Species number, coverage and species diversity index of plants at sampling points: the control area > Nature reserve > the contaminated area. The vegetation in the contaminated area is mainly Reaumuria soongarica, Tetraena mongolica, and Achnatherum splendens. The vegetation in the Nature reserve is mainly composed of Tetraena mongolica and Zygophyllum xanthoxylon. Tetraena mongolica and Phragmites australis are the main vegetation in the control area. Different locations have different vegetation types.

Table 1. Changes in plant community structure at sampling sites.

Sample Vegetation Shannon- Pielou’s Simpson Main Code Coverage (%) Index Index Index Companion Species P1 23.33 1.05 0.71 0.87 Achnatherum splendens P2 28.49 1.23 0.72 0.89 Reaumuria soongarica P3 21.29 1.18 0.69 0.90 Achnatherum splendens H1 46.79 1.35 0.67 0.86 Zygophyllum xanthoxylon H2 51.31 1.47 0.64 0.82 Zygophyllum xanthoxylon CK 72.34 1.53 0.68 0.72 Phragmites australis

3.2. Eﬀect of Open Pit Mining on Soil Quality The soil particle size distribution is indicated in Figure2. The contaminated area and conservation area mainly consisted of ﬁne sand and moderate sand, where 15–30 cm is considered coarse sand. The control area mainly contained sand and silt, while sand was the major component of both the contaminated and conservation areas. The conservation area had coarse sand. There was more moderate sand and silt present in the contaminated area than in the control area. There was also less clay in the topsoil, but the amount of clay at 15–30 cm increased. Int. J. Environ. Res. Public Health 2020, 17, x FOR PEER REVIEW 5 of 16 more moderate sand and silt present in the contaminated area than in the control area. There was also less clay in the topsoil, but the amount of clay at 15–30 cm increased.

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moreInt. J. Environ.moderate Res. Publicsand Healthand silt2020 present, 17, 5142 in the contaminated area than in the control area. There 5was of 16 also less clay in the topsoil, but the amount of clay at 15–30 cm increased.

Figure 2. Soil particle size distribution of samples. Soil particles classifications: clay (0–5 μm); silt (5–50 μm); fine sand (50–250 μm), moderate sand (250–500 μm) and coarse sand (500–1000 μm).

As shownFigure 2. inSoil Figure particle 3, the size soil distribution SWC and of EC samples. of the Soil samples particles showed classifications: different clay trends. (0–5 µm); The soil EC −1 ranged fromFiguresilt (5–50 1.80 2. µSoil–m);2.62 particle fine dS·m sand size (50–250. These distributionµm), soil moderate variables of samples. sand gradually Soil (250–500 particlesµ m)increased classifications: and coarse with sand clay depth, (500–1000 (0–5 andμm);µm) thesilt. values in the contaminated(5–50 μm); fine area sand were (50– 250statistically μm), moderate higher sand than (250 –those500 μm) in and the coarse control sand area (500 –at1000 the μm) same. depth. The SWC rangedAs shown from in 5Figure.95% 3to, the 15.6 soil0 SWC% in and the EC control of the samples and contaminated showed di ff erent areas. trends. The The SWC soil of EC the T. rangedAs shown from 1.80–2.62 in Figure dS 3, mthe1 .soil These SWC soil and variables EC of the gradually samples increasedshowed different with depth, trends. and The the soil values EC mongolica regions increased· − with the increase in depth. The surface water content of the rangedin the contaminatedfrom 1.80–2.62 area dS·m were−1. These statistically soil variables higher gradually than those increased in the control with depth, area at and the the same values depth. in contaminated area was at a low level. There were same trends in the severe group and the controls theThe contaminated SWC ranged fromarea 5.95were to statistically 15.60% in the higher control than and those contaminated in the control areas. area The at SWC the same of the depth.T. mongolica The with regard to soil nutrients. For these groups, the TN, AP, AK, and SOC exhibited similar changes SWCregions ranged increased from with 5.95 the% increaseto 15.60 in% depth. in the The control surface and water contaminated content of the areas. contaminated The SWC area of the was T. at withmongolicaa lowdepth, level. butregions There the were values increased same were trends with statistically in the the increase severe higher group in depth. in and the the The control controls surface area with waterthan regard in content to the soil contaminated nutrients. of the areas.contaminatedFor The these soil groups, TN area of the allwas TN,studied at AP,a low AK, areas level. and ranged There SOC exhibited were from same 0. similar11 trendsg·kg changes−1 into the0.65 withsevere g·kg depth,−1 group. The but soiland the APthe values controof all were studiedls areaswithstatistically ranged regard from higherto soil 3. 48nutrients. in mg·kg the control−1 For to areathese8.91 thanmg groups,·kg in the−1 .the The contaminated TN, AP AP, content AK areas., and decreased SOC The soilexhibited with TN of the similar all increase studied changes areas in depth, 1 1 1 andwith rangedthe surface depth, from but 0.11soil the gvaluekg values− toin 0.65the were control g kgstatistically− . Thearea soil washigher AP significantly of in all the studied control higher areas area ranged thanthan thatin from the in 3.48 contaminatedthe mg contaminatedkg− to 1 · · −1 −1 · area.areas.8.91 The mg The soilkg soil −AK. TN The ranged of AP all content studied from decreased95. areas39 rangedmg·kg with −1from theto 160. increase0.1129 g·kg mg·kg in to depth, 0.65−1. The g·kg and AK the. The surfacecontent soil AP soil in of value theall studied topsoil in the was · −1 −1 higherareascontrol in ranged areathe was controlfrom significantly 3.48 area mg·kg compared higher to 8.9 than1 mg to that·kg the in. The the contaminated AP contaminated content decreased area. area. The The with soil SOC the AK increase ranged of the from in control depth, 95.39 areas and the 1surface soil value 1in the control area was significantly higher than that in the contaminated decreasedmg kg− fromto 160.29 0–15 mg cmkg to− . 15 The–30 AK cm content by 16.8 in1 the%, topsoil while wasthese higher percent in the decreases control area were compared 19.32% to from area.· The soil AK ranged· from 95.39 mg·kg−1 to 160.29 mg·kg−1. The AK content in the topsoil was 0–15the cm contaminated to 15–30 cm area. in the The contaminated SOC of the control areas. areas The decreasedcontrol areas from had 0–15 a cm lower to 15–30 decrease cm by in 16.81%, rates over higherwhile these in the percent control decreases area compared were 19.32% to the from contaminated 0–15 cm to 15–30 area. cm The in the SOC contaminated of the control areas. areas The the soil depth range than the contaminated areas. decreasedcontrol areas from had 0– a15 lower cm to decrease 15–30 cm in ratesby 16.8 over1%, the while soil depththese range percent than decreases the contaminated were 19.32 areas.% from 0–15 cm to 15–30 cm in the contaminated areas. The control areas had a lower decrease in rates over the soil depth range than the contaminated areas.

Figure 3. Physicochemical properties of the soil samples. Different lowercase letters indicate significant differences at 0.05 (p < 0.05) levels.

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Figure 3. Physicochemical properties of the soil samples. Different lowercase letters indicate significant differences at 0.05 (p < 0.05) levels.

The activities of four enzymes (NAG, sucrose, β-glucosidase, and alkaline phosphatase) in the T. mongolicaInt. J.regions Environ. Res. are Public displayed Health 2020, 17in, 5142Figure 4. Soil enzyme activity decreased with increasing 6 of 16 depth in both the control and contaminated areas. The surface enzymes activity in the control area was significantly higherThe activities than of that four inenzymes other (NAG,areas. sucrose, Soil NAGβ-glucosidase, activity and in thealkaline T. mongolica phosphatase) regions in the ranged from 2.88T. to mongolica 11.75 μg·gregions−1·h are−1. Soil displayed sucrose in Figure activity4. Soil in enzyme the T. activitymongolica decreased regions with ranged increasing from depth 1.50 to 6.31 mg·g−1·24in h both−1. The the controlactivity and of contaminated β-glucosidase areas. ranged The surface from enzymes 14.69 to activity 41.17 in mg·g the control−1·24 harea−1. wasThe highest significantly higher than that in other areas. Soil NAG activity in the T. mongolica regions ranged −1 −1 activity of alkaline phosphatase1 1 was 1.92 mg·g ·24 h in the control area at 0–15 cm depth. The from 2.88 to 11.75 µg g− h− . Soil sucrose activity in the T. mongolica regions ranged from 1.50 to 1 1 · · 1 1 activity levels6.31 mg ing −contaminated24 h− . The activity areas of β were-glucosidase significantly ranged from lower 14.69 tothan 41.17 those mg g− in24 the h− . control The highest areas. The · · 1 1 · · overall activityactivity of alkalinethe four phosphatase enzymes was in 1.92 the mg controlg− 24 harea− in thesoil control was areasignificantly at 0–15 cm depth. higher The than activity that in the · · contaminatedlevels in area contaminated soil. Therefore, areas were all significantly the results lower show than those that in open the control pit areas.mining The reduces overall activity soil enzyme activity. of the four enzymes in the control area soil was significantly higher than that in the contaminated area soil. Therefore, all the results show that open pit mining reduces soil enzyme activity.

Figure 4. Changes in soil enzyme activities in the control and contaminated areas. Different lowercase Figure 4.letters Changes indicate in significant soil enzyme differences activities at 0.05 (p < in0.05) the levels. control and contaminated areas. Different lowercase letters indicate significant differences at 0.05 (p < 0.05) levels. 3.3. Effect of Open Pit Mining on Bacterial Community Structure and Composition 3.3. Effect of OThepen coveragePit Mining index on actually Bacterial reflects Community whether the Structure sequencing and results Composition represent the real situation of the sample. The coverage of all samples was above 95%, indicating that the results of sequencing The werecoverage reliable index and reflected actually the reflects real situation whether of soil the bacteria. sequencing As shown results in Table represent2, the ACE, the Shannon real situation of the sample.and Chao1 The indicescoverage for six of sitesall samples revealed similar was above trends and95%, exhibited indicating a gradual that decreasethe results with depth;of sequencing were reliablehigher and values reflected were recorded the real for situation the control of area soil at thebacteria. same depth. As shown The Simpson in Table diversity 2, the index ACE, was Shannon the highest among two samples collected at 15–30 cm depth, and the value of the control area was and Chao1lower indices than thatfor ofsix the sites contaminated revealed area. similar These trends results and show exhibited that the soil a structure gradual induced decrease by open with depth; higher valuespit mining were gradually recorded decreases for the the control bacterial area richness at the and same diversity. depth. The Simpson diversity index was the highest among two samples collected at 15–30 cm depth, and the value of the control area was lower than that Tableof the 2. Characteristicscontaminated of soil area. bacterial These richness results and diversity show that indices the in disoilfferent structure soil samples. induced by open pit mining gradually decreasesSample Code the Shannon bacterial richness ACE and Chao1 diversity. Coverage Simpson P1-1 6.69 8139.96 7828.36 0.95 0.06 P1-3 5.93 7124.62 7228.36 0.95 0.09 Table 2. CharacteristicsP2-1 of soil bacterial 6.87 richness 8496.79 and 7847.48 diversity indices 0.95 in different 0.07 soil samples. P2-3 5.84 7051.32 7025.39 0.95 0.10 P3-1 6.82 8308.25 7956.83 0.96 0.07 Sample CodeP3-3 Shannon 5.75 ACE 7150.23 7081.62Chao1 0.97Coverage 0.09 Simpson H1-1 7.27 10,527.34 9096.68 0.96 0.05 P1-1 H1-36.69 6.36 8139.96 9488.99 8049.607828.36 0.950.95 0.08 0.06 P1-3 H2-15.93 7.15 7124.62 11,035.31 8847.987228.36 0.960.95 0.05 0.09 H2-3 6.56 9468.66 7766.70 0.95 0.07 P2-1 CK-16.87 7.43 8496.79 12,458.37 10,510.847847.48 0.980.95 0.02 0.07 P2-3 CK-35.84 6.71 7051.32 10,703.70 9766.707025.39 0.970.95 0.04 0.10 P3-1 6.82 8308.25 7956.83 0.96 0.07 P3-3 5.75 7150.23 7081.62 0.97 0.09 H1-1 7.27 10,527.34 9096.68 0.96 0.05 H1-3 6.36 9488.99 8049.60 0.95 0.08 H2-1 7.15 11,035.31 8847.98 0.96 0.05

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H2-3 6.56 9468.66 7766.70 0.95 0.07 CK-1 7.43 12,458.37 10,510.84 0.98 0.02 CK-3 6.71 10,703.70 9766.70 0.97 0.04 Int. J. Environ. Res. Public Health 2020, 17, 5142 7 of 16 Bacterial 16S rRNA genes collected from 12 soil samples were sequenced by high-throughput sequencing and cluster analysis, and a total of 11 phyla were obtained (Figure 5). At the phylum Bacterial 16S rRNA genes collected from 12 soil samples were sequenced by high-throughput level, Actinobacteria, Proteobacteria, Acidobacteria, Planctomycetes, Gemmatimonadetes, sequencing and cluster analysis, and a total of 11 phyla were obtained (Figure5). At the phylum level, Bacteroidetes, and Chloroflexi were the 7 most abundant phyla. In addition, approximately 7% of Actinobacteria, Proteobacteria, Acidobacteria, Planctomycetes, Gemmatimonadetes, Bacteroidetes, the sequences were not assigned to any of the existing phyla, suggesting that there are unrecognized and Chloroflexi were the 7 most abundant phyla. In addition, approximately 7% of the sequences were microbial resources in the region. As shown in Figure 5, the community structure of each site was not assigned to any of the existing phyla, suggesting that there are unrecognized microbial resources in the same at the phylum level, but the relative abundance of each genus was different. Actinobacteria the region. As shown in Figure5, the community structure of each site was the same at the phylum were abundant in the contaminated areas at 15–30 cm depths, but in other areas the opposite was level, but the relative abundance of each genus was different. Actinobacteria were abundant in the true. Proteobacteria were the most abundant at a soil depth of 0–15 cm. Acidobacteria were more contaminated areas at 15–30 cm depths, but in other areas the opposite was true. Proteobacteria were abundant at soil depth of 0–15 cm than at soil depth of 15–30 cm. Increased levels of Planctomycetes thewere most detected abundant in the at a surface soil depth soil of of 0–15 the contaminated cm. Acidobacteria areas. were Gemmatimonadetes more abundant at were soil depththe most of 0–15 cm than at soil depth of 15–30 cm. Increased levels of Planctomycetes were detected in the surface abundant at a soil depth of 0–15 cm in the contaminated area. Chloroflexi were more abundant than soil of the contaminated areas. Gemmatimonadetes were the most abundant at a soil depth of 0–15 cm the deep layer when the soil depth is 0–15 cm. In the desert, Actinobacteria, Proteobacteria, in the contaminated area. Chloroflexi were more abundant than the deep layer when the soil depth is Bacteroidetes, and Candidatus Saccharibacteria were the 4 most abundant phyla. Actinobacteria and 0–15Proteobacteria cm. In the desert,were more Actinobacteria, abundant at Proteobacteria, soil depth of Bacteroidetes,15–30 cm than and at Candidatussoil depth of Saccharibacteria 0–15 cm; they werewere the the 4 opposite most abundant of the levels phyla. observed Actinobacteria for Candidatus and Proteobacteria Saccharibacteria were more and abundant Bacteroidetes. at soil At depth the of 15–30 cm than at soil depth of 0–15 cm; they were the opposite of the levels observed for Candidatus phylum level, the contaminated area of the soil bacterial community structure was similar to the Saccharibacteriacontrol area, and and the Bacteroidetes. relative abundance At the ofphylum bacteria level, belonging the contaminated to each phylum area of exhibited the soil bacterial obvious communitydifferences, structure verifying was that similar coal mining to the control has an area, apparent and the influence relative abundance on the soil of bacteria bacteria community belonging tostructure. each phylum exhibited obvious differences, verifying that coal mining has an apparent influence on the soil bacteria community structure.

Figure 5. At the phylum level, soil bacterial community composition in the T. mongolica regions. Figure 5. At the phylum level, soil bacterial community composition in the T. mongolica regions. At the genus level, the thermal maps of microbial communities in different sampling areas wereAt constructed the genus (Figurelevel, the6). thermal At the maps genus of level, microbial 628 bacterial communities genera in weredifferent detected. sampling The areas 20 most were abundantconstructed genera, (Figure with 6). aAt frequency the genus> 1.5%level, in628 at bacterial least one genera sample, were were detected. selected The for heat20 most map abundant analysis. Basedgenera, on with Figure a frequency6, the main >1.5% genus in at least types one in sample, soil samples were selected were as for follows: heat mapSphingomonas analysis. Based, Gp6 on, GemmatimonasFigure 6, the main, WPS-1_genera_incertae_sedis genus types in soil samples, Nocardioides were as follows:, and Saccharibacteria_genera_incertae_sedis Sphingomonas, Gp6, Gemmatimonas., TheWPS main-1_genera_incertae genus types_sedis in the, Nocardioides conservation, and area Saccharibacteria_genera_incertae_sedis and contaminated area were Sphingomonas. The main ,genusGp6, andtypesGemmatimonas in the conservation. Nocardioides area and, and contaminatedSaccharibacteria_genera_incertae_sedisere area were Sphingomonas, Gp6were, and the Gemmatimonas main genus. typesNocardioides in the desert, and Saccharibacteria_genera_incertae_sedis area. In both the control and contaminatedere were areas, the mainSphingomonas genus typeswas in the the primary desert genus,area. In representing both the control 2.52–8.67% and contaminated the sequences. areas, GemmatimonasSphingomonas waswas the the primary next major genus, component representing of the2.52 bacterial–8.67% the community sequences. in Gemmatimonas all samples. Gaiellawas therepresented next major component 0.45–12.37% of of the the bacterial sequences, community among whichin all samples.the contaminated Gaiella represented area harbored 0.45– a12.37% significantly of the sequences,high percentage. among Nocardioides which the contaminatedrepresented 0.45–2.12% of the sequences obtained from the control and contaminated areas. Acidobacteria, particularly subgroups Gp6, Gp4, Gp16, and Gp7, represent approximately 10% of all bacteria found in the soil. Saccharibacteria_genera_incertae_sedis represented between 0.52% and 1.99% of the sequences discovered in the control and contaminated areas, respectively, among which the desert area harbored Int. J. Environ. Res. Public Health 2020, 17, x FOR PEER REVIEW 8 of 16 area harbored a significantly high percentage. Nocardioides represented 0.45–2.12% of the sequences obtained from the control and contaminated areas. Acidobacteria, particularly subgroups Gp6, Gp4, Gp16, and Gp7, represent approximately 10% of all bacteria found in the soil. Saccharibacteria_genera_incertae_sedis represented between 0.52% and 1.99% of the sequences discovered in the control and contaminated areas, respectively, among which the desert area harbored a significantlyInt. J. Environ. Res. high Public Health percentage2020, 17, 5142 (10%). Other core genera included Pirellula, 8 of 16 Thiobacillus, Sphingomonas, and Rubrobacter. a significantly high percentage (10%). Other core genera included Pirellula, Thiobacillus, Sphingomonas, and Rubrobacter.

Figure 6. Heat map of microbial community structure in soil.

BasedFigure on the Kyoto 6. Heat Encyclopedia map of microbial of Genes and community Genomes (KEGG) structure database, in soil. the abundance of functional genes is predicted. Functional genes with statistical differences at p < 0.05 between Based on contaminatedthe Kyoto area Encyclopedia and control area wereof Genes shown in and Figure Genomes7. At the second (KEGG) level, most database, KEGG pathways the abundance of significantly different were increased by open pit mining. The larger differences between contaminated functional genesarea and is predicted. control area samples Functional were found genes for the with terms “Carbohydrate statistical Metabolism”,differences “Membrane at p < 0.05 between contaminated Transport”, area and and control “Amino Acid area Metabolism”. were shown The results in indicateFigure that 7. coal At mining the causessecond changes level, in most KEGG soil bacterial metabolism. At the third level, the larger differences between contaminated area and pathways significantlycontrol area samplesdifferent were were found forincreased the terms “Peptidases by open “, pit “Phenylalanine, mining. The tyrosine larger and tryptopha” differences between contaminated areaand “Pantothenate and control and CoAarea biosynthesis”. samples were Functional found genes f relatedor the to terms peptidases “Carbohydrate had enriched. Metabolism”,

“Membrane Transport”3.4. Response, of and Bacterial “Amino Community Acid to Soil Metabolism Environmental Factors”. The results indicate that coal mining causes changes in soil bacterialPearson’s metabolism. correlation coeffi Atcient the was third used to level, determine the the larger relationship differences between soil between chemical contaminated area and controlproperties area samples and soil bacterial were community found for (Table the3). terms Among “ thePeptidases main environmental “, “Phenylalanine, factors, there is tyrosine and tryptopha” anda significant“Pantothenate relationship and between CoA every biosynthesis”. factors and soil bacterial Functional community. genes The changesrelated of to eight peptidases had dominant bacteria are related to soil properties. The correlation between different bacterial groups and enriched. each soil environmental factor is different. Sphingomonas showed a significant positive correlation with AP (p < 0.05); Gp6 showed a significant positive correlation with SWC, and Gemmatimonas exhibited a significant positive correlation with EC and SWC (p < 0.05). Nocardioides showed a significant negative correlation with EC and SWC (p < 0.05), and there was a significant negative correlation with SOC and TN (p < 0.01). Saccharibacteria_genera_incertae_sedis showed a significant negative correlation with SWC and Sand (p < 0.05), and there was a significant negative correlation with EC, SOC, and TN (p < 0.01).

Int. J. Environ.Int. J. Environ. Res. Public Res. Public Health Health 20202020, 17,,17 x ,FOR 5142 PEER REVIEW 9 of 169 of 16

Figure 7. Prediction of abundance of functional gene contents of rhizosphere soil. The microbial Figurefunctions 7. Prediction were predicted of abundance at the second of functional (top) and the gene third contents (bottom) levelof rhi ofzosphere the KEGG soil. pathway. The microbial functions were predicted at the second (A) and the third (B) level of the KEGG pathway.

3.4. Response of Bacterial Community to Soil Environmental Factors Pearson’s correlation coefficient was used to determine the relationship between soil chemical properties and soil bacterial community (Table 3). Among the main environmental factors, there is a significant relationship between every factors and soil bacterial community. The changes of eight dominant bacteria are related to soil properties. The correlation between different bacterial groups

Int. J. Environ. Res. Public Health 2020, 17, x FOR PEER REVIEW 10 of 16

and each soil environmental factor is different. Sphingomonas showed a significant positive correlation with AP (p < 0.05); Gp6 showed a significant positive correlation with SWC, and Gemmatimonas exhibited a significant positive correlation with EC and SWC (p < 0.05). Nocardioides showed a significant negative correlation with EC and SWC (p < 0.05), and there was a significant negative correlation with SOC and TN (p < 0.01). Saccharibacteria_genera_incertae_sedis showed a significant negative correlation with SWC and Sand (p < 0.05), and there was a significant negative correlation with EC, SOC, and TN (p < 0.01).

Table 3. Pearson correlations coefficient between the relative abundance of the dominant bacterial genera and environment factors.

Sphingomon Gemmatimo Nocardioid Gaiell Aciditerrimo Saccharibacte Gp6 Int. J. Environ. Res.as Public Health 2020, 17, 5142nas es a nas ria 10 of 16 EC 0.135 0.463 0.643 * −0.747 * 0.365 −0.201 −0.764 ** SW 0.785 0.351 0.722 * −0.678 * 0.146 −0.434 −0.763 * C Table 3. Pearson correlations* coeﬃcient between the relative abundance of the dominant bacterial genera and environment factors. SO 0.576 0.414 0.347 −0.820 ** −0.106 −0.321 −0.797 ** C Sphingomonas Gp6 Gemmatimonas Nocardioides Gaiella Aciditerrimonas Saccharibacteria TNEC 0.1350.253 0.4630.239 0.6430.292 * −0.7470.839 * ** 0.3650.055 0.0230.201 0.764−0.870 ** ** − − − SWC 0.351 0.785−0.19 * 0.722 * 0.678 * 0.146 0.434 0.763 * AP 0.602 * −0.191 − −0.522 −0.317 −−0.151 − −0.485 SOC 0.576 0.4140 0.347 0.820 ** 0.106 0.321 0.797 ** − − − − TN 0.253 0.239−0.11 0.292 0.839 ** 0.055 0.023 0.870 ** AK 0.526 −0.054 − −0.581 * −0.115 −0.177 − −0.582 AP 0.602 * 0.1902 0.191 0.522 0.317 0.151 0.485 San − −0.27 − − − − − AK 0.526−0.139 0.112 −0.0540.508 0.5810.576 * −0.1150.399 0.1090.177 0.5820.621 * d − 7 − − − − − Sand 0.139 0.277 0.508 0.576 0.399 0.109 0.621 * − The correlations− are significant− at the 0.05 level. * indicates− p < 0.05; ** indicates p < 0.01. The correlations are signiﬁcant at the 0.05 level. * indicates p < 0.05; ** indicates p < 0.01.

ToTo further further explore explore the the relationships relationships between between environmental environmental factors factors and and bacterial bacterial community community structures,structures, a a canonical canonical correspondence correspondence analysis analysis (CCA) (CCA) tri-plot tri-plot was was constructed, constructed, as shownas shown in Figurein Figure8. CCA18. CCA1 and and CCA2 CCA explained2 explained 40.01% 40.01% and and 35.82% 35.82% of of the the variance, variance, respectively. respectively. The The environmental environmental factorsfactors and and soil soil nutrients nutrients were were found found to to be be closely closely related related to to the the structure structure and and composition composition of of the the soil soil microbialmicrobial communities. communities. The The SWC SWC and and EC EC have have the the longest longest arrow arrow lines, lines, indicating indicating that that they they are are both both majormajor factors factors driving driving changes changes in in soil soil nutrients nutrients and and enzyme enzyme activity. activity. Thus, Thus, the the SWC SWC and and EC EC were were the the strongeststrongest determinants, determinants, respectively, respectively, with with respect respect to to the the microbial microbial community community composition. composition. AK, AK, AP, AP, TN,TN, and and SOC SOC were were positivity positivity correlated correlated with the with soil thebacterial soil community bacterial community composition. compositio Soil enzymaticn. Soil activitiesenzymatic were activities positivity were correlated positivity with correlated the soil with bacterial the soil community bacterial composition.community composition.

FigureFigure 8. 8.Canonical Canonical correspondence correspondence analysis analysis (CCA) (CCA) of of environment environment variables, variables, enzymatic enzymatic activity,activity, andand bacterial bacterial diversity. diversity. 4. Discussion

4.1. Open Pit Mining Reduced Soil Nutrient and Enzyme Activity Open pit mining damages soil structure, decreases soil water holding capacity, and seriously affects plant growth. The closer to the coal mining area, the lower the vegetation coverage and species diversity. Although the T. mongolica Nature Reserve is far away from the mining area, it is also polluted, and the vegetation coverage and species diversity are reduced. In this study, we found that the particle size distribution in the contaminated area was mainly sandy soil, whereas open pit mining increases the content of sand and decreases the content of silt. The EC of soil in the contaminated area was significantly higher than that in the control area at the same depth. At the same time, the soil moisture content was low, while the soil EC value was relatively high, which reflects the Int. J. Environ. Res. Public Health 2020, 17, 5142 11 of 16 desertification characteristics of poor-quality alkaline soil in this region. The SOC in the distribution area of T. mongolica was low, and the TN, AP, and AK were also very low. This indicates that soil fertility was very low, and the amount of nitrogen phosphorus content that could be directly absorbed by plants was very low in the contaminated areas. T. mongolica regions are located in the arid and semi-arid area of Northwest China. Water is the main ecological limiting factor in the study area. The decrease in field moisture capacity may be related to the loss of physical clay in soil [21]. Severe soil erosion and wind erosion in this area are some of the most important factors causing soil desertification, with open pit mining exacerbating the problem. In arid and semi-arid areas, the rainfall rate is much slower than the evaporation rate, and the salt content accumulates in the soil and groundwater. The soil is generally alkaline, while coal mining aggravates the evaporation of water, thus causing soil salinity risk. The available nutrients in the soil are decreased in polluted areas, which may be caused by soil structure destruction due to open pit mining. Processes such as coal mining, coal transportation, coal gangue heap, discharge at will, and wind dust will intensify the accumulation of heavy metals in the surrounding soil, and this will have an impact on soil nutrients [22]. Soil enzyme activity is a sensitive and reliable index of soil biological activity and soil fertility [23,24]. For instance, NAG and phosphatase participate in soil the C, N and P cycles, which may affect soil fertility [25]. β-glucosidase can change cellulose, starch, and sugar into low molecular weight compounds [26]. Urease plays a vital role in the hydrolysis of urea into carbon dioxide and ammonia [27]. Phosphatase catalyzes organophosphate mineralization and immobilization processes, affecting the potential supply of organophosphorus. There are many factors affecting the enzyme activity, and its mechanism is complex. Soil water is one of the main factors affecting soil enzyme activity, which can regulate soil microbial activity by affecting soil osmotic potential, nutrients, energy transfer, and microbial cell metabolism, thereby affecting soil enzyme activity [28]. The soil nutrient content and enzyme activity were significantly different in among separate farmlands, and the soil urease, phosphatase and catalase activity were significantly linearly correlated with the soil organic matter and nutrient content [29]. The catalase, polyphenol oxidase, and sucrase activities in the soil were significantly correlated with the available nitrogen in the soil and that urease and polyphenol oxidase were significantly correlated with the total nitrogen content, with the sucrase activity significantly correlated with the level of organic matter present [30]. As pollution increases, the soil enzyme synthesis function declines, microbial growth is restrained, soil organic matter decomposes at a slower pace, respiration is restrained, N fixation effects are reduced, and the turnover rate of C and N elements and the energy cycle are reduced; this results in lower soil organic matter and TN content. As a consequence, the soil water capacity, EC, soil nutrients, and other factors can change the soil enzyme activity, while coal mining increases the soil heterogeneity and causes changes to the soil enzyme activity. Changes in soil nutrients and uenzyme activity indicate that open pit mining leads to soil degradation, affecting the TOC, N, and P cycles of the soil, and thus affecting the total number and density of T. mongolica.

4.2. The Soil Bacterial Community Shift due to Open Pit Mining Soil microorganisms are an important part of the rhizosphere soil ecosystem, and their quantity and distribution are influenced by many factors, which can reflect the subtle changes within the soil ecosystem. The secretion from the roots of plants have a serious effect on microorganisms of the rhizosphere. Soil microbial communities play an important role in ecosystems by regulating soil processes. In the soil environments, bacteria species or strains are very abundant. The diversities are related to the vegetation, soil moisture, and depth. Open pit mining had severe effects on soil properties such as the SWC, soil EC, nutrient levels, and enzyme activities. Thus, the soil structure induced by open pit mining gradually decreases the bacterial richness and diversity. In the present study, the 7 dominant bacterial communities present in the soils belonged to the phyla Actinobacteria, Proteobacteria, Acidobacteria, Planctomycetes, Gemmatimonadetes, Bacteroidetes, and Chloroflexi in Int. J. Environ. Res. Public Health 2020, 17, 5142 12 of 16 the same pattern observed from the above index. Among the bacteria from various topsoil sources, 92% belong to the 9 dominant phyla: Proteobacteria, Acidobacteria, Actinobacteria, Verrucomicrobia, Bacteroidetes, Chloroflexi, Planctomycetes, Gemmatimonadetes, and Firmicutes [31]. Li studied the effects of vegetation restoration among different soil bacterial communities, and the dominant phyla were Proteobacteria, Actinobacteria, and Acidobacteria [7]. Li [32] also investigated the diversity and composition of bacterial communities in response to reclamation of a soil subsidence area affected by mining activities, and found that the predominant phyla in present in the soils were Proteobacteria, Actinobacteria, Acidobacteria, and Planctomycetes. This indicates that the soil bacterial community in the Tetraena mongolica region is consistent with other types of soil at the phyla level. In addition, the abundance of some core genera in the soil also changed substantially, such as the increase in Sphingomonas, Gemmatimonas and Gaiella and the decrease in Nocardioides. Sphingomonas sp. was a member of the hydrocarbon degrading family Sphingomonadaceae and relies on vitamins for its growth [33]. Vitamins are an essential micronutrient to promote biodegradation activity and accelerate the degradation of tributyl phosphate. Therefore, Sphingomonas was abundant in the contaminated area. Sphingomonas sp. Cra20 promotes the growth of Arabidopsis thaliana [34]. Gemmatimonas plays a key role in the fixation of organic carbon [35]. Gemmatimonas also participates in SOC dynamics because Gemmatimonas can metabolize fibers, thus promoting the degradation of cellulose [36]. Consequently, the Gemmatimonas content is higher in the topsoil. Gaiella and Solirubrobacter have also been reported to degrade polycyclic aromatic hydrocarbons [37]. Nocardioides species degrade aromatic amines by utilizing a mixture of aromatic amines as a source of carbon, nitrogen, energy, and sulfur [38]. Nocardioides sp. strain PD653 are capable of denitrating chloroaromatic compounds [39]. Acidobacterial subgroups Gp4 and Gp6 are more abundant in soils with relatively high SOC [40]. It is reported that Saccharibacteria_genera_incertae_sedis mostly exists in desert areas [41]. Prediction of functional gene in rhizosphere soil showed that the largest differences between contaminated area and control area samples were the terms “Carbohydrate Metabolism” and “Peptidases”. The activity of all cysteine peptidases depends on a catalytic dyad of cysteine and histidine [42]. Rhizosphere soil increased the carbohydrate and amino acid metabolism, which were related to higher rates of soil C turnover [43]. Soil microbial richness and activity are closely related to the availability of soil nutrients. “Amino Acid Metabolism” were the energy and carbon sources of bacterial metabolism [44]. Thus, rhizosphere soil could promote more amino acid production and humic substance synthesis. During the metabolism, the microbes were accumulated in soil and probably concerned in further carbon and nitrogen cycling. Open pit mining has no significant effect on the composition of the soil’s microbial community, but open pit mining reduces the diversity of soil bacteria, and the soil environment in the polluted area was poor, which changed the relative abundance of dominant bacteria. Thus, the dominant bacteria had strong anti-interference ability and open pit mining increased the abundance of bacteria for metabolism and degradation of organic pollutants. The dominant bacteria played an important role in nutrient cycling in poor soil. Microbial degradation is not only the main pattern of degrading mining area pollution but also one of powerful means to treat pollution in the environment.

4.3. Response of the Soil Bacterial Community to Changes in Soil Properties Soil properties can affect the composition of bacterial communities, and those soil microbial communities play an important role in the ecosystem by regulating soil processes [9,45]. In this study, the correlation coefficient of CCA and Pearson’s correlation coefficients showed the relationship between the relative abundance of dominant flora and soil properties, indicating the response of the change of soil bacterial community to the change of soil biochemical characteristics. There was a significant correlation between the bacteria and the soil EC, SWC, TN, AP, and SOC, indicating that the soil bacterial community in Wuhai was also affected by the soil properties. Zeng [46] reported a significant correlation between soil bacterial communities and the levels of soil organic carbon, total nitrogen [24], and total phosphorus. Gao [47] observed that soil TP was positively correlated with Actinobacteria and negatively correlated with Firmicutes. Taylor [48] believed that soil enzyme activity Int. J. Environ. Res. Public Health 2020, 17, 5142 13 of 16 was significantly correlated with soil microbial quantity and microbial diversity. Torres-Cortes found that the diversity of rhizosphere bacteria in central and southern Mexico changed with different seasons, and Acosta-Martínez identified positive correlations between Proteobacteria, Firmicutes, Chloroflexi, Verrucomicrobia, and Fibrobacteres and the activity of alkaline phosphatase and β-glucosidase or β-glucosaminidase [49]. Li studied the effects of vegetation restoration on the soil bacterial community, enzyme activity, and nutrients in the Loess Plateau and found that soil organic matter, available phosphorus, urease, and sucrase significantly increased, with the dominant phyla being Proteobacteria, Actinobacteria, and Acidobacteria [7]. Our result, among the main environmental factors, is that there is a significant relationship between every factor and soil bacterial community. Changes in dominant bacteria are related to soil properties, consistent with previous studies. Thus, the changes of soil environmental factors and nutrients are the main factors controlling the changes of soil bacterial community structure. It may be because the area is located in the arid and semi-arid area, with less rainfall and large water evaporation. Microorganisms are involved in the decomposition of plant litter, and changes in the quality and quantity of litter lead to differences in soil TOC. However, open pit mining destroys the soil structure, which leads to the decrease of soil water holding capacity, the increase of electrical conductivity, and the accumulation of soil salt. The loss of water will be accompanied by the loss of nutrients. The accumulation of salt in the soil will hinder the nutrient cycle in the soil. Soil microbial communities play an important role in ecosystems by regulating soil processes. Therefore, the soil water and electrical conductivity are environmental factors that disturb the soil plants and microorganisms. This discovery indicates that open pit mining changes the physical and chemical properties of soil, decreases carbon and nitrogen cycling, and changes soil nutrients, which changes the structure of soil microbial communities in the Tetraena mongolica region.

5. Conclusions Open pit mining leads to surface damage and deterioration of the ecological environment. Our study showed that open pit mining had severe effects on Tetraena mongolica soil properties such as the SWC, soil EC, nutrient levels, and enzyme activities. The results showed that the electrical conductivity (EC) increased; the TN, AP, AK, and SOC decreased, and the activity of four kinds of enzymes (NAG, sucrose, β-glucosidase, and alkaline phosphatase) in the soil decreased. In the present study, seven dominant phyla were found, in which the relative abundance of Actinobacteria, Proteobacteria, and Gemmatimonadetes increased while the abundance of Acidobacteria, Planctomycetes, Bacteroidetes, and Chloroflexi decreased. In addition, the abundance of some core genera in the soil also changed substantially, such as the increase in Sphingomonas, Gemmatimonas, and Gaiella and the decrease in Nocardioides. These results indicate that open pit mining caused the soil microbial community structure to shift, bacterial diversity and relative abundance to decrease, the carbon-nitrogen cycle to alter, the enzyme activity to decrease, the salinization of the soil to reach serious conditions, and the soil nutrient content to be reduced. This paper discusses the effects of open pit mining on Tetraena mongolica soil that were revealed; further studies are needed to investigate the harm brought by soil changes on Tetraena mongolica, and to reveal the potential interaction between plant community and soil microbial community.

Author Contributions: Conceptualization, M.R. and Y.Z.; methodology, M.R.; software, M.R.; validation, M.R., Y.Z. and T.C.; formal analysis, M.R.; investigation, M.R.; resources, Y.Z. and T.C.; data curation, M.R.; writing—original draft preparation, M.R.; writing—review and editing, Y.Z. and M.R.; visualization, M.R.; supervision, Y.Z. and T.C.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the National Key Research and Development Program of China (2017YFC0504400) and the Fundamental Research Funds for the Central Universities (2020YJSHH06). Conﬂicts of Interest: The authors declare no conﬂicts of interest. Int. J. Environ. Res. Public Health 2020, 17, 5142 14 of 16

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