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Article

Assessment of the Pollution of Soil Heavy Metal(loid)s and Its Relation with Soil Microorganisms in Wetland Soils

by
Chunyong Wang
1,2,*,
Bo Zhu
1,
Yitong Guo
1,
Shasha Tian
1,
Zhenbin Zhang
1 and
Xintong Hou
1
1
School of Chemical and Environmental Engineering, Liaoning University of Technology, Jinzhou 121001, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(19), 12164; https://doi.org/10.3390/su141912164
Submission received: 23 August 2022 / Revised: 20 September 2022 / Accepted: 23 September 2022 / Published: 26 September 2022
(This article belongs to the Section Soil Conservation and Sustainability)
Browse Figures
Versions Notes

Abstract

:
This study was performed to provide a comprehensive assessment of the pollution of soil heavy metal(loid)s (HMs) and the relationships between HMs (including As, Cd, Cr, Cu, Ni, Se, Pb, Hg, and Mn) and soil microorganisms in the different utilized types of wetland soils (agricultural soils (AS), natural wetland soils (NWS), and restored wetland soils (RWS)). Our results indicated that As and Cd in the studied region accumulated to different degrees in AS, NWS, and RWS. The microbial community compositions and heatmaps showed that the compositions of bacterial, fungal, and archaeal communities had differences in various degrees. A comprehensive assessment was performed including the geoaccumulation index (Igeo), the pollution load index (PLI), and the potential ecological risk index (ERI) to evaluate the pollution of HMs. Based on the results, Cd and As were identified as the major pollutants that contributed to the potential ecological risk in AS, NWS, and RWS. The co-occurrence network analysis indicated that the bacterial genera Bryobacter and Acidothermus, the fungal genera Fusarium and Anguillospor, and the archaeal unclassified genus Nitrososphaeraceae were the key taxa in the microbial networks. Based on the interactive forward selection method in redundancy analysis (RDA), it could be concluded that microbial community compositions were mainly controlled by As.

1. Introduction

Currently, the environmental problems caused by heavy metals and metalloids (HMs) in soils have attracted more and more attention in the world [1,2]. Among soil HMs, the most typical pollutants include Ni, Cd, Cr, etc. [3]. The main sources of HMs in soils are various anthropogenic activities such as the different types of waste and poor management of them [4,5]. HMs in soils are very difficult to degrade, resulting in persistent damage to the soil ecosystem. They can threaten soil organisms and affect soil enzyme activity [6,7]. In addition, HMs can also migrate to plants and humans, posing a direct or indirect threat to human health [8,9]. In particular, HMs can cause pathological changes in the human body and even malignant diseases such as cancer, Alzheimer’s disease, etc. [10,11]. Due to the toxicity, persistence, and bioaccumulation of HMs in soils, it has been a research hotspot for many years [12]. For wetland soils, the pollution and risk assessment of HMs have attracted considerable attention in recent years [13]. It was found that the changes in the utilized types of soils could cause varying degrees of differences in the concentrations of HMs [14,15]. In particular, wetland soils, which are more likely to be disturbed by human activities, have also been reported to have differences in the accumulation of HMs depending on the different types of soils utilized [16]. However, little is known about the assessment of HM pollution in the different utilized types of wetland soils.
Soil microorganisms, mainly bacteria, fungi, and archaea, are the vital living components of soil and important parts of maintaining the stability of soil [17,18]. In wetland ecosystems, soil microorganisms not only play important roles in the transformation and recycling of carbon, sulfur, nitrogen, etc., but also play key roles in cellulose degradation and nitrogen fixation [19]. Previous studies were conducted to discuss the effects of HMs on the soil’s microbial community structure [13,14,20]. For example, studies on soil microorganisms could be conducted to evaluate the ecological status of HMs through observations and comparisons of the changes in microbial populations and activities [21]. However, whether there are correlations between HMs and soil microorganisms is still controversial [13]. Therefore, it is necessary to discuss the relationships between wetland soil microorganisms and HMs.
The aims of the present study are as follows: (1) to explore the accumulation level of soil HMs and the pollution degree of soil HMs in the different utilized types of wetland soils (agricultural soils (AS) and natural wetland soils (NWS) and in restored wetland soils (RWS)), (2) to comprehensively assess the ecological risks caused by different soil HMs, and (3) to discuss the relationships between microbial community compositions and HMs. This study can provide new insights into understanding the relationships between soil microbial communities and HM pollution.

2. Materials and Methods

2.1. Soil Sampling and the Determination of Soil HMs

The soil sampling sites were located in the Sanjiang Plain, which belongs to a typical freshwater wetland and faces marsh loss and cropland expansion [22,23]. Thus far, positive measures such as repurposing farmlands into wetlands have effectively restored wetland areas [24]. Therefore, the different utilized types of wetland soils (AS, NWS, and RWS) that could be clearly distinguished were shown during the restoration of wetlands in the Sanjiang Plain [24].
Soil sampling was performed on May 2019. The specific location of the sampling sites is shown in Figure 1. A composite soil sample in each site was collected by mixing five surface subsamples (0–10 cm). Three soil samples were taken in the different utilized types of wetland soils (AS, NWS, and RWS), respectively. The collected six fresh samples in AS, NWS, and RWS were each mixed into one soil sample for Illumina MiSeq sequencing. The air-dried soil samples were ground and sieved (100 mesh). Then, 0.1 g of each air-dried soil sample was mixed with 10 mL HNO3-HF-H2O2 and digested using a closed microwave digestion system (Mars 6, CEM, Matthews, NC, USA), as indicated in previous studies [25,26]. The digested solution was added with 1 mL HClO4 to remove the remaining HNO3-HF-H2O2 and then diluted with ultrapure water and passed through a 0.22 mm filter [25]. The HMs (including As, Cd, Cr, Cu, Ni, Se, Pb, Hg, and Mn) in the treated solution were measured using inductively coupled plasma optical emission spectrometry (ICP-OES, OPTIMA 5300DV, Perkin-Elmer Co., Ltd. Waltham, MA, USA) [25,27]. The detection limit was 0.1 mg/kg for each metal. The standard soil GSS-5 (GBW07453) and reagent blanks were used to guarantee quality assurance and quality control (QA/QC). The average recovery was between 95.1% and 106.3%.

2.2. A Comprehensive Assessment of HM Pollution

The geoaccumulation index (Igeo), the pollution load index (PLI), and the potential ecological risk index (ERI) assessment were used to comprehensively reflect the accumulation level of soil HMs, the pollution degree of soil HMs, and the ecological risks caused by HMs [28,29].
Igeo is calculated based on the formula in Equation (1) [28].
I g e o = l o g 2 C n 1.5 × B n
where Cn is the measured concentration of metal i, and Bn is the geochemical background values in soils. In the present study, the geochemical background values of HMs in Heilongjiang Province were selected (As = 7.3 mg/kg, Cd = 0.086 mg/kg, Cr = 58.6 mg/kg, Cu = 20 mg/kg, Ni = 228 mg/kg, and Mn = 1065 mg/kg) [30]. The divided grades of Igeo based on the study of Barkett and Akün [28] are shown in Table 1.
PLI can be used to assess the average contributions of HMs [31]. Its calculation formulae are shown in Equations (2) and (3).
  P i = C i B i
P L I = II i = 1 n P i n
where Ci is the measured concentration of metal i, and Bi is the background values of metal i. Herein, we selected the background values in soils of the Sanjiang Plain as Bi. The values of Bi were the same as Ti [29,32,33,34,35], as shown in Table 2. PLI can be divided into four grades: no pollution (PLI < 1), moderate pollution (1 < PLI < 2), heavy pollution (2 < PLI < 3), or extremely heavy pollution (3 < PLI) [31].
ERI is calculated as shown in Equation (4) [33]:
C o m p r e h e n s i v e   E R I = ∑ i = 1 n E R I i = ∑ i = 1 n C i S i × T i
where ERIi is the ERI of the given metal i, Ci is the average concentration of metal i, and Si is the background values of metal i in the soils of the Sanjiang Plain [32,35]. Ti is the toxic-response factor values of metal i [33,34]. The values of Si and Ti are shown in Table 2. The divided grades of ERI and comprehensive ERI values reported by Xiang et al. [33] are shown in Table 3.

2.3. Illumina MiSeq Sequencing and the Processing of Raw Data

The DNA from fresh soil samples was extracted using an OMEGA-soil DNA kit (Omega Bio-Tek, Norcross, GA, USA) [25,36]. The quality and size of the extracted DNA concentration were determined using a spectrophotometer (NanoDrop2000, Thermo Fisher Scientific, Waltham, MA, USA) and then purified with a DNeasy Tissue kit (Qiagen, Valencia, CA, USA) and checked via 1% agarose gel electrophoresis [21,33]. The processes of PCR amplification were the same as those in our previous studies [25]. The bacterial 16S rRNA, the fungal ITS genes, and the archaeal 16S rRNA were amplified using the 338F/806R primer pairs, ITS1F/ITS2R primer pairs, and 524F10extF/Arch958RmodR primer pairs, respectively [37,38,39,40]. Illumina MiSeq sequencing was achieved through an Illumina MiSeq platform at Majorbio Co., Ltd., (Shanghai, China) [41]. The raw fastq files were quality-filtered using Trimmomatic and merged using the FLASH software [42,43]. The raw sequencing data were submitted to the NCBI Sequence Read Archive (SRA) under the accession number PRJNA788198.

2.4. Data Analysis

The SPSS 20.0 software was selected for data processing. A one-way analysis of variance (ANOVA) was conducted to identify the differences in concentrations of HMs. The microbial community compositions and heatmaps were achieved using the online platform of I-Sanger (www.i-sanger.com, accessed on 6 June 2020) [36]. The “psych” package in the R software was selected to visualize the microbial co-occurrence network construction [44]. The co-occurrence patterns were built at the genus level. The construction of the network was based on significant differences, absolute correlation values, and relative abundance [44]. A redundancy analysis (RDA) was conducted to study the relationships between the HMs and microbial communities [45].

3. Results

3.1. Characteristics of HMs in Different Utilized Types of Wetland Soils

The descriptive statistics of HMs in the different utilized types of wetland soils (AS, NWS, and RWS) are shown in Table 4. The concentrations of HMs (As, Cd, Cr, Cu, Ni, and Mn) varied between 18.54 and 41.05, 0.18 and 0.72, 12.02 and 28.17, 6.61 and 12.04, 8.28 and 18.03, 341.1 and 985.2 mg/kg, respectively. Significant differences in the concentrations of As, Cd, Cr, and Ni were observed, whereas there were no significant differences in the concentrations of Cu and Mn. The average concentrations of HMs except Mn followed a decreasing order as follows: AS > RWS > NWS. The minimum concentrations of As and Cd were always higher than the background values in the Sanjiang Plain (Table 4). Compared with the background values in the Sanjiang Plain, the average concentrations of Cr, Cu, and Ni in all the soil samples did not exceed the background values, respectively. These results indicated that As and Cd accumulated in three different utilized types of wetland soils. The average concentrations of Cr, Cu, Ni, and Mn were lower than those of the Grade II values indicated in the Environmental Quality Standard for Soils (EQSS, GB15618-1995), while the maximum concentrations of As in AS and Cd in NWS were higher than those of the Grade II values of the EQSS. The concentration levels of HMs except As were commonly lower than the target limits of the Canadian Council of Ministers of the Environment (CCME) in AS, NWS, and RWS. In general, the average concentrations of As in all the sampled soils were higher than the background values in the Sanjiang Plain and the target limits of the CCME but lower than the Grade II values of the EQSS. The average concentrations of Cd were below the target limits of the CCME and above the background values in the Sanjiang Plain. The above results indicated that the As and Cd in the studied region had accumulated to different degrees. Meanwhile, some sampling sites were polluted by As and Cd.
The Pearson correlation coefficients of HMs in the different utilized types of wetland soils are shown in Table 5. There were significantly positive correlations between As and Cd (r = 0.848, p < 0.001), As and Cr (r = 0.714, p < 0.005), Ni and As (r = 0.675, p < 0.005), Cr and Cd (r = 0.742, p < 0.005), and Cu and Cr (r = 0.745, p < 0.005). However, no correlations were detected between Mn and other HMs. No negative correlations were shown between the different HMs.

3.2. Assessment of HM Pollution in AS, NWS, and RWS

The calculation results of Igeo, PLI, ERI, and comprehensive ERI are indicated in Table 6. The Igeo values for As and Cd were above zero, while the Igeo values for Cr, Cu, Ni, and Mn were below zero. These results indicated that the cumulative levels of As and Cd were higher than those of the other HMs. Cd had the maximal Igeo among all the HMs in AS, NWS, and RWS. Based on the grades of the Igeo classification (Table 1), the highest value of Igeo (2.22) was detected in the Cd of AS, which reached a moderately to heavily contaminated level. Compared with the grades of the PLI classification, the PLI values of Cd in AS (6.7) and RWS (4.07) and As in AS (3.46) exceeded their respective highest grades and were all in the extremely heavy pollution level (Table 6). Conversely, Cr, Cu, and Ni exhibited low PLI values in AS, NWS, and RWS, the values of which were all less than 1, indicating that Cr, Cu, and Ni resulted in no pollution based on the calculation of PLI. The sampling sites of NWS were the only sites with moderate pollution levels caused by As (PLI = 1.9).
As shown in Table 6, the average ERI values of HMs were in the descending order of Cd > As > Ni > Cu > Mn > Cr. The ERI values caused by Cd were 202.2 in AS, 82.42 in NWS, and 123.08 in RWS, respectively. The values of the ERI of As, Cr, Cu, Ni, and Mn were lower than 40, which meant that the potential ecological risks posed by As, Cr, Cu, Ni, and Mn in AS, NWS, and RWS were at the slight risk level, according to the grades of the ERI classification in Table 3. Conversely, the individual index values of the ERI of Cd indicated a moderate risk level for NWS and RWS but a severe risk level for AS. The comprehensive ERI levels of all the HMs (As, Cd, Cr, Cu, Ni, and Mn) in AS and RWS exceeded the corresponding value (150), which indicated a mild ecological risk caused by these HMs. Meanwhile, the ERI of Cd contributed 83.1% and 77.5% to the comprehensive ERI in AS and RWS, respectively. As contributed the second highest comprehensive ERI after Cd. Therefore, Cd and As were the primary contributors to the potential ecological risk in the different utilized types of wetland soils (AS, NWS, and RWS).

3.3. Analyses of Microbial Community Compositions in AS, NWS, and RWS

In the present study, high-quality microbial sequences comprising 174,054 bacterial sequences with an average length of 411.03 bp, 189,025 fungal sequences with an average length of 228.44 bp, and 169,450 archaeal sequences with an average length of 430.04 bp were identified. Those sequences were classified into 1635 bacterial OTUs, 910 fungal operational taxonomic units (OTUs), and 90 archaeal OTUs at a 97% similarity level. The total bacterial domain consisted of 30 phyla, 80 classes, 181 orders, 278 families, and 401 genera, while the total fungal domain consisted of 9 phyla, 29 classes, 71 orders, 146 families, and 228 genera. The total archaeal domain had 6 phyla, 11 classes, 17 orders, 19 families, and 23 genera.
Based on Figure 2, the compositions of bacterial, fungal, and archaeal communities in the phylum level were substantially affected by the different utilized types of wetland soils. The phyla of Proteobacteria, Acidobacteria, Chloroflexi, and Actinobacteria were the most abundant bacterial communities in AS, NWS, and RWS. The bacterial phylum of Actinobacteria was less abundant in NWS than in AS and RWS, while Proteobacteria was more abundant in RWS than in AS and NWS. The compositions of fungal communities mostly included Ascomycetes, Basidiomycota, and Mortierellomycota at the level of phylum. Based on the average abundance, Ascomycetes was the most abundant phylum, followed by Basidiomycota and Mortierellomycota. The archaeal phylum of Thaumarchaeota was dominant in all the soil samples, especially in AS. The heatmaps of bacterial, fungal, and archaeal communities based on the relative abundances of the top 50 OTUs are shown in Figure 3. It could be detected that considerable differences existed in the relative abundances of the different utilized types of wetland soils.

3.4. Co-Occurrence Network of Soil Microbiomes

To discuss the co-occurrence patterns of soil bacteria, fungi, and archaea, three networks were constructed. Based on Figure 4, the bacterial networks had more nodes and edge numbers than those of the fungal and archaeal networks. The negative links were more than the positive links in bacterial, fungal, and archaeal communities. The network analysis further indicated that the bacterial genera Bryobacter and Acidothermus, the fungal genera Fusarium and Anguillospor, and the archaeal unclassified genus Nitrososphaeraceae were the key taxa in maintaining the stability of the soil ecosystem.

3.5. Relationships between Microbial Community Compositions and HMs

In the present study, RDA could reflect the correlations between the community compositions and HMs, as shown in Figure 5. The first two canonical axes could explain 47.64% and 28.41% of the total variations, respectively. It is apparent from Figure 5 that As, Cd, Cr, Cu, and Ni were positively correlated with the bacterial community compositions but showed negative correlations with the fungal community compositions. We used a function in RDA known as interactive forward selection to determine the potential predictors among HMs. The results showed that As (Pseudo-F = 3.4, p < 0.05) was the key factor that significantly affected the soil bacterial, fungal, and archaeal community compositions.

4. Discussion

It was reported that HMs can chronically exist in soils because they are non-biodegradable [33]. In the present study, HM pollution in the different utilized types of wetland soils (AS, NWS, and RWS) was evaluated. The average concentrations of As and Cd in AS, NWS, and RWS were lower than those in the wetland sediments/soils from the Panyu District of the Pearl River Delta, the wetland soils from the Karuola Glacier in the Qinghai-Tibetan Plateau, and the wetland soils from the tidal wetlands in the Yellow River estuary [48,49,50]. The concentrations of As and Cd higher than the background values in the Sanjiang Plain indicated that As and Cd accumulated in AS, NWS, and RWS. The results of the Igeo caused by Cd and As, compared with the divided grades, also illustrated the accumulation of Cd and As in the study region. Furthermore, there were no negative correlations between the different HMs. These results indicated that HMs in the different utilized types of wetland soils (AS, NWS, and RWS) might be from a common anthropogenic source [51]. It was reported that the main source of Cd is related to the use of phosphate fertilizers [52]. Previous studies have shown that the accumulation of As in cultivated soils is due to the application of pesticides, fungicides, herbicides, etc. [53]. In the region of this study, crops such as Zea mays were planted and fertilized [24]. Due to continuous farming and fertilization activities, Cd and As accumulated in the studied region. In summary, the sources of toxic HMs (Cd and As) in AS, NWS, and RWS were mainly from human activities, including fertilization and pesticides.
In this study, a comprehensive assessment including the geoaccumulation index (Igeo), the pollution load index (PLI), and the potential ecological risk index (ERI) was conducted to evaluate the pollution of HMs. The above three evaluation indices were considered to also be important means to judge the state of heavy metal accumulation in soils and their levels of pollution [28,29,31,33]. Our results indicated that the values of the PLI highlighted the severity of Cd pollution since extremely heavy pollution levels of Cd were observed in AS and RWS because its values were higher than the grades of the PLI classification (3 < PLI), while As in AS was at the same pollution level. The PLI values of Cd and As suggested that Cd and As were the potential primary contaminants. In the present study, the ERI values of Cd were highest, compared with the other HMs, which indicated that Cd poses a substantial risk to humans [51]. Meanwhile, Cd significantly contributed to the potential ecological risk of the different utilized types of wetland soils. This was consistent with the previous study conducted by Shen et al. [54]. In the present study, the values of the ERI caused by As were higher than those in the surface sediments of the Huixian Karst Wetland [55]. The HM As was identified as having the second highest contribution to the comprehensive ERI.
In the present study, the bacterial phyla of Proteobacteria, Acidobacteria, Chloroflexi, and Actinobacteria dominated in AS, NWS, and RWS. They were always the most dominant phyla in HM-contaminated soils [56,57]. It was reported that Proteobacteria can exist in many soils contaminated with HMs [58]. In particular, Proteobacteria were found to be tolerant to Cd- and As-contaminated soils [59]. Furthermore, it is generally accepted that Acidobacteria can resist the toxicity of HMs via their complexation and adsorption capacities [58]. The phyla Chloroflexi and Actinobacteria were also found to be tolerant to Cd and As [21,60,61,62]. For the fungal phyla, it was found that Ascomycetes, Basidiomycota, and Mortierellomycota were dominant in HM-contaminated soils [63,64]. The most dominant archaea in AS, NWS, and RWS was Thaumarchaeota, which could be detected in HM-contaminated soils [65].
Co-occurrence network analyses could reflect the putative interactions among microbial communities and the stability of the microbial communities in response to changes in the environment [66,67]. Higher complexity in the bacterial networks than in the fungal and archaeal networks was detected, and this result indicated that a more intense activity and higher resilience to perturbation existed in bacterial communities, compared with fungal and archaeal communities [68]. This result also meant that bacterial communities had the most ecological interactions and niche sharing [69]. Meanwhile, it could be concluded that bacterial communities had more tolerance to these HMs than fungal and archaeal communities [58]. Co-occurrence network analyses were also used to identify the key nodes that are the most influential and essential members of the microbial communities [70]. In the present study, the bacterial genera Bryobacter and Acidothermus, the fungal genera Fusarium and Anguillospor, and the archaeal unclassified genus Nitrososphaeraceae were the key taxa. These results meant that the above microorganisms could drive the structure and function of the microbial communities [70].
Soil microorganisms are considered to be indicative of disturbance and might be significantly changed due to the pollution of HMs [45]. In the present study, RDA was conducted to reflect the relationships between microbial community compositions and HMs. The HMs As, Cd, Cr, Cu, and Ni were positively correlated with the bacterial community compositions. It was reported that As, Cd, and Ni can shape the bacterial community structure [71]. Meanwhile, the HMs Cr and Cu were also found to be driving the changes in the bacterial community compositions [72]. The RDA results indicated that As was a vital factor driving the microbial community compositions in AS, NWS, and RWS. These results were in accordance with those of the previous studies reported by Li et al. [71] and Zhang et al. [73]. Previous studies also indicated that As can change the compositions of the soil bacterial community and influence the microbial community diversity [74,75]. Thus, As was considered to be a key factor driving the composition of microbial communities in the different utilized types of wetland soils.
Although the interaction between the dominant microbial communities and HMs was not thoroughly studied, the results of this study could still provide a basis for studying the association between the soil microbial communities and HM pollution in wetland soils.

5. Conclusions

This study assessed soil HM pollution and discussed the relationships between microbial community compositions and HMs in the different utilized types of wetland soils (AS, NWS, and RWS). Cd and As showed accumulation characteristics. They were also major contributors to the potential ecological risk in AS, NWS, and RWS. The bacterial phyla Proteobacteria, Acidobacteria, Chloroflexi, and Actinobacteria, the fungal phyla Ascomycetes, Basidiomycota, and Mortierellomycota, and the archaeal phylum Thaumarchaeota were the dominant microbial communities. According to RDA, As had a key impact on the microbial community compositions in these types of soil. Future studies should be conducted to investigate the interactions between the dominative microbial communities, which may be crucial in regulating adaption to HMs.

Author Contributions

Conceptualization, C.W. and Z.Z.; experiments, C.W.; software, Y.G. and S.T.; writing—original draft preparation, C.W., X.H. and B.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by the Natural Science Foundation of Liaoning Province (No. 2021-BS-256) and the Scientific Research Funding Project of the Education Department of Liaoning Province (No. JQL202015402).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are publicly available with accession number BioProject ID PRJNA788198.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 14 12164 g001 550
Figure 1. The location of sampling sites in Sanjiang Plain.
Figure 1. The location of sampling sites in Sanjiang Plain.
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Sustainability 14 12164 g002a 550Sustainability 14 12164 g002b 550
Figure 2. The bacterial (a), fungal (b), and archaeal (c) community compositions in different utilized types of wetland soils (agricultural soils (AS), natural wetland soils (NWS), and restored wetland soils (RWS)) at the phylum level.
Figure 2. The bacterial (a), fungal (b), and archaeal (c) community compositions in different utilized types of wetland soils (agricultural soils (AS), natural wetland soils (NWS), and restored wetland soils (RWS)) at the phylum level.
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Figure 3. Heatmaps based on the relative abundances of the top 50 OTUs in soil samples from agricultural soils (AS), natural wetland soils (NWS), and restored wetland soils (RWS): (a) bacterial community, (b) fungal community, and (c) archaeal community. The difference in color represents the difference in OTU abundance. Red represents high abundance, and blue represents low abundance.
Figure 3. Heatmaps based on the relative abundances of the top 50 OTUs in soil samples from agricultural soils (AS), natural wetland soils (NWS), and restored wetland soils (RWS): (a) bacterial community, (b) fungal community, and (c) archaeal community. The difference in color represents the difference in OTU abundance. Red represents high abundance, and blue represents low abundance.
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Figure 4. The co-occurrence patterns of soil bacteria (a), fungi (b), and archaea (c) at the genus level. Connecting lines of a node to other nodes stand for the strong positive (blue) or negative (yellow) interactions. Different colored circles represent different genera.
Figure 4. The co-occurrence patterns of soil bacteria (a), fungi (b), and archaea (c) at the genus level. Connecting lines of a node to other nodes stand for the strong positive (blue) or negative (yellow) interactions. Different colored circles represent different genera.
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Figure 5. RDA between microbial community compositions and HMs in different utilized types of wetland soils (agricultural soils (AS), natural wetland soils (NWS), and restored wetland soils (RWS)) at the phylum level.
Figure 5. RDA between microbial community compositions and HMs in different utilized types of wetland soils (agricultural soils (AS), natural wetland soils (NWS), and restored wetland soils (RWS)) at the phylum level.
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Table
Table 1. The divided grades of Igeo.
Table 1. The divided grades of Igeo.
GradesIgeo
UncontaminatedIgeo ≤ 0
Uncontaminated to moderately contaminated0 < Igeo ≤ 1
Moderately contaminated1 < Igeo ≤ 2
Moderately to heavily contaminated2 < Igeo ≤ 3
Heavily contaminated3 < Igeo ≤ 4
Heavily to extremely contaminated4 < Igeo ≤ 5
Extremely contaminatedIgeo > 5
Table
Table 2. The background values and the toxic-response factor value of metal i.
Table 2. The background values and the toxic-response factor value of metal i.
HMsAsCdCrCuNiMn
Si110.091702428600
Ti10302551
Table
Table 3. The divided grades of ERI and comprehensive ERI.
Table 3. The divided grades of ERI and comprehensive ERI.
GradesERIComprehensive ERI
Slight risk<40<150
Mild risk40–80150–300
Moderate risk80–160300–600
Severe risk160–320>600
Extremely severe risk>320
Table
Table 4. The descriptive statistics of HMs in different utilized types of wetland soils (agricultural soils (AS), natural wetland soils (NWS), and restored wetland soils (RWS)).
Table 4. The descriptive statistics of HMs in different utilized types of wetland soils (agricultural soils (AS), natural wetland soils (NWS), and restored wetland soils (RWS)).
Sampling Sites (HMs)MinMaxMean ± SDBackground Values in Sanjiang Plain [35] (mg/kg)Grade II Values of EQSS 1CCME 2
AS (As)34.5941.0538.1 ± 3.3 b114012
AS (Cd)0.510.720.6 ± 0.1 b0.0910.31.4
AS (Cr)20.5528.1724.8 ± 3.9 b7015064
AS (Cu)8.3412.0410.4 ± 1.9 a245063
AS (Ni)12.7818.0315.4 ± 2.6 b284050
AS (Mn)341.1694543.6 ± 182.1 a600--
NWS (As)18.5422.1920.5 ± 1.8 a114012
NWS (Cd)0.180.360.3 ± 0.1 a0.0910.31.4
NWS (Cr)12.0215.513.6 ± 1.8 a7015064
NWS (Cu)6.619.618.2 ± 1.5 a245063
NWS (Ni)8.2811.179.6 ± 1.5 a284050
NWS (Mn)486.1886.7639.8 ± 215.9 a600--
RWS (As)30.737.1533.1 ± 3.5 b114012
RWS (Cd)0.280.450.4 ± 0.1 a0.0910.31.4
RWS (Cr)17.2222.8419.5 ± 2.9 ab7015064
RWS (Cu)7.2111.188.7 ± 2.2 a245063
RWS (Ni)9.8912.0811.1 ± 1.2 a284050
RWS (Mn)547.3985.2737.6 ± 224.5 a600--
SD: standard deviation. The superscripts denote differences based on one-way ANOVA (p < 0.05). Se, Pb, and Hg were not detected (below detection limit). Unit is mg/kg for As, Cd, Cr, Cu, Ni, and Mn. 1 Environmental Quality Standard for Soils (EQSS, GB15618-1995) [46]. 2 Canadian Council of Ministers of the Environment (CCME) [47].
Table
Table 5. Pearson’s correlation coefficients of HMs in different utilized types of wetland soils (agricultural soils (AS), natural wetland soils (NWS), and restored wetland soils (RWS)).
Table 5. Pearson’s correlation coefficients of HMs in different utilized types of wetland soils (agricultural soils (AS), natural wetland soils (NWS), and restored wetland soils (RWS)).
HMsAsCdCrCuNiMn
As1
Cd0.848 **1
Cr0.714 *0.742 *1
Cu0.290.6240.745 *1
Ni0.675 *0.6280.6520.2841
Mn−0.060.048−0.1710.25−0.211
** Significant at the 0.01 level. * Significant at the 0.05 level.
Table
Table 6. The calculations of Igeo, PLI, ERI, and comprehensive ERI in agricultural soils (AS), natural wetland soils (NWS), and restored wetland soils (RWS).
Table 6. The calculations of Igeo, PLI, ERI, and comprehensive ERI in agricultural soils (AS), natural wetland soils (NWS), and restored wetland soils (RWS).
Risk IndexIgeo
(AS)		Igeo
(NWS)		Igeo
(RWS)		PLI
(AS)		PLI
(NWS)		PLI
(RWS)		ERI
(AS)		ERI
(NWS)		ERI
(RWS)
As1.80.91.593.461.9334.6618.6630.08
Cd2.221.221.636.72.644.07202.282.42123.08
Cr−1.84−2.74−2.180.350.190.280.710.390.56
Cu−1.51−1.89−1.790.430.340.362.161.711.81
Ni−1.15−1.84−1.640.540.340.42.741.711.99
Mn−1.56−1.32−1.120.871.031.190.911.071.23
Comprehensive ERI 243.38105.96158.75
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Wang, C.; Zhu, B.; Guo, Y.; Tian, S.; Zhang, Z.; Hou, X. Assessment of the Pollution of Soil Heavy Metal(loid)s and Its Relation with Soil Microorganisms in Wetland Soils. Sustainability 2022, 14, 12164. https://doi.org/10.3390/su141912164

AMA Style

Wang C, Zhu B, Guo Y, Tian S, Zhang Z, Hou X. Assessment of the Pollution of Soil Heavy Metal(loid)s and Its Relation with Soil Microorganisms in Wetland Soils. Sustainability. 2022; 14(19):12164. https://doi.org/10.3390/su141912164

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Wang, Chunyong, Bo Zhu, Yitong Guo, Shasha Tian, Zhenbin Zhang, and Xintong Hou. 2022. "Assessment of the Pollution of Soil Heavy Metal(loid)s and Its Relation with Soil Microorganisms in Wetland Soils" Sustainability 14, no. 19: 12164. https://doi.org/10.3390/su141912164

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