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Article

Assessment of Polluted Soil Remediation Using Bacterial Community Tolerance to Heavy Metals as an Indicator

by
Claudia Campillo-Cora
1,*,
Diego Soto-Gómez
2,3,
Manuel Arias-Estévez
1 and
David Fernández-Calviño
1
1
Departamento de Bioloxía Vexetal e Ciencia do Solo, Facultade de Ciencias, Universidade de Vigo, As Lagoas s/n, 32004 Ourense, Spain
2
Department of Agricultural Engineering, Technical University of Cartagena, Paseo Alfonso XIII, 48, 30203 Cartagena, Spain
3
Department of Biology, Microbial Ecology—MEMEG, Lund University, Ecology Building, SE-223 62 Lund, Sweden
*
Author to whom correspondence should be addressed.
Agronomy 2022, 12(10), 2280; https://doi.org/10.3390/agronomy12102280
Submission received: 5 September 2022 / Revised: 17 September 2022 / Accepted: 20 September 2022 / Published: 23 September 2022
(This article belongs to the Special Issue Remediation of Soil Pollution and Improvement of Soil Health)
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Abstract

:
The assessment of remediation on metal-polluted soils is usually focused on total and/or bioavailable metal content. However, these chemical variables do not provide direct information about reductions in heavy metals pressure on soil microorganisms. We propose the use of bacterial communities to evaluate the efficiency of three remediation techniques: crushed mussel shell (CMS) and pine bark (PB) as soil amendments and EDTA-washing. A soil sample was polluted with different doses of Cu, Ni, and Zn (separately). After 30 days of incubation, the remediation techniques were applied, and bacterial community tolerance to heavy metals determined. If bacterial communities develop tolerance, it is an indicator that the metal is exerting toxicity on them. Soil bacterial communities developed tolerance to Cu, Ni, and Zn in response to metal additions. After remediation, bacterial communities showed decreases in bacterial community tolerance to Cu, Ni, and Zn for all remediation techniques. For Cu and Ni, soil EDTA-washing showed the greatest reduction of bacterial community tolerance to Cu and Ni, respectively, while for Zn the soil amendment with PB was the most effective remediation technique. Thus, bacterial community tolerance to heavy metals successfully detect differences in the effectiveness of the three remediation techniques.

1. Introduction

Heavy metal pollution in soils is a global concern, increasing mainly due to anthropogenic contribution through industrialization [1]. Recently, in industrial regions and surrounding areas affected by mining, values up to 1504 mg·kg−1 for Cu, 2580 mg·kg−1 for Ni, and 4851 mg·kg−1 for Zn were reported [2,3]. Some elevated heavy metal values were also recorded in agricultural soils, some of them located close to industrial areas or due to intensive management: values up to 2522 mg·kg−1 for Cu, 1757 mg·kg−1 for Ni, and 16,400 mg·kg−1 for Zn [4,5]. Elevated metal concentrations in soils may lead to serious environmental consequences, including soil functions alteration, such as organic matter cycling or carbon sequestration [6,7]. Various remediation techniques have been widely used to stabilize heavy metals avoiding their impacts on soils. Those techniques based on the application of by-products from some industries are highly appreciated due to their ability to reduce metal toxicity and valorize waste products, such as mussel shell, pine park, or biochar from different sources [8,9,10,11,12,13,14]. For example, crushed mussel shell (CMS), a by-product of the seafood industry, has been satisfactorily used to remediate heavy metal polluted soils since it reduces their bioavailability by increasing soil pH [15,16,17]. Pine bark (PB) from the forest industry also has been applied as an amendment in metal-contaminated soils. PB amendment increases soil organic matter, enhancing the organometallic complexes formation and thus decreasing metal availability [18,19,20]. In addition, the use of these by-products as bio-sorbents is highly recommended due to their cost-effectiveness and low environmental impact. Other techniques were also used to remove metals from polluted soils, such as soil washing using chelating agents such as EDTA (ethylenediaminetetraacetic acid) [21].
After remediation, soil health is usually evaluated by the final total and/or bioavailable metal contents. However, sometimes these parameters may not be sufficient to provide information on the real state of the soil. The use of soil microorganisms as indicators may be very useful to complement the evaluation of remediated soils since the essential role they perform in biogeochemical cycles, organic matter recycling, etc., [22,23,24,25,26]. Enhanced microbial properties were found in remediated soils with bio-sorbents versus metal-contaminated sites [27,28,29,30]. Mora et al. [31] showed improved enzymatic activities in soils remediated with organic amendments than in metal-polluted soils, while Kaurin et al. [32] reported decreased enzymatic activities after EDTA soil washing. Pollution-Induced Community Tolerance (PICT) is a specific methodology to assess metal toxicity, that is based on the development of tolerance by a microbial community following exposure to a metal [33,34]. The microbial community will show increased tolerance (relative to unpolluted soil) if metal exerts toxicity and vice versa. This technique has been applied successfully to assess the metal pollution effect on soil microorganisms [35,36,37,38,39]. Previous authors showed that PICT was a highly sensitive method to detect metal effects, more than other measurements [37,40,41,42]. However, there is a lack of studies which use PICT to assess the status of microbial community after remediation of a metal-polluted soil. Previously, various authors adequately tested this technique for remediation assessment with other pollutants, such as antibiotics [43,44].
In the present study, we tested the effectiveness of three remediation techniques (crushed mussel shell or pine bark soil amendment, and EDTA-washing) on metal-polluted soil samples by using bacterial community tolerance to heavy metal metals (Cu, Ni, and Zn), using bacterial growth as PICT endpoint [38]. The aim is to test this methodology as a direct indicator of reductions of heavy metals pressure on soil microbial communities.

2. Materials and Methods

2.1. Soil, Bio-Sorbents and Chemicals

A composite sample was taken from a soil developed on amphibolite in Galicia (NW Spain). In the selected sampling area, surface soil samples (0–20 cm) were taken with an Edelman probe. A representative plot of approximately 0.25 ha was delimited and 20 random sub-samples were collected and pooled. Once in the laboratory, the soil sample was air-dried, homogenized, sieved (2-mm mesh), and stored until analysis. Soil pH was determined both in water (pHW) and in 0.1 M KCl (pHK) (soil:solution 1:2.5) using potentiometric determinations (pHmeter model 2001, Crison, Barcelona, Spain) [45]. Particle size distribution was estimated by wet sieving and the international pipette method [46]. Dissolved organic carbon (DOC) was measured in a Total Carbon Analyzer Multi N/C 2100 (Analytic Jena, Jena, Germany) after mixing soil with distilled water (soil:solution 1:5) for 1 h. Organic matter content (OM) was estimated by loss on ignition during 3 h at 550 °C [47]. Total metal contents (Cu, Ni, and Zn) were extracted with HNO3, HF, and HCl using a microwave oven (MarsXpress, CEM Corporation, Matthews, NC, USA) [48] and measured using atomic absorption spectroscopy (Thermo, Waltham, MA, USA). Briefly, pHW was 4.70 and pHK was 4.32. Particle size analysis showed that sand was the most abundant fraction (45%), followed by silt (35%) and clay (19%). The soil sample presented 0.25 g DOC·kg−1 and 19.6% OM. Regarding total metal contents, 63 mg·kg−1 were determined for Cu, 79 mg·kg−1 for Ni, and 117 mg·kg−1 for Zn.
Crushed mussel shell (CMS), pine bark (PB), and EDTA were used for the remediation treatments. CMS was supplied by Abonomar S.L. (Illa de Arousa, Galicia, Spain), and PB was supplied by Geolia (Madrid, Spain). Both bio-sorbents were crushed, sieved (2 mm-mesh), and characterized by Romar-Gasalla et al. [49] (Table S1, Supplementary Materials). Briefly, CMS showed a high pHW (9.4) and 11.4% C, while PB showed low pHW (4.0) and high carbon content (47%). Soil washing was performed with 0.1 M EDTA-Na2 (ethylenediaminetetraacetic acid, 2 Na; CAS No: 6381-92-6) + 2.5 M NH4Ac (CAS No: 631-61-8) with adjusted pH at 4.65 using acetic acid (CAS No: 64-19-7) [50,51,52].

2.2. Experimental Design

The experimental procedure consists of three parts. (1) First, bacterial communities were exposed to studied metals (individually to Cu, Ni, and Zn) and were incubated for a sufficient period to allow their adaptation to metals. (2) Later, remediation techniques were performed in polluted samples followed by a new incubation. (3) Finally, bacterial community tolerance to heavy metals was estimated using bacterial growth through the leucine incorporation technique [53,54].

2.2.1. Microbial Reactivation and Soil Spiking with Metals

Soil bacterial communities were reactivated by rewetting up to 50–60% water holding capacity [55]. To expose the bacterial communities to studied metals separately (Cu, Ni and Zn), soil rewetting (20 g) was performed with three metal solutions (500, 1000, and 2000 mg·kg−1, individually for each metal) and distilled water as control (0 mg·kg−1). Metal solutions were made from Cu(NO3)2·3H2O, Ni(NO3)2·6H2O and Zn(NO3)2·6H2O. Each metal and concentration were added in triplicate. Finally, 36 microcosms were obtained: 3 metal x 4 metal concentrations x 3 replicates. Microcosms were incubated at 22 °C in the dark for 30 days using a climatic chamber, to ensure the reactivation of bacterial communities and their adaptation to the new environment with a metal.

2.2.2. Soil Remediation

Once incubation was completed, each microcosm (20 g) was subdivided into four sub-samples (5 g), one for each remediation treatment:
(i).
Crushed mussel shell (CMS) was added to one of the sub-samples (5 g) as a remediation treatment. Sub-sample was amended with 48 g·kg−1 [56,57]. Soil moisture was restored and microcosms were incubated for 60 days at 22 °C in the dark.
(ii).
Another sub-sample (5 g) was amended with 48 g·kg−1 of pine bark (PB) [19,56]. Soil moisture was readjusted (23%) and microcosms were incubated for 60 days at 22 °C in the dark.
(iii).
Another sub-sample (5 g) was subjected to soil washing with 0.1 M EDTA through a laboratory column experiment. Sub-samples were placed in vertically oriented glass columns (100 mm long x 10 mm inner diameter) up to 3.9 cm in height. To set up the experiment, a peristaltic pump (Gilson Minipuls 3, Middelton, WI, USA) was connected to the lower side of the column (input). A two-way valve was used to connect the pump to two bottles containing distilled water or 0.1 M EDTA. The outgoing liquid was collected at the upper side of the column (output). Once the experiment has been set up, a 0.1 M EDTA solution was circulated through the column for 5 h (2.5 mL·h−1 flow rate), followed by 5 h of distilled water (2.5 mL·h−1 flow rate) [58]. After soil remediation, water-saturated soil was retrieved from the column, air-dried, and rewetted to restore the initial moisture of the experiment. Later, microcosms were incubated for 60 days at 22 °C in the dark.
(iv).
The last sub-sample (5 g) was established as control since non-remediation treatment was performed.
Later, remediated soils were re-incubated to guarantee bacterial adaptation to the new conditions.

2.2.3. Determination of Bacterial Community Tolerance to Cu, Ni and Zn

Bacterial community tolerance to heavy metals was estimated following Bååth [53], Bååth et al. [54] and Fernández-Calviño et al. [38] with the modifications proposed by Lekfeldt et al. [59]. To extract bacterial communities, soil samples (5 g) were mixed with 20 mM MES buffer (pH 6; 4-Morpholineethanesulfonic acid, CAS no: 4432-31-9) in a 1:12 soil:solution ratio using a multi-vortexer (Multi Reax, Heidolph, Schwabach, Germany). To remove most of the fungal biomass low-speed centrifugation step was performed (1000× g, 10 min) [53,54,60]. The supernatant, i.e., bacterial suspension, was distributed into ten 2 mL centrifuge micro-tubes (1.35 mL). Depending on the tested metal in the microcosm (Cu, Ni, or Zn), 0.15 mL of respective metal solution (made from metal salts) were added to bacterial suspensions, obtaining nine final metal concentrations (10−8 to 3.3 × 10−4 M) plus a blank of distilled water. In each micro-tube, the bacterial growth was estimated using the 3H-leucine incorporation technique [54]. A volume of 0.2 μL [3H]-Leu (37 MBq mL−1 and 5.74 TBq mmol−1. Amersham) with non-labelled Leu (19.8 μL) was added to each micro-tube, resulting in 300 nM Leu in the bacterial suspensions. Bacterial suspensions with added 3H-leucine were incubated at 22 °C for 8 h, and 75 μL of 100% trichloroacetic acid was added to stop the bacterial growth. Later, a washing procedure was performed to measure radioactivity [54]. Radioactivity was determined by liquid scintillation counting (Tri-Carb 2810 TR, PerkinElmer, Waltham, MA, USA).

2.3. Data Analysis

Each microcosm yielded an inhibition curve (dose-response curves). To compare one microcosm with another, in dose-response curves bacterial growth of each was expressed as relative bacterial growth. First, for each microcosm, the average bacterial growth was determined for the four lowest metal concentrations (including distilled water blank) that, generally, did not show inhibition. Then, each bacterial growth data was divided by this average, obtaining relative bacterial growth.
The bacterial community tolerance to Cu, Ni, and Zn was determined from each inhibition curve as IC50, which is defined as the metal concentration necessary to inhibit bacterial growth by 50%. IC50 was obtained as log IC50 from the following logistic model (Equation (1)) [38]
Y = c/[1 + eb(X−a)]
where Y is the measured level of Leu incorporation, c is the bacterial growth rate without metal addition, b is the slope parameter indicating inhibition rate, X is the logarithm of the added metal, and a is the log IC50. The higher the IC50 increase compared to unpolluted soil (∆IC50), the higher the metal tolerance developed by the bacterial communities, and vice versa. To determine significant differences between ∆IC50 values, standard error was used to determine the interval of each ∆IC50 value. If the interval of another ∆IC50 value does not overlap this, then there will be significant differences. If, on the other hand, the intervals overlap, then there are no significant differences.
To compare the effect of remediation techniques between metals, only data from 1000 mg·kg−1 were used, since not all bacterial communities were able to grow at 2000 or developed tolerance at 500 mg·kg−1. The effect of remediation techniques was determined to each metal (1000 mg·kg−1) as the percentage of decreased bacterial community tolerance to heavy metals following the Equation (2):
((∆IC50 remediated − ∆IC50 non-remediated)/(∆IC50 non-remediated)) × 100
where ∆IC50 remediated is the increased bacterial community tolerance to a metal after remediation in a metal-polluted soil, and ∆IC50 non-remediated is the increased bacterial community tolerance to a metal with no remediation treatment in a metal-polluted soil.

3. Results and Discussion

Inhibition curves showed a sigmoidal shape, showing the highest relative bacterial growth data at the lowest added metal concentration and with a tendency to zero as metal concentration increases in bacterial suspensions (Figure 1, Figure 2 and Figure 3 and Table S1 in Supplementary Information). From inhibition curves, bacterial community tolerance values to studied metals were determined as log IC50 (Table 1). All inhibition curves for Cu, Ni, and Zn were successfully adjusted to the logistic model (R2 ≥ 0.95, 0.96 and 0.90, respectively; Table 1), except bacterial growth data from non-remediated soil polluted with 2000 mg Ni·kg−1, probably because of too high Ni toxicity (Figure 3). Figure 4 shows the effect of studied remediation techniques on increased bacterial community tolerance (∆IC50) to Cu, Ni, and Zn in metal-polluted soil with different concentrations of each metal.

3.1. Effect of Heavy Metal Addition to Soil on the Development of Bacterial Community Tolerance

Bacterial communities developed tolerance to Cu, Ni, and Zn in response to heavy metal additions to soil. In the case of Cu, bacterial communities developed tolerance at all studied Cu levels in soil, increasing as Cu content in soil increases (Figure 4). The ∆IC50 values after the addition of 500, 1000, and 2000 mg Cu·kg−1 were 0.054, 0.066, and 0.223 mM, respectively. Bacterial communities showed a tendency to develop tolerance to Ni in response to Ni additions to soil, showing tolerance to Ni only at 1000 mg·kg−1 (∆IC50 = 0.184 mM). Bacterial community tolerance to Zn was developed for all the Zn concentrations studied, increasing as the Zn level in soil increases. The ∆IC50 values were 0.623 mM at 500 mg Zn·kg−1, 0.930 mM at 1000 mg Zn·kg−1, and 1.023 mM at 2000 mg Zn·kg−1. The increased bacterial community tolerance to heavy metals reflects the increased toxicity of metals [37,38]. Various studies have also reported this relationship between soil metal level and microbial community response in terms of increased tolerance to Cu [61,62,63], Ni [64,65], and Zn [66,67,68]. However, bacterial communities have not developed the same tolerance to all metals. These differences in developed tolerance may be due to different toxicity exerted by metals, which followed the order Zn > Ni > Cu. Similarly, Díaz-Raviña and Bååth [69] reported that, after 14 months of incubation, Ni and Zn showed the highest increase in bacterial community tolerance, followed by Cu. But this sequence may be different as a function of the studied soil. Santás-Miguel et al. [65] found a different toxicity sequence in terms of increased bacterial community tolerance: Cu > Zn > Ni. Díaz-Raviña et al. [70] showed that Cu pollution caused the largest increase in bacterial community tolerance, followed by Zn and Ni. That is, metal toxicity also depends on soil properties, such as soil pH, texture, or organic matter content [71,72]. Hence, soil properties have a direct influence on the development of bacterial community tolerance to metals [39,73,74].

3.2. Changes in Bacterial Community Tolerance to Cu, Ni and Zn after Soil Remediation

3.2.1. Mussel Shell

The soil amendment with CMS reduced the bacterial community tolerance to Cu, Ni, and Zn. The CMS addition was effective in decreasing Cu toxicity at all studied Cu levels in soil (∆IC50 decreased from 0.223, 0.066 and 0.054 mM to below 0.006 mM at 2000, 1000, and 500 mg·kg−1, respectively; Figure 4). Regarding Ni, CMS application reduced bacterial community tolerance to Ni at 1000 mg·kg−1 (∆IC50 decreased from 0.184 mM to 0.034 mM), while at 2000 mg·kg−1 bacterial communities were able to grow normally (R2 = 0.99, Table 1), but showing very high tolerance (∆IC50 = 0.214 mM). In the case of Zn, CMS application decreased bacterial community tolerance to Zn at 500 and 1000 mg·kg−1 (∆IC50 decreased from 0.474 and 0.781 mM to 0.156 and 0.450 mM, respectively) but not at 2000 mg·kg−1 CMS (∆IC50 decreased from 0.874 to 0.645 mM). The mode of action of CMS amendment to reduce the bacterial community tolerance to Cu, Ni, and Zn was to increase the soil pH, thus reducing the toxicity of metals [15,75]. Fernández-Calviño et al. [76] determined an increase of at least 1 pH units at zero days of incubation after CMS amendment (48 g·kg−1) in a soil with pH 4.5, increasing with time. After 51 days of incubation with 48 g·kg−1, Fernández-Calviño et al. [57] determined an increase of ≈2 pH unit in a pot experiment. Increased adsorption of Cu, Ni, and Zn after CMS application was showed by Fernández-Calviño et al. [77], Garrido-Rodríguez et al. [16], and Ramírez-Pérez et al. [17], thus reducing the soluble and/or available metal content. Our results showed that in Ni-polluted soil with the highest metal level studied (2000 mg·kg−1), bacteria could not grow normally, but this tendency was reversed after CMS addition. CMS has the ability to increase Ni retention [77], reducing Ni availability and allowing the development of bacteria. Hence, the CMS treatment was effective in reducing Cu, Ni, and Zn toxicity. At 1000 mg·kg−1, the greatest effect of CMS addition was on Cu toxicity which reduced the bacterial community tolerance by −91%, followed by Ni (−82%) and Zn (−42%) (Table S2, Supplementary Information). In this way, bacterial community tolerance to heavy metals seems a good indicator to evaluate the soil status, in terms of metal toxicity, after metal remediation.

3.2.2. Pine Bark

Soil amendment with PB reduced the bacterial community tolerance at all studied Cu levels in soil (∆IC50 decreased from 0.223, 0.066 and 0.054 mM to below 0.0.012 mM at 2000, 1000 and 500 mg·kg−1, respectively; Figure 4). Concerning Ni, bacterial community tolerance decreased at 1000 mg·kg−1 (∆IC50 decreased from 0.184 mM to 0.030 mM). At 2000 mg Ni·kg−1, bacterial communities could develop after PB addition (R2 = 0.96, Table 1), although they presented a high tolerance (∆IC50 = 0.230 mM). The PB amendment reduced bacterial community tolerance for all studied Zn levels in the soil. The ∆IC50 values decreased from 0.874 mM to 0.197 mM at 2000 mg·kg−1, from 0.781 mM to 0.022 at 1000 mg·kg−1, and from 0.474 mM to 0.046 mM at 500 mg·kg−1. Contrary to CMS, PB amendment was associated with insignificant pH changes, both in soil and bacterial suspensions [10,56]. Soil remediation by PB application is based on the organic matter input in soil, which enhances the formation of organometallic complexes [19], reducing metal toxicity. Cu, Ni, and Zn may be retained in presence of PB [18,78], thus reducing their toxicity. Bacterial communities showed less tolerance to Cu, Ni (at 1000 mg·kg−1), and Zn as indicators of reduced toxicity after remediation. Regarding Ni at 2000 mg·kg−1, the effect of the PB amendment was similar to the CMS amendment. The soil polluted with 2000 mg·kg−1 was too toxic for bacteria, which initially could not develop. But after PB addition, bacterial communities were able to normally grow, probably because of Ni immobilization [18] and decreased toxicity. The highest decrease in bacterial community tolerance was determined for Zn (−97%), followed by Ni and Cu: −84% and −81%, respectively (Table S2, Supplementary Information). The soil amendment with PB successfully reduced Cu, Ni, and Zn toxicity, determined by bacterial community tolerance to heavy metals.

3.2.3. Soil Washing with EDTA

In the case of Cu, soil washing with EDTA reduces Cu toxicity for bacterial communities: the ∆IC50 values decreased from 0.223 mM to −0.017 mM at 2000 mg·kg−1, from 0.066 mM to −0.026 mM at 1000 mg·kg−1, and from 0.054 mM to −0.0003 mM at 500 mg·kg−1 (Figure 4). Regarding Ni, bacterial communities from EDTA-washed soil presented a decrease in tolerance to Ni. At 1000 mg·kg−1, bacterial community tolerance decreased up to 0.170 mM with respect to non-remediated soil (from 0.184 mM to 0.014 mM). Bacterial communities from 2000 mg·kg−1 after EDTA washing were able to grow normally (R2 = 0.97; Table 1), and showed almost no tolerance (∆IC50 = 0.005 mM). Soil washing with EDTA reduced bacterial community tolerance to Zn. The ∆IC50 values decreased from 0.874 to 0.100 mM at 2000 mg·kg−1, from 0.781 to 0.101 mM at 1000 mg·kg−1, and from 0.474 to −0.003 mM at 500 mg·kg−1. If the decrease of bacterial community tolerance was slightly below zero (∆IC50 < 0), these negative signs indicate that, after remediation, bacterial communities need less metal to inhibit the 50% of growth than non-remediated soil, i.e., the bacterial community becomes a little bit more sensitive to metal. Soil washing with EDTA decreased bacterial community tolerance to Cu, Ni, and Zn, which reflects the reduced Cu, Ni, and Zn toxicity. When soil is washed with EDTA, the Cu, Ni, and Zn mobile fractions are removed from the soil [79,80,81], decreasing metal toxicity. The highest decrease in tolerance after EDTA-washing was for Cu, where bacterial communities not only recovered their initial tolerance to Cu but also developed more sensitivity to Cu (−140%; Table S2, Supplementary Information). A substantial decrease in bacterial community tolerance was also determined for Ni (−92%) and Zn (−87%) after EDTA washing. Our results showed that bacterial community tolerance to heavy metals can be used to assess metal toxicity after soil remediation by EDTA-washing.

3.3. Concluding Remarks

Three remediation techniques for soils polluted with Cu, Ni, and Zn were applied: crushed mussel shell amendment (CMS), pine bark amendment (PB), and soil washing with EDTA. Bacterial community tolerance to heavy metals was satisfactorily used to detect reductions in Cu, Ni, and Zn toxicity after the application of these remediation techniques, since reductions in bacterial community tolerance to a metal indicated a decrease in metal toxicity. Regarding the effect of remediation procedures on the reduced toxicity of each metal, for Cu and Ni soil washing with EDTA showed the highest reduction of bacterial community tolerance, while for Zn the PB amendment was the most effective method. Despite this, it should be noted that all remediation techniques reduced more than 80% of bacterial community tolerance to Cu, Ni, and Zn, except CMS for Zn (−42%). However, if a remediation procedure has been effective in reducing metal toxicity, its effect on microbial activity should also be evaluated. Some authors determined that mussel shell amendment to soil stimulates bacterial growth and enzymatic activity, and decreased fungal growth but does not significantly affect microbial biomass [10,75,76]. Kokalis-Burelle and Rodríguez-Kábana [82] and Santás-Miguel et al. [10] found that PB amendment may enhance the enzymatic activity and fungal growth, but not bacterial growth, leading to changes in microbial structure. Regarding soil washing with EDTA, Kaurin et al. [32], Wei et al. [83], and Zhang et al. [84] reported decreased enzymatic activity and shifted microbial community after remediation. Hence, when a remediation procedure is performed in a polluted soil, not only the final metal concentration (total and/or available) should be considered, but attention should also be paid to the microbial communities of the soil.
Our study shows that bacterial community tolerance to three heavy metals (Cu, Ni, and Zn) can be used to assess the efficiency of three techniques of soil remediation, providing direct evidences of reductions of those heavy metals pressure on bacterial communities. As a practical example, our results are very relevant for agricultural soils, since if microbial communities are not in optimal conditions, the functions they perform in soils may be compromised (e.g., nutrient recycling, carbon sequestration), and may alter crop yields. This is the case of vineyard soils which usually present high Cu levels and has to be remediated [16,57], so future research should also include some microbial property such as bacterial community tolerance to Cu, in order to complement soil restoration. On the other hand, in this study, although metal spiking in soil was performed individually for each metal, heavy metals do not usually occur singly in real scenarios because typical anthropogenic sources release them in combination, but sometimes also occur singly, e.g., Cu in vineyard soils [85], Pb in firing range soils [86], etc. Our results showed that the single-metal methodology is useful to determine which remediation technique is most effective for each metal, which will allow for more efficient remediation in future research addressing a metal combination.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy12102280/s1, Table S1. Bio-sorbent properties (crushed mussel shell and pine bark). Average values (n = 3) with coefficients of variation <5%. Data from Romar-Gasalla et al. [49]; Table S2: Percentage of decreased bacterial community tolerance to Cu, Ni and Zn in a polluted soil (1000 mg·kg−1) after three remediation techniques: mussel shell application (CMS), pine bark application (PB) or soil washing with EDTA (EDTA).

Author Contributions

Conceptualization, M.A.-E. and D.F.-C.; methodology, M.A.-E. and D.F.-C.; software, D.F.-C. and C.C.-C.; validation, D.F.-C., C.C.-C. and D.S.-G.; formal analysis, C.C.-C. and D.S.-G.; investigation, C.C.-C. and D.F.-C.; resources, M.A.-E. and D.F.-C.; data curation, C.C.-C. and D.S.-G.; writing—original draft preparation, C.C.-C.; writing—review and editing, D.F.-C., C.C.-C. and D.S.-G.; visualization, C.C.-C. and D.S.-G.; supervision, M.A.-E. and D.F.-C.; project administration, D.F.-C.; funding acquisition, D.F.-C. and M.A.-E. All authors have read and agreed to the published version of the manuscript.

Funding

This study has been funded by the Spanish Ministry of Economy and Competitiveness through the project CTM2015-73422-JIN (FEDER Funds) David Fernández Calviño holds a Ramón y Cajal contract (RYC-2016-20411) financed by the Spanish Ministry of Economy, Industry and Competitiveness. Diego Soto-Gómez has a post-doctoral contract “Margarita Salas” funded by European Union—NextGenerationEU, and a Seneca Foundation grant for research stays in international centres (21525/EE/21). Claudia Campillo-Cora holds a predoctoral fellowship with Xunta de Galicia (ED401A-2020/084) funded by Consellería de Educación, Universidade e Formación Profesional.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Nagajyoti, P.C.; Lee, K.D.; Sreekanth, T.V.M. Heavy Metals, Occurrence and Toxicity for Plants: A Review. Environ. Chem. Lett. 2010, 8, 199–216. [Google Scholar] [CrossRef]
  2. Liu, H.; Qu, M.; Chen, J.; Guang, X.; Zhang, J.; Liu, M.; Kang, J.; Zhao, Y.; Huang, B. Heavy Metal Accumulation in the Surrounding Areas Affected by Mining in China: Spatial Distribution Patterns, Risk Assessment, and Influencing Factors. Sci. Total Environ. 2022, 825, 154004. [Google Scholar] [CrossRef] [PubMed]
  3. Wang, S.; Zhang, Y.; Cheng, J.; Li, Y.; Li, F.; Li, Y.; Shi, Z. Pollution Assessment and Source Apportionment of Soil Heavy Metals in a Coastal Industrial City, Zhejiang, Southeastern China. Int. J. Environ. Res. Public Health 2022, 19, 3335. [Google Scholar] [CrossRef]
  4. Marrugo-Negrete, J.; Pinedo-Hernández, J.; Díez, S. Assessment of Heavy Metal Pollution, Spatial Distribution and Origin in Agricultural Soils along the Sinú River Basin, Colombia. Environ. Res. 2017, 154, 380–388. [Google Scholar] [CrossRef] [PubMed]
  5. Zhang, C.; Li, Z.; Yang, W.; Pan, L.; Gu, M.; Lee, D. Assessment of Metals Pollution on Agricultural Soil Surrounding a Lead–Zinc Mining Area in the Karst Region of Guangxi, China. Bull. Environ. Contam. Toxicol. 2013, 90, 736–741. [Google Scholar] [CrossRef]
  6. Enya, O.; Heaney, N.; Iniama, G.; Lin, C. Effects of Heavy Metals on Organic Matter Decomposition in Inundated Soils: Microcosm Experiment and Field Examination. Sci. Total Environ. 2020, 724, 138223. [Google Scholar] [CrossRef]
  7. Medyńska-Juraszek, A.; Kuchar, L. Carbon Sequestration Rates in Organic Layers of Soils under the Grey Poplar (Populus x Canescens) Stands Impacted by Heavy Metal Pollution. In Functions of Natural Organic Matter in Changing Environment; Springer: Dordrecht, The Netherlands, 2012; pp. 365–369. [Google Scholar] [CrossRef]
  8. Tauqeer, H.M.; Basharat, Z.; Adnan Ramzani, P.M.; Farhad, M.; Lewińska, K.; Turan, V.; Karczewska, A.; Khan, S.A.; Faran, G.; Iqbal, M. Aspergillus Niger-Mediated Release of Phosphates from Fish Bone Char Reduces Pb Phytoavailability in Pb-Acid Batteries Polluted Soil, and Accumulation in Fenugreek. Environ. Pollut. 2022, 313, 120064. [Google Scholar] [CrossRef] [PubMed]
  9. Rasool, B.; Mahmood-ur-Rahman; Zubair, M.; Khan, M.A.; Ramzani, P.M.A.; Dradrach, A.; Turan, V.; Iqbal, M.; Khan, S.A.; Tauqeer, H.M.; et al. Synergetic Efficacy of Amending Pb-Polluted Soil with P-Loaded Jujube (Ziziphus Mauritiana) Twigs Biochar and Foliar Chitosan Application for Reducing Pb Distribution in Moringa Leaf Extract and Improving Its Anti-Cancer Potential. Water. Air. Soil Pollut. 2022, 233, 344. [Google Scholar] [CrossRef]
  10. Santás-Miguel, V.; Cutillas-Barreiro, L.; Nóvoa-Muñoz, J.C.; Arias-Estévez, M.; Díaz-Raviña, M.; Fernández-Sanjurjo, M.J.; Álvarez-Rodríguez, E.; Núñez-Delgado, A.; Fernández-Calviño, D. By-Products as an Amendment of a Mine Soil: Effects on Microbial Biomass Determined Using Phospholipid Fatty Acids. Span. J. Soil Sci. 2018, 8, 1–11. [Google Scholar] [CrossRef]
  11. Turan, V.; Khan, S.A.; Mahmood-ur-Rahman; Iqbal, M.; Ramzani, P.M.A.; Fatima, M. Promoting the Productivity and Quality of Brinjal Aligned with Heavy Metals Immobilization in a Wastewater Irrigated Heavy Metal Polluted Soil with Biochar and Chitosan. Ecotoxicol. Environ. Saf. 2018, 161, 409–419. [Google Scholar] [CrossRef]
  12. Turan, V. Confident Performance of Chitosan and Pistachio Shell Biochar on Reducing Ni Bioavailability in Soil and Plant plus Improved the Soil Enzymatic Activities, Antioxidant Defense System and Nutritional Quality of Lettuce. Ecotoxicol. Environ. Saf. 2019, 183, 109594. [Google Scholar] [CrossRef] [PubMed]
  13. Turan, V.; Ramzani, P.M.A.; Ali, Q.; Abbas, F.; Iqbal, M.; Irum, A.; Khan, W.-u.-D. Alleviation of Nickel Toxicity and an Improvement in Zinc Bioavailability in Sunflower Seed with Chitosan and Biochar Application in PH Adjusted Nickel Contaminated Soil. Arch. Agron. Soil Sci. 2018, 64, 1053–1067. [Google Scholar] [CrossRef]
  14. Turan, V. Potential of Pistachio Shell Biochar and Dicalcium Phosphate Combination to Reduce Pb Speciation in Spinach, Improved Soil Enzymatic Activities, Plant Nutritional Quality, and Antioxidant Defense System. Chemosphere 2020, 245, 125611. [Google Scholar] [CrossRef] [PubMed]
  15. Ahmad, M.; Moon, D.H.; Lim, K.J.; Shope, C.L.; Lee, S.S.; Usman, A.R.A.; Kim, K.-R.; Park, J.-H.; Hur, S.-O.; Yang, J.E.; et al. An Assessment of the Utilization of Waste Resources for the Immobilization of Pb and Cu in the Soil from a Korean Military Shooting Range. Environ. Earth Sci. 2012, 67, 1023–1031. [Google Scholar] [CrossRef]
  16. Garrido-Rodríguez, B.; Fernández-Calviño, D.; Nóvoa Muñoz, J.C.; Arias-Estévez, M.; Díaz-Raviña, M.; Álvarez-Rodríguez, E.; Fernández-Sanjurjo, M.J.; Núñez-Delgado, A. PH-Dependent Copper Release in Acid Soils Treated with Crushed Mussel Shell. Int. J. Environ. Sci. Technol. 2013, 10, 983–994. [Google Scholar] [CrossRef]
  17. Ramírez-Pérez, A.M.; Paradelo, M.; Nóvoa-Muñoz, J.C.; Arias-Estévez, M.; Fernández-Sanjurjo, M.J.; Álvarez-Rodríguez, E.; Núñez-Delgado, A. Heavy Metal Retention in Copper Mine Soil Treated with Mussel Shells: Batch and Column Experiments. J. Hazard. Mater. 2013, 248–249, 122–130. [Google Scholar] [CrossRef] [PubMed]
  18. Cutillas-Barreiro, L.; Ansias-Manso, L.; Fernández-Calviño, D.; Arias-Estévez, M.; Nóvoa-Muñoz, J.C.; Fernández-Sanjurjo, M.J.; Álvarez-Rodríguez, E.; Núñez-Delgado, A. Pine Bark as Bio-Adsorbent for Cd, Cu, Ni, Pb and Zn: Batch-Type and Stirred Flow Chamber Experiments. J. Environ. Manag. 2014, 144, 258–264. [Google Scholar] [CrossRef] [PubMed]
  19. Fernández-Calviño, D.; Cutillas-Barreiro, L.; Paradelo-Núñez, R.; Nóvoa-Muñoz, J.C.; Fernández-Sanjurjo, M.J.; Álvarez-Rodríguez, E.; Núñez-Delgado, A.; Arias-Estévez, M. Heavy Metals Fractionation and Desorption in Pine Bark Amended Mine Soils. J. Environ. Manag. 2017, 192, 79–88. [Google Scholar] [CrossRef] [PubMed]
  20. Paradelo, R.; Cutillas-Barreiro, L.; Soto-Gómez, D.; Nóvoa-Muñoz, J.C.; Arias-Estévez, M.; Fernández-Sanjurjo, M.J.; Álvarez-Rodríguez, E.; Núñez-Delgado, A. Study of Metal Transport through Pine Bark for Reutilization as a Biosorbent. Chemosphere 2016, 149, 146–153. [Google Scholar] [CrossRef]
  21. Leštan, D.; Luo, C.-L.; Li, X.-D. The Use of Chelating Agents in the Remediation of Metal-Contaminated Soils: A Review. Environ. Pollut. 2008, 153, 3–13. [Google Scholar] [CrossRef] [PubMed]
  22. Kaurin, A.; Lestan, D. Multi-Substrate Induced Microbial Respiration, Nitrification Potential and Enzyme Activities in Metal-Polluted, EDTA-Washed Soils. Environ. Pollut. 2018, 243, 238–245. [Google Scholar] [CrossRef]
  23. Nannipieri, P.; Ascher, J.; Ceccherini, M.T.; Landi, L.; Pietramellara, G.; Renella, G. Microbial Diversity and Soil Functions. Eur. J. Soil Sci. 2003, 54, 655–670. [Google Scholar] [CrossRef]
  24. Pascual, J.A.; Garcia, C.; Hernandez, T.; Moreno, J.L.; Ros, M. Soil Microbial Activity as a Biomarker of Degradation and Remediation Processes. Soil Biol. Biochem. 2000, 32, 1877–1883. [Google Scholar] [CrossRef]
  25. Tang, J.; Zhang, J.; Ren, L.; Zhou, Y.; Gao, J.; Luo, L.; Yang, Y.; Peng, Q.; Huang, H.; Chen, A. Diagnosis of Soil Contamination Using Microbiological Indices: A Review on Heavy Metal Pollution. J. Environ. Manag. 2019, 242, 121–130. [Google Scholar] [CrossRef] [PubMed]
  26. Udovic, M.; Lestan, D. EDTA and HCl Leaching of Calcareous and Acidic Soils Polluted with Potentially Toxic Metals: Remediation Efficiency and Soil Impact. Chemosphere 2012, 88, 718–724. [Google Scholar] [CrossRef] [PubMed]
  27. Clemente, R.; de la Fuente, C.; Moral, R.; Bernal, M.P. Changes in Microbial Biomass Parameters of a Heavy Metal-Contaminated Calcareous Soil during a Field Remediation Experiment. J. Environ. Qual. 2007, 36, 1137–1144. [Google Scholar] [CrossRef] [PubMed]
  28. Kelly, J.J.; Häggblom, M.M.; Tate, R.L. Effects of Heavy Metal Contamination and Remediation on Soil Microbial Communities in the Vicinity of a Zinc Smelter as Indicated by Analysis of Microbial Community Phospholipid Fatty Acid Profiles. Biol. Fertil. Soils 2003, 38, 65–71. [Google Scholar] [CrossRef]
  29. Kelly, J.J.; Tate, R.L. Effects of Heavy Metal Contamination and Remediation on Soil Microbial Communities in the Vicinity of a Zinc Smelter. J. Environ. Qual. 1998, 27, 609–617. [Google Scholar] [CrossRef]
  30. Liu, S.J.; Liu, Y.G.; Tan, X.F.; Zeng, G.M.; Zhou, Y.H.; Liu, S.B.; Yin, Z.H.; Jiang, L.H.; Li, M.F.; Wen, J. The Effect of Several Activated Biochars on Cd Immobilization and Microbial Community Composition during In-Situ Remediation of Heavy Metal Contaminated Sediment. Chemosphere 2018, 208, 655–664. [Google Scholar] [CrossRef] [PubMed]
  31. De Mora, A.P.; Ortega-Calvo, J.J.; Cabrera, F.; Madejón, E. Changes in Enzyme Activities and Microbial Biomass after “in Situ” Remediation of a Heavy Metal-Contaminated Soil. Appl. Soil Ecol. 2005, 28, 125–137. [Google Scholar] [CrossRef]
  32. Kaurin, A.; Cernilogar, Z.; Lestan, D. Revitalisation of Metal-Contaminated, EDTA-Washed Soil by Addition of Unpolluted Soil, Compost and Biochar: Effects on Soil Enzyme Activity, Microbial Community Composition and Abundance. Chemosphere 2018, 193, 726–736. [Google Scholar] [CrossRef] [PubMed]
  33. Blanck, H. A Critical Review of Procedures and Approaches Used for Assessing Pollution-Induced Community Tolerance (PICT) in Biotic Communities. Hum. Ecol. Risk Assess. 2002, 8, 1003–1034. [Google Scholar] [CrossRef]
  34. Tlili, A.; Berard, A.; Blanck, H.; Bouchez, A.; Cássio, F.; Eriksson, K.M.; Morin, S.; Montuelle, B.; Navarro, E.; Pascoal, C.; et al. Pollution-Induced Community Tolerance (PICT): Towards an Ecologically Relevant Risk Assessment of Chemicals in Aquatic Systems. Freshw. Biol. 2016, 61, 2141–2151. [Google Scholar] [CrossRef]
  35. Aliasgharzad, N.; Molaei, A.; Oustan, S. Pollution Induced Community Tolerance (PICT) of Microorganisms in Soil Incubated with Different Levels of Pb. Int. J. Environ. Ecol. Eng. 2011, 5, 838–842. [Google Scholar] [CrossRef]
  36. Bérard, A.; Capowiez, L.; Mombo, S.; Schreck, E.; Dumat, C.; Deola, F.; Capowiez, Y. Soil Microbial Respiration and PICT Responses to an Industrial and Historic Lead Pollution: A Field Study. Environ. Sci. Pollut. Res. 2016, 23, 4271–4281. [Google Scholar] [CrossRef] [PubMed]
  37. Díaz-Raviña, M.; Calvo De Anta, R.; Bååth, E. Tolerance (PICT) of the Bacterial Communities to Copper in Vineyards Soils from Spain. J. Environ. Qual. 2007, 36, 1760–1764. [Google Scholar] [CrossRef]
  38. Fernández-Calviño, D.; Arias-Estévez, M.; Díaz-Raviña, M.; Bååth, E. Bacterial Pollution Induced Community Tolerance (PICT) to Cu and Interactions with PH in Long-Term Polluted Vineyard Soils. Soil Biol. Biochem. 2011, 43, 2324–2331. [Google Scholar] [CrossRef]
  39. Kamitani, T.; Oba, H.; Kaneko, N. Microbial Biomass and Tolerance of Microbial Community on an Aged Heavy Metal Polluted Floodplain in Japan. Water. Air. Soil Pollut. 2006, 172, 185–200. [Google Scholar] [CrossRef]
  40. Brandt, K.K.; Frandsen, R.J.N.; Holm, P.E.; Nybroe, O. Development of Pollution-Induced Community Tolerance Is Linked to Structural and Functional Resilience of a Soil Bacterial Community Following a Five-Year Field Exposure to Copper. Soil Biol. Biochem. 2010, 42, 748–757. [Google Scholar] [CrossRef]
  41. Niklińska, M.; Chodak, M.; Laskowski, R. Pollution-Induced Community Tolerance of Microorganisms from Forest Soil Organic Layers Polluted with Zn or Cu. Appl. Soil Ecol. 2006, 32, 265–272. [Google Scholar] [CrossRef]
  42. Viti, C.; Quaranta, D.; De Philippis, R.; Corti, G.; Agnelli, A.; Cuniglio, R.; Giovannetti, L. Characterizing Cultivable Soil Microbial Communities from Copper Fungicide-Amended Olive Orchard and Vineyard Soils. World J. Microbiol. Biotechnol. 2008, 24, 309–318. [Google Scholar] [CrossRef]
  43. Demoling, L.A.; Bååth, E. No Long-Term Persistence of Bacterial Pollution-Induced Community Tolerance in Tylosin-Polluted Soil. Environ. Sci. Technol. 2008, 42, 6917–6921. [Google Scholar] [CrossRef] [PubMed]
  44. Santás-Miguel, V.; Fernández-Sanjurjo, M.J.; Núñez-Delgado, A.; Álvarez-Rodríguez, E.; Díaz-Raviña, M.; Arias-Estévez, M.; Fernández-Calviño, D. Use of Waste Materials to Prevent Tetracycline Antibiotics Toxicity on the Growth of Soil Bacterial Communities. Environ. Res. 2021, 193, 110404. [Google Scholar] [CrossRef]
  45. Guitián, F.; Carballas, T. Técnicas de Análisis de Suelos; Pico Sacro: Santiago de Compostela, Spain, 1976; ISBN 8485170091/97884485170098. [Google Scholar]
  46. Day, P.R. Particle Fractionation and Particle-Size Analysis. In Methods of Soil Analysis: Part 1 Physical and Mineralogical Properties, Including Statistics of Measurement and Sampling; American Society of Agronomy: Madison, WI, USA, 1965; pp. 545–567. [Google Scholar] [CrossRef]
  47. Hoogsteen, M.J.J.; Lantinga, E.A.; Bakker, E.J.; Groot, J.C.J.; Tittonell, P.A. Estimating Soil Organic Carbon through Loss on Ignition: Effects of Ignition Conditions and Structural Water Loss. Eur. J. Soil Sci. 2015, 66, 320–328. [Google Scholar] [CrossRef]
  48. Reed, S.T.; Martens, D.C. Copper and Zinc. In Methods of Soil Analysis Part 3; Bigham, J., Ed.; Chemical Methods; Soil Science Society of America: Madison, WI, USA, 1996; pp. 703–722. [Google Scholar]
  49. Romar-Gasalla, A.; Santás-Miguel, V.; Nóvoa-Muñoz, J.C.; Arias-Estévez, M.; Álvarez-Rodríguez, E.; Núñez-Delgado, A.; Fernández-Sanjurjo, M.J. Chromium and Fluoride Sorption/Desorption on Un-Amended and Waste-Amended Forest and Vineyard Soils and Pyritic Material. J. Environ. Manag. 2018, 222, 3–11. [Google Scholar] [CrossRef] [PubMed]
  50. Lakanen, E.; Erviö, R. A Comparison of Eight Extractans for the Determination of Plant Available Micronutrients in Soils. Acta Agric. Scand. 1971, 17, 131–139. [Google Scholar] [CrossRef]
  51. Naghipour, D.; Jaafari, J.; Ashrafi, S.D.; Mahvi, A.H. Remediation of Heavy Metals Contaminated Silty Clay Loam Soil by Column Extraction with Ethylenediaminetetraacetic Acid and Nitrilo Triacetic Acid. J. Environ. Eng. 2017, 143, 04017026. [Google Scholar] [CrossRef]
  52. Wasay, S.A.; Barrington, S.; Tokunaga, S. Organic Acids for the In Situ Remediation of Soils Polluted by Heavy Metals: Soil Flushing in Columns. Water Air Soil Pollut. 2001, 127, 301–314. [Google Scholar] [CrossRef]
  53. Bååth, E. Thymidine and Leucine Incorporation in Soil Bacteria with Different Cell Size. Microb. Ecol. 1994, 27, 267–278. [Google Scholar] [CrossRef]
  54. Bååth, E.; Pettersson, M.; Söderberg, K.H. Adaptation of a Rapid and Economical Microcentrifugation Method to Measure Thymidine and Leucine Incorporation by Soil Bacteria. Soil Biol. Biochem. 2001, 33, 1571–1574. [Google Scholar] [CrossRef]
  55. Meisner, A.; Bååth, E.; Rousk, J. Microbial Growth Responses upon Rewetting Soil Dried for Four Days or One Year. Soil Biol. Biochem. 2013, 66, 188–192. [Google Scholar] [CrossRef]
  56. Fernández-Calviño, D.; Cutillas-Barreiro, L.; Nóvoa-Muñoz, J.C.; Díaz-Raviña, M.; Fernández-Sanjurjo, M.J.; Álvarez-Rodríguez, E.; Núñez-Delgado, A.; Arias-Estévez, M.; Rousk, J. Using Pine Bark and Mussel Shell Amendments to Reclaim Microbial Functions in a Cu Polluted Acid Mine Soil. Appl. Soil Ecol. 2018, 127, 102–111. [Google Scholar] [CrossRef]
  57. Fernández-Calviño, D.; Cutillas-Barreiro, L.; Núñez-Delgado, A.; Fernández-Sanjurjo, M.J.; Álvarez-Rodriguez, E.; Nóvoa-Muñoz, J.C.; Arias-Estévez, M. Cu Immobilization and Lolium Perenne Development in an Acid Vineyard Soil Amended with Crushed Mussel Shell. Land Degrad. Dev. 2017, 28, 762–772. [Google Scholar] [CrossRef]
  58. Conde-Cid, M.; Fernández-Calviño, D.; Fernández-Sanjurjo, M.J.; Núñez-Delgado, A.; Álvarez-Rodríguez, E.; Arias-Estévez, M. Adsorption/Desorption and Transport of Sulfadiazine, Sulfachloropyridazine, and Sulfamethazine, in Acid Agricultural Soils. Chemosphere 2019, 234, 978–986. [Google Scholar] [CrossRef] [PubMed]
  59. Lekfeldt, J.D.S.; Magid, J.; Holm, P.E.; Nybroe, O.; Brandt, K.K. Evaluation of the Leucine Incorporation Technique for Detection of Pollution-Induced Community Tolerance to Copper in a Long-Term Agricultural Field Trial with Urban Waste Fertilizers. Environ. Pollut. 2014, 194, 78–85. [Google Scholar] [CrossRef]
  60. Rousk, J.; Bååth, E. Growth of Saprotrophic Fungi and Bacteria in Soil. FEMS Microbiol. Ecol. 2011, 78, 17–30. [Google Scholar] [CrossRef]
  61. Berg, J.; Brandt, K.K.; Al-Soud, W.A.; Holm, P.E.; Hansen, L.H.; Sørensen, S.J.; Nybroe, O. Selection for Cu-Tolerant Bacterial Communities with Altered Composition, but Unaltered Richness, via Long-Term Cu Exposure. Appl. Environ. Microbiol. 2012, 78, 7438–7446. [Google Scholar] [CrossRef]
  62. Fernández-Calviño, D.; Bååth, E. Interaction between PH and Cu Toxicity on Fungal and Bacterial Performance in Soil. Soil Biol. Biochem. 2016, 96, 20–29. [Google Scholar] [CrossRef]
  63. Li, Y.; Liu, B.; Zhang, X.; Gao, M.; Wang, J. Effects of Cu Exposure on Enzyme Activities and Selection for Microbial Tolerances during Swine-Manure Composting. J. Hazard. Mater. 2015, 283, 512–518. [Google Scholar] [CrossRef]
  64. Fait, G.; Broos, K.; Zrna, S.; Lombi, E.; Hamon, R. Tolerance of Nitrifying Bacteria to Copper and Nickel. Environ. Toxicol. Chem. 2006, 25, 2000–2005. [Google Scholar] [CrossRef] [PubMed]
  65. Santás-Miguel, V.; Núñez-Delgado, A.; Álvarez-Rodríguez, E.; Díaz-Raviña, M.; Arias-Estévez, M.; Fernández-Calviño, D. Tolerance of Soil Bacterial Community to Tetracycline Antibiotics Induced by As, Cd, Zn, Cu, Ni, Cr and Pb Pollution. Soil 2022, 8, 437–449. [Google Scholar] [CrossRef]
  66. Lock, K.; Janssen, C.R. Influence of Soil Zinc Concentrations on Zinc Sensitivity and Functional Diversity of Microbial Communities. Environ. Pollut. 2005, 136, 275–281. [Google Scholar] [CrossRef] [PubMed]
  67. Rutgers, M.; Van’t Verlaat, I.M.; Wind, B.; Posthuma, L.; Breure, A.M. Rapid Method for Assessing Pollution-Induced Community Tolerance in Contaminated Soil. Environ. Toxicol. Chem. 1998, 17, 2210–2213. [Google Scholar] [CrossRef]
  68. Van Beelen, P.; Fleuren-Kemila, A.K.; Aldenberg, T. The Relation between Extrapolated Risk, Expressed as Potentially Affected Fraction, and Community Effects, Expressed as Pollution-Induced Community Tolerance. Environ. Toxicol. Chem. 2001, 20, 1133–1140. [Google Scholar] [CrossRef]
  69. Díaz-Raviña, M.; Bååth, E. Development of Metal Tolerance in Soil Bacterial Communities Exposed to Experimentally Increased Metal Levels. Appl. Environ. Microbiol. 1996, 62, 2970–2977. [Google Scholar] [CrossRef] [PubMed]
  70. Díaz-Raviña, M.; Bååth, E.; Frostegard, A. Multiple Heavy Metal Tolerance of Soil Bacterial Communities and Its Measurement by a Thymidine Incorporation Technique. Appl. Environ. Microbiol. 1994, 60, 2238–2247. [Google Scholar] [CrossRef]
  71. Bradl, H.B. Adsorption of Heavy Metal Ions on Soils and Soils Constituents. J. Colloid Interface Sci. 2004, 277, 1–18. [Google Scholar] [CrossRef]
  72. Uchimiya, M.; Bannon, D.; Nakanishi, H.; McBride, M.B.; Williams, M.A.; Yoshihara, T. Chemical Speciation, Plant Uptake, and Toxicity of Heavy Metals in Agricultural Soils. J. Agric. Food Chem. 2020, 68, 12856–12869. [Google Scholar] [CrossRef] [PubMed]
  73. Macdonald, C.A.; Yang, X.; Clark, I.M.; Zhao, F.-J.; Hirsch, P.R.; McGrath, S.P. Relative Impact of Soil, Metal Source and Metal Concentration on Bacterial Community Structure and Community Tolerance. Soil Biol. Biochem. 2010, 42, 1408–1417. [Google Scholar] [CrossRef]
  74. Stefanowicz, A.M.; Niklińska, M.; Laskowski, R. Pollution-Induced Tolerance of Soil Bacterial Communities in Meadow and Forest Ecosystems Polluted with Heavy Metals. Eur. J. Soil Biol. 2009, 45, 363–369. [Google Scholar] [CrossRef]
  75. Abd El-Azeem, S.A.M.; Ahmad, M.; Usman, A.R.A.; Kim, K.-R.; Oh, S.-E.; Lee, S.S.; Ok, Y.S. Changes of Biochemical Properties and Heavy Metal Bioavailability in Soil Treated with Natural Liming Materials. Environ. Earth Sci. 2013, 70, 3411–3420. [Google Scholar] [CrossRef]
  76. Fernández-Calviño, D.; Garrido-Rodríguez, B.; Arias-Estévez, M.; Díaz-Raviña, M.; Álvarez-Rodríguez, E.; Fernández-Sanjurjo, M.J.; Nuñez-Delgado, A. Effect of Crushed Mussel Shell Addition on Bacterial Growth in Acid Polluted Soils. Appl. Soil Ecol. 2015, 85, 65–68. [Google Scholar] [CrossRef]
  77. Fernández-Calviño, D.; Pérez-Armada, L.; Cutillas-Barreiro, L.; Paradelo-Núñez, R.; Núñez-Delgado, A.; Fernández-Sanjurjo, M.J.; Álvarez-Rodriguez, E.; Arias-Estévez, M. Changes in Cd, Cu, Ni, Pb and Zn Fractionation and Liberation Due to Mussel Shell Amendment on a Mine Soil. Land Degrad. Dev. 2016, 27, 1276–1285. [Google Scholar] [CrossRef]
  78. Cutillas-Barreiro, L.; Paradelo, R.; Igrexas-Soto, A.; Núñez-Delgado, A.; Fernández-Sanjurjo, M.J.; Álvarez-Rodriguez, E.; Garrote, G.; Nóvoa-Muñoz, J.C.; Arias-Estévez, M. Valorization of Biosorbent Obtained from a Forestry Waste: Competitive Adsorption, Desorption and Transport of Cd, Cu, Ni, Pb and Zn. Ecotoxicol. Environ. Saf. 2016, 131, 118–126. [Google Scholar] [CrossRef]
  79. Barona, A.; Aranguiz, I.; Elías, A. Metal Associations in Soils before and after EDTA Extractive Decontamination: Implications for the Effectiveness of Further Clean-up Procedures. Environ. Pollut. 2001, 113, 79–85. [Google Scholar] [CrossRef]
  80. Sun, B.; Zhao, F.J.; Lombi, E.; McGrath, S.P. Leaching of Heavy Metals from Contaminated Soils Using EDTA. Environ. Pollut. 2001, 113, 111–120. [Google Scholar] [CrossRef]
  81. Zhang, W.; Huang, H.; Tan, F.; Wang, H.; Qiu, R. Influence of EDTA Washing on the Species and Mobility of Heavy Metals Residual in Soils. J. Hazard. Mater. 2010, 173, 369–376. [Google Scholar] [CrossRef] [PubMed]
  82. Kokalis-Burelle, N.; Rodríguez-Kábana, R. Changes in Populations of Soil Microorganisms, Nematodes, and Enzyme Activity Associated with Application of Powdered Pine Bark. Plant Soil 1994, 162, 169–175. [Google Scholar] [CrossRef]
  83. Wei, H.; Liu, Y.; Chao, Y.; Tsang, D.C.W.; Zhao, N.; Liu, K.; Zhang, W.; Qiu, R. Recovery of the Biological Function of Ethylenediaminetetraacetic Acid-Washed Soils: Roles of Environmental Variations and Microbes. Sci. Total Environ. 2020, 715, 137032. [Google Scholar] [CrossRef]
  84. Zhang, Y.; Wu, C.; Deng, S.; Zhang, J.; Hou, J.; Wang, C.; Fu, Z. Effect of Different Washing Solutions on Soil Enzyme Activity and Microbial Community in Agricultural Soil Severely Contaminated with Cadmium. Environ. Sci. Pollut. Res. 2022, 29, 54641–54651. [Google Scholar] [CrossRef] [PubMed]
  85. Mirlean, N.; Roisenberg, A.; Chies, J.O. Metal Contamination of Vineyard Soils in Wet Subtropics (Southern Brazil). Environ. Pollut. 2007, 149, 10–17. [Google Scholar] [CrossRef] [PubMed]
  86. Rodríguez-Seijo, A.; Alfaya, M.C.; Andrade, M.L.; Vega, F.A. Copper, Chromium, Nickel, Lead and Zinc Levels and Pollution Degree in Firing Range Soils. Land Degrad. Dev. 2016, 27, 1721–1730. [Google Scholar] [CrossRef]
Agronomy 12 02280 g001 550
Figure 1. Dose–response curves of relative bacterial growth versus added Cu to bacterial suspensions from a polluted soil with four Cu levels: 0 (A), 500 (B), 1000 (C), and 2000 (D) mg Cu·kg−1. Each Cu level was remediated with four treatments: crushed mussel shell amendment (CMS, blue dots and line), pine bark amendment (PB, green dots and line), EDTA washing (black dots and line), and control (red dots and line).
Figure 1. Dose–response curves of relative bacterial growth versus added Cu to bacterial suspensions from a polluted soil with four Cu levels: 0 (A), 500 (B), 1000 (C), and 2000 (D) mg Cu·kg−1. Each Cu level was remediated with four treatments: crushed mussel shell amendment (CMS, blue dots and line), pine bark amendment (PB, green dots and line), EDTA washing (black dots and line), and control (red dots and line).
Agronomy 12 02280 g001
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Figure 2. Dose–response curves of relative bacterial growth versus added Ni to bacterial suspensions from a polluted soil with four Ni levels: 0 (A), 500 (B), 1000 (C), and 2000 (D) mg Ni·kg−1. Each Ni level was remediated with four treatments: crushed mussel shell amendment (CMS, blue dots and line), pine bark amendment (PB, green dots and line), EDTA washing (black dots and line), and control (red dots and line).
Figure 2. Dose–response curves of relative bacterial growth versus added Ni to bacterial suspensions from a polluted soil with four Ni levels: 0 (A), 500 (B), 1000 (C), and 2000 (D) mg Ni·kg−1. Each Ni level was remediated with four treatments: crushed mussel shell amendment (CMS, blue dots and line), pine bark amendment (PB, green dots and line), EDTA washing (black dots and line), and control (red dots and line).
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Figure 3. Dose–response curves of relative bacterial growth versus added Zn to bacterial suspensions from a polluted soil with four Zn levels: 0 (A), 500 (B), 1000 (C), and 2000 (D) mg Zn·kg−1. Each Zn level was remediated with four treatments: crushed mussel shell amendment (CMS, blue dots and line), pine bark amendment (PB, green dots and line), EDTA washing (black dots and line), and control (red dots and line).
Figure 3. Dose–response curves of relative bacterial growth versus added Zn to bacterial suspensions from a polluted soil with four Zn levels: 0 (A), 500 (B), 1000 (C), and 2000 (D) mg Zn·kg−1. Each Zn level was remediated with four treatments: crushed mussel shell amendment (CMS, blue dots and line), pine bark amendment (PB, green dots and line), EDTA washing (black dots and line), and control (red dots and line).
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Figure 4. Effect of four remediation techniques (crushed mussel shell –CMS-, pine bark –PB-, EDTA, and control –no treatment-) on increased bacterial community tolerance (∆IC50) to Cu, Ni, and Zn in polluted soil samples with four levels of each heavy metal: 0, 500, 1000, and 2000 mg·kg−1. Blue bars represent CMS amendment, green bars represent PB amendment, grey bars represent EDTA treatment, and red bars represent the control. Error bars represent standard error (n = 3).
Figure 4. Effect of four remediation techniques (crushed mussel shell –CMS-, pine bark –PB-, EDTA, and control –no treatment-) on increased bacterial community tolerance (∆IC50) to Cu, Ni, and Zn in polluted soil samples with four levels of each heavy metal: 0, 500, 1000, and 2000 mg·kg−1. Blue bars represent CMS amendment, green bars represent PB amendment, grey bars represent EDTA treatment, and red bars represent the control. Error bars represent standard error (n = 3).
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Table
Table 1. Bacterial community tolerance to Cu, Ni, and Zn in a polluted soil with four metal levels (0, 500, 1000, and 2000 mg·kg−1). Each metal level was remediated with four remediation treatments: crushed mussel shell amendment (CMS), pine bark amendment (PB), EDTA, and control (no treatment).
Table 1. Bacterial community tolerance to Cu, Ni, and Zn in a polluted soil with four metal levels (0, 500, 1000, and 2000 mg·kg−1). Each metal level was remediated with four remediation treatments: crushed mussel shell amendment (CMS), pine bark amendment (PB), EDTA, and control (no treatment).
Remediation
Treatment		Metal LevelCopperNickelZinc
mg·kg−1Log IC50 ± SDR2Log IC50 ± SDR2Log IC50 ± SDR2
Control2000−3.64 ± 0.040.99--−2.99 ± 0.150.97
1000−4.13 ± 0.170.95−3.66 ± 0.070.99−3.03 ± 0.070.99
500−4.21 ± 0.100.97−4.35 ± 0.160.96−3.21 ± 0.100.97
0−5.11 ± 0.080.99−4.47 ± 0.080.99−3.83 ± 0.130.96
CMS2000−5.04 ± 0.070.99−3.56 ± 0.100.99−3.11 ± 0.180.96
1000−5.03 ± 0.020.99−4.02 ± 0.070.99−3.24 ± 0.090.97
500−5.20 ± 0.030.99−4.20 ± 0.100.98−3.55 ± 0.090.97
0−5.44 ± 0.060.99−4.21 ± 0.070.99−3.91 ± 0.100.98
PB2000−4.65 ± 0.030.99−3.57 ± 0.140.96−3.34 ± 0.070.96
1000−4.60 ± 0.040.99−4.14 ± 0.120.97−3.55 ± 0.090.97
500−4.89 ± 0.040.99−4.50 ± 0.040.99−3.52 ± 0.110.97
0−4.88 ± 0.020.99−4.38 ± 0.070.99−3.59 ± 0.100.98
EDTA2000−4.47 ± 0.110.97−4.31 ± 0.160.97−3.45 ± 0.120.93
1000−4.62 ± 0.050.99−4.23 ± 0.040.99−3.45 ± 0.070.90
500−4.30 ± 0.110.96−5.11 ± 0.060.99−3.60 ± 0.140.96
0−4.30 ± 0.090.98−4.35 ± 0.040.99−3.59 ± 0.070.98
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Campillo-Cora, C.; Soto-Gómez, D.; Arias-Estévez, M.; Fernández-Calviño, D. Assessment of Polluted Soil Remediation Using Bacterial Community Tolerance to Heavy Metals as an Indicator. Agronomy 2022, 12, 2280. https://doi.org/10.3390/agronomy12102280

AMA Style

Campillo-Cora C, Soto-Gómez D, Arias-Estévez M, Fernández-Calviño D. Assessment of Polluted Soil Remediation Using Bacterial Community Tolerance to Heavy Metals as an Indicator. Agronomy. 2022; 12(10):2280. https://doi.org/10.3390/agronomy12102280

Chicago/Turabian Style

Campillo-Cora, Claudia, Diego Soto-Gómez, Manuel Arias-Estévez, and David Fernández-Calviño. 2022. "Assessment of Polluted Soil Remediation Using Bacterial Community Tolerance to Heavy Metals as an Indicator" Agronomy 12, no. 10: 2280. https://doi.org/10.3390/agronomy12102280

APA Style

Campillo-Cora, C., Soto-Gómez, D., Arias-Estévez, M., & Fernández-Calviño, D. (2022). Assessment of Polluted Soil Remediation Using Bacterial Community Tolerance to Heavy Metals as an Indicator. Agronomy, 12(10), 2280. https://doi.org/10.3390/agronomy12102280

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