Long-term adaptive evolution of Shewanella oneidensis MR-1 for establishment of high concentration Cr(VI) tolerance

Yong Xiao, Changye Xiao, Feng Zhao

Front. Environ. Sci. Eng. ›› 2020, Vol. 14 ›› Issue (1) : 3.

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Front. Environ. Sci. Eng. ›› 2020, Vol. 14 ›› Issue (1) : 3. DOI: 10.1007/s11783-019-1182-8
RESEARCH ARTICLE
RESEARCH ARTICLE

Long-term adaptive evolution of Shewanella oneidensis MR-1 for establishment of high concentration Cr(VI) tolerance

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Highlights

Shewanella oneidensis MR-1 was acclimated to grow with Cr(VI) of 190 mg/L.

• Whole genomes from 7 populations at different acclimation stages were sequenced.

• Gene mutations mainly related to efflux pumps and transporters.

• An adaptation mechanism of MR-1 to high concentration of Cr(VI) was proposed.

Abstract

Acclimation is the main method to enhance the productivity of microorganisms in environmental biotechnology, but it remains uncertain how microorganisms acquire resistance to high concentrations of pollutants during long-term acclimation. Shewanella oneidensis MR-1 was acclimated for 120 days with increasing hexavalent chromium (Cr(VI)) concentrations from 10 to 190 mg/L. The bacterium was able to survive from the highly toxic Cr(VI) environment due to its enhanced capability to reduce Cr(VI) and the increased cell membrane surface. We sequenced 19 complete genomes from 7 populations of MR-1, including the ancestral strain, the evolved strains in Cr(VI) environment on days 40, 80 and 120 and their corresponding controls. A total of 27, 49 and 90 single nucleotide polymorphisms were found in the Cr(VI)-evolved populations on days 40, 80 and 120, respectively. Nonsynonymous substitutions were clustered according to gene functions, and the gene mutations related to integral components of the membrane, including efflux pumps and transporters, were the key determinants of chromate resistance. In addition, MR-1 strengthened the detoxification of Cr(VI) through gene variations involved in adenosine triphosphate binding, electron carrier activity, signal transduction and DNA repair. Our results provide an in-depth analysis of how Cr(VI) resistance of S. oneidensis MR-1 is improved by acclimation, as well as a genetic understanding of the impact of long-term exposure of microorganisms to pollution.

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Keywords

Environmental biotechnology / Acclimation / Chromate resistance / Efflux pumps / Detoxification

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Yong Xiao, Changye Xiao, Feng Zhao. Long-term adaptive evolution of Shewanella oneidensis MR-1 for establishment of high concentration Cr(VI) tolerance. Front. Environ. Sci. Eng., 2020, 14(1): 3 https://doi.org/10.1007/s11783-019-1182-8

1 1 Introduction

Municipal solid waste incineration (MSWI) has gained increasing popularity in China due to its high disposal capacity, environmental benefits, and minimal land area requirements. MSWI can reduce the volume of waste by approximately 90% and its weight by 70% (Wang et al., 2015; Li et al., 2023). As shown in Fig.1, only 45 waste incineration plants were operational in 2001, with a total combustion mass of 2.75 × 106 t. By 2020, the number of plants had surged to 463, handling a total combustion mass of 1.46 × 108 t. However, MSWI inevitably generates fly ash (FA), which accounts for approximately 3%–5% of the total waste mass. This amounted to at least 4.38 × 106 t of FA produced nationwide in 2020, with an upward trend expected (Yakubu et al., 2018).
Fig.1 The national incineration plant No. and incinerated MSW mass (a), the data were collected from the annual statistical yearbooks, and the estimated FA amount from 2001 to 2020, 4% is selected as the ratio to determine the FA mass (b).

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In many countries, landfilling remains the primary disposal method for FA (Xin et al., 2023). However, it contains toxic organic compounds, such as dioxins and furans, leachable heavy metals (e.g., Zn, Pb, Cu, Ni, Cd, Cr), and high concentrations of chloride, classifying it as hazardous waste. Therefore, FA requires environmentally sound treatment prior to disposal (Ferreira et al., 2003; Chang et al., 2023; Wang et al., 2023). The existing waste incineration fly ash main treatment technologies includes sintering, melting/vitrification, hydrothermal treatment, mechanochemistry and cement-based processing. However, it normally caused high energy consumption, complex equipment and high investment, producing harmful gases and large footprint (Lin et al., 2022). Chemical stabilization/solidification (S/S) techniques are typically employed to stabilize the contaminants in FA for the further landfill disposal process due to the advantages of easy to operate and mature technology (Zhao et al., 2002; Shao et al., 2016; Zhang et al., 2020). Common stabilizing agents include organic chelators, such as tetrathio bicarbamic acid, sixthio guanidine acid, and ethylenediaminetetraacetic acid (EDTA) (Ma et al., 2019; Wang et al., 2022; Zhou et al., 2023). However, even after stabilization, stabilized fly ash (SFA) retains its fine particle size, low density, and porous structure, similar to untreated FA (Wu et al., 2018). These characteristics pose challenges during handling, packaging, transportation, and landfilling, as SFA can easily become airborne or come into contact with workers through skin or respiratory exposure, leading to potential health risks (Matzenbacher et al., 2017; Zheng et al., 2020). Additionally, the increasing volume of SFA generated has intensified concerns about the limited landfill capacity in China, as well as the potential for hazardous material leaching from SFA due to rainfall exposure (Zhang et al., 2016).
One potential solution to these issues is the compaction of SFA, which can serve as a pre-treatment strategy. Densification of SFA into shapes such as pellets, briquettes, or cubes addresses the challenges associated with low bulk density and fine particle size, improving handling, transport, and landfilling efficiency (Lin, 2006; Zhang and Guo, 2014). Compacted ash has been successfully utilized in various civil engineering applications, such as the construction of highway embankments, landfill liners, and covers (Kaniraj and Havanagi, 2001). Similarly, lime-fly ash-stabilized macadam has been developed for use in pavement base and subbase layers due to its enhanced mechanical properties (Deng et al., 2020). Furthermore, the compaction of mixtures of iron powder and coal fly ash during powder metallurgy processing has shown that the resulting products meet ASTM standards for mineralogy, morphology, and physio-mechanical properties (Singh et al., 2021). FA (or SFA) has also been investigated as an additive material in construction applications such as building bricks (Turgut, 2012), concrete (Sua-Iam and Makul, 2015) and landfill liners (Gupt et al., 2021), where compaction is often employed. Key parameters that influence the effectiveness of powder compaction include pressure, moisture content, and holding duration (Kaniraj and Havanagi, 2001; Patel et al., 2007). Pressure determines the extent to which the powder can be deformed, moisture acts as a lubricant, reducing friction between particles, and holding duration ensures that the powders have sufficient time to fully shift and deform under pressure, resulting in optimal compaction (Zabielska-Adamska, 2008).
Despite these advancements, limited research has been conducted on the compaction of SFA to reduce environmental health risks and optimize landfill capacity. Thus, the stabilized fly ash (SFA) samples, after undergoing commercially chemical stabilization, were also collected from Laogang Incineration Plant in Shanghai and Rugao Incineration Plant in Jiangsu, China, which denoted as SFA-G and SFA-F, respectively. Subsequently, compacted SFA was a physical stabilization treatment serving as a pre-treatment strategy before the SFA landfill disposal. The compacted SFA can significantly reduce the volume to maximize landfill capacity, and environmental risks during handling, packaging, transportation, and landfilling through avoiding fine particle size of SFA. Key parameters, including pressure, moisture content, and holding duration, were systematically investigated to determine their impact on compact density. Additionally, widely recognized compaction models were applied to describe the relationship between compact density and pressure. The physical and mechanical properties of compacted SFA, such as density, unconfined compressive strength, and three-point bending strength, were analyzed to assess the material’s suitability for practical applications. Given its potential advantages, this study highlights the economic feasibility of compacted SFA.

2 2 Materials and methods

2.1 2.1 Sample collection and microstructural characterization

Two types of FAs generated from grate furnance (FA-G) and fluidized bed (FA-F) were selected for this study because they are the most common incinerator types used in MSW management in China, and these FAs exhibit significant differences in their physicochemical properties. The stabilized fly ash (SFA) samples, after undergoing chemical stabilization, were also collected from each plant and denoted as SFA-G and SFA-F, respectively. The SFA samples were dried in an oven at 105 °C for at least 72 h until fully dried. To investigate the influence of moisture content, the samples were conditioned to the target moisture level by adding a predetermined amount of water. The SFA, and water were thoroughly mixed and stored in zip-lock plastic bags at 5 °C for 48 h. Unless otherwise specified, all moisture content values in this study are reported on a wet mass basis.
The SFA samples were thoroughly characterized, including the surface morphology, chemical composition, porosity. The SFA samples were allowed to air-dry completely and then mounted on SEM stubes using conductive carbon tape. The carbon tape ensures good electrical conductivity between the sample and the SEM stub, preventing charging during analysis. To enhance surface conductivity and minimize charging during SEM analysis, the mounted SFA samples were coated with a thin layer of gold using a sputter coater. The gold coating was applied at a deposition thickness of approximately 10 nm. The gold-coated SFA samples were then analyzed using a Field Emission Scanning Electron Microscope (SEM) equipped with energy-dispersive X-ray spectroscopy and mapping (Hitachi S-4800, Japan).
Moreover, the chemical compositions of the samples were determined using X-ray fluorescence (XRF) spectrometry (Shimadzu XRF-1800, Japan) and X-ray Diffractometer (XRD, Bruker, Germany) with a Cu Kα (λ = 1.5406 Å) radiation source (40 kV, 40 mA), the diffraction angel (2θ) is recorded from 10° to 80°, with a scanning speed of 1°/min and a step size of 0.02°. In addition, the dried samples were degassed under vacuum or in an inert gas environment at elevated temperatures (typically around 200–300 °C) for a few hours. The Brunauer–Emmet–Teller (BET) surface area was measured by nitrogen adsorption–desorption isotherm at 77K (Micrometrics model 3Flex 3500 analyzer, USA). The sample was exposed to increasing pressures of nitrogen gas, and the volume of gas adsorbed at each pressure was measured until the relative pressure approached 1.0.

2.2 2.2 SFA compaction and parameter optimization

Compaction experiments were performed using a microcomputer-controlled electronic universal testing machine (CMT 4104, MTS Industrial Systems Co., Ltd., Shenzhen, China). The compaction process is illustrated in Fig.2. SFA powder samples weighing 10 g were manually loaded into a die and compressed into flat-faced tablets with a diameter of 15 mm. The loading rate was set to 20 kN/min, and the frequency was 5 Hz. No binding agents were used during the compaction tests. The bulk density (ρbulk), tap density (ρtap), green density (ρgreen), and true density (ρtrue) of the compacted SFA were determined by Eqs. (1)–(4), respectively. Among them, the bulk density is defined as the mass of the powder per unit volume of the powder in its freely settled state. It reflects the packing efficiency of the powder in its loose state. The tap density is measured by tapping the powder container until a constant volume is achieved. It represents the density of the powder when it is tightly packed by shaking or tapping. The true density refers to the density of the solid material itself, excluding any void spaces within the powder. It provides information about the inherent density of the individual particles. The volume reduction ratio (RV,B) after compaction was determined by Eq. (5).
Fig.2 Schematic flow of the whole compaction process for SFA.

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ρbulk=mVbulk×100%,
ρtap=mVtap×100%,
ρgreen=mVgreen×100%,
ρtrue=mVtrue×100%,
RV,B=ρgreenρbulkρgreen×100%,
where m is the mass of the sample, and Vbulk, Vtap, Vgreen, and Vtrue are the bulk, tap, green, and true volumes, respectively, of the compacted SFA.
To optimize the compaction process, key parameters such as compaction pressure (50, 100, 150, 200, and 250 MPa), moisture content (5%, 10%, 15%, 20%, and 25%) and holding duration (0–100 s) were systematically varied. Density-pressure curves were generated for each sample to provide quantitative insights into the compaction process.

2.3 2.3 Modeling the compaction process with empirical equations

Empirical models are often employed to investigate the deformation mechanisms of solid materials during compaction. Three commonly used equations, Heckel, Kawakita, and Huang Peiyun double-logarithmic compaction equations were applied to describe the compaction behavior of the SFA (Xiao et al., 2009).
1) Heckel equation
The Heckel equation, originally developed for ceramic and metallic materials, is widely used in pharmaceutical research to describe powder compaction. It treats pore reduction as analogous to a first-order chemical reaction, with pores acting as the reactant and the densified bulk as the product. The densification kinetics are proportional to the pressure-induced pore reduction, as described by Eq. (6) (Çomoğlu, 2007).
ln11D=k×P+A,
where D is the relative density to the true density when the compaction is conducted; P is the applied pressure, k and A are constants.
2) Kawakita equation
The Kawakita equation, developed by Kawakita and Lüdde (1971), is another widely used model for powder compaction. It is expressed as follows (Eqs. (7) and (8)):
1C=1ab×1P+1a,
C=V0VV0,
where P is the applied pressure, C is the volume reduction ration (equals to RV,B), V0 and V are the solid volume before and after compaction, and a and b are constants.
3) Huang Peiyun double-logarithm equation
The Huang Peiyun double-logarithm equation is frequently used to describe the compaction behavior of powder systems. It is given by Eq. (9) (Hu, 1987).
M×lg[ln((ρmρ0)×ρ(ρmρ)×ρ0)]=lgPlgM,
where ρm is the green density, ρ0 is the tap density, P is the applied pressure, M is the compaction modulus, m is a nonlinear compaction index. The constants m and M are system-specific, with m determined as the slope of the linear regression.

2.4 2.4 Mechanical properties of compacted SFA

As the compaction pressure increases, SFA transitions from a loosely packed powder to a compact solid, exhibiting improved mechanical properties. The unconfined compressive strength (Pc) and three-point bending (σb) of the compacted SFA were measured to assess the strength of the material. Cylindrical models (Φ 15 mm × 15 mm) were prepared, and the unconfined compressive strength was tested at a loading rate of 0.5 MPa/s, according to ASTM C39/C39M-17a. The compressive strength was calculated using Eq. (10).
Pc=k4000PmaxπD2,
where Pc is the unconfined compressive strength (MPa), Pmax is the failure load (kN), k is the correction factor, adopting 0.87 for height-to-diameter ratio of 1:1 in this study, D is the diameter of cylindrical model (mm).
Prismoid samples (12 mm × 6 mm × 30 mm) were prepared, and their three-point bending strength was calculated using Eq. (11).
σb=3PL2bh2,
where P is the breaking load (N), L is the distance between the two fixed support points (25 mm in this study), b is the width of sample (mm), h is the thickness of sample (mm).

2.5 2.5 Leaching behaviors of SFAs

Batch leaching tests were performed on uncompacted and compacted SFAs, following the EN 12457-2 standard, with a liquid-to-solid ratio of 2:1. The leachate was filtered using a 0.45 µm cellulose acetate filter. Heavy metal concentrations in the leachate were determined using inductively coupled plasma mass spectrometry (ICP-MS) with a PerkinElmer Elan DRC-e.

3 3 Results and discussion

3.1 3.1 Physicochemical properties of FA/SFA samples

The densities of the FA and SFA samples are summarized in Tab.1. Both the tap and true densities of the FA samples were significantly higher than their bulk density, indicating the potential for ash compaction. After chemical stabilization, the SFA samples exhibited higher densities than the untreated FA samples due to the coating of FA particles with stabilizers, which increased particle size. The addition of coal and silica sand to the fluidized bed incinerator leads to a larger particle size distribution and create more void spaces in SFA-F compared to SFA-G (Chang and Wey, 2006). This increased particle size contributes to a higher bulk density and true density in SFA-F, while the lower porosity can result in a higher tap density due to less empty space within the material in SFA-G. This is further supported by the higher silicon content and loss on ignition (LOI) observed in SFA-F compared to SFA-G (Tab.2). Their XRD patterns in Fig.3 also showed that SFA-F contains obviously higher content of silicon than that of SFA-G. Fig.3 also reveals the morphological differences between the two SFA samples. SFA-G exhibited a finer particle size distribution with numerous pores on the ash surface, while SFA-F had a denser surface with fewer pores. This fact is evidenced by BET surface areas, namely 45.5 m2/g for SFA-G and 21.2 m2/g for SFA-F, respectively, and the nitrogen adsorption-desorption isotherms are shown in Fig. S1. These morphological differences suggest that the pore structure in SFA-G may facilitate deformation and improve compaction performance.
Tab.1 Densities of FA and SFA for the compaction experiment
Sample Bulk density (g/cm3) Tap density (g/cm3) True density (g/cm3)
Fly ash
 FA-G 0.363 ± 0.009 0.695 ± 0.002 2.290 ± 0.016
 FA-F 0.627 ± 0.005 0.870 ± 0.005 2.541 ± 0.035
Stabilized fly ash
 SFA-G 0.372 ± 0.033 1.227 ± 0.015 2.513 ± 0.057
 SFA-F 0.627 ± 0.005 0.870 ± 0.005 2.515 ± 0.041
Tab.2 Chemical composition of SFA for the compaction experiment
Composition SFA-G (%) SFA-F (%) Composition SFA-G (%) SFA-F (%)
CaO 37.89 25.36 Fe2O3 0.64 6.85
Cl 28.03 6.09 TiO2 0.44 1.25
Na2O 11.96 2.44 PbO 0.37 0.14
K2O 8.37 1.88 Br 0.22 0.03
SO3 5.19 3.11 F 0.22 a
SiO2 3.54 28.41 CuO 0.13 0.19
ZnO 1.36 0.6 LOIb 2.1 8.14
Al2O3 0.82 18.99 Alkalinity 10.7 0.89
MgO 0.65 3.08 SiO2/Al2O3 4.38 1.5

Notes: a –, Undetected; b LOI, Loss on ignition.

Fig.3 XRD patterns and SEM images of SFA-G (a, b, 10000 ×; c, 30000 ×) and SFA-F (d, e, 10000 ×; f, 30000 ×).

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3.2 3.2 Compaction behavior of SFA under gradient pressure

The effect of compaction pressure on the physical properties of SFA is shown in Fig.4. As compaction pressure increased, the SFA samples became denser, resulting in significant volume reduction. At compaction pressures ≤ 200 MPa, the density and volume reduction ratio of both SFA-G and SFA-F increased rapidly as the porosity of the SFA decreased. On average, as shown in Fig.4(a), the densities of SFA-G and SFA-F increased by 0.336 g/cm3 and 0.317 g/cm3 per 100 MPa, respectively. However, when the pressure exceeded 200 MPa, the rate of density increase slowed significantly, with growth rates of 0.110 g/cm3 for SFA-G and 0.077 g/cm3 for SFA-F per 100 MPa. This suggests that the SFA samples encountered resistance to further deformation, consistent with previous observations for coal FA compaction (Zabielska-Adamska, 2008). The smaller particle size and lower density of SFA-G made it easier to compact compared to SFA-F, as indicated in Tab.1. The fluidized bed incineration process produced larger particles in SFA-F due to the inclusion of silica sand as a fluidizing material, which contributed to particle attrition and the formation of larger ash particles (Chang and Wey, 2006).
Fig.4 Effect of compaction pressure on density (a), displacement-pressure curves of SFA-G (b) and SFA-F (c), and RV,B of SFA (d).         

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The pressure-displacement curves for SFA during compaction (Fig.4(b) and Fig.4(c)) showed an initial sharp increase in displacement, followed by a gradual plateau. At the early stages of compaction, the particles easily shifted to fill voids with minimal resistance. As compaction progressed, increased pressure was required to overcome friction between particles and induce further deformation. Eventually, the ash particles fully contacted each other, and the pore structure collapsed, leading to mechanical interlocking between particles (Kawakita and Lüdde, 1971; Alimardani et al., 2016). At this stage, further deformation became increasingly difficult, indicating that the maximum compressible state of SFA had been reached. The consolidation modulus of SFA-G increased from 2.47 × 107 kN/m3 to 8.34 × 107 kN/m3 as the compaction pressure increased from 50 MPa to 200 MPa, while the modulus of SFA-F increased from 1.69 × 107 kN/m3 to 9.49 × 107 kN/m3. However, the modulus showed little further increase beyond 200 MPa, reaching 8.51 × 107 kN/m3 for SFA-G and 9.52 × 107 kN/m3 for SFA-F at 250 MPa.
The volume reduction ratio (RV,B) of SFA exhibited trends similar to density (Fig.4(d)). For SFA-G and SFA-F, RV,B reached 55% and 63%, respectively, at 200 MPa, with limited further increases as the pressure was raised to 800 MPa. Therefore, a compaction pressure of 100–200 MPa is recommended for optimal volume reduction (40%–60%), maximizing landfill capacity and offering a cost-effective solution for regions with limited land resources (Kaladharan et al., 2019).

3.3 3.3 Effects of moisture content and holding duration

The moisture content of SFA, a byproduct of the chemical stabilization process, was adjusted to 5%, 10%, 15%, 20%, and 25% to study its influence on compaction behavior. Fig.5(a) and Fig.5(b) show that increasing moisture content improved the compaction process. For example, the density increment ratio for SFA-G increased from 1.6% to 2.2%, while for SFA-F, it increased from 1.2% to 1.6% as moisture content rose from 5% to 15% with a holding duration of 20 s. This effect can be attributed to the presence of silicon, iron, aluminum, calcium, and sulfur compounds in SFA, which react with calcium hydroxide in the presence of moisture to form cementitious structures (Cheerarot and Jaturapitakkul, 2004; Schneider et al., 2011). However, when moisture content exceeded 20% for both SFAs, the density increment ratios diminished. Excessive moisture caused water overflow and compromised the structural integrity of the compacted samples (Patel et al., 2007).
Fig.5 Density increment ratio-holding duration curves of SFA-G (a) and SFA-F (b), loading pressure-displacement curves with various moisture contents under the compaction pressure of 100 MPa for SFA-G (c) and SFA-F (d), density increment ratio-holding duration curves with different compaction pressure under the moisture content of 10% for SFA-G (e) and SFA-F (f).

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Fig.5(c) and Fig.5(d) illustrate the loading pressure-displacement curves of SFA during compaction process under various moisture contents. It showed that the loading pressure-displacement curves of SFA-G and SFA-F obviously fluctuated when the moisture contents were larger than 20% and 15%, respectively, indicating the plastic flow prone to be appeared during the compaction process. Water overflow was also observed at moisture contents above 15% for SFA-G and 10% for SFA-F. Therefore, the optimal moisture content for smooth and efficient compaction should not exceed 15% for SFA-G and 10% for SFA-F. This discrepancy is likely due to the lower salinity and reduced CaO content in SFA-F (Tab.2).
In compaction process, if there were still deformation occurring after reaching the set value, the pressure shall be maintained for a while to ensure full compaction (Hafizpour et al., 2010). As shown in Fig.5(e), SFA densities increased sharply during the first 20 s of holding, followed by slower increases thereafter. The effect of holding duration was more pronounced for SFA-F than for SFA-G (Fig.5(f)), likely due to the higher abundance of water-soluble components in SFA-G (Tab.2). Overall, the recommended holding durations are 80–100 s for SFA-G and 20–40 s for SFA-F.

3.4 3.4 Modeling the compaction process

To better understand the relationship between compaction behavior and pressure, empirical models were applied to the experimental data. As shown in Fig.6, the compaction curves for both SFA-G and SFA-F exhibited an “S” shape, with tailing observed at both low and high pressures. Specially, Huang Peiyun equation provided the best linear fit with an R2 of 0.965, particularly in the low-pressure range as shown in Tab.3. The Heckel equation also performed well, particularly for describing the compaction process at low and moderate pressures. Therefore, the Heckel equation may be more suitable for further studies on the compaction of SFA-G. On the other hand, the Huang Peiyun equation provided an excellent linear fit for SFA-F across the full range of compaction pressures, with an R2 of 0.991. This suggests that the Huang Peiyun equation is better suited for modeling the compaction behavior of SFA-F, particularly at low pressures.
Tab.3 Fitting parameters of SFA compaction process using empirical equations
Equation Pressure(MPa) Intercept Slope Pearson’r R2 Adj. R2
SFA-G SFA-F SFA-G SFA-F SFA-G SFA-F SFA-G SFA-F SFA-G SFA-F
Heckel 1–800 0.619 0.798 0.003 0.001 0.990 0.995 0.980 0.991 0.980 0.991
20–800 0.617 0.815 0.003 0.001 0.989 0.998 0.979 0.997 0.979 0.997
50–800 0.610 0.822 0.003 0.001 0.989 0.999 0.978 0.998 0.978 0.998
100–800 0.584 0.832 0.003 0.001 0.988 0.999 0.976 0.999 0.975 0.999
20–250 0.708 0.759 0.002 0.002 0.999 0.992 0.998 0.984 0.998 0.984
20–400 0.722 0.792 0.002 0.001 0.999 0.993 0.999 0.987 0.999 0.987
20–600 0.712 0.814 0.002 0.001 0.999 0.996 0.999 0.993 0.999 0.993
Kawakita 1–800 1.874 1.765 −1.53 −1.08 −0.349 −0.387 0.122 0.150 0.121 0.147
20–800 2.036 1.883 −38.31 −28.01 −0.762 −0.772 0.581 0.596 0.581 0.595
50–800 2.123 1.945 −64.79 −46.61 −0.859 −0.864 0.739 0.747 0.738 0.746
100–800 2.235 2.021 −106.4 −74.96 −0.920 −0.920 0.846 0.846 0.846 0.845
Huang Peiyun 1–250 −0.972 −0.862 0.437 0.378 0.996 0.999 0.991 0.999 0.991 0.999
1–400 −1.019 −0.874 0.466 0.386 0.994 0.999 0.988 0.998 0.988 0.998
1–600 −1.084 −0.899 0.501 0.400 0.991 0.998 0.981 0.996 0.981 0.996
1–800 −1.175 −0.935 0.547 0.418 0.982 0.995 0.965 0.991 0.965 0.991
Fig.6 Description of empirical equations for the compaction process of SFA-G (a, b, c) and SFA-F (d, e, f).

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Overall, no single empirical model fits both SFA samples due to their compositional differences, which lead to distinct physical and mechanical properties (Denny, 2002). The Kawakita equation failed to describe the compaction behavior of either sample, likely because it is best suited for low-pressure, high-porosity materials, such as soft pharmaceutical powders (Kawakita and Lüdde, 1971).

3.5 3.5 Mechanical behaviors of compacted SFA

Before landfill disposal, compacted SFA must undergo various handling processes, such as falling, collision, clamping, stacking, and rolling. Therefore, adequate unconfined compressive strength (Pc) is necessary to maintain structural integrity and prevent material loss. Fig.7 shows that under a compaction pressure of 50 MPa, the Pc of compacted SFA-G and SFA-F were 26.8 and 15.6 MPa, respectively, meeting the strength requirements for MU10-class solid bricks (GB 11945-1999). The stabilizers in the SFA likely filled gaps between ash particles, promoting cementation and enhancing compressive strength. As compaction pressure increased, the Pc of both materials also increased. However, SFA-F exhibited lower strength than SFA-G, likely due to higher Al2O3 and SiO2 content in SFA-F, which contributed to increased hardness and reduced mechanical engagement during compaction (Chang and Wey, 2006).
Fig.7 Effect of compaction pressures on the Pc of compacted SFA.

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Fig.8 shows the influence of compaction pressure on the three-point bending strength (σb) of compacted SFA. Similar to Pc, σb increased with compaction pressure, with SFA-G exhibiting higher values than SFA-F. This increase is likely due to mechanical interlocking of particles, as well as carbonization and alkali-activation reactions involving water, calcium oxide, and calcium hydroxide, which contribute to further strength gains in compacted SFA (Justnes et al., 2020; Lu et al., 2024).
Fig.8 Effect of compaction pressure on the σb and ρgreen of compacted SFA-G (a) and SFA-F (b).

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SFA-G with its higher calcium content, underwent prolonged carbonization, resulting in sustained increases in σb over curing periods. In contrast, SFA-F with lower calcium content, exhibited shorter carbonization times. Fig.8 shows that after 3 days of curing, SFA-G exhibited significantly higher σb than other curing conditions, while SFA-F showed little difference after 1 day of curing. The rate of σb increase also depended on the diffusion rate of airborne CO2, which initiated carbonization. Lower compaction pressures, which led to higher porosity, facilitated faster CO2 diffusion, resulting in higher σb in low-pressure compacts compared to those subjected to higher pressures.
Overall, the strength increment in compacted SFA is due to two key mechanisms, including carbonization and alkali activation, which lead to significant phase transformations. Carbonization transforms weaker phases like calcium hydroxide into calcium carbonate, densifying the structure. Meanwhile, alkali activation dissolves and re-polymerizes the fly ash components to form C-S-H and geopolymer gels, which act as strong binding agents. The combined effect of these processes results in a compacted material with enhanced mechanical properties and long-term durability (John et al., 2021; Suescum-Morales et al., 2022).

3.6 3.6 Compaction-mediated procedure for SFA disposal

The effects of key parameters on SFA compaction demonstrate its feasibility for practical applications. Fig.9(a) shows a typical green compacted SFA brick, with an experimental scale-up to ~1 kg per run to form bricks measuring 240 mm × 240 mm × 50 mm. Fig.9(b) highlights the key advantages of the compaction-mediated FA disposal process. Importantly, Tab.4 illustrates the leaching concentration of heavy metals from uncompacted and compacted fly ash in a side-by-side analysis to show the potential inhibition effect of compaction. The compaction inhibits the leaching of heavy metals to some extent.
Tab.4 Leached concentrations of typical heavy metals from uncompacted and compacted SFA
Sample Pb (mg/L) Zn (mg/L) Cu (mg/L) Cd (mg/L) Cr (mg/L)
Uncompacted
 SFA-G 0.12 4.32 2.85 0.06 1.23
 SFA-F 0.10 5.45 2.48 0.08 0.98
Compacted
 SFA-G 0.05 1.34 1.19 a 0.79
 SFA-F 0.04 1.16 0.98 0.65

Notes: a –, indicates not detected.

Fig.9 Image of the green compacted SFA (a), flow-chart of compaction-mediated process for FA disposal (b).

Full size|PPT slide

The techno-economic analysis in Tab.5 demonstrates that integrating compaction into the disposal process yields significant economic and environmental benefits. Specifically, the stabilization agent consumption decreased from 3 wt% to 1 wt% with the compaction-mediated process because compaction increases the density of the stabilized fly ash (SFA), which reduces the amount of stabilization agent required per unit weight of SFA. The stabilization agent is used to react with the fly ash to form a stable matrix. Compaction reduces the porosity of the SFA, which means that the stabilization agent has a smaller surface area to react with. As a result, less stabilization agent is needed to achieve the same level of stabilization. The total cost can be reduced by approximately 26.5 $/t, factoring in stabilizing agent consumption and compaction costs. Most importantly, compaction minimizes ash dispersion during transport and increases landfill capacity, which is critical given the rise in MSW incineration. Furthermore, the increased density of compacted SFA may reduce weathering and pollutant leaching, which warrants further investigation.
Tab.5 Comparative techno-economics parameters of classical and compaction-mediated processes for FA disposal
Technical indicators Performance metrics Financial input ($/t)*
classical process compaction-mediated process classical process compaction-mediated process
Landfilling capacity (t/m3) 0.8–1.1 2–3
Stabilization agent consumption 3 wt.% 1 wt.% 41.8 13.9
Compaction power consumption (kW·h/t) 0 10 0 1.4
Landfill lifespan (year) 10 30–40

Notes: *All the financial inputs are based on engineering practice in China, capital investment required for the compaction equipment and the associated labor cost are not included, because they depend on the scale.

4 4 Conclusions

This study introduces a cost-effective compaction method into the mainstream disposal pathway for treating stabilized fly ash (SFA). The experimental results demonstrated that the density of compacted SFA more than doubled compared to untreated SFA, with a volume reduction exceeding 60%. This significant volume reduction increases landfill capacity, making it highly beneficial, especially given the growing reliance on incineration for municipal solid waste disposal. Key factors influencing the compaction performance of SFA include compaction pressure, holding duration, and moisture content. Optimal values for practical applications are recommended as 100–200 MPa for pressure, 20 s for holding duration, and 10%–15% for moisture content, depending on the type of incinerator used. Additionally, the compaction process of SFA-G and SFA-F can be effectively modeled using the Heckel and Huang Peiyun equations, respectively. The mechanical properties of compacted SFA, including unconfined compressive strength and three-point bending strength, were found to meet the MU10 class lime-sand brick standard. This ensures sufficient strength for safe transportation and landfilling. The compaction-mediated method is more cost-effective because it requires less stabilization agent, less energy to compact the SFA, and results in a smaller volume of SFA to be landfilled. Overall, this study provides valuable insights for the improved disposal and management of fly ash, offering a viable, cost-effective solution for optimizing landfill usage and enhancing environmental sustainability.
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References

[1]
Aguilera S, Aguilar M E, Chávez M P, López-Meza J E, Pedraza-Reyes M, Campos-García J, Cervantes C (2004). Essential residues in the chromate transporter ChrA of Pseudomonas aeruginosa. FEMS Microbiology Letters, 232(1): 107–112
CrossRef Pubmed Google scholar
[2]
Barrick J E, Yu D S, Yoon S H, Jeong H, Oh T K, Schneider D, Lenski R E, Kim J F (2009). Genome evolution and adaptation in a long-term experiment with Escherichia coli. Nature, 461(7268): 1243–1247
CrossRef Pubmed Google scholar
[3]
Bhatter P, Chatterjee A, D’souza D, Tolani M, Mistry N (2012). Estimating fitness by competition assays between drug susceptible and resistant Mycobacterium tuberculosis of predominant lineages in Mumbai, India. PLoS One, 7(3): e33507
CrossRef Pubmed Google scholar
[4]
Blount Z D, Barrick J E, Davidson C J, Lenski R E (2012). Genomic analysis of a key innovation in an experimental Escherichia coli population. Nature, 489(7417): 513–518
CrossRef Pubmed Google scholar
[5]
Brockhurst M A, Colegrave N, Rozen D E (2011). Next-generation sequencing as a tool to study microbial evolution. Molecular Ecology, 20(5): 972–980
CrossRef Pubmed Google scholar
[6]
Brown S D, Thompson M R, Verberkmoes N C, Chourey K, Shah M, Zhou J, Hettich R L, Thompson D K (2006). Molecular dynamics of the Shewanella oneidensis response to chromate stress. Molecular & Cellular Proteomics, 5(6): 1054–1071
CrossRef Pubmed Google scholar
[7]
Carlson H K, Price M N, Callaghan M, Aaring A, Chakraborty R, Liu H, Kuehl J V, Arkin A P, Deutschbauer A M (2019). The selective pressures on the microbial community in a metal-contaminated aquifer. The ISME Journal, 13(4): 937–949
CrossRef Pubmed Google scholar
[8]
Cervantes C, Silver S (1992). Plasmid chromate resistance and chromate reduction. Plasmid, 27(1): 65–71
CrossRef Pubmed Google scholar
[9]
Cheung K H, Gu J D (2007). Mechanism of hexavalent chromium detoxification by microorganisms and bioremediation application potential: A review. International Biodeterioration & Biodegradation, 59(1): 8–15
CrossRef Google scholar
[10]
Choi O, Hu Z (2008). Size dependent and reactive oxygen species related nanosilver toxicity to nitrifying bacteria. Environmental Science & Technology, 42(12): 4583–4588
CrossRef Pubmed Google scholar
[11]
Chourey K, Thompson M R, Morrell-Falvey J, Verberkmoes N C, Brown S D, Shah M, Zhou J, Doktycz M, Hettich R L, Thompson D K (2006). Global molecular and morphological effects of 24-hour chromium(VI) exposure on Shewanella oneidensis MR-1. Applied and Environmental Microbiology, 72(9): 6331–6344
CrossRef Pubmed Google scholar
[12]
Cingolani P, Platts A, Wang L, Coon M, Nguyen T, Wang L, Land S J, Lu X, Ruden D M (2012). A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly, 6(2): 80–92
CrossRef Pubmed Google scholar
[13]
Conrad T M, Lewis N E, Palsson B Ø (2011). Microbial laboratory evolution in the era of genome-scale science. Molecular Systems Biology, 7(1): 509
CrossRef Pubmed Google scholar
[14]
Dettman J R, Rodrigue N, Melnyk A H, Wong A, Bailey S F, Kassen R (2012). Evolutionary insight from whole-genome sequencing of experimentally evolved microbes. Molecular Ecology, 21(9): 2058–2077
CrossRef Pubmed Google scholar
[15]
Deutscher J, Francke C, Postma P W (2006). How phosphotransferase system-related protein phosphorylation regulates carbohydrate metabolism in bacteria. Microbiology and Molecular Biology Reviews, 70(4): 939–1031
CrossRef Pubmed Google scholar
[16]
Gang H, Xiao C, Xiao Y, Yan W, Bai R, Ding R, Yang Z, Zhao F (2019). Proteomic analysis of the reduction and resistance mechanisms of Shewanella oneidensis MR-1 under long-term hexavalent chromium stress. Environment International, 127: 94–102
CrossRef Pubmed Google scholar
[17]
Gao H, Yang Z K, Wu L, Thompson D K, Zhou J (2006). Global transcriptome analysis of the cold shock response of Shewanella oneidensis MR-1 and mutational analysis of its classical cold shock proteins. Journal of Bacteriology, 188(12): 4560–4569
CrossRef Pubmed Google scholar
[18]
Gerrish P J, Lenski R E (1998). The fate of competing beneficial mutations in an asexual population. Genetica, 102/103(1-6): 127–144
CrossRef Pubmed Google scholar
[19]
Groh J L, Luo Q, Ballard J D, Krumholz L R (2007). Genes that enhance the ecological fitness of Shewanella oneidensis MR-1 in sediments reveal the value of antibiotic resistance. Applied and Environmental Microbiology, 73(2): 492–498
CrossRef Pubmed Google scholar
[20]
He Q, Xu P, Zhang C, Zeng G, Liu Z, Wang D, Tang W, Dong H, Tan X, Duan A (2019). Influence of surfactants on anaerobic digestion of waste activated sludge: acid and methane production and pollution removal. Critical Reviews in Biotechnology, 39(5): 746–757
CrossRef Pubmed Google scholar
[21]
Heidelberg J F, Paulsen I T, Nelson K E, Gaidos E J, Nelson W C, Read T D, Eisen J A, Seshadri R, Ward N, Methe B, Clayton R A, Meyer T, Tsapin A, Scott J, Beanan M, Brinkac L, Daugherty S, DeBoy R T, Dodson R J, Durkin A S, Haft D H, Kolonay J F, Madupu R, Peterson J D, Umayam L A, White O, Wolf A M, Vamathevan J, Weidman J, Impraim M, Lee K, Berry K, Lee C, Mueller J, Khouri H, Gill J, Utterback T R, McDonald L A, Feldblyum T V, Smith H O, Venter J C, Nealson K H, Fraser C M (2002). Genome sequence of the dissimilatory metal ion-reducing bacterium Shewanella oneidensis. Nature Biotechnology, 20(11): 1118–1123
CrossRef Pubmed Google scholar
[22]
Holmes D E, O’Neil R A, Chavan M A, N’Guessan L A, Vrionis H A, Perpetua L A, Larrahondo M J, DiDonato R, Liu A, Lovley D R (2009). Transcriptome of Geobacter uraniireducens growing in uranium-contaminated subsurface sediments. The ISME Journal, 3(2): 216–230
CrossRef Pubmed Google scholar
[23]
Hou X, Huang L, Zhou P, Xue H, Li N (2018). Response of indigenous Cd-tolerant electrochemically active bacteria in MECs toward exotic Cr (VI) based on the sensing of fluorescence probes. Frontiers of Environmental Science & Engineering, 12(4): 7
CrossRef Google scholar
[24]
Hu H, Jin Q, Kavan P (2014). A study of heavy metal pollution in China: Current status, pollution-control policies and countermeasures. Sustainability, 6(9): 5820–5838
CrossRef Google scholar
[25]
Huang W, Sherman B T, Lempicki R A (2009a). Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Research, 37(1): 1–13
CrossRef Pubmed Google scholar
[26]
Huang W, Sherman B T, Lempicki R A (2009b). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols, 4(1): 44–57
CrossRef Pubmed Google scholar
[27]
Korotkov K V, Sandkvist M, Hol W G J (2012). The type II secretion system: Biogenesis, molecular architecture and mechanism. Nature Reviews. Microbiology, 10(5): 336–351
CrossRef Pubmed Google scholar
[28]
Kouzuma A, Oba H, Tajima N, Hashimoto K, Watanabe K (2014). Electrochemical selection and characterization of a high current-generating Shewanella oneidensis mutant with altered cell-surface morphology and biofilm-related gene expression. BMC Microbiology, 14(1): 190
CrossRef Pubmed Google scholar
[29]
Lenski R E, Rose M R, Simpson S C, Tadler S C (1991). Long-term experimental evolution in Escherichia coli. I. Adaptation and divergence during 2000 generations. American Naturalist, 138(6): 1315–1341
CrossRef Google scholar
[30]
Li H, Durbin R (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics (Oxford, England), 25(14): 1754–1760
CrossRef Pubmed Google scholar
[31]
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, and the 1000 Genome Project Data Processing Subgroup (2009). The sequence alignment/map format and SAMtools. Bioinformatics (Oxford, England), 25(16): 2078–2079
CrossRef Pubmed Google scholar
[32]
Li Z, Ma Z, van der Kuijp T J, Yuan Z, Huang L (2014). A review of soil heavy metal pollution from mines in China: Pollution and health risk assessment. The Science of the Total Environment, 468–469: 843–853
CrossRef Pubmed Google scholar
[33]
Liang Q, Liu X, Zeng G, Liu Z, Tang L, Shao B, Zeng Z, Zhang W, Liu Y, Cheng M, Tang W, Gong S (2019). Surfactant-assisted synthesis of photocatalysts: Mechanism, synthesis, recent advances and environmental application. Chemical Engineering Journal, 372: 429–451
CrossRef Google scholar
[34]
Liu Z, Liu Y, Zeng G, Shao B, Chen M, Li Z, Jiang Y, Liu Y, Zhang Y, Zhong H (2018). Application of molecular docking for the degradation of organic pollutants in the environmental remediation: A review. Chemosphere, 203: 139–150
CrossRef Pubmed Google scholar
[35]
Lytle C M, Lytle F W, Yang N, Qian J H, Hansen D, Zayed A, Terry N (1998). Reduction of Cr (VI) to Cr (III) by wetland plants: Potential for in situ heavy metal detoxification. Environmental Science & Technology, 32(20): 3087–3093
CrossRef Google scholar
[36]
McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo M A (2010). The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20(9): 1297–1303
CrossRef Pubmed Google scholar
[37]
Mikolay A, Nies D H (2009). The ABC-transporter AtmA is involved in nickel and cobalt resistance of Cupriavidus metallidurans strain CH34. Antonie van Leeuwenhoek, 96(2): 183–191
CrossRef Pubmed Google scholar
[38]
Nishioka H (1975). Mutagenic activities of metal compounds in bacteria. Mutation Research, 31(3): 185–189
CrossRef Pubmed Google scholar
[39]
Orellana R, Hixson K K, Murphy S, Mester T, Sharma M L, Lipton M S, Lovley D R (2014). Proteome of Geobacter sulfurreducens in the presence of U(VI). Microbiology, 160(12): 2607–2617
CrossRef Pubmed Google scholar
[40]
Park C H, Keyhan M, Wielinga B, Fendorf S, Matin A (2000). Purification to homogeneity and characterization of a novel Pseudomonas putida chromate reductase. Applied and Environmental Microbiology, 66(5): 1788–1795
CrossRef Pubmed Google scholar
[41]
Petrilli F L, De Flora S (1977). Toxicity and mutagenicity of hexavalent chromium on Salmonella typhimurium. Applied and Environmental Microbiology, 33(4): 805–809
Pubmed
[42]
Puentes-Téllez P E, Hansen M A, Sørensen S J, van Elsas J D (2013). Adaptation and heterogeneity of Escherichia coli MC1000 growing in complex environments. Applied and Environmental Microbiology, 79(3): 1008–1017
CrossRef Pubmed Google scholar
[43]
Qiu X, Sundin G W, Wu L, Zhou J, Tiedje J M (2005). Comparative analysis of differentially expressed genes in Shewanella oneidensis MR-1 following exposure to UVC, UVB, and UVA radiation. Journal of Bacteriology, 187(10): 3556–3564
CrossRef Pubmed Google scholar
[44]
Rittmann B E, Hausner M, Löffler F, Love N G, Muyzer G, Okabe S, Oerther D B, Peccia J, Raskin L, Wagner M (2006). A vista for microbial ecology and environmental biotechnology. Environmental Science & Technology, 40(4): 1096–1103
CrossRef Pubmed Google scholar
[45]
Torres-García W, Brown S D, Johnson R H, Zhang W, Runger G C, Meldrum D R (2011). Integrative analysis of transcriptomic and proteomic data of Shewanella oneidensis: Missing value imputation using temporal datasets. Molecular BioSystems, 7(4): 1093–1104
CrossRef Pubmed Google scholar
[46]
Venkateswaran K, Moser D P, Dollhopf M E, Lies D P, Saffarini D A, MacGregor B J, Ringelberg D B, White D C, Nishijima M, Sano H, Burghardt J, Stackebrandt E, Nealson K H (1999). Polyphasic taxonomy of the genus Shewanella and description of Shewanella oneidensis sp. nov. International Journal of Systematic Bacteriology, 49(2): 705–724
CrossRef Pubmed Google scholar
[47]
Viamajala S, Peyton B M, Apel W A, Petersen J N (2002). Chromate/nitrite interactions in Shewanella oneidensis MR-1: Evidence for multiple hexavalent chromium [Cr(VI)] reduction mechanisms dependent on physiological growth conditions. Biotechnology and Bioengineering, 78(7): 770–778
CrossRef Pubmed Google scholar
[48]
Wang C, Chen J, Hu W J, Liu J Y, Zheng H L, Zhao F (2014). Comparative proteomics reveal the impact of OmcA/MtrC deletion on Shewanella oneidensis MR-1 in response to hexavalent chromium exposure. Applied Microbiology and Biotechnology, 98(23): 9735–9747
CrossRef Pubmed Google scholar
[49]
Yang Z, Bielawski J P (2000). Statistical methods for detecting molecular adaptation. Trends in Ecology & Evolution, 15(12): 496–503
CrossRef Pubmed Google scholar
[50]
Zeng Z, Liu X, Yao J, Guo Y, Li B, Li Y, Jiao N, Wang X (2016). Cold adaptation regulated by cryptic prophage excision in Shewanella oneidensis. The ISME Journal, 10(12): 2787–2800
CrossRef Pubmed Google scholar

Acknowledgements

This study was supported by grants from the National Natural Science Foundation of China (51878640, 51478451), and the Youth Innovation Promotion Association of Chinese Academy of Sciences (2018344).

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11783-019-1182-8 and is accessible for authorized users.

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