Anionic Effects on Flocculation and Consolidation of Sediments Contaminated by Heavy Metals

Abstract

The remediation of heavy metal-contaminated sediments is a significant environmental challenge. While cation effects are well studied, the influence of common co-existing anions on treatment efficiency remains poorly quantified. This study systematically investigates the effects of nitrate (NO₃⁻), chloride (Cl⁻), and sulfate (SO₄²⁻) ions on the flocculation and consolidation of copper (Cu)- and zinc (Zn)-contaminated sediments through settling column tests, turbidity measurements, and oedometer consolidation tests. Results demonstrated that NO₃⁻ achieved the highest flocculation efficiency, with a final settling height of 3.52 cm and a supernatant turbidity of 4.6 NTU, and the best consolidation performance, with a coefficient of 1.27 × 10⁻³ cm²/s. In contrast, SO₄²⁻ yielded the poorest outcomes. The superior performance of NO₃⁻ is attributed to its low charge density, which promotes the formation of denser flocs. These findings underscore that anion selection is a critical factor for optimizing sediment dewatering processes, suggesting that strategies favoring nitrate conditions can enhance the efficiency of techniques like pressure filtration and vacuum pre-compression.

1. Introduction

Against the backdrop of rapid industrialization, the management of dredged contaminated sediments has emerged as a formidable challenge in civil engineering and environmental protection [1,2,3,4]. Copper (Cu) and zinc (Zn), in particular, are prevalent pollutants in aquatic systems due to their extensive use in metallurgy, chemical manufacturing, and electroplating processes [5]. Consequently, riverine sediments tend to accumulate substantial concentrations of these metals over time. Heavy metals are characterized by pronounced toxicity, significant bioaccumulation potential, and resistance to degradation [6,7,8]. Predominantly, these contaminants become immobilized within sediment matrices through adsorption and complexation mechanisms, processes that considerably modify the inherent physicochemical properties of the sediments [9,10,11]. Owing to their high toxicity, bioaccumulative nature, and recalcitrance, these heavy metals are typically immobilized within the sediments through adsorption and complexation mechanisms, thereby significantly altering their physicochemical properties. Such contamination not only endangers aquatic ecosystems but also poses severe risks to public health via trophic transfer into the human food chain [12].

However, these contaminated dredged sediments, which are rich in clay and silt, can often be repurposed as backfill material, thereby contributing to resource recovery [13,14]. Prior to their utilization as backfill, it is generally necessary to remove the heavy metals from the sediments via curing stabilization (physicochemical methods) while concurrently dewatering and drying the material to enhance its strength for transportation and backfill construction [15,16]. One commonly employed approach involves the use of chemical reagents in conjunction with pressure filtration or vacuum pre-compression [17]. In this method, a reagent possessing both heavy metal removal and flocculation–dewatering functionalities (e.g., anionic polyacrylamide (APAM) [18] and chelating agents [19]) is first mixed with the contaminated sediments as a pretreatment. Thereafter, physical dewatering—primarily through pressure filtration or vacuum pre-compression—is applied to dry the sediments before they are transported to designated backfill sites [20,21].

In addition to heavy metal cations—which are known to significantly alter the physicochemical properties of contaminated sediments and thus affect the efficiency of heavy metal removal and dewatering/drying processes [4]—dissolved anions commonly present in such sediments (e.g., sulfate (SO₄²⁻), chloride (Cl⁻), and nitrate (NO₃⁻)) also interact with heavy metal ions in aqueous systems and influence sediment characteristics [22,23]. Although most studies have predominantly focused on the impact of heavy metal cations on decontamination and dewatering efficiency [24], the effects of these anions should not be overlooked. In particular, the presence of SO₄²⁻, Cl⁻, and NO₃⁻ can exacerbate drainage difficulties and lead to the filter cloth surface forming a soil layer with an extremely low permeability coefficient [25]. Therefore, elucidating the influence of different anions on the physicochemical properties of copper- and zinc-contaminated sediments is crucial for advancing remediation strategies and enhancing sediment management practices.

In real sediment environments, electrical conductivity serves as an indicator of the ionic strength of heavy metal ions in sediments [26], with higher ionic strengths reflecting increased concentrations of both heavy metal cations and anions [27]. While the detrimental impact of heavy metal cations has been extensively quantified—for instance, Roberts et al. (2023) [24] documented a 40% reduction in dewatering rate correlated with high Cu²⁺ concentration—the systematic influence of common anions remains poorly defined in quantitative terms. Previous research has offered qualitative hints; Wu et al. (2021) [28] observed that SO₄²⁻ presence could exacerbate drainage difficulties and lead to clogging, but a direct, side-by-side comparison of key anions like NO₃⁻, Cl⁻, and SO₄²⁻ with measured performance metrics, is lacking. This constitutes a critical knowledge gap, as the anionic composition can vary widely in different water bodies (e.g., from NO₃⁻-rich agricultural runoff to SO₄²⁻-laden industrial effluents), yet there is no clear guidance on how this variation affects sediment treatment design and efficiency.

The primary aim of this study is to quantitatively elucidate and compare the specific effects of NO₃⁻, Cl⁻, and SO₄²⁻ on the flocculation and consolidation characteristics of Cu-Zn-contaminated sediments. Through a combined approach of settling column experiments, turbidity and zeta potential measurements, particle size analysis, and consolidation tests, this work provides a definitive, side-by-side comparison of anion-specific impacts.

Our study contributes a missing quantitative framework to the field, directly linking anion type to changes in floc structure, settling efficiency, and compressibility. The findings are expected to provide concrete guidance for optimizing sediment remediation strategies in diverse anionic environments. The primary aim of this study is to quantitatively elucidate and compare the specific effects of NO₃⁻, Cl⁻, and SO₄²⁻ on the flocculation and consolidation characteristics of Cu-Zn-contaminated sediments. Through a combined approach of settling column experiments, turbidity and zeta potential measurements, particle size analysis, and consolidation tests, this work provides a definitive, side-by-side comparison of anion-specific impacts. Our study contributes a missing quantitative framework to the field, directly linking anion type to changes in floc structure, settling efficiency, and compressibility. The findings are expected to provide concrete guidance for optimizing sediment remediation strategies in diverse anionic environments.

2. Materials and Methods

2.1. Preparation of Sediment Samples

2.1.1. Sediment Samples

In this study, a silty clay with low organic content, obtained from an engineering construction site, was employed. Sediment samples were collected from a depth of 7 to 9 m. The primary physical properties of the sediment are summarized in

Table 1. The main physical properties of test sediment.

Items	Sampling Depth (m)	Water Content (%)	Specific Gravity	Density (g/cm3)	Liquid Limit wl (%)	Plastic Limit, wp (%)
sludge	7–9 m	38.4%	2.62	1.61	41.92%	20.14%

, and the selected sediment samples exhibited characteristic particle size distribution patterns, as presented in Figure 1.

The results of the major chemical element analysis of the raw material, conducted using X-ray fluorescence (XRF) spectroscopy, are as follows. The XRF analysis indicates that the chemical composition of the remolded sediment is primarily composed of SiO₂, CaO, Al₂O₃, and Fe₂O₃. This composition is attributed to the sediment’s derivation from weathered rocks—particularly granite and sedimentary rocks—wherein the parent material’s minerals decompose under the influence of both physical and chemical weathering processes, thereby releasing elements such as silicon, calcium, aluminum, and ferrum.

2.1.2. Pretreatment Configuration of the Contaminated Sediments

In this study, sediments collected from a construction site were first oven-dried at 105 °C for 24 h. After cooling to room temperature, the dried sediments were ground using a planetary ball mill and subsequently stored for the preparation of contaminated sediment. A measured volume of deionized water was then used to fully dissolve CuSO₄·5H₂O and ZnSO₄·7H₂O in a mechanical stirrer, yielding their respective solutions. These solutions were added to the oven-dried, ground, and sieved natural silt clay, and the mixture was thoroughly stirred until the water content reached approximately 160%, the prepared copper ion concentration was 142.6 mg/kg and the zinc ion concentration was 384.2 mg/kg [29]. The resulting mixture was weighed and transferred into sealed plastic containers. To maintain the desired moisture content, water was periodically added, and the samples were cured in a standard conditioning room for 48 h (at 22 °C and relative humidity > 90%) to simulate Cu–Zn contamination in the sediment [30]. We used inductively coupled plasma emission spectrometry to test the copper and zinc contents in the prepared contaminated soil, as can be seen in

Table 2. Cu and Zn contents of the original soil and contaminated soil.

Basic Properties	Original Soil	NO3−	Cl−	SO42−
Cu content (mg/kg)	30.4	143.6	142.5	140.3
Zn content (mg/kg)	54.7	384.9	382.5	380.4

. Similarly, to introduce nitrate ions, Cu(NO₃)2 and Zn(NO₃)2·6H₂O were employed, while CuCl₂ solutions and ZnCl₂ solutions were used to introduce chloride ions, following the same procedure, to simulate contaminated sediments. The production process of the simulated contaminated sediments is illustrated in Figure 2.

2.2. Physical Properties Testing

2.2.1. Sedimentation Column Test and Turbidity Determination

The flocculation performance was assessed via settling column tests. Briefly, 300 mL of sediment slurry was mixed with a 0.2% (w/v) flocculant solution for 2 min. The mixture was transferred to a graduated cylinder, and the settling interface height was recorded at 1 min, 1 h, 3 h, 6 h, and 24 h. After 24 h, the supernatant turbidity was measured.

2.2.2. Particle Size Analysis Testing

The particle size distribution of the flocculated sludge was characterized using a water sieving technique. Complementary laser diffraction analysis was also performed. Samples treated with different anionic solutions (NO₃⁻, SO₄²⁻, and Cl⁻) and anionic polyacrylamide (APAM) were analyzed to determine the effect of anions on floc size.

2.2.3. Consolidation Testing

A series of drained consolidation tests were performed using a precision consolidation device. Flocs formed from the polluted sludge with APAM under different anionic conditions were layered onto an oedometer ring (61.8 mm diameter) and subjected to sequential vertical loads of 12.5 kPa, 25 kPa, 50 kPa, 100 kPa, and 200 kPa. Key consolidation parameters, including the compression index, void ratio, and consolidation coefficient, were determined at each loading stage.

During one-dimensional consolidation tests, sediment compressibility is primarily influenced by changes in the void ratio and effective stress. It is commonly characterized by the coefficient of compressibility (${\mathrm{a}}_{\mathrm{v}}$), which is calculated using the formula shown in Equation (1). A lower compression coefficient indicates lower sediment compressibility.

$${\mathrm{a}}_{\mathrm{v}}={\displaystyle \frac{\mathsf{\Delta }\mathrm{e}}{\mathsf{\Delta }\mathrm{p}}}$$

(1)

where $\mathsf{\Delta }\mathrm{e}$ denotes the change in the void ratio and $\mathsf{\Delta }\mathrm{p}$ represents the change in effective stress.

In practice, the consolidation coefficient (C_v) is typically used as a characteristic parameter to assess sediment consolidation and drainage performance. C_v is calculated using the square-root-of-time method, as presented in Equation (2), and its relationship with the permeability and compression coefficients is given in Equation (3). A lower consolidation coefficient indicates a slower drainage rate.

$${\mathrm{c}}_{\mathrm{v}}={\displaystyle \frac{0.848\times {\mathrm{H}}^2}{{\mathrm{T}}_{90}}}$$

(2)

$$\mathrm{k}={\mathsf{\gamma }}_{\mathrm{w}}\times {\mathrm{a}}_{\mathrm{v}}\times {\mathrm{C}}_{\mathrm{v}}$$

(3)

where H: the drainage path length (H = h for one-way drainage; H = h/2 for two-way drainage), T₉₀: the time corresponding to 90% settlement on the time–settlement curve, k: the permeability coefficient (cm/s or m/s), γ_w: the unit weight of water (kN/m³, typically assumed to be 9.81 kN/m³), and C_v: the sediment compressibility coefficient (m²/kN).

2.3. Microscopic Testing

2.3.1. Zeta Potential Measurement

To elucidate the impact mechanisms of various anions on heavy metal-contaminated sludge, the surface charge distribution of the flocs was systematically characterized using a commercial SurPASS 3 Zeta potential analyzer (Anton Paar, Graz, Austria). Finely ground floc samples were precisely weighed and carefully introduced into the analyzer’s measurement cell. Prior to initiating the measurement protocol, the electrolyte solution was adjusted to the desired pH using diluted NaOH and HCl solutions to ensure optimal and consistent testing conditions. The measurement process was rigorously conducted in accordance with the instrument’s guidelines, with three consecutive readings obtained and averaged to enhance data reliability and precision.

2.3.2. Scanning Electron Microscopy (SEM)

Scanning electron microscopy (SEM) was employed using a Hitachi Regulus 8100 (Hitachi Ltd., Tokyo, Japan) at an accelerating voltage of 10 kV to characterize the microstructural features of the samples. High-resolution stereoscopic images were acquired to facilitate a detailed observation and analysis of the microstructural changes in heavy metal-contaminated sludge and sediment samples treated with various anions. Prior to imaging, samples underwent meticulous preparation—including dehydration and gold sputter-coating—to enhance electron conductivity and ensure optimal image quality. SEM analysis provided critical insights into the influence of different anions on the pore structure, surface morphology, and aggregation behavior of the sediment matrix.

3. Results and Discussion

3.1. Micro Characterization and Zeta Potential Value

The flocculation microstructure and surface charge characteristics of sediments treated with different anions were systematically characterized using SEM and zeta potential analysis. Figure 3a–c show SEM images at 5000× magnification, which reveal distinct morphological differences. A quantitative analysis of these images, corroborated by zeta potential measurements (Figure 3d), provides a clear mechanistic explanation for the observed behaviors.

The nitrate (NO₃⁻)-treated sediment (Figure 3a) exhibited the most compact structure, with the smallest observable interparticle spacing and minimal pore sizes. This dense morphology is directly linked to its lowest measured absolute zeta potential of −22.14 mV, indicating weakened electrostatic repulsion between particles. Conversely, the sulfate (SO₄²⁻)-treated sediment (Figure 3c) produced the smallest, loosest floc aggregates with the largest interparticle spacing and pore volumes, which corresponded to the highest absolute zeta potential of −14.51 mV. The chloride (Cl⁻)-treated system (Figure 3b) showed an intermediate microstructure, consistent with its zeta potential value of −17.67 mV.

This clear correlation can be fundamentally attributed to the charge density of the anions. SO₄²⁻, as a divalent anion, possesses a high charge density, which leads to strong electrostatic repulsion between sediment particles and anionic polyacrylamide (APAM) chains. This repulsion, quantified by the high zeta potential, hinders close particle approach, resulting in the observed open, loose floc structure. In contrast, the monovalent NO₃⁻ ion has a larger ionic radius and a more diffuse charge distribution, resulting in a lower charge density. This minimizes interparticle repulsion (evidenced by the low zeta potential), thereby promoting tighter particle packing and the formation of larger, denser flocs. The properties of Cl⁻ are intermediate, leading to the observed transitional state in both zeta potential and floc morphology.

3.2. Flocculation Sedimentation Efficiency

Three particle size distribution curves for contaminated sediments treated with APAM flocculation in the presence of different anions (nitrate, sulfate, and chloride) are displayed by Figure 4a. It can be observed that the flocs formed with nitrate are larger than those formed with chloride, which, in turn, are larger than those formed with sulfate. Specifically, the fraction of particles smaller than 0.075 mm decreased to 3.98% for the nitrate-treated flocs, to 5.32% for the chloride-treated flocs, and to 6.22% for the sulfate-treated flocs.

This behavior is mainly attributed to the characteristics of the anions. Nitrate, as a monovalent anion with a relatively low charge, is more readily net captured and subject to sweeping action onto sediment particle surfaces via electrostatic interactions. Moreover, because the ionic radius of the nitrate ion is larger than that of the chloride ion, its negative charge is more diffusely distributed, resulting in weaker electrostatic forces. Consequently, sediment particles are net captured and subject to sweeping action more efficiently, with a smaller interparticle spacing (denoted as d_a) along a flocculant chain of a given length (l_a), as shown in Figure 5a. This leads to the formation of larger flocs when nitrate is introduced.

Although chloride is also a monovalent anion, its higher charge density and smaller ionic radius result in a less pronounced effect. The interparticle spacing (denoted as db) is larger compared to that of nitrate, so fewer sediment particles are net captured and subject to sweeping action on a flocculant chain of the same length, yielding smaller flocs than those formed with nitrate.

In contrast, sulfate is a divalent anion with the highest charge density. Its double negative charge may induce an excessively strong electrostatic repulsion, resulting in an even larger interparticle spacing (denoted as dc). This leads to the adsorption of the fewest sediment particles along a flocculant chain of length l_a, hindering the formation of bridging structures and thereby limiting its effective binding with APAM. As a result, sulfate produces the smallest flocs.

Here, we mainly use sedimentation height to qualitatively characterize sedimentation rate and flocculation efficiency, and verify the flocculation efficiency with the flocculent particle size results under different anions later. Therefore, only sedimentation height is listed here. Figure 4b presents the sedimentation behavior of copper–zinc-contaminated sediments in the presence of different anions. The results indicate that, over a 24 h period, the final sedimentation height follows the following order: nitrate > chloride > sulfate. Sedimentation is most rapid in the nitrate system, where an initial height of 3.31 cm is achieved within the first hour and then stabilizes at 3.52 cm. In contrast, chloride-induced sedimentation occurs at a slightly slower rate, with an initial height of 3.13 cm and a final height of 3.35 cm. Sulfate produces the smallest flocs, resulting in the lowest sedimentation performance, with heights of 2.36 cm in the first hour and 2.67 cm at 24 h.

This behavior is primarily attributed to the floc characteristics: the nitrate system forms the largest flocs, which, due to their greater mass, experience a stronger gravitational force that more effectively overcomes fluid resistance. Field observations in the Yangtze Estuary further validate this mechanism: during flood seasons, flocs with equivalent diameters exceeding 80 μm exhibited settling velocities up to 2.5 mm/s, consistent with Stokes’ law predictions after accounting for fractal structure and density adjustments. According to Stokes’ law [31], the settling velocity of particles is proportional to the square of their diameter, meaning that larger flocs settle more quickly and achieve a higher sedimentation height. In comparison, the flocs formed with chloride are smaller, leading to a slightly reduced sedimentation height, while the smallest flocs in the sulfate system result in the lowest sedimentation.

Turbidity, serving as a critical parameter for quantifying suspended particulate matter and colloidal substances in aqueous systems, provides a quantitative indicator for evaluating the flocculation efficiency of sedimentary aggregates through supernatant turbidity measurements. This analytical approach fundamentally bridges the colloidal behavior characterization with the optimization of solid–liquid separation processes in environmental engineering applications. As shown in Figure 4c, the supernatant turbidity exhibited significant variations depending on the introduced anions. The nitrate-modified sludge demonstrated the lowest turbidity value of 4.6 NTU, followed by the chloride-modified sludge with a slightly higher turbidity of 8.1 NTU. Notably, the sulfate-modified sludge exhibited the highest supernatant turbidity (17.1 NTU), exceeding the nitrate and chloride systems by 12.5 NTU and 9 NTU, respectively.

This phenomenon occurs because measurable turbidity in the supernatant indicates that some sediment particles remain unflocculated. In the nitrate system, the small interparticle spacing (d_a) allows a greater number of sediment particles to be net captured and sweeping action along a flocculant chain of fixed length (l_a). As a result, the flocculation efficiency is high, enabling the flocs to aggregate and settle effectively, which reduces the concentration of suspended particles. Since these particles contribute little to light scattering and absorption [32], the measured turbidity is the lowest. In the chloride system, the interparticle spacing (db) is larger than in the nitrate system, leading to fewer sediment particles being captured per flocculant chain and a slight reduction in flocculation efficiency; thus, more particles remain suspended and the turbidity is higher. Conversely, although the sulfate system exhibits small interparticle spacing (dc), it adsorbs the fewest sediment particles on an equal-length flocculant chain, resulting in the lowest flocculation efficiency. Consequently, a larger number of suspended particles—characterized by a wider particle size distribution that scatters and absorbs light more strongly—remains in the supernatant, yielding the highest turbidity.

Table 3. Quantitative comparison of flocculation and consolidation parameters for sediments under different anionic conditions.

Basic Properties	NO3−	Cl−	SO42−
Fraction of Particles < 0.075 mm (%)	3.98	5.32	6.22
Final Settling Height (cm)	3.52	3.35	2.67
Supernatant Turbidity (NTU)	4.6	8.1	17.1
Compression Coefficient, av (MPa−1, at 100–200 kPa)	1.52	1.56	1.58
Consolidation Coefficient, Cv (10−3 cm2/s, at 100–200 kPa)	1.27	1.10	1.06

shows a quantitative comparison of sediment flocculation and sedimentation parameters under different anion conditions.

3.2.1. Porosity

According to the one-dimensional consolidation compression theory, the porosity ratio and the consolidation coefficient have a regular consistency. When other conditions remain unchanged, the larger the porosity ratio, the larger the consolidation coefficient. We have conducted parallel tests on the porosity ratio results. From the porosity ratio results, the porosity ratio of the sediment introduced with nitrate is the largest, and the porosity ratio of the sediment introduced with sulfate is the smallest, which is completely consistent with our consolidation coefficient relationship curve. Figure 6d illustrates the variation in the porosity of copper–zinc-contaminated sediments treated with different anions under various loads. Initially, all three sediment systems exhibit relatively high porosity values: the nitrate-treated sediment has a porosity of 2.79, the chloride-treated sediment a porosity of 2.84, and the sulfate-treated sediment a porosity of 3.08. As the applied load increases, the porosity decreases rapidly with rising consolidation stress before gradually leveling off; at a load of 100 kPa, the porosity is reduced to a range of 1.48–1.52—corresponding to an 89–91% decrease in the total porosity. This indicates that the sediments are highly sensitive under low stress, exhibiting significant deformation.

Moreover, the final porosity differs among the systems: the sulfate-treated sediment retains the highest porosity, followed by the chloride-treated sediment, while the nitrate-treated sediment shows the lowest porosity. This behavior is primarily due to the differences in electrostatic interactions among sediment particles [33]. Sulfate ions, with their high charge density, induce strong electrostatic repulsion that maintains a larger interparticle spacing; consequently, for a given amount of flocculant per unit area, a smaller fraction of sediment particles aggregate, resulting in a greater pore area and the highest porosity. In contrast, chloride ions have a lower charge density and smaller ionic radius, which produce weaker repulsive forces and slightly reduce interparticle spacing. This allows more sediment particles to aggregate—yielding a lower porosity than the sulfate system. Nitrate ions, having a larger ionic radius than chloride ions and an even lower charge density, generate the weakest repulsive force. Thus, the interparticle spacing is minimized, enabling the greatest particle aggregation and leading to the smallest pore area and the lowest overall porosity, as shown in Figure 5b.

3.2.2. Compression Factor

Figure 6e presents the variation in compression coefficients of copper–zinc-contaminated sediments treated with different anions under various loads. The data indicate that the initial compression coefficients for all three anion-treated sediments are relatively high, with the nitrate system reaching 54.13 MPa⁻¹, the chloride system reaching 67.98 MPa⁻¹, and the sulfate system reaching 69.26 MPa⁻¹. As the applied load increases, the compression coefficient decreases rapidly at first and then gradually levels off. Under typical engineering consolidation pressures (100–200 kPa), the nitrate-treated sediment exhibited the lowest compression coefficient (1.52 MPa⁻¹), followed by chloride (1.56 MPa⁻¹) and sulfate (1.58 MPa⁻¹), highlighting the critical role of anion-specific interactions in governing sediment compressibility.

The observed compressibility hierarchy (SO₄²⁻ > Cl⁻ > NO₃⁻) aligns with the fundamental geotechnical principle that high porosity leads to greater compressibility. The novelty of our findings lies in demonstrating that this principle can be controlled chemically through anion selection. While a previous work [34] has noted that anions could affect dewatering, our study quantitatively links specific anions to defined changes in pore structure and subsequent engineering properties. This advances the field by providing a predictive framework where the choice of anion can be used as a tool to pre-determine the compressibility and dewatering behavior of treated sediments.

This behavior aligns with broader observations in geotechnical studies of compressible materials. For instance, it has been demonstrated that high-frequency vibration loads (45 Hz, 8 kPa amplitude) enhanced the compressibility of normally consolidated soft clay by effectively reducing porosity (from 0.93 to 0.78 under 50–200 kPa). While their work focused on mechanical stimulation rather than chemical modification, both studies underscore the importance of pore structure evolution in determining compressibility. Similarly, revealed that increasing clay content from 20% to 50% amplified the compression coefficient by 80%, emphasizing the sensitivity of compressibility to microstructural configurations. In our study, sulfate-treated sediments—characterized by loose floc structures and high porosity—exhibited analogous behavior to high-clay-content soils, where weaker particle associations promote greater compressibility. Conversely, nitrate-induced dense aggregation mimics low-clay systems with reduced deformation potential.

Further parallels emerge in studies employing chemical additives to optimize compressibility. Reference [35] achieved a 24% reduction in the post-yield compression index (Cc) of dredged clay through synergistic solidification with red mud and phosphogypsum, attributing this improvement to pore-filling effects and alkaline cementation. Our findings reveal a comparable mechanism: nitrate ions act as “natural modifiers” by minimizing interparticle spacing and creating a compact pore network, thereby reducing the compression coefficient more effectively than chloride or sulfate. This similarity highlights the universal principle that material compressibility is fundamentally governed by interparticle interactions and pore geometry, whether modified through external additives or intrinsic ionic properties.

3.2.3. Consolidation Coefficient

Based primarily on Taylor’s theory [36], the consolidation and drainage performance of sediments is mainly affected by their permeability, compressibility, and void ratio.

The consolidation characteristics of copper–zinc-contaminated sediments with different anions were systematically investigated using oedometer tests under incremental loading conditions. As illustrated in Figure 6a–c, which depict the time-dependent compression behavior of sludge specimens (initial height = 2 mm) amended with nitrate (NO₃⁻), chloride (Cl⁻), and sulfate (SO₄²⁻) anions, the specimen height decreased progressively with increasing load duration. The final compressed heights measured 1.25 mm (NO₃⁻), 1.21 mm (Cl⁻), and 1.17 mm (SO₄²⁻), respectively. Notably, the sulfate-modified sludge exhibited the highest compression susceptibility, followed by the chloride- and nitrate-amended specimens, revealing anion-specific consolidation patterns under equivalent geotechnical stress conditions.

Figure 6f illustrates the changes in the consolidation coefficient of copper–zinc-contaminated sediments treated with different anions under various loads. The figure shows that, as the load increases, the consolidation coefficient decreases gradually. Initially, the consolidation coefficient is quite high, with the nitrate-treated sediment decreasing from 2.36 × 10⁻³ cm²/s to 1.27 × 10⁻³ cm²/s, the chloride-treated sediment from 2.13 × 10⁻³ cm²/s to 1.10 × 10⁻³ cm²/s, and the sulfate-treated sediment from 2.11 × 10⁻³ cm²/s to 1.06 × 10⁻³ cm²/s. Under a load range of 100–200 kPa, the final consolidation coefficients are ranked as follows: nitrate > chloride > sulfate.

In the sediments treated with nitrate, the absolute value of the zeta potential is the lowest, as nitrate ions gather near the surface of particles and the ion concentration is high. According to the Jones–Dole equation, a high ion concentration in the solution will significantly increase the viscosity coefficient of the system. The increase in the viscosity coefficient further hinders the permeability of the fluid in the pores of the sediment, which is manifested as a decrease in the permeability coefficient. The lower zeta potential makes the van der Waals attraction between particles dominate, promotes particle aggregation to form a dense structure, accelerates the self-weight consolidation process of the sediment, and leads to an increase in the consolidation coefficient. In the sediments treated with sulfate, the absolute value of the zeta potential is the highest, and the concentration of ions gathered on the surface of the particles is low, resulting in a low viscosity coefficient of the system, good permeability, and a low consolidation coefficient. In the sediments treated with chloride ions, the absolute value of the zeta potential is between the nitrate and sulfate systems, and the ion concentration, viscosity coefficient, and permeability are moderate, so its consolidation coefficient lies between the two [37]. The consolidation rate hierarchy (NO₃⁻ > Cl⁻ > SO₄²⁻) observed in this study provides important insights for optimizing dewatering operations in real-world applications where different anions may dominate the sediment environment.

Let us consider a practical case, for which, the PVD spacing is 1.0 m with a square pattern; the equivalent diameter of the PVD d_w = 0.05 m; the diameter of smear zone d_s = 0.2 m; k_h/k_s = 2; the discharge capacity of a PVD q_w = 100 m³/year; the hydraulic conductivity in the horizontal direction k_h = 2 × 10⁻⁹ m/s; the coefficient of consolidation in the horizontal direction c_h = 1.27 × 10⁻³ cm²/s; and the drainage length of PVD l = 5 m [38].

The consolidation caused by radial drainage of the PVDs can be calculated using Hansbo’s consolidation theory. The calculation Equation (4) is as follows:

$$U_t=1-\mathrm{e}\mathrm{x}\mathrm{p}({\displaystyle \frac{-8T_h}{\left(F_n+F_s+F_r\right)}})$$

(4)

where T_h is the time factor of the horizontal drainage, F_n is the drain spacing influence factor, F_s is the smear zone influence factor, and F_r is the well resistance influence factor. T_h can be calculated using Equation (5)

$${\mathrm{T}}_{\mathrm{h}}={\displaystyle \frac{{\mathrm{c}}_{\mathrm{h}}\mathrm{t}}{{\mathrm{D}}_{\mathrm{e}}^2}}$$

(5)

where c_h is the horizontal consolidation coefficient, t is the duration after pressurization, and D_e is the influence diameter of the drain. F_n can be calculated using Equation (6):

$${\mathrm{F}}_{\mathrm{n}}=\ln \left({\displaystyle \frac{D_e}{d_w}}\right)-{\displaystyle \frac{3}{4}}$$

(6)

where d_w is the equivalent diameter of the drainage board. F_s can be calculated using Equation (7):

$${\mathrm{F}}_{\mathrm{s}}=\left({\displaystyle \frac{{\mathrm{k}}_{\mathrm{h}}}{{\mathrm{k}}_{\mathrm{s}}}}-1\right)\mathrm{l}\mathrm{n}({\displaystyle \frac{{\mathrm{d}}_{\mathrm{s}}}{{\mathrm{d}}_{\mathrm{w}}}})$$

(7)

where k_h is the horizontal permeability coefficient of the undisturbed zone, k_s is the horizontal permeability coefficient of the smear zone, and d_s is the diameter of the smear zone. F_r can be calculated using Equation (8):

$${\mathrm{F}}_{\mathrm{r}}={\displaystyle \frac{\mathsf{\pi }\mathrm{z}(\mathrm{L}-\mathrm{z}){\mathrm{k}}_{\mathrm{h}}}{{\mathrm{q}}_{\mathrm{w}}}}$$

(8)

where L is the length of the drainage board, z is the distance from a point on the drainage board to the bottom of the drainage board, and qw is the discharge capacity of the drainage board. It should be noted that in the case calculation presented in this paper, the value of Fr is the average, with z set to 0.5 L.

From the consolidation results, as seen in Figure 7, the nitrate treatment demonstrates distinct temporal advantages. At an 80% consolidation degree, the nitrate-amended sediment required 79 days, 8.1% faster than the chloride system (86 days) and 16.5% faster than the sulfate system (92 days). This acceleration pattern intensified at 90% consolidation, where nitrate achieved target compaction in 117 days, 4.1% and 9.4% quicker than chloride (122 days) and sulfate (128 days), respectively. The consolidation rate hierarchy (NO₃⁻ > Cl⁻ > SO₄²⁻) aligns with previously discussed permeability–viscosity tradeoffs, reinforcing the anion-specific process optimization potential for sludge dewatering operations.

4. Conclusions

This study provides a definitive quantitative evaluation of anion-specific effects on the flocculation and consolidation of Cu-Zn-contaminated sediments. Nitrate (NO₃⁻) consistently yielded superior performance, forming the largest flocs, achieving the fastest settling (final height of 3.52 cm), the clearest supernatant (turbidity of 4.6 NTU), and the most efficient consolidation (coefficient of 1.27 × 10⁻³ cm²/s). In contrast, sulfate (SO₄²⁻) resulted in the poorest outcomes due to strong electrostatic repulsion, while chloride (Cl⁻) exhibited intermediate behavior. These trends, corroborated by SEM and zeta potential analyses, are governed by the charge density of the anions. The findings establish anion selection as a critical design parameter for optimizing sediment remediation. Future work should address the challenges of scaling up nitrate-based conditioning, particularly, managing nitrate leaching and its behavior in complex multi-anion systems under field conditions.