Facile Recovery of Polycationic Metals from Acid Mine Drainage and Their Subsequent Valorisation for the Treatment of Municipal Wastewater

Abstract

The presence of toxic and hazardous chemical species in municipal wastewater poses a significant environmental and public health challenge, necessitating innovative, sustainable, and cost-effective treatment solutions. This study pioneers the recovery and valorisation of polycationic metals from real acid mine drainage (AMD) for municipal wastewater treatment, demonstrating a novel approach that integrates resource recovery with wastewater remediation. A key strength of this study is the use of real municipal wastewater (authentic MWW) in the treatment phase, ensuring that the findings accurately reflect real-world conditions. Advanced analytical techniques were employed to characterise both aqueous and solid samples, and batch experiments were conducted to assess the removal efficiency of polycationic metals for key contaminants: ammonium (NH₄⁺), sulphate (SO₄²⁻), phosphate (PO₄³⁻), and nitrate (NO₃⁻). The optimised conditions are 2 g of polycationic metals per 100 mL, 90 min of contact time, and 35 °C. The yielded exceptional removal efficiencies are PO₄³⁻ (>99.9%), NH₄⁺ (>99.7%), NO₃⁻ (>99%), and SO₄²⁻ (>96%), achieving final concentrations of <0.5 mg/L for PO₄³⁻ and NH₄⁺, 2.1 mg/L for NO₃⁻, and 9.1 mg/L for SO₄²⁻. Adsorption kinetics followed a pseudo-first-order model, indicating physisorption, while the Two-Surface Langmuir model suggested a combination of homogeneous and heterogeneous adsorption mechanisms. FTIR, SEM-EDX mapping, and XRF analyses confirmed the retention of P, S, and N in the product sludge, validating the adsorption process. This study is the first of its kind to recover Al-rich Fe species from real AMD and activate them for municipal wastewater remediation using authentic MWW, bridging the gap between laboratory-scale research and real-world applications. By simultaneously addressing AMD pollution and municipal wastewater treatment, this research advances circular economy principles, promotes sustainable water management, and contributes to national and global efforts toward water security and environmental protection.

1. Introduction

Water pollution is a critical environmental issue with direct implications for several United Nations Sustainable Development Goals (SDGs). It poses significant public health risks by contaminating water sources with harmful pathogens and chemicals, which directly threatens SDG 3: Good Health and Well-Being, by increasing the spread of waterborne diseases and contributing to health crises [1]. The pollution of freshwater resources also impedes progress toward SDG 6: Clean Water and Sanitation, as contaminated water increases the need for more complex and costly treatment processes to ensure safe water access [2]. Economically, water pollution imposes substantial financial burdens due to the increased costs of water treatment and purification processes required to make water safe for consumption and use (SDG 12: Responsible Consumption and Production) [3]. Additionally, water pollution disrupts aquatic ecosystems, causing biodiversity loss and degrading natural habitats, which impacts SDG 14: Life Below Water, by harming marine and freshwater species and reducing the sustainability of aquatic environments [4]. Sustainable practices for water pollution control are essential for achieving these interconnected goals, ensuring the well-being of human populations and the preservation of ecosystems.

In recent years, acid mine drainage (AMD) has emerged as a significant environmental challenge due to its high acidity and elevated concentrations of dissolved heavy metals, such as iron (Fe), aluminium (Al), zinc (Zn), copper (Cu), manganese (Mn), and cadmium (Cd) [5]. Acid mine drainage is generated when sulphide minerals, commonly found in mine waste, are exposed to air and water, leading to a series of oxidation reactions [6]. The sulphide oxidation reaction of pyrite (FeS₂) in the presence of water is shown in Equation (1). This reaction produces ferrous iron (Fe²⁺), sulphate ions (SO₄²⁻), and hydrogen ions (H⁺), which contribute to the high acidity of AMD.

$$2{\mathrm{F}\mathrm{e}\mathrm{S}}_{2\left(\mathrm{s}\right)}+7{\mathrm{O}}_{2\left(\mathrm{g}\right)}+2{\mathrm{H}}_2{\mathrm{O}}_{\left(\mathrm{l}\right)}\to {2\mathrm{F}\mathrm{e}}_{\left(\mathrm{a}\mathrm{q}\right)}^{2+}+{4\mathrm{S}\mathrm{O}}_{4\left(\mathrm{a}\mathrm{q}\right)}^{2-}+{4\mathrm{H}}_{\left(\mathrm{a}\mathrm{q}\right)}^{+}$$

(1)

The ferrous iron can further oxidise to ferric iron (Fe³⁺), which precipitates as iron hydroxide, creating even more acidity, as illustrated in Equations (2) and (3).

$${\mathrm{F}\mathrm{e}}_{\left(\mathrm{a}\mathrm{q}\right)}^{2+}+{\displaystyle \frac{1}{4}}{\mathrm{O}}_{2\left(\mathrm{g}\right)}+{\mathrm{H}}_{\left(\mathrm{a}\mathrm{q}\right)}^{+}\to {\mathrm{F}\mathrm{e}}_{\left(\mathrm{a}\mathrm{q}\right)}^{3+}+{\displaystyle \frac{1}{2}}{\mathrm{H}}_2{\mathrm{O}}_{\left(\mathrm{l}\right)}$$

(2)

$${\mathrm{F}\mathrm{e}}_{\left(\mathrm{a}\mathrm{q}\right)}^{3+}+{3\mathrm{H}}_2{\mathrm{O}}_{\left(\mathrm{l}\right)}\to {\mathrm{F}\mathrm{e}\left(\mathrm{O}\mathrm{H}\right)}_{3\left(\mathrm{s}\right)}+{3\mathrm{H}}_{\left(\mathrm{a}\mathrm{q}\right)}^{+}$$

(3)

The high acidity and metal concentrations in AMD pose severe threats to aquatic ecosystems and water quality. The low pH of AMD can lead to the dissolution of toxic metals, such as aluminium, manganese, and other heavy metals, which can harm aquatic life and contaminate drinking water sources [7]. Equation (4) illustrates the dissolution of aluminium as one of the heavy metals in the AMD.

$${\mathrm{A}\mathrm{l}\left(\mathrm{O}\mathrm{H}\right)}_{3\left(\mathrm{s}\right)}+{3\mathrm{H}}_{\left(\mathrm{a}\mathrm{q}\right)}^{+}\to {\mathrm{A}\mathrm{l}}_{\left(\mathrm{a}\mathrm{q}\right)}^{3+}+{3\mathrm{H}}_2{\mathrm{O}}_{\left(\mathrm{l}\right)}$$

(4)

The treatment of acid mine drainage (AMD) involves a variety of technologies, including adsorption, which uses high-surface-area materials to selectively capture metals from AMD [8]. Membrane filtration technologies, such as microfiltration, ultrafiltration, nanofiltration, and reverse osmosis, are also commonly employed to separate dissolved metals from AMD using semi-permeable membranes [9]. Another effective approach is ion exchange, where ion exchange resins selectively remove metal ions from AMD through functional group interactions [10]. Additionally, bioremediation uses biological agents, such as bacteria and algae, to bioaccumulate or precipitate metals. Anekwe et al. [11] conducted a review on the bioremediation of AMD, highlighting the use of biological agents like bacteria and algae to facilitate the bioaccumulation or precipitation of metals through metabolic processes.

Beyond these treatment methods, the recovery and utilisation of heavy metals and minerals from AMD have gained significant interest due to the abundance of valuable metals like iron and aluminium. These metals, found in high concentrations in AMD, are ideal candidates for recovery and valorisation, particularly for environmental remediation and wastewater treatment applications [6,12]. AMD-recovered minerals have shown great potential in these fields due to their unique physicochemical properties, including a high surface area, enhanced reactivity, and superior adsorption capacity [13,14]. These properties make them particularly effective in removing contaminants from wastewater through adsorption, as the high surface area of the nanocomposites allows for greater interaction with pollutants, while their enhanced reactivity ensures efficient contaminant removal [15,16]. The recovery of minerals from AMD involves a range of advanced technologies aimed at reclaiming valuable metals and mitigating environmental impacts. One common method is precipitation, where chemical reagents are added to AMD to form insoluble metal compounds that are then separated [17,18,19]. This method is effective in recovering metals such as Fe, Al, and Mn by adjusting the pH of AMD to induce the formation of metal hydroxides [20,21]. Building on the previous work of our research group [22,23,24], this study explores the recovery of Fe and Al species from AMD and evaluates their application for the removal of contaminants from real municipal wastewater (authentic MWW). By combining recovery and remediation strategies, this approach offers a sustainable solution to AMD pollution.

Among the various sources of water pollution, authentic MWW is a significant contributor, often containing harmful contaminants such as ammonium (NH₄⁺), sulphate (SO₄²⁻), phosphate (PO₄³⁻), and nitrate (NO₃⁻) [25]. These pollutants stem from domestic activities, industrial processes, and agricultural runoff, posing serious risks to both human health and the environment [26]. Akinnawo et al. [27] highlighted that NH₄⁺ is toxic to aquatic organisms, while high levels of SO₄²⁻, PO₄³⁻, and NO₃⁻, can lead to eutrophication, causing algal blooms and oxygen depletion in water bodies. Consequently, the effective treatment of authentic MWW is crucial to ensure compliance with acceptable water standards and to prevent adverse ecological impacts [28].

Traditional methods for removing NH₄⁺, SO₄²⁻, PO₄³⁻, and NO₃⁻, such as chemical precipitation, ion exchange, and biological treatment, often face limitations in terms of efficiency and cost [29,30,31]. This has led to a growing demand for innovative and sustainable alternatives that not only improve the efficiency of contaminant removal but also offer cost-effective and environmentally friendly solutions. One promising approach is the use of advanced nanostructured materials, such as polycationic metals, which demonstrate superior performance in the removal of pollutants from wastewater through adsorption.

This study explores the feasibility and efficacy of polycationic metals recovered from real AMD for the removal of SO₄²⁻, NH₄⁺, PO₄³⁻, and NO₃⁻ from authentic MWW through adsorption. To the best of the authors’ knowledge, this is the first study to investigate the recovery of polycationic species from authentic AMD and their application for the effective removal of contaminants from authentic MWW. The findings of this research offer significant environmental benefits, advancing circular economy principles by promoting waste valorisation and beneficiation while simultaneously minimising ecological footprints. Unlike conventional approaches that focus solely on AMD neutralisation and secondary sludge disposal, this study demonstrates a resource-recovery-driven approach that repurposes AMD-derived materials for pollutant removal. The accumulation of secondary sludge from hazardous AMD has been widely recognized as a major environmental and economic burden [13], increasing remediation costs for mining operations and posing risks to human and ecological health.

By recovering valuable minerals from real AMD and synthesising a polycationic adsorbent for wastewater treatment, this study introduces a sustainable and cost-effective solution that addresses both AMD pollution and municipal wastewater contamination, offering a dual environmental benefit.

2. Materials and Methods

2.1. Feedstock Collection and Sample Preparation

Raw acid mine drainage (AMD) was sourced from industrial coal mining activities in the Mpumalanga Province, South Africa, while authentic MWW was collected from a wastewater treatment plant in Gauteng Province. Chemical reagents including ammonium chloride (NH₄Cl), disodium phosphate (Na₂HPO₄), sodium nitrate (NaNO₃), caustic soda (NaOH), magnesium sulphate heptahydrate (MgSO₄·7H₂O), and hydrochloric acid (37% HCl) were procured from Sigma-Aldrich and used without further purification. Ultra-pure water (18.2 MΩ cm resistivity) generated by the Elga PURELAB^® Flex system was used for all aqueous solutions. The prepared solutions were comprised of 420 mg/L, 180 mg/L, 226 mg/L, and 253 mg/L for phosphate, ammonia, nitrate, and sulphate, respectively, to correspond to the measured values in the authentic MWW. All glassware was meticulously cleaned before and after each use to prevent cross-contamination.

2.2. Synthesis of Polycationic Metals

Polycationic metals were synthesised following an established procedure [22]. Acid mine drainage was diluted with NaOH to adjust the pH to 4.5, followed by agitation for 60 min at room temperature. The solution was then heated to 100 °C with continuous stirring, after which the precipitate was collected via vacuum filtration, dried, and ground into fine powder using a vibrating ball mill. The material was further calcined at 750 °C and sieved to a particle size of 32 µm. The final product was stored in airtight bags to avoid contamination.

2.3. Preparation of Synthetic Wastewater

Synthetic wastewater stock solutions were prepared by dissolving NH₄Cl, Na₂HPO₄, MgSO₄·7H₂O, and NaNO₃ in 1 L of ultra-pure water. Fresh solutions were made from this stock for each batch experiment to optimise removal parameters.

2.4. Obtaining and Preparation of Authentic MWW Samples

Authentic MWW was sourced from the inlet of the activated sludge treatment process at an operational authentic MWW plant in Tshwane, South Africa (coordinates: 25°44′01.9″ S, 28°10′40.4″ E). The plant layout and operations are detailed by Muloiwa et al. [32]. To achieve the required nutrient concentrations for the isotherm experiments, the authentic MWW was spiked with varying amounts of NH₄Cl, Na₂HPO₄, MgSO₄·7H₂O, and NaNO₃.

2.5. Batch Adsorption Experiments

Two separate batch adsorption experiments were performed to assess the removal of SO₄²⁻, NH₄⁺, PO₄³⁻, and NO₃⁻ using the synthesised polycationic metals. In the first experiment, synthetic wastewater was used to reduce potential confounding factors, and the variables such as adsorbent dosage (ranging from 0.1 to 2 g), agitation time (10 to 300 min), operational temperature (25 to 55 °C), and pH (5 to 10) were optimised. All reactions were carried out at 300 rpm in 100 mL vessels.

The second experiment used authentic MWW, with unspiked authentic MWW for kinetic studies and spiked authentic MWW for isotherm analysis. This provided a more realistic evaluation of the adsorbent’s performance in a real wastewater setting.

All results were obtained in triplicate and are reported as mean values.

2.6. Sample Characterisation

Aqueous samples were characterised for concentrations of SO₄²⁻, NH₄⁺, PO₄³⁻, and NO₃⁻ using Ion Chromatography Mass Spectrometry (IC-MS: 940 Professional IC Vario). The pH of the solutions was measured using a Thermo Scientific™ Orion 3 Star portable pH meter, and electrical conductivity (EC), total dissolved solids (TDSs), and salinity were measured using a Mettler Toledo FiveGo EC/TDS/Salt/Temperature portable multimeter.

2.7. Sludge Characterisation

Solid samples were analysed before and after the authentic MWW adsorption experiments (experiment 2) using Fourier Transform Infrared Spectroscopy (FTIR—Bruker FTIR Spectrometer Alpha II Platinum-ATR with OPUS Version 8.2 TOUCH IR Spectroscopy Software, Bruker BioSpin AG, Fällanden, Switzerland), High-Resolution Scanning Electron Microscopy with Energy Dispersive X-Ray Spectroscopy (HR-SEM-EDX) (Carl Zeiss Sigma VP FE-SEM with Oxford EDX Sputtering System, Carl Zeiss AG, Oberkochen, Germany), and X-Ray Fluorescence (XRF) (Thermo Fisher ARL Perform’X Sequential XRF instrument with Uniquant Sulphide software, Thermo Fisher Scientific, Waltham, MA, USA) to evaluate functional groups, surface morphology, and elemental composition, respectively.

The HR-SEM-EDX elemental mapping was utilised to analyse the correlation between elements on the adsorbent surface, following the approach initially presented by Kpai et al. [33]. In brief, EDS maps for different elements were quantitatively compared by standardising image dimensions and pixel alignment using the Python Image Library (PIL Version 1.1). The images were cropped to uniform sizes and coordinates, converted to grayscale, and transformed into numerical matrices representing pixel luminescence values, which correspond to relative elemental concentrations. These matrices were then flattened and analysed using Pearson’s correlation coefficient (numpy corrcoef) to evaluate elemental distribution patterns and potential interactions or bonding relationships across the adsorbent surface.

3. Results and Discussion

3.1. Water Quality Results

This section outlines the process to determine the optimised conditions under which the contaminants NH₄⁺, SO₄²⁻, PO₄³⁻, and NO₃⁻ can be most effectively removed from authentic MWW. The results for the dosage, contact time, and temperature variations are reported in Figure 1.

3.1.1. Effect of Dosage

The attenuation of contaminants from authentic MWW as a function of dosage is shown in Figure 1a. The relationship between the residual concentrations of SO₄²⁻, NH₄⁺, PO₄³⁻, and NO₃⁻ and polycationic metal dosage is presented. The polycationic metals showed high potential for removing SO₄²⁻ from the wastewater, achieving a removal efficiency of ≥90%. For PO₄³⁻, the highest removal (≥90%) was observed at an adsorbent concentration of 2 g/100 mL. However, some resistance to removal was noted between 0.2 and 1 g/100 mL, which was overcome at higher dosages of 1.5–2 g/100 mL. NO₃⁻ removal was ≥90% at a dosage of 0.5 g/100 mL, while NH₄⁺ was completely removed (≥99.99%) at a dosage of 1 g/100 mL. The increase in dosage resulted in higher removal efficiencies, indicating that more surface area was available for the adsorption process. Based on these results, a dosage of 1.5 g/100 mL was identified as the optimum condition for the removal of contaminants from wastewater using polycationic metals recovered from acid mine drainage.

These findings are consistent with previous studies. For instance, Cheng et al. [34] reported 90% removal of NO₃⁻ from wastewater using 50 mg/L of Fe-Al bimetal. Similarly, Xu et al. [35] achieved 90% removal of PO₄³⁻ using 18.25 mg/L of FeSO₄-Al₂(SO₄)₃ coagulant. Additionally, Nurmesniemi et al. [36] demonstrated that SO₄²⁻ can be removed from wastewater with 87% efficiency using calcium sulfoaluminate (Ca₄Al₆O₁₂SO₄).

3.1.2. Effect of Contact Time

The attenuation of contaminants from synthetic municipal wastewater as a function of contact time is shown in Figure 1b. This figure illustrates the relationship between the residual concentrations of SO₄²⁻, NH₄⁺, PO₄³⁻, and NO₃⁻ and the agitation time. The polycationic metals demonstrated the ability to remove approximately 80% of SO₄²⁻ from the aqueous solution after 60 min of equilibration. For NO₃⁻, a removal efficiency of ≥99% was achieved after 90 min. PO₄³⁻ was almost completely removed (≥99.99%) at 90 min, while NH₄⁺ was fully removed by the 60 min mark. Based on these findings, 90 min was determined to be the optimum equilibration time for the removal of SO₄²⁻, NH₄⁺, PO₄³⁻, and NO₃⁻ from the wastewater system.

These results align with previous studies, such as Cheng et al. [34], who reported a 90% removal of NO₃⁻ from wastewater using Fe-Al bimetal, with an optimal contact time of 60 min.

3.1.3. Effect of Temperature

The attenuation of contaminants from synthetic municipal wastewater as a function of temperature is summarised in Figure 1c. This figure highlights the relationship between the residual concentrations of SO₄²⁻, NH₄⁺, PO₄³⁻, and NO₃⁻ at different temperatures. Polycationic metals demonstrated the potential to remove approximately 80% of SO₄²⁻ from the aqueous solution at temperatures between 25 and 35 °C. The decline in SO₄²⁻ removal efficiency at higher temperatures can be attributed to changes in the adsorption process and sulphate release from the adsorbent itself, which was originally derived from sulphate-rich acid mine drainage (AMD). As temperature increases, the kinetic energy of SO₄²⁻ ions rises, potentially weakening electrostatic interactions and reducing their binding affinity to adsorption sites. Furthermore, SO₄²⁻ trapped within Al-rich Fe(III) species during adsorbent synthesis may undergo thermal desorption, leading to its release back into the solution, sometimes resulting in negative removal values.

Additionally, competitive adsorption from H₂PO₄⁻ ions further limits the availability of active binding sites for SO₄²⁻, displacing it from the adsorbent surface. This effect is exacerbated by the declining solubility of SO₄²⁻ at elevated temperatures, in accordance with Le Chatelier’s principle, which predicts that higher temperatures reduce SO₄²⁻ retention on solid surfaces. Consequently, at temperatures above 35 °C, less SO₄²⁻ remains adsorbed, while increased desorption from the adsorbent matrix leads to a rise in the SO₄²⁻ concentration in the solution. These findings underscore the influence of the adsorbent’s initial sulphate content on SO₄²⁻ behaviour under varying temperature conditions, highlighting the need for adsorbent optimisation to mitigate sulphate leaching in high-temperature applications [37,38].

For NO₃⁻ removal, an efficiency of ≥99% was observed at 25 °C, while PO₄³⁻ was almost completely removed (≥99.99%) at 35 °C. NH₄⁺ was also fully removed at 25 °C. Therefore, 35 °C was identified as the optimum temperature for the removal of NH₄⁺, PO₄³⁻, and NO₃⁻ from the aqueous system.

3.1.4. Effect of pH

Figure 1d illustrates the attenuation of contaminants from synthetic municipal wastewater as a function of contact time, demonstrating exceptional stability in the removal of NO₃⁻, NH₄⁺, and PO₄³⁻, with removal efficiencies exceeding 95% across the entire pH range. This high adsorption performance aligns with previous studies that have explored similar adsorbent materials for nutrient removal. Ma et al. [39] demonstrated that a ferric sulphate-modified carbon/zeolite composite effectively removed NH₄⁺ and PO₄³⁻, achieving high adsorption capacities across varying pH conditions due to the strong binding affinity of iron-based functional groups for these ions. Similarly, Cheng et al. [34] reported that Fe-Al bimetal composites efficiently removed NO₃⁻ from wastewater, maintaining stable performance despite fluctuations in solution chemistry, which is consistent with the findings in this study. The polycationic metals demonstrated effective SO₄²⁻ removal from the aqueous solution, particularly within the pH range of 5–8. At a lower pH, the adsorbent surface is positively charged, enhancing Coulombic attraction with negatively charged SO₄²⁻ ions. However, as the pH increases, surface deprotonation reduces the positive charge density, weakening electrostatic attraction and lowering sulphate adsorption efficiency. Additionally, H₂PO₄⁻ acts as a competing anion, preferentially binding to active adsorption sites and displacing weakly held SO₄²⁻ ions. Since SO₄²⁻ has little competitive influence on H₂PO₄⁻, its removal efficiency continues to decline as pH increases. A similar trend was observed by Yamba et al. [40], who reported that sulphate removal efficiency decreases with increasing pH, further supporting the observed behaviour in this study. Overall, a pH of 7–8 was determined as the optimum range for the combined removal of SO₄²⁻, NH₄⁺, PO₄³⁻ and NO₃⁻.

3.1.5. Treatment of an Authentic MWW Stream at Optimised Conditions

The attenuation of contaminants from authentic MWW under optimised conditions is summarised in

Table 1. Attenuation results of contaminants from authentic MWW under optimised conditions.

Parameters	World Health Organization (WHO)	South African National Standards (SANS) 241	Co (mg/L)	Ce (mg/L)	% Removal
PO43−	No specific guideline	No specific standard for drinking water	420.8	<0.5	99.9
NH4+	≤1.5 mg/L	≤1.5 mg/L	180.7	<0.5	99.7
NO3−	≤50 mg/L	≤11 mg/L	226.0	2.114	99
SO42−	≤250 mg/L	≤250 mg/L	253.9	9.138	96

.

As depicted in Table 1, the raw wastewater contained significant concentrations of oxyanions and ammonia, all of which exceeded the thresholds established by the World Health Organization (WHO) [41] and the South African National Standards (SANS) for drinking water. These elevated concentrations pose a significant environmental risk, as they promote the excessive growth of aquatic plants and complicate water treatment. However, after treatment with polycationic metals, the contaminants were efficiently removed, with the sequence of removal efficacy being PO₄³⁻ ≥ NH₄⁺ ≥ NO₃⁻ ≥ SO₄²⁻.

PO₄³⁻ and NH₄⁺ were particularly easy to remove, achieving near-complete removal rates. NO₃⁻, while effectively removed, faced some challenges, likely due to the limited capacity of the polycationic metals or their specific reactivity with NO₃⁻, which may require complementary treatment processes, such as reverse osmosis, for more complete removal [42]. Additionally, the presence of multiple competing ions in the wastewater could reduce the availability of reactive sites on the polycationic metals, further impacting NO₃⁻ removal efficiency [43,44].

A study by Ordonez et al. [45] utilised iron fillings to remove nutrients from wastewater, achieving removal efficiencies of 42% for NO₃⁻ and 98% for PO₄³⁻. Similarly, Ma et al. [39] reported removal rates of up to 88% for NH₄⁺ and 99% for PO₄³⁻ when using a ferric sulphate-modified carbon/zeolite composite. In terms of SO₄²⁻ removal, Nurmesniemi et al. [36] demonstrated that calcium sulphoaluminate can remove 98% of SO₄²⁻ from synthetic wastewater and 87% from industrial wastewater. Additionally, Xie et al. [46] investigated the removal of PO₄³⁻ from wastewater using calcium–aluminium layered double oxides with aluminium sludge, achieving a 98.85% removal rate. Overall, the use of polycationic metals recovered from real acid mine drainage in this study proved to be highly effective in removing contaminants from authentic MWW.

3.2. Characterisation Results of Sludge Samples

3.2.1. Functional Groups

Fourier Transform Infrared Spectroscopy (FTIR) was employed to characterise the functional groups of the recovered and valorised polycationic metals before and after their interaction with fortified authentic MWW [47]. Specifically, Figure 2 presents the functional groups identified in the polycationic metals following the removal of contaminants from the aqueous media. This analysis provides insights into the chemical changes and bonding interactions that occurred during the adsorption process.

A summary of the peaks and bands corresponding to the different functional groups depicted in Figure 2 is provided in Table 2 to support and substantiate the results. This table highlights the key wavenumbers and their associated functional groups, offering a detailed understanding of the chemical interactions involved during the removal of contaminants.

As shown in Figure 2 and summarised in Table 2, the FTIR analysis reveals the peaks and bands associated with various functional groups in the polycationic metals after their reaction with SO₄²⁻, NH₄⁺, PO₄³⁻, and NO₃⁻. The results indicate Fe–O bending and stretching peaks between 438 and 528 cm⁻¹ [48], signifying the presence of iron-based minerals. The Al–O peak signifies the involvement of aluminium-based minerals, indicating that aluminium oxides or hydroxides play a significant role in the formation of complexes with contaminants like phosphates and sulphates [49]. The Al–O bond is critical for binding phosphate ions, as aluminium exhibits a strong affinity for phosphate. The SO₄ stretching peak at 1082 cm⁻¹ [50] suggests the formation of aluminium sulphate or oxyhydrosulphates.

Table 2. Functional groups and their respective wavenumbers and references.

Wavenumber (cm−1)	Functional Group	Reference
438–528	Fe–O	[48]
911	Al–O	[49]
1082	–SO4	[51]
1425	N=O	[52]
1061–1645	–PO4	[53]
3367	H–O–H	[54]
3404–3678	O–H stretching	[55]
3200–3500	N–H	[56]

Table 2. Functional groups and their respective wavenumbers and references.

Wavenumber (cm−1)	Functional Group	Reference
438–528	Fe–O	[48]
911	Al–O	[49]
1082	–SO4	[51]
1425	N=O	[52]
1061–1645	–PO4	[53]
3367	H–O–H	[54]
3404–3678	O–H stretching	[55]
3200–3500	N–H	[56]

The N=O peak suggests the presence of nitrate (NO₃⁻) in the adsorbed state. Nitrates are likely bound to the surface of the polycationic metals through electrostatic interactions or coordination bonds with iron or aluminium [52]. The broad range of peaks corresponds to phosphate (PO₄³⁻) groups, suggesting that phosphates are strongly bound to the surface of the polycationic metals, likely through the formation of aluminium or iron phosphate complexes [53]. The affinity of aluminium and iron for phosphate enhances the removal efficiency of PO₄³⁻ from wastewater [57].

The H–O–H bending peak indicates the presence of water molecules, suggesting that the polycationic metals are hydrated and that water plays a role in the adsorption process [54]. The hydration of the adsorbent is common in aqueous environments and can influence the adsorption capacity. The detection of hydroxyl (–OH) groups confirm that the sludge produced during the reaction is hydrated [55]. The presence of hydroxyl groups also implies that the metal oxides or hydroxides are in a hydrated state, which may enhance their reactivity and adsorption capacity.

The presence of the N–H group further demonstrates that the polycationic metals effectively removed NH₄⁺ from the authentic MWW, as evidenced by its incorporation into the sludge [56].

Overall, the FTIR analysis provides strong evidence of the chemical interactions taking place between the polycationic metals and the various contaminants (SO₄²⁻, NH₄⁺, PO₄³⁻, and NO₃⁻). The identified functional groups confirm that iron and aluminium oxides or hydroxides are the primary active sites responsible for the adsorption of these contaminants. The presence of hydroxyl and water molecules further supports the notion that hydration plays a role in the overall adsorption process. The ability of these polycationic metals to adsorb multiple contaminants, including SO₄²⁻, NH₄⁺, PO₄³⁻, and NO₃⁻, highlights their versatility and effectiveness in wastewater treatment applications.

3.2.2. Elemental Properties

The elemental composition of the product sludge, following the removal of contaminants from authentic MWW, was analysed using Energy Dispersive X-Ray Spectroscopy (EDX), with the results presented in Figure 3.

The EDX results confirm that iron (Fe), oxygen (O), and aluminium (Al) are the predominant elements in the polycationic metals, indicating the presence of Fe–O and Al–O compounds. This finding aligns with the functional groups identified in the FTIR analysis, further supporting the involvement of iron and aluminium oxides in the adsorption process. Additionally, the presence of phosphorus (P), nitrogen (N), and sulphur (S) suggests that the contaminants, including SO₄²⁻, NH₄⁺, PO₄³⁻, and NO₃⁻, were successfully removed from the wastewater and incorporated into the sludge.

The detection of trace elements such as sodium (Na), potassium (K), magnesium (Mg), chlorine (Cl), calcium (Ca), lanthanides (Ln), silicon (Si), and carbon (C) provides further insights into the composition of the sludge. The presence of carbon is likely due to the sample coating used during the SEM measurements, rather than an inherent component of the sludge.

This comprehensive elemental analysis highlights the effectiveness of the polycationic metals in capturing and removing a wide range of contaminants from the authentic MWW matrix. The identification of key elements involved in the adsorption process validates the role of these metals in wastewater treatment and underscores their potential for targeting diverse chemical species.

3.2.3. Elemental Composition

The elemental composition of the product sludge after the removal of contaminants from authentic MWW was determined using X-Ray Fluorescence (XRF), with the results presented in

Table 3. The elemental composition (wt.%) of the product sludge after the removal of contaminants from authentic MWW as measured by XRF.

Chemical Species	Composition (wt.%)	Chemical Species	Composition (wt.%)
Fe	46.82	Cl	1.14
P2O5	17.65	CaO	0.43
Al2O3	17.31	SiO2	0.22
SO3	4.02	BaO	0.06
Na2O	3.53	Cs2O	0.05
K2O	3.29	Co3O4	0.04
MgO	1.30	Gd2O3	0.03
Chemical species	Composition (ppm)	Chemical species	Composition (ppm)
Mn	301	Yb	79
Zn	250	Bi	45
Pb	147	Cr	43
Ag	128

.

As shown in Table 3, the product sludge’s composition was dominated by iron (Fe) and aluminium (Al), which are key elements in AMD. The high concentrations of Fe (46.82 wt.%) and Al (17.31 wt.% in the form of Al₂O₃) reflect the polycationic metals’ predominant role in the sludge. Phosphorus (P₂O₅, 17.65 wt.%) and sulphur (SO₃, 4.02 wt.%) were also abundant, confirming the effective removal of phosphate (PO₄³⁻) and sulphate (SO₄²⁻) from the wastewater.

Trace elements such as manganese (Mn), zinc (Zn), lead (Pb), silver (Ag), ytterbium (Yb), bismuth (Bi), and chromium (Cr) were present in smaller quantities. These elements were likely introduced from the AMD and retained in the sludge during the treatment process. On the other hand, the presence of sodium (Na₂O), potassium (K₂O), magnesium (MgO), and calcium (CaO) in lower concentrations points to their association with the authentic MWW.

The XRF results align well with the findings from other analytical techniques, such as FTIR and SEM-EDX, confirming the effectiveness of the polycationic metals in capturing a wide range of contaminants from both AMD and authentic MWW. These results highlight the sludge’s complex composition, reflecting both the original constituents of the AMD and the treated authentic MWW.

3.2.4. Morphological and Microstructural Properties

The morphological and microstructural properties of the product sludge were examined using Field Emission Scanning Electron Microscopy (FE-SEM). High-resolution images were captured, clearly revealing the fine microstructural details of the product sludge, as shown in Figure 4.

The product sludge exhibited a notable degree of homogeneity, with the surface predominantly consisting of spherical-like structures [58] interspersed with octagonal-like formations. These structures were uniformly distributed across the surface, indicating a consistent microstructural pattern throughout the sludge. A detailed examination across various magnifications, ranging from 2 µm to 200 nm, showed that the sludge retained its microstructural and morphological properties consistently. This uniformity across different scales confirms that the physical characteristics of the sludge do not vary significantly, suggesting structural stability [59].

Furthermore, the analysis confirmed that the product sludge exhibits a fully crystallised structure, with the SEM images revealing well-defined and uniform crystal lattices. This observation reinforces the conclusion that the minerals in the sludge are evenly distributed and crystallised [60]. The presence of these uniform crystal structures at all magnification levels supports the sludge’s homogeneity and highlights its predominantly amorphous nature.

The SEM analysis underscores the stability and consistency of the sludge’s composition and morphology, validating the efficiency of the treatment process in producing a uniform end product. The consistent microstructure and crystallinity suggest that the polycationic metals effectively facilitated the formation of a well-structured, stable sludge during the removal of contaminants from authentic MWW.

3.2.5. Elemental Mapping

In this section, the SEM micrograph and corresponding EDX elemental mapping of the product sludge are presented, as shown in Figure 5. The primary goal was to gain insights into the elemental composition of the product sludge after the removal of contaminants from authentic MWW [47]. The elemental mapping confirmed the presence of Fe, O, Al, P, S, N, Mg, and Ca, as depicted in Figure 5. These elements correspond to some of the peaks observed in the FE-SEM images, further substantiating the formation of polycationic metals. The rod-like structures primarily represent Fe, Al, and O, indicating the formation of polycationic metal complexes, while spherical structures suggest the presence of Mg, Ca, K, Mn, and Cl [47]. Overall, these results confirm the high efficacy of polycationic metals in removing contaminants from authentic MWW, with the adsorption process being particularly effective for capturing these elements from the aqueous matrix.

The chemical composition of the sludge, as revealed through the elemental analysis, suggests the formation of various compounds during the adsorption of contaminants. The following reactions illustrate the formation of different hydroxide compounds during the treatment process (Equations (5)–(8)).

Equation (5) illustrates the reaction between aluminium ions with hydroxide ions to denote the possible formation of aluminium hydroxide [61] from AMD.

$${\mathrm{A}\mathrm{l}}^{3+}+3{\mathrm{O}\mathrm{H}}^{-}\to \mathrm{A}\mathrm{l}(\mathrm{O}\mathrm{H}{)}_{3\left(\mathrm{s}\right)}$$

(5)

Similarly, Equation (6) shows that iron ions combine with hydroxide ions to potentially form iron (III) hydroxide [62] from AMD.

$${\mathrm{F}\mathrm{e}}^{3+}+3{\mathrm{O}\mathrm{H}}^{-}\to \mathrm{F}\mathrm{e}(\mathrm{O}\mathrm{H}{)}_{3\left(\mathrm{s}\right)}$$

(6)

In addition to the hydroxides, before the removal of contaminants from authentic MWW, the sludge contains magnesium and Ca, which can react with hydroxide ions to form potentially magnesium hydroxide, as illustrated in Equations (7) and (8).

$${\mathrm{M}\mathrm{g}}^{2+}+2{\mathrm{O}\mathrm{H}}^{-}\to \mathrm{M}\mathrm{g}(\mathrm{O}\mathrm{H}{)}_{2\left(\mathrm{s}\right)}$$

(7)

$${\mathrm{C}\mathrm{a}}^{2+}+2{\mathrm{O}\mathrm{H}}^{-}\to \mathrm{C}\mathrm{a}(\mathrm{O}\mathrm{H}{)}_{2\left(\mathrm{s}\right)}$$

(8)

Throughout the removal of contaminants from authentic MWW, the polycationic metals, predominantly composed of Fe and Al, adsorb ions such as NH₄⁺, PO₄³⁻, NO₃⁻, and SO₄²⁻. This process results in clean water that meets both World Health Organization (WHO) guidelines and South African National Standards (SANS 241) for various water uses. These reactions further demonstrate the efficiency of polycationic metals in removing a wide range of contaminants from wastewater while producing a stable, crystalline sludge.

3.3. Correlations Between Elements in SEM-EDX

Applying the method developed in Kpai et al. in 2023 [33], the elemental maps as presented in Figure 5 were correlated to establish the similarities between the elemental distributions in the maps. The resulting Pearson correlation coefficients are shown in Figure 6.

The Pearson correlation coefficient matrix highlights several key relationships between the elements, reflecting their co-localisation and interaction in the material recovered from acid mine drainage (AMD) and utilisation for the adsorption of NH₄⁺, PO₄³⁻, NO₃⁻, and SO₄²⁻. Notably, the strong correlations involving phosphorus (P), nitrogen (N), sulphur (S), iron (Fe), and aluminium (Al) provide critical insights into the adsorption mechanisms.

3.3.1. Strong Correlations Involving P, N, and S

The correlations reveal that P and N are strongly associated with Fe and Al. P shows strong correlations with Fe (r = 0.69) and Al (r = 0.82), indicating that these elements form stable associations, likely as iron and aluminium phosphates. These phosphates are essential for the adsorption of PO₄³⁻ from wastewater, with iron and aluminium oxides acting as the primary adsorption sites [63].

Similarly, N, which may exist in the form of NH₄⁺ or NO₃⁻, also correlates strongly with Fe (r = 0.52) and Al (r = 0.65). This suggests that iron and aluminium oxides or hydroxides serve as efficient adsorbents for nitrogen species, particularly NH₄⁺. The material’s ability to capture nitrogen compounds is closely tied to these strong elemental associations, with iron and aluminium compounds providing a reactive surface for adsorption through ion exchange or electrostatic interactions [45].

3.3.2. Role of Sulphur in Adsorption

S also demonstrates a strong correlation with Fe (r = 0.69) and Al (r = 0.77), indicating the presence of sulphur in the form of SO₄²⁻ or sulphide compounds. This strong association with iron and aluminium suggests that the material is well-suited for adsorbing sulphur-based contaminants, particularly SO₄²⁻ ions. Sulphur’s strong correlation with iron likely reflects the formation of iron sulphides or the adsorption of SO₄²⁻ onto iron oxides, both of which are common in materials recovered from AMD [22].

The correlation between S and P (r = 0.65) further indicates the possibility of the co-adsorption of PO₄³⁻ and SO₄²⁻ ions in the same regions of the material. This could suggest the formation of mixed anionic adsorption sites on iron and aluminium oxides, where both SO₄²⁻ and PO₄³⁻ compete for surface binding, a behaviour observed in multi-anion adsorption systems [22].

3.3.3. Implications for Adsorption of NH4+, PO43−, NO3−, and SO42−

The strong correlations between P, N, S, Fe, and Al highlight the material’s dual functionality, effectively adsorbing both cations (e.g., NH₄⁺) and anions (e.g., PO₄³⁻ and SO₄²⁻). Iron and aluminium oxides, formed during AMD treatment, serve as primary adsorption sites for both PO₄³⁻ and SO₄²⁻ ions, while their interaction with nitrogen species, particularly NH₄⁺, enhances the material’s overall adsorption capacity.

PO₄³⁻ and SO₄²⁻ are likely adsorbed through anion exchange processes, where they interact with the positively charged surface sites of iron and aluminium oxides [64].

NH₄⁺ is adsorbed through ion exchange with the oxides, while NO₃⁻ may be removed through similar electrostatic interactions [65,66].

The coexistence of S and P with Fe and Al indicates that the material’s surface is well-adapted to handling complex mixtures of contaminants, which are typical of wastewater streams that contain high levels of nitrogen, phosphorus, and sulphur compounds.

3.3.4. Moderate Correlations with Other Elements

While P, N, and S exhibit strong correlations with Fe and Al, moderate correlations were also observed with other elements. For example, magnesium (Mg) shows a moderate correlation with iron (Fe) (r = 0.36), suggesting that magnesium may also contribute to the structural stability of the material or act as a secondary adsorbent for certain ions.

3.3.5. Negative Correlations

A negative correlation was observed between Fe and Al with carbon (C) (r = −0.41 and r = −0.25, respectively), suggesting that C is likely present in a distinct phase, separate from the metal-based compounds. This carbon is likely a result of the sputtering process used to coat the material with carbon in preparation for SEM-EDX analysis. However, it appears that the carbon tends to “avoid” regions dominated by the metal phases, which is a point of interest.

The strong correlations between P, N, and S with Fe and Al underscore the material’s effectiveness in adsorbing key contaminants such as PO₄³⁻, NH₄⁺, NO₃⁻, and SO₄²⁻. The interactions between these elements suggest that iron and aluminium oxides provide a highly reactive surface for the adsorption of both cations and anions, making the material versatile for treating wastewater streams with mixed contaminants. Sulphur’s strong association with iron and aluminium further highlights the material’s ability to capture SO₄²⁻ ions, a common pollutant in acid mine drainage-affected waters. These findings emphasise the material’s broad applicability in environmental remediation, particularly for the removal of nitrogen, phosphorus, and sulphur compounds from aqueous solutions.

3.4. Adsorption Kinetics

The transient data from the experimental runs were fitted using the pseudo-first-order and pseudo-second-order kinetics models (Equations (9) and (10), respectively [67]), and the kinetic data on the removal of contaminants from authentic MWW are shown in Figure 7 and summarised in

Table 4. Kinetics data on the removal of contaminants from aqueous solution.

Model/Model Parameters	NO3− (mg/L)	SO42− (mg/L)	PO43− (mg/L)	NH4+ (mg/L)
Pseudo first order
Best-fit values
Qe	23.50	20.82	44.05	17.97
k1	0.01864	0.02700	0.01800	0.3448
Goodness of Fit
R squared	0.8718	0.9510	0.9548	0.9998
Sum of Squares	107.5	28.88	112.9	0.06530
Sy.x	3.919	2.031	4.017	0.09659
Pseudo second order
Best-fit values
k1	0.0006240	0.001260	0.0003477	0.1436
Qe	29.13	24.34	53.87	18.06
Goodness of Fit
R squared	0.8407	0.9130	0.9332	0.9999
Sum of Squares	133.6	51.32	166.7	0.01568
Sy.x	4.369	2.708	4.881	0.04733

.

$${\displaystyle \frac{d{Q}_t}{dt}}=k_1\left(Q_e-Q_t\right)$$

(9)

$${\displaystyle \frac{d{Q}_t}{dt}}=k_1{\left(Q_e-Q_t\right)}^2$$

(10)

To assess the reliability and variability of the kinetic data, a 95% prediction interval was calculated for the adsorption kinetics model fits. The prediction interval provides an estimate of the range within which future observations are expected to fall with 95% confidence, accounting for both model uncertainty and inherent data variability.

For the pseudo-first-order and pseudo-second-order kinetic models (Equations (9) and (10)), the prediction interval was determined using the standard error of regression and the t-distribution [22].

In Figure 7, the 95% prediction intervals are shown as shaded regions around the model curves, representing the expected variation in contaminant removal efficiency. The narrow prediction intervals indicate a high degree of model reliability and strong agreement between experimental data and the fitted kinetic models.

By including this statistical analysis, the study ensures the robust interpretation of adsorption kinetics, improving the reliability of the findings for real-world wastewater treatment applications.

In analysing the adsorption kinetics for the removal of NO₃⁻, SO₄²⁻, PO₄³⁻, and NH₄⁺ from authentic MWW using polycationic metals, it is essential to determine the most suitable kinetic model that accurately reflects the adsorption behaviour of the system. Given the coexistence of multiple contaminants in the complex wastewater matrix, both pseudo-first-order and pseudo-second-order kinetic models were exhaustively examined to identify the best fit.

The pseudo-first-order kinetic model exhibited strong correlations with the adsorption data, particularly for SO₄²⁻ (R² = 0.9510), PO₄³⁻ (R² = 0.9548), and NH₄⁺ (R² = 0.9998), as shown in Table 4. These elevated R² values indicate a high degree of agreement between the experimental data and the model predictions, especially for NH₄⁺, where the fit was nearly perfect. For NO₃⁻, however, the pseudo-first-order model displayed a slightly weaker fit (R² = 0.8718), suggesting that the adsorption mechanism for this contaminant may be more complex or influenced by additional factors not fully captured by the model. Despite this minor deviation, the pseudo-first-order model generally provides a robust representation of the adsorption kinetics for most contaminants.

On the other hand, the pseudo-second-order kinetic model performed exceptionally well for NH₄⁺ (R² = 0.9999), indicating that the adsorption of ammonium ions is likely governed by chemisorption [22,67], characterised by stronger interactions between the adsorbate and the adsorbent. However, for contaminants like NO₃⁻ (R² = 0.8407) and SO₄²⁻ (R² = 0.9130), the pseudo-second-order model exhibited weaker correlations, implying that this model does not fully capture the adsorption kinetics for these species. The lower fit suggests that physisorption still plays a significant role [22,67], and the assumption of chemisorption may be less applicable to the adsorption of NO₃⁻ and SO₄²⁻.

Considering the simultaneous presence of these contaminants in the same wastewater system, the overall adsorption kinetics align more closely with the pseudo-first-order model. The stronger and more consistent fit across key contaminants like SO₄²⁻, PO₄³⁻, and NH₄⁺ supports this conclusion, particularly in scenarios where multiple ions are competing for adsorption sites. While the pseudo-second-order model performs significantly well for NH₄⁺, its weaker fit for NO₃⁻ and SO₄²⁻ indicates that physisorption is the dominant mechanism driving the overall adsorption process. Consequently, the pseudo-first-order model emerges as the more appropriate representation of the adsorption kinetics for the removal of contaminants from authentic MWW using polycationic metals, suggesting that physical adsorption, with some influence of chemisorption for NH₄⁺, largely characterises the process.

3.5. Adsorption Isotherms

The adsorption isotherms for the removal of contaminants from fortified authentic MWW using polycationic metals were examined to better understand the mechanisms governing the adsorption process. The Langmuir, Freundlich, and Two-Surface Langmuir isotherm models, shown in Equations (11)–(13), were applied to fit the equilibrium data [22,67].

$$Q_e={\displaystyle \frac{k_L{Q}_{max,L}{C}_e}{1+k_L{C}_e}}$$

(11)

This model assumes monolayer adsorption onto a surface with a finite number of identical sites.

• $Q_e$: amount of contaminant adsorbed per unit mass of adsorbent at equilibrium (mg/g);
• $Q_{max,L}$: maximum adsorption capacity (mg/g);
• $k_L$: Langmuir adsorption constant (L/mg), related to the affinity of binding sites;
• Cₑ: equilibrium concentration of the contaminant in solution (mg/L).

$$Q_e=K_F{C}_e^{{\displaystyle \frac{1}{n}}}$$

(12)

The Freundlich model describes multilayer adsorption on a heterogeneous surface.

• $K_F$: Freundlich constant, indicative of adsorption capacity;
• n: heterogeneity factor, indicating the favourability of adsorption;

$$Q_e={\sum }_{i=1}^2{\displaystyle \frac{k_{L,i}\left(T\right)Q_{max,i}{C}_e}{1+k_{L,i}\left(T\right)C_e}}$$

(13)

This model considers adsorption on two types of surfaces with different affinities and capacities.

• Qₑ: amount of contaminant adsorbed per unit mass of adsorbent at equilibrium (mg/g);
• $Q_{max,i}$: maximum adsorption capacity of the i-th surface (mg/g);
• $k_{L,i}$: Langmuir constant for the i-th surface (L/mg);

These models provide insight into the nature of the adsorption process and help identify whether the adsorption is monolayer, multilayer, or occurs on multiple types of surfaces.

As depicted in

Table 5. Adsorption isotherm models for removal of contaminants from fortified authentic MWW using polycationic metals.

Model/Model Parameters	NO3− (mg/L)	SO42− (mg/L)	PO43− (mg/L)	NH4+ (mg/L)
Langmuir
Best-fit values
QMAX	249.6	26.62	142.6	120.0
KL	0.0073	0.06083	0.0714	0.003672
Goodness of Fit
R squared	0.9842	0.9988	0.8844	0.9379
Sum of Squares	170.3	0.3875	1486	121.6
Sy.x	4.932	0.3594	14.57	6.367
Freundlich
Best-fit values
K	3.459	6.436	24.52	1.435
n	1.346	3.675	2.576	1.486
Goodness of Fit
R squared	0.971	0.991	0.9629	0.9511
Sum of Squares	311.7	3.012	476.8	95.84
Sy.x	6.673	1.002	8.253	5.652
Two-Surface Langmuir
QMAX1	0	0	13.58	3.61
QMAX2	249.6	26.62	169.3	170.8
KL2	0.0073	0.06083	0.03042	0.001959
Goodness of Fit
R squared	0.9842	0.9988	0.9416	0.9426
Sum of Squares	170.3	0.3875	750.8	112.4
Sy.x	5.327	0.4402	11.19	7.498

and Figure 8, the adsorption isotherm results offer insights into the adsorption behaviour of SO₄²⁻, NH₄⁺, PO₄³⁻ and NO₃⁻ using polycationic metals from fortified authentic MWW, elucidating the efficacy of different adsorption models.

For the contaminants under investigation, the Langmuir isotherm model demonstrates a robust fit for NO₃⁻ (R² = 0.9842), SO₄²⁻ (R² = 0.9988), and PO₄³⁻ (R² = 0.9379), signifying strong correlations in adsorption. Conversely, the fit for NH₄⁺ is less robust (R² = 0.8844), indicating that the Langmuir model may not adequately encapsulate the adsorption characteristics of NH₄⁺. The elevated Q_MAX values for NO₃⁻ (249.6 mg/g) and NH₄⁺ (142.6 mg/g) denote a high adsorption capacity for these ions, while SO₄²⁻ and PO₄³⁻ exhibit moderate capacities of 26.62 mg/g and 120.0 mg/g, respectively. Despite the strong fit for most contaminants, the diminished correlation for NH₄⁺ implies that the Langmuir model may not represent the most appropriate framework for elucidating the overall adsorption process within this system.

Similarly, the Freundlich isotherm model yields a reasonably good fit for all contaminants, with R² values of 0.971 for NO₃⁻, 0.991 for SO₄²⁻, 0.9629 for NH₄⁺, and 0.9511 for PO₄³⁻. This observation suggests that the Freundlich model can accommodate variations in adsorption energies across disparate sites. The parameter n, indicative of the intensity of adsorption, exceeds 1 for all contaminants (ranging from 1.346 for NO₃⁻ to 3.675 for SO₄²⁻), thereby affirming favourable adsorption conditions. Although the Freundlich model does not yield the highest R² values in comparison to the Langmuir isotherm, it performs satisfactorily across all contaminants, indicating that adsorption may not exclusively transpire on a homogeneous surface and that multilayer adsorption may be influential, particularly for NH₄⁺ and PO₄³⁻.

Furthermore, the Two-Surface Langmuir isotherm model exhibits high performance for SO₄²⁻ (R² = 0.9988) and demonstrates strong correlations for NO₃⁻ (R² = 0.9842), NH₄⁺ (R² = 0.9416), and PO₄³⁻ (R² = 0.9426). Additionally, this model indicates an elevated Q_MAX2 for NH₄⁺ (169.3 mg/g) and PO₄³⁻ (170.8 mg/g), signifying a superior adsorption capacity for these ions in contrast to the traditional Langmuir model. The enhanced fitting for NH₄⁺ implies that this isotherm more accurately encompasses the intricate nature of the adsorption mechanism for NH₄⁺, which may be affected by the existence of multiple adsorption sites. The Two-Surface Langmuir model, through its capacity to consider various types of adsorption sites, facilitates a more thorough comprehension of the adsorption dynamics of all contaminants, particularly within systems where adsorption may exhibit non-uniform characteristics.

Considering that the contaminants coexist within the same authentic MWW matrix, the Two-Surface Langmuir isotherm emerges as the most appropriate model for elucidating the overall adsorption dynamics. It provides exemplary fits for SO₄²⁻ and robust correlations for NO₃⁻, NH₄⁺, and PO₄³⁻, while also accommodating the presence of multiple adsorption sites, which is likely in a complex wastewater environment. The increased adsorption capacities (Q_MAX2 values) for NH₄⁺ and PO₄³⁻ further substantiate its relevance. Although the Langmuir and Freundlich isotherms yield insights into specific facets of the adsorption behaviour, the Two-Surface Langmuir model presents a more integrative representation of the adsorption phenomena concerning contaminants from authentic MWW utilising polycationic metals.

3.6. Proposed Adsorption Mechanism

The adsorption of contaminants such as SO₄²⁻, NH₄⁺, PO₄³⁻, and NO₃⁻ from authentic MWW using polycationic metals recovered from acid mine drainage (AMD) operates through a combination of electrostatic interactions, ion exchange, and surface complexation mechanisms. The positively charged surfaces of the polycationic metals, primarily Fe and Al, attract negatively charged anions like SO₄²⁻ and PO₄³⁻ through electrostatic attraction. These anions then form surface complexes with the Fe and Al hydroxides present on the adsorbent’s surface [22].

For cationic species such as NH₄⁺, the removal occurs primarily through ion exchange mechanisms. The strong evidence for this mechanism comes from the rapid and nearly complete removal of NH₄⁺ at a relatively low dosage of 1 g, indicating a strong affinity for NH₄⁺. This removal occurs through the displacement of other cations (such as H⁺) on the surface of the iron and aluminium oxides present in the polycationic metals. Furthermore, the adsorption of NH₄⁺ follows pseudo-second-order kinetics, a characteristic behaviour of chemisorption, including ion exchange. This suggests that the interaction between NH₄⁺ and the adsorbent is governed by a chemical process rather than mere physical adsorption, further supporting the ion exchange mechanism [45].

The presence of iron and aluminium oxides, which are known for their ion exchange capabilities, also provides structural evidence. The functional groups associated with these oxides, particularly hydroxides (–OH), act as exchange sites where NH₄⁺ can displace other ions. Elemental analysis, such as the detection of nitrogen (N) in the post-adsorption material, supports the conclusion that NH₄⁺ is successfully adsorbed onto the polycationic metals, reinforcing the role of ion exchange in the removal process [44].

In systems with multiple ions, such as PO₄³⁻, NO₃⁻, and SO₄²⁻, competition for available adsorption sites occurs. PO₄³⁻ ions, due to their strong electrostatic interactions with Fe and Al hydroxides, have a higher affinity for the adsorption sites, resulting in superior removal efficiency compared to NO₃⁻. The kinetic analysis suggests that the adsorption process follows pseudo-first-order kinetics for most contaminants, especially SO₄²⁻ and PO₄³⁻, indicating that physical adsorption plays a significant role. However, the pseudo-second-order kinetics observed for NH₄⁺ confirm that ion exchange dominates its removal [45].

Adsorption isotherms reveal that the Langmuir model fits the data well for NO₃⁻ and PO₄³⁻, indicating monolayer adsorption on homogeneous sites. For NH₄⁺, the Freundlich model suggests multilayer adsorption, pointing to a more heterogeneous process, likely due to the presence of multiple sites for ion exchange or chemisorption. Temperature also plays a key role in adsorption efficiency. As temperature increases, the removal of SO₄²⁻ decreases, consistent with the exothermic nature of the process. Conversely, higher temperatures enhance the adsorption of PO₄³⁻ and NH₄⁺, likely due to increased diffusion rates or the overcoming of activation energy barriers.

In systems with multiple contaminants, co-adsorption and competition between PO₄³⁻ and SO₄²⁻ occur, as both are attracted to the same Fe and Al surface sites. This competition can reduce adsorption efficiency, particularly at higher temperatures or varying dosages. Ultimately, the polycationic metals demonstrate their versatility and effectiveness as adsorbents, capable of removing a wide range of contaminants from wastewater through both physical adsorption and chemical bonding mechanisms, with ion exchange playing a key role in the removal of NH₄⁺.

4. Conclusions and Recommendations

This study successfully demonstrated the recovery of polycationic metals (Fe³⁺ and Al³⁺) from acid mine drainage (AMD) and their subsequent valorisation for municipal wastewater (MWW) treatment. The process effectively removed NH₄⁺, SO₄²⁻, PO₄³⁻, and NO₃⁻ ions, achieving final water quality that meets WHO and South African National Water Standards under optimal conditions of 2 g of polycationic metals, 90 min of contact time, and a temperature of 35 °C.

The removal of contaminants was driven by a combination of ion exchange, surface complexation, and co-precipitation mechanisms, with pseudo-first-order kinetics indicating physisorption as the dominant process. Isotherm analysis best fit the Two-Surface Langmuir model, suggesting a mix of homogeneous and heterogeneous adsorption due to varying ion affinities for adsorption sites. Sludge characterisation confirmed the presence of Fe, Al, P, S, N, Mg, and Ca, further validating the adsorbent’s ability to capture multiple contaminants through chemisorptive and electrostatic interactions.

Despite the clear potential of the recovered adsorbent, several challenges must be addressed to ensure large-scale applicability. These include scalability, competition from coexisting ions in complex wastewater matrices, long-term adsorbent stability, and regeneration efficiency. Understanding the mechanistic interactions between the polycationic metals and various contaminants will be crucial for optimising performance and improving adsorption selectivity. The further refinement of operational parameters, such as dosage control, contact time, and pH adjustments, will be necessary to enhance efficiency across different wastewater conditions.

Future research will focus on pilot-scale validation and industrial-scale implementation, ensuring that the system remains efficient, cost-effective, and adaptable to real-world treatment settings. Additionally, assessing the reusability and regeneration potential of the adsorbent will be critical in evaluating its economic and environmental feasibility. A comprehensive technoeconomic assessment will provide insights into the financial viability of the technology, while a life cycle assessment (LCA) will quantify its environmental impact and sustainability over time.

To further enhance contaminant removal efficiency and broaden applicability, hybrid treatment approaches combining adsorption with advanced oxidation processes, membrane-based separation, or biological treatments may be explored. These integrated strategies could improve pollutant removal while addressing challenges such as ion competition and adsorbent exhaustion, making the system more resilient and effective in diverse wastewater treatment applications.