Investigating the sustainable performance of a nanoscale zerovalent iron permeable reactive barrier for removal of nitrate, sulfide, and arsenic

Due to climate change, an extensive drought has spread worldwide. This drought's severity and prolonged duration put the water conditions at a catastrophic point. Efficient and sustainable management of freshwater resources, mainly groundwater, is necessary to deal with this crisis (Hussain 2020; Mao et al. 2022). Improving this water source is a high priority because many countries use it as drinking water. Shahdadi & Sari (2011) showed that nitrate and arsenic are the most prominent contaminants in the existing groundwater resources of most watersheds in Iran.

Nitrate is an essential nutrient for living organisms; however, the release of excessive amounts in aquatic systems often causes environmental and ecological challenges such as eutrophication (Dauvin et al. 2007; Roy & Malenica 2013; Zabaleta & Rodic 2015). Kishida et al. (2009) and Gao et al. (2018) have reported that nitrate concentration in livestock wastewater can be up to 650 mg/L. According to health studies, a high nitrate concentration can cause methemoglobinemia (blue baby syndrome) and brain damage (Majumdar & Gupta 2000; Ward et al. 2005). Arsenic is one of the crucial carcinogens that cause severe diseases like pulmonary disorders, endocrine disorders, cardiovascular effects, immunity, neurological disorders, and genetic toxicity, in which exposure can alter the integrity of human cells and genetic material. Sulfides (H₂S_(aq), HS⁻ and S²⁻) are considered one of the most unwanted contaminants in groundwater resources. Because of their unpopular taste and odor, sulfides at a negligible concentration make aquifers non-operational for municipal or industrial use (Mahmood et al. 2007).

Since the beginning of the recent decade, efforts have been made to integrate sustainability and green technology practices, which is considered a vital parameter in all treatment strategies by ITRC¹ (Barrier 2011). Conventional technologies used to treat contaminated groundwater are based on pumping and treatment systems, although the primary treatment goals are rarely achieved with these systems. This technique has become obsolete in the last two decades with the modern sustainability and renewable energy concept. Permeable reactive barrier (PRB) technology could be an alternative to traditional pumping and treatment systems to treat contaminated groundwater (Henderson & Demond 2007). Since 1990, many PRBs have been designed and implemented in different forms, including funnel and gat (Thiruvenkatachari et al. 2008). The reactants are inserted in front of the subsurface contaminated flow path to convert contaminants into environmentally acceptable by-products and reduce the contaminant concentration downstream. This technology is passive, meaning that all the energy required is provided by natural energy sources (gravity, microbial, metabolic, and photosynthesis), and these systems have appropriate longevity (Younger 2009). Among the various studies, PRBs have been recognized as helpful technologies for treating the majority of groundwater contaminants (Obiri-Nyarko et al. 2014; Robertson et al. 2014; Mondal et al. 2016; Jeen 2017; Faisal et al. 2020).

PRB's footprint during the operation phase is green and sustainable because of using recycled materials and hiring locally based suppliers of materials and equipment (Torres & Gómez 2020). Many studies have suggested that nanozerovalent iron (nZVI) allows an effective, safe, and economical reduction of arsenic from groundwater (Siddiqui et al. 2019). Research shows that nZVI PRBs will perform efficiently for 10–30 years, depending on the flow rate through the system and the amount of dissolved solids (EPA E.P.A 2002). Rahmani et al. (2011) comprehended that a 1.0 g/L nZVI could decrease the As(III) concentration to the WHO² limit due to their small particle size and high inherent activity. The high cost of ZVI-PRBs to treat many pollutants led to a search for more cost-competitive alternatives to reactive media. PRBs made with plant mulch have successfully treated groundwater contamination in recent years more economically than ZVIs (Bombaywala 2020). The performance of nZVIs and mZVIs in contaminant treatment is limited by particle aggregation, instability, and phase separation, even at low iron concentrations (Gastone et al. 2014). Precipitation of reactive materials and a decrease in the longevity of PRB are fundamental challenges and need further studies (Thakur et al. 2020).

Some studies showed that several modifications had been made to the design of PRBs to improve their performance. To date, various compounds have been developed to stabilize nZVIs, such as polyacrylic (Pei et al. 1995; Schrick et al. 2004; Kanel et al. 2005), tri-block copolymer (Shihadeh & Saleh 2005), guar gum (Tiraferri et al. 2008), carboxymethylcellulose (He et al. 2007), starch (He et al. 2007), and 4-polyester sulfonate (Dorsey et al. 1996). In 2022 and 2023, Ibrahim Maamoun et al. confirmed that magnesium hydroxide-coated iron nanoparticles (Fe⁰ @ Mg(OH)₂) protect Fe⁰ from rapid aqueous corrosion for the removal of Cr(VI) (Maamoun et al. 2022; Maamoun et al. 2023). Surface modifiers increase the stability of mZVI and nZVI by providing a steady repulsion force between particles, decreasing the viscosity of the suspension, and facilitating the transfer of particles through the porous media (Xue & Sethi 2012). Most commercial biopolymers, xanthan gum (XG) and guar gum have been used broadly in soil stabilization. Their positive impact on soil sustainability has been recorded in several studies (Dehghan et al. 2019; Lee et al. 2019), such as improved strength of soil (Fatehi et al. 2018; Arab et al. 2019), stiffness (Ayeldeen et al. 2017), hydraulic conductivity (Bouazza et al. 2009; Cabalar et al. 2017), and dust resistance (Chen et al. 2015, 2019). XG increases the stability of nZVI particle suspension and potentially facilitates their transfer in a porous saturated medium (Zhong et al. 2013). According to Xin et al. (2015), the transport and sustainability of nanoparticles modified with xanthan biopolymer compared to the net utilization. On the other hand, organic mulch is one of the lowest-cost alternatives among the various reactant materials in PRBs. Organic mulch is a complex (insoluble) carbon source that is naturally filled with an accumulation of microorganisms. Microorganisms remove chlorinated contaminants through catalytic transport pathways. In addition to the low cost of PRB systems, mulch systems provide a more extensive refining media than ZVI-PRBs (Robertson et al. 2000). Robertson et al. (2008) showed that a sawdust-based PRB could effectively treat nitrate for about 15 years. In the review article, Santanu Maitra 2019 (Naghikhani et al. 2021) mentioned various adsorbents used to treat water contaminated with mineral and organic compounds.

If a single remediation system can remove nutrients simultaneously, it can offer a cost-effective solution for groundwater contamination (Buyanjargal et al. 2021). There are numerous papers on PRBs, most of which focus on removing contaminants with multi-barrier systems (Noubactep 2010). Two-layer barriers with nanoparticles are suitable for removing contaminants such as heavy metals, nitrates, and sulfates so that high concentrations of contaminants are significantly reduced to a minimum (Barrier 2011).

Since the appearance of the first nZVI PRB in the United States, this technology has evolved from an innovative to an accepted standard method. However, the nZVI PRB field application is hampered by the lack of stability of NZVI suspensions. Accordingly, extensive studies and additional efforts are required to select new materials, determine their properties, increase absorbent longevity, and thus identify their suitability for use in PRB. On the other hand, predicting the longevity of PRBs and several other issues have yet to be conclusively answered, so the main focus for additional research should be on innovative technologies to increase a system's longevity (Thiruvenkatachari et al. 2008). Two-barrier PRB was examined in this study to determine whether it was effective in removing contaminants simultaneously, increasing the longevity of nZVIs, and its performance over time. As a part of the study, we compared bare nZVI PRB with a PRB that removes nitrate, sulfide, and arsenic from groundwater using mulch and XG-nZVI. This study aimed to develop a method for treating nitrate, sulfide, and arsenic simultaneously in groundwater with organic mulch and XG-nZVI; and to determine whether XG-nZVI + Mulch and bare nanoparticles can be utilized for longevity over 10 days (Table 1).

Table 1

Research questions and a summary of the answering and interpreting processes

Question .	Sampling plan .	Analysis plan .	Interpretation .
Does the XG-nZVI + Mulcha PRB have a proper filtration function?	The average of two samples in a day and two repeated. Totally 40 samples for each contaminant	✓ Eliminations graphs (Figures 7–12)	Bare nanoparticle barrier performance was significantly reduced, but the XG-nZVI + Mulch barrier was more stable during the experiment
What is the difference between XG-nZVI + Mulch and bare nZVI PRB performance and stability?	The average of two samples in a day and two repeated. Totally 40 samples for each contaminant	✓ Correlation Pearsonb ✓ Descriptive Statisticsc likes Std. Deviation	The XG-nZVI + Mulch barrier's performance for eliminating ⁠, As, and S2− is generally improved compared to the Bare nZVI barriers by 5.7 and 19.2, and 10.9%, respectively.
What is the correlation and effect of the three contaminants with each other?	The average of two samples in a day and two repeated. Totally 40 samples for each contaminant	✓ Correlation Pearson ✓ hierarchical clusteringd	It was apparent in the correlation matrix that and S2− interact the most.

Question .	Sampling plan .	Analysis plan .	Interpretation .
Does the XG-nZVI + Mulcha PRB have a proper filtration function?	The average of two samples in a day and two repeated. Totally 40 samples for each contaminant	✓ Eliminations graphs (Figures 7–12)	Bare nanoparticle barrier performance was significantly reduced, but the XG-nZVI + Mulch barrier was more stable during the experiment
What is the difference between XG-nZVI + Mulch and bare nZVI PRB performance and stability?	The average of two samples in a day and two repeated. Totally 40 samples for each contaminant	✓ Correlation Pearsonb ✓ Descriptive Statisticsc likes Std. Deviation	The XG-nZVI + Mulch barrier's performance for eliminating ⁠, As, and S2− is generally improved compared to the Bare nZVI barriers by 5.7 and 19.2, and 10.9%, respectively.
What is the correlation and effect of the three contaminants with each other?	The average of two samples in a day and two repeated. Totally 40 samples for each contaminant	✓ Correlation Pearson ✓ hierarchical clusteringd	It was apparent in the correlation matrix that and S2− interact the most.

^aA permeable reactive barrier designed in research.

^bCorrelation is significant at the p-value = 0.01 in one-tailed.

^cSuch as range, min, max, mean, median, std. deviation, skewness, and kurtosis.

^dAgglomeration clustering with the Wards and Pearson computational methods.

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The iron nanoparticles with a purity of 98% and a particle diameter of about 20–60 nm were provided by Iranian Nanomaterials Pioneers (INP)³. Some details of these particles are shown in Table 2.

The solid phase needs a widespread morphological profile, composition, internal structure, and chemistry of the nZVI. The laboratory of Iranian Pioneers in nanomaterials implemented this analysis. Approval of nanoparticles is based on X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Brunaeur–Emmett–Teller (BET) (Naghikhani et al. 2021).

The structural characteristics, including BET, and porosity and pore size distribution, are measured by nitrogen absorption and isotherm desorption at INP (Table 3).

Table 4

Standard limits and the initial density of experiments (mg/L)

Contaminant .	WHO .	Iran .	Experiment .
	44.43	50	100
As	0.01	0.01	1
S2−	250	250	200

Contaminant .	WHO .	Iran .	Experiment .
	44.43	50	100
As	0.01	0.01	1
S2−	250	250	200

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Table 5

Descriptive statistical analyzes of bare nZVI

.	Range statistic .	Min. statistic .	Max statistic .	Mean .		Median .	Std. deviation .	Variance .
				Statistic .	Std. error .
	48.45	15.3	63.75	33.985	5.425	29.82	17.15	294.35
S2−	58.15	93.85	152.00	117.26	5.843	112.85	18.47	341.46
As(V)	0.36	0.29	0.65	0.487	0.043	0.5048	0.136	0.019
.	Skewness .		Kurtosis .		.	.	.	.
	statistic .	Sth. error .	statistic .	Sth. error .	.	.	.	.
	0.651	0.687	−0.910	1.334
S2−	0.813	0.687	−0.203	1.334
As(V)	−0.116	0.687	−1.788	1.334

.	Range statistic .	Min. statistic .	Max statistic .	Mean .		Median .	Std. deviation .	Variance .
				Statistic .	Std. error .
	48.45	15.3	63.75	33.985	5.425	29.82	17.15	294.35
S2−	58.15	93.85	152.00	117.26	5.843	112.85	18.47	341.46
As(V)	0.36	0.29	0.65	0.487	0.043	0.5048	0.136	0.019
.	Skewness .		Kurtosis .		.	.	.	.
	statistic .	Sth. error .	statistic .	Sth. error .	.	.	.	.
	0.651	0.687	−0.910	1.334
S2−	0.813	0.687	−0.203	1.334
As(V)	−0.116	0.687	−1.788	1.334

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Table 6

Descriptive statistical analyzes of XG-nZVI

.	Range statistic .	Min. statistic .	Max statistic .	Mean .		Median .	Std. deviation .	Variance .
				Statistic .	Std. Error .
	25.70	18.60	44.30	28.22	2.85	25.35	9.01	81.281
S2−	38.15	82.85	121.00	95.44	4.16	90.46	13.15	173.081
As(V)	0.22	0.20	0.20	0.295	0.023	0.2815	0.0754	0.006
.	Skewness .		Kurtosis .		.	.	.	.
	statistic .	Sth. error .	statistic .	Sth. error .	.	.	.	.
	0.889	0.687	−0.512	1.334
S2−	1.052	0.687	−0.053	1.334
As(V)	0.435	0.687	−1.022	1.334

.	Range statistic .	Min. statistic .	Max statistic .	Mean .		Median .	Std. deviation .	Variance .
				Statistic .	Std. Error .
	25.70	18.60	44.30	28.22	2.85	25.35	9.01	81.281
S2−	38.15	82.85	121.00	95.44	4.16	90.46	13.15	173.081
As(V)	0.22	0.20	0.20	0.295	0.023	0.2815	0.0754	0.006
.	Skewness .		Kurtosis .		.	.	.	.
	statistic .	Sth. error .	statistic .	Sth. error .	.	.	.	.
	0.889	0.687	−0.512	1.334
S2−	1.052	0.687	−0.053	1.334
As(V)	0.435	0.687	−1.022	1.334

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A dynamic shear rheometer determines the rheology characteristics of the XG solution mass. The viscosity-dependent shear rate can be determined with high accuracy by the following power equation (Xue & Sethi 2012):

When n is zero, Newtonian current behavior occurs. For larger values of n, the nature of the shear shell rate of the solution becomes more important.

With increasing XG concentration, the stability of particles in an aqueous solution has increased significantly. Hydrophilic polymers with long, trailing rings enter the solution and achieve steric stability (Tiraferri et al. 2008).

The stabilizing effect of XG gel is attributed to

• High static viscosity of the solution.

• The polymer structure pressure, as opposed to the nZVI particle forces.

The deionized water solution of XG is stirred for about 5 min to reach specific polymer concentrations (0.7 g/L). Then, remove the gases in the XG solution and place them with N₂ flow to remove air bubbles and kept for 12 h at room temperature (22–25 °C) to facilitate hydration and complete dissolution. Then, 2 g of nZVI particles are dissolved in 1 L of aerated XG solution, homogenized, and dispersed with vigorous shaking.

To homogenize the mulches, they must be washed several times with deionized water to remove fine sand, waste, and leachate. After washing, they should be heated twice to remove all bacterial and fungal seeds and then dried at 60 °C. The mulches are powdered with mechanical mixers and then passed through a sieve (#10) (2 mm) and 16 (1.18 mm). The sieved mulch is then reheated to determine the physicochemical and isothermal properties.

Columns were conducted to evaluate the removal of nitrate, sulfide, and arsenic in groundwater by a PRB system. Two columns were set up: column 1 assessed the effectiveness of treating contaminants using bare nZVI, whereas column 2 was designed to treat using organic mulch and XG-nZVI. The two columns were connected to the source of the same contaminants.

Two reactive barriers were used for treating nitrate, sulfide, and arsenic (As(V)): mulch and XG-nZVI. The mulch consisted of a mixture of plant remains and hardwood shredded with a ball-mill device of the Membrane Processes Laboratory⁴ for 25 h.

The bare nZVI column consisted of 2 g of nZVI mixed with the gravel in each barrier, and XG-nZVI + Mulch column was packed with 3 g of mulch and 2 g/L of nZVI/XG solution mixed with the gravel for the first and second barriers. Mulch was sieved to sizes between 1.18 and 2 mm.

The input solution for the columns was prepared by dissolving ⁠, Na₂S, and NaAsO₂ analytical grade in deionized water. The initial concentrations for the input contaminants were N = 100 mg/L, As(V) = 1 mg/L, and S²⁻ = 200 mg/L. The contaminant limitation standards based on WHO and Iranian standards are as shown in Table 4.

As ITRC mentioned (Barrier 2011), the flow velocity proportion of the barrier and aquifer should be 1/2–3/4, so an aquifer is implemented by graded sand with specifications of minimum and maximum diameters of 0.075 and 6 mm and an effective diameter(d₅₀) of 1.50. A reactive barrier is implemented by graded sand with specifications of minimum and maximum diameter of 0.075 and 4.75 mm and d₅₀ = 1.20 (Naghikhani et al. 2021).

For each contaminant, two columns were considered (Figure 3). The natural aquifer's performance was estimated before conducting the main tests to determine the designed system operation. Also, possible errors in the measurement method were corrected by evaluating zero samples. An average of two samples was performed at the end of each day. Forty samples were studied for each contaminant. Spectrophotometer DR-5000 and inductively coupled plasma mass spectrometry (ICP-MS) were used for (⁠⁠, S²⁻) and As, respectively.

The average of two sequential samples was used in the final calculations to reduce sampling errors. Descriptive statistics, correlation, and hierarchical clustering analysis were performed by SPSS 26 to understand the relationships among contaminants.

According to Table 5, and S²⁻ have positive moderate skewness, so most observations are concentrated in the left tail. As(V) is symmetrical, so median and mean are equal. The data kurtosis of all three contaminants is platykurtic; the peak of the distribution diagram is lower and wider, and the tails are thinner.

According to Table 6, and S²⁻ have positive moderate and high skewness, respectively, so most observations are concentrated in the left tail. As(V) is symmetrical, so median and mean are equal. The data kurtosis of all three contaminants is platykurtic; the peak of the distribution diagram is lower and wider, and the tails are thinner (research data are available in the Supplementary section).

The standard deviation of contaminants in the XG-nZVI barrier is 9.01, 13.15, and 0.075, contrary to 17.15, 18.47, and 0.136 in the bare nZVI. In conclusion, since bare nZVI outputs are more scattered than XG-nZVI, its performance stability is worthen. On the other hand, results are more widely distributed around the mean, which indicates decreased performance and instability compared to the XG-nZVI + Mulch PRB.

Correlation analysis and hierarchical clustering have been used to investigate contaminants’ rate and order of correlation.

According to the correlation matrix (Figure 5), it is evident that all three contaminants have a prominent interaction with each other. The highest interaction is between (⁠⁠, S²⁻) and (⁠⁠, As (V)), equal to 0.993 and 0.985, respectively, and the lowest value is between (S²⁻, As(V)). It should be noted that the lowest amount of interaction has occurred in the bare nZVI column between (S²⁻, As(V)). The stability of the correlation matrix in the XG-nZVI is more than the bare nZVI, which promises better performance, prevention of nanoparticle flocculation, and impressive longevity. Correlation is significant at the p-value = 0.01 in one-tailed. The matrix is about the effects of contaminants on each other, so one-tailed correlation is more precise.

where is the standard score of ⁠; is the average of the data set; is the standard deviation.

Hierarchical cluster analysis is suitable when the data set is small, and the dissimilarity among clusters is essential. Agglomeration clustering with the Wards and Pearson computational methods is used for better performance.

The dendrogram chart shows the number of similar clusters in the and S²⁻ data sets. So, firstly, and S²⁻ are combined, and secondly, As (V) at a higher level joined them (Figure 6). It was apparent in the correlation matrix that and S²⁻ interact the most.

As mentioned in the introduction, research works by Araújo et al. (2016) also showed PRBs to be a promising technology. Still, the long-term impact of nano-sized iron in this modification process on the environment and human health is far from being conveniently known. Appropriate stabilizers must be safe, degradable by microorganisms, easy to use on an accurate scale, and effective during the time required for nZVI to transfer to the aquifer (e.g., 10 days) (Phenrat et al. 2009).

As a result, by stabilizing nanoparticles with biodegradable and safe biopolymers such as XG, the systems’ longevity, and performance can be increased compared to the bare use of nanoparticles. In all experiments, the elimination ratio increased initially and decreased in the ultimate process. Bare nanoparticle barrier performance was significantly reduced, but the XG-nZVI + Mulch barrier was more stable during the experiment. Tests were repeated to determine the repeatability of the tests. Finally, the average results for each day were drawn in the graphs.

According to Figures 7 and 8, nitrate remaining and removal percentage in both columns decreased and increased, respectively, then reversed beyond 4 days. The column containing bare nanoparticles shows a better removal performance in the initial 4 days than the XG-nZVI + Mulch barrier, which results in less stability. This decrease in performance can be related to the flocculation of particles and the barrier-clogging phenomenon. The typical system has almost lost its function since the eighth day of the experiment. The XG-nZVI + Mulch column with fluctuation in the range of 60–80% shows a stable performance, and this stability promises to improve the longevity of nanoparticles (Figure 8). As mentioned above, previous studies showed transport and sustainability of nanoparticles increased with xanthan biopolymer compared to the net utilization (Barrier 2011; Xue & Sethi 2012; Zhong et al. 2013; Xin et al. 2015). According to Edgar et al. (Edgar & Boyer 2022), a limited amount of is absorbed by the mulch particles on the first barrier and finally degraded by XG-nZVI.

The arsenic remaining in the XG-nZVI + Mulch barrier shows a significant difference from the nZVI barrier in performance and stability. Besides implementing the XG-nZVI barrier, arsenate [As(V)] can be reduced by the microbial process of the mulch barrier (Woolson 1977). Hence, the removal performance improved until the fifth day of the test, which dropped with a gentle slope (Figure 9). Not only in the nZVI barrier, after the second day, the amount of arsenic removal decreases drastically, but also in the XG-nZVI + Mulch state, the system's performance improves until the fourth day. After that, it decreases steadily compared to the common barrier. The excellent removal performance of arsenate [As(V)] is because of its oxidation through the mulch barrier. At the end of 10 days, the common barrier's performance fluctuates between 30 and 70%, but the XG-nZVI + Mulch system changes in 60–80% (Figure 10). Previous studies comprehended that a 1.0 g/L nZVI could outstandingly decrease the As(III) concentration to the standard limits, and XG and biowells increase the stability of nZVI particle suspension (Rahmani et al. 2011; Obiri-Nyarko et al. 2014; Xin et al. 2015). Results confirm the effectiveness of nZVI and mulch for removing As (V) from the water body through adsorption and precipitation processes.

Sulfide removal is affected by various parameters like absorption with the aquifer's material and reaction with hydrogen and other contaminants. In the preliminary test, a significant amount of sulfide (about 20 ppm) was absorbed while passing through the typical aquifer. Therefore, the results of the tests do not only reflect the performance of barriers. According to studies, sulfide is found in most groundwater wells, which were considered to reach natural conditions. It should be mentioned that the XG-nZVI + Mulch system has far better and more stable performance. At the end of the 10th day, the typical approach was not significantly different from the natural aquifer (Figure 11). Sulfide can increase the precipitation of iron and arsenic to a safe concentration (Jusoh et al. 2011); in this project, it can have a dual function. The bare nanoparticles column did not perform well, and the removal percentage varies in the 22–50% range. Still, a significant improvement has been shown in the XG-nZVI + Mulch column, and the removal range fluctuates between 45 and 60%. According to the influencing parameters and the different forms of sulfide in the water body, despite the significant improvement in the removal rate and stability, the final percentage is less than 45% (Figure 12).

Since there are many effective parameters, we will focus on one factor at a time in natural groundwater to investigate different conditions in our future study.

It is obtained to increase the efficiency of multi-contaminant groundwater treatment by designing multi-barrier systems. Due to the characteristics of iron nanoparticles, they have been widely used as adsorbents in PRBs. As experiments’ results showed, bare iron nanoparticles cause clogging of the pores due to the flocculation phenomena, so the system's longevity has been considered in future works. There is less variation around the mean in XG-nZVI + Mulch standard deviations, indicating better performance stability than bare nZVI for simultaneously removing ⁠, S²⁻, and As (V). Furthermore, according to std deviation, the highest interaction is between (⁠⁠, S²⁻) and (⁠⁠, As (V)), equal to 0.993 and 0.985, respectively, and the lowest value is between (S²⁻, As(V)). Correlation analysis and hierarchical clustering showed that and S²⁻ interact the most. Results indicated that absorbed XG significantly affected the electrostatic stability of XG-nZVI and hindered aggregation and sedimentation of XG-nZVI. It is possible to stabilize with biodegradable biopolymers like XG and use multi-mulch barriers to improve the performance and longevity of nanoparticles. As a result, the percentage of removal and longevity of the XG-nZVI + Mulch system shows a significant increase. Due to arsenate-consuming microbial processes, the operating capacity of the XG-nZVI + Mulch barrier to remove arsenic sustainably increases impressively. The XG-nZVI + Mulch barrier's performance for eliminating ⁠, As(V), and S²⁻ is generally improved compared to the bare nZVI barriers by 5.7, 19.2, and 10.9%, respectively. Finally, XG-nZVI + Mulch PRB's performance is impressive despite needing a long-term sustainability assessment. This stability is expected to make nanoparticles used in PRBs last longer because of the greater dimensions of the field barrier and the potential to remain in them for extended periods.

All the experiments were done at the University of Tehran. Statistical analyses were performed by SPSS 26, and the authors received no specific funding for this work.

Experiments and analysis were carried out by A.N., under the supervision of professors A.K., A.S., and M.B. as A.N.'s PhD dissertation. The funding was acquired by A.K, A.S., and M.B. The analyses and results were supervised and validated by A.K., A.S., and M.B. All authors read and approved the final version of the manuscript.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.