A continuous method for arsenic removal from groundwater using hybrid biopolymer‐iron‐nanoaggregates: improvement through factorial designs
Summary (4 min read)
- Arsenic (As) in water represents a global problem, affecting low- and high-income countries1.
- About 50% of the population in rural areas is exposed to As poisoning, including several clinical manifestations such as cancer, hypertension, diabetes, and hyperpigmentation9.
- Biosorption is a novel method that has demonstrated a high capacity to remove several contaminants14-17, mainly when applied in continuous flow systems16,18.
- This has been reported previously in the literature20-22.
- The aim of this work was the synthesis of a new material based on chitosan and iron derived nanopartiples (CIN) and its use in continuous treatment of natural contaminated groundwater.
2.1 Analytical methods
- Groundwater natural samples were obtained from Piamonte Town, Santa Fe, Argentina.
- Arsenic concentration was increased until 1.0 mg/L in column experiments in order to work with a value within the concentration range found in natural groundwater of the area under study.
- All the reagents for the current research were of analytical grade.
- As(V) quantification in aqueous solutions was performed applying a self-made modification of molybdenum blue method16.
2.2 Chitosan Iron Nanoaggegates synthesis (CIN)
- Iron nanoparticles synthesis and stabilization was achieved from a stock solution containing 1:2 molar ratio ferrous: ferric species, which was slowly poured (drop-wise) into an alkali source, composed of sodium hydroxide, under vigorous stirring and nitrogen sparging.
- Core-shell magnetic crystals (Feº core - magnetite and / or maghemite shell) formed and precipitated.
- CIN was prepared by mixing a water-based suspension of iron nanoaggregates, functionalized with starch to promote interaction and linking to chitosan, incorporated as a powder.
- 20 w/w iron/chitosan) was stirred to achieve homogenization, also known as The mixture (1.
- Then it was allowed to settle, supernatant water was eliminated, and the material was dried at 60 °C for 72 hours.
2.3 CIN stability and total iron quantification
- The filtered supernatant was analyzed to measure the presence of iron using ICP-MS.
- Quantification of total iron was carried out by disaggregation of samples, and iron in aquous phase were measured by ICP-MS.
2.4 pH zero-point charge (pHZPC) determination
- After that, 1.0 g of CIN was added to each solution and stirring for 24 hours at room temperature (25°C).
- The final pH (pHf) was measured and the ΔpH was calculated.
- The intersection point on the x-axis in the ΔpH vs pH0 graph indicates the pHZPC14.
2.5 Sorbent composition effect on the As(V) sorption
- Batch experiments were carried out using chitosan, iron nanoaggregates, and CIN individually.
- Arsenic concentration was raised up to 20 mg/L in order to magnified differences between the three materials.
- Groundwater was divided into three equal parts, and each one was placed in a beaker with constant stirring.
- At the end of the reaction time, solutions were filtered under reduced pressure using cellulose nitrate filters (0.45 µm pore size).
2.6 FT-IR, XRD and TGA
- FT-IR spectroscopy (Perkin Elmer FT-IR Spectrum One spectrophotometer) was performed to identify the chemical functional groups present on CIN.
- TGA was performed in Thermogravimetric equipment DTG-60H, Shimadzu, made in Japan.
2.7 SEM and EDAX
- Iron nanoaggregates size and morphology were analyzed in a Zeiss Supra 40VP fieldemission scanning electron microscope (SEM).
- A SEM Philips 515 with EDS probe focused on individual agglomerates was used to assess the composition of nanoparticles.
2.8 Continuous Up-Flow Fixed-Bed Column Sorption Experiments
- Polypropylene columns of 2.0 cm internal diameter and 9.5 cm long were used for sorption experiments.
- CIN sorbent was hydrated in 100 mL of Milli-Q water (pH 4.5) at room temperature with constant agitation.
- Groundwater containing 1.0 mg L-1 of As(V) was pumped through the bed column with a peristaltic pump (Gilson This article is protected by copyright.
- Volumetric flow-rate and pH were periodically controlled.
- Finally, As(V) removal percentage (%R) can be obtained from min and mout, through Eq. 2: %R = 𝑚𝑚𝑖𝑖𝑖𝑖−𝑚𝑚𝑜𝑜𝑜𝑜𝑜𝑜 𝑚𝑚𝑖𝑖𝑖𝑖 x 100 Eq. 2 Desorption studies was performed employing 0.10 M solution of NaOH eluent and an upward flow of 8.5 mL min-1.
2.9 Statistical, mathematical modeling of experimental column data
- Different mathematical models are used to describe the sorption process behavior.
- These models include Thomas23, Yoon−Nelson24, and Modified Dose Response25.
- After preliminary tests, a central composite design (CCD) was selected for As(V) removal improvement.
- Table I presents the range and levels of independent variables: volumetric flow-rate (Q) and column bed height (H). Insert Table I here For RSM, experimental conditions were: pH = 4.5, packing density = 270 kg / m3, [As(V)]0 = 1.0 mg / L. Evaluated factors were: H and Q. All rights reserved.
- The study of the adjustment of experimental data to the mathematical model was carried out through Analysis of the Variance .
3.1 pHzpc determination
- An increase in the pHZPC of CIN (8.6) compared to chitosan (6.3)15 can be explained by the presence of iron oxides/hydroxides, which give a higher positive charge density on its surface27.
- At pH 4.5, CIN has a positive surface charge, which favors As(V) sorption, because it is present predominantly as H2AsO4- see Figure S228.
- Under these conditions, As(V) could form the ferric arsenate species according to the following reaction29: Fe(OH)2+ + H2AsO4- = FeAsO4 + 2H2O.
- The formation of this specie explains that, in addition to physical sorption, chemical sorption can also take place.
3.2. Effect of pH and iron content
- At this pH there is a favorable electrostatic attraction between As(V) and CIN, allowing efficient removal of arsenate ions.
- Table II details the batch experiment results, carried out with solutions of As(V) and: a) CIN, b) iron nanoaggregates, and c) chitosan.
- It can be seen that the CIN material has a higher retention capacity of As(V).
- The more surface area exposed implies more binding sites available and a higher retention capacity.
- Insert Table II here Elution profiles of columns filled with chitosan and CIN are compared in Figure 2.
3.3 FTIR, XRD and TGA characterization
- In the spectra shown below , the characteristic signals of chitosan and CIN after being exposed to arsenate ions are observed.
- In the CIN-As diffractogram, a marked decrease in the intensity of the peaks mentioned above is observed, so it can be affirmed that the sorption of arsenate ions breaks the semi-crystallinity state of the polymeric portion of the material and iron oxides present in it.
3.5 Improvement of the contaminant sorption process in fixed bed columns
- Values for each factor generated by the CCD and the responses obtained are shown in Table III.
- An acceptable tb indicates the column effluent must have an As(V) concentration of 0.05 mg L-1 according to the Argentine drinking water standards12.
- Both responses (tb and Vol) were optimized, generating the desirability function.
- The results of these analyses are detailed below.
3.6. tb improvement
- ANOVA analysis for this model is shown in Table S2.
- The lack of adjustment is not significant and indicates that the model has a good correlation with the experimental values.
- Statistical parameters of the model prediction are shown in Table S3.
- The response Surface obtained, Figure S6, showed that tb increase when H increases and decrease when Q increases.
- All rights reserved. of As(V) is entering the column in less time, which causes saturation of the active sorption sites.
3.7. Vol improvement
- The quadratic model for Vol response was significant (Table S4) Values of R2predicted and R2-adjusted did not show significant differences (Table S5), discarding block effects, and the presence of outliers, Figure S7.
- The signal-to-noise ratio (adequate accuracy), is greater than 4.
- The lack of adjustment is not significant, indicating the model has a good correlation with experimental values.
- The graph of the response surface, Figure S8, showed that increasing H increases the Vol of purified water due to a more significant number of active sites of the sorbent and therefore the amount of As(V) retained will be higher.
3.8 Desirability Function
- When a simple response is being analyzed, the model analysis indicates areas in the design region where the system is likely to give desirable results.
- Two responses were simultaneously optimized: minimizes tb and maximizes Vol are desirable.
- The breakthrough curve for this experimental condition and the adjustment to experimental data made by Thomas32, Yoon Nelson33, and Dose-Response34 models are shown in Figure S9 and Table S6.
- Thus, it can be said that the desirability function validates the objective of optimizing both responses studied satisfactorily.
- The percentage (%) of As(V) desorption was 75% in the second cycle and breakthrough time (tb) decreased to 35 min in the second cycle (over 40 % loss of removal capacity).
- Continuous sorption of As(V) in groundwater was studied using a hybrid material as sorbent: CIN.
- SEM, EDS, TGA, XRD and FTIR spectroscopic techniques were applied to characterize the sorbent.
- FTIR spectra of As and the decrease in the degree of crystallinity by XRD confirm As(V) sorption.
- The use of the experimental design in fixed bed column studies was successfully applied.
- Having a multi-response model allowed the generation of the desirability function by optimizing the process for the two responses studied: tb and Vol.
Did you find this useful? Give us your feedback
Related Papers (5)
Frequently Asked Questions (1)
Q1. What are the contributions in "A continuous method for arsenic removal from groundwater using hybrid biopolymer-iron-nanoaggregates: improvement through factorial designs" ?
This article is protected by copyright. This study demonstrates the great capacity of the hybrid sorbent to eliminate As ( V ) working with a continuous system ( columns ).