The biosorption potential of orange waste (OW) was investigated using synthetic solutions of arsenic and contaminated drinking water under different parameters, e.g. biosorbent dose, initial concentrations of solution, contact time, and pH in a batch system. The optimum conditions were identified as a contact time of 30 minutes, pH 6, biosorbent dose of 1 g L−1, and initial arsenic concentration of 250 ppb. A fluidized bed column was used to study the removal of arsenic in the column. The results showed that biosorption of arsenic gave promising results in batch and continuous system, lowering the arsenic concentration down to WHO standards (10 ppb) for drinking water. The Fourier transform infrared spectra indicated that hydroxyl and carboxyl groups were major active sites for biosorption, while the results of scanning electron microscopy showed obvious changes in surface morphology of OW after the biosorption process. With 90% removal efficiency, results indicated that OW is a cost-effective and eco-friendly biosorbent and comparable to current drinking water treatment technologies. Further research is needed to get the optimum conditions for pilot-scale testing of the biosorption process by OW as well as evaluation of treated water for food quality parameters in order to commercialize the process.
Around the globe, arsenic is a naturally occurring metalloid (Iriel et al. 2015) and being part of the earth's crust, it causes contamination of water resources. Contamination of drinking water with arsenic is a serious issue due to its toxicity and carcinogenicity (Yazdani et al. 2016) and it is well known that continuous exposure to low arsenic concentrations can lead to chronic diseases (Zhang et al. 2013). Due to its high toxicity and carcinogenicity arsenic is considered a class 1 toxicant and the World Health Organization has set a permissible limit of 10 ppb in drinking water. Arsenic exposure has been linked with a high risk of malignant arsenical skin lesions (Brahman et al. 2016), diabetes mellitus, and carcinomas in humans. Chronic exposure can disrupt several biological processes of the body and can cause severe effects on the body's organs, mainly the liver. Furthermore, alteration of growth factor, suppression of proteins involved in cell cycle checkpoints, and disruption of the DNA methylation and repair system are major pathways that are mainly affected by arsenic toxicity (Kumar et al. 2015).
Arsenic contamination of water resources occurs due to natural as well as anthropogenic activities (Islam et al. 2015; Yazdani et al. 2016) as arsenic is used in different industries. Natural geochemical processes, use of arsenic-containing pesticides, irrigation with contaminated water, and fertilization with municipal solid wastes are major entry points of arsenic into the food chain (Finnegan & Chen 2012). Depending on the redox environment, arsenic mainly exists in two oxidation states in water known as arsenate(V) and arsenite(III) (Zhang & Gao 2013). In Pakistan, soil and water resources of different regions have substantially high concentrations of arsenic (Brahman et al. 2016). In Sindh province, the groundwater arsenic concentration has reached up to 1,100 μg/L against the permissible limit of the World Health Organization (10 μg/L) for drinking water. Moreover, about 20% of the population in Punjab province and 36% of the population in Sindh province is exposed to arsenic contamination above the prescribed limits of the World Health Organization (Haque et al. 2007). In order to ensure the use of safe drinking water and to meet water quality standards there is an urgent need to develop a new treatment system that can efficiently remove arsenic from drinking water.
A number of treatment methods are known to eradicate metals from water. For the removal of arsenic from drinking water, adsorption is emerging as a useful technology keeping in considerations the cost, availability, simplicity and operation of the process (Yazdani et al. 2016). Different biosorbents are used for the removal of toxic compounds from water out of which agricultural wastes are more popular due to their cost effectiveness, availability and efficiency (Paradelo et al. 2016). Activated carbon papered from grape bagasse has been used as a low-cost adsorbent to remove copper from aqueous solution (Demiral & Güngör 2016). Adsorption using cashew nut shell showed 92.55% removal efficiency for the adsorption of arsenic(III) in a three-phase fluidized bed reactor (Dora et al. 2013). Pakistan is a citrus-producing country, producing 95% of the Citrus reticulata variety (mandarin orange) and orange waste (OW) is freely available in huge quantities (Irem et al. 2013; Qureshi et al. 2014) having no other use and also causing problems of solid waste management. Therefore using this waste as a biosorbent will not only remove arsenic from water but also resolve the issue of waste management. Furthermore use of natural OW is safe for humans as it can be directly used for water treatment without any modifications.
In the last decade several studies were conducted to evaluate the capabilities of OW for the removal of heavy metals from water. Orange juice residue was chemically modified and its biosorption efficiency for removing the arsenic from aqueous media was evaluated at different contact times and pH after loading the gels with iron (Ghimire et al. 2003). Saponified OW was used to study the effect of adsorbate dose, arsenic(III) concentration and contact time in a batch system (Qureshi et al. 2014). Chemically modified orange peel was characterized by Fourier transform infrared (FT-IR) and scanning electron microscopy (SEM) and it showed high efficiency for copper(II) removal from aqueous solution in a batch system (Feng et al. 2009). Similarly removal of cadmium by OW revealed that the process is highly dependent on pH and maximum removal was observed at pH 6 (Pérez-Marín et al. 2007). In another study Schiewer & Iqbal (2010) reported that the hydroxyl and carboxyl groups of orange peel are responsible for cadmium removal from aqueous media. In adsorption-based water treatment processes, batch and fixed bed column systems are most commonly used. However, in fixed bed columns, the process encounters several problems such as channeling, development of dead zones, improper mixing, temperature gradient and clogging. So, fluidized bed reactors are being widely tested for upscaling of the treatment process to avoid these problems and for the efficient removal of pollutants (Bello et al. 2016). In a fluidized bed reactor the static solid particles behave as fluid as they are suspended by the velocity of the passing fluid (Khan et al. 2014). Uniform mixing, low operational cost, higher rate for mass transfer and resistance to system upsets are some of the key features of the fluidized bed reactor (Andalib et al. 2014; Ahmadi et al. 2015). Due to these features researchers are incorporating it in wastewater treatment, especially in biological and advanced oxidation processes. Zou et al. (2016) reported the effective use of a fluidized bed reactor for biological processes. Several large-scale fluidized bed bioreactors are being widely used in biological wastewater treatment for the removal of recalcitrant pollutants due to the advantages of efficient mixing and high mass transfer (Bello et al. 2016). Micro-grain activated carbon has been used in a fluidized bed reactor as a tertiary treatment process to remove the emerging pollutants from the discharge of a wastewater treatment plant (Mailler et al. 2016).
The objectives of the present work were to: (1) evaluate the efficiency of powdered OW in its natural form to remove arsenic from water in batch- and column-scale systems, (2) explain the influence of different experimental conditions such as biosorbent dose, pH, contact time, and initial arsenate concentration on arsenate sorption capacity in order to optimize the process for treatment of contaminated drinking water, and (3) investigate the mechanism of the biosorption process using FT-IR, electron microscopy and equilibrium as well as kinetic studies.
MATERIALS AND METHODS
Sodium arsenate was used to prepare the stock solution (1,000 ppm) of arsenic(V) from which dilutions were prepared for working solutions using distilled water. All chemicals and reagents used in the study were of analytical grade and supplied by Merck. The pH of the solutions was maintained using 0.1 M solutions of NaOH and H2SO4.
Preparation of the biosorbent
OW (peel along with pulp) was collected from the local juice shop in Faisalabad, Pakistan, and thoroughly washed with tap water to remove dust and adhering particles. After washing with distilled water, the waste was oven dried at 65 °C until it attained a constant weight. Dried OW was crushed and ground by an electric ball mill and sieved through mesh #40 and #80 (corresponding to particle sizes of 425 μm and 180 μm, respectively). The sieved biomass was stored in airtight plastic jars for further use in biosorption experiments.
Characterization of biosorbent
The biosorbent was characterized using a surface area analyzer and porosimeter (ASAP-2020, Germany). The Brunauer–Emmett–Teller (BET) surface area of the OW was 0.2661 m2 g−1 with a micropore volume of 0.000044 cm3 g−1 calculated by N2 adsorption isotherm. Functional group identification was carried out by FT-IR (IR Prestige, SHIMADZU). SEM was used to study the surface morphology of the biosorbent before and after adsorption.
Batch- and column-scale experiments
The biosorption of arsenic was carried out in a laboratory-scale batch system and column under different parameters. The laboratory-scale biosorption experiments were carried out in batch reactors and all experiments were conducted in triplicate. In a series of experiments, sorption capacity was determined in a batch system, over a pH range of 4–8, using biosorbent dose of 1–3 g/L, at initial arsenic concentrations in the range of 50–500 μg/L (based on our findings during a survey in a well-known arsenic-contaminated area, Manga Mandi, in the Punjab province of Pakistan) and different contact times using 100 mL solutions in Erlenmeyer flasks (250 mL). The mixture was agitated at 120 rpm at room temperature (28 ± 3 °C) for different contact times (0.5–4 h) until equilibrium was achieved. At the end of the adsorption period, samples were filtered and analyzed using atomic absorption spectroscopy (AAS) to measure the concentration of the remaining arsenic in the water. The adsorbed amount of arsenic was calculated from the concentrations in the water before and after adsorption. Water with biosorbent was used as a control while deionized water was used as a blank containing no arsenic in all experiments.
Upscaling for the biosorption of arsenic was carried out in a fluidized bed column of 1.6 cm diameter with bed length of 2.8 cm using 3 g/L of biosorbent (coarse particle size). About 4 L of water with initial arsenic concentration of 50 μg/L was allowed to pass through it at a flow rate of 50 ml/min under the natural pH of water at room temperature, until the complete exhaustion of the column occurred. Breakthrough volume was determined from the plot of volume (L) vs residual arsenic concentration (ppb).
Equilibrium and kinetic studies
The plot of t versus t/qt gives a linear relationship from which qe, K and h can be determined from the slope and intercept, respectively. All graphical work was carried out in MS-Excel 2007.
Calculation of sorption capacity and arsenic removal efficiency
RESULTS AND DISCUSSION
Surface characterization of the biosorbent
OW mainly consists of cellulose, hemi-cellulose, pectin, limonene and many other low molecular weight compounds (Pérez-Marín et al. 2008). The presence of cellulose and hydroxyl and carboxyl groups plays an important part in biosorption. Surface area analysis provides the surface properties and pore volume of the material that play an important role in adsorption by banana and orange peel (Thirumavalavan et al. 2009). In the present study, the BET surface area and the t-plot pore volume of the biosorbent were found to be in the range of 0.0701 m2/g – 0.2661 m2/g, and −0.000035 cm3/g – 0.000044 cm3/g respectively. The very small pore volume of OW indicates the microporous structure of the biosorbent that causes the surface adsorption of larger adsorbate molecules, making transport limited through the pores (Rodríguez et al. 2009). Although the pore size and surface area of the orange peel was very small, it exhibited effective adsorption properties (Thirumavalavan et al. 2009).
The comparison of OW spectra before and after adsorption of arsenic shows the significant shift in intensity and frequency range of the carboxyl and hydroxyl stretches (1,537.8, 1,406.1, 1,240.2 and 1,055.1 cm−1) and appearance of new peaks (2,858.51, 2,308.79), while some peaks at 773.46 and 3,265.49 cm−1 disappeared (Figure 1(b)). The change in intensity and appearance of new peaks can be attributed to the binding of metal ions to the functional groups, while the absence of peaks indicates the involvement of chemical reactions, leading to the decomposition of chemical bonds (Schiewer & Iqbal 2010). Band shifts towards lower frequencies designate the weakening of bonds while a shift towards high frequency is a sign of strong bonds (Gutha et al. 2014). The results indicate that carboxyl and hydroxyl functional groups are mainly involved in arsenic adsorption, while, besides the surface adsorption, chemisorption is also involved in the biosorption process, however, the exact mechanism of chemisorption is not known yet.
Biosorption of arsenic in a batch adsorption system
Biosorption of metal is affected by different parameters such as pH, contact time, initial metal ion concentration and adsorbent dose. Change in removal efficiency at different contact times, pH, initial metal ion concentrations and adsorbent doses were studied for the adsorption of arsenic(III) using iron oxide-coated sand by Gupta et al. (2005). Our preliminary studies for biosorption of arsenic by OW indicated that particle size slightly impacts the removal efficiency of arsenic in a batch system (unpublished results) but it does not exhibit any difference in column-scale experiments, indicating that spontaneous chemisorption is also involved for arsenic uptake.
Effect of contact time and biosorbent dose
Effect of pH
Initial pH of the solution plays an important role in biosorption. A pH-dependent variation in adsorption occurs due to the fact that change in pH modifies the charge of both adsorbent and adsorbate molecules leading to change in interactions among functional groups (Chen et al. 2015). There was a slight difference in the arsenic adsorption capacity of OW when tested under different pH. The optimum pH observed was 6.0 at which maximum sorption capacity was obtained, so further experiments were conducted without changing the pH of the solution as the natural pH of the arsenic solution was 6.0 at room temperature (Figure 3(b)). Furthermore, when tested with drinking water samples, the biosorbent showed similar results as shown with synthetic solutions suggesting that it can be used directly for arsenic removal from drinking water as biosorption of arsenic was less influenced by pH. The results of the present study are similar to those reported by Zhang et al. (2015). FT-IR analysis of the OW has pointed out the presence of –COOH and –OH functional groups that can be considered possible active sites for sorption of arsenic. Arsenic is able to form oxyanions and is also redox sensitive and thus its adsorption is dependent on the pH of the environment. Moreover, under acidic or near-neutral pH, As(V) is more strongly adsorbed than As(III) (Shafique et al. 2012).
Effect of initial arsenic concentration and adsorption isotherms
The initial concentration of contaminant has a strong influence on the percentage removal of the adsorbate and also helps to determine the sorption capacity of the biosorbent. In the present study, the sorption capacity of arsenic on OW showed a linear increasing trend with increase of initial arsenic concentration from 50 μg/L to 500 μg/L (Figure 3(d)) while a decrease in sorption capacity was observed with increase in biosorbent dose from 1 g/L to 3 g/L (Figure 3(c)). A similar pattern has been observed during the biosorption of lead on modified orange peel (Lugo-Lugo et al. 2009). Adsorption of copper(II) on modified orange peel also increased with increasing initial metal concentration (Feng et al. 2009).
|Kinetic and equilibrium models .||Parameters .||Arsenic .|
|Kinetic and equilibrium models .||Parameters .||Arsenic .|
Kinetic models such as pseudo-first-order and pseudo-second-order kinetics were used to test the experimental data to examine the mass transfer and chemical reactions of the adsorption processes. The data do not show any fit to pseudo-first-order kinetics while they give a straight line for pseudo-second-order kinetics with R2 = 0.999 (Table 1 and Figure 4(b)). The similarity of calculated and experimental adsorption capacities supported the good correlation of the data with the pseudo-second-order model and it implied that chemical adsorption between As(V) and OW was the rate-limiting step of the process. Similar results have been observed for biosorption of arsenic on pine leaves (Shafique et al. 2012; Brahman et al. 2016).
Column studies for biosorption of arsenic
Column-scale studies for the biosorption process are required in order to develop a feasible technique for commercial and large-scale application. The column-scale approach has been used for effective removal of arsenic(III) using iron-oxide coated sand as a biosorbent (Gupta et al. 2005). Similarly the effect of different parameters on arsenic removal has been investigated in a column study using plant biomass as an adsorbent (Kamala et al. 2005). Arsenic removal efficiency and the breakthrough curve for biosorption of arsenic by OW is shown in Figure 4(a).
The fluidized bed column was found to be highly efficient at removing the arsenic and was comparable to that observed in batch experiments (90%) for 0.6 L solutions and reached a breakthrough point at 1 L. The column studies showed promising results giving 50% removal efficiency up to 2 L treated volume and reached exhaustion point after treating 4 L solutions. It could be due to the fact that initially the adsorbent was fresh and all the active sites were available for metal binding and not a single ion escaped from the column. However, as time passed, the functional groups were occupied by metal ions and some of the metal ions started escaping the column leading towards the exhaustion of biosorbent. Being low cost and nontoxic, and having outstanding adsorption capacity and biocompatibility make OW a cost-effective and eco-friendly biosorbent and comparable to the current drinking water treatment technologies. Further research is needed to get the optimum conditions for pilot-scale testing of the biosorption process by OW as well as evaluation of treated water for food quality parameters in order to commercialize the process.
OW was found to be a low cost and abundantly available biosorbent for removing arsenic from contaminated water.
The decrease of arsenic concentrations in water after adsorption in a fluidized bed column met the WHO standard (10 μg/L) with a flow rate of 50 ml/min under the natural pH of the water at room temperature.
FT-IR spectra of the biosorbent before and after adsorption indicated that carboxylic and hydroxyl groups are the major active sites involved in metal removal, while SEM confirmed the adsorption process showing obvious changes in surface morphology.
The removal of arsenic by OW was independent of most of the parameters with the exception of initial arsenic concentration and can be safely used for drinking water treatment.
The study was financially supported by a grant (No. 1887) from the Higher Education Commission of Pakistan. The authors are greatly thankful to the Institute of Chemical Engineering, University of the Punjab, Pakistan, for FT-IR analysis and the Nano-biotechnology group, National Institute for Biotechnology and Genetic Engineering (NIBGE), Faisalabad, for SEM analysis of the OW samples.