Biosorption of Chromium (II) Ion from Textile Effluent Using Watermelon Shell-Activated Carbon

. Watermelon Shell, an agricultural waste, was employed for the adsorptive removal of chromium (II) ion Cr 2+ from textile effluent. This study analysed the adsorbent's active sites and morphological structures using FTIR, SEM, XRD, and XRF. The independent variables' effect, contact time, adsorbent dosage, and pH were predicted using Response Surface Methodology (RSM) for chromium adsorption onto Watermelon Shell Activated Carbon (WSAC). The experimental results indicated that NaOH activation effectively improved WSAC's adsorption capacity. The maximum adsorption capacity was 54.53 %, with an adsorbent dosage of 0.6 g/l, pH of 6.0, and agitation time of 40 min. The high correlation coefficient (R 2 =0.978) between the model and the experimental data showed that the model predicted the removal of Cr 2+ from textile effluent using Watermelon Shell Activated Carbon efficiently.


INTRODUCTION
Wastewater is any water whose quality has been negatively impacted by human activity. Wastewater can originate from domestic, industrial, commercial, or agricultural activities, surface runoff or stormwater, and sewer inflow or infiltration [1]. Significant health hazards are related to using untreated wastewater for domestic purposes, agriculture, etc. The presence of heavy metals in wastewater is one of the most challenging environmental problems due to their toxicity, persistence, and bioaccumulation tendencies [2]. Many industries produce and discharge metalcontaining wastes mostly into water bodies. This affects the water's aesthetic quality and increases the concentrations of metals present [3]. These heavy metals commonly include; Cd, Pb, Cu, Fe, Ni, Mn, and Cr. Heavy metal contamination is not a recent problem, but its management and prevention are still of global concern [4]. Textile industries contribute immensely to surface water deterioration and are categorised among the most polluting in all industrial sectors [5]. The dyeing and printing of textiles have a significant environmental impact since they use a lot of water and generate a lot of highly contaminated wastewater.
The pollutants created by the textile dyeing and printing industries are influenced by the chemicals used in the various dyeing and printing processes. The receiving water thus becomes brackish. Textile dyes are toxic, highly stable and do not degrade quickly, and are not removed by conventional wastewater treatment methods. Due to the environment's non-degradable nature and long-time persistence, the toxic waste often accumulates at a low level, causing a harmful biological effect [6].
In addition, effluent or wastewater from textile production discharged into the water body without proper treatment also seeps through the aquifer and pollutes the underground water in many ways. Besides colour visibility which brings displeasing aesthetics, heavy metal constituents in the effluent also result in adverse environmental impacts on the water body and environment as well as deterioration of human health [7].
During operations, they also produce heat from effluents released, increased pH, and water saturation with dyes, defoamers, bleaches, detergents, optical brighteners, and equalisers. Due to these, pollutants from the textile production sector are being released into the environment at various stages of operation. Heavy metals, notably lead (Pb), Chromium (Cr), cadmium (Cd), and copper (Cu), are used widely for the colour pigment production of textile dyes. Such heavy metals can exist naturally in the structures of textiles or penetrate textile fibres during the show, the dyeing process, or through protective agents used during storage. These heavy metals, which have been transferred to the environment, are highly toxic and can bioaccumulate in the human body, aquatic life, and natural water bodies and possibly be trapped in the soil [8].
Lead (II) ion has been reported to be responsible for intellectual disabilities in children and causes about 143,000 deaths annually in developing countries [9]. Young children are vulnerable to lead exposure because it affects the development of the brain and nervous system [10]. It can also result in miscarriage, low birth weight, stillbirth, premature birth in pregnant women, kidney damage, and high blood pressure in adults [11]. Ingestion of lead-contaminated water has been implicated as a significant route of lead toxicity.
Several techniques have been designed for heavy metals removal from aqueous solutions, including ion exchange, chemical precipitation/coprecipitation, filtration, coagulation, membrane technologies, and commercial activated carbon [12]. The significant drawbacks to these methods lie in the cost involved, the efficiency of the processes, and the disposal of wastes generated [13]. These disadvantages have made researchers seek alternative techniques for heavy metal remediation.
Hazardous metals can reportedly be removed from industrial effluents by adsorbing using naturally occurring elements. This method uses dead waste biomass, microorganisms, and other naturally plentiful plant components as raw materials [14]. The sorption capacity of some bio sorbents is high due to the presence of adequate functional groups that sequester metals from aqueous solutions [13]. This method's application is affordable, sustainable, and readily available. These bio-based materials have demonstrated a propensity to eliminate metals at trace levels, thereby addressing some of the major draw-backs of conventional techniques [15]. Several adsorbents from plant origin have been used and modified for heavy metal removal from wastewater and aqueous solution, which include: maise tassels, coffee beans, coconut shells, peanut shells, Annona squamosal shells, rice husks, rice bran, orange peels, sunflower stem, groundnut shells and avocado seed [16,17]. This study presents Watermelon Shell Activated Carbon as a potential, environmentally friendly, and low-cost adsorbent for the adsorption of metals from textile wastewater samples. In Watermelon, the red flesh inside is sweet, edible, and used for juices and salads, but the outer shell is considered waste and has no commercial value [18].
The watermelon shell consists of pectin, citrulline, cellulose, proteins, and carotenoids [19]. The polymers are rich in functional groups like hydroxyl (cellulose) and carboxylic (pectin). They can easily bind metal ions by changing their hydrogen ions for metal ions or giving an electron pair to form complexes with the metal ions [20]. The idea behind the adsorption of heavy metals using watermelon shells is to treat waste with waste and become even more effective because these agricultural by-products are easily accessible and frequently cause issues with garbage disposal.
Because they are waste items, they can be found for little to no money. Also, this makes treating wastewater with agricultural by-product adsorbents more economical than using conventional adsorbents like activated carbon. This research conducted characterisation and optimisation studies for textile effluent and watermelon shells.

Effluent Collection.
The raw effluent was collected from Rosie's textile Company in Aba, Abia state, in a sterile 20 litres gallon from its point of discharge to the environment. It was stored at room temperature without further purification [21].

Collection of Adsorbent.
Watermelon shells (WS) were obtained from different fruit-selling sources in Umuahia, Abia state, and washed with clean water. The watermelon shells were cut into small pieces and dried in sunlight for 14 days to remove all moisture content present. The dried watermelon shells were stored in an air-tight container.
Carbonisation. Carbonisation (1500 grams of washed, cut, and dried watermelon shells) was carried out in a muffle furnace (KGYV BUDAPEST KCC086/50-120.3 phases) at about 500-700 °C for two hours and was held at this temperature for 60 min, after which the charred products were allowed to cool to room temperature.
Preparation of 60% Alkaline (NaOH) Used for Activation. 40 g of NaOH pellet was carefully weighed using an electronic balance (YP502N) and dissolved in 1000 ml of distilled water using a measuring cylinder. 400 ml of the dissolved NaOH was removed and stored in an air-tight container. The remaining 600 ml of the dissolved NaOH was diluted up to 1000 ml of NaOH solution with distilled water. This gave the required 60% solution of NaOH.
Preparation of Activated Carbon. The charred material obtained from watermelon shell carbonisation was crushed into smaller sizes to pass through a 3 mm sieve and retained in a 1.5 mm sieve. 302 g of the sample was weighed using an electronic balance (YP502N) and impregnated in the alkaline solution (60 % NaOH) in a beaker. The mixture was stirred with a glass rod to mix well and left for 24 hours for proper impregnation. At the end of the 24 hours, the mixture was washed with distilled water until the tested pH of the water washed out became near neutral [22]. The sample was dried in an oven for 1 hour at 105 °C and stored in an air-tight container.
Textile Effluent Characterization. The effluent was analysed for the presence of heavy metals (nickel, vanadium, zinc, lead, calcium, manganese, chromium, sodium, iron, and copper) by digesting 100 ml of the effluent using a 10 ml triple acid mixture (5:1:1 -HNO3:HClO4:H2SO4) in a 250 ml conical flask. The sample was properly covered with aluminium foil to avoid spillage and heated on a hot plate until the solution was reduced to 10 ml. After that, it was allowed to cool and make up to a mark by topping with distilled water before it was filtered into a 50 ml standard flask ready for further analysis. The concentrations of the heavy metals in the wastewater were determined using Atomic Absorption Spectrometer (AA-7000). The result is given in Table 1.
Batch Adsorption. 100 ml of the textile effluent was transferred to 250 cm 3 Erlenmeyer flasks after the initial pH of the sample's effluent had been determined. 0.2 g of Watermelon Shell Activated Carbon (WSAC) was weighed and added to the effluent. The solution was agitated for 10 min using a speed-adjusting multipurpose vibrator (HY-2) at 100 rpm. Adsorbent dose, contact time, pH, and beginning concentration were among the adsorption parameters whose effects were investigated. Using 0.1 M HCl and 0.1 M NaOH as adjustments, the impact of pH on adsorption was assessed by altering the pH from 2-10. The contact period was changed from 10 to 70 min, and the adsorbent dosage was changed from 0.2 to 1 gram per litre to determine its impact. The solid phase was separated for each parameter study using filter paper, and the remaining metal concentration in the supernatant was assessed using an atomic absorption spectrophotometer (MODEL: AA-700).

Adsorbent Characterization
Proximate Analysis. Proximate analysis was carried out on the WSAC to determine the moisture content, ash content, fat content, crude protein, crude fibre, and carbohydrate using AOAC 2005 method. The result is presented in Figure 5.
FTIR Analysis. The sample was analysed using FTIR technology of the mark SPECTRUM ONE FTIR combined with software (Perkin Elmer Instruments version 3.02.01) to study the spectra. 0.5 g of activated carbon was added to the spectrophotometer for sample analysis. For sample analysis, 0.5 g of activated carbon was introduced into the spectrophotometer for analysis. The wave number varied between 4000 and 350 cm −1 .
X-ray Fluorescence (XRF) Analysis. This was performed to know the chemical compositions of the minerals in the adsorbent. X-ray fluorescence analysis of the adsorbent was done using model PW 2400/0. X-ray diffraction (XRD) Analysis. X-ray (XRD-6000 SHIMADZU Japan) was used to scan the activated carbon used in this work. This was done at 2θ value between 100 and 800 at a scan rate of 2 degrees per minute.
Scanning Electron Microscope Analysis. A scanning electron microscope (SEM) was used to determine the morphological structure of the activated carbon before and after adsorption.

Adsorption Kinetics and Isotherms
Kinetics of Adsorption. The kinetics of adsorption was examined by examining the adsorptive uptake of heavy metals from wastewater at various time intervals. The pseudo-first-order and pseudo-second-order model equations were used to simulate the rate at which heavy metals bind to the created activated carbon.
The linearity of each model was plotted. As a result, in this work, the model parameters were calculated utilising the data produced by the experiments performed and considering the linearised forms of the pseudo-first-order and pseudosecond-order reaction models, respectively.
Adsorption Isotherm. With the aid of the data generated from the adsorption studies of this work, the parameters contained in the Langmuir adsorption isotherm were estimated.
The linearisation of equation (3) leads to the following form: Experimental Design. The design and analysis of variables were evaluated using Design-Expert V6.0.8. Response surface methodology (RSM) is a statistical method that uses quantitative data from appropriate experiments to determine regression model equations and operating conditions. A standard RSM design called Box-Behnken Design (BBD) was applied in this work to study the variables for removing chromium from an aqueous solution using a batch process. BBD was used as an experimental design model for three variables (adsorbent dosage, pH of resolution, and agitation time), each with three levels (the minimum, medium and maximum). Seventeen experiments [22] are needed to conduct and determine ten coefficients of the second-order polynomial equation [23]. In the experimental design model, adsorbent dosage (0.2-1.0 g/l), pH (2-10), and agitation time (10-70 min) were taken as input variables. The percentage removal of chromium was taken as the response of the system. Three factors were studied, and their low and high levels are given in Table 1.

RESULTS AND DISCUSSION
Textile effluent and proximate analysis. The result of the characterisation of the textile effluent before adsorption (Table 2), conducted using an Atomic Absorption Spectrometer (AA-7000), indicated the presence of heavy metals. After the proximate composition characterisation of the activated carbon (Table 3), the result stated fixed carbon content of 80.37% at a 25.7% yield. FTIR Analysis. The FT-IR spectra of unloaded WSAC (Fig. 1a) indicated a band at 3395.79 cm -1 due to the O-H stretching of water. The band observed at 2935.76 cm -1 corresponded to methylene asymmetric, H-H stretching. The band at 1592.29 cm -1 indicated the presence of pyridine, C=N stretching, while the band at 1402.3 cm -1 was ascribed to azo compound, N=N stretching. Finally, the band at 1084.03 cm -1 was found to be due to aliphatic C-N stretching, while the band at 683.79 cm -1 was due to P=S stretching. After the adsorption of the metal ions present in the effluent by the activated carbon, the FT-IR spectra (Figure 1b) (Table 4), the concentration of silica oxide (SiO2) was 83.70%, the highest among the chemical composition. It shows that the loading of Cr was 24.40% after biosorption. X-ray diffraction analysis (XRD). The WSAC has an utterly amorphous structure which is expected for organic materials. Sharp peaks are absent, revealing a predominantly amorphous structure, an advantageous property of a well-defined porous adsorbent [24]. The X-ray diffraction pattern of the samples in Figure 2 shows the presence of mica/illites, kaolinite, and quartz.
The reflection associated with mica/illites is characterised by reflection located at 2θ=17° and 25° and kaolinite at 2θ=20° and 21° for WSAC before adsorption. For WSAC after adsorption, mica/illites were located at 15°, 18°, and 24°, and kaolinite at 20° and 22°.   (Figure 3a), the watermelon shell gave a porous surface texture, enabling metal ions' adsorption onto the surface. Besides, it also shows that the surface morphology of the watermelon shell consisted of two kinds of colour tones: lighter and darker [26]. The lighter shades represented the inorganic component, while the darker shades were organic components. From the spectrum of the watermelon shell (Figure 3a), Si was observed. After the biosorption of Cr using a watermelon shell, the morphology underwent a physical change. Lump-like deposits or shiny particles were formed (Figure 3b).
Adsorption Kinetics. The pseudo-first-order and pseudo-second-order model equations were fitted to model the kinetics of heavy metal adsorption onto the produced activated carbon. When plotted, the linearity of each model was used to determine how suitable each model was for the adsorption. As a result, the model parameters were calculated in this study utilising the data produced by the experiments conducted and taking into account the linearised versions of the pseudo-first-order and pseudo-second-order reaction models provided in Equations (5) and (6), respectively.
It was found that the pseudo-second-order model fitted better than the pseudo-first-order model for the removal of Cr 2+ by Watermelon with R 2 =0.9983 (Figure 4).

Pseudo-first-order kinetic
Pseudo-second-order kinetic  Statistical Optimization of Removal of Chromium Using WSAC. A BBD with a total of 17 experiments was employed for RSM. Table 6 reports the experimental and predicted values of the adsorption capacity of WSAC.  The coefficient of determination (R 2 ) presents the quality of the polynomial model [28]. The predicted R 2 considers all effects, and the adjusted R 2 considers only square effects and interaction effects between two input variables. This study's predicted R 2 and adjusted R 2 were 0.8225 and 0.9867, respectively, indicating that the model could not explain only 17.75 % of total variations. "Adeq Precision" measures the signalto-noise ratio, and a ratio greater than four is desirable. The balance was 17.36 in this study, indicating an adequate signal. Therefore, the RSM model could be used to navigate the design space [12].
The experimental and predicted values closely match the R 2 value of 0.978 ( Figure 6).
So, the high correlation coefficient (R 2 =0.978) between the model and the experimental data showed that the model could efficiently predict the removal of Cr 2+ from textile effluent using WSAC. This methodology could be successfully employed to study the importance of the test variables' individual, cumulative, and interactive effects in biosorption. The optimum values of adsorbent dosage, pH and agitation time from BBD were found to be 0.6 g/l, six and 40 min, respectively. The maximum predicted removal of Cr 2+ was found to be 54.06%.
The developed model equation in terms of coded factors is as follows: where the response was the removal of chromium from textile effluent, A was the coded value of dosage of adsorbent, B was the coded value of pH of the solution, and C was agitation time.
The coefficients with one factor represent the effect on the particular factor, while the coefficients with two elements represent the interaction between the two factors. The positive sign in the equation indicates a synergistic effect, whereas the negative sign indicates an antagonistic effect.
From the analysis of variance (ANOVA) for the response surface quadratic model (Table 7), the model F-value of 34.50 implied that the RSM model is significant. The coefficient of variation (the ratio of the standard error of estimate to the mean value stated as a percentage) and F-value tests have also been carried out to assess the model's suitability.
The F-distribution is a probability distribution that compares variances by looking at their ratio. If they are equal, then the F-value would equal one. The balance of the model means square (MS) to the relevant error means the F-value represents square in the ANOVA table.
The F-value increases along with the ratio, increasing the likelihood that the model's mean square contribution will be much more significant than the error mean square. For R 2 closer to unity, the better the estimated regression equation fits the response data. The closeness of R 2 and Adj. R 2 signified that the significant terms and effects are genuinely substantial. Hence the model worked well. The agreement of Adj. R 2 and Pred. R 2 (i.e., a difference of < 0.2) authenticated that the model truly fits.
Effect of pH and dosage. pH and adsorbent dosage is the essential process parameters for assessing the removal capacity of a biosorbent. Adsorption experiments were carried out per the selected model with a ph range and WSAC dosage. The maximum removal of Cr 2+ metal ions was 54.53% for WSAC at pH six and WSAC dosage of 0.6 g/l ( Figure 7).

CONCLUSIONS
A detailed batch experimental study was carried out for the removal of Cr 2+ from wastewater by using Watermelon shell-activated carbon. The current study aimed to identify optimum process conditions using response surface methodology for removing Cr 2+ from textile effluent by WSAC as biosorbent. Response surface methodology using BBD proved a very effective and timesaving model for studying the influence of process parameters on response factors by significantly reducing the number of experiments and facilitating the optimum conditions. The Experimental values and the predicted values are in perfect match with an R 2 value of 0.978. This methodology could be successfully employed to study the importance of the test variables' individual, cumulative, and interactive effects in biosorption. The optimal removal of Cr was obtained at initial pH of 6.0 solution, WSAC dosage of 0.6 g/l and contact time of 40 min, resulting in 97.80% of the maximum predicted removal of Cr 2+ .
FTIR results confirmed the presence of different functional groups such as phenol, alcohol, carboxylic acid, alkanes, amines, amino acids, and aromatic alkyl halide in the Watermelon. Furthermore, the SEM image showed macro, meso, and micro-pore [26], which enhanced metal uptake.
Since the studied adsorption process in removing metal from textile effluent is efficient, further studies on the use of WSAC with high fixed carbon content should be carried out to investigate the effect on removal and compared with commercial activated carbon, which has been frequently employed in the industry. Also, further studies on the use of response surface methodology (RSM-BBD) should be employed for the adsorption of heavy metals.