Absorption of hexavalent chromium by green micro algae Chlorella sorokiniana: live planktonic cells

Water pollution with heavy metals is a severe socio-environmental problem that caused by discharging industrial effluents into water streams. Such effluents are highly contaminated with heavy metals that show many hazardous effects on the aquatic ecosystem (Mahdavi et al. 2012; Shanab et al. 2012). Hexavalent chromium [Cr (VI)] is a toxic heavy metal that is used in many industries, such as electroplating, leather tanning and metal finishing. It has been reported that Cr (VI) has been found in ground water at concentrations that range from between 0.2 and 180 μg/l. Furthermore, it has been detected in soil and rocks at an average concentration of 2,200 and 2,650 mg/kg, respectively (Chrysochoou et al. 2016). Cr (VI) contaminated aquifers have been found in two locations. According to Dermatas et al. 2017, the first, Cr (VI) contaminated aquifer was in Northern Greece with a concentration of 64 μg/l and it was considered to be the highest detected geogenic concentration to have been found in regions that have this geological background. The second Cr (VI) contaminated aquifer was at INofyta (Central Greece), with a peak concentration of 10,000 μg/l as a result of anthropogenic activities (Dermatas et al. 2017). Furthermore, Cr (VI) gets to humans through drinking water or food such as fish or plants, where it has many hazardous effects on human health. Therefore, many conventional methods were used for chromium removal: ion exchange, electrodialysis, ultrafiltration, chemical precipitation and reverse osmosis (Nataraj et al. 2007; Rajput et al. 2016; An et al. 2017; Yan et al. 2017; Zamri et al. 2017). However, these technologies are very expensive, generate toxic non-ecofriendly waste material and are not effective at a low chromium concentration (Singh et al. 2014). Alternatively, many researchers were directed to use several non-conventional methods such as biosorption for better and safe removal. Biosorption is the process of binding cations passively by both live and dead biomass (Abdi & Kazemi 2015). Agriculture wastes, plants, fungal, bacterial and algal biomass have been used as potential cost-effective biosorbents (Abioye et al. 2018; Almeida & Corso 2014; Fawzy et al. 2016; Kavitha et al. 2016; Naghipour et al. 2016; Thatheyus & Ramya 2016). As algae are photosynthetic organisms, both living and non-living cells are capable of Cr (VI) removal (Sen & Dastidar 2010). Algal strain selection is a key consideration in the chromium removal process. There are many species of algae that can accumulate high concentrations of Cr (VI), such as Scenedesmus dimorphous, Nanoclropsis, Spirulina, Neochloris oleoabundans, Nannochloroposis, Botrycoccus braunii, Sargassum sp, Dunaliella salina, Chlorella colonials and Chroococcus disperses (Aslan & Kapdan 2006; Chalivendra 2014; Hedayatkhah et al. 2018; Kaparapu & Prasad 2018; Jaafari & Yaghmaeian 2019). Although many researchers have investigated Chlorella sp. for heavy metal removal, most of them used the dead form only. In this study, a live planktonic form was used in order to investigate the ability of live Chlorella sp. to remove Cr (VI) and to estimate its tolerance to chromium toxicity. One factor at a time (OFAT) experiments and general full factorial design were performed to obtain the optimum conditions that maximize the removal of Cr (VI). Chlorophyll A was estimated in order to investigate the ability of Chlorella sp. to grow in the presence of Cr (VI). Morphologically identified Chlorella sp. was further identified using 18S rRNA. FT-IR analysis of the algal cell was used in order to identify the functional groups that found on the surface of the Chlorella sp. and helped in the removal process.

Soil samples were collected from 6th of October industrial zone, Cairo, Egypt. Micro-algal species were isolated and Chlorella sp. (microscopically confirmed) was purified from the cultivated sample and sub-cultured using Z-media (see Table 1) under light intensity (3000 lux) with pH 7 and temperature 25 ± 1 °C.

Different concentrations of chromium were prepared by dissolving an equivalent amount of potassium dichromate (K₂Cr₂O₇) in deionized water. Diphenyl carbazide has been used for chromium estimation using (M-ETKAL) 721 spectroscopy (Rice et al. 2012).

The previous OFAT experiments were displayed as preliminary experiments in order to determine the low and the high levels of each factor.

The 2⁵ general full factorial design experiments were performed according to the low and the high levels of the all factors that listed in Table 2. Factorial design matrix and the measured RE were demonstrated in Table 3 with fits and residuals values. The run orders of the experiments were randomized to avoid the systematic errors. The obtained results were analyzed using Minitab 18 software. Furthermore, the main and interaction effects, pareto chart and normal probability were plotted and interpreted.

The OFAT experiments were analyzed using one-way analyses of variance (ANOVA) in order to determine the significance between the different levels at alpha level (α) = 0.05. Shapiro-Wilk's test was used to assess the assumptions of normality, while Levene's test was used to determine homogeneity of variances. Data transformation was performed prior to running the ANOVA models. In full-factorial model, analysis of variance (ANOVA) (see Table 4) was performed to determine the significant differences between and within (i.e. low and high levels) factors at α = 0.05. Model reduction was displayed to identify all potential terms in the model. Backward elimination method at alpha to remove equal to 0.05 was used to reduce the model. The fitted values and the residuals were calculated for each run in order to calculate the error estimation. Moreover, the response optimizer was used to identify the combination of levels settings from all the factors that maximize the removal efficiency of Cr (VI).

Effect of chromium on Chlorella sp. growth was studied through an estimation of chlorophyll ‘a’ where different time intervals (1, 2, 3, 4, 6, 7, 9 days) were tested with a fixed pH 7, a chromium dose of 20 mg/100 ml, and a Cr (VI) concentration of 100 ppm. Furthermore, different Cr (VI) concentrations (0, 100, 200, 400, 600 ppm) were investigated when the pH was 7 and the Ch dose 20 mg/100 ml Cr (VI) solution. At the end, the effect of the Ch dose (5, 10, 15, and 20 mg/100 ml Cr (VI) solution) was studied at 100 ppm Cr (VI), pH 7. All the experiments were undertaken at a room temperature 25 °C ± 1. Dimethyl sulfoxide (DMSO) method was used for chlorophyll ‘a’ extraction and estimation as follow:

The chlorophyll ‘a’ was extracted using 1:1 (v/v) dimethyl sulfoxide (DMSO) and 90% acetone as a solvent. Afterwards, the absorbance was determined at 664, 647 nm.

• E₆₆₄ = absorbance at 664 nm.

• E₆₄₇ = Absorbance at 647 nm.

• = volume of extracting solvent in ml.

• V = volume in sample in L.

• L = the path-length of cuvette in cm.

Chlorella sp. that was previously confirmed microscopically was identified using 18S rRNA as follows:

The sample was enriched and cultivated on Z-media under a light intensity (3000 lux) with pH 7 at a temperature 25 ± 1 °C for seven days. After cultivation, the sample was collected and centrifuged at 20,000 rpm for 15 min. The total genomic of micro-algal sample was extracted and purified using a GeneJet Genomic DNA purification Mini Kit (Thermo Scientific) according to manufacturer's protocol. The extracted DNA was diluted to an adequate concentration with sterile water for subsequent PCR reactions to minimize the formation of heteroduplexe molecules and stored at −20 °C until further use.

Primer set, forward (5′-AACCTGGTTGATCCTGCCAG-3′) and Reverse (5′-CACCAGACTTGCCCTCCA-3′) was used to get a fragment of approximately 500 bp. PCR amplification was carried out in a thermal cycler (Creacon, Holland). The reaction mixture (50 μl) contained 1 μl template DNA (50 ng/μl), 25 μl hot start master mix, 1 μl from each primer (5 pmol/μl) and 23 μl biodest water (Hot Start Taq QIAGEN). Thermal cycling was carried out by using an initial denaturation step of 94 °C for 3 min, followed by 40 cycles of denaturation at 94 °C for 30 sec, annealing at 55 °C for 30 sec, and elongation at 72 °C for 1 min. Cycling was completed by a final elongation step of 72 °C for 7 min. Final product for specific amplicon (approx., 869 bp) was photograph and detection using Dig-doc, UVP, INC, England. DNA molecular weight ladder 1 kbp DNA marker (PeqGold 1 Kb, Peqlab, GmH) was used to estimate final amplified product length. The PCR product was loaded on 1.5% (w/v) Agarose gel, stained with ethidium bromide, separated by electrophoresis (75 V, 150 mA) and viewed on UV plate. Gene JET PCR Purification Kit (Thermo Scientific) was used for DNA purification. ABI PRISM^® 3100 Genetic Analyzer 3100 (Applied Bio-system) was applied for PCR products and performed by Macrogen Inc. Seal, Korea. Gel documentation system (Geldoc-it, UVP, and England) was applied for data analysis using Total lab analysis software, ww.totallab.com, (Ver.1.0.1). Aligned sequences were analyzed on the NCBI website (http://www.ncbi.nlm.nih.gov) using BLAST to confirm their identity. The genetic distances and multi-alignments were computed by Pairwise Distance method using Clustalw software analysis (www.ClustalW.com). The sequence was deposited in the Gen Bank with a number of Banklt2088194.

Algal cells were dried overnight and the dried material encapsulated with KBr discs. Fourier transform infrared spectroscopy (FT-IR) was used in order to detect the functional groups of the algal cell. Spectra were detected in the wavelength range from 400–4,000 Cm⁻¹.

The vegetative Chlorella sp. cells, shown in Figure 1, were spherical, 5.5–8.4 μm in diameter, oval after division, with a fluffy, smooth, and colorless membrane. Chloroplast ‘a’ was a parietal cup with basal pyrenoid and during reproduction, the cell contents divide into (2–4) auto-spores. These are spherical cells 4.5–7.5 μm in diameter.

The results of a full factorial design experiment were analyzed. Furthermore, the main and the interaction effects, the pareto chart and the normal probability plot were obtained and interpreted as follows:

Plot of the main effects was designed to show the results of the regression analysis. Significant factors of 95% intervals have been displayed by this plot. The main effects were expressed a deviation of the average between the high and the low levels for each factor. The RE was increased as the factor change increased from the low to the high level and this indicated the positive effect of this factor. In contrast to the negative effect, the RE was decreased as a result of reduction that occured from the low to the high level of the factor (Ponnusami et al. 2007). Furthermore, the larger the vertical line between the two levels, the larger the effect on the RE. This result was attributed to the length of the vertical line that was directly proportional to the statistical significant of a factor (Palanikumar & Davim 2009). Table 4 indicated that time, temperature, Cr (VI) concentration and algae dose (Ch dose) were significant factors, which aligns with previous studies (Saadat & Karimi-Jashni 2011). Furthermore, the main effects plot (Figure 2) indicates that Cr (VI) concentration and algae dose (Ch dose) were the most significant factors. However, Ch dose had a positive effect on the Cr (VI) RE. Nevertheless, Cr (VI) concentration had a negative effect. Furthermore, the plot interpretation suggests that the low level of Cr (VI) concentration factor, the low level of time and the high level of algal dose achieved the highest Cr (VI) RE.

It is well known that effective interaction is achieved when there are changes from the low to the high levels of a factor, dependent on the levels of other factors. For instance, when lines of the levels of two factors don't run in parallel (Mathialagan & Viraraghavan 2005). Table 4 states that the two-way-interactions between ‘Time and pH’, ‘Time and Cr (VI) conc.’, ‘Time and Ch dose’, and ‘Cr (VI) and Ch dose’ were statistically significant, which means it affected the Cr (VI) RE. In addition, Figure 3 exhibites that the interactions between ‘Time and pH’ had a positive effect on the Cr (VI) RE due to the non-parallel lines that occurred between the levels of the two factors. Furthermore, Table 4 demonstrates that the three-way interactions ‘Time, pH, and temperature’, ‘Time, Cr (VI) concentration, and Ch dose’, ‘pH, temperature, and Cr (VI) concentration’, and the four-way interactions ‘Time, pH, temperature, and Cr (VI) concentration affected the Cr (VI) RE.

A Student's t-test was performed to estimate if the calculated effects were significantly different from zero or not (Ponnusami et al. 2007). For the 95% of confidence intervals and 16 degrees of freedom, the t-value reference line (Figure 4) was 4.30. It is well known that values that exceed the reference line are significant values, while values that do not exceed the reference line are non-significant values. Based on the previous rule, it was observed that algal dose (E), Cr (VI) concentration (D), time & Ch dose (AE), time (A), time & Cr (VI) concentration (AD), pH & temperature & Cr (VI) concentration (BCD), time & pH & temperature & Cr (VI) concentration (ABCD), time & pH (AB), time & pH & temperature (ABC), temperature (C) and time & Cr (VI) concentration & algal dose (ADE) were significant values. The (E) and (D) were higher significantly than the other values that exhibited their positive effect on the Cr (VI) RE (Mathialagan & Viraraghavan 2005). These results were in a good a agreement with previous studies where the concentration of pollutants was decreased from the low to the high levels of the factor (Boubakri et al. 2014).

In the statistical data analysis, it's necessary to know if the data comes from normal distribution or not (Ruiz Espejo 2006). Data plotted in Figure 5 shows the normal probability distribution of the data. The points that fall close enough to the straight are normally distributed. Moreover, appearance of far points of algal dose (E) and Cr (VI) concentration (D) shows that they are real factors that had a great influence on the Cr (VI) RE (Palanikumar & Davim 2009; Brasil et al. 2006). This result confirmed the previous result obtained from the pareto chart.

From the all of previous results, it can be observed that the Cr (VI) RE was increased on increasing the Ch dose and decreased on increasing the Cr (VI) concentration. This may be attributed to the large number of the functional groups, which occurred on the cell wall surface of Chlorella, resulting from the increasing dose. Many researchers have reported that increasing doses caused an increase of pollutants' RE due to the availability of functional groups on the cell wall of Chlorella, such as carboxylic, amino, hydroxyl, amines, sulphonic etc. that increased the RE (Volesky 2007; Gupta & Rastogi 2008; Jaafari & Yaghmaeian 2019). On the other hand, the RE was decreased by increasing the Cr (VI) concentration due to the damage inflicted on the Chlorella cells by the higher concentrations of Cr (VI), as previously stated (Singh et al. 2016).

Response optimizer is a tool that is used to determine the optimum conditions that achieve the highest RE. In the current work, the optimum combined conditions were a time of three days in the static state, at pH 6.2, temperature 29 °C, Cr (VI) concentration 100 mg/l and algal dose 20 mg/100 ml Cr (VI)-solution for a predicted RE of 99.67%, with a desirability score of 1 (see Figure 6).

The phylogenetic analysis of the band sequence revealed that the sample was related to Chlorella sorokiniana strain KU-1019 18S ribosomal RNA gene, partial sequence with accession number of KF444207.1 with Query coverage of 97% and Identity of 86%.

In order to investigate the ability of Chlorella sorokiniana to tolerate Cr (VI) stress, chlorophyll ‘a’ (growth indicator) was estimated. For instance, Figure 7 showed a comparison of chlorophyll ‘a’ estimation between the Cr (VI)-treated and the non-treated (control) Chlorella sorokiniana. It was observed that the Chlorella sorokiniana can tolerate Cr (VI) until the third day where the growth in the third day was nearly 1,890.177 μg/l in comparison to the control 6,680.79 μg/l. After that, a slight decrease was observed from the fourth day where the chlorophyll ‘a’ result recorded 163.023 μg/l. Furthermore, Figure 8 demonstrated the effect of increasing algal dose with a correlation to its growth in Cr (VI)-treated medium when Cr (VI) concentration was 100 ppm, and pH 7. Furthermore, it was noticed that a slight increase in chlorophyll ‘a’ that resulted from increasing the Ch dose (see Figure 8). Nevertheless, increasing in the Cr (VI) concentration has displayed a negative effect on the Chlorella sorokiniana growth where it exhibited 256.55 μg/l at 100 ppm when the algal dose was 20 mg/100 ml, and the pH of 7. After increasing the Cr (VI) concentration to 100 ppm, the chlorophyll ‘a’ content was reduced respectively to the half of the value. Figure 9 illustrates the decreasing in the detected chlorophyll ‘a’ by increasing the concentration of Cr (VI) from (0–600). Error bars illustrated the standard deviation of each values.

The FT-IR spectrum of the Chlorella sorokiniana (Figure 10) displays a number of absorption peaks that indicate the complex nature of the examined sample. The appearance of different characteristic peaks refer to the heterogeneity of Chlorella sorokiniana as a biosorbent material. The IR absorption of each peak indicated a certain functional group: a peak of 3,422 showed the appearance of a strong, broad –OH group. The 2,953 band formation suggested a stretch –CH group and the peak 1,653 was stretch –C=O. Moreover, 1,544, 1,454, and 1,404 indicate the formation of stretch –C=C. SO₃²⁻ and PO₄³⁻ were formed at 1,244.46 and 1,072.74, respectively, and the peak 669 showed the appearance of halogens. Copious functional groups appeared on the cell wall of Chlorella sorokiniana, indicating its affinity for metal ions removal.

Live planktonic cells of Chlorella sorokiniana have successfully removed Cr (VI) from polluted wastewater through a process called biosorption. Biosorption as a word consists of two parts: sorption, that means attachment of one substance on the surface of another substance via physico-chemical process, and bio ‘prefix’, that means by using of biological material. Furthermore, this method has the ability to remove heavy metals by two mechanisms (Gadd 2009). The first mechanism is a slow metabolic dependent that occurs in living cells only where metal ion uptake into the cell occurs through the cell membrane and cytoplasm of the cell. This type of mechanism was known as intracellular uptake or active uptake and bioaccumulation (Vymazal 1990; Herrero et al. 2008). Accordingly, death of Chlorella cells after three days' exposure to 100 ppm Cr (VI) suggested that the amount of absorbed Cr (VI) was toxic to the cells and caused its death. Furthermore, the second mechanism, which is regarded as a rapid metabolic independent and known as adsorption or passive uptake, was observed. In this mechanism, metal ion binds to the surface of the cell wall and extracellular material and is common between both live and dead cells. This return to multiple chemical groups carboxyl, sulphonate, hydroxyl and amino groups' availability on the surface of the cell wall of the algal cell that was confirmed by the FT-IR spectrum (Davis 2003). Another concerning point, heavy metals binding on the surface of the algal cells are dependent on many factors, such as the availability of active sites, their accessibility and the affinity between site and metal (Lacher & Smith 2002). Additionally, the role of shaking in the adsorption mechanism can't be denied where 99.07% RE was observed after 24 h shaking and this result was nearly the same obtained after three days' static time: 99.02%. This may be attributed to passive uptake discussed previously and as stated in previous research (Friis & Myers-Keith 1986; Al-Rub et al. 2006; Murphy et al. 2008).

Chlorella sp. was successfully isolated, purified and applied in the Cr (VI) removal. The highest Cr (VI) RE of 99.6793% was achieved after 24 h contact time, pH 7, temperature 40 °C, shaking model and Cr (VI) concentration 100 ppm. Based on this result, the live planktonic form of Chlorella sp. was stated as an efficient biomass for Cr (VI) removal. Additionally, the chlorophyll ‘a’ result was nearly 1,890 μg/l after three days' exposure to 100 ppm of Cr (VI); after that growth declined. This suggested the ability of Chlorella sp. to tolerate Cr (VI) toxicity. Additionally, Chlorella sp. was successfully identified using 18Sr RNA as Chlorella sorokiniana. However, further studies are still needed to investigate a new method that facilitates harvesting and dewatering of algae cells after treatment processes such as biofilm formation from micro-algae.

The research was supported by Academy of Scientific Research and Technology (ASRT), Egypt through a master grant, Scientific Next Generation grant cycle 5 (SNG5), to the first author.