This article focuses on the phycoremediation of pollutants from secondary treated coke-oven effluent through a green and economical route. A microalgal sample was collected and identified as a consortium of Chlorella sp. and Synechococcus sp. The culture cost was reduced by using poultry litter extract as supplementary material to BG-11 medium. Since the major pollutants present in real secondary treated coke-oven wastewater are phenol, ammoniacal-N (NH4+) and cyanide, several matrices were designed with these three major pollutants by varying their initial concentrations such as phenol (2–10 mg/L), cyanide (0.3–1 mg/L) and NH4+ (100–200 mg/L), termed as simulated secondary treated coke-oven wastewater. Maximum removal was observed with individual solutions of phenol (4 mg/L), cyanide (0.6 mg/L) and NH4+ (175 mg/L), while maximum removal in simulated secondary treated coke-oven wastewater was observed at higher concentrations of phenol (8 mg/L) and cyanide (0.8 mg/L) and the same concentration of NH4+ (175 mg/L). A consortium was found effective to meet statutory limits of pollutants. Kinetic model was developed for predicting growth of consortium and observed that the poultry litter extract-enriched BG-11 medium showed higher values of maximum specific growth rate (0.56 per day) and carrying capacity (1,330 mg/L) than that in BG-11 medium only.

  • A consortium was identified for tertiary treatment of coke-oven wastewater.

  • Poultry litter extract was found effective as a growth medium for consortium.

  • Phycoremediation of NH4+, phenol and cyanide was examined with the present consortium.

  • Biomass and biomolecules were extracted and estimated.

  • A consortium was found effective for real wastewater treatment.

Graphical Abstract

Graphical Abstract
Graphical Abstract

The coke-oven plant is the integrated part of the steel industry where the coal pyrolysis process occurs at a higher temperature (1,000–1,200 °C) to produce coke (Kwiecińska-Mydlak et al. 2019). Wastewater (approximately 1–1.5 m3/tonnes coke production) is generated during the byproducts recovery processes and is known as coke-oven wastewater (Kwiecińska-Mydlak et al. 2019). Generally, raw coke-oven wastewater is characterized by high contents of chemical oxygen demand (COD), biochemical oxygen demand (BOD), phenol, ammoniacal-N (NH4+), cyanide, sulfide and thiocyanate (Kwiecińska-Mydlak et al. 2019). While the physico-chemical methods such as pH neutralization, flow equalization, coagulation and chemical oxidation were used as primary treatment for reduction of COD to BOD ratio of coke-oven wastewater, a membrane-based technique, ion exchange separation, electro-coagulation, photocatalysis and adsorption were used as a tertiary treatment method or as a polishing method in the effluent treatment plant (Maiti et al. 2019). Coagulation and precipitation methods and chemical oxidation methods have disadvantages of high chemical cost, generation of sludge and are inefficient at removing pollutants at the parts per million (ppm) level (Waghmare & Arfin 2015). The generation of secondary pollutants is another characteristic problem of chemical oxidation methods (Waghmare & Arfin 2015). Bacterial treatment of wastewater is a widely accepted secondary treatment method due to its economical and ecofriendly nature. However, it has several limitations such as generation of secondary sludge and maintenance of proper nutrient condition (Waghmare & Arfin 2015). Biological treatment of physico-chemical treated coke-oven wastewater is also found inefficient sometimes due to the presence of refractory compounds and toxic chemicals in the influent of the biochemical oxidation and dephenolisation (BOPD) plants (Sharma & Philip 2016). Usage of non-specific bacterial strain and poor operation conditions are the major challenges faced by bacterial treatment processes. Even after primary and secondary treatment processes, the wastewater may contain an array of pollutants above permissible limits as set by authorized bodies (Central Pollution Control Board (CPCB), India). For wastewater, the permissible limits of phenol (1 mg/L), cyanide (0.2 mg/L) and NH4+ (50 mg/L) have been fixed by the CPCB. Scientists reported the presence of pollutants even in biologically treated coke-oven wastewater as phenol (2–4 mg/L) (Das et al. 2020), cyanide (0.4–0.5 mg/L, 0.8 mg/L) (Biswas et al. 2010; Das et al. 2020) and NH4+ (210 mg/L) (Biswas et al. 2010). These toxic chemicals have adverse effects on terrestrial and aquatic ecosystems. Therefore, tertiary treatment of coke-oven wastewater is essentially required for the attainment of stipulated water quality before its discharge to the environment. Adsorption and membrane-based separation are commonly used tertiary treatment methods. Although adsorption is a technically simple method, it has the problem of disposal of spent adsorbent (Waghmare & Arfin 2015). Membrane-based separation requires an extensive pretreatment process to prevent membrane fouling. In addition, the operational cost is also high (Waghmare & Arfin 2015). Therefore, an apposite ecofriendly and economic tertiary end-of-pipe treatment method of coke-oven wastewater is required. More information related to primary and secondary methods for treatment of raw coke-oven wastewater and the degradation potential are shown in Table S1, attached as Annexure I in the Supplementary Material.

The application of cyanobacteria and microalgae for nutrient recovery such as nitrate and phosphate from different sources has been well explored. Cyanobacteria and microalgae are capable of growing in agricultural, industrial and municipal wastewater. Tertiary treatment of wastewater using cyanobacteria and microalgae is a unique method and the production of value-added products from produced biomass makes this process economically feasible. Phycoremediation has several advantages such as (i) it can remove the pollutants in an environmentally and ecofriendly manner to ppm levels and (ii) generated biomass can be used for the production of value-added products which makes the process profitable one. Gurbuz et al. (2004) tested the three microalgal cultures Chlorella sp. Arthrospira maxima and Scenedesmus obliqus for the biological detoxification of cyanide from the mining process wastewater. Sen et al. (2018b) used the cyanobacterial consortium for phycoremediation of cyanide from coke-oven wastewater. Thakurta et al. (2018) used Leptolyngbya sp. for the bioremoval of phenol from the synthetic and real wastewater. Thus, it can be stated that while a few studies were carried out for removal of individual pollutants such as phenol, cyanide and NH4+ using microalgae/cyanobacteria, the studies on the application of microalgae/cyanobacteria for the removal of multipollutant from coke-oven wastewater as tertiary treatment is very limited. Again, the main objective of all these studies was to establish the applicability of microalgae/cyanobacteria in the removal of pollutants and to ascertain proper operating conditions, and no discussion was made to curtail the cost of operation by supplementing any nutrient-rich waste material to BG-11 for growth of algae. Therefore, in the current study, an attempt has been made to fulfil the present knowledge gap by using a phycoremediation method for tertiary treatment of coke-oven wastewater in an economic and ecofriendly way.

In this study, the phycoremediation technique has been applied to remediate pollutants (NH4+, phenol and cyanide) from both simulated secondary treated coke-oven wastewater (SSTCW) and from real secondary treated coke-oven wastewater (RSTCW). Several researchers reported that microalgal cultivation requires macronutrients (nitrogen and phosphorus) from different organic and inorganic nutrient sources, which increases the cost of microalgal production (Bhatnagar et al. 2011; Singh & Das 2014). Poultry litter is a hazardous waste of poultry farms and is a rich source of nitrogen and phosphorus (Lynch et al. 2013). In this work, such waste has been utilized as a food supplement to grow the consortium of cyanobacteria and microalgae. Furthermore, kinetic modelling is an essential part of designing a bioreactor and for the development of a proper control strategy. In this article, a kinetic model was developed to predict growth of a consortium of cyanobacteria and microalgae during the phycoremediation process. Therefore, a comprehensive study of the simultaneous removal of the three pollutants, and an assessment of biomolecule production and kinetic modelling is the strength of this study.

All experiments were done in three sets and obtained results were shown as mean ± standard deviation (SD).

Collection, identification and cultivation of microalgal sample

Since the natural adaptation of the microalgal sample in the contaminated site is the key point for the phycoremediation process, a microbial sample was collected from the runoff of secondary treated coke-oven wastewater from a nearby coke-oven plant. The microalgal sample was incubated in collected native wastewater (100 mL) mixed with BG-11 (blue-green) medium (100 mL) (HIMEDIA, India) in a 500 mL of Erlenmeyer flask (Borosil, Mumbai, India). The flask was placed in an algal incubator at specified culture conditions as described by Sen et al. (2018a). The composition of the BG-11 medium is shown in Annexure II in the Supplementary Material. The isolation and identification of the microalgal sample were performed following basic spreading and streaking techniques, and the grown cultures were observed under a microscope (Premium, Dewinter Technologies, Italy) for assessing microalgal morphology.

Media preparation with poultry litter extract

Poultry litter was collected from a broiler farm of Naihati, North 24 Parganas (22.8895° N, 88.4220° E) West Bengal, India, and nutrients were extracted in liquid form following the protocol as suggested by Bhatnagar et al. (2011). Collected poultry litter was dried in a hot air oven (Universal Hot Air Oven, India) for 16 h at 60 ± 1 °C. Dried poultry litter (5 g) was added to 1,000 mL of distilled water, and the mixture was stirred using a magnetic stirrer for 2 h. The solution was then filtered with Whatman filter paper, and the filtrate was autoclaved. Finally, the solution was stored at 4 °C. The stored solution was termed as poultry litter extract (PLE). The characterization of PLE is shown in Table S2 attached as Annexure III in the Supplementary Material. PLE was diluted with sterile BG-11 medium in three ratios (v/v) so that the PLE extract was BG-11–1:5, 1:10, 1:15. The isolate was cultured in these three solutions individually. Culture broths were collected after 14 days of incubation and centrifuged at 5,000 rpm for 15 min. Pellets were washed twice with deionized water, and finally, placed in a hot air oven at a temperature (65 ± 1 °C) for 18 h. The weight of dried biomass was taken, and the growth of the culture was measured in terms of dry biomass concentration (DBC). Maximum growth was seen in the 1:10 dilution, and to compare the isolated sample growth in pure BG-11 medium and PLE-enriched medium, the PLE which was diluted 10 times with BG-11 medium was used for the next stage of the study.

To perform growth studies, the isolated sample was inoculated in BG-11 medium and in PLE-enriched BG-11 medium (10 times diluted with BG-11 medium) separately. In both media, inoculum size (IS) and initial pH were kept constant at 10%, and pH 9, respectively. The high alkaline pH helps to prevent the growth of other microorganisms. Both cultures were then placed in an algal incubator at specific conditions (Sen et al. 2018a) and incubated for 18 days. Samples were collected from both cultures at 2-day intervals and centrifuged. The growth of the culture was measured for DBC, and the best one was selected. Biomolecules such as chlorophyll, carbohydrate and protein were extracted from the produced biomass following the methods described by Biswas et al. (2018). A detailed description of standard protocols for biomolecules estimation are described in the Supplementary Material as Annexure IV.

Design of experiments and preparation of SSTCW solutions

Since the major pollutants present in RSTCW are phenol, ammoniacal-N (NH4+) and cyanide, several matrices were designed with these three pollutants by varying their initial concentrations, i.e. phenol (2–10 mg/L), cyanide (0.3–1 mg/L) and NH4+ (100–200 mg/L), to mimic the real wastewater. The concentrations of these three pollutants are shown in Table 1, where rows describe the concentrations of a single pollutant and columns represent the concentrations of mixed pollutants.

Table 1

Matrix of pollutants for experiments

Name of pollutantsM1M2M3M4M5
Phenol (mg/L) 10 
Ammoniacal-N (NH4+) (mg/L) 100 125 150 175 200 
Cyanide (mg/L) 0.3 0.4 0.6 0.8 
Name of pollutantsM1M2M3M4M5
Phenol (mg/L) 10 
Ammoniacal-N (NH4+) (mg/L) 100 125 150 175 200 
Cyanide (mg/L) 0.3 0.4 0.6 0.8 

An aqueous solution of a mixture of these pollutants was prepared by adding all the pollutants phenol, cyanide and NH4+ as per Table 1. The solution, thus prepared, was named as SSTCW labelled as M1–M5 in Table 1. The ranges of model pollutants were selected based on the previous studies.

Standard solutions of these model pollutants were prepared following the protocol as described by Rai et al. (2021b). Required concentrations of pollutants were made by adding the required amount of PLE-enriched BG-11 medium.

Tolerance level study of isolated sample in simulated solutions

To determine the susceptibility of isolated sample in diverse concentrations of pollutants, a tolerance level study (TLS) was performed by growing the isolated strains in simulated solutions, as prepared row-wise (aqueous solution of individual pollutants) and column-wise (SSTCW) of Table 1. The isolated sample was cultivated in all prepared solutions at initial pH 9 and IS 10%, and placed in the algal incubator. Samples were collected after 10 days of incubation period and centrifuged. The supernatant was examined for analysis of residual concentrations of pollutants (NH4+, phenol and cyanide) following the methods described by Rai et al. (2021b). Pellets were dried at 65 ± 1 °C in a hot air oven for 18 h. The DBC was measured for each study. For every set of experiments, the following controls were kept as (i) biotic control – PLE-enriched BG-11 medium with the isolated sample, and (ii) abiotic control – pollutant(s) containing PLE-enriched BG-11 medium without isolated sample. The controls were incubated under the same condition as for the experimental ones. To see the effect of pH on the growth and removal of multipollutants, the test consortium was grown in M1–M5 solutions where the initial pH was varied from 8 to 10.

Time variation studies

Time variation studies were performed to analyze the trend in bioremoval of NH4+, phenol and cyanide from simulated solution with time. The isolated sample inoculate (14 days old and IS: 10%) was added to SSTCW (M4) at pH 9, and thereafter, the culture was incubated for 14 days in the microalgal growth chamber. Samples were collected after at 2-day intervals. The biomass was harvested by centrifugation, and the supernatant was analyzed for remaining concentrations of pollutants. Finally, chlorophyll, carbohydrate and protein were estimated using standard protocols.

Application of isolated sample for treatment of RSTCW

RSTCW was collected from the runoff of the BOPD plant of the coke-oven plant in West Bengal, India. RSTCW was filtered with Whatman filter paper (grade no. 42). Generally, RSTCW was found to be polluted with three major pollutants – phenol, NH4+ and cyanide (Singh & Mishra 2018) – and the secondary clarifier effluent was reported to be loaded mainly with phenol, cyanide and NH4+ (Biswas et al. 2010; Das et al. 2020). Therefore, the RSTCW was characterized in terms of pH, phenol, NH4+, cyanide, total suspended solids and total dissolved solids as shown in Table 2.

Table 2

Characterization of RSTCW

Sl. no.ParameterValueStandard value (Kwiecińska et al. 2017; Maiti et al. 2019)
pH 6.9 ± 0.127 5.5–9.0 
Cyanide (mg/L) 0.65 ± 0.042 0.2 
Phenol (mg/L) 8.05 ± 0.057 1.0 
NH4+ (mg/L) 157.3 ± 3.05 50.0 
Total suspended solids (mg/L) 98 ± 11.2 15–50 
Total dissolved solids (mg/L) 3,645 ± 19.55 1,000–2,000 
Sl. no.ParameterValueStandard value (Kwiecińska et al. 2017; Maiti et al. 2019)
pH 6.9 ± 0.127 5.5–9.0 
Cyanide (mg/L) 0.65 ± 0.042 0.2 
Phenol (mg/L) 8.05 ± 0.057 1.0 
NH4+ (mg/L) 157.3 ± 3.05 50.0 
Total suspended solids (mg/L) 98 ± 11.2 15–50 
Total dissolved solids (mg/L) 3,645 ± 19.55 1,000–2,000 

The growth of isolate was examined in both diluted and non-diluted RSTCW. Bioremoval of NH4+, phenol and cyanide from RSTCW using microalgae isolate was investigated. The filtrate of wastewater (RSTCW) was diluted 50% with a requisite amount of PLE-enriched BG-11 medium. Inoculum (size: 10%) was added to wastewater and kept in the microalgal growth chamber for 14 days. After that, samples were collected and centrifuged at 5,000 rpm for 15 min. The supernatant was analyzed for all residual concentrations of pollutants. The growth of the isolated sample was determined by DBC and chlorophyll content.

Theoretical analysis

Kinetic model for biomass growth

Kinetic modelling is imperative in understanding the mechanisms of growth of biomass. Determination of kinetic parameters is important for (i) suitable bioreactor designing, (ii) scaling-up of process, (iii) developing adequate control strategies and (iv) cost optimization. While the kinetics of growth of indigenous novel strains in elemental growth medium (like BG-11) can be explained by conventional kinetic equations, the behaviour of strains in wastewater containing a number of pollutants is somewhat complex. An insightful, experienced and pragmatic approach is therefore required to obtain a unified model to explain the growth of microbial strains in complex systems.

Kinetic models for predicting the growth of the isolated sample in different systems, including (i) different medium composition, (ii) different pollutant systems like phenol, NH4+ and cyanide in their individual solutions and (iii) in their mixed conditions (M1–M5), were developed. To avoid the limitations of the Monod model, a logistic model was used to represent the kinetics of biomass growth. The main characteristic of this model is that it provides the maximum sustainable carrying capacity of a biomass population under constant environmental conditions as a parameter. The integral form of the logistics model (Sen et al. 2018a) is:
formula
(1)
where biomass concentration at time t (mg/L); maximum specific growth rate (per day); carrying capacity (mg/L); initial concentration of biomass (mg/L), and time of incubation (days).

Maximum specific growth rate and carrying capacity of the microorganism are evaluated by fitting the logistic model with experimental biomass growth data obtained during the exponential growth phase in different systems using the generalized reduced gradient optimization technique. A novel approach was explored to define the maximum specific growth rate for mixed pollutant systems.

For analyzing the growth kinetics of the isolated sample in SSTCW solutions (mixed pollutant systems), a modified logistic model was used. In this model, the maximum specific growth rate for the mixed pollutant system is assumed to be a function of the maximum specific growth rate for the individual pollutant systems as shown below.
formula
(2)

The coefficients () of the above equations are regressed along with carrying capacity () using the generalized reduced gradient optimization technique, where maximum specific growth rate (per day) for mixed pollutant systems (M1–M5); maximum specific growth rate for NH4+ pollutant systems (per day); maximum specific growth rate for phenol pollutant systems (per day); and maximum specific growth rate for cyanide pollutant systems (per day).

The logistic kinetic model was used to estimate the maximum specific growth rate and carrying capacity for the biomass production in the log phase by fitting the experimental biomass growth data. For regressing the model parameters (, ), the generalized reduced gradient optimization technique was used to minimize the average absolute relative deviation (AARD) between experimental ( being the number of data points) and model-predicted data. The objective function used for this method is:
formula
(3)

Statistical analysis

Graph Pad Prism 5.01 (software) was used for statistical analysis. Nonparametric t-test, and unpaired t-test were performed to obtain the level of significance for the production of biomass between PLE-enriched BG-11 medium and BG-11 medium only. One-way analysis of variance (ANOVA) test was used for the (i) bioremoval of pollutants and the production of biomass during the time variation study and (ii) produced biomass and biomolecules during time variation study.

Microalgal isolate

Microscopic observation shows that the isolated sample is a consortium consisting of a cyanobacterium Synechococcus sp. (rod-shaped non-motile, unicellular) and green algae Chlorella sp. (round-shaped, unicellular) as shown in Figure S1 which is attached as Annexure V in the Supplementary Material. A consortium culture is a robust candidate for treatment methods compared to a uniculture (Gonçalves et al. 2017). In the present consortium, the dominant strain is cyanobacteria over green algae. Generally, cyanobacterial samples have more tolerance and resistance for different wastewater such as agricultural, municipal and industrial than green algae (Renuka et al. 2015), and hence, it can be stated that the present consortium is suitable for the treatment of wastewater. Furthermore, Synechococcus sp. is a unicellular rod-shaped cyanobacterium and its unicellular structure is effective for the treatment of wastewater (Thangavel et al. 2018). In the case of green algae, Chlorella sp. is also effective for the treatment of different wastewater. Thus, the present consortium can be considered as an viable bioremediant for the treatment of wastewater.

Comparative growth studies

The consortium was cultured in two different media, BG-11 and PLE-enriched BG-11 media (PLE: BG-11 medium = 1:10). PLE-enriched BG-11 medium was found to be the best alternative for the growth of consortium (Figure 1(a)). Extracted biomolecules such as chlorophyll, carbohydrate and protein are shown in Figure 1(b). Throughout the experiment, the production of biomass from PLE-enriched BG-11 compared to only BG-11 medium was found to be significant with time (). In BG-11, the consortium showed a regular growth pattern with four consecutive phases: (i) preparatory phase – where culture was acclimatized to the surrounding environment, (ii) exponential phase – in this phase microorganism synthesize primary metabolites for their exponential growth, thus the growth rate of cells become high, (iii) plateau phase – here the culture growth rate is equal to the death rate due to nutrient deficiency and (iv) decline phase – cells die due to lack of nutrients (Sen et al. 2018a). Figure 1(a) shows that the growth of the consortium in PLE-enriched BG-11 medium increased 8.75-fold from the initial DBC, while in the BG-11 medium, growth increased 2.9-fold under identical conditions after 18 days of incubation. A similar increment was obtained for biomolecules chlorophyll (0.3 ± 0.001 mg/L to 3.8 ± 0.012 mg/L and 0.015 ± .002 mg/L to 0.88 ± .031 mg/L), carbohydrate (13.4 ± 1.1 mg/L to 374 ± 11.5 mg/L and 7.2 ± 1.0 mg/L to 142 ± 18.4 mg/L) and protein (17.0 ± 1.7 mg/L to 341.5 ± 10.9 mg/L and 7.5 ± 2.9 mg/L to 102 ± 2.3 mg/L) in PLE-enriched BG-11 medium and in only BG-11 medium, respectively (Figure 1(b)). Maximum growth (390 ± 6.0 mg/L) was seen on the 14th day of incubation in the BG-11 medium while in PLE-enriched BG-11 medium the biomass increased to 1,240 ± 17.0 mg/L on the 18th day. Bhatnagar et al. (2011) showed higher growth of microalgae (180%) than that obtained in the BG-11 medium, and total chlorophyll was obtained as 2.7 mg/L in the PLE-enriched BG-11 medium. This might be due to the availability of more nutrients in the case of the PLE-enriched medium, which favoured the growth. Due to its rich source of macronutrients, PLE supports the growth of the consortium compared to the BG-11 medium only. Therefore, it can be stated that the PLE-enriched BG-11 media is an effective alternative nutrient source for microalgal growth.

Figure 1

(a) Comparative growth study on PLE-enriched media BG-11 and BG-11 media, (b) comparative study of extracted biomolecules in PLE-enriched BG-11 media and BG-11 media.

Figure 1

(a) Comparative growth study on PLE-enriched media BG-11 and BG-11 media, (b) comparative study of extracted biomolecules in PLE-enriched BG-11 media and BG-11 media.

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Tolerance level study

Growth of the consortium was observed in both the simulated individual solutions and in SSTCW (M1–M5 solutions) at a varying range of initial concentrations of the pollutants (Figure 2(a)–2(d)).

Figure 2

(a) TLS of consortium in simulated ammoniacal-N solutions, (b) TLS of consortium in simulated phenol solutions, (c) TLS of consortium in simulated cyanide solutions, (d) TLS of consortium in SSTCW solutions.

Figure 2

(a) TLS of consortium in simulated ammoniacal-N solutions, (b) TLS of consortium in simulated phenol solutions, (c) TLS of consortium in simulated cyanide solutions, (d) TLS of consortium in SSTCW solutions.

Close modal

Bioremoval of NH4+ from simulated NH4+ solution

NH4+ is the preferred inorganic source of nitrogen for microalgal growth; cyanobacteria and microalgae can assimilate it directly (Rai et al. 2020). The growth of the consortium was examined in simulated solutions of NH4+, which is shown in Figure 2(a). Maximum growth (0.365 ± 0.001 g/L) of test strain was observed at 175 mg/L NH4+ while maximum removal (75.464 ± 0.581%) occurred at 100 mg/L. The test consortium grew well in all concentrations of NH4+. This might be due to the utilization of NH4+ (pollutant) as the nitrogen source by the strain. When NH4+ concentration increased from 100 mg/L to 175 mg/L, DBC increased from 0.329 ± 0.003 to 0.365 ± 0.001 g/L. Beyond this concentration, DBC of the consortium decreased to 0.315 ± 0.025 g/L (at 200 mg/L NH4+). Collos & Harrison (2014) reported that a concentration of ammoniacal-N <30.54 mg/L would not generally affect the growth rate of microalgal cells, and chlorophytes were found the most tolerant toward the ammoniacal-N. Therefore, it can be stated that the growth of consortium was restricted at a higher concentration of NH4+, and this value varies from species to species of cyanobacteria and microalgae (Collos & Harrison 2014).

Bioremoval of phenol from simulated phenol solutions

The growth of test strain in different concentrations of phenol is shown in Figure 2(b). Maximum DBC (0.335 ± 0.75 g/L) and maximum removal (91.072 ± 0.471%) were observed at 4 mg/L of phenol concentration. The consortium was found to grow in all concentrations of phenol (2–10 mg/L), but limited growth was observed beyond 6.0 mg/L phenol concentration. Aerobic biodegradation of phenol by cyanobacteria and microalgae provides CO2 and other small organic molecules which help in the growth of strains (Stephen & Ayalur 2017). Similarly, Duan et al. (2017) performed a growth inhibition test in phenolic solutions using different microalgal cells and stated that the tolerance level of pollutants for microalgal samples varies from species to species (Duan et al. 2017). The lowering of biomass production at a higher concentration of phenol might be due to the generation of reactive oxygen (Duan et al. 2017).

Bioremoval of cyanide from simulated cyanide solutions

The microalgal consortium was grown in simulated cyanide solutions with different initial cyanide concentrations (0.3–1.0 mg/L) (Figure 2(c)). Growth of the test strain was observed in all cyanide concentrations; however, DBC of the test strain increased up to 0.6 mg/L cyanide concentration; beyond this, growth was restricted. Hammed et al. (2016) reviewed cyanide removal using microalgae, and stated that some microalgal cells used cyanide as a carbon and nitrogen source. In the present study, while maximum biomass (0.255 ± 0.046 g/L) was obtained at 0.6 mg/L, maximum removal (72.814 ± 1.192%) was observed at 0.3 mg/L cyanide concentration. Lower growth at higher cyanide concentrations might be due to its inhibitory effect. Cyanide is highly toxic due to its high affinity towards the metals, which block the metabolic activity at higher concentrations (Gupta et al. 2010). Furthermore, Choi et al. (2012) analyzed the toxic effect of cyanide on the chlorophyll of five different microalgal species and observed the threshold concentration of cyanide below 0.305 mg/L.

Effects of initial pH on the growth, and bioremoval of multipollutant from all SSTCW (M1–M5 solutions) are shown in Figure S2(a–c), attached as Annexure VI in the Supplementary Material. pH 9 was found suitable for both removal and growth perspectives.

Simultaneous bioremoval of NH4+, phenol and cyanide from SSTCW solutions

The microalgal consortium was grown in all SSTCW (M1–M5) solutions, and their DBC was measured as shown in Figure 2(d). It is noteworthy that the consortium can grow in all SSTCW (M1–M5) solutions with varying initial concentrations of pollutants. Therefore, it can be stated that the present consortium might be a suitable option for the treatment of RSTCW. Maximum biomass (0.328 ± 0.78 g/L) was observed at SSTCW with initial concentrations of pollutants: NH4+-100 mg/L, cyanide 0.3 mg/L and phenol 2 mg/L (M1). Maximum removal of all pollutants such as phenol (94.551 ± 1.213%), NH4+ (70.832 ± 1.076%) and cyanide (74.702 ± 2.014%) was also observed in M1 solutions. However, for further studies, the selection of a suitable set of SSTCW was done based on maximum consumption of pollutants, and this was achieved with M4 solution with the following composition: phenol 8 mg/L, NH4+ 175 mg/L and cyanide 0.8 mg/L. Maximum consumption in such case (M4 solution) was: phenol 0.284 ± 0.023 mg, NH4+ 5.71 ± 2.7 mg and cyanide 0.27 ± 0.01 mg. Therefore, the M4 set of SSTCW was chosen for further studies. At the highest concentrations (M5 solution), the removal of all pollutants was found least. In M1–M4 solutions, biomass was found to increase with an increase in the concentrations of all pollutants. The pollutants present in the mixed solutions might be helping the growth of the test consortium due to the increased biodegradation of pollutants through their positive interaction and, thereby, increased bioavailability of carbon and nitrogen (Rai et al. 2020).

The DBC obtained for biotic control (PLE-enriched BG-11 medium) is shown in Figure 2(a)–2(d). From the data, it can be stated that although the enhanced biomass was not obtained during the treatment of pollutant-loaded simulated solutions, the values were nearly the same as that obtained for growth in the PLE-enriched BG-11 medium without pollutant. Therefore, the robustness of the isolated consortium in the treatment of such wastewater was preliminarily established.

Time variation studies on the treatment of SSTCW solution in batch mode

Time variation studies are important from a chemical kinetics point of view. Therefore, variation of simultaneous removal of all three pollutants NH4+, phenol and cyanide and variation of pH of medium with time were investigated (Figure 3(a)). The final concentrations of all three pollutants (NH4+: 48.19 ± 0.28 mg/L, phenol: 0.33 ± 0.08 mg/L and cyanide: 0.2 ± 0.001 mg/L) met the permissible levels within 12 days of incubation of the test strain with M4 solution (SSTCW). Maximum removal was observed for NH4+ (72.804 ± 0.275%), phenol (95.852 ± 0.075%) and cyanide (74.449 ± 0.001%) after 12 days of incubation.

Figure 3

Time variation studies on: (a) the removal of pollutants from SSTCW solution (M4) and variation of pH of the SSTCW and (b) the production of biomass and biomolecules during treatment of SSTCW solution (M4). (c) Proposed pathways for pollutants removal from SSTCW (Gurbuz et al. 2004; Wang et al. 2019; Rai et al. 2020).

Figure 3

Time variation studies on: (a) the removal of pollutants from SSTCW solution (M4) and variation of pH of the SSTCW and (b) the production of biomass and biomolecules during treatment of SSTCW solution (M4). (c) Proposed pathways for pollutants removal from SSTCW (Gurbuz et al. 2004; Wang et al. 2019; Rai et al. 2020).

Close modal

Carbohydrate (167.67± 6.1 mg/L), protein (129.6± 2.45 mg/L) and chlorophyll (1.15 ± 0.021 mg/L) were estimated from the produced biomass (356 ± 0.25 mg/L) after the treatment of SSTCW (Figure 3(b)). Among all biomolecules, the concentration of carbohydrates was highest. Therefore, produced biomass can be used for the production of carbohydrate or protein-based value-added products like biofuels, cattle feed or biochar. The increase in biomass and biomolecules concentrations of the microalgal consortium with time after treatment of SSTCW has demonstrated that the microalgal consortium is utilizing the pollutants as a nutrient source.

The pH of the medium was also tracked with time for 12 days of incubation period, as shown in Figure 3(a). Initially, the pH was constant for 4 days, after which there was a pH drop. A pH of 6.3 was observed at the end of the experiment. The change in the pH during the phycoremediation process might be due to the formation of intermediate compounds. pH plays a key role in the availability of pollutants for their removal in the solution. High alkaline solution prefers the conversion of NH4+ to free NH3. Free ammonia directly affects the photosynthesis process. Rossi et al. (2020) studied thoroughly the inhibitory effect of free ammonia on the microalgae/cyanobacteria. Phenol and cyanide were previously removed when the pH was more than 8, and after the 6th day, NH4+ removal was observed. Uptake of NH4+ causes efflux of the H+ (proton) from the cells, which shifts the pH from alkaline to slightly acidic (pH 6.3). In the early days (up to the 6th day), alkaline pH causes some ammonia formation, which is assimilated in the cell by the alleviation of the α-Keto-Glutarate (αKG), an intermediate of the Krebs cycle (Lu et al. 2018). Phenol and products obtained by cyanide degradation are directly or indirectly associated with the Krebs cycle (Figure 3(c)), which might be helpful for the growth of the present strain at a higher pH. Since the growth of the test strain was observed instead of the inhibitory effect of NH3, it can be said that the consortium might have developed defence or tolerance mechanisms to overcome the NH3 inhibition during the growth of cells. NH3 inhibition towards the consortium can be overcome by either (i) regulating the pH or (ii) utilizing other nitrogen sources with or without NH4+ (Gutierrez et al. 2016). The uptake of NH4+ from the solution and its assimilation followed the GDH (glutamate dehydrogenase) and GS-GOGAT (glutamine synthetase-glutamine:2-oxoglutarate aminotransferase) pathways, as described by Rai et al. (2021a). Generally, a high concentration of NH3 has a negative effect on the GS-GOGAT pathway, while another GDH pathway will continue and NH3 is assimilated (Perez-Garcia et al. 2011). Phenol was utilized for the growth of cyanobacteria and microalgae through the catabolism process (Rai et al. 2020). The aerobic phenol biodegradation pathway is shown in Figure 3(c) (Wang et al. 2019). Phenol monooxygenase enzyme produces catechol from phenol. Catechol is converted into 2-hydroxymuconic semi-aldehyde in the presence of the enzyme catechol 2,3-dioxygenase (cyclic breakdown). The 2-hydroxymuconic semi-aldehyde is oxidized by dehydrogenase (NAD+ dependent) enzyme and generates 4-oxalocrotonate. A cascade of reactions is followed for the conversion of 4-oxalocrotonate into pyruvate, CO2 and alkyl aldehyde. This pyruvate is utilized in the Krebs cycle for energy generation (Figure 3(c)).

Cyanide degradation pathways in cyanobacteria and microalgae are not well explored compared to bacterial and fungal strains (Hammed et al. 2016). Scientists proposed that cyanide is converted into NH4+ and CO2 after the cascade of enzymatic reactions (Gupta et al. 2010). Therefore, this NH4+ is again assimilated into the cells. The nitrogen assimilation process is regulated with C/N ratios (Collos & Harrison 2014). In this work, NH4+ assimilation might probably be regulated with carbon compounds in the form of CO2, as well as with Krebs cycle intermediates (αKG) (Lu et al. 2018) by the degradation of phenol and cyanide in the SSTCW solution.

One-way ANOVA was performed for the analysis of removal of pollutants and production of biomass and biomolecules with time variation. Both the removal of NH4+–cyanide and NH4+–phenol were found significant while phenol–cyanide was not significant during the study (0–12th day). Only NH4+ was observed to be significant for biomass production during the phycoremediation, while phenol and cyanide were not significant. In the case of biomass and biomolecules, only carbohydrate was found to be significant.

Phycotreatment of RSTCW for removal of NH4+, phenol and cyanide using a consortium of Synechococcus sp. and Chlorella sp.

Analysis of collected RSTCW showed it contained NH4+ (157.2 mg/L) phenol (8.05 mg/L), and cyanide (0.65 mg/L). Removal of these three pollutants from undiluted (RSTCW) and diluted (RSTCW + PLE-enriched BG-11 medium) using a consortium of Synechococcus sp. and Chlorella sp. is shown in Figure 4(a). The test strain growth was measured for DBC (0.183 ± 1.01 g/L, 0.754 ± 1.17 g/L) and chlorophyll content (0.53 ± 1.1 mg/L, 1.0 ± 1.32 mg/L) during the treatment of undiluted and diluted RSTCW, respectively (Figure 4(b)). The removal of NH4+ (66.121± 1.665%, 96.127 ± 1.354%), phenol (62.152 ± 0.788%, 90.176 ± 2.341%) and cyanide (87.553 ± 1.335%, 95.972 ± 1.757%) was observed from undiluted and diluted RSTCW, respectively. Sen et al. (2018b) used cyanobacterial consortium for treatment of RSTCW where cyanide (1.2 mg/L) was found to be higher than the permissible limit. The cyanobacterial consortium was able to remove 60–80% of cyanide (Sen et al. 2018b). The present consortium was found effective for lowering the concentrations of NH4+, phenol and cyanide below the permissible limit from diluted RSTCW. In the case of undiluted RSTCW, the growth of the consortium and bioremoval of all pollutants except cyanide (87.553 ± 1.335%) were found to be less than in the SSTCW. The lower growth of the test strain in the undiluted RSTCW might be due to (i) the dark colour of wastewater, which prevents the entry of light into the solution and (ii) the pH (6.9) of the wastewater.

Figure 4

(a) Bioremoval of NH4+ (ammoniacal-N), phenol and cyanide using a consortium of Synechococcus sp. and Chlorella sp. from RSTCW and (b) production of DBC and chlorophyll content during the treatment of RSTCW.

Figure 4

(a) Bioremoval of NH4+ (ammoniacal-N), phenol and cyanide using a consortium of Synechococcus sp. and Chlorella sp. from RSTCW and (b) production of DBC and chlorophyll content during the treatment of RSTCW.

Close modal

Liu et al. (2018) observed that by increasing the cyanide concentration (0.1–10 mg/L), the removal of cyanide increased (38–61%) while the cell count decreased below 0.1 mg/L cyanide concentration (Liu et al. 2018). However, in the current study, the maximum removal (72.814 ± 1.192%) and maximum DBC (0.365 ± .001 g/L) were obtained when the consortium was used to treat an aqueous solution of cyanide with initial concentrations of 0.3 mg/L and 0.6 mg/L, respectively. In SSTCW (M4 solution), the maximum removal of pollutants was observed as 74.449 ± 0.001%, 72.804 ± 0.275% and 95.852 ± 0.0751% for cyanide, NH4+ and phenol, respectively. Thakurta et al. (2018) used Leptolyngbya sp. for the removal of phenol (98.5%) from the synthetic phenolic (50 mg/L) wastewater. Limited studies were carried out in the removal of multipollutants from secondary treated coke-oven wastewater. In this study, solutions of mixed pollutants (SSTCW) and RSTCW (both diluted and undiluted forms) were treated with a test consortium to investigate its efficacy in the simultaneous removal of multipollutant. The pollutants concentrations reaching statutory limits for both SSTCW (M4 solution) and diluted RSTCW has preliminarily established the efficacy of the strain and opens up a new avenue for tertiary treatment of coke-oven wastewater in an environmentally friendly manner.

Comparative study of removal of NH4+, phenol and cyanide from SSTCW and RSTCW using the consortium of Synechococcus sp. and Chlorella sp.

The consortium was applied to remove major pollutants such as NH4+, phenol and cyanide from SSTCW and from RSTCW, both diluted and undiluted forms. The study showed that the consortium was able to remove all the pollutants below permissible limits within 12 days and 14 days when it was grown in SSTCW (M4 solution) and RSTCW (diluted), respectively. However, in the case of undiluted RSTCW, the residual concentrations of NH4+, phenol and cyanide were obtained as 53.2 mg/L, 3.04 mg/L and 0.081 mg/L, respectively. From this result, only cyanide could achieve below the permissible limit, while the concentrations of the other two pollutants could not meet the stipulated values. However, the concentrations of the other two pollutants are very close to the stipulated ones. Although the initial concentrations of model pollutants in SSTCW were higher than RSTCW, the removal was higher in SSTCW (M4 solution) NH4+ (72.804 ± 0.275%), phenol (95.852 ± 0.0751%) and cyanide (74.449 ± 0.001%) than in the undiluted RSTCW (NH4+ (66.121± 1.665%), phenol (62.152 ± 0.788%) and cyanide (87.553 ± 1.335%)). The lower removal of pollutants in the case of undiluted RSTCW might be due to less nutrient availability of real wastewater (Rai et al. 2021a).

Kinetics of growth of consortium in BG-11 and PLE enriched BG-11 medium

The model-predicted data are compared with the experimental ones as shown in Figure 5(a). From the figure, it is clear that the model holds good to represent experimental data. Regressed model parameters obtained in this work is presented in Table 3. As shown in the table; maximum specific growth rates and carrying capacity are estimated to be 0.56 per day and 1,330 mg/L, 0.45 per day and 351 mg/L for PLE + BG-11 and BG-11 medium, respectively. The higher values of maximum specific growth rate and carrying capacity indicate the vigorous growth of the present consortium in PLE enriched BG-11 medium.

Table 3

Kinetic parameters for algal growth in BG-11 and BG-11 + PLE

ParametersBiomass (mg/L)
BG-11BG-11 + PLE
Maximum specific growth rate (per day) 0.446 0.56 
Carrying capacity (mg/L) 350.7 1,330 
AARD (%) 5.204 7.913 
ParametersBiomass (mg/L)
BG-11BG-11 + PLE
Maximum specific growth rate (per day) 0.446 0.56 
Carrying capacity (mg/L) 350.7 1,330 
AARD (%) 5.204 7.913 
Figure 5

Comparison of experimental and model-predicted DBC during exponential phase at different systems: (a) in different media (BG-11 and PLE-enriched BG-11 media), (b) in simulated solutions of NH4+, (c) in simulated solutions of phenol, (d) in simulated solutions of cyanide and (e) in SSTCW solutions.

Figure 5

Comparison of experimental and model-predicted DBC during exponential phase at different systems: (a) in different media (BG-11 and PLE-enriched BG-11 media), (b) in simulated solutions of NH4+, (c) in simulated solutions of phenol, (d) in simulated solutions of cyanide and (e) in SSTCW solutions.

Close modal

Kinetic modelling for growth of the consortium in individual aqueous solutions of NH4+, phenol and cyanide

For simulated NH4+ solutions

Experimental log phase data were used to find the kinetic parameters of the logistic model for predicting the growth of microalgal consortium in NH4+ solutions. The maximum specific growth rate was estimated to be 0.133 per day in presence of NH4+ pollutants. Silva et al. (2015) studied the growth kinetics for microalgal biomass at different N:P molar ratios, and observed the specific growth rate (0.8 per day) of C. vulgaris at 8:1 (N:P) ratio where nitrogen source was only NH4+. The higher specific growth rate might be due to the higher N:P ratio (Silva et al. 2015). The maximum carrying capacity was estimated as 1,009 mg/L for 150 mg/L NH4+ solutions and AARD of 8.472% was obtained. The model-predicted data have been found to match reasonably well with experimental ones (Figure 5(b)). It is observed that the carrying capacity increases with an increase in initial concentration from 100 mg/L to 150 mg/L. Further increase in the pollutant concentration (175 mg/L) results in a very small decrease (920 mg/L) of carrying capacity. The values of maximum specific growth rate and carrying capacity are shown in Table S3(a), which is attached as Annexure VII in the Supplementary Material.

For simulated phenol solutions

A kinetic model for predicting the growth of the consortium in aqueous phenol solutions was developed. The maximum specific growth rate was found to be 0.167 per day and the maximum carrying capacity (699 mg/L) was obtained for 4.0 mg/L of phenol solution with 7.774% AARD. Lee et al. (2015) used Spirulina maxima for phenol removal, and a maximum specific growth rate of 0.088 per day at 50 mg/L phenol concentration was obtained. The lower specific growth rate as observed by Lee et al. (2015) might be due to a higher concentration of phenol. The model-predicted growth data in this study are compared with the experimental data (Figure 5(c)). From Figure 5 it can be stated that the developed model is good enough to predict the kinetic data during the log phase. The values of kinetic parameters are shown in Table S3(b), which is attached as Annexure VII in the Supplementary Material. The carrying capacity increased with an increase in initial concentration from 2 to 4.0 mg/L of phenol. Beyond that a sudden decrease was observed (Table S3(b)).

For simulated cyanide solutions

A growth kinetic model was developed to predict the growth of microalgal consortium in cyanide solution during the log phase. The maximum specific growth rate of 0.13 per day in cyanide solutions was observed. Mekuto & Musingadi (2019) obtained a maximum specific growth rate of 0.16 per day at 150 mg/L cyanide solution using Scenedesmus obliqus. The growth rate of microalgae depends on microenvironmental conditions and also varies from species to species (Mekuto & Musingadi 2019). The maximum carrying capacity of 990 mg/L for 0.4 mg/L of cyanide solution with 8.541% of AARD was obtained in this study. The values of kinetic parameters are shown in Table S3(c), which is attached as Annexure VII in the Supplementary Material. Carrying capacity varied with an increase in initial concentrations of cyanide (Table S3(c)). The model-predicted DBC data match quite well with experimental ones (Figure 5(d)).

Kinetic modelling for growth of consortium in SSTCW solutions

The values of maximum specific growth rate () and corresponding carrying capacity () for the mixed pollutant system with varying pollutant compositions (M1–M5) are estimated by fitting the experimental data obtained during the log phase in the modified logistic model. The values are shown in Table S3(d), which is attached as Annexure VII in the Supplementary Material. Maximum value of (0.12 per day) (Equation (3)) is obtained for the mixed pollutant system with composition M1: NH4+ = 100 mg/L, phenol = 2.0 mg/L and cyanide = 0.3 mg/L. Model predictions are compared with the experimental data for all these systems, as shown in Figure 5(e). From this figure, it can be stated the developed model has superior prediction capability with average absolute relative deviations of 7.232%, 6.872%, 9.103%, 17.70% and 9.031%.

The obtained maximum specific growth rate was also compared with other studies from the literature, as shown in Table S4 attached as Annexure VIII in the Supplementary Material. It is clear from Table S4 that the maximum specific growth rate of the present strain in both BG-11 and in PLE-enriched BG-11 are much more than those reported in the literature. Further, the values of maximum specific growth rates in polluted systems are comparable with others.

A consortium of Synechococcus sp. and Chlorella sp. was isolated from a contaminated site and was used for tertiary treatment of coke-oven wastewater. PLE obtained from poultry litter, a nitrogen and phosphorus-rich waste material, was mixed with BG-11 medium to curtail the cost of the growth medium. The consortium showed an enormous increase in growth (8.75-fold) in the PLE-enriched BG-11 medium after 18 days, while in the BG-11 medium the growth was observed to increase only 2.9-fold for the same time period. The consortium was found effective for the removal of multipollutants such as NH4+, phenol and cyanide from both the synthetic and RSTCW. The maximum removal of pollutants obtained from SSTCW (M4 solution) was NH4+ (72.804 ± 0.275%), phenol (95.852 ± 0.0751%) and cyanide (74.449 ± 0.001%). Bioremoval of NH4+ (66.121 ± 1.665%, 96.127 ± 1.354%), phenol (62.152 ± 0.788%, 90.176 ± 2.341%) and cyanide (87.553 ± 1.335%, 95.972 ± 1.757%) was achieved from undiluted and diluted RSTCW, respectively. The consortium was found efficient to reduce the concentrations of all the said pollutants below the permissible limits from both SSTCW (M4 solution) and the diluted RSTCW within the 12 days and 14 days, respectively. The consortium was also found suitable for the removal of a single pollutant from their aqueous solution. Therefore, it can be stated that the present consortium can be used for tertiary treatment of such pollutant-laden industrial effluent emitted from industries.

Other advantages of the phycoremediation technique include the fact that the produced biomass can further be used for the production of value-added products such as biodiesel, cattle feed, biofertilizer and others, depending on its macromolecule content. Moreover, the self-adjusting behaviour of pH during phycoremediation process makes it cost-effective. Hence, it can be stated that this study opens up a new avenue for tertiary treatment of industrial wastewater in an ecofriendly and economic manner. However, this work is a preliminary work performed under laboratory conditions. Therefore, a comprehensive field study encompassing all real-time parameters such as ambient temperature and light intensity using real wastewater is needed before its commercial application. Furthermore, efficient handling of other operational problems during continuous operation such as contamination with other microbes and frothing is a challenging job for process engineers for real field application of such a technique.

The authors are thankful to the IMPRINT, Government of India (Sanction order no: F.No.3-18/2015-T.S.-I (Vol. III) dated 24 April 2017) and National Institute of Technology Durgapur (Sanction order no: NITD/Regis/702/17 dated 15 February 2017) for providing the project funds. Authors are also thankful to DST-FIST for providing the equipment for carrying out the research work.

The authors declare that they do not have any conflict of interest.

Data cannot be made publicly available; readers should contact the corresponding author for details.

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