Impact of textile dyes on human health and bioremediation of textile industry effluent using microorganisms: current status and future prospects

Abstract

Environmental contamination brought on by the discharge of wastewater from textile industries is a growing concern on a global scale. Textile industries produce a huge quantity of effluents containing a myriad of chemicals, mostly dyes. The discharge of such effluents into the aquatic environment results in pollution that adversely affects aquatic organisms. Synthetic dyes are complex aromatic chemical structures with carcinogenic and mutagenic properties in addition to high biological oxygen demand (BOD) and chemical oxygen demand (COD). This complex aromatic structure resists degradation by conventional techniques. The bioremediation approach is the biological clean-up of toxic contaminants from industrial effluents. Biological treatment methods produce less or no sludge and are cost-effective, efficient, and eco-friendly. Microorganisms, mostly microalgae and bacteria, and, in some instances, fungi, yeast, and enzymes decolorize textile dye compounds into simple, non-toxic chemical compounds. Following a thorough review of the literature, we are persuaded that microalgae and bacteria might be one of the potential decolorizing agents substituting for most other biological organisms in wastewater treatment. This article presents extensive literature information on textile dyes, their classification, the toxicity of dyes, and the bioremediation of toxic textile industry effluent utilizing microalgae and bacteria. Additionally, it combines data on factors influencing textile dye bioremediation, and a few suggestions for future research are proposed.

bioremediation, decolorization, textile industry, dye, microalgae, bacteria, fungi

Introduction

The world’s first synthetic dye, “Mauvine”, was discovered by “W.H. Perkin” in 1856. Since then, a large number of synthetic dyes have been manufactured in huge quantities and are widely used for various commercial purposes (Greluk and Hubicki 2011; Ajaz et al. 2020). The estimated number of dyes and pigments generated worldwide stands at >100000 (de Araújo et al. 2020). Commercial synthetic dyes find their application in various industries such as textiles, leather, paper printing, paint, food, cosmetics, pigments, plastics, and pharmaceutical industries, with an annual consumption of >7 × 10⁵ metric tons (Chen et al. 2003; Pandey et al. 2013; Ahmed et al. 2022). Synthetic dyes are in great demand as a result of the textile industry’s rapid developments. In particular, the textile industry accounts for more than half of the global consumption of synthetic dyes (Wang et al. 2022). The textile industry plays a significant role in the economic development of any country (Rafique et al. 2022a), with China being the largest exporter of textile products, followed by the European Union, India, the USA, and Turkey (WTO 2019).

The process of dyeing textiles is a critical one. A variety of salts and chemicals are employed to enhance the adsorption process between the dyes and the fibers during dyeing (Yaseen and Scholz 2019). The textile dyeing industry is one of the main contributors to environmental contamination among dye-consuming sectors (Hoseinzadeh and Rezaee 2015). During this process, around 10–15% of the dye typically remains unfixed, and this fraction is directly released into the industrial effluent (Rehman et al. 2018b). As a consequence, 2–20% of used dyes are directly discharged as aqueous effluents into water bodies (Carmen and Daniela 2012). The tinctorial value of dye is high, and even concentrations <1 mg L⁻¹ of dye produce visible coloration of water bodies. Worse, such contamination makes the water unfit for human consumption (Gupta et al. 2003; Sugumar and Sadanandan 2010; Kabra et al. 2011; Garg et al. 2020). The global textile industry utilizes many kinds of chemicals and consumes large volumes of water. It is estimated that ∼50–90 g of dye (Bera and Tank 2021) and 200 L of water are required to produce one kilogram of the textile product (Ghaly et al. 2014). Globally, ∼280 000 tons of textile dyes are released into the environment via textile industry wastewater per year (Berradi et al. 2019). These dyes may persist in the environment if wastewater is not properly treated. For instance, the environmental half-life of hydrolyzed reactive blue 19, a frequently used dye, is 46 years at pH 7 and a temperature of 298 K (Carmen and Daniela 2012; Awasthi and Datta 2019). Textile dyes can enter the food chain, bioaccumulate, affect photosynthesis, and have the potential to generate ecotoxic, mutagenic, and carcinogenic consequences if these wastewaters are discharged untreated into aquatic habitats (Desai et al. 2021). As a result, toxic textile dye wastewater treatment is one of the most pressing issues of the present time (Seo et al. 2015; Goswami et al. 2020; Kiran et al. 2022).

Several biological and physicochemical approaches with varying efficacies have been used to treat textile dye effluent (Kiran et al. 2013; Rafique et al. 2022a). Bioremediation of dyes is most interesting in treating textile effluents since it has numerous advantages over traditional treatment procedures (Desai et al. 2021; Rafique et al. 2022b). Recent research has shown that micro-biotechnology techniques, such as textile dye bioremediation employing algal, bacterial, yeast, and fungal processes, are successful in the environmentally friendly treatment of textile dye wastewater (Bhatia et al. 2017). The research on the bioremediation of dyes has been strongly increasing in recent years, as a search of ScienceDirect with the search string “bioremediation of dyes” illustrates (Fig. 1). Researchers have developed a variety of bioremediation technologies to treat textile effluents over the years, but little effort has been taken to compile a comprehensive evaluation of these approaches in a single peer-reviewed paper. Therefore, in this review paper, an effort has been made to review the existing literature on biological (primarily bacterial and algal) approaches for the treatment of textile wastewater containing colors. The authors also summarized the scattered information available on textile dye toxicity in aquatic environments and humans. The novel component of this review paper is the presentation of up-to-date research results on diverse dye bioremediation technologies and their effectiveness in the removal of various dyes, as well as the critical analysis and identification of factors influencing dye bioremediation and their maximum bioremediation efficiency.

Figure 1.

Number of publications (from 2000 to 2022) about bioremediation of dyes (Source: ScienceDirect).

Types of dyes

A dye typically consists of a chromophore and an auxochrome component. The dye molecules’ ability to absorb light is due in large part to the chromophores, while auxochromes function as color enhancers. The latter makes a dye molecule more soluble in water and increases its affinity for fiber, often by chemical reduction (electron donation) to the chromophore (Pereira and Alves 2012; Gowri et al. 2020). Commercial dyes can be classified in terms of origin (organic compounds isolated from natural sources versus chemically synthesized products), chemical structure, color, and application methods. Dyes are further divided into triphenylmethane, azo, nitro, anthraquinone, indigoid, nitroso, and phthalein groups depending on their chemical structures. Based on the chromophore, dyes may be classified into a variety of further categories, as indicated in Table 1. Based on the particle charge, they can be classified into non-ionic (disperse dyes), anionic (direct, acidic, and reactive dyes), and cationic forms (basic dyes) (Fu and Viraraghavan 2001; Yagub et al. 2014; Vikrant et al. 2018; Ihsanullah et al. 2020; Varjani et al. 2020). Azo dyes generally contain one or more azo groups (-N = N-) and account for up to 60–70% of all dye structures known to exist (Sathishkumar et al. 2019; Shetty and Krishnakumar 2020; Prabhakar et al. 2022). Due to their high dyeing performance, ease of usage, and cheap production costs, anthraquinone dyes are the second most used dyes after azo dyes (Varjani et al. 2020). It is estimated that 25 different types of chromophore-based dye groups are available (Varjani et al. 2020).

Table 1.

Classification of dye compounds based on chemical structure (Ramalho 2005).

Characterization of textile dye effluent

Based on the production volume and composition of effluent, the textile industry’s wastewater is the most polluted among all industrial sectors (Ben Mansour et al. 2012). In addition to surplus dye remaining in the effluent as a result of the production process, sometimes dyes are also disposed of by direct release into the environment (Ergene et al. 2009). Textile industry effluents are characterized by high salinity and alkalinity (pH = 11.0–11.5) because textile industries utilize various salts and sodium hydroxide in wet processing steps for color fixing, and wastewater from the textile sector typically has a dye content between 16 and 20 mg L⁻¹ (Guadie et al. 2017). Intense color, high biological oxygen demand (BOD) and chemical oxygen demand (COD), salinity, temperature, pH, salts, and the presence of hazardous metals including cadmium, chromium, copper, lead, nickel, and zinc, etc. and surfactants characterize such textile effluent (Kalyani et al. 2008; Lim et al. 2010; Hussain et al. 2013). Synthetic dyes in wastewater make the water unfit for human consumption (Kumar et al. 2006; Kabra et al. 2011), and the pollution can disrupt aquatic ecosystems and water resources (Hernández-Zamora and Martnez-Jerónimo 2019; Kiran et al. 2022). This pollution affects both human health and the environment. Color, smell, pH, total solids (TS), total dissolved solids (TDS), total suspended solids (TSS), chloride, and COD can all exceed regulatory limits in effluent from the dyeing industry, as reported by Elango et al. (2017), Gowri et al. (2020)), and Behl et al. (2020).

Textile waste toxicity

Medical conditions such as nausea, haemorrhage, skin and mucous membrane ulceration, dermatitis, perforation of the nasal septum, and numerous respiratory tract irritations may result from the usage of textile dyes (Shah et al. 1999), as well as causing breathing problems and possibly causing nausea, vomiting, diarrhea, gastritis, and mental disorientation when inhaled (Sen et al. 2011). Improperly disposed textile dye effluent into water bodies results in intense coloration of the water, which reduces light penetration in the aquatic environment, thus ultimately affecting photosynthetic activity. This is coupled with increased heterotrophic activity, which lowers dissolved oxygen levels, adversely affecting aquatic life (Verma et al. 2012; Kiran et al. 2013). Aquatic life is also adversely affected by the carcinogenic, mutagenic, and teratogenic activity of a particular dye (Sen et al. 2011). Cationic dyes are generally more harmful than anionic dyes, as shown in eco-toxicological studies (Hao et al. 2000).

Environmental and human health impacts of some commonly used dyes

Malachite green is a triphenylmethane dye that is used as an antifungal and antiprotozoal agent in aquaculture. It is also widely used in the food, textile, leather, paper, and paint industries (Srivastava et al. 2004; Han et al. 2020). It affects the immune and reproductive systems of humans. Srivastava et al. (2004) reported that it is highly cytotoxic to mammalian cells, with genotoxicity, carcinogenicity, and teratogenic properties. Commercial xanthene class dye, rhodamine B, is widely used as a water tracer dye (Richardson et al. 2004), food colorant, and in the textile, paper, paint, and leather industries (Jain et al. 2007; Baldev et al. 2013), and this has genotoxic, carcinogenic, and neurotoxic activity in humans and animals. Rhodamine B contaminated drinking water can cause subcutaneous tissue-borne sarcoma. It is harmful if swallowed and causes irritation to the skin, eyes, and respiratory tract (Jain et al. 2007; Singh et al. 2017).

In addition to its usage in the textile industry, triphenylmethane class dye methylene blue is used as a medicine to treat methemoglobinemia, psoriasis, West Nile virus, and duck hepatitis B infections. It can cause increased blood pressure, myocardial depression, and the development of Heinz bodies, as well as cyanosis and jaundice, which are all possible side effects of induced tissue necrosis in humans (Gupta et al. 2016). Crystal violet (a triphenylmethane) which in the lab is used as employed as a biological stain, in the past was used as a bacteriostatic and dermatological agent in humans. It was widely used as a feed supplement in poultry production to prevent mould, intestinal parasites, and fungal infections (Blanco-Flores et al. 2014; Sadeghi and Nasehi 2018). Some fish species are more likely to develop tumours when exposed to this substance (Chen et al. 2007). It can cause irritation of the skin and the gastrointestinal system, and it may potentially cause respiratory and renal failure; in severe circumstances, it is mutagenic, carcinogenic, and mitotically poisonous (Kumar and Ahmad 2011; Blanco-Flores et al. 2014). Brilliant green is a triphenylmethane dye widely used in the paper, textile, rubber, and plastic industries (Kumar et al. 2012). Carcinogens and mutagens, both towards humans and aquatic life (Kumar et al. 2012).

Azo dyes are used in the textile, leather, food, cosmetics, paper printing, paint, and pharmaceutical industries (Saratale et al. 2011). They are mutagenic, carcinogenic, and genotoxic compounds (Yadav et al. 2021). Workers handling azo dyes have been reported to suffer from bladder cancer, splenic sarcoma, and hepatic carcinomas, as demonstrated in experimental animals and chromosomal aberrations in mammalian cells (Puvaneswari et al. 2006). A monoazo dye, metanil yellow, is widely used as a colorant in food, beverages, soap, spirit lacquer, shoe polish, wood paints, leather dyeing, ink, and for the manufacturing of colored paper (Anjaneya et al. 2011). It is carcinogenic, can alter gene expression, and has been shown to decrease the rate of spermatogenesis. Oral consumption causes toxic methaemoglobinaemia and cyanosis in humans (Anjaneya et al. 2011). It is an extremely toxic carcinogen and important dye (Mota et al. 2015). Another azo class dye, methyl orange, is used in the textile industry and in the laboratory as a pH indicator (Azami et al. 2012). It is reported as toxic and mutagenic and can produce acute and chronic effects on aquatic life (Tan et al. 2016).

Removal of dyes from aquatic ecosystems

Textile dyes are typically designed to resist fading by light, oxidizing agents, and chemicals (Aksu and Tezer 2005). As a consequence, decolorization of dye effluent by traditional physical–chemical methods is not very efficient, with limited applicability and high operating costs. However, these methods result in the production of large amounts of toxic sludge, creating a disposal problem (Robinson et al. 2001b; Kuppusamy et al. 2017a; Rafique et al. 2022a). Synthetic dyes are complex, aromatic molecules that are often relatively stable and resistant to degradation (Ahmed et al. 2022). This explains why the treatment of dye-containing effluents by conventional physico–chemical treatment methods is difficult (Lim et al. 2010). Adsorption (Nandi and Walker 1971; Thakur and Kaur 2017; Jawad et al. 2020), ion exchange (Karcher et al. 2002; Greluk and Hubicki 2011; Saruchi and Kumar 2019), chemical coagulation (Huang et al. 2014; Zhou et al. 2019), membrane separation (Rondon et al. 2015; Yang et al. 2020), photo-degradation (Gupta al. 2011; Li et al. 2021), and oxidative remediation (Arslan et al. 2000; Rehman et al. 2018a) have all been applied for effluent treatment. Metal or metal oxide nanoparticles have recently received a lot of attention as improved adsorption materials in the cleanup of textile dyes from the environment (Zhang et al. 2016; Afzal et al. 2022; Ahmed et al. 2022; Rafique et al. 2022a, b). However, they have disadvantages such as difficulty in large-scale implementation, recovery of nanoparticles after usage, high energy usage, cost of fabrication, and generation of toxic and polluting waste (Nandhini et al. 2019; Karthik et al. 2021; Magalhães-Ghiotto et al. 2021).

An attractive alternative is provided by biological treatment, as it produces little or no sludge. Based on the applied methodology, textile dye bioremediation can be separated into biodegradation and biosorption (Kaushik and Malik 2009). Biological adsorption removes dyes quickly by concentrating on the cell wall, but enzyme-mediated biodegradation is a slower process (Shah et al. 2001). A combination of biological adsorption and enzyme-mediated degradation is enabled by the application of microorganisms such as bacteria, algae, and fungi that can adsorb and subsequently degrade the chemicals in wastewater (Solís et al. 2012; Wu et al. 2012).

Biosorption and biodegradation

The removal of textile dyes by microorganisms involves several methods, including degradation, accumulation, and adsorption, which are briefly explained in Fig. 2. Non-biodegradable dyes may be removed from aqueous solutions by the application of either dead or living cells (Kumar et al. 2006). Dead microbial matter has an advantage over living organisms, as it is not affected by toxicity, doesn’t depend on a constant supply of nutrients, and may be regenerated and reused. Moreover, dead cells exhibit equal or greater pollution accumulation capability than living cells. The adsorption capacity depends on the dye’s chemistry, biomass type, a dose of biosorbent, the surface property of biosorbent, and ambient factors such as pH, temperature, ionic strength, and the presence of competing molecules (Aksu and Tezer 2005). Biosorbents can be produced from algae, bacteria, yeasts, and other fungi and have been used to treat agricultural and industrial wastes (Vijayaraghavan and Yun 2008). Because of their inherent ability to decompose waste and their ability to survive in the most extreme conditions, microorganisms are efficiently employed as an adsorbing agent for waste removal (Sharma et al. 2020; Wang et al. 2022). Biosorption by dead cells is a metabolically independent process that occurs mostly in the cell wall of the organism and includes adsorption by physical and chemical means as well as ion exchange, complexation, micro-precipitation, and chelation. The efficiency and selectivity of biosorption technologies, as well as their superior removal performance and cost-effectiveness, make this a highly recommended approach, although the pH of the water must be critically controlled: the adsorbent surface properties and the degree of ionization of the contaminants can both be affected by pH (Vijayaraghavan and Yun 2008; Yagub et al. 2014).

Figure 2.

Mechanisms of microorganism-mediated bioremediation of textile dyes.

Biodegradation is most effectively performed by microorganisms that can degrade dyes that have been isolated from textile dye-contaminated environments like soil, effluent, and sludge from textile dye wastewater treatment plants since they are adapted to grow in the presence of the toxic dye compounds (Ayed et al. 2009). In general, bacteria degrade the dye molecules to use them as a carbon source (Bheemaraddi et al. 2014). For instance, particular microalgae can degrade dye molecules and use this as a nitrogen source (Abdel-Raouf et al. 2012) or as a source for phosphorous (Znad et al. 2018). The use of microalgae in textile dye wastewater bioremediation helps in algal biomass production and CO₂ fixation (Wang et al. 2016). In a complete biodegradation or mineralization process, organic compounds are converted into water and carbon dioxide (Vikrant et al. 2018). Decolorization of textile dye wastewater is carried out by microbial enzymes that cleave the complex chemical structures of toxic dyes into non-toxic, simpler molecules (Baldev et al. 2013). In the next sections, bioremediation by bacteria and microalgae is summarized in more detail. Since relatively few applications rely on the use of fungi (Kirby et al. 2000; Robinson et al. 2001a; Akar et al. 2006; Renganathan et al. 2006; Kumari and Abraham 2007; Levin et al. 2012; Ashrafi et al. 2013; Rani et al. 2014; Taha et al. 2014; Mahmoud et al. 2017; Olicón-Hernández et al. 2017; Alam et al. 2018; Góralczyk-Bińkowska et al. 2021; Juárez-Hernández et al. 2021; Zhang et al. 2021) these are not further treated here.

Bioremediation of dye contaminated textile effluents by bacteria

Bacteria and cyanobacteria have been shown to be the most effective degraders of synthetic dyes due to their short life cycles, low secondary waste generation, and adaptability to a variety of substrates (Cepoi et al. 2016; Chen et al. 2018), and these cyanobacteria do not need organic carbon as a source of energy since they are photosynthetic bacteria (Han et al. 2020). A summary of the biodegradation of dyes by pure and mixed bacterial cultures, along with their efficiency and degradation conditions, is summarized in Table 2, and the biosorption efficiency of bacteria for selected dyes is represented in Table 4. Microorganisms acclimatize themselves to the hazardous chemical environment, and dye-resistant strains develop naturally, which then transform various highly toxic chemicals into less or non-harmful forms (Saratale et al. 2011). The inherent ability of microorganisms to breakdown, detoxify, and mineralize synthetic dyes exists under specific environmental circumstances (Sathishkumar et al. 2018; Goud et al. 2020). Pure bacterial cultures of Bacillus cereus, Aeromonas hydrophila, and B. subtilis, capable of degrading azo dyes, were reported for the first time in the 1970s (Horitsu et al. 1977; Wuhrmann et al. 1980). Bacteria that can degrade such dyes have now been found in many different places and sources (Goud et al. 2020). When compared to commercially available strains, the isolated strain was more successful in decolorizing colors (Unnikrishnan et al. 2018). Degradation levels can reach 100% (Table 2), although for some dyes (e.g. Reactive black 5 [molecular weight: 991.8]), efficiencies remain <80%.

Table 2.

Summary of decolorization of various dyes by pure and mixed bacterial cultures.

Dye	Bacteria	Enzymes involved	Efficiency (%)	Condition (Time, pH, T [°C], Dye conc.,)	Reference
Azo dyes
Red HE7B	Pseudomonas desmolyticum NCIM 2112	Extracellular lignin peroxidase, laccase, tyrosinase, and reductases	95 (COD–71%)	Static condition 72 h, pH = 6.8–7.8, T = 30°C, conc = 100 mg L−1	Kalme et al. (2007a)
Reactive red BLI	Pseudomonas sp. SUK1	Aminopyrine N-demethylase and NADH-DCIP reductase	99.28	Static anoxic condition 1 h, pH = 6.5–7, T = 30°C, conc = 50 mg L−1	Kalyani et al. (2008)
Direct black 38	Enterococcus gallinarum	Azoreductase	85	-	Bafana et al. (2008)
Reactive red 2	Pseudomonas sp. SUK1	Lignin peroxidase and azoreductase	96	Static condition 6 h, pH = 6.2–7.5 T = 30°C, conc = 5 g L−1	Kalyani et al. (2009)
Reactive red 180	Citrobacter sp. CK3	Unknown	96.2	Anaerobic condition 36 h, pH = 7, T = 32°C, conc = 200 mg L−1	Wang et al. (2009a)
Reactive black 5	Enterobacter sp. EC3	Unknown	92.56	Static condition 108 h, pH = 7, T = 37°C	Wang et al. (2009b)
Reactive green 19 A	Micrococcus glutamicus NCIM 2168	Oxidoreductive enzymes	100	Static condition 42 h, pH = 6.8, T = 37°C, conc = 50 mg L−1	Saratale et al. (2009a)
Metanil yellow	Bacillus sp. AK1	Azoreductase	100	Static condition 24 h, pH = 7.2, T = 37°C, conc = 200 mg L−1	Anjaneya et al. (2011)
Metanil yellow	Lysinibacillus sp. AK2	Azoreductase	100	Static condition 12 h, pH = 7.2, T = 37°C, conc = 200 mg L−1	Anjaneya et al. (2011)
Orange II	Pseudomonas putida SKG-1	Unknown	92.8%	Static condition 96 h, pH = 8, T = 30°C, conc = 100 mg L−1	Garg et al. (2012)
Remazol red	Lysinibacillus sp. RGS	Oxidoreductive enzymes	100 (COD–92%)	Static condition 6 h, pH = 7.0, T = 30°C conc = 50 mg L−1	Saratale et al. (2013)
Reactive black 5	Bacillus sp. YZU1	Azoreductase	95	Static condition 120 h, pH = 7.0, T = 40°C, conc = 100 mg L−1	Wang et al. (2013)
Reactive black 5	Pseudomonas sp. RA20	Unknown	98.5	Static condition 48 h, pH = 8.0, T = 25°C conc = 100 mg L−1	Hussain et al. (2013)
Methyl red	Staphylococcus saprophyticus AUCASVE3	Unknown	97	48 h, T = 30 to 40°C, conc = 600 mg L−1	Hakim et al. (2014)
Reactive violet 5	Paracoccus sp	Unknown	100	Static condition 16 h, pH = 7, T = 30°C, conc = 100 mg L−1	Bheemaraddi et al. (2014)
Acid Orange	Bacillus megaterium PMS82	Unknown	100	Static condition 16 h, pH = 7, T = 30°C, conc = 100 mg L−1	Shah (2014)
Reactive Red 239	Bacillus sp. strain CH12	Unknown	100	Anaerobic 96 h, pH = 10, T = 30°C, conc = 100 mg L−1	Guadie et al. (2017)
Reactive Red 239	Bacillus sp. strain CH12	Unknown	18.6	Shaking condition 96 h, pH = 10, T = 30°C, conc = 100 mg L−1	Guadie et al. (2017)
Reactive red 198	Immobilized Acinetobacter baumannii	Unknown	96.2	72 h, pH = 7.0, T = 37°C, conc = 500 mg L−1	Unnikrishnan et al. (2018)
Reactive yellow 145	Pseudomonas aeruginosa	Unknown	100	Static condition 96 h, pH = 7.0, T = 37°C, conc = 50 mg L−1	Garg et al. (2020)
Disperse blue-284	Klebsiella pneumoniae GM-04	Azoreductase	95	Static condition 24 h, pH = 7.0, T = 37°C, conc = 200 mg L−1	Mustafa et al. (2021)
Diazo dyes
Direct blue-6	Pseudomonas desmolyticum NCIM 2112	Lignin peroxidase (LiP), laccase and tyrosinase	92(COD -88.95%)	Static anoxic condition 72 h, pH = 7–7.9, T = 30°C, conc = 100 mg L−1	Kalme et al. (2007b)
Reactive red HE8B	Pseudomonas aeruginosa	Unknown	86	Static condition 48 h, pH = 7.0, T = 30°C, conc = 100 mg L−1	Patel and Gupte (2016)
Reactive Black 5	Aeromonas hydrophila	Unknown	76	Static condition 24 h, pH = 7.0, T = 35°C, conc = 100 mg L−1	El Bouraie and El Din (2016)
Reactive red 120	Shewanella haliotis RDB_1	Oxidoreductive enzymes like azoreductase, NADH-DCIP reductase, lignin peroxidase, manganese peroxidase and tyrosinase	100	Static anoxic condition 2.25 h, pH = 7.4, T = 35°C, conc = 50 mg L−1	Birmole and Aruna (2019)
Acid blue 113	Pseudomonas stutzeri AK6	Azoreductase and laccase	86.2	Static condition 96 h, pH = 7.4, T = 37°C, conc = 300 mg L−1	Joshi et al. (2020)
Congo Red	Enterococcus faecalis R1107	Unknown	90.17	48 h, conc = 50 mg L−1	Wang et al. (2022)
Congo Red	Enterococcus faecalis R1107	Unknown	65.57	48 h, conc = 1000 mg L−1	Wang et al. (2022)
Reactive Black 5	Enterococcus faecalis R1107	Unknown	96.82	48 h, conc = 50 mg L−1	Wang et al. (2022)
Reactive Black 5	Enterococcus faecalis R1107	Unknown	72.64	48 h, conc = 1000 mg L−1	Wang et al. (2022)
Triazo dyes
Direct Black 38	Enterococcus faecalis R1107	Unknown	81.95	48 h, conc = 50 mg L−1	Wang et al. (2022)
Direct Black 38	Enterococcus faecalis R1107	Unknown	23.39	48 h, conc = 1000 mg L−1	Wang et al. (2022)
Triphenylmethane dyes
Malachite green	Kocuria rosea MTCC 1532	Malachite green reductase and DCIP reductase	100	Static anoxic condition 5 h, pH = 6.8–6.9, conc = 50 mg L−1	Parshetti et al. (2006)
Crystal violet	Pseudomonas putida	Unknown	78.5	7 days, pH = 7.5, T = 37°C, conc = 60 μM L−1	Chen et al. (2007)
Crystal violet	Pseudomonas putida	Unknown	80	7 days, pH = 7.5, T = 37°C, conc = 0.022 mg L−1	Chen et al. (2007)
Malachite green	Sphingomonas paucimobilis	Unknown	82.49	Shaking condition 4 h, pH = 9, T = 25°C, conc = 50 mg L−1	Ayed et al. (2009)
Malachite green	Pseudomonas pulmonicola YC32	Unknown	85.2	3.5 h, pH = 7–10, conc = 50 mg L−1	Chen et al. (2009)
Malachite green	Pseudomonas sp. DY1	Manganese peroxidase, NADH-DCIP and malachite green reductase	>96	Shaking condition 24 h, pH = 6.6, T = 28–30°C, conc = 100–800 mg L−1	Du et al. (2011)
Malachite green	Micrococcus sp. BD15	Laccase and NADH-DCIP reductase	100	Static condition 30mins, T = 30°C, conc = 100 mg L−1	Du et al. (2013)
Malachite green	Bacillus cereus KM201428	Crude protease	>98	Static condition 12 h, pH = 8, T = 25°C, conc = 1.0 × 10−5 M L−1	Wanyonyi et al. (2017)
Crystal violet	Enterobacter sp. CV–S1	Unknown	100	Aerobic shaking condition 72 h, pH = 6.5, T = 35°C, conc = 150 mg L−1	Roy et al. (2018)
Malachite green	Enterobacter sp. CV-S1	Unknown	100	Shaking condition 78 h, pH = 6.5, T = 35°C, con = 15 mg L−1	Roy et al. (2020)
Malachite green	Enterobacter sp. CM-S1	Unknown	100	Shaking condition 144 h, pH = 6.5, T = 35°C, conc = 15 mg L−1	Roy et al. (2020)
Procion red H‐3B	Pseudomonas stutzeri	Unknown	96	24 h, pH = 7.5, T = 37°C, conc = 50 mg L−1	Bera and Tank (2021)
Thiazine dyes
Methylene blue	Bacillus subtilis MTCC 441	Unknown	91.68	Shaking condition 6 h, T = 30°C, conc = 20 mg L−1	Upendar et al. (2017)
Azo dyes
Reactive blue	Bacterial consortium PMB11 1. Bacillus odysseyi SUK32. Morganella morganii SUK53. Proteus sp. SUK7	Lignin peroxidase, Laccase, Tyrosinase and NADH-DCIP reductase	100	Static incubation; 1 h, T = 30°C, conc = 50 mg L−1	Patil et al. (2008)
Reactive orange 16	Bacterial consortium DAS 1. Pseudomonas sp. SUK1 2. Pseudomonas sp. LBC2 3. Pseudomonas sp. LBC3	Laccase, Azoreductase	100	Static condition 48 h, pH = 7.0, T = 30°C, conc = 100 mg L−1	Jadhav et al. 2010
Green HE4BD	Bacterial consortium GR 1. Proteus vulgaris NCIM-2027 2. Micrococcus glutamicus NCIM-2168	Oxidoreductive enzymes	100	Static condition 24 h, pH = 8.0, T = 37°C, conc = 50 mg L−1	Saratale et al. (2010)
Scarlet R and mixture of 8 dyes	Bacterial consortium-GR 1. Proteus vulgaris NCIM-2027 2.Micrococcus glutamicus NCIM-2168	Riboflavin reductase and NADH-DCIP reductase	100	Static anoxic condition 3 h, pH = 7.0, T = 37°C, conc = 50 mg L−1	Saratale et al. (2009b)
Direct black 22	1. Pseudomonas aeroginosa,2. Stenotrophomonas.maltophila 3. Pseudomonas mirabilis	Unknown	91	Static condition 12 h, pH = 7, T = 45°C, conc = 100 mg L−1	Mohana et al. (2008)
Congo red	1. Pseudomonas stutzeri L1 2. Acinetobacter baumannii L2	Laccase	97	5 days, T = 37°C, conc = 100 mg L−1	Kuppusamy et al. (2017b)
Gentian violet	1. Pseudomonas stutzeri L1 2. Acinetobacter baumannii L2	Laccase	95	5 days, T = 37°C, conc = 100 mg L−1	Kuppusamy et al. (2017b)
Reactive Yellow 174	1. Sphingomonas paucimobilis,2. Pseudomonas putida3. Lactobacillus acidophilus.	Unknown	90(COD–86%)	Shaking condition 5 days, T = 35°C	Ayed et al. (2021)
Miscellaneous
Acid red 119	Phormidium valderianum BDU 20 041	Bioadsorption	>90	pH = 11, conc = 500 mg L−1	Shah et al. (2001)
FF sky blue	Gloeocapsa pleurocapsoides	Unknown	90	26 days, T = 27°C, conc = 100 mg L−1	Parikh and Madamwar (2005)
Acid red 97	Phormidium ceylanicum	Unknown	89	26 days, T = 27 °C, conc = 100 mg L−1	Parikh and Madamwar (2005)
Acid black 1	Oscillatoria curviceps BDU92191	Unknown	84	8 days, conc = 100 mg L−1	Priya et al. 2011
Indigo BANN 30	Phormidium sp. CENA135	Unknown	100	Static condition 26 days, T = 23°C, conc = 0.02%	Silva-Stenico et al. (2012)
Congo red	Arthospira maxima	Unknown	46	Conc = 2 mg L−1	Mahalakshmi et al. (2015)
Basic fuchsin	Hydrocoleum oligotrichum	Unknown	92.44	Shaking condition 7 days, conc = 5 mg L−1	Abou-El-Souod and El-Sheekh (2016)
Indigo dye	Phormidium. autumnale UTEX1580	Unknown	100	19 days, T = 25°C	Dellamatrice et al. (2017)
Malachite green	Synechococcus elongatus	Bioadsorption	99.5	Static condition 12 h, pH = 6.0, T = 30°C, conc = 100 mg L−1	Han et al. (2020)

Dye decomposition may be carried out using a pure bacterial culture or through the use of mixed cultures, often described as a “consortium” (Goud et al. 2020). The latter makes use of synergistic metabolic effects, resulting in particularly shown to enable effective dye degradation (Kalyani et al. 2009). This synergistic interaction of microbes or their enzymes may result from the use of breakdown products generated by another strain for further decomposition (Jadhav et al. 2010). A bacterial consortium can have a broader activity spectrum than a pure culture. For instance, a bacterium consortium GR comprising Proteus vulgaris and Micrococcus glutamicus created by Saratale et al. (2010) demonstrated degradation of six harmful sulfonated reactive dyes (Green HE4BD, Golden yellow HE4R, Orange 3R, Violet 5R, Red ME4BL, and Red M2BN) and resulted in faster decolorization of Green HE4BD than the individual bacterial strains could achieve.

The biodegradation of two azo dyes (basic fuschin and methyl red) by cyanobacterial strains, Hydrocoleum oligotrichum and Oscillatoria limnetica, was studied by Abou-El-Souod and El-Sheekh (2016). It was discovered that H. oligotrichum and O. limnetica degraded >90% of basic fuschin after 7 days. But, the degradation of methyl red by H. oligotrichum and O. limnetica after 7 days was found to be 53.23 and 50.18%, respectively. The performance of the dye-degrading bacterium Enterococcus faecalis R1107 was investigated by Wang et al. (2022) for the decolorization of diazo dye (Congo red and reactive black 5) and triazo dye (direct black 38). Results of this study suggest that the increase of azo bonds in synthetic dyes and the increase in the concentration of dye might reduce the decolorization efficiency of microorganisms. It was discovered that the rate of degradation was affected by the dye’s molecular structure, the concentration of dye, and the kind of microorganism utilized in the experiment.

To demonstrate the advantage of biodegradation in comparison to biosorption, Wang et al. (2009a) demonstrated that Citrobacter CK3 (isolated from textile mill activated sludge) degraded >95% of reactive red 180 in 48 h, but heat-killed cells only resulted in 29.7% decolorization after 120 h of incubation. Wang et al. (2013) obtained comparable results with an azo dye, reactive black 5, which was degraded by living Bacillus sp. YZU1 after 120 h of incubation but only 3.1% was decolored by heat-killed cells. In the living cells, azoreductase was responsible for much higher efficiency. Other enzymes involved in biodegradation include laccase and NADH-DCIP reductase, which are highly active in Micrococcus sp. BD15 to biodegrade malachite green (Du et al. 2013), and Pseudomonas desmolyticum NCIM 2112 was able to entirely biodegrade the diazo dye direct blue-6 by means of the oxidative enzymes lignin peroxidase, laccase, and tyrosinase (Kalme et al. 2007b). Enzymes such as azoreductase (Jinqi and Houtian 1992), peroxidase (Baldev et al. 2013), NADH-DCIP reductase (Du et al. 2013), manganese peroxidase, lignin peroxidase, azoreductase (Chen et al. 2018), laccase (Kalme et al. 2007b; Du et al. 2013; Chen et al. 2018), tyrosinase (Kalme et al. 2007b), veratryl alcohol oxidase (Srinivasan and Sadasivam 2021), and catalase (El-Sheekh et al. 2016) were capable of decolorizing dyes.

A few degradation pathways are summarized here. Elimination of the azo linkage was demonstrated to be responsible for the degradation of direct black 38 by azoreductase produced by Enterococcus gallinarum. This produces benzidine and 4-aminobiphenyl (4-ABP), as illustrated in Fig. 3 (Bafana et al. 2008, 2009). These secondary metabolites are less hazardous and mutagenic than the original dye. Likewise, the cyanobacterium Phormidium autumnale UTEX 1580 entirely decomposed the indigo dye into two secondary metabolites, which were identified as anthranilic acid (m/z 399) and isatin or indole-2,3 dione (m/z 421). The first stage in the decomposition of indigo is the oxidative breakage of the central link, which creates isatin as a metabolite, which is then hydrolyzed, reduced, and decarboxylated to form anthranilic acid. These metabolites were not harmful in any way (Dellamatrice et al. 2017). These metabolites were not harmful (Dellamatrice et al. 2017). The degradation pathway of indigo dye by P. autumnale is represented in Fig. 4.

Figure 3.

Degradation pathway of the azo dye, direct black 38 by E. gallinarum (adapted from Bafana et al. 2009, 2011).

Figure 4.

Degradation pathway of the indigo dye by P. autumnale (adapted from Dellamatrice et al. 2017).

In some instances, the biological removal of multiple compounds can also be achieved by a single strain, such as P. valderianum, a marine cyanobacterium that successfully decolorized three textile dyes (Acid red, Acid red 119, and Direct black 155). By adsorption, this species efficiently decolorized >90% of these dyes (Shah et al. 2001). According to Bheemaraddi et al. (2014), Paracoccus sp. GSM2 was shown to be an ideal strain for the treatment of textile wastewater containing reactive azo dyes. Not only reactive violet 5, but a mixture of six different azo dyes could be decolorized with >70% in 38 h. According to the research conducted by Shah (2014), B. megaterium PMS82, an isolated strain, totally decomposed acid orange as a single source of carbon under static conditions. The same bacterial strain was able to decolorize five distinct azo dyes with physically diverse structures by >70% in only 38 h. It demonstrates the broad range of applications of microbial strains in textile wastewater treatment. Although in some cases the dye serves as the sole or main carbon, nitrogen, or phosphorus source, in other cases co-substrates are needed. This was demonstrated, for instance, by Karim et al. (2018). By using indigenous bacterial isolates such as Neisseria, Vibrio, Bacillus sp., and Aeromonas sp. Karim et al. (2018) investigated the decolorization of five commercially available textile reactive dyes (Novacron orange FN, Novacron brilliant blue, Bezema yellow, and Bezema red) and their mixture by indigenous bacterial species. Results showed that in the absence of co-substrates (glucose and yeast extract), none of this isolate could begin decolorization. It suggests that the dyes were not the main source of energy for the microorganisms.

Application of microalgae in textile dye bioremediation

Microalgae are chlorophyll-bearing photosynthetic eukaryotic microorganisms found in freshwater, marine, and brackish waters. Phyco-remediation can be defined as the use of algae to convert hazardous contaminants present in water or soil into non-hazardous materials. Similarly, to bacteria, Algae have the potential because of their high growth rate in a simple medium and cost-effectiveness (Chakravarty et al. 2015). Microalgae degrade dye from N and P sources, removing N and P concentrations to extremely low levels. It aids in the management of eutrophication in aquatic environments (Wang et al. 2016). A summary of selected literature on bioremediation of dyes using microalgae is summarized in Table 3, and the biosorption efficiency of microalgae for different dyes is represented in Table 4. Dyes may be removed by microalgae via three different mechanisms: (1) for the creation of algal biomass; (2) for the mineralization of dye molecules into H₂O and CO₂ because of the conversion of colored to non-colored molecules; and (3) for chromophores’ adsorption on algal biomass (Ajaz et al. 2020). The degradation pathway of azo dye by microalgae is represented in Fig. 5. Peroxidase, malate dehydrogenase, catalase, bromo, and chloroperoxidase enzymes generated by microalgae were involved in the dye degradation experiments. Three marine microalgal species, Porphyridium purpureum, Phaeodactylum tricornutum, and Dunaliella tertiolecta, were shown to have peroxidase activity in cell-free extracts (Murphy et al. 2000). Coelastrella sp., a green alga, was able to biodegrade rhodamine B into the simplest, non-toxic compounds. It can decolorize 80% of the dye in 20 days, and the biodegradation process was carried out by the peroxidase enzyme by cleaving the aromatic ring structure of rhodamine B. According to Jinqi and Houtian (1992), the presence of the azoreductase enzyme allows Chlorella pyrenoidosa, C. vulgaris, and Oscillatoria tenuis to biodegrade and decolorize >30 different azo dye compounds into simpler aromatic amines. The azo link is broken down by the azoreductase enzyme of algae, and an aromatic amine is generated as a secondary product. These metabolites are eventually mineralized, resulting in the production of CO₂ and simple chemical compounds (Jinqi and Houtian 1992). Microalgae outperformed terrestrial plants in photosynthetic efficiency, efficiently converting carbon dioxide and solar energy into biofuels such as biodiesel (Wang et al. 2016). Following the bioremediation investigation, algal species such as Coelastrella sp. employed for biodegradation produce a high output of biomass and are viable species for biodiesel productivity (Baldev et al. 2013).

Figure 5.

Degradation pathway of azo dyes by microalgae (adapted from Jinqi and Houtian 1992).

Table 3.

Summary of selected literature on bioremediaton of dyes by microalgae.

Dye	Microalgae	Mechanism	Condition (Time, pH, T (°C), Dye conc.,)	Efficiency (%)	Reference
Triphenylmethane dyes
Malachite green	Cosmarium sp.	Biodegradation	pH = 9.0, T = 25°C, conc = 10 mg L−1	92.4	Daneshvar et al. (2007)
Malachite green	Chlorella sp.	Biosorption	4 h, T = 35°C, conc = 10 mg L−1	99.4	Al-Fawwaz and Jacob (2011)
Malachite green	Chlamydomonas sp.	Biosorption	4 h, T = 35°C, conc = 10 mg L−1	99.2	Al-Fawwaz and Jacob (2011)
Malachite green	Chlorella vulgaris	Biosorption	7 days, pH = 7.0, conc = 5 mg L−1	>90	Abd-El-Kareema et al. (2012)
Malachite green	Immobilized Desmodesmus sp.	Biosorption	conc = 20 mg L−1	89.1	Al-Fawwaz and Abdullah (2016)
Xanthene
Rhodamine B	Coelastrella sp.	Biodegradation	pH = 8.0, T = 30°C, conc = 100 mg L−1	80	Baldev et al. (2013)
Thiazine dyes
Methylene blue	Chlorella sp.	Biosorption	4 h, T = 35°C, conc = 10 mg L−1	99	Al-Fawwaz and Jacob (2011)
Methylene blue	Chlamydomonas sp.	Biosorption	4 h, T = 35°C, conc = 10 mg L−1	99	Al-Fawwaz and Jacob (2011)
Miscellaneous
Textile wastewater	Consortium: Scenedesmus sp., Chlorella sp., Synedra sp., Achnanthidium sp.	Biodegradation	20 days, pH = 8.0, T = 30°C	82.6 (BOD-91.9% COD-91.5%)	Aragaw and Asmare (2018)

Table 4.

Biosorption efficiency of microalgae and bacteria for different dyes.

Dye	Microalgae	Condition	Biosorbent Dosage	Biosorption capacity qmax (mg/g)	Adsorption isotherms	Adsorption kinetic	Reference
Remazol black B	Chlorella vulgaris (Dried biomass)	pH = 2, T = 35.0°C, conc = 800 mg L−1	1 g L−1	419.5 mg g−1	Freundlich, Redlich–Peterson and Koble– Corrigan adsorption	Second-order and saturation type adsorption kinetics	Aksu and Tezer, (2005)
Remazol red RR	Chlorella vulgaris (Dried biomass)	pH = 2, T = 25.0°C, conc = 800 mg L−1	1 g L−1	181.9 mg g−1	Langmuir model	Second-order and saturation type adsorption kinetics	Aksu and Tezer, (2005)
Remazol golden yellow RNL	Chlorella vulgaris (Dried biomass)	pH = 2, T = 25.0°C, conc = 200 mg L−1	1 g L−1	52.8 mg g−1	Langmuir model	Second-order and saturation type adsorption kinetics	Aksu and Tezer, (2005)
Malachite green	Pithophora sp	pH = 5, T = 30°C, conc = 100 mg L−1	0.03 g	117.647 mg g−1	Redlich Peterson isotherm model, Freundlich and Langmuir isotherm	Pseudo-second order model	Kumar et al. (2005)
Malachite green	Pithophora sp	1 h, pH = 6, T = 30°C, conc = 100 mg L−1	0.03 g	59.038 mg g−1	Freundlich isotherm equation	First order rate kinetics	Kumar et al. (2006)
Rhodamine B	Chlorella pyrenoidosa	4 h, pH = 8, T = 25°C, conc = 100 mg L−1	0.1 g	63.14 mg g−1	Sips isotherm equation	Pseudo-second order model	da Rosa et al. (2018)
Malachite green	Exiguobacterium sp. VK1	0.5 h, pH = 6, T = 40°C, conc = 100 mg L−1	2 g L−1	684.38 mg g−1	Freundlich isotherm equation	Pseudo-second order model	Kalpana et al. (2020)

Process optimizing and modeling

For the mathematical description of the biosorption isotherm and for assessing the adsorption type, adsorption isotherm models such as Freundlich, Langmuir, Temkin, Redlich–Peterson, Sips, Redlich–Peterson, and Koble-Corrigan were used (Vijayaraghavan and Yun 2008). The Langmuir model (Aksu and Tezer 2005) was followed in the adsorption of reactive dyes (e.g. Remazol red and Remazol golden yellow) by dried biomass of Chlorella vulgaris, implying that there was a monolayer or homogenous adsorption of dye molecules on the algal cell wall. The presence of a heterogenous or multilayer sorption of dye chemicals on the algal cell wall is suggested by the biosorption of malachite green dye onto Pithophora sp. biomass, which fits with the Freundlich model (Kumar et al. 2006). The study demonstrated that malachite green equilibrium uptake is pH-dependent, with maximum absorption occurring at a pH of 6.

Factors affecting biodegradation of dyes

The elements influencing dye bioremediation may be split into two groups. The first is environmental, and the next is nutritional (Varjani et al. 2020). Jinqi and Houtian (1992), Khataee et al. (2010), Dellamatrice et al. (2017), El-Sheekh et al. (2018)), and Khan et al. (2020) discovered that the biological decolorization of dyes is connected to the dye’s molecular structure, species employed for bioremediation, biomass content, and physicochemical factors such as pH and temperature. Optimization of the physicochemical parameters of the bioremediation process will be very beneficial in the development of a commercial-scale textile dye removal technology. Higher temperatures, low pH, and anaerobic conditions are the key operating parameters limiting the rate of bioremediation of dyes in industrial effluent (Shetty and Krishnakumar 2020).

Effect of pH

The biological decolorization process is highly dependent on the pH of the treatment environment. Algal cell walls play a major role in the adsorption process and provide a site for the electrostatic attraction of dye molecules (Pathak et al. 2015). The charge on the surface of the microbial biomass depends on functional groups like hydroxyl and carboxyl groups. These groups tend to act as adsorbing agents or sites (Shetty and Krishnakumar 2020). The pH of the solution influences the ionic form of dye in water as well as the dye binding sites at the cell surface. The pH can be acidic, alkaline, or neutral based on the type of dyes and salts used (Varjani et al. 2020). Mahmood et al. (2017) revealed that the maximum decolorization of Congo red occurred when the bacterial strain Bacillus sp. SR-2–1/1 at pH 7.0. Under highly alkaline and acidic pH values, there is relatively lower decolorization of the selected azo dyes by the strain SR-2–1/1. The pH values of neutral or slightly alkaline were found optimal for maximal decolorization, and decolorization rates tend to drop rapidly under strong alkaline pH or acidic pH conditions (Vargas-de la Cruz and Landa-Acuña 2020). Textile wastewater is often buffered to enhance the color removal performance of the microbial cells (Majhi et al. 2021). When the pH value is too high or low, it will significantly affect the azoreductase enzyme activities and survival of the bacterial cells, resulting in a decreased rate of decolorization (Solis et al. 2012; Mahmood et al. 2017). This statement is also supported by Wang et al. (2009b), finding that Enterobacter sp. EC3 showed a high decolorization rate (92.6%) at pH 7.0 after 108 h of incubation for synthetic dyeing wastewater containing reactive black 5, and the rate of color removal was much lower (<30%) at acidic pH 4.0.

Effect of temperature

The decolorization efficiency is high at temperatures optimal for bacterial growth, which is generally reported as 30–40°C for most bacteria (Rakkan and Sangkharak 2020). Mahmood et al. (2017) revealed that the maximum decolorization of reactive red 120 and reactive black 5 was achieved by the bacterial strain Bacillus sp. SR-2–1/1 at a temperature of 30°C. Xie et al. (2020) observed that the extent of reactive black 5 decolorization increased with the increase in temperature from 30 to 40°C. The maximum decolorization rate of 82% was reached at 40°C, whereas beyond the temperature of 45°C, the decolorization efficiency of reactive black 5 decreased to 43% after 48 h. This might be due to a rapid increase in enzyme activity with a rise in temperature up to a specific range. But very high temperatures may cause thermal inactivation of enzymes responsible for decolorization, causing loss of cell viability and affecting cell structures such as the cell membrane, leading to a lower decolorization rate (Shah et al. 2013; Rakkan and Sangkharak 2020). Banat et al. (1996) reported that some of the textile and other dye effluents are produced at relatively high temperatures (50–60°C) even after a cooling or heat-exchange step. This indicates that the biomass of living microorganisms utilized, whether live or dead, must be active and capable of decolorizing the effluent at this temperature range (Shetty and Krishnakumar 2020). Immobilization of the microbial cell culture in a support material such as sodium alginate beads causes a shift in the maximum color removal temperature toward higher values because of the alteration in the cells’ microenvironment. Cell immobilization has the benefits of a higher dye degradation rate, high biomass loading, and protection of cells from the negative effects of dye and its by-products (Pandey et al. 2020; Vargas-de la Cruz and Landa-Acua 2020). According to Chu et al. (2009), immobilized C. vulgaris in alginate was able to remove up to 48.9% of the color from textile effluent, outperforming free suspension cultures of algal cells (34.9%). Majhi et al. (2021) also discovered that immobilized live C. pyrenoidosa algae cells had remarkable thermal stability at 60°C, with a decolorization efficiency of 97.2% at 60°C. This might be owing to the higher temperature stability of algal cells with alginate binding, which offers an alternate monolayer for dye molecule adsorption.

The effect of initial dye concentration and molecular structure

The presence of a high concentration of dye in the aquatic environment may disrupt light transmission in water, thus impacting photosynthesis and the development of aquatic microalgae (Hu and Wu 2001). Toxicity caused by dye at greater concentrations may reduce microbial decolorization rate, survival, and efficiency (Afrin et al. 2021, Sudarshan et al. 2022). According to Parshetti et al. (2006), increasing the methyl orange concentration from 10 to 100 mg L⁻¹ dramatically lowered the percentage of dye decolorization by Kocuria rosea. This might be owing to the large amount of biomass concentration required to remove larger quantities of color. Khataee et al. (2011) investigated the biotreatment of the triphenylmethane dye, malachite green, by a xanthophyta alga, Vaucheria species, which exhibited a decline in decolorization efficacy as the initial dye concentration was increased. According to Xie et al. (2016), the degradation rate of dispersed blue 2BLN by the oil-producing freshwater microalgae Chorella sorokiniana was reduced with increasing dye concentration.

Initial dye concentration and the molecular structure of the dye influence the rate of decolorization (Kaushik and Malik 2009; Garg et al. 2012). The impact of the initial dye concentration in the bioremediation process is determined by the direct relationship between the dye concentration in an aqueous solution and the accessible sites on an adsorbent surface (Yagub et al. 2014). Low dye concentrations may have gone undetected by enzymes released by dye-degrading microorganisms. On the other hand, high dye concentration is toxic to microorganisms and affects dye degradation by blocking enzyme active sites, having a toxic impact on dye-degrading microorganisms, and saturating adsorption sites on the adsorbent surface (Varjani et al. 2020). According to Wang et al. (2009a), the maximum decolorization extent was 96.20% for reactive red 180, and for six other dyes, azo dyes (Reactive black 5; Reactive yellow 81; Reactive blue 17); and anthraquinone dyes (Reactive blue 19; Reactive blue 49; Reactive blue 74); the decolorization value ranged from 73.11 to 85.28% by the bacterial strain Citrobacter sp. strain CK3, and results demonstrated that anthraquinone dyes are more resistant to degradation than azo dyes. Similarly, Pseudomonas sp. RA20 culture decolorized 100 mg L⁻¹ of azo dyes, Reactive black 5, Reactive orange 16, Reactive red 120, and Reactive yellow 2, to 98.6, 92.6, 23.7, and 49.6%, respectively, in 72 h (Hussain et al. 2013). This discrepancy in the decolorization percentage of various dyes might be attributed to differences in the dyes’ molecular structures, and dyes with a simpler structure and lower molecular weight decolored more faster than those with a more intricate structure and large molecular weight (Sha et al. 2016).

Oxygen’s influence

The most important factor to consider is the effect of oxygen on cell growth and dye reduction. Typically, aerobic and anaerobic routes are used by bacteria to degrade azo dyes (Chen et al. 2021). Guadie et al. (2017) reported the 90–100% decolorization of reactive red 239 dye by Bacillus sp. strain CH12 under anoxic and anaerobic conditions, where they observed a lower decolorization rate (2–18.6%) in shaking conditions. Oxygen was superior to the azo groups as a terminal electron acceptor, and the anaerobic decolorization of azo dyes was typically hindered by dissolved oxygen. Because oxygen was the most preferred electron acceptor throughout the aerobic dye reduction process, which results in inhibition of dye reduction (Pearce et al. 2003). Similarly, Hussain et al. (2013) observed that electrons are required for azoreductase to carry out the reduction of azo bonds of reactive black-5 dye and that the presence of oxygen inhibits azoreductase’s capacity to get electrons from NADH/NADPH. Anaerobic degradation of azo dyes occurs rapidly but results in the production of colorless aromatic amines as metabolites. It produces toxic, carcinogenic, and mutagenic colorless aromatic amines as a byproduct, and no further breakdown of the dye molecule is detected once the “N = N” (azo) link is reduced (Senan and Abraham 2004). However, the inhibition of the azo dye reduction process under aerobic conditions lasts only for a limited period and is an irreversible effect (Pearce et al. 2003). A biological treatment system using aerobic bacteria further degrades the aromatic amines that arise from this process into innocuous chemicals via monooxygenase or dioxygenase, enabling further degradation and mineralization of the dyes (Chen 2006; Chen et al. 2021). To fully mineralize the reactive azo dye, aerobic conditions are required. While, Du et al. (2011) reported that Pseudomonas sp. strain DY1 was able to decolorize malachite green with high decolorizing capability (90.3%) of malachite green (1000 mg L⁻¹) under shaking conditions within 24 h. But under the static condition, it decolorizes only 78.9% of malachite green at 1000 mg L⁻¹ in 24 h. Hence, the combination of anaerobic treatment followed by aerobic treatment is the safest method for the complete mineralization of dye molecules and is greatly favored (Senan and Abraham 2004; Pandey et al. 2020).

The influence of nutrients

Most of the dyes are deficient in carbon level. Due to this, the microorganism-based dye removal process without any supplemental carbon source is difficult. Carbon sources such as glucose, starch, and yeast served as enhancers of the bacterial growth rate and metabolism, which led to a higher dye decolorization percentage (Bayoumi et al. 2010; Garg et al. 2012). More than 96% decolorization of reactive red 180 by Citrobater sp. was reported by Wang et al. (2009a), with the supplemental glucose at 4 g L⁻¹ serving as an optimum level of carbon source for decolorization of reactive red 180 by Citrobater sp. While decolorization of reactive black 5 by Enterobacter sp. EC3 shows 90% decolorization efficiency in 48 h with the supplementation of glucose at 2 g L⁻¹ (Wang et al. 2009b). The difference in the nutrient requirement may depend on the molecular structure of the dye and the microorganism involved in the decolorization process. Kumari et al. (2016) found that Bacillus endophyticus LWIS1 decolored 98% of the azo dye Remazol black-B after 10 h of incubation, and yeast extract was used as a carbon source in this dye decolorization study. It proves yeast extract acts as a co-substrate and enhances the decolorization activity of B. endophyticus LWIS1. Any increase or decrease in the concentration of the carbon source reduced the growth response as well as the rate of dye removal. Lowering the level of carbon sources could not meet the growth and metabolic performance of bacterial strains. A high level of carbon concentration results in the utilization of carbon sources preferentially for other metabolic activities and the inhibition of dye decolorization (Wang et al. 2009a).

Among the nitrogen sources in the decolorization study, ammonium sulfate, ammonium nitrate, ammonium chloride, and peptone are widely used as nitrogenous supplements. Ammonium sulfate at 0.1% (w/v) was optimum for the decolorization of monoazo dye orange II with an efficiency of 92.8% by Pseudomonas putida SKG-1 (MTCC 10510). Higher or lower nitrogen concentrations than the optimum level were inhibitory (Garg et al. 2012).

Future outlook

Bioremediation of dyes using microorganisms has gained attention in the last two decades, and employing microalgae and bacteria as biodegrading agents or biosorbents might be a potential strategy to mitigate the detrimental effects of dye effluent. Textile dye-containing wastewater may be effectively treated using bioremediation methods. However, there are a few issues that restrict the practical implementation of bioremediation. Bioremediation technologies for commercial and industrial-scale applications in textile dye wastewater treatment must overcome the following problems in order to improve their efficacy:

1. The experimental factors for bioremediation of resistant dyes, favorable circumstances for the growth of microorganisms, the process of dye degradation, and the toxicity of degraded products have to be extensively studied in order to obtain greater decolorization effectiveness.

2. There is very little data available regarding the bioremediation of real textile dye wastewater, while most of the studies are based upon laboratory made synthetic dye-containing wastewater. The use of pure microorganisms or consortiums of microorganisms in the bioremediation of real wastewater requires extensive research to determine their commercial viability.

3. The toxic accumulation potential of biomass during the bioremediation process, as well as the biomass production potential and use of produced biomass as animal feed, must all be investigated.

Conclusion

A significant cause of water contamination across the world is due to textile industry effluents. Because of their xenobiotic nature, they pose a threat to both people and aquatic life. Bioremediation in the textile industry may make use of bacteria, algae and dead cells as adsorbents. Bioremediation has evolved as an environmentally benign, low-cost, and efficient method for treating textile effluents. Microorganisms such as bacteria and algae isolated from textile dye-contaminated environments can be effectively taken for bioremediation due to their well-developed mechanisms such as oxidative and reductive enzymes that assist in cleaving the chemical molecules of dye. In addition, microalgae and bacteria may effectively treat dye-polluted wastewaters due to their several additional benefits, including their shorter life cycles, great capacity for biosorption due to their huge surface area, and the presence of chemical groups on their cell walls, which gives numerous sites for electrostatic adsorption. Although various issues require urgent attention, a future study in this field may be able to overcome the majority of the existing obstacles. The existing bioremediation strategies are primarily laboratory-scale. Integration of these systems for large-scale commercial applications is a major technological problem. There are several factors to consider when using bioremediation, including the kind of effluent, the toxicity of the metabolites, the costs involved, and the intended use of the treated water. The efficiency of bioremediation could be enhanced by optimizing and manipulating parameters such as temperature, pH, biomass concentration, agitation, and dye concentration, and these parameters must be taken into account. To create the best combination, it is necessary to carefully consider the advantages of the various processes and the potential interactions of combining such technologies, making use of a variety of strains and consortiums isolated from dye-contaminated sites, the isolation of new microorganisms, such as halotolerant or thermophilic microorganisms, or the adaptation of existing ones to consume dye molecules as their carbon and nitrogen sources in a way that achieves the desired results. Therefore, a further study focused on selecting a variety of microorganisms living in the polluted areas and then employing them for the full breakdown and mineralization of the harmful textile dyes will undoubtedly function as a sustainable, economical, and environmentally beneficial strategy.

Consent to participate

All authors have given their consent for their participation in this submission and the possible publication of this study.

Acknowledgements

The authors are thankful to the Director, Indian Council of Agricultural Research, and the Central Institute of Fisheries Education, Mumbai. The authors would like to thank the anonymous reviewers who gave their feedback on the manuscript.

Conflict of interest

No conflict of interest declared.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Ethical approval

Not applicable.

Consent for publication

All authors are aware of this submission and have consented to the publication of this study.

Author contribution statement

1. Shanmugam Sudarshan—Conceptualization, Data Curation, Formal Analysis, Visualization, and Writing—Original Draft Preparation.

2. Sekar Harikrishnan—Data Curation and Resources.

3. Govindarajan Rathi Bhuvaneswari—Visualization, Writing—Review and Editing.

4. Venkatesan Alamelu—Writing—Review and Editing.

5. Samraj Aanand—Supervision, Writing—Review and Editing.

6. Aruliah Rajasekar—Writing—Review and Editing.

7. Muthusamy Govarthanan—Data Curation, Formal Analysis, and Writing—Review and Editing.

Data availability statement

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.