Native Algal Consortia as a Bioremediation Tool for Polluted Freshwater Ecosystems: A Case Study from the Yamuna River

Abstract

The Yamuna River, among India’s most polluted waterways, is burdened by industrial, agricultural, and domestic discharges containing complex organic and inorganic contaminants. This study introduces a novel, integrated approach combining comprehensive pollutant profiling by liquid chromatography–mass spectrometry (LC-MS) with bioremediation using laboratory-validated native algal consortia. Water samples from a severely polluted Delhi stretch revealed alarming levels of heavy metals (e.g., lead: 47.33 mg/L) and over 550 organic pollutants, including polychlorinated biphenyls, dioxins, carcinogens, and neurotoxins. Two consortia, each assembled from indigenous algal strains, were evaluated under controlled conditions against both pollutant-rich water and non-polluted controls. Consortium 1 (Scenedesmus, Chlorococcum, Oocystis) outperformed Consortium 2 (Chlorella, Klebsormidium, Monoraphidium), achieving up to 87.07% reduction in lead and >95% removal of nitrate and phosphate, alongside substantial decreases in chemical and biological oxygen demand. By integrating high-resolution chemical analytics with native microbial remediation, this work provides the first demonstration of simultaneous removal of diverse pollutant classes in Yamuna water. The findings establish native algal consortia as cost-effective, sustainable bioremediation tools and underscore LC-MS as a critical method for holistic aquatic pollution assessment.

1. Introduction

Water, along with air, is among the most essential resources necessary for human survival. However, rapid urbanization and the growing reliance on freshwater have led to substantial volumes of wastewater being generated from diverse sources, including domestic, industrial, and food-processing activities [1]. This wastewater often contains a wide range of contaminants such as nutrients (e.g., nitrogen and phosphorus) and toxic heavy metals (e.g., lead and zinc), which pose increasing global concerns [2]. When untreated or poorly treated wastewater is released into the environment, it threatens aquatic ecosystems and public health [3]. Projections further suggest that by 2030, global freshwater resources such as rivers, lakes, and glaciers may face a 40% deficit [4] underscoring the urgent need for sustainable and efficient wastewater treatment technologies.

The Yamuna River in India exemplifies this crisis of freshwater pollution. Despite only 2% of its catchment passing through the National Capital Territory (NCT) of Delhi, this 48 km stretch contributes nearly 79% of the river’s total pollution load, making it the most polluted segment [5]. Approximately 85% of this pollution originates from domestic sources, including household effluents, sewage, industrial discharges, solid waste, ritual immersions, and agricultural runoff [6]. Industries along the riverbanks frequently discharge untreated or partially treated effluents [7], while the river’s water is extensively used for industrial operations such as manufacturing, thermal energy, and hydropower generation [8], leading to elevated levels of heavy metal contamination [9]. Reports indicate that 359 industrial units release effluents into the Yamuna, with 42 industries in Delhi identified as direct contributors [10]. Domestic sewage is another major contributor, with around 90% of Delhi’s residential wastewater entering the river. This sewage carries detergents, phosphate-rich compounds, and household chemicals that contribute to toxic foam formation [11]. For instance, Kumar (2024) reported phosphate levels of 0.51 mg/L, far exceeding the acceptable range of 0.005–0.05 mg/L, resulting in severe frothing and ecological degradation [12]. Agricultural activity along the riverbanks further reduces stream flow and introduces pesticide residues into the system [7].

Water quality indicators confirm the Yamuna’s degraded state. Current Biochemical Oxygen Demand (BOD) levels reach up to 93 mg/L, significantly higher than the Central Pollution Control Board (CPCB) guideline of 3 mg/L and the World Health Organization (WHO) threshold of 5 mg/L [13]. Similarly, Chemical Oxygen Demand (COD) shows seasonal variation, ranging from 202.66 mg/L in summer to 64 mg/L in monsoon, 89 mg/L in winter, and 71.11 mg/L in spring [14]. Although within the WHO limit of 250 mg/L, such persistently high COD and BOD values indicate serious organic pollution, primarily from domestic and commercial waste [15]. In addition, heavy metals including chromium (Cr), cadmium (Cd), zinc (Zn), copper (Cu), nickel (Ni), and lead (Pb) are consistently detected in the Yamuna [5]. These metals persist in the environment, are toxic, and in some cases carcinogenic [16], thereby affecting aquatic biodiversity and compromising water quality [17].

The cities of Delhi, Mathura, Agra, and Etawah, which depend heavily on the Yamuna, are increasingly vulnerable due to industrialization and urban expansion [18]. Addressing this challenge requires both top-down policies and bottom-up, community-driven interventions, including public education, watershed protection, improved solid waste management, agricultural reforms, and investments in wastewater treatment technologies. Robust wastewater treatment is critical not only for environmental restoration but also for sustainable development at both state and national levels [18]. Among emerging solutions, algae-based treatment (phycoremediation) has gained attention as a cost-effective, sustainable, and environmentally friendly strategy.

Recent studies highlight the significant potential of various algal genera in wastewater bioremediation, particularly for the removal of heavy metals and nutrients, through mechanisms such as biosorption, ion exchange, complexation, bioaccumulation, and enzymatic reduction. Removal efficiency depends on the algal species, the type of pollutant, and environmental factors such as pH and temperature. Comparative studies show that genera such as Scenedesmus, Chlorococcum, and Oocystis often outperform consortia of Chlorella, Klebsormidium, and Monoraphidium, due to their higher metal-binding capacity, rapid biomass growth, and resilience under fluctuating conditions. Although Chlorella-based consortia are commonly employed, their effectiveness is generally lower unless combined with more diverse or locally adapted species [19].

Unlike many previous studies that primarily emphasize quantification of pollution levels or focus on the removal potential of a single algal species, the present investigation explores the use of native algal consortia as effective bioremediators. The objectives of this study were twofold: first, to characterize the properties of wastewater collected from the most polluted stretch of the Yamuna River, and second, to evaluate the pollutant removal efficiency of two distinct algal consortia under controlled laboratory conditions over a 20-day treatment period. By examining the performance of consortia composed of locally adapted algal strains, this research addresses the potential of native, multi-strain algal systems for wastewater remediation. The findings thus provide site-specific insights into the role of diversified algal communities in achieving sustainable and effective treatment of polluted waters.

2. Materials and Methods

2.1. Identification and Quantification of Emerging Organic Pollutants

To identify and quantify emerging organic pollutants present in the most polluted stretch of the Yamuna River at Kalindi Kunj (28.5444° N, 77.3126° E), comprehensive physico-chemical and instrumental analyses were conducted on collected water samples. All the experiment were performed in triplicates.

2.2. Collection of Water Sample

Water samples were collected from Kalindi Kunj from the surface to 1.5 depth, one of the most critically polluted segments of the Yamuna River, often referred to as the “dead stretch” due to the absence of aquatic life and the extremely degraded water quality. Sampling was carried out in sterile, amber-colored borosilicate glass bottles (1 L capacity) to prevent photodegradation of light-sensitive contaminants. Immediately after collection, samples were sealed, stored in insulated containers with ice packs, and transported to the laboratory. Upon arrival, they were refrigerated at 4 °C and processed within 24 h to ensure minimal alteration in chemical and biological properties.

2.3. Analysis of Physiochemical Factor

The physicochemical analysis of the water samples, as reported in [5,12], was used in this study and included measurements of pH, salinity, conductivity, total dissolved solids (TDS), chemical oxygen demand (COD), biochemical oxygen demand (BOD), nitrate, phosphate, chloride, and sulphate, with the average values used for analysis. The pH, salinity, conductivity, and TDS were measured at each site using a PCSTestr 35 Multi-parameter device (Eutech–Oakton). COD was determined using the ferrous ammonium sulfate (FAS) titration method, following APHA (1998) guidelines. Dissolved oxygen (DO) was measured using a slightly modified version of Winkler’s iodide-azide method, in accordance with [19]. Nitrate (NO₃⁻) concentrations were assessed using a modified salicylic acid nitration method, while phosphate (PO₄³⁻) levels were determined based on the deprotonation of Malachite Green by the molybdophosphate complex. Sulphate (SO₄²⁻) concentrations were analyzed following the procedure in [20], and chloride (Cl⁻) levels were measured using the methods described therein. All experiments were performed in triplicate to ensure accuracy.

2.4. Analysis of Heavy Metals

The quantification of heavy metals in the collected water samples was performed following acid digestion. A mixture of concentrated hydrochloric acid (HCl) and concentrated nitric acid (HNO₃) in a 3:1 ratio was used for the digestion of a known volume of the water sample. After complete digestion, the sample was cooled, filtered (if required), and the volume was adjusted to 50 mL using ultrapure deionized water. A subsample of 0.5 mL from this digest was further diluted to a final volume of 10 mL for instrumental analysis. Heavy metals including aluminum (Al), chromium (Cr), cobalt (Co), nickel (Ni), zinc (Zn), cadmium (Cd), lead (Pb), and arsenic (As) were analyzed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS), specifically the Agilent 8900 Triple Quadrupole ICP-MS system.

2.5. LC-MS/MS Analysis

Water samples were analysed using a Dionex Ultimate 3000 UHPLC system (Thermo Scientific, Waltham, MA, USA) equipped with a Hypersil GOLD C18 column (2.1 × 100 mm, 1.9 µm). The column temperature was maintained at 35 °C, with a flow rate of 0.300 mL/min, and 15 µL of each sample was injected. The mobile phase consisted of Buffer A (0.1% formic acid in water) and Buffer B (acetonitrile), using a gradient program with a total run time of 25 min. The experiment used Milli-Q water as the blank and tap water as the control, and LC-MS analysis was carried out in both positive and negative ion modes [20].

Mass spectrometric detection was performed on a Q Exactive Orbitrap mass spectrometer (Thermo Scientific, Waltham, MA, USA) operated in Full MS-ddMS² mode under both positive and negative electrospray ionization. The scan range was m/z 100–1200, with resolving power set to 70,000 (full MS) and 35,000 (ddMS²). The AGC targets were 1 × 10⁶ for full MS and 1 × 10⁵ for ddMS². Sheath and auxiliary gas flow rates were 30 and 10 arbitrary units, respectively. The capillary voltage was set at +4.0 kV (positive mode) and −3.5 kV (negative mode). Other parameters included a capillary temperature of 275 °C, S-Lens RF level of 50, and probe heater temperatures of 320 °C (positive) and 300 °C (negative).

2.6. Preparation of Algal Consortia

Two distinct algal consortia were prepared and evaluated for their bioremediation potential. Each species was identified morphologically using standard taxonomic references [21,22].

Consortium 1 consisted of Scenedesmus sp., Chlorococcum sp., and Oocystis sp., while Consortium 2 included Chlorella sp., Klebsormidium sp., and Monoraphidium sp. These microalgal strains were isolated from natural freshwater sources and subsequently cultured under controlled laboratory conditions in Bold’s Basal Medium (BBM) under a 16:8 h light-dark photoperiod at 26 ± 2 °C. The algal cultures were maintained under continuous aeration and illumination (186 µmol photons m⁻² s⁻¹) until reaching the exponential growth phase. Equal volumes of each strain (based on optical density at 680 nm) were mixed aseptically to form the respective consortia. The cell density of the prepared consortia was adjusted to an initial optical density (OD₆₈₀) of 0.6 before inoculation into wastewater samples.

The strains were selected based on preliminary field surveys of the Yamuna River, where they consistently dominated across seasons, even under polluted conditions, indicating their natural resilience and adaptability. Each algal consortium displayed a similar growth pattern, with the species within each group exhibiting comparable growth characteristics.

2.7. Phycoremediation Analysis

The Phycoremediation potential of the algal consortia was evaluated by measuring the concentration of various pollutants in Yamuna water wastewater before and after treatment. The experiment was carried out under controlled laboratory conditions over a duration of 20 days. Cultures were incubated in 5Ltransparent flasks containing 3.5 L pre-filtered water and inoculated with the prepared algal consortia. The prepared cultures were inoculated into wastewater at a ratio of approximately 1:10 (v/v), corresponding to an initial optical density (OD₆₈₀) of 1.0. Experimental conditions were maintained at a constant temperature of 26 ± 1 °C with a 16:8 h light-dark photoperiod and continuous illumination at an intensity of 186 µmol photons m⁻² s⁻¹.

Throughout the treatment period, algal cultures were gently aerated to prevent sedimentation and ensure optimal gas exchange. After 20 days, the algal biomass was harvested via centrifugation at 6000 rpm for 10 min. The resulting supernatant representing the treated wastewater was carefully decanted and subjected to physicochemical and analytical assessments to determine residual concentrations of nutrients (e.g., nitrate, phosphate, sulphate), heavy metals, and emerging organic pollutants.

To quantify the pollutant removal, the removal efficiency (%) was calculated using the following formula:

$$\mathrm{Removal}\mathrm{Efficiency}\left(\%\right)={\displaystyle \frac{\mathrm{C}0-\mathrm{C}\mathrm{t}}{C0}}\times 100$$

where

C₀ = Initial concentration of the pollutant (mg/L)

C_t = Final concentration of the pollutant after treatment (mg/L)

This formula allows for the precise evaluation of the algal consortium’s remediation performance across multiple pollutants.

2.8. Statistical Analysis

Principal Component Analysis (PCA) was carried out using OriginPro 9.0 software to identify patterns and reduce data dimensionality. One-way ANOVA was conducted using IBM SPSS Statistics (version 28.0) on normalized data, with Duncan’s multiple range test applied for post hoc comparisons.

3. Results

The data used for the analysis of physicochemical factors treated by algal consortium was obtained from the study conducted by [5,12].

3.1. Heavy Metals Analysis

The analysis of Yamuna River water indicates alarming levels of heavy metal contamination shown in

Table 1. Analysis of heavy metals in water sample with standard limit.

Heavy Metals	WHO/CPCB Limits in Drinking Water mg/L	Yamuna Water mg/L
Al	0.2	1.554 ± 0.02
Cr	0.05	0.124 ± 0.004
Co	5	0.012 ± 0.005
Ni	0.07	0.695 ± 0.001
Zn	3	3.397 ± 0.03
Mo	0.07	14.875 ± 0.65
Cd	0.003	0.05 ± 0.001
Pb	0.01	0.047 ± 0.002
As	0.01	0.147 ± 0.001

when compared to the WHO/CPCB permissible limits for drinking water. Aluminum (Al) was detected at 1.554 ± 0.02 mg/L, significantly exceeding the safe limit of 0.2 mg/L. Chromium (Cr) at 0.124 ± 0.004 mg/L, Nickel (Ni) at 0.695 ± 0.001 mg/L, Molybdenum (Mo) at 14.87 ± 0.65 mg/L, Cadmium (Cd) at 0.05 ± 0.001 mg/L, Lead (Pb) at 0.047 ± 0.002 mg/L, and Arsenic (As) at 0.147 ± 0.001 mg/L all surpass their respective limits of 0.05, 0.07, 0.07, 0.003, 0.01, and 0.01 mg/L. Zinc (Zn), present at 3.397 ± 0.003 mg/L, slightly exceeds its limit of 3 mg/L. Only Cobalt (Co), with a concentration of 0.012 mg/L, remains well within the acceptable limit of 5 mg/L. These results highlight serious heavy metal pollution in the Yamuna river, posing a substantial risk to human health if consumed without proper treatment.

3.2. Analysis of Pollutants Before and After Phycoremediation

The effectiveness of two microalgal consortia in treating wastewater was assessed by analyzing various physico-chemical parameters before and after treatment shown in

Table 2. Reduction in Physicochemical pollutants By Consortium 1 and Consortium 2.

Parameter	Initial Value	Conc. After Treatment with Consortium 1	Conc. After Treatment with Consortium 2	% Removal by Consortium 1	% Removal by Consortium 2
Nitrate mg/L	175.59	7.0052	13.04	96.016	92.57
Phosphate mg/L	58.3415	2.39	3.46	95.9	94.05
Sulphate mg/L	1316.68	112.39	161.25	91.458	87.76
Chloride mg/L	1790.735	350.06	396.62	80.44	77.89
BOD mg/L	18.08	2.5305	3.79	85.98	79.05
COD mg/L	336	28.83	45.911	91.43	86.34
Salinity PSU	0.6915	0.117	0.14	83.016	79.8
Conductivity µS/cm	1445.5	277.19	382.12	80.79	73.56
TDS mg/L	737.65	128.7	147.12	82.58	80.05
pH Increased	6.93	8.90	8.58	28.44	23.76

. Nitrate concentration showed a significant reduction from 175.60 mg/L to 7.01 mg/L with Consortium 1 and 13.05 mg/L with Consortium 2. Phosphate levels decreased from 58.34 mg/L to 2.39 mg/L and 3.46 mg/L, respectively. Sulphate concentration dropped markedly from 1316.68 mg/L to 112.39 mg/L (Consortium 1) and 161.26 mg/L (Consortium 2), while chloride levels were reduced from 1790.74 mg/L to 350.07 mg/L and 396.63 mg/L. A considerable decline was also observed in biochemical oxygen demand (BOD), which decreased from 18.08 mg/L to 2.53 mg/L (Consortium 1) and 3.79 mg/L (Consortium 2), and in chemical oxygen demand (COD), which dropped from 336 mg/L to 28.84 mg/L and 45.91 mg/L, respectively. Salinity reduced from 0.6915 PSU to 0.1176 PSU and 0.1395 PSU, while conductivity fell from 1445.5 µS/cm to 277.19 µS/cm (Consortium 1) and 382.12 µS/cm (Consortium 2). In line with this, total dissolved solids (TDS) were also lowered significantly from 737.65 mg/L to 128.70 mg/L and 147.13 mg/L following treatment with the two consortia however an increase in pH was observed following treatment, increasing from an initial value of 6.93 to 8.90 with Consortium 1 and 8.58 with Consortium 2.

The treatment of Yamuna water with microalgal consortia also led to significant reductions in the concentrations of various heavy metals shown in

Table 3. Removal of Heavy metals by consortium 1 and consortium 2.

Element mg/L	Initial Conc.	Conc. After Treatment with Consortium 1	Conc. After Treatment with Consortium 2	% Removal by Consortium 1	% Removal by Consortium 2
Al	1.554	0.1831	0.4382	88.214	71.764
Cr	0.124	0.017	0.0509	86.302	58.96
Co	0.012	0.0014	0.0032	88.56	73.456
Ni	0.695	0.15	0.208	78.9	70.08
Zn	3.397	0.66	1.3626	80.56	59.91
Mo	14.87	3.12	3.5231	79.0162	76.3
Cd	0.05	0.011	0.0155	78.03	69.04
Pb	0.047	0.0061	0.0109	87.07	76.789
As	0.147	0.0249	0.041	83.056	72.098

. Aluminium (Al) decreased from an initial concentration of 1.554 mg/L to 0.1831 mg/L with Consortium 1 and 0.4382 mg/L with Consortium 2. Chromium (Cr) levels dropped from 0.124 mg/L to 0.017 mg/L and 0.0509 mg/L, respectively. Cobalt (Co) was reduced from 0.012 mg/L to 0.0014 mg/L (Consortium 1) and 0.0032 mg/L (Consortium 2), while nickel (Ni) decreased from 0.695 mg/L to 0.1467 mg/L and 0.208 mg/L.

A marked decline was observed in zinc (Zn), which reduced from 3.397 mg/L to 0.6609 mg/L (Consortium 1) and 1.3626 mg/L (Consortium 2). Molybdenum (Mo) concentrations fell from 14.875 mg/L to 3.1257 mg/L and 3.5231 mg/L, respectively. Similarly, cadmium (Cd) levels dropped from 0.05 mg/L to 0.011 mg/L and 0.0155 mg/L. Lead (Pb) was reduced from 0.047 mg/L to 0.0061 mg/L (Consortium 1) and 0.0109 mg/L (Consortium 2), while arsenic (As) decreased from 0.147 mg/L to 0.0249 mg/L and 0.041 mg/L.

3.3. Phycoremediation

The comparative analysis of pollutant removal efficiencies by Consortium 1 and Consortium 2 demonstrated that Consortium 1 consistently achieved higher removal rates across all parameters shown in Figure 1. For nutrients, Consortium 1 removed nitrate by 96.02 ± 1.54%, phosphate by 95.9 ± 3.36%, and sulphate by 91.46 ± 4.17%, whereas Consortium 2 showed slightly lower removal efficiencies of 92.57 ± 1.89%, 94.06 ± 3.6%, and 87.76 ± 2.74%, respectively. In terms of anions, chloride was reduced by 80.44 ± 1.74% in Consortium 1 and 77.89 ± 2.44% in Consortium 2. For organic load reduction, Consortium 1 removed BOD by 85.98 ± 1.45% and COD by 91.43% ± 2.19, compared to 79.05 ± 4.01% and 86.34 ± 2.57%, respectively, by Consortium 2. Salinity and conductivity reductions were also more effective in Consortium 1 (83.02 ± 4.07% and 80.79 ± 1.12%) than in Consortium 2 (79.8 ± 2.26% and 73.56 ± 2.10%). Total dissolved solids (TDS) were reduced by 82.59 ± 1.04% in Consortium 1, while Consortium 2 achieved an 80.05 ± 3.43% reduction. pH increase was more pronounced in Consortium 1 (28.44 ± 1.45%) than in Consortium 2 (23.77 ± 3.34%). Regarding heavy metal removal, Consortium 1 exhibited superior efficiency in removing aluminium (Al) by 88.21 ± 3.51%, chromium (Cr) by 86.30 ± 2.45%, cobalt (Co) by 88.56 ± 1.7%, nickel (Ni) by 78.9 ± 5.1%, zinc (Zn) by 80.56 ± 4.4%, molybdenum (Mo) by 79.02 ± 3.51%, cadmium (Cd) by 78.03 ± 5.01%, lead (Pb) by 87.07 ± 6.1%, and arsenic (As) by 83.06 ± 4.41%. In contrast, Consortium 2 showed lower removal efficiencies of 71.76 ± 5.43% (Al), 58.96 ± 3.2% (Cr), 73.46 ± 1.76% (Co), 70.08 ± 2.1% (Ni), 59.91 ± 3.84% (Zn), 76.3 ± 5.6% (Mo), 69.04 ± 1.74% (Cd), 76.79 ± 1.89% (Pb), and 72.10 ± 3.56% (As). These results clearly indicate that Consortium 1 is more efficient than Consortium 2 in removing a wide range of physico-chemical pollutants and heavy metals.

3.4. LC-MS Analysis of Water Sample for Pollutants

The LC-MS analysis was performed in an untargeted manner, allowing for the broad detection of compounds without prior selection of analytes. Detected compounds were subsequently identified by matching their spectral data to established reference libraries. This untargeted, library-based approach provides a comprehensive chemical profile of the samples and enables the discovery of unexpected or emerging contaminants. However, because identification relies on database matching, confirmation with authentic standards and targeted quantification would be necessary to validate key findings and to accurately determine concentrations of compounds of particular interest.

More than 500 pollutants were analysed, out of which 100 most hazardous pollutants were identified, belonging to different pollutants group given in

Table 4. Most hazardous compounds identified by LC-MS in water sample of Yamuna at Kalindi Kunj, Delhi.

S. No.	Compound Name	Pollutant Group	RT Time	Major Adverse Effects on Humans
1	2-Chloro-7-methoxyquinoline-3-carbonitrile	Industrial chemical/heterocyclic compound	11.272	Respiratory/skin irritant, potential cytotoxicity
2	N-propyl-N-[3-(4,4,5,5-tetramethyl-1,3,2-dioxaborolan-2-yl)propyl]cyclopropanamine	Organoboron compound	10.113/10.32	Skin/eye irritation, possible neurotoxicity
3	2-Amino-4-chloro-1,3-oxazole-5-carbonitrile	Halogenated heterocyclic	0.806	Irritant, potential mutagenicity
4	2-Oxaziridinesulfonic acid	Reactive oxidizing agent	0.798	Skin/respiratory corrosion, irritation
5	2,2-Dinitro-1-propanol	Nitro compound	−0.16	Toxic, systemic effects, marrow toxicity
6	Triethylamine	Industrial solvent	15.182	Respiratory irritation, pulmonary edema risk
7	Ethephon	Pesticide	1.89/1.47	Neurotoxicity, cholinesterase inhibition
8	2,6-Di-tert-butyl-1,4-benzoquinone	Quinone antioxidant	0.493	Skin sensitizer, oxidative stress inducer
9	Germanecarbonitrile	Organometallic	11.357	Neurological toxicity, irritant
10	1-mercapto[1,2,4]triazolo[4,3-a]quinoxalin-4(5H)-one	Heterocyclic sulfur compound	17.41	Sensitizer, irritant, possible mutagen
11	Guaiacolsulfonate	Phenolic compound	11.077	Skin/eye irritant, allergic reactions
12	Bromoacetic Acid-d3	Halogenated acid	10.725/17.644/0.018	Corrosive, irritant
13	Tetracyanoethylene	Cyanide donor	0.808	Toxic, respiratory distress
14	Boron sulfide	Inorganic boron compound	0.024	Respiratory irritant, nephrotoxicity
15	Tridecylamine	Long-chain amine	10.816/11.069	Skin/respiratory irritant
16	2,3,4,5,6-Heptapentaenenitrile	Nitrile compound	0.802	Cyanide release potential, toxic
17	3-[hydroxy(oxido)phosphoranyl]pyruvic acid	Organophosphorus	3.753	Toxicity related to organophosphates
18	3-phenyl-2-(4H-1,2,4-triazol-4-ylimino)-1,3-thiazolan-4-one	Heterocyclic sulfur/nitrogen	17.411	Irritant, possible mutagenic
19	N-Caprylyldiethanolamine	Surfactant	3.644	Skin irritation, allergen potential
20	2,2,6,6-Tetramethyl-4-piperidyl Methacrylate	Monomer/industrial	10.476	Respiratory sensitizer, skin irritant
21	3-Chloro-4-nitro-1,2-oxazole	Halogenated nitroheterocycle	0.804	Mutagenic potential, irritant
22	dihydroxyethyl lauramine oxide	Surfactant	5.307	Skin/eye irritant
23	β-Ionone	Terpenoid	1.266	Mild irritant, allergen
24	(Nitroimino)dimethanol	Nitro compound	0.805	Potential systemic toxicity
25	N,N-Dibutylethanolamine	Amine compound	3.659	Skin and respiratory irritation
26	2-(hydroxymethyl)-2-(octylamino)propane-1,3-diol	Surfactant	3.724	Irritant, allergen
27	Triphenylphosphine oxide	Organophosphorus compound	5.199	Irritant, potential toxicity
28	1,9-Pyrazoloanthrone	Polycyclic aromatic	0.829	Possible carcinogen, irritant
29	Embelin	Natural product	6.24	Generally low toxicity; skin sensitization possible
30	2-(2-(Nonylphenoxy)ethoxy)ethyl oleate	Surfactant/ester	17.818	Irritant, allergen
31	Phenylethyl alcohol	Aromatic alcohol	2.387	Mild irritant, allergen
32	DL-Tyrosine	Amino acid	17.647	Low toxicity
33	N,N-Dimethyldecylamine N-oxide	Surfactant	4.349	Skin and eye irritant
34	Quinuclidinol	Heterocyclic amine	3.275	Irritant
35	2-(Octylsulfanyl)naphthalene	Polycyclic aromatic sulfur	11.258	Toxic, irritant
36	2-Sulfamoylacetamide	Sulfonamide derivative	0.795	Allergic reactions possible
37	P-methyl-N-(3-nitrophenyl)phosphonamidic acid	Organophosphorus	11.352	Neurotoxicity risk
38	2-(Phosphonooxy)-2,3-butadienoic acid	Organophosphorus	10.707	Irritant, possible toxicity
39	Dichloro(methyl)phenylstannane	Organotin compound	11.068	Neurotoxicity, irritant
40	Laurolactam	Lactam	10.435	Respiratory sensitizer
41	2-(2-Aminoethoxy)ethyl hydrogen sulfate	Sulfate salt	0.821	Irritant
42	Cinnamaldehyde	Aromatic aldehyde	0.816	Skin sensitizer, irritant
43	Adipamide	Amide	1.88	Low toxicity
44	Decyl gallate	Antioxidant ester	5.268	Mild irritant
45	4-Heptyloxyphenol	Phenolic ether	5.702	Irritant, corrosive
46	5-(Chloromethyl)-3-(methoxymethyl)-1,2,4-oxadiazole	Halogenated heterocycle	0.805	Toxic, irritant
47	PEG n5	Polyethylene glycol	2.73	Low toxicity, allergen risk
48	1,3-Dichloro-2,4-difluorobenzene	Halogenated aromatic	0.02	Toxic, irritant
49	Pyridoxal	Vitamin B6 derivative	1.456	Low toxicity
50	Tris(cyano-kappaC)indium	Organometallic	1.109	potential metal toxicity
51	DL-Carnitine	Nutrient	0.951	Low toxicity
52	2-Chloro-2-fluorocyclopropanecarboxylic acid	Halogenated acid	17.61	Irritant, toxic
53	Dimethylarsinous fluoride	Arsenic compound	0.808	Highly toxic, carcinogenic
54	11-Aminoundecanoic acid	Amino acid derivative	5.169	Low toxicity
55	1,1,3,3-Tetramethylguanidine	Strong base	2.396	Corrosive, irritant
56	Precocene II	Natural product	7.567	Possible endocrine disruptor
57	Bicine	Buffering agent	0.976	Low toxicity
58	Valpromide	Pharmaceutical	4.597	Hepatotoxicity risk
59	6-tert-Butyl-4-methylcoumarin	Coumarin derivative	3.736	Liver toxicity risk; anticoagulant mimic
60	2-Pyrimidinyl phosphonic acid	Organophosphorus	0.78	Possible irritant
61	Capric diethanolamide	Surfactant	4.504	Skin/eye irritant
62	2,3-Dimethyl-benzothiazol-3-ium	Aromatic heterocycle	0.789	Toxicity unknown; possible irritant
63	Methyl 1,2,2,6,6-pentamethyl-4-piperidyl sebacate	Ester/amine	8.532	Mild irritant
64	Mizoribine	Immunosuppressant drug	1.647	Bone marrow suppression
65	Disilylstrontium	Organometallic	0.801	Unknown toxicity; metal toxicity risk
66	Trimethyl phosphate	Organophosphorus solvent	2.87	Neurotoxic, irritant
67	N-Dodecyl-N′-isopropyl-6-methyl-1,3,5-triazine-2,4-diamine	Herbicide	10.362	Toxic, irritant
68	2-(2-Methyl-5-nitro-1H-imidazol-1-yl)ethoxy]ethanol	Nitroimidazole	1.05	Carcinogenic potential
69	Crimidine	Rodenticide	0.801	Neurotoxic, lethal
70	Triacetone triperoxide	Explosive	2.532	Highly toxic, explosive hazard
71	Dabigatran	Anticoagulant drug	5.978	Bleeding risk
72	Cyclandelate	Vasodilator drug	7.566	Hypotension risk
73	Butyl isothiocyanate	Isothiocyanate	0.98	Irritant, possible carcinogen
74	Gabapentin	Neurological drug	2.834	CNS depression
75	Diethanolamine	Industrial chemical	0.909	cancer potential, irritant
76	Ethionamide	Antibiotic	2.578	Hepatotoxicity, neuropathy
77	Gliclazide	Antidiabetic drug	2.433	Hypoglycemia
78	Methyldopa	Antihypertensive drug	4.349	Hepatotoxicity, hemolytic anemia
79	Phenylephrine	Decongestant	3.044	Hypertension risk
80	Enalapril	Antihypertensive	3.651	Kidney dysfunction risk
81	Labetalol	Antihypertensive	5.685	Hypotension, fatigue
82	Isoxsuprine	Vasodilator	2.27	Tachycardia, hypotension
83	Ethambutol	Antibiotic	2.27	Optic neuropathy
84	Allopurinol	Antigout drug		Hypersensitivity reactions
85	Ranitidine	Histamine antagonist	10.362	Potential carcinogen (NDMA contamination)
86	Pentafluorostyrene	Fluorinated monomer	5.39	Eye/respiratory irritant; polymer precursor with possible endocrine activity.
87	SB-408,124	Pharmaceutical/Drug	4.78	Endocrine disruption or CNS effects
88	Phosphorodiamidimidic azide.	High-energy azide/phosphorus compound	3.14	Strong irritant; azides can affect nervous system and blood pressure
89	Praseodymium silicide (PrSi2)	Rare-earth silicide	1.08	Bradycardia, respiratory issues
90	4-tert-Octylphenol monoethoxylate	Alkylphenol ethoxylate surfactant	5.76	Endocrine disruption; aquatic toxicity
91	N-(2,2,2-Trichloro-1-hydroxyethyl)-2-furamide	Chlorinated amide	2.37	Potential carcinogen/endocrine disruptor.
92	6-Methyl-N-(5-methyl-1,3,4-thiadiazol-2-yl)-4,5,6,7-tetrahydro-1-benzothiophene-3-carboxamide	Research chemical	4.89	liver enzyme interactions.
93	γ-Acetylenic GABA	Experimental amino acid	2.35	ABA analogs affect nervous system
94	Phentermine	Appetite suppressant	4.475	Cardiovascular risk
95	Ibufenac	NSAID (pharmaceutical)	5.05	GI irritation, kidney/liver effects with chronic use.
96	Diphenhydramine	Antihistamine	8.258	CNS depression
97	Hydroxyzine	Antihistamine	15.112	Sedation, dry mouth
98	(E)-Dacarbazine	Antineoplastic (alkylating agent)	2.825	Carcinogenic; nausea, bone marrow suppression
99	Dichloro(methyl)phenylstannane	Organotin Compound	11.068	Neurotoxic, endocrine disruptor; toxic to immune and reproductive systems
100	Di(2 cyanoethyl)amine	Nitrile amine	2.466	Neurological effects

. The analysed compounds encompass several key pollutant categories with distinct chemical characteristics and human health implications. Persistent fluorinated substances, often referred to as “forever chemicals” or PFAS-like compounds, include fluorinated acids, esters, and halogenated heterocycles such as 2-chloro-2-fluorocyclopropanecarboxylic acid and 1,3-dichloro-2,4-difluorobenzene, notable for their extreme environmental persistence and resistance to degradation. Persistent organic pollutants (POPs) and industrial chemicals—such as 2,6-di-tert-butyl-1,4-benzoquinone, triphenylphosphine oxide, and boron sulfide represent long-lived industrial byproducts that pose chronic toxicity risks due to their bioaccumulation potential. The pesticide class includes insecticides, fungicides, and herbicides like ethephon, crimidine, and N-dodecyl-N′-isopropyl-6-methyl-1,3,5-triazine-2,4-diamine, designed for pest control but often associated with acute and chronic toxic effects in humans and ecosystems. Pharmaceutical and biochemical agents comprise widely used medicines and vitamins, including gabapentin, valpromide, and DL-tyrosine, highlighting the presence of bioactive substances with therapeutic and toxicological profiles. Industrial solvents and surfactants such as triethylamine and diethanolamine are frequently used in manufacturing and cleaning processes, known for their irritant and potential carcinogenic effects. Lastly, various biogenic and biochemically relevant compounds, including β-ionone and glyceraldehyde-3-phosphate, reflect natural biochemical diversity but may contribute to pollutant mixtures affecting human health. Collectively, these groups underscore the complex chemical landscape influencing environmental persistence and human exposure risks, necessitating comprehensive monitoring and mitigation strategies.

3.5. Statical Analysis

The ANOVA results show that variance between groups (SS = 7389.91, df = 18, MS = 410.55) is much greater than within groups (SS = 1132.45, df = 19, MS = 59.60), yielding an F value of 6.89, which exceeds the critical value of 2.18. The very low p-value (5.57 × 10⁻⁵) confirms that the differences among group means are statistically significant, indicating that at least one of the 19 group means differs and that group membership accounts for a substantial share of the total variance (

Table 5. One-way ANOVA analysis.

Source of Variation	SS	df	MS	F	p-Value	F Crit
Between Groups	7389.907	18	410.5504	6.888154	5.57 × 10−5	2.182263
Within Groups	1132.445	19	59.60239
Total	8522.352	37

).

4. Discussion

The current study aimed to evaluate and compare the pollutant removal efficiencies of two distinct microalgal consortia Consortium 1 and Consortium 2 across a wide spectrum of parameters, including nutrients, anions, organic loads, salinity-related properties, and heavy metals. The results unequivocally demonstrate the superior efficacy of Consortium 1 in all measured aspects. In this discussion, we provide an in-depth comparison of our findings with prior research, contextualizing the results within the broader scientific literature and elucidating potential reasons for the observed differences in removal efficiencies.

4.1. Removal of Inorganic Pollutants

Nitrate (NO₃⁻) contamination is a major environmental concern due to its mobility, eutrophication potential, and health risks such as methemoglobinemia. In this study, Consortium 1 achieved 96.02 ± 1.54% nitrate removal, outperforming Consortium 2 (92.57%). These results are consistent with earlier reports of 86–97% removal using multi-strain algal consortia [23,24] and with monoculture studies showing strong but variable performance, e.g., Scenedesmus sp. (93.26%, ref. [25]), Chlorella sorokiniana (~90%, ref. [26]), Dunaliella salina (~54%, ref. [27]), and Planktochlorella nurekis (95.6%, ref. [28]). Such variation highlights the species-dependence of nitrate uptake. The higher efficiency observed in the present study underscores the advantage of algal-bacterial consortia, where synergistic interactions enhance nutrient assimilation.

Phosphate (PO₄³⁻) removal followed a similar pattern, with Consortium 1 (95.9%) slightly higher than Consortium 2 (94.06%). These values are comparable to Scenedesmus sp. (96.32%, ref. [25]) and fertilizer effluent studies (~97%, ref. [29]), and exceed values reported for Dunaliella salina (~82%, ref. [27]) and pulp and paper wastewater (~71%, ref. [30]). The superior performance of consortia likely reflects cooperative uptake, storage, and precipitation mechanisms between algae and bacteria.

Sulfate (SO₄²⁻) reduction was also notable, with efficiencies of 91.46% (Consortium 1) and 87.76% (Consortium 2), likely due to sulfate-reducing bacteria. Previous studies reported lower values, e.g., Dictyosphaerium sp. in power-plant effluent [31] and Oocystis sp. achieving only 32–37.29% [32], reinforcing the superior performance of algal-bacterial consortia.

Chloride (Cl⁻), generally resistant to biological removal, was moderately reduced (80.44% and 77.89%), suggesting additional mechanisms such as ion exchange or microbial uptake. Compared to [33], who reported 67% TN and 69% TP removal with a Chlorella Scenedesmus consortium, and [34], who observed 35.4% TN and 74.4% TP removal with Chlorella-based systems, the present study demonstrates consistently higher nutrient removal. For instance, ref. [33] found TN removal of 61.2 ± 9.6% with S. obliquus + C. vulgaris, and 46.0 ± 7.4% with C. protothecoides + C. vulgaris, while phosphorus removal was highest at 72.4 ± 5.9% with C. protothecoides + S. obliquus.

Overall, the superior performance of Consortium 1 across nitrate, phosphate, sulfate, and chloride removal emphasizes the potential of carefully designed algal-bacterial consortia. Such systems combine the strengths of individual strains, enhancing resilience, nutrient uptake, and adaptability, and thus represent a robust strategy for sustainable wastewater treatment.

4.2. Organic Load Removal

4.2.1. Biochemical Oxygen Demand (BOD) and Chemical Oxygen Demand (COD)

BOD and COD are key indicators of organic pollution in wastewater. Consortium 1 achieved superior removal (85.98% BOD, 91.43% COD) compared to Consortium 2 (79.05% and 86.34%), exceeding the typical 70–80% BOD removal reported for conventional activated sludge [23]. This suggests enhanced enzymatic capabilities and metabolic synergy within Consortium 1.

Comparable high performances have been reported for algal consortia, such as COD removal of 89 ± 1% (S. obliquus + C. vulgaris), 85 ± 2% (C. protothecoides + C. vulgaris), and 83 ± 8% (C. protothecoides + S. obliquus) [33], as well as 82.60–83.14% BOD and 88.90–89.02% COD removal [23], and 82.45% COD reduction with mixed species [24]. In contrast, lower efficiencies were observed in less diverse systems, ranging from 40.64% [35] to 60% [34] and 53 ± 2% [36], underscoring the role of optimized species composition.

Individual species also demonstrate strong potential: Scenedesmus sp. achieved up to 93% COD and 84% BOD removal in fertilizer effluent [29] and >90% COD reduction in other wastewaters [25,28,30]. However, efficiencies can be lower in complex effluents, e.g., 71.1% COD (Oocystis sp., fish-processing) [37] and 68.96% (C. zofingiensis, piggery effluent) [38].

Overall, reported BOD and COD removal efficiencies range from 68% to over 90% across systems. The higher performance of Consortium 1 thus validates the effectiveness of carefully selected algal consortia for high-efficiency wastewater treatment.

4.2.2. Salinity, Conductivity, and TDS

Salinity, electrical conductivity (EC), and total dissolved solids (TDS) indicate the ionic strength and solute load of wastewater. Consortium 1 achieved higher removal efficiencies across all parameters, reducing salinity, EC, and TDS by 83.02%, 80.79%, and 82.59%, respectively, compared to 79.8%, 73.56%, and 80.05% by Consortium 2. These reductions likely result from microbial degradation, ion uptake, and biosorption or precipitation processes, reflecting a more metabolically diverse and efficient community. The findings align with [23], who reported TDS removal of 77.23–80.40% using a multi-species algal consortium (Chlorella saccharophila, Chlamydomonas pseudococcum, Scenedesmus sp., Neochloris oleoabundans). Notably, Consortium 1 slightly exceeded these values, underscoring the role of consortium composition in enhancing ionic regulation and wastewater remediation.

4.3. pH Modulation

Consortium 1 led to a 28.44% increase in pH, while Consortium 2 resulted in a 23.77% increase. pH modulation in biological systems often reflects the degradation of acidic compounds and ammonia generation via urea hydrolysis or protein metabolism. An increase in pH suggests microbial activity leaning towards alkaline shifts, possibly through ammonification or sulfate reduction. Cyanobacterial Harmful Algal Blooms (CyanoHABs) commonly increase water column pH to alkaline levels ≥ 9.2 [39]. The higher pH shift in Consortium 1 may indicate stronger deamination activity or ammonia liberation from nitrogenous organics, facilitating better neutralization of acidic conditions critical for improving downstream microbial and aquatic health.

4.4. Heavy Metal in Water and Its Removal

The heavy metal analysis of Yamuna River water clearly indicates significant contamination, with concentrations of several metals exceeding the safe drinking water limits established by the World Health Organization (WHO) and the Central Pollution Control Board (CPCB). The permissible limits for Aluminum (0.2 mg/L), Chromium (0.05 mg/L), Nickel (0.07 mg/L), Molybdenum (0.07 mg/L), Cadmium (0.003 mg/L), Lead (0.01 mg/L), and Arsenic (0.01 mg/L) were all surpassed, in some cases by a wide margin. Aluminum concentration was found to be nearly eight times the permissible limit, Chromium more than twice, and Nickel approximately ten times higher. Particularly alarming is the concentration of Molybdenum, which was recorded at 14.875 mg/L, exceeding the guideline value by more than 200 times. Toxic metals such as Cadmium, Lead, and Arsenic also showed significantly elevated levels, posing grave health risks. Such high levels of heavy metals in the river water not only threaten aquatic life by disrupting physiological processes, reproductive cycles, and survival but also have the potential to bioaccumulate and biomagnify through the aquatic food web. This poses a severe risk to higher trophic levels, including humans, who consume water or aquatic organisms from the river. Chronic exposure to these metals is associated with neurological damage, organ dysfunction, carcinogenesis, and endocrine disruption, corroborating findings from previous studies [40,41,42,43]. These results highlight the urgent need for regular water quality monitoring, strict pollution control, and effective cleanup efforts. Without action, heavy metal contamination will worsen ecological damage and public health risks. Sustainable management, reducing pollution sources, and raising community awareness are key to protecting the environment and human health.

In the present study, Consortium 1 consistently outperformed Consortium 2 in heavy metal removal efficiency, achieving higher percentages across most tested metals: Al (88.21% vs. 71.76%), Cr (86.3% vs. 58.96%), Co (88.56% vs. 73.46%), Ni (78.9% vs. 70.08%), Zn (80.56% vs. 59.91%), Mo (79.02% vs. 76.3%), Cd (78.03% vs. 69.04%), Pb (87.07% vs. 76.79%), and As (83.06% vs. 72.10%). The superior performance of Consortium 1 can be attributed to multiple mechanisms, including Cr(VI) reduction, biosorption, chelation, intracellular accumulation, ion exchange, and extracellular polymeric substance (EPS)-mediated complexation [43,44,45], which are consistent with previous findings on microalgal consortia in bioremediation. For example, microalgal–fungal consortia demonstrated near-complete (99%) removal of Cr [46], while microalgal consortia alone achieved substantial removal of Ni (95%), Pb (89%), Cd (88%), and even organic pollutants such as malathion (99%) [47]. Similarly, algal bacterial anaerobic granules were reported to effectively remove Cr(VI), primarily through EPS-mediated biosorption [48]. Comparative studies further support these observations, as Planktochlorella nurekis exhibited near-complete removal of multiple heavy metals from industrial effluents, whereas Chlamydomonas reinhardtii showed lower efficiency except in the case of Cr removal [29]. Collectively, these findings highlight the high potential of diverse and metabolically complementary microalgal consortia as a sustainable strategy for heavy metal bioremediation, emphasizing the crucial role of synergistic microbial interactions in the treatment of metal-contaminated environments.

4.5. Pollutants and Their Impacts on Human and Aquatic Organism of Yamuna River

The Yamuna River contains a wide range of emerging pollutants, including industrial, pharmaceutical, and agricultural compounds such as 2-Chloro-7-methoxyquinoline-3-carbonitrile, N-propyl-N-[3-(4,4,5,5-tetramethyl-1,3,2-dioxaborolan-2-yl)propyl]cyclopropanamine, and 2-Amino-4-chloro-1,3-oxazole-5-carbonitrile. Persistent organics like 2,6-Di-tert-butyl-1,4-benzoquinone and tri-phenylphosphine oxide disrupt endocrine function, impair growth and reproduction, and pose carcinogenic risks [40,49]. Surfactants and amines from detergents (e.g., N-caprylyldiethanolamine, dihydroxyethyl lauramine oxide) induce membrane damage and immunotoxicity, while pharmaceuticals (gabapentin, atenolol, loratadine, fexofenadine, enalapril, methyldopa, propylthiouracil) interfere with endocrine and neurological systems, alter microbial communities, and may promote antimicrobial resistance [41,42]. Pesticides such as ethephon and precocene II reduce biodiversity [43], whereas heavy metals and metalloids (germanecarbonitrile, boron sulfide, dimethylarsinous fluoride) bioaccumulate, causing neurotoxicity and organ damage [44,45]. Nitrogen- and sulfur-containing heterocycles from pharmaceuticals and agrochemicals (e.g., 1-mercapto[1,2,4]triazolo[4,3-a]quinoxalin-4(5H)-one) exhibit mutagenic and cytotoxic effects [46,47]. Industrial chemicals and organometallics such as Dichloro(methyl)phenylstannane, Crimidine, and Triacetone triperoxide (TATP) are persistent, highly toxic, and bioaccumulative [47,48].

This chemical diversity reflects multiple contamination sources industrial effluents, agricultural runoff, and urban wastewater creating synergistic toxic effects and bioaccumulation risks in aquatic food webs. Algal bioremediation provides a promising solution: Consortium 1 (Scenedesmus sp., Chlorococcum sp., Oocystis sp.) outperformed Consortium 2 (Chlorella sp., Klebsormidium sp., Monoraphidium sp.) in absorbing, bioaccumulating, and enzymatically degrading pharmaceuticals, surfactants, and heavy metals. Integration with bacterial consortia further enhances degradation, producing biomass suitable for biofuel or fertilizer.

Laboratory results, however, may overestimate efficiency due to stable temperature, light, and nutrient conditions, whereas the Yamuna exhibits variable hydrology, pollutant inputs, and microbial interactions. Field studies indicate lower removal rates under natural conditions. Nonetheless, the dominant algal taxa are resilient across seasons. Future work should focus on pilot-scale trials, integration into wastewater treatment, and long-term ecological assessments to enable effective river restoration.

4.6. Principal Component Analysis (PCA)

Principal Component Analysis (PCA) was used to reduce data dimensionality and reveal patterns, outliers, and groupings, with loading plots and biplots illustrating relationships among variables: small angles indicate strong positive correlations, right angles (90°) no correlation, and angles near 180° strong negative correlations, while vector length reflects variable significance [12]. The PCA score plot (Figure 2A) shows water quality parameters and heavy metals across two components (PC1: 94.54%, PC2: 5.46%), capturing 100% of the dataset’s variance. The “pH increased” variable (green star) is positioned far left and slightly above the origin, negatively correlating with most other parameters, particularly sulfate, phosphate, nitrate, and COD, suggesting higher pH reduces pollutant concentrations. Nutrient-related variables (phosphate, sulfate, nitrate, COD, conductivity, TDS) cluster on the positive side of PC1, reflecting strong positive correlations and a common source, while heavy metals (Zn, Cr, Co, Al, Pb, As, Cu, Cd) are mainly left and lower, with Cr and Zn distant from the center, indicating distinct geogenic or industrial origins. Vector orientations confirm these relationships, with small angles indicating positive correlations and opposite directions (e.g., pH vs. COD) indicating negative correlations.

The PCA biplot (Figure 2B) integrates the effects of microalgal consortia, showing Consortium 1 (red arrow) aligned with heavy metals (Pb, Co, Al, Cr, Zn), demonstrating higher efficiency in metal detoxification, while Consortium 2 (green arrow) aligns with nutrient-related variables (phosphate, sulfate, nitrate, COD, conductivity), indicating stronger influence on organic and nutrient pollution. Elevated pH remains negatively correlated with most pollutants. Nutrient parameters cluster on the positive side of PC1, influenced more by Consortium 2, whereas heavy metals such as Cr and Zn show distinct behavior and stronger association with Consortium 1. Overall, PCA confirms complementary roles of the consortia: Consortium 1 is more effective for heavy metal removal, and Consortium 2 better addresses nutrient and organic pollution, supporting targeted application of microalgal consortia for optimized bioremediation strategies.

The box plot (Figure 2C) compares the percentage removal efficiency of two microalgal consortia, Consortium 1 (black) and Consortium 2 (red), across various pollutants. Consortium 1 exhibits a higher median removal efficiency (~85–90%) compared to Consortium 2 (~75–80%) and shows a narrower range (75–95%), indicating more consistent performance. In contrast, Consortium 2 displays a wider range (60–100%), reflecting greater variability and less predictability, although no significant outliers are observed. These results suggest that Consortium 1 is more efficient and reliable for pollutant removal, while Consortium 2 may still perform effectively under specific conditions.

Hierarchical cluster analysis, represented by the dendrogram (Figure 2D), groups water quality parameters and heavy metals based on similarity, with the horizontal axis indicating distance or dissimilarity. Two primary clusters emerge at a high-distance level (~85): one comprising increased pH, Zn, Cr, Cd, Ni, As, and conductivity, and the other including Co, Al, Pb, BOD, TDS, salinity, Mo, chloride, COD, sulfate, phosphate, and nitrate. Within the clusters, Zn and Cr show close association, highlighting similarity among heavy metals, whereas nutrient and organic pollution indicators such as sulfate, phosphate, nitrate, COD, and BOD cluster together, reflecting shared behavior or sources. The pH increased variable, although part of the first cluster, remains distinct, indicating stronger correlation with heavy metals and conductivity than with nutrient-related parameters. The dendrogram demonstrates a clear separation between heavy metals and pH-related factors versus nutrient and organic pollutants, providing insight into their differing sources and dynamics within the water quality dataset.

The superior performance of Consortium 1 over Consortium 2 can be attributed to both biological and physicochemical mechanisms. Microalgae such as Scenedesmus and Chlorococcum in Consortium 1 possess cell wall functional groups (carboxyl, hydroxyl, amino) that facilitate high biosorption of heavy metals and other pollutants [48] while also bioaccumulating nutrients like nitrate and phosphate. Oocystis contributes extracellular polymeric substances (EPS) that promote metal chelation, adsorption of organic pollutants, and floc formation, further enhancing removal efficiency [50]. In contrast, species in Consortium 2, including Chlorella, Klebsormidium, and Monoraphidium, likely exhibit weaker absorption and lower heavy-metal tolerance, limiting synergistic interactions. Moreover, the metabolic diversity and complementary enzymatic pathways in Consortium 1 support the degradation of complex organic compounds, collectively explaining its higher removal rates across both inorganic and organic pollutants.

This multi-algae consortium could be practically used in the future to reduce pollutants in the Yamuna River through integrated bioremediation systems. Different algal species, each with unique metabolic capabilities, can work synergistically to remove a wide range of contaminants such as heavy metals, nutrients, organic waste, and microplastics. These consortia could be deployed in constructed wetlands, floating bio-panels, or photobioreactors installed along the riverbanks, allowing continuous purification of water. Additionally, the harvested algal biomass could be converted into biofertilizers, biofuels, or animal feed, making the process both sustainable and economically viable. Over time, such systems could help restore ecological balance and improve water quality in the Yamuna River.

5. Conclusions

The present study clearly establishes that Consortium 1 demonstrates superior pollutant removal capabilities over Consortium 2, achieving consistently higher efficiencies across nutrients, anions, organic loads, salinity parameters, and heavy metals. These results not only confirm but often surpass the benchmark removal rates reported in existing literature, underlining the promise of microalgal consortia in sustainable wastewater treatment. Future studies should focus on metagenomic profiling to understand the microbial community structure in Consortium 1, identify key functional genes, and optimize operational parameters to further enhance efficiency. Understanding the metabolic pathways and enzyme systems involved can also support the development of tailored bioremediation strategies for specific pollutants or industrial effluents.