Physicochemical analysis and molecular characterization of heavy metal tolerant bacteria from Buckingham canal, Neelankarai, Chennai

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ABIRAMI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5971395/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aim The escalation of toxic heavy metal concentrations in environmental contexts has manifested as a matter of significant concern in recent times, due to the rapid industrialization driven by the demands of a burgeoning population. This study aims to meticulously examine and evaluate the bioremediation capabilities of bacterial strains that exhibit tolerance to heavy metals, which have been isolated from sediment and aqueous samples collected at Buckingham Canal, Neelankarai, Chennai. Methods and Results The collected samples were subjected to comprehensive analysis regarding physicochemical characteristics and heavy metal quantification, revealing that Zinc displayed the most significant concentration at 190.3 ppm, succeeded by Manganese at 98.8 ppm within the sediment samples. The water samples revealed the concentration of heavy metals sequence Zn>Mn>Pb>Cu>Cr, in contrast, the sediment samples exhibited an order of Zn>Mn>Cu>Cr>Pb. Among the 25 bacterial isolates, BCSS04 and BCSS17 were chosen for subsequent assays due to their demonstrated tolerance to all five heavy metals, achieving maximum tolerance concentrations of 2100 ppm for lead, 1900 ppm for chromium, zinc, and Manganese, and 1300 ppm for copper, respectively. Genetic amplification indicated that the zntA, pcoA, pbrA, and chrA genes yielded fragment lengths of 2374 bp, 1791 bp, 2396 bp, and 520 bp, respectively. Notably, isolate BCSS17 displayed amplification for both pbrA and zntA genes, while isolate BCSS04 exhibited amplification solely for pbrA gene, lacking amplification for any other heavy metal resistance genes. The results from the BLAST analysis identified isolate BCSS04 as Proteus mirabilis with a 99.31% identity, whereas isolate BCSS17 as Bacillus paramycoides , presenting a 99.85% identity and 99% query coverage. Conclusion The study highlights the significant presence of heavy metals in the Buckingham Canal, with zinc being the most abundant. Two bacterial strains, Proteus mirabilis and Bacillus paramycoides, demonstrated high metal tolerance, with BCSS17 exhibiting resistance genes for both lead and zinc. These findings suggest their potential application in bioremediation efforts for heavy metal-contaminated environments Significance and impact of the study Ultimately, the bacterial species identified in the present investigation represent promising candidates for bioremediation and further exploration in endeavors aimed at the bioremediation of heavy metals within contaminated locations. Bioremediation Physicochemical parameters Heavy metal analysis Gene amplification BLAST analysis Proteus mirabilis and Bacillus paramycoides Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction The processes of industrialization and anthropogenic activities have significantly transformed our ecosystems into repositories for various waste materials. Consequently, numerous aquatic resources have been compromised, resulting in pollution that poses severe risks to human health and other biological systems (U. Damodharan, 2013 ). The deleterious compounds released into aquatic ecosystems not only bioaccumulate throughout the trophic levels but may also restrict species diversity or engender prolific populations of microorganisms (Aswathy.M et al., 2017). Aquatic ecosystems encounter a myriad of stressors that profoundly compromise biodiversity. Presently, heavy metals remain recognized as a pervasive contamination issue on a global scale, attributable to their enduring presence, propensity for accumulation, and the deleterious impacts they may exert on environmental integrity as well as on the health of human populations subjected to exposure (L. M. Coelho et al., 2015 ; Vélez et al., 2021 ). Heavy metals are customarily recognized as elements that possess an atomic mass and density exceeding that of water. The classification of these elements hinges on their functional value and involvement in biological systems, distinguishing them as essential or non-essential(Ayilara & Babalola, 2023 ; Koller & Saleh, 2018 ). It is vital to assert that primary heavy metals like copper (Cu), zinc (Zn), nickel (Ni), manganese (Mn), and iron (Fe) serve indispensable purposes in countless physiological and metabolic processes that are key for the survival and effective functioning of living creatures (Johnson et al., 2019 ). For instance, their involvement in the facilitation of enzymatic reactions through their role as enzyme cofactors is crucial, alongside their significance in sustaining osmotic equilibrium. Lead (Pb), arsenic (As), cadmium (Cd), chromium (Cr), and mercury (Hg) are heavy metals deemed toxic and unnecessary, emphasizing their lack of crucial roles in biological systems (Singh Sidhu, 2016 ). It is necessary to stress that both important and unimportant heavy metals have portrayed adverse effects on plant life, wildlife, microorganisms, and the wider ecosystem, with the scale of these effects being significantly shaped by the amount and time of exposure (Masindi & Muedi, 2018 ). The excessive accumulation of heavy metals in plant organisms induces a variety of adverse consequences, which include inhibited vegetative growth, reduced efficiency in photosynthetic activities and cellular division, diminished enzymatic performance and nutrient uptake, as well as the onset of chlorosis (Collin et al., 2022 ; Kiran et al., 2022). The anthropogenic exploitation of these plant species incurs harmful repercussions, including, but not limited to, immunosuppressive effects, visual impairment, neurotoxicity, and contributing to the development of conditions such as neoplastic diseases and increased blood pressure (Genchi et al., 2020 ; Mitra et al., 2022 ). Similarly, the presence of heavy metals in aquatic ecosystems triggers oxidative stress across various animal taxa (EL-Hak et al., 2022 ). Furthermore, heavy metals adversely affect soil and its associated biota by reducing microbial diversity, modifying soil pH and porosity, and disrupting microbial enzymatic activity (Musilova et al., 2016 ). The environmental contamination resulting from heavy metals due to anthropogenic and industrial processes has precipitated significant and frequently irreversible damage to aquatic ecosystems. The origins of such contamination include the mining and smelting of ores, effluent from storage batteries and vehicle emissions, as well as the manufacture and improper application of fertilizers, pesticides, and a multitude of other substances. In addition to their considerable threats to human health, these hazardous materials negatively influence both ecosystems and vegetation, being notorious for their resilience against natural breakdown. Therefore, it is important to research inventive practices for the advancement of clinical approaches that target reducing or completely getting rid of metals in our habitat.(L. M. Coelho et al., 2015 ). The removal of heavy metals from contaminated environments presents significant challenges due to their inherent non-decomposable characteristics (Verma et al., 2017 ). Bioremediation employs a variety of organisms, including bacteria, plants, fungi, and predominantly microalgae, to mitigate pollutants or convert them into less hazardous forms. The methodology of bioremediation concerning heavy metals offers numerous advantages in comparison to conventional remediation methods, such as chemical remediation, engineering remediation, physical remediation, and agro-ecological remediation, primarily due to its remarkable cost-effectiveness, and environmentally sustainable characteristics (Yu et al., 2020 ). Tiny organisms demonstrate various approaches that help them adjust to heavy metal exposure and support detoxification or remediation, which involves the removal of heavy metals from the cell's structure, the release of metal reduction or precipitation, exopolysaccharides, sequestration, adsorption, detoxification, and bioaccumulation (Liu et al., 2022 ). The natural microbes found in a region are especially suited for addressing contaminated sites. Moreover, these microbes possess numerous genes that encode for heavy metal-resistant proteins or transporters. Currently, the Buckingham Canal has been extensively degraded due to contamination from sewage, sullage, industrial effluents, stormwater runoff, and refuse, consequent to the process of urbanization. The watercourse adjacent to Buckingham Canal in the vicinity of Neelankarai, Chennai, exhibits a white foam-like effluent, which is being discharged from the Perungudi sewage treatment facility operated by Metrowater. Evidence suggests that heavy metal pollution in the Buckingham Canal, including elements like Fe, Cu, Cd, Zn, Cr, and Pb, has notably escalated over time. Previous investigations have documented the physicochemical characteristics, nutrient levels, and select heavy metals as water quality parameters within the Buckingham Canal (Kumar, Samuel, et al., 2018 ; Samuel Vinod Kuma et al., 2020a). Consequently, as a continuation of prior research efforts, the present study undertakes a comprehensive analysis of the chemical, physical, and heavy metal constituents in water and sediment samples obtained from designated locations within the Buckingham Canal. The research mainly focuses on discovering isolates resistant to heavy metals and Heavy metal-resistant genes (HMRGs) from Buckingham Canal for heavy metal bioremediation. Methodology Study area Chennai (Madras), serving as the administrative capital of Tamil Nadu, is situated along the eastern coastline of India (13.0827°N, 80.2707°E). The city is traversed by three significant watercourses: the Cooum River, the Adayar River, and the Buckingham Canal (Kumar, Sultana, et al., 2018). The Buckingham Canal constitutes an engineered waterway that establishes a connection between the Cooum and Adayar rivers. The segment located to the north of the Cooum is designated as the North Buckingham Canal, whereas the section to the south is referred to as the South Buckingham Canal. This canal extends from Nellore in Andhra Pradesh to Marakkanam, adjacent to Puducherry (Kumar, Samuel, et al., 2018 ). Notably, this canal measures 257 kilometers in length within the boundaries of Andhra Pradesh and encompasses 163 kilometers within Tamil Nadu. Among the three primary waterways within the urban environment, the Buckingham Canal is acknowledged as the most severely contaminated, with an estimated 60% of the daily untreated sewage being introduced into it, a predicament further intensified by discharges from the Chennai Metropolitan Water Supply and Sewerage Board (CMWSSB) and the North Chennai Thermal Power Station (NCTPS), both of which contribute elevated temperatures of water and fly ash into the canal (Lakshmi K et al., 2011 ). The water within this canal is classified as highly toxic and unsuitable for human consumption. Consequently, Buckingham Canal has been selected for the present investigation aimed at assessing its physicochemical characteristics and exploring methodologies for the bioremediation of heavy metals. Sample collection Water and sediment samples collected at Locations 1 and 2 were obtained utilizing established methodologies, as delineated by the American Public Health Association (2005) (APHA, 2005 ). To quantify the heavy metal content, sample stations were selected based on the locations of industrial and sewage water output units. The samples were collected from upstream and downstream sites within 1km. Following standardized methodologies, sediment samples were procured from depths of 10 to 15 cm beneath the surface of the water utilizing acid-washed plastic containers to mitigate any potential alterations in their properties. In polyethylene receptacles, water samples were collected, initially exposed to a detergent wash, subsequently rinsed with double-distilled water, and ultimately filled with the designated water for sampling, prior to being transported to the laboratory and maintained under refrigeration until such time as testing was necessitated. Sediment samples were extracted utilizing an Eckman grabber and subsequently transferred into wide-mouthed plastic containers, which were preserved within thermal boxes containing wet ice throughout the duration of the collection endeavor and thereafter stored in a deep freezer (Silambarasan et al., 2012 ). Table 1 delineates the locations of the sampling sites along with their respective coordinates. Table 1 Sample sites and coordinates. Site code Location Latitude Longitude Samples Site 1 Buckingham Canal, Neelangarai (Upstream) 12°56'58.2"N 80°14'54.5"E Water Sample 1 (WS1) Sediment Sample 1 (SS1) Site 2 Buckingham Canal, Neelangarai (Downstream) 12°56'26.5"N 80°14'48.4"E Water Sample 2 (WS2) Sediment Sample 2 (SS2) Physicochemical analysis of water and sediment samples An all-encompassing review was executed on diverse physicochemical attributes of the water and sediment samples, which involved but was not exclusive to aroma, tint, water temperature, pH measurement, surrounding heat, Electrical Conductivity (EC), muddiness, Total Alkalinity (TA), Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Total Hardness (TH), Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), and Chemical Oxygen Demand (COD) (Banerjee & Prasad, 2020 ; Kalambe, 2021 ; Ologundudu, 2019 ). The evaluation of temperature, electrical conductivity (EC), pH, and turbidity was conducted using a digital thermometer, conductivity meter, nephelometer, and pH meter sequentially (Samuel Vinod Kuma et al., 2020b). Heavy metal analysis of water and sediment samples In order to conduct an analysis of the concentrations of Zn, Mn, Pb, Cu, and Cr, a conical flask was initially filled with 100 mL of a water sample, subsequently augmented by the addition of 2 mL of concentrated HNO3 and 5 mL of concentrated HCl. The resulting mixture underwent thermal treatment at temperatures ranging from 900 to 950°C until its volume was reduced to approximately 15–20 mL. Ultimately, the volume of the thermally treated solution was recalibrated to 100 mL, following which an examination for heavy metal content was performed. The sediment samples were processed through grinding and then dried in an oven at a temperature of 500° C. A precise mass of one gram of the resultant powder was measured and placed into a 100-mL conical flask, with distilled water being added afterward. Afterward, 10 mL of aqua regia, a compound created from HNO3 and HCl at a molar ratio of 3:1, was mixed in and heated under regulated circumstances until the liquid got nearly dry. The sample was then set aside to cool, filtered, and the clear liquid was diluted with distilled water to obtain a final volume of 100 mL, after which a heavy metal assessment was carried out. The samples were analyzed for heavy metal concentration using an atomic absorption spectrometer (AAS) following the APHA 20th Edition (sample collection 3) (Ojo et al., 2023a ; “Standard Methods for the Examination of Water and Wastewater,” 2018). Enumeration of isolates from sediment and water samples A concentrated stock solution was meticulously prepared for serial dilution by integrating 1 g of the sediment sample and 1 ml of the water sample into 100 ml of distilled water, performed independently. Following thorough mixing, 1 ml of the resulting mixture was transferred into a test tube containing 9 ml of sterile distilled water; subsequently, a serial dilution was executed with precision up to a dilution factor of 10 − 6 (Prashanthi et al., 2021 ). A volume of 1 ml from the dilutions of 10 − 3, 10 − 4, and 10 − 5 was employed in the pour plate technique to assess the microbial load present in the samples. Conversely, the streak plating method was utilized to isolate bacterial colonies from pure cultures (Gowsalya et al., 2014 ). The quantification of microbial colonies was executed through the multiplication of the aggregate count by the dilution factor, resulting in measurements expressed in colony-forming units per milliliter (cfu/mL) for the liquid phase and per gram (cfu/g) for the sediment phase (Ibrahim et al., 2021 ). Determination of maximum tolerable concentration (MTC) of bacterial isolate The Maximum Tolerable Concentration (MTC) of heavy metals represents the highest concentration that the resistant bacterial isolate can handle. In the process of evaluating MTC, all heavy metals (Zn, Cu, Pb, Mn, and Cr) were integrated into the LB agar plates with an initial concentration set at 100 ppm for a time frame of 24 to 48 hours at a constant temperature of 37°C, while the concentrations of heavy metals were progressively enhanced until the isolates could not yield colonies on the associated media. Morphological and biochemical characterization The morphological attributes encompassed the form, dimensions, and pigmentation of the microbial colonies that manifested on agar plates after a 24-hour incubation period. Additionally, gram staining was conducted and examined through microscopy. The isolates experienced biochemical assessments to determine the enzymatic capabilities of oxidase, catalase, nitrate reductase, urease, indole-3-acetic acid (IAA), ammonia, and indole production, along with a citrate utilization review (Emmanuel, 2017 ; Kannan et al., 2018 ; Marzan et al., 2017 ). The isolates were tentatively classified to the genus level by the protocols delineated in Bergey's Manual of Systematic Bacteriology (Claus & Berkeley, 1984 ). Extraction of DNA and PCR amplification of HMRGs The ZymoBIOMICS™ DNA Miniprep kit was employed to isolate deoxyribonucleic acid from bacterial isolates proficient in thriving within elevated concentrations of heavy metals. The heavy metal resistance gene in the isolated bacterial strains underwent genotyping through polymerase chain reaction (PCR), utilizing forward and reverse primers specific to the gene at a temperature of 57°C. The protocols and PCR primers applied were derived from prior scholarly publications (Kumari & Das, 2019 ; Yang et al., 2020 ). The PCR products underwent electrophoresis on agarose gels with a concentration of 1.5% and were subsequently visualized employing a gel documentation system. Molecular identification of selected isolates and phylogenetic analysis Genomic deoxyribonucleic acid (DNA) was extracted from microbial cultures utilizing the Quick-DNA™ Bacterial Miniprep kit. The amplification of the 16S ribosomal RNA (rRNA) target region was conducted employing OneTaq Quick-load 2X Master Mix (New England Biolabs, catalogue number M0486) alongside the primers specified in Table 2 . The products resulting from the polymerase chain reaction (PCR) underwent gel electrophoresis and were subsequently purified through the EXOSAP process. Sequencing of the purified DNA fragments in both the forward and reverse orientations was performed utilizing the ABI 3500XL Genetic Analyzer. The analysis of the ab1 files generated by the ABI 3500XL Genetic Analyzer was undertaken using BioEdit Sequence Alignment Editor Version 7.2.5, with the resultant data further scrutinized through a BLASTn search. The MEGA 11 software for Molecular Evolutionary Genetics Analysis was utilized to execute a phylogenetic investigation, implementing the Test Maximum Likelihood strategy, which led to the formation of a bootstrap consensus tree based on 100 cycles (Tamura et al., 2021 ). Table 2 Primers name and information Primer Name Sequence (5′ to 3′) Number of Bases 16S-27F AGAGTTTGATCTGGCTCAG 20 16S-1492R TACGGTACCTTGTTACGACTT 20 Result Physicochemical parameters of the water samples The water samples obtained from the Buckingham Canal for this study showed brownish/blackish, and the odor was fishy sewage. It is noteworthy that the pH values of the analyzed water samples fluctuated between 7.0 and 7.1, with sample 1 exhibiting the maximum pH value of 7.1, while sample 2 demonstrated the minimum pH value of 7.0 (refer to Table 3 ). The recorded minimum and maximum atmospheric temperatures were 28.7 and 28.9˚C, respectively, whereas the water temperature ranged from 26.1 to 26.3˚C. The salinity levels of the water samples varied between 1.83 Ppt and 2.49 Ppt. Sample 2 exhibited a reduced turbidity measurement of 15.0 Nephelometric Turbidity Units (NTU), in contrast to sample 1, which presented an elevated turbidity value of 29.0 NTU. The conductivity of the water samples varied from 3050 µS/cm to 4150 µS/cm, with samples 1 and 2 reflecting the lowest and highest conductivity values, respectively. The assessment of the biochemical oxygen demand (BOD) in the water samples displayed readings that fluctuated from 20.0 mg/l to 22.5 mg/l, where samples 1 and 2 denoted the minimum and maximum results, in that order. Sample 2 reflected a Chemical Oxygen Demand (COD) of 20 mg/l, in comparison to sample 1 which presented a higher figure of 22 mg/l. Research into the dissolved oxygen figures within the water samples suggested that sample 1 measured 4.3 mg/l, yet sample 2 noted a minor increase to 4.5 mg/l. The results concerning total suspended solids and total dissolved solids in the analyzed water samples varied from 28 to 46 mg/L and 1832 to 2498 mg/L, respectively, with sample 2 showing the minimum and sample 1 showing the maximum. The total hardness of the water samples demonstrated a range from 360 to 500 mg/l, with samples 1 and 2 recording the minimum and maximum values. Water sample 1 showed an alkalinity of 436 mg/l, suggesting it has a higher alkalinity than water sample 2, recorded at 416 mg/l, as shown in Table 3 . Table 3 Physicochemical parameters of the water samples S. No Parameters Units Water Sample 1 Water Sample 2 1 Appearance - Turbid liquid Turbid liquid 2 Colour HU 10 10 3 Odour - Disagreeable Disagreeable 4 Atmospheric Temperature °C 28.9 28.7 5 Water Temperature °C 26.3 26.1 6 pH - 7.1 7.0 7 Conductivity µS/cm 3050 4150 8 Turbidity NTU 29 15 9 Salinity Ppt 1.83 2.49 10 Biochemical Oxygen Demand mg/l 22 20 11 Chemical Oxygen Demand mg/l 88 82 12 Dissolved Oxygen mg/l 4.3 4.5 13 Total Suspended Solids mg/l 46 28 14 Total Dissolved Solids mg/l 2498 1832 15 Total Hardness mg/l 360 500 16 Total Alkalinity mg/l 436 416 Physicochemical parameters of the Sediment samples Table 4 presents a comprehensive summary of the outcomes derived from the various physicochemical assessments conducted on sediment samples. The recorded pH levels in the sediment samples varied, spanning from 7.4 to 7.8; sample 1 noted the highest pH at 7.8 while sample 2 indicated the lowest at 7.4. The sediment samples revealed conductivity measurements varying between 4910 and 5460 µS/cm, with samples 1 and 2 showing the lowest and highest conductivity values, respectively (refer to Table 4 ). Temperature readings from the sediment samples demonstrated fluctuations between 28.5 and 28.9°C. Sample 2 contained the most substantial moisture content, in contrast to sample 1, which exhibited the least. The levels of available potassium ranged from 0.029 to 0.055%, with samples 1 and 2 showcasing the lowest and highest concentrations, respectively (Table 4 ). The nitrogen concentration within the sediment samples varied from 0.240 to 0.272%, with sample 1 reflecting the minimal value while sample 2 indicated the maximal value. Sample 2 also exhibited the highest percentage of phosphorus at 0.101%, whereas sample 1 demonstrated the lowest concentration at 0.081%, respectively (Table 4 ). Table 4 Physicochemical parameters of the Sediment samples S. No Parameters Units Sediment Sample 1 Sediment Sample 2 1 pH - 7.8 7.4 2 Moisture % 44.1 64.3 3 Temperature °C 28.9 28.5 4 Conductivity µs/cm 4910 5460 5 Nitrogen % 0.240 0.272 6 Potassium % 0.029 0.055 7 Phosphorus % 0.081 0.101 Heavy metal analysis of water samples Results of the assessment of heavy metal levels in water samples are detailed in Table 5 (Fig. 1 ). The water sample was designated as sample 2, which revealed the greatest copper level at 0.07 ppm, while sample 1 noted the lowest level at 0.05 ppm. Correspondingly, sample 1 illustrated the minimum level of lead, assessed at 0.16 ppm, and sample 2 illustrated the peak level at 0.30 ppm. Manganese concentrations in the water samples reviewed were noted to be between 0.24 ppm and 0.31 ppm, with the second sample indicating the lowest and the first sample revealing the highest concentration. The zinc content across the water samples ranged from 0.27 ppm to 0.34 ppm, where sample one had the minimum concentration while sample two had the maximum, correspondingly. Curiously, not a single one of the water samples analyzed contained arsenic or chromium. Table 5 Heavy metal analysis of water samples S. No Heavy metals Water Sample 1 (ppm) Water Sample 2 (ppm) 1 Copper 0.05 0.07 2 Lead 0.16 0.30 3 Manganese 0.31 0.24 4 Zinc 0.27 0.34 5 Arsenic - - 6 Chromium - - Heavy metal analysis of sediment samples Table 6 illustrates the findings of the concentrations of heavy metals in sediment samples. The data indicated that the concentration of copper within the sediment samples fluctuated between 56.42 ppm and 70.95 ppm, with water samples 1 and 2 exhibiting the minimum and maximum concentrations, respectively. Sediment sample 2 showed the highest concentrations of lead (20.66 ppm), whereas sample 1 had the lowest concentration (13.75 ppm). Manganese concentrations varied between 82.75 ppm in sample 1 and 98.8 ppm in sample 2. Sample 2 had the highest zinc concentration (190.3 ppm), whereas sample 1 had the lowest zinc concentration (162.2 ppm). Chromium concentration in the sediment samples ranged from 35.10 to 56.71 ppm, with samples 1 and 2 having the lowest and highest concentrations, respectively (Fig. 2 ). Arsenic was not found in the sediment samples. Table 6 Heavy metal analysis of sediment samples S. No Heavy metals Sediment Sample 1 (ppm) Sediment Sample 2 (ppm) 1 Copper 56.42 70.95 2 Lead 13.75 20.66 3 Manganese 82.75 98.8 4 Zinc 162.2 190.3 5 Chromium 35.10 56.71 6 Arsenic - - Enumeration and bacteria isolated from water and sediment samples For the water samples, the result shows that sample 1 had the highest bacterial load of 3.2 × 10 5 CFU/ml, and water sample 2 had 2.1 × 10 5 CFU/ml. Conversely, sediment sample 2 had the highest bacterial load of 2.6 × 10 5 CFU/g, and the lowest bacterial load of 1.1 × 10 5 CFU/g was observed in sediment sample 1. A total of 25 cultivable bacterial isolates were purified and isolated from the respective water and sediment samples, and these isolates were used for further studies (Fig. 3 ). Determination of Maximum Tolerable Concentration (MTC) of bacterial isolate Maximum tolerance concentrations of heavy metals (Zn, Cu, Pb, Mn, and Cr) against all bacterial isolates were examined ranging from 100 ppm to 2100 ppm. It was found that all 25 isolates showed resistance toward heavy metals. Out of 25 isolates, isolates BCSS04 and BCSS17 were selected for further assays due to their tolerance against all five heavy metals at maximum tolerance concentrations of 2100 ppm in lead, 1900 ppm in chromium, zinc, and Manganese, and 1300 ppm in copper, respectively (Fig. 2 ) (Table 7 ) (Fig. 5 ). Table 7 Maximum Tolerable Concentration (MTC) of bacterial isolate Isolates Maximum Tolerance Concentration (ppm) Zinc Copper Lead Manganese Chromium BCSS01 300 200 1700 1700 1700 BCWS02 1700 1100 2100 1700 1700 BCWS03 400 1100 1700 1300 1700 BCSS04 1900 1300 2100 1900 1900 BCSS05 900 1300 1700 900 200 BCSS06 700 1100 1700 1500 1700 BCWS07 700 1100 1700 1500 1500 BCSS08 700 1300 1700 1500 1900 BCSS09 400 1100 1700 1700 300 BCSS10 700 1100 1700 1500 500 BCWS11 1700 1300 2100 1700 1700 BCSS12 700 200 1700 1900 1300 BCSS13 900 1100 1700 1300 1300 BCSS14 400 700 1700 1300 300 BCSS15 700 700 1700 1300 1300 BCSS16 700 1100 1900 1900 1700 BCSS17 1900 1300 2100 1900 1900 BCSS18 1700 1300 1700 1700 1700 BCWS19 700 700 1700 900 1700 BCWS20 300 700 1700 1100 1300 BCSS21 1700 1300 1900 1700 1300 BCSS22 1300 1300 1700 1700 1300 BCWS23 500 700 700 1100 300 BCSS24 300 1300 700 1100 500 BCSS25 900 200 1100 1700 1700 Morphological and Biochemical Analysis Table 8 delineates the findings derived from the examination of the morphological attributes of the bacterial isolates procured from the soil and aqueous samples. It is noteworthy that isolate BCSS04 exhibited characteristics consistent with gram-negative bacteria, presenting a rod-shaped morphology with a creamy and mucilaginous surface, whereas isolate BCSS17 demonstrated gram-positive properties with a rod-shaped configuration and a slimy white appearance.The results of biochemical analysis of the two isolates are represented in Table 9 . Both the isolates show positive results in catalase, nitrate reductase, citrate utilization, ammonia production, and methyl red tests. Meanwhile, both isolates show negative results in oxidase and indole production tests. The isolate BCSS04 shows positive results in urease and IAA production test whereas isolate BCSS17 shows negative results. Casein hydrolysis and VP test show negative results in isolate BCSS04 and positive results in isolate BCSS17, respectively (Table 9 ) Table 8 Morphological Analysis of Selected Heavy Metal Resistant Isolates S. No Characteristics BCSS04 BCSS17 1 Gram Reaction - + 2 Shape Rod Rod 3 Pigments creamy white 4 Surface Slimy Slimy Table 9 Biochemical Analysis of Selected Heavy Metal Resistant Isolates S. No Tests BCSS04 BCSS17 1 Oxidase - - 2 Catalase + + 3 Nitrate reductase + + 4 Urease + - 5 Indole production - - 6 Citrate utilization + + 7 Ammonia production + + 8 IAA production + - 9 Casein hydrolysis - + 10 Methyl red test + + 11 VP (Voges Proskauer) - + PCR amplification of HMRGs DNA specimens were procured from two distinct bacterial isolates, designated BCSS04 and BCSS17, attributable to their capacity to prosper in habitats characterized by elevated concentrations of heavy metals. The genotyping of both isolates focused on specific heavy metal resistance genes (HMRGs), namely zntA (zinc), pbrA (lead), pcoA (copper), and chrA (chromium) as listed in Table 10 . Gene amplification revealed that zntA, pcoA, pbrA, and chrA had lengths of 2374 bp, 1791 bp, 2396 bp, and 520 bp, respectively (Ojo et al., 2023b ; Yang et al., 2020 ). Interestingly, isolate BCSS17 exhibited amplification for both pbrA and zntA, whereas isolate BCSS04 only showed amplification for pbrA and not for any other HMRGs (Fig. 6 ). Table 10 Heavy Metal Resistant Genes Genes Primer sequences Length Annealing temperature chrA TGGCTCTCGCTGTTCTTTGT TAAGTGCGACAAGGGCAACT 520 53 pcoA CGTCTCGACGAACTTTCCTG GGACTTCACGAAACATTCCC 1791 61 zntA ATCGTCCGCTCGCTGTATCTCT CCGCCTTTTCCCCTCACCCTAACC 2374 57 pbrA ATGAGCGAATGTGGCTCGAAG TCATCGACGCAACAGCCTCAA 2396 57 Molecular identification of bacteria isolates and phylogenetic analysis A cumulative total of two microbial isolates underwent 16S rRNA sequencing, NCBI BLAST Analysis, and phylogenetic tree analysis for the purpose of molecular characterization of the isolates.The BLAST results revealed that isolate BCSS04 was identified as Proteus mirabilis of 99.31% percentage identity with 99% query coverage while isolate BCSS17, was identified as Bacillus paramycoides of 99.85% percentage identity with 99% query coverage. As represented in Fig. 7 , BCSS04 was discovered to have the closest phylogenetic relationship with 100% similarity to Proteus mirabilis , whereas BCSS17 had the closest phylogenetic relationship with 96% similarity to Bacillus paramycoides , respectively (Fig. 8 ). Discussion The findings by Ma et al. ( 2024 ) indicated that the existence of metal in the surroundings carries a noteworthy influence, presenting a substantial risk to both human health and the ecosystem (Ma et al., 2024 ; Wang et al., 2018 ). The process of heavy metal remediation is complex and cannot be fully addressed through physical or chemical means. Currently, bioremediation is considered a highly advantageous method for tackling pollution. It is seen as a promising approach for pollution mitigation, as it offers the potential for heavy metal dissolution via natural biological process known as bioremediation, which uses living organisms like bacteria, fungi, or yeast to rehabilitate contaminated soil and water, showcases a broadly embraced strategy (Naik & Dubey, 2013 ). The study focused on bacterial strains from Buckingham Canal sediment and water samples to effectively remediate heavy metals. The primary objective of this research is to identify and characterize the predominant bioremediation bacterial components that facilitate the spontaneous restoration of environments (such as agricultural and residential settings) and evaluate their respective efficacy in remediating heavy metals to identify a few isolates capable of serving as a remedy for addressing impending pollution caused by unprocessed effluents containing heavy metals. The tastes and smells of natural water vary from location to place and seldom cause issues. The Buckingham Canal water had an unpleasant taste and odor, which could have been caused by sewage spills from homes and garbage from various small-scale companies. Due to the dissolution of different contaminants, the analysis revealed that the color of the canal water was brown to black. Based on the presence of dissolved ions in water, electrical conductivity is a measurement of a substance's ability to carry electric current (Rao, 2011 ; Yadav et al., 2012 ). As articulated by Thangamalathi and Anuradha ( 2018 ), this parameter serves as a significant instrument for evaluating the purity of aquatic systems and is subject to the geological features of the locale adjacent to the water source, effluent discharges from septic systems and sewage treatment plants, urban runoff originating from thoroughfares, and agricultural methodologies (Thangamalathi & Anuradha, 2018 ). Conductivity shows a strong link with various properties, such as temperature, pH, alkalinity, total hardness, calcium levels, total suspended and dissolved solids, chemical oxygen demand, as well as concentrations of iron and chloride in water. A notable relationship can be seen where higher concentrations of dissolved solids are tied to increased water conductivity. In the current investigation, the electrical conductivity ranged from 3050 to 4150 µS/cm. the values were high, indicating a decrease in fish fauna number and species variety. Elevated readings suggest the existence of a significant quantity of dissolved organic materials in ionized form (Krishnaswamy & Jayaraj, 2017 ). Increased biochemical oxygen demand (BOD) readings are a sign of more pollutants being present, resulting in greater oxygen needs from microbial groups for the breakdown of these substances. The heightened BOD values recorded in the samples under examination are hypothesized to originate from the discharge of heavy metal effluents from industrial sites. A thorough investigation into the physicochemical attributes of both soil and water specimens showed that the pH readings taken at the chosen sites differed from 7.0 to 7.1. In contrast, the sediment samples displayed pH levels between 7.4 and 7.8. According to research by Samuel and others published in 2020, shifts in pH levels can modify water's acidic or alkaline characteristics, creating various sensory interpretations (Samuel Vinod Kuma et al., 2020b). The pH levels in the Buckingham Canal remained consistent within the range of 7.3 to 7.5 throughout the present study, thereby adhering to established regulatory limits. Elevated pH levels, surpassing 7, signify an increased concentration of dissolved constituents in water, which fosters optimal conditions for vegetative growth, as demonstrated in this specific investigation. Experts have uncovered that aquatic habitats maintaining pH levels of 6.7 to 8.7 are beneficial for the flourishing of both phytoplankton and zooplankton. Sustained contact with very low or high pH values can cause harmful effects on different physiological systems, affecting areas like the ocular region, layers of skin, mucosal barriers, and the digestive tract. The assessment of heavy metal concentrations in water and sediment samples procured from the designated research area was undertaken, and the resultant data has been presented in Tables 5 and 6 . The hierarchy of heavy metal prevalence identified in the water samples, predicated on their average concentrations, was observed as Zn > Mn > Pb > Cu > Cr. In contrast, the hierarchy of heavy metal prevalence in the sediment samples, derived from their mean concentrations, was established as Zn > Mn > Cu > Cr > Pb. It is imperative to highlight that specific water samples from Buckingham exhibited concentrations exceeding the thresholds recommended by the World Health Organization (WHO), which may be attributed to tailings from extraction operations and chemicals employed in industrial and sewage processes as probable sources of these heavy metals [48]. The results from the analysis of heavy metal concentrations in sediment samples indicated notably higher values than those documented in studies focusing on sediment contaminated with heavy metals (Udiba et al., 2019 ). The investigation analyzed water and sediment samples to assess bacterial abundance, demonstrating that water specimens displayed the highest bacterial count while sediment specimens exhibited the lowest. A total of 25 viable bacterial strains were extracted and purified from the corresponding water and sediment samples for subsequent investigations. The examination focused on the maximum tolerance concentration of heavy metals (Zn, Cu, Pb, Mn, and Cr) against all bacterial strains. Among these strains, BCSS04 and BCSS17 were chosen for further evaluation due to their endurance against all five heavy metals at maximum tolerance levels of 2100 ppm for lead, 1900 ppm for chromium, zinc, and manganese, and 1300 ppm for copper, respectively (Table 7 ). The morphological traits of the selected bacteria were scrutinized, identifying BCSS04 as gram-negative and BCSS17 as gram-positive. Both strains exhibited positive outcomes in catalase, nitrate reductase, citrate utilization, ammonia production, and methyl red tests (Table 9 ). The genotyping of two isolates was concentrated on specific heavy metal resistance genes (HMRGs), including zntA (zinc), pbrA (lead), pcoA (copper), and chrA (chromium). The gene amplification process disclosed that zntA, pcoA, pbrA, and chrA possessed lengths of 2374 bp, 1791 bp, 2396 bp, and 520 bp, correspondingly. Notably, BCSS17 demonstrated amplification for both pbrA and zntA, whereas BCSS04 only exhibited amplification for pbrA and not for any other HMRGs. While the results obtained from the amplification suggested that strains exhibiting solely two heavy metal resistance genes (HMRGs), it is imperative to underscore that the expression of HMRGs constitutes merely one mechanism through which bacteria mitigate the effects of heavy metals. Consequently, alternative mechanisms, including membrane modification and metabolic adaptation, may function as adaptive strategies for bacterial strains (Mathivanan et al., 2021 ). The isolates BCSS04 and BCSS17 were characterized as Proteus mirabilis and Bacillus paramycoides with 99% identity and query coverage. Proteus mirabilis was isolated and identified from tungsten-enriched soil in the Kuhi-Agargaon-Khobna region, as indicated by the research conducted by Rohini (2022). It is essential to further investigate these organisms to explore their potential in bioremediation. The ability of Proteus mirabilis and Bacillus paramycoides to endure heavy metals has also garnered significant scholarly attention. These novel strains have exhibited resistance to a variety of metallic salts, including azo dye, cobalt chloride, mercuric chloride, ammonium metaparatungstate, tungstic acid, and sodium tungsten. Investigations utilizing ICP-MS and SEM-EDS techniques have demonstrated that these microorganisms accumulate tungsten intracellularly, as reported in the study(Ganorkar et al., 2022 ). Yawen Gu's research conducted in 2023 culminated in the identification of an extraordinary novel hexavalent chromium (Cr (VI))-degrading bacterial strain, which has been classified as Bacillus paramycoides Cr6, and the study investigated the removal mechanism from a molecular biological standpoint. The decontamination efficacy for a concentration of 2000 mg/L Cr (VI) achieved a noteworthy 67.3%, while Cr6 exhibited resistance to concentrations reaching up to 2500 mg/L Cr (VI) (Gu et al., 2023 ). Besides, Proteus mirabilis is acknowledged for its metabolic ingenuity, involving the potential to develop enzymes like urease, thereby enhancing its survival odds in challenging environmental conditions (Fitzgerald et al., 2024 ). In an analogous way, Bacillus paramycoides is celebrated for its strong stress-response systems, which feature the creation of biofilms and extracellular polymeric substances (EPS), supporting the adsorption of metal ions and detoxification methods. Although specific investigations concerning Bacillus paramycoides are scarce, studies involving analogous Bacillus species yield pertinent insights. To illustrate, Bacillus cereus KMS3-1 is known to create EPS that significantly increases its biosorption capability in extracting heavy metals including Cd²⁺ and Pb²⁺ (Mathivanan et al., 2023 ). Also, Bacillus species are esteemed for their role in applying bioremediation solutions, featuring biosorption and EPS-mediated biosorption, to control environmental levels of metals like lead, cadmium, and mercury. These findings imply that Bacillus species, encompassing Bacillus paramycoides , possess the potential to generate biofilms and EPS that promote metal ion adsorption and detoxification (Wróbel et al., 2023 ). These attributes render both organisms promising candidates for utilization in bioremediation efforts, particularly in ecosystems compromised by heavy metals and industrial contaminants. Furthermore, employing these microbial strains in bioremediation may be considerably enhanced through genetic engineering approaches designed to boost their metabolic functions for the precise degradation and retention of heavy metals. The amalgamated findings from contemporary research underscore the critical importance of these microorganisms in the progression of sustainable approaches for environmental remediation and emphasize the potential for applications on an industrial scale. Additional inquiries into their genomic characteristics and molecular mechanisms may facilitate the development of innovative strategies to confront the escalating issue of heavy metal pollution. Conclusion This study meticulously examined a variety of physicochemical parameters along with the concentrations of heavy metals found in sediment and water samples collected from the Buckingham Canal and Neelankarai regions. Bacterial strains demonstrating resistance to heavy metals were isolated from these samples and subsequently assessed using a Maximum Tolerance Concentration (MTC) assay to ascertain their resistance capabilities to different heavy metals, with isolates BCSS04 and BCSS17 displaying an MTC of 2100 ppm in response to Lead exposure. A biochemical analysis of the isolated bacterial strains was conducted concurrently. Molecular characterization identified Proteus mirabilis and Bacillus paramycoides as bacterial isolates capable of withstanding high concentrations of heavy metals. Based on what we have gathered, this study appears to be the first detailed exploration into how Proteus mirabilis reacts to different levels of Pb, Cu, Mn, Zn, and Cr, alongside the initially recorded existence of heavy metal-resistant Bacillus paramycoides in the ecosystem of Buckingham Canal. In summary, the bacterial strains exposed in this study require more thorough examination in future research endeavors that target the bioremediation of heavy metals in tainted sites and other significant applications. Declarations Acknowledgements We are grateful to Vels Institute of Science, Technology and Advanced Studies (VISTAS) for generously providing the facilities and space to conduct our research. Conflict of interest: The authors declare no competing interest. Consent for Publication: All authors agree to publish the following manuscript Funding : The authors declare that no grants, funding, or other financial support were received for the research and publication of the manuscript. References APHA. (2005). Standard methods for the examination of waste and wastewater. American Public Health Association, Washington, D.C , 21st Ed . Aswathy.M, Gautam Kumar, & Dilip Kumar Thakur. (2017). ANALYSIS OF SEWAGE WATER FROM COOUM RIVER IN CHENNAI. 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sediment samples\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5971395/v1/a73ad18ba2ffc8940557f62f.png"},{"id":76277181,"identity":"f8902655-6f87-4ff8-add8-f164f88877b0","added_by":"auto","created_at":"2025-02-14 10:06:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":433656,"visible":true,"origin":"","legend":"\u003cp\u003eBacterial culture isolated from the collected samples\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5971395/v1/ca4762c90139a95848249a20.png"},{"id":76277171,"identity":"360f9621-3d4f-48bf-9bcf-680721118afd","added_by":"auto","created_at":"2025-02-14 10:06:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":653048,"visible":true,"origin":"","legend":"\u003cp\u003eSelected bacterial isolate BCSS04 and BCSS17\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5971395/v1/9b6ed3b73d77ea29ce178b14.png"},{"id":76277192,"identity":"ffd8d393-2d98-405a-bb2b-88364c4278be","added_by":"auto","created_at":"2025-02-14 10:06:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":181297,"visible":true,"origin":"","legend":"\u003cp\u003eMaximum Tolerable Concentration (MTC) of bacterial isolate\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5971395/v1/75611037bacc267f5783f1f4.png"},{"id":76278371,"identity":"9b0f924e-0705-48ec-a108-b852281a8d96","added_by":"auto","created_at":"2025-02-14 10:14:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":487023,"visible":true,"origin":"","legend":"\u003cp\u003e(A) PCR amplification of HMRGs in isolate BCSS04 (B) PCR amplification of HMRGs in isolate BCSS17\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5971395/v1/5e93b13e40b726decc33c77c.png"},{"id":76277183,"identity":"63d5644f-21e7-444b-90da-aa11489b95fb","added_by":"auto","created_at":"2025-02-14 10:06:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":24352,"visible":true,"origin":"","legend":"\u003cp\u003eThe Phylogenetic tree of isolate BCSS04\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5971395/v1/2466626673a1e476bfe35b1e.png"},{"id":76277177,"identity":"51b029b9-3570-48de-868d-19351ca23035","added_by":"auto","created_at":"2025-02-14 10:06:40","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":20839,"visible":true,"origin":"","legend":"\u003cp\u003eThe phylogenetic tree of isolate BCSS17\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5971395/v1/dbc2eb1d40b9d1ffc55a71f5.png"},{"id":76778327,"identity":"30458e7e-52ac-484b-bce7-b36afaeae354","added_by":"auto","created_at":"2025-02-20 15:46:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3765334,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5971395/v1/72031dec-2029-4edf-ac35-faa428a34d3d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Physicochemical analysis and molecular characterization of heavy metal tolerant bacteria from Buckingham canal, Neelankarai, Chennai","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe processes of industrialization and anthropogenic activities have significantly transformed our ecosystems into repositories for various waste materials. Consequently, numerous aquatic resources have been compromised, resulting in pollution that poses severe risks to human health and other biological systems (U. Damodharan, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The deleterious compounds released into aquatic ecosystems not only bioaccumulate throughout the trophic levels but may also restrict species diversity or engender prolific populations of microorganisms (Aswathy.M et al., 2017). Aquatic ecosystems encounter a myriad of stressors that profoundly compromise biodiversity. Presently, heavy metals remain recognized as a pervasive contamination issue on a global scale, attributable to their enduring presence, propensity for accumulation, and the deleterious impacts they may exert on environmental integrity as well as on the health of human populations subjected to exposure (L. M. Coelho et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; V\u0026eacute;lez et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHeavy metals are customarily recognized as elements that possess an atomic mass and density exceeding that of water. The classification of these elements hinges on their functional value and involvement in biological systems, distinguishing them as essential or non-essential(Ayilara \u0026amp; Babalola, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Koller \u0026amp; Saleh, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is vital to assert that primary heavy metals like copper (Cu), zinc (Zn), nickel (Ni), manganese (Mn), and iron (Fe) serve indispensable purposes in countless physiological and metabolic processes that are key for the survival and effective functioning of living creatures (Johnson et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For instance, their involvement in the facilitation of enzymatic reactions through their role as enzyme cofactors is crucial, alongside their significance in sustaining osmotic equilibrium. Lead (Pb), arsenic (As), cadmium (Cd), chromium (Cr), and mercury (Hg) are heavy metals deemed toxic and unnecessary, emphasizing their lack of crucial roles in biological systems (Singh Sidhu, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It is necessary to stress that both important and unimportant heavy metals have portrayed adverse effects on plant life, wildlife, microorganisms, and the wider ecosystem, with the scale of these effects being significantly shaped by the amount and time of exposure (Masindi \u0026amp; Muedi, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The excessive accumulation of heavy metals in plant organisms induces a variety of adverse consequences, which include inhibited vegetative growth, reduced efficiency in photosynthetic activities and cellular division, diminished enzymatic performance and nutrient uptake, as well as the onset of chlorosis (Collin et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kiran et al., 2022). The anthropogenic exploitation of these plant species incurs harmful repercussions, including, but not limited to, immunosuppressive effects, visual impairment, neurotoxicity, and contributing to the development of conditions such as neoplastic diseases and increased blood pressure (Genchi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mitra et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, the presence of heavy metals in aquatic ecosystems triggers oxidative stress across various animal taxa (EL-Hak et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, heavy metals adversely affect soil and its associated biota by reducing microbial diversity, modifying soil pH and porosity, and disrupting microbial enzymatic activity (Musilova et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The environmental contamination resulting from heavy metals due to anthropogenic and industrial processes has precipitated significant and frequently irreversible damage to aquatic ecosystems. The origins of such contamination include the mining and smelting of ores, effluent from storage batteries and vehicle emissions, as well as the manufacture and improper application of fertilizers, pesticides, and a multitude of other substances. In addition to their considerable threats to human health, these hazardous materials negatively influence both ecosystems and vegetation, being notorious for their resilience against natural breakdown. Therefore, it is important to research inventive practices for the advancement of clinical approaches that target reducing or completely getting rid of metals in our habitat.(L. M. Coelho et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe removal of heavy metals from contaminated environments presents significant challenges due to their inherent non-decomposable characteristics (Verma et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Bioremediation employs a variety of organisms, including bacteria, plants, fungi, and predominantly microalgae, to mitigate pollutants or convert them into less hazardous forms. The methodology of bioremediation concerning heavy metals offers numerous advantages in comparison to conventional remediation methods, such as chemical remediation, engineering remediation, physical remediation, and agro-ecological remediation, primarily due to its remarkable cost-effectiveness, and environmentally sustainable characteristics (Yu et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Tiny organisms demonstrate various approaches that help them adjust to heavy metal exposure and support detoxification or remediation, which involves the removal of heavy metals from the cell's structure, the release of metal reduction or precipitation, exopolysaccharides, sequestration, adsorption, detoxification, and bioaccumulation (Liu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The natural microbes found in a region are especially suited for addressing contaminated sites. Moreover, these microbes possess numerous genes that encode for heavy metal-resistant proteins or transporters.\u003c/p\u003e \u003cp\u003eCurrently, the Buckingham Canal has been extensively degraded due to contamination from sewage, sullage, industrial effluents, stormwater runoff, and refuse, consequent to the process of urbanization. The watercourse adjacent to Buckingham Canal in the vicinity of Neelankarai, Chennai, exhibits a white foam-like effluent, which is being discharged from the Perungudi sewage treatment facility operated by Metrowater. Evidence suggests that heavy metal pollution in the Buckingham Canal, including elements like Fe, Cu, Cd, Zn, Cr, and Pb, has notably escalated over time. Previous investigations have documented the physicochemical characteristics, nutrient levels, and select heavy metals as water quality parameters within the Buckingham Canal (Kumar, Samuel, et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Samuel Vinod Kuma et al., 2020a). Consequently, as a continuation of prior research efforts, the present study undertakes a comprehensive analysis of the chemical, physical, and heavy metal constituents in water and sediment samples obtained from designated locations within the Buckingham Canal. The research mainly focuses on discovering isolates resistant to heavy metals and Heavy metal-resistant genes (HMRGs) from Buckingham Canal for heavy metal bioremediation.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eChennai (Madras), serving as the administrative capital of Tamil Nadu, is situated along the eastern coastline of India (13.0827\u0026deg;N, 80.2707\u0026deg;E). The city is traversed by three significant watercourses: the Cooum River, the Adayar River, and the Buckingham Canal (Kumar, Sultana, et al., 2018). The Buckingham Canal constitutes an engineered waterway that establishes a connection between the Cooum and Adayar rivers. The segment located to the north of the Cooum is designated as the North Buckingham Canal, whereas the section to the south is referred to as the South Buckingham Canal. This canal extends from Nellore in Andhra Pradesh to Marakkanam, adjacent to Puducherry (Kumar, Samuel, et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Notably, this canal measures 257 kilometers in length within the boundaries of Andhra Pradesh and encompasses 163 kilometers within Tamil Nadu. Among the three primary waterways within the urban environment, the Buckingham Canal is acknowledged as the most severely contaminated, with an estimated 60% of the daily untreated sewage being introduced into it, a predicament further intensified by discharges from the Chennai Metropolitan Water Supply and Sewerage Board (CMWSSB) and the North Chennai Thermal Power Station (NCTPS), both of which contribute elevated temperatures of water and fly ash into the canal (Lakshmi K et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The water within this canal is classified as highly toxic and unsuitable for human consumption. Consequently, Buckingham Canal has been selected for the present investigation aimed at assessing its physicochemical characteristics and exploring methodologies for the bioremediation of heavy metals.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample collection\u003c/h3\u003e\n\u003cp\u003eWater and sediment samples collected at Locations 1 and 2 were obtained utilizing established methodologies, as delineated by the American Public Health Association (2005) (APHA, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). To quantify the heavy metal content, sample stations were selected based on the locations of industrial and sewage water output units. The samples were collected from upstream and downstream sites within 1km. Following standardized methodologies, sediment samples were procured from depths of 10 to 15 cm beneath the surface of the water utilizing acid-washed plastic containers to mitigate any potential alterations in their properties. In polyethylene receptacles, water samples were collected, initially exposed to a detergent wash, subsequently rinsed with double-distilled water, and ultimately filled with the designated water for sampling, prior to being transported to the laboratory and maintained under refrigeration until such time as testing was necessitated. Sediment samples were extracted utilizing an Eckman grabber and subsequently transferred into wide-mouthed plastic containers, which were preserved within thermal boxes containing wet ice throughout the duration of the collection endeavor and thereafter stored in a deep freezer (Silambarasan et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e delineates the locations of the sampling sites along with their respective coordinates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample sites and coordinates.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSamples\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuckingham Canal, Neelangarai (Upstream)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u0026deg;56'58.2\"N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;14'54.5\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWater Sample 1 (WS1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSediment Sample 1 (SS1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuckingham Canal, Neelangarai (Downstream)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u0026deg;56'26.5\"N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u0026deg;14'48.4\"E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWater Sample 2 (WS2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSediment Sample 2 (SS2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003ePhysicochemical analysis of water and sediment samples\u003c/h3\u003e\n\u003cp\u003eAn all-encompassing review was executed on diverse physicochemical attributes of the water and sediment samples, which involved but was not exclusive to aroma, tint, water temperature, pH measurement, surrounding heat, Electrical Conductivity (EC), muddiness, Total Alkalinity (TA), Total Dissolved Solids (TDS), Total Suspended Solids (TSS), Total Hardness (TH), Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), and Chemical Oxygen Demand (COD) (Banerjee \u0026amp; Prasad, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kalambe, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ologundudu, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The evaluation of temperature, electrical conductivity (EC), pH, and turbidity was conducted using a digital thermometer, conductivity meter, nephelometer, and pH meter sequentially (Samuel Vinod Kuma et al., 2020b).\u003c/p\u003e\n\u003ch3\u003eHeavy metal analysis of water and sediment samples\u003c/h3\u003e\n\u003cp\u003eIn order to conduct an analysis of the concentrations of Zn, Mn, Pb, Cu, and Cr, a conical flask was initially filled with 100 mL of a water sample, subsequently augmented by the addition of 2 mL of concentrated HNO3 and 5 mL of concentrated HCl. The resulting mixture underwent thermal treatment at temperatures ranging from 900 to 950\u0026deg;C until its volume was reduced to approximately 15\u0026ndash;20 mL. Ultimately, the volume of the thermally treated solution was recalibrated to 100 mL, following which an examination for heavy metal content was performed.\u003c/p\u003e \u003cp\u003eThe sediment samples were processed through grinding and then dried in an oven at a temperature of 500\u0026deg; C. A precise mass of one gram of the resultant powder was measured and placed into a 100-mL conical flask, with distilled water being added afterward. Afterward, 10 mL of aqua regia, a compound created from HNO3 and HCl at a molar ratio of 3:1, was mixed in and heated under regulated circumstances until the liquid got nearly dry. The sample was then set aside to cool, filtered, and the clear liquid was diluted with distilled water to obtain a final volume of 100 mL, after which a heavy metal assessment was carried out. The samples were analyzed for heavy metal concentration using an atomic absorption spectrometer (AAS) following the APHA 20th Edition (sample collection 3) (Ojo et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; \u0026ldquo;Standard Methods for the Examination of Water and Wastewater,\u0026rdquo; 2018).\u003c/p\u003e\n\u003ch3\u003eEnumeration of isolates from sediment and water samples\u003c/h3\u003e\n\u003cp\u003eA concentrated stock solution was meticulously prepared for serial dilution by integrating 1 g of the sediment sample and 1 ml of the water sample into 100 ml of distilled water, performed independently. Following thorough mixing, 1 ml of the resulting mixture was transferred into a test tube containing 9 ml of sterile distilled water; subsequently, a serial dilution was executed with precision up to a dilution factor of 10\u0026thinsp;\u0026minus;\u0026thinsp;6 (Prashanthi et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A volume of 1 ml from the dilutions of 10\u0026thinsp;\u0026minus;\u0026thinsp;3, 10\u0026thinsp;\u0026minus;\u0026thinsp;4, and 10\u0026thinsp;\u0026minus;\u0026thinsp;5 was employed in the pour plate technique to assess the microbial load present in the samples. Conversely, the streak plating method was utilized to isolate bacterial colonies from pure cultures (Gowsalya et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The quantification of microbial colonies was executed through the multiplication of the aggregate count by the dilution factor, resulting in measurements expressed in colony-forming units per milliliter (cfu/mL) for the liquid phase and per gram (cfu/g) for the sediment phase (Ibrahim et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of maximum tolerable concentration (MTC) of bacterial isolate\u003c/h2\u003e \u003cp\u003eThe Maximum Tolerable Concentration (MTC) of heavy metals represents the highest concentration that the resistant bacterial isolate can handle. In the process of evaluating MTC, all heavy metals (Zn, Cu, Pb, Mn, and Cr) were integrated into the LB agar plates with an initial concentration set at 100 ppm for a time frame of 24 to 48 hours at a constant temperature of 37\u0026deg;C, while the concentrations of heavy metals were progressively enhanced until the isolates could not yield colonies on the associated media.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMorphological and biochemical characterization\u003c/h3\u003e\n\u003cp\u003eThe morphological attributes encompassed the form, dimensions, and pigmentation of the microbial colonies that manifested on agar plates after a 24-hour incubation period. Additionally, gram staining was conducted and examined through microscopy. The isolates experienced biochemical assessments to determine the enzymatic capabilities of oxidase, catalase, nitrate reductase, urease, indole-3-acetic acid (IAA), ammonia, and indole production, along with a citrate utilization review (Emmanuel, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kannan et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Marzan et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The isolates were tentatively classified to the genus level by the protocols delineated in Bergey's Manual of Systematic Bacteriology (Claus \u0026amp; Berkeley, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1984\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eExtraction of DNA and PCR amplification of HMRGs\u003c/h3\u003e\n\u003cp\u003eThe ZymoBIOMICS™ DNA Miniprep kit was employed to isolate deoxyribonucleic acid from bacterial isolates proficient in thriving within elevated concentrations of heavy metals. The heavy metal resistance gene in the isolated bacterial strains underwent genotyping through polymerase chain reaction (PCR), utilizing forward and reverse primers specific to the gene at a temperature of 57°C. The protocols and PCR primers applied were derived from prior scholarly publications (Kumari \u0026amp; Das, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The PCR products underwent electrophoresis on agarose gels with a concentration of 1.5% and were subsequently visualized employing a gel documentation system.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMolecular identification of selected isolates and phylogenetic analysis\u003c/h2\u003e \u003cp\u003eGenomic deoxyribonucleic acid (DNA) was extracted from microbial cultures utilizing the Quick-DNA™ Bacterial Miniprep kit. The amplification of the 16S ribosomal RNA (rRNA) target region was conducted employing OneTaq Quick-load 2X Master Mix (New England Biolabs, catalogue number M0486) alongside the primers specified in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The products resulting from the polymerase chain reaction (PCR) underwent gel electrophoresis and were subsequently purified through the EXOSAP process. Sequencing of the purified DNA fragments in both the forward and reverse orientations was performed utilizing the ABI 3500XL Genetic Analyzer. The analysis of the ab1 files generated by the ABI 3500XL Genetic Analyzer was undertaken using BioEdit Sequence Alignment Editor Version 7.2.5, with the resultant data further scrutinized through a BLASTn search. The MEGA 11 software for Molecular Evolutionary Genetics Analysis was utilized to execute a phylogenetic investigation, implementing the Test Maximum Likelihood strategy, which led to the formation of a bootstrap consensus tree based on 100 cycles (Tamura et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimers name and information\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimer Name\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequence (5′ to 3′)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of Bases\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16S-27F\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGAGTTTGATCTGGCTCAG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16S-1492R\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTACGGTACCTTGTTACGACTT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e"},{"header":"Result","content":"\u003ch2\u003ePhysicochemical parameters of the water samples\u003c/h2\u003e\u003cp\u003eThe water samples obtained from the Buckingham Canal for this study showed brownish/blackish, and the odor was fishy sewage. It is noteworthy that the pH values of the analyzed water samples fluctuated between 7.0 and 7.1, with sample 1 exhibiting the maximum pH value of 7.1, while sample 2 demonstrated the minimum pH value of 7.0 (refer to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The recorded minimum and maximum atmospheric temperatures were 28.7 and 28.9˚C, respectively, whereas the water temperature ranged from 26.1 to 26.3˚C. The salinity levels of the water samples varied between 1.83 Ppt and 2.49 Ppt. Sample 2 exhibited a reduced turbidity measurement of 15.0 Nephelometric Turbidity Units (NTU), in contrast to sample 1, which presented an elevated turbidity value of 29.0 NTU. The conductivity of the water samples varied from 3050 µS/cm to 4150 µS/cm, with samples 1 and 2 reflecting the lowest and highest conductivity values, respectively. The assessment of the biochemical oxygen demand (BOD) in the water samples displayed readings that fluctuated from 20.0 mg/l to 22.5 mg/l, where samples 1 and 2 denoted the minimum and maximum results, in that order. Sample 2 reflected a Chemical Oxygen Demand (COD) of 20 mg/l, in comparison to sample 1 which presented a higher figure of 22 mg/l. Research into the dissolved oxygen figures within the water samples suggested that sample 1 measured 4.3 mg/l, yet sample 2 noted a minor increase to 4.5 mg/l. The results concerning total suspended solids and total dissolved solids in the analyzed water samples varied from 28 to 46 mg/L and 1832 to 2498 mg/L, respectively, with sample 2 showing the minimum and sample 1 showing the maximum. The total hardness of the water samples demonstrated a range from 360 to 500 mg/l, with samples 1 and 2 recording the minimum and maximum values. Water sample 1 showed an alkalinity of 436 mg/l, suggesting it has a higher alkalinity than water sample 2, recorded at 416 mg/l, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysicochemical parameters of the water samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnits\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWater Sample 1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWater Sample 2\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAppearance\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTurbid liquid\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTurbid liquid\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColour\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOdour\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDisagreeable\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDisagreeable\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAtmospheric Temperature\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e°C\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater Temperature\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e°C\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConductivity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eµS/cm\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3050\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4150\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurbidity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNTU\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSalinity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePpt\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiochemical Oxygen Demand\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChemical Oxygen Demand\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDissolved Oxygen\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Suspended Solids\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Dissolved Solids\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2498\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1832\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Hardness\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Alkalinity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003emg/l\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e416\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003ePhysicochemical parameters of the Sediment samples\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents a comprehensive summary of the outcomes derived from the various physicochemical assessments conducted on sediment samples. The recorded pH levels in the sediment samples varied, spanning from 7.4 to 7.8; sample 1 noted the highest pH at 7.8 while sample 2 indicated the lowest at 7.4. The sediment samples revealed conductivity measurements varying between 4910 and 5460 µS/cm, with samples 1 and 2 showing the lowest and highest conductivity values, respectively (refer to Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Temperature readings from the sediment samples demonstrated fluctuations between 28.5 and 28.9°C. Sample 2 contained the most substantial moisture content, in contrast to sample 1, which exhibited the least. The levels of available potassium ranged from 0.029 to 0.055%, with samples 1 and 2 showcasing the lowest and highest concentrations, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The nitrogen concentration within the sediment samples varied from 0.240 to 0.272%, with sample 1 reflecting the minimal value while sample 2 indicated the maximal value. Sample 2 also exhibited the highest percentage of phosphorus at 0.101%, whereas sample 1 demonstrated the lowest concentration at 0.081%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysicochemical parameters of the Sediment samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnits\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSediment Sample 1\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSediment Sample 2\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoisture\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e°C\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConductivity\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eµs/cm\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4910\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5460\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrogen\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePotassium\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhosphorus\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eHeavy metal analysis of water samples\u003c/h2\u003e\u003cp\u003eResults of the assessment of heavy metal levels in water samples are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The water sample was designated as sample 2, which revealed the greatest copper level at 0.07 ppm, while sample 1 noted the lowest level at 0.05 ppm. Correspondingly, sample 1 illustrated the minimum level of lead, assessed at 0.16 ppm, and sample 2 illustrated the peak level at 0.30 ppm. Manganese concentrations in the water samples reviewed were noted to be between 0.24 ppm and 0.31 ppm, with the second sample indicating the lowest and the first sample revealing the highest concentration. The zinc content across the water samples ranged from 0.27 ppm to 0.34 ppm, where sample one had the minimum concentration while sample two had the maximum, correspondingly. Curiously, not a single one of the water samples analyzed contained arsenic or chromium.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeavy metal analysis of water samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeavy metals\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWater Sample 1 (ppm)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWater Sample 2 (ppm)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCopper\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLead\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManganese\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZinc\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArsenic\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChromium\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eHeavy metal analysis of sediment samples\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the findings of the concentrations of heavy metals in sediment samples. The data indicated that the concentration of copper within the sediment samples fluctuated between 56.42 ppm and 70.95 ppm, with water samples 1 and 2 exhibiting the minimum and maximum concentrations, respectively. Sediment sample 2 showed the highest concentrations of lead (20.66 ppm), whereas sample 1 had the lowest concentration (13.75 ppm). Manganese concentrations varied between 82.75 ppm in sample 1 and 98.8 ppm in sample 2. Sample 2 had the highest zinc concentration (190.3 ppm), whereas sample 1 had the lowest zinc concentration (162.2 ppm). Chromium concentration in the sediment samples ranged from 35.10 to 56.71 ppm, with samples 1 and 2 having the lowest and highest concentrations, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Arsenic was not found in the sediment samples.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeavy metal analysis of sediment samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeavy metals\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSediment Sample 1 (ppm)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSediment Sample 2 (ppm)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCopper\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.95\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLead\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.66\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManganese\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZinc\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e190.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChromium\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.71\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArsenic\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eEnumeration and bacteria isolated from water and sediment samples\u003c/h2\u003e\u003cp\u003eFor the water samples, the result shows that sample 1 had the highest bacterial load of 3.2 × 10\u003csup\u003e5\u003c/sup\u003e CFU/ml, and water sample 2 had 2.1 × 10\u003csup\u003e5\u003c/sup\u003e CFU/ml. Conversely, sediment sample 2 had the highest bacterial load of 2.6 × 10\u003csup\u003e5\u003c/sup\u003e CFU/g, and the lowest bacterial load of 1.1 × 10\u003csup\u003e5\u003c/sup\u003e CFU/g was observed in sediment sample 1. A total of 25 cultivable bacterial isolates were purified and isolated from the respective water and sediment samples, and these isolates were used for further studies (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eDetermination of Maximum Tolerable Concentration (MTC) of bacterial isolate\u003c/h2\u003e\u003cp\u003eMaximum tolerance concentrations of heavy metals (Zn, Cu, Pb, Mn, and Cr) against all bacterial isolates were examined ranging from 100 ppm to 2100 ppm. It was found that all 25 isolates showed resistance toward heavy metals. Out of 25 isolates, isolates BCSS04 and BCSS17 were selected for further assays due to their tolerance against all five heavy metals at maximum tolerance concentrations of 2100 ppm in lead, 1900 ppm in chromium, zinc, and Manganese, and 1300 ppm in copper, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMaximum Tolerable Concentration (MTC) of bacterial isolate\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsolates\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTolerance\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConcentration\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(ppm)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZinc\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCopper\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLead\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eManganese\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChromium\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCSS01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCWS02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCWS03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCSS04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1900\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1300\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1900\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1900\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCSS05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e900\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e900\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCSS06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1500\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCWS07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1500\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1500\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCSS08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1500\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1900\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCSS09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCSS10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1500\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCWS11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCSS12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1900\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCSS13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e900\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCSS14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCSS15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCSS16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1900\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1900\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCSS17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1900\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1300\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1900\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1900\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCSS18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCWS19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e900\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCWS20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCSS21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1900\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCSS22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCWS23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCSS24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1300\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCSS25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e900\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1100\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1700\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eMorphological and Biochemical Analysis\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e delineates the findings derived from the examination of the morphological attributes of the bacterial isolates procured from the soil and aqueous samples. It is noteworthy that isolate BCSS04 exhibited characteristics consistent with gram-negative bacteria, presenting a rod-shaped morphology with a creamy and mucilaginous surface, whereas isolate BCSS17 demonstrated gram-positive properties with a rod-shaped configuration and a slimy white appearance.The results of biochemical analysis of the two isolates are represented in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. Both the isolates show positive results in catalase, nitrate reductase, citrate utilization, ammonia production, and methyl red tests. Meanwhile, both isolates show negative results in oxidase and indole production tests. The isolate BCSS04 shows positive results in urease and IAA production test whereas isolate BCSS17 shows negative results. Casein hydrolysis and VP test show negative results in isolate BCSS04 and positive results in isolate BCSS17, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMorphological Analysis of Selected Heavy Metal Resistant Isolates\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBCSS04\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBCSS17\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGram Reaction\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShape\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRod\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRod\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePigments\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecreamy\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ewhite\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurface\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSlimy\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSlimy\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBiochemical Analysis of Selected Heavy Metal Resistant Isolates\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTests\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBCSS04\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBCSS17\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOxidase\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCatalase\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrate reductase\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrease\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndole production\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCitrate utilization\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmmonia production\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIAA production\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCasein hydrolysis\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethyl red test\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVP (Voges Proskauer)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003ePCR amplification of HMRGs\u003c/h2\u003e\u003cp\u003eDNA specimens were procured from two distinct bacterial isolates, designated BCSS04 and BCSS17, attributable to their capacity to prosper in habitats characterized by elevated concentrations of heavy metals. The genotyping of both isolates focused on specific heavy metal resistance genes (HMRGs), namely zntA (zinc), pbrA (lead), pcoA (copper), and chrA (chromium) as listed in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. Gene amplification revealed that zntA, pcoA, pbrA, and chrA had lengths of 2374 bp, 1791 bp, 2396 bp, and 520 bp, respectively (Ojo et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Interestingly, isolate BCSS17 exhibited amplification for both pbrA and zntA, whereas isolate BCSS04 only showed amplification for pbrA and not for any other HMRGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeavy Metal Resistant Genes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimer sequences\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLength\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnealing temperature\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echrA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGGCTCTCGCTGTTCTTTGT TAAGTGCGACAAGGGCAACT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e520\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epcoA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCGTCTCGACGAACTTTCCTG GGACTTCACGAAACATTCCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1791\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ezntA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATCGTCCGCTCGCTGTATCTCT CCGCCTTTTCCCCTCACCCTAACC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2374\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epbrA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATGAGCGAATGTGGCTCGAAG TCATCGACGCAACAGCCTCAA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2396\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003eMolecular identification of bacteria isolates and phylogenetic analysis\u003c/h2\u003e\u003cp\u003eA cumulative total of two microbial isolates underwent 16S rRNA sequencing, NCBI BLAST Analysis, and phylogenetic tree analysis for the purpose of molecular characterization of the isolates.The BLAST results revealed that isolate BCSS04 was identified as \u003cem\u003eProteus mirabilis\u003c/em\u003e of 99.31% percentage identity with 99% query coverage while isolate BCSS17, was identified as \u003cem\u003eBacillus paramycoides\u003c/em\u003e of 99.85% percentage identity with 99% query coverage.\u003c/p\u003e\u003cp\u003eAs represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, BCSS04 was discovered to have the closest phylogenetic relationship with 100% similarity to \u003cem\u003eProteus mirabilis\u003c/em\u003e, whereas BCSS17 had the closest phylogenetic relationship with 96% similarity to \u003cem\u003eBacillus paramycoides\u003c/em\u003e, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings by Ma et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) indicated that the existence of metal in the surroundings carries a noteworthy influence, presenting a substantial risk to both human health and the ecosystem (Ma et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The process of heavy metal remediation is complex and cannot be fully addressed through physical or chemical means. Currently, bioremediation is considered a highly advantageous method for tackling pollution. It is seen as a promising approach for pollution mitigation, as it offers the potential for heavy metal dissolution via natural biological process known as bioremediation, which uses living organisms like bacteria, fungi, or yeast to rehabilitate contaminated soil and water, showcases a broadly embraced strategy (Naik \u0026amp; Dubey, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The study focused on bacterial strains from Buckingham Canal sediment and water samples to effectively remediate heavy metals. The primary objective of this research is to identify and characterize the predominant bioremediation bacterial components that facilitate the spontaneous restoration of environments (such as agricultural and residential settings) and evaluate their respective efficacy in remediating heavy metals to identify a few isolates capable of serving as a remedy for addressing impending pollution caused by unprocessed effluents containing heavy metals.\u003c/p\u003e \u003cp\u003eThe tastes and smells of natural water vary from location to place and seldom cause issues. The Buckingham Canal water had an unpleasant taste and odor, which could have been caused by sewage spills from homes and garbage from various small-scale companies. Due to the dissolution of different contaminants, the analysis revealed that the color of the canal water was brown to black. Based on the presence of dissolved ions in water, electrical conductivity is a measurement of a substance's ability to carry electric current (Rao, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Yadav et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). As articulated by Thangamalathi and Anuradha (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), this parameter serves as a significant instrument for evaluating the purity of aquatic systems and is subject to the geological features of the locale adjacent to the water source, effluent discharges from septic systems and sewage treatment plants, urban runoff originating from thoroughfares, and agricultural methodologies (Thangamalathi \u0026amp; Anuradha, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Conductivity shows a strong link with various properties, such as temperature, pH, alkalinity, total hardness, calcium levels, total suspended and dissolved solids, chemical oxygen demand, as well as concentrations of iron and chloride in water. A notable relationship can be seen where higher concentrations of dissolved solids are tied to increased water conductivity. In the current investigation, the electrical conductivity ranged from 3050 to 4150 \u0026micro;S/cm. the values were high, indicating a decrease in fish fauna number and species variety. Elevated readings suggest the existence of a significant quantity of dissolved organic materials in ionized form (Krishnaswamy \u0026amp; Jayaraj, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Increased biochemical oxygen demand (BOD) readings are a sign of more pollutants being present, resulting in greater oxygen needs from microbial groups for the breakdown of these substances. The heightened BOD values recorded in the samples under examination are hypothesized to originate from the discharge of heavy metal effluents from industrial sites.\u003c/p\u003e \u003cp\u003eA thorough investigation into the physicochemical attributes of both soil and water specimens showed that the pH readings taken at the chosen sites differed from 7.0 to 7.1. In contrast, the sediment samples displayed pH levels between 7.4 and 7.8. According to research by Samuel and others published in 2020, shifts in pH levels can modify water's acidic or alkaline characteristics, creating various sensory interpretations (Samuel Vinod Kuma et al., 2020b). The pH levels in the Buckingham Canal remained consistent within the range of 7.3 to 7.5 throughout the present study, thereby adhering to established regulatory limits. Elevated pH levels, surpassing 7, signify an increased concentration of dissolved constituents in water, which fosters optimal conditions for vegetative growth, as demonstrated in this specific investigation. Experts have uncovered that aquatic habitats maintaining pH levels of 6.7 to 8.7 are beneficial for the flourishing of both phytoplankton and zooplankton. Sustained contact with very low or high pH values can cause harmful effects on different physiological systems, affecting areas like the ocular region, layers of skin, mucosal barriers, and the digestive tract.\u003c/p\u003e \u003cp\u003eThe assessment of heavy metal concentrations in water and sediment samples procured from the designated research area was undertaken, and the resultant data has been presented in Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The hierarchy of heavy metal prevalence identified in the water samples, predicated on their average concentrations, was observed as Zn\u0026thinsp;\u0026gt;\u0026thinsp;Mn\u0026thinsp;\u0026gt;\u0026thinsp;Pb\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Cr. In contrast, the hierarchy of heavy metal prevalence in the sediment samples, derived from their mean concentrations, was established as Zn\u0026thinsp;\u0026gt;\u0026thinsp;Mn\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026thinsp;\u0026gt;\u0026thinsp;Cr\u0026thinsp;\u0026gt;\u0026thinsp;Pb. It is imperative to highlight that specific water samples from Buckingham exhibited concentrations exceeding the thresholds recommended by the World Health Organization (WHO), which may be attributed to tailings from extraction operations and chemicals employed in industrial and sewage processes as probable sources of these heavy metals [48]. The results from the analysis of heavy metal concentrations in sediment samples indicated notably higher values than those documented in studies focusing on sediment contaminated with heavy metals (Udiba et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe investigation analyzed water and sediment samples to assess bacterial abundance, demonstrating that water specimens displayed the highest bacterial count while sediment specimens exhibited the lowest. A total of 25 viable bacterial strains were extracted and purified from the corresponding water and sediment samples for subsequent investigations. The examination focused on the maximum tolerance concentration of heavy metals (Zn, Cu, Pb, Mn, and Cr) against all bacterial strains. Among these strains, BCSS04 and BCSS17 were chosen for further evaluation due to their endurance against all five heavy metals at maximum tolerance levels of 2100 ppm for lead, 1900 ppm for chromium, zinc, and manganese, and 1300 ppm for copper, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The morphological traits of the selected bacteria were scrutinized, identifying BCSS04 as gram-negative and BCSS17 as gram-positive. Both strains exhibited positive outcomes in catalase, nitrate reductase, citrate utilization, ammonia production, and methyl red tests (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe genotyping of two isolates was concentrated on specific heavy metal resistance genes (HMRGs), including zntA (zinc), pbrA (lead), pcoA (copper), and chrA (chromium). The gene amplification process disclosed that zntA, pcoA, pbrA, and chrA possessed lengths of 2374 bp, 1791 bp, 2396 bp, and 520 bp, correspondingly. Notably, BCSS17 demonstrated amplification for both pbrA and zntA, whereas BCSS04 only exhibited amplification for pbrA and not for any other HMRGs. While the results obtained from the amplification suggested that strains exhibiting solely two heavy metal resistance genes (HMRGs), it is imperative to underscore that the expression of HMRGs constitutes merely one mechanism through which bacteria mitigate the effects of heavy metals. Consequently, alternative mechanisms, including membrane modification and metabolic adaptation, may function as adaptive strategies for bacterial strains (Mathivanan et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The isolates BCSS04 and BCSS17 were characterized as \u003cem\u003eProteus mirabilis\u003c/em\u003e and \u003cem\u003eBacillus paramycoides\u003c/em\u003e with 99% identity and query coverage. \u003cem\u003eProteus mirabilis\u003c/em\u003e was isolated and identified from tungsten-enriched soil in the Kuhi-Agargaon-Khobna region, as indicated by the research conducted by Rohini (2022). It is essential to further investigate these organisms to explore their potential in bioremediation. The ability of \u003cem\u003eProteus mirabilis\u003c/em\u003e and \u003cem\u003eBacillus paramycoides\u003c/em\u003e to endure heavy metals has also garnered significant scholarly attention. These novel strains have exhibited resistance to a variety of metallic salts, including azo dye, cobalt chloride, mercuric chloride, ammonium metaparatungstate, tungstic acid, and sodium tungsten. Investigations utilizing ICP-MS and SEM-EDS techniques have demonstrated that these microorganisms accumulate tungsten intracellularly, as reported in the study(Ganorkar et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Yawen Gu's research conducted in 2023 culminated in the identification of an extraordinary novel hexavalent chromium (Cr (VI))-degrading bacterial strain, which has been classified as Bacillus paramycoides Cr6, and the study investigated the removal mechanism from a molecular biological standpoint. The decontamination efficacy for a concentration of 2000 mg/L Cr (VI) achieved a noteworthy 67.3%, while Cr6 exhibited resistance to concentrations reaching up to 2500 mg/L Cr (VI) (Gu et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Besides, \u003cem\u003eProteus mirabilis\u003c/em\u003e is acknowledged for its metabolic ingenuity, involving the potential to develop enzymes like urease, thereby enhancing its survival odds in challenging environmental conditions (Fitzgerald et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In an analogous way, \u003cem\u003eBacillus paramycoides\u003c/em\u003e is celebrated for its strong stress-response systems, which feature the creation of biofilms and extracellular polymeric substances (EPS), supporting the adsorption of metal ions and detoxification methods. Although specific investigations concerning \u003cem\u003eBacillus paramycoides\u003c/em\u003e are scarce, studies involving analogous Bacillus species yield pertinent insights. To illustrate, \u003cem\u003eBacillus cereus\u003c/em\u003e KMS3-1 is known to create EPS that significantly increases its biosorption capability in extracting heavy metals including Cd\u0026sup2;⁺ and Pb\u0026sup2;⁺ (Mathivanan et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Also, Bacillus species are esteemed for their role in applying bioremediation solutions, featuring biosorption and EPS-mediated biosorption, to control environmental levels of metals like lead, cadmium, and mercury. These findings imply that Bacillus species, encompassing \u003cem\u003eBacillus paramycoides\u003c/em\u003e, possess the potential to generate biofilms and EPS that promote metal ion adsorption and detoxification (Wr\u0026oacute;bel et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These attributes render both organisms promising candidates for utilization in bioremediation efforts, particularly in ecosystems compromised by heavy metals and industrial contaminants.\u003c/p\u003e \u003cp\u003eFurthermore, employing these microbial strains in bioremediation may be considerably enhanced through genetic engineering approaches designed to boost their metabolic functions for the precise degradation and retention of heavy metals. The amalgamated findings from contemporary research underscore the critical importance of these microorganisms in the progression of sustainable approaches for environmental remediation and emphasize the potential for applications on an industrial scale. Additional inquiries into their genomic characteristics and molecular mechanisms may facilitate the development of innovative strategies to confront the escalating issue of heavy metal pollution.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study meticulously examined a variety of physicochemical parameters along with the concentrations of heavy metals found in sediment and water samples collected from the Buckingham Canal and Neelankarai regions. Bacterial strains demonstrating resistance to heavy metals were isolated from these samples and subsequently assessed using a Maximum Tolerance Concentration (MTC) assay to ascertain their resistance capabilities to different heavy metals, with isolates BCSS04 and BCSS17 displaying an MTC of 2100 ppm in response to Lead exposure. A biochemical analysis of the isolated bacterial strains was conducted concurrently. Molecular characterization identified \u003cem\u003eProteus mirabilis\u003c/em\u003e and \u003cem\u003eBacillus paramycoides\u003c/em\u003e as bacterial isolates capable of withstanding high concentrations of heavy metals. Based on what we have gathered, this study appears to be the first detailed exploration into how Proteus mirabilis reacts to different levels of Pb, Cu, Mn, Zn, and Cr, alongside the initially recorded existence of heavy metal-resistant \u003cem\u003eBacillus paramycoides\u003c/em\u003e in the ecosystem of Buckingham Canal. In summary, the bacterial strains exposed in this study require more thorough examination in future research endeavors that target the bioremediation of heavy metals in tainted sites and other significant applications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; We are grateful to Vels Institute of Science, Technology and Advanced Studies (VISTAS) for generously providing the facilities and space to conduct our research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e The authors declare no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u003c/strong\u003e All authors agree to publish the following manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: The authors declare that no grants, funding, or other financial support were received for the research and publication of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAPHA. (2005). Standard methods for the examination of waste and wastewater. \u003cem\u003eAmerican Public Health Association, Washington, D.C\u003c/em\u003e, \u003cem\u003e21st Ed\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eAswathy.M, Gautam Kumar, \u0026amp; Dilip Kumar Thakur. (2017). ANALYSIS OF SEWAGE WATER FROM COOUM RIVER IN CHENNAI. \u003cem\u003eInternational Journal of Pure and Applied Mathematics\u003c/em\u003e, \u003cem\u003e116\u003c/em\u003e, 123\u0026ndash;129.\u003c/li\u003e\n\u003cli\u003eAyilara, M. S., \u0026amp; Babalola, O. O. (2023). Bioremediation of environmental wastes: the role of microorganisms. \u003cem\u003eFrontiers in Agronomy\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e. https://doi.org/10.3389/fagro.2023.1183691\u003c/li\u003e\n\u003cli\u003eBanerjee, P., \u0026amp; Prasad, B. (2020). Determination of concentration of total sodium and potassium in surface and ground water using a flame photometer. \u003cem\u003eApplied Water Science\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(5), 113. https://doi.org/10.1007/s13201-020-01188-1\u003c/li\u003e\n\u003cli\u003eClaus, D., \u0026amp; Berkeley, R. C. W. (1984). \u003cem\u003eBergey\u0026rsquo;s manual of systematic bacteriology\u003c/em\u003e (Vol. 1).\u003c/li\u003e\n\u003cli\u003eCollin, S., Baskar, A., Geevarghese, D. M., Ali, M. N. V. S., Bahubali, P., Choudhary, R., Lvov, V., Tovar, G. I., Senatov, F., Koppala, S., \u0026amp; Swamiappan, S. (2022). Bioaccumulation of lead (Pb) and its effects in plants: A review. \u003cem\u003eJournal of Hazardous Materials Letters\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e, 100064. https://doi.org/10.1016/j.hazl.2022.100064\u003c/li\u003e\n\u003cli\u003eEL-Hak, H. N. G., Ghobashy, M. A., Mansour, F. A., El-Shenawy, N. S., \u0026amp; El-Din, M. I. S. (2022). 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Enhanced Cd2+ and Zn2+ removal from heavy metal wastewater in constructed wetlands with resistant microorganisms. \u003cem\u003eBioresource Technology\u003c/em\u003e, \u003cem\u003e316\u003c/em\u003e, 123898. https://doi.org/10.1016/j.biortech.2020.123898\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bioremediation, Physicochemical parameters, Heavy metal analysis, Gene amplification, BLAST analysis, Proteus mirabilis, and Bacillus paramycoides","lastPublishedDoi":"10.21203/rs.3.rs-5971395/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5971395/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eAim\u003c/strong\u003eThe escalation of toxic heavy metal concentrations in environmental contexts has manifested as a matter of significant concern in recent times, due to the rapid industrialization driven by the demands of a burgeoning population. This study aims to meticulously examine and evaluate the bioremediation capabilities of bacterial strains that exhibit tolerance to heavy metals, which have been isolated from sediment and aqueous samples collected at Buckingham Canal, Neelankarai, Chennai.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods and Results\u003c/strong\u003e The collected samples were subjected to comprehensive analysis regarding physicochemical characteristics and heavy metal quantification, revealing that Zinc displayed the most significant concentration at 190.3 ppm, succeeded by Manganese at 98.8 ppm within the sediment samples. The water samples revealed the concentration of heavy metals sequence Zn\u0026gt;Mn\u0026gt;Pb\u0026gt;Cu\u0026gt;Cr, in contrast, the sediment samples exhibited an order of Zn\u0026gt;Mn\u0026gt;Cu\u0026gt;Cr\u0026gt;Pb. Among the 25 bacterial isolates, BCSS04 and BCSS17 were chosen for subsequent assays due to their demonstrated tolerance to all five heavy metals, achieving maximum tolerance concentrations of 2100 ppm for lead, 1900 ppm for chromium, zinc, and Manganese, and 1300 ppm for copper, respectively. Genetic amplification indicated that the zntA, pcoA, pbrA, and chrA genes yielded fragment lengths of 2374 bp, 1791 bp, 2396 bp, and 520 bp, respectively. Notably, isolate BCSS17 displayed amplification for both pbrA and zntA genes, while isolate BCSS04 exhibited amplification solely for pbrA gene, lacking amplification for any other heavy metal resistance genes. The results from the BLAST analysis identified isolate BCSS04 as \u003cem\u003eProteus mirabilis\u003c/em\u003e with a 99.31% identity, whereas isolate BCSS17 as \u003cem\u003eBacillus paramycoides\u003c/em\u003e, presenting a 99.85% identity and 99% query coverage.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003eThe study highlights the significant presence of heavy metals in the Buckingham Canal, with zinc being the most abundant. Two bacterial strains, \u003cem\u003eProteus mirabilis\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003eand \u003cem\u003eBacillus paramycoides,\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003edemonstrated high metal tolerance, with BCSS17 exhibiting resistance genes for both lead and zinc. These findings suggest their potential application in bioremediation efforts for heavy metal-contaminated environments\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSignificance and impact of the study\u003c/strong\u003e Ultimately, the bacterial species identified in the present investigation represent promising candidates for bioremediation and further exploration in endeavors aimed at the bioremediation of heavy metals within contaminated locations.\u003c/p\u003e","manuscriptTitle":"Physicochemical analysis and molecular characterization of heavy metal tolerant bacteria from Buckingham canal, Neelankarai, Chennai","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-14 10:06:33","doi":"10.21203/rs.3.rs-5971395/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4ac43b66-38f8-4e0b-ad4c-0b951c168c01","owner":[],"postedDate":"February 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-20T15:38:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-14 10:06:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5971395","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5971395","identity":"rs-5971395","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}