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Considering the actual and predicted values, the model generated through the different inputted parameters should be diagnosed satisfactorily. It is pretty understanding that agreement between the actual and predicted values given the effectiveness and accuracy of the generated model, as shown in Fig. 2. The following polynomial regression model equations were obtained:$$begin{aligned} COD;removal , % , & = 76.63 – 0.019*A , + , 0.064*B , – 0.511*C , – 0.405*AB , – 0.153*AC , \ &quad – 0.099*BC , + , 0.263*A^{2} + , 0.479*B^{2} – 0.303*C^{2} \ end{aligned}$$
(1)
$$begin{aligned} Nitrate;Removal , % , & = 72.04 , – 1.881*A – 0.142* , B , + , 2.384*C , + , 2.623*AB , + , 8.579*AC , \ &quad – 2.626*BC , – 10.783*A^{2} + , 0.223*B^{2} + , 0.963*C^{2 } hfill \ end{aligned}$$
(2)
$$begin{aligned} & Phosphate , Removal , % , = \ & 67.179 – 1.215*A , + , 3.539*B , – 1.068*C , + , 1.610*AB , – 2.559*AC , + , 0.392*BC , + , 0.788*A^{2} – 2.943*B^{2} + , 0.564*C^{2} \ end{aligned}$$
(3)
where A is initial pH, B is current time (min), C is MLSS concentration (mg L−1) at which the study was carried out.Figure 2Normal probability versus studentized residuals and predicted versus actual plots for (i) COD removal, (ii) nitrate removal, and (iii) phosphate removal.Full size imageIt has been observed that statistics for the model having low values represent well for the system and its predictions.Statistical analysis of COD, nitrate and phosphate removalIt was seen that 3D surface plots could provide a better understanding of the interactive effects of the parameters. The 3D surface plots are illustrated in Figs. 3, 4, and 5, respectively. It was observed that the maximum removal efficiency for COD, nitrate, and phosphate is in the range of 59% to 74%.Figure 3Model generated surface plot of % COD removal (i) pH versus current time (ii) pH vs. MLSS (iii) MLSS vs. current time.Full size imageFigure 4Model generated surface plot of %nitrate removal (i) pH versus current time (ii) pH vs. MLSS (iii) MLSS vs. current time.Full size imageFigure 5Model generated surface plot of %phosphate removal (i) pH versus current time (ii) pH versus MLSS (iii) MLSS versus current time.Full size imageTable 4 (i) shows the statistics for COD removal. Adeq Precision is desirable, which measures the signal-to-noise ratio and a ratio greater than 4. For the COD removal, Adeq Precision was 19.255, indicating an adequate signal. It was also observed that the adjusted R2 is 0.9118 (difference less than 0.2), and the predicted R2 of 0.8601 was significant, implying that the predictions are in good agreement with experimental values.Table 4 Fit statistics for (i) COD removal, (ii) Nitrate removal, (iii) Phosphate removal.Full size tableFigure 3 illustrates the effect of current flow time and pH concerning the percentage removal of COD. The model predicted values observed were seen to lie in the range of 73.1% at MLSS values of 2500 mg L−1, keeping initial COD values as 200 mg L−1. As the COD load increases, it seems to be predicted that the overloading of bacteria occurs, thereby slowing down the consumption of organics. In Fig. 4, the expected removal efficacy shows upward trends with an increase in the values of MLSS, which also coincided with previous studies. As the value of MLSS increases, the contact time of biomass in the system increases, hence producing more effective results than others.Table 4 (ii) shows the statistics for nitrate removal. The predicted R2 of 0.9164 was in reasonable agreement with the adjusted R2 of 0.9730. For the nitrate removal, Adeq Precision was 29.608, indicating an adequate signal. This model can be used to navigate the design space.Table 4 (iii) shows the statistics for phosphate removal. The predicted R2 of 0.9165 was in reasonable agreement with the adjusted R2 of 0.9720. For the phosphate removal, Adeq Precision was 34.945, indicating an adequate signal. This model can be used to navigate the design space.Figure 5 illustrates that as we reduce the cycle time from 24 to 18 h, the system efficacy, i.e., COD removal effectiveness shows a downward trend due to less contact time with biomass. Meanwhile, if we increase the cycle time, we observe higher efficacy in the system. The model generated surface plot in Fig. 5 illustrated that increasing MLSS values by 3000 mg L−1 will enhance the COD removal by 73.1%, keeping the initial pH constant. This may be due to many microbes that can break down organic matter. In aerobic reactors, pH is an essential factor in the growth of the microbial population. To create granules, the pH of the reactor has a direct impact. Studies have shown that granule formation occurs when bacteria grow at the ideal pH level, whereas mass proliferation of fungus occurs in an acidic environment.COD removal in EBR and tubesettlerThe Influence, effluent, and removal of COD in EBR & tubesettler are illustrated in Fig. 6a,b. Results demonstrate that the COD concentration is consistent and better COD removal efficacy rate. The average removal rate values observed in the EBR were between 74 and 79%, with the initial COD concentration kept around 360–396 mg L−1. It was also observed that tubesettler resulted in approximately 25–36% efficacy when the initial concentration was between 75 and 97 mg L−1. The results of EBR are promising and can be attributed to the fact that electrocoagulation takes place along with the oxidation and biodegradation process. It was also observed that the percentage removal of COD shows downward trends due to electrochemical oxidation and adsorption, thereby resulting in physical entrapment and electrostatic attraction30. It has also been reported in many other studies that COD removal of around 85–90% was observed using composite cathode membrane using MRB/MFC system19 for the specialized treatment of landfill leachate. It was seen with the electrooxidation process having COD removal of around 80–84% and 84–96% with submerged membrane bioreactors, using Iron electrode6. For the Coal industry, it was found to be around 85% using membrane electro bioreactors31.Figure 6(a) Influent, effluent and removal of COD in EBR (IEBR = Influent Electrobioreactor, EEBR = Effluent Electrobioreactor, STD = Standard, REBR = Removal Electrobioreactor), (b) Influent, effluent, and removal of COD in tubesettler (IT = Influent tubesettler, ET = Effluent tubesettler, STD = Standard, RT = Removal tubesettler).Full size imageIn the current study, results seemed to be lower than the values reported in the previous studies. The main reason might be the employment of a modified EBR system and the production of biomass species. When the overall COD removal with tubesettler is considered, up to 83.58% removal efficiency is observed. The overall COD removal efficiency is significant and is at par with other studies3,4,5. This signifies that EBR performed better than tubesettler in COD removal. The tubesettler’s lower removal efficiency can be attributed to lower influent concentration from already reduced wastewater from EBR.Nitrate removal in EBR and tubesettlerIt was observed in many studies that nitrifying is the leading cause of nitrification, i.e., conversion of NH3-N to nitrate NO3-N10. The indirect method of system nitrification process claudication was to be ascertained using measurements concerning ammonia values32,33. In the current study, the nitrification process was considered using the nitrate concentration measurement from the influent and effluent in both systems, i.e., EBR and tubesettler34,35,36. The nitrate concentration of influent and effluent was observed and illustrated in Fig. 7a,b. The system stabilized and produced enhanced results up to 70% of nitrate removal, and it was seen to be in the range of 40–45% for the tubesettler. It has been observed that EBR produced better results than the tubesettler. The results variation in both the systems were reasonably attributed mainly to two primary reasons (1) low influent concentration in the influent compared to the EBR system and (2) inhibition effect due to the applied DC field, which was absent in tubesettlers.Figure 7(a) Influent, effluent, and removal of nitrate in EBR (IEBR = Influent Electrobioreactor, EEBR = Effluent Electrobioreactor, STD = Standard, REBR = Removal Electrobioreactor), (b) Influent, effluent, and removal of nitrate in tubesettler (IT = Influent tubesettler, ET = Effluent tubesettler, STD = Standard, RT = Removal tubesettler).Full size imageThe removal efficiency of around 70% was achieved, lower than the values in submerged membrane bioreactors, i.e., 82%6. However, including a membrane would have enhanced the removal efficiency and considered a hybrid EBR system. The results of the current study are close enough to many other studies with a similar system and different operating parameters. Hence, a combined approach can be used for better efficacy. During the weekly analysis, the nitrate concentration during the 1st to 3rd week is lower than in the following weeks. As the concentration of nitrifying bacteria decreased, they had less to work with. Thus, the substrate concentration grew, and so did the removal rate. Nitrate concentrations rose by more than twice the previous week during Week 7. They slowed the bacterial activity, resulting in an efficiency decline to 47% from 70% during the last week’s study period and weeks 6 and 8. A similar pattern emerged for the seventh week in a row in tubesettler. On the other hand, microorganisms overcame differences in engagement because the nitrate content was low in other weeks.Phosphate removal in EBR and tubesettlerMany researchers have looked at nitrate content, but none have looked at phosphate concentration. Eutrophication in receiving water bodies, on the other hand, is predominantly caused by phosphate and nitrate. Additionally, there is a lack of information available on hospital wastewater. The influent and effluent phosphate concentrations in the Electro bioreactor and the tubesettler is shown in Fig. 8a,b. A 75% reduction in the effluent phosphate content in EBR was achieved tubesettler had a 67% effectiveness in phosphate removal but a lower efficiency in nitrate reduction. A previous similar study that used a Submerged Membrane Electro bioreactor claimed a clearance rate of 76% to 95%, which is lower than this study’s results6. Phosphate removal was reported at 50–70% using the electrocoagulation process for different Ph and current6.Figure 8(a) Influent, effluent, and removal of phosphate in EBR (IEBR = Influent Electrobioreactor, EEBR = Effluent Electrobioreactor, STD = Standard, REBR = Removal Electrobioreactor), (b) Influent, effluent, and removal of phosphate in tubesettler (IT = Influent tubesettler, ET = Effluent tubesettler, STD = Standard, RT = Removal tubesettler).Full size imageIn week 6 and week 8, the EBR’s phosphate removal efficiency fluctuated dependent on the weekly average concentration in EBR. This volatility can be linked to a shift in the composition of hospital wastewater. tubesettler had a modest variation ranging from 5 to 6%. Although phosphate concentrations rose in week two, tubesettler removal efficiency improved. As demonstrated in Fig. 8a,b, the arriving wastewater ingredient exhibited a strong affinity in terms of phosphate reduction.Excess effluent concentration and standard deviation from EBR and tubesettler are shown in Table 5. EBR performed better than tubesettler in COD reduction when nitrate and phosphate were compared. Because tubesettler solely employs a physical process to remove contaminants, this is to be anticipated. Effluent from the secondary treatment facility is sent to a tubesettler, which acts as a polishing unit. EBR eliminated COD by 91%, nitrate by 85%, and Phosphate reduction by 81% compared to tubesettler’ s total efficiency. At the same time, tubesettler reduced COD by 37%, nitrate by 51%, and phosphate by 53%. Hence, EBR primarily removed pollutants from wastewater while tubesettler acted as a polishing unit. Table 5 illustrates the effluent wastewater characteristics of EBR and tubesettler.Table 5 Effluent wastewater characteristics of EBR and tubesettler.Full size tableKinetic models post optimizationFirst-order modelA first-order linear model was analyzed on the experimental data by plotting (So − Se)/Se against hydraulic retention time (HRT), providing K1 and R2. For COD, R2 values were 0.761 with a constant value of 1.213, as shown in Table 6. Henceforth based on the results, the obtained model did not seem to fit well for either of the cases.Table 6 Analyzed kinetic models.Full size tableGrau second-order modelA Grau second-order model was analyzed on the experimental data by plotting HRT/((So − Se)/So) versus HRT. The COD constant obtained was Ks = 10–5, as shown in Table 6. The R2 value of 0.99 suggests a good correlation coefficient. Therefore, the obtained results fit well for AOX and COD.Modified Stover–Kincannon modelSubstrate utilization rate expressed as organic loading in this model is widely used in biological reactor kinetic modelling of wastewater. The developed model can evaluate the performance of the biological system and estimate its efficiency based on the input parameters. The kinetic constant KB and Umax for COD were 0.35 and 1.73 g L−1 d−1, respectively. The R2 was 0.98 for the substrate removal, as presented in Table 6.Monod modelCOD utilization rate was obtained by plotting VX/Q (So − Se) against 1/Se. The value of 1/K (0.421) was obtained from the intercept, while the Ks/K value (1.235) was the slope of the line. COD removal half-saturation values were 0.045 and 0.056 g L−1. These values infer a high affinity of bacteria for the substrate. The R2 value of 0.95 depicted an excellent correlation coefficient in the case of COD. The Monod model fits well for COD, resulting in R2 = 0.98, as shown in Table 6. More

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The datasetWe curated and explored 695 published 16S rRNA gene samples from cryospheric ecosystems (Methods section and Supplementary Table 7), including polar ice sheets, mountain glaciers and their proglacial lakes, permafrost soils and the coastal ocean under the influence of glacier runoff, and compared these to 3552 published 16S rRNA gene samples from non-cryospheric ecosystems, including temperate and tropical lakes and soils (Supplementary Table 7). This approach allowed us to identify and explore features specific to the cryospheric microbiome and compare it to other environmental microbiomes. However, we note a geographical bias towards polar regions in current publicly available repositories, and the paucity of alpine samples specifically highlights the need to further characterise these habitats given that they are among the most endangered cryospheric ecosystems globally. This bias is further compounded by the inconsistent methodologies applied across studies (e.g. primer pairs and sequencers used). To account for potential primer biases, we analysed two 16S rRNA primer pairs (Primer Pair 1, PP1: 341f-785r; Primer Pair 2, PP2: 515f-806r)12,13 commonly used in amplicon high-throughput sequencing. In total, this dataset contains 241,502,708 paired sequence reads, resulting in 530,254 and 410,931 amplicon sequence variants (ASVs) for PP1 and PP2, respectively. Moreover, all taxonomic analyses were performed at the genus level, to account for the limitations of 16s rRNA amplicon data. To gain deeper insights into the functional space of the cryospheric microbiome, we compared 34 published metagenomes from cryospheric ecosystems with 56 metagenomes from similar but non-cryospheric ecosystems (Fig. 1A). Given the difficulty of obtaining high-quality metagenomes from cryospheric ecosystems, we restricted our analyses to glacier surfaces, ice-covered lakes, and Antarctic soils. Although our analyses were limited to samples where raw sequence data are available (Methods section), the breadth of habitats covered are representative of the most abundant cryospheric ecosystems, e.g., glacier ice, cryoconites, subglacial lakes and sea ice. On the other hand, several niches such as glacier snow, glacier-fed rivers/streams, and the full-breadth of permafrost may not entirely be represented due to data unavailability. We reanalysed all metagenomes using the same bioinformatic pipeline (IMP3; see Methods section) to avoid analytical biases. Overall, the metagenomic analyses from 2,427,818,072 paired reads yielded 41,068,842 gene sequences. Thus, we here present a catalogue representing a snapshot of the functional diversity in the cryospheric microbiome, integrating across diverse habitats. This represents what we believe to be the first global overview of the functional repertoire of the Earth’s cryosphere compared to other ecosystems.Fig. 1: A unique cryospheric microbiome.A Geographic distribution of the 16 S rRNA gene samples for the two primer pairs (PP) and metagenomes for both cryospheric and non-cryospheric ecosystems, where GPS coordinates were available on NCBI. Symbol size denotes the number of samples per site (see Supplementary Table 7). B Phylogenetic tree based on abundant ASVs ( >0.5% relative abundance in at least one sample) in the PP1 dataset. The heatmap (inner rings) shows the presence (at a  > 0.5% relative abundance threshold) of ASVs in the four ecosystem types of the cryosphere (ice and snow, terrestrial, coastal ocean and freshwater). The barplot (outer ring) represents the coefficient for the SVM classifier analysis, highlighting discriminating ASVs. C Sorensen’s phylogenetic index of β-diversity (n1 = n2 = 84,461 for PP1, and n1 = n2 = 99,000 for PP2) and D β-MNTD calculated across pairs of samples in the cryospheric samples (Cryo-Cryo), pairs of cryospheric and non-cryospheric samples (Cryo-Others) and pairs of non-cryospheric (Others-Others) samples (sample sizes are listed in Supplementary Table 2). The top panel (shades of blue) is for PP1, the bottom one (shades of red) for PP2; two-sided Wilcoxon tests were performed to assess significance in panels C and D; the Holm method was used to correct for multiple testing (****: 0–0.0001). Boxplots depict the median and the 25th and 75th quartiles, whiskers extend to values within 1.5 times the interquartile range, and the remaining points are outliers. Effect sizes and exact p-values are available in Supplementary Table 2. Source data are provided as a Source Data file.Full size imageA cryospheric microbiomeGiven the communal constraints imposed by the harsh environment of cryospheric ecosystems (e.g., low temperature, oligotrophy), we expected them to harbour a specific microbiome. Accordingly, machine-learning classification (logistic regression models, Methods) based on community composition was able to differentiate between cryospheric and non-cryospheric microbiomes with high accuracy (balanced accuracy >0.96, Supplementary Table 1). Both primer pairs consistently yielded a high classification accuracy and especially a high precision. Interestingly, many of the discriminating cryospheric ASVs were spread widely across the bacterial tree of life (Fig. 1A and Supplementary Fig. 1).The notion that a part of the microbiome is specific to the cryosphere is also strongly supported by phylogenetic analyses of the 16 S rRNA gene amplicon dataset. First, we found higher pairwise phylogenetic overlap among cryospheric samples than among cryospheric/non-cryospheric or non-cryospheric samples (Sorensen’s index, Fig. 1C; Wilcoxon test, Holm adj. p  More

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