MnO2 decoration onto the guava leaves: A sustainable and cost-effective material for methylene blue dye removal.

Excessive number of dyes in water is becoming the main cause of water pollution, which is very important to remove because it is harmful. Dye contaminated water is being treated by various methods. Adsorption method can be considered best for the study of dye removal due to several technological reasons. The adsorption method has also been emphasized in this study. In the present work, a nano-bio-composite was fabricated by growing manganese oxide nanoparticles on abundant cellulosic guava leaf powder. This allows nanocomposite to be prepared in large quantities at nominal cost. The characterization technique confirmed the irregular growth of manganese oxide nanoparticles onto the guava leaf powder. The electrostatic and non-electrostatic interactions was confirmed in between manganese oxide nanoparticles and the carbon structure of guava leaf powder. The massive functional groups were found to be in the prepared nano-bio-composite. The grain size of prepared material was in nano range. The developed nano-bio-composite was used to remove methylene blue from water. This showed a very good adsorptive capacity for methylene blue. The analyzed adsorption data was modelled through isotherms, kinetics and thermodynamics models. The nature of the adsorption process was determined to be spontaneous and exothermic. The reusability test was carried out for five adsorption-desorption cycles. The reusability results suggested the better removal efficiency (%) in the first two cycles with only 20 % reduction in removal efficiency (%). The leaching test result revealed the good stability of MnO2/GL at neutral pH. It was a unique and cheap adsorbent of its kind, which had not been noticed anywhere before.

Biodegradable Acid-Based Fe2MnO4 Nanoparticles for Water Remediation.

This study demonstrated the synthesis of Fe2MnO4 modified by citric acid, a biodegradable acid, using a simple co-precipitation method. Characterization was performed using qualitative analysis techniques such as Fourier-transformed infrared spectroscopy, scanning electron microscopy equipped with energy-dispersive X-ray spectroscopy, X-ray diffraction, selected-area electron diffraction, N2 adsorption-desorption, and zero-point charge. The prepared nanoparticles had a rough and porous surface, and contained oxygenous (-OH, -COOH, etc.) functional groups. The specific surface area and average pore size distribution were 83 m2/g and 5.17 nm, respectively. Net zero charge on the surface of the prepared nanoparticles was observed at pH 7.5. The prepared nanoparticles were used as an adsorbent to remove methylene blue dye from water under various conditions. Using small amounts of the adsorbent (2.0 g/L), even a high concentration of MB dye (60 mg/L) could be reduced by about ~58%. Exothermic, spontaneous, feasible, and monolayer adsorption was identified based on thermodynamics and isotherm analysis. Reusability testing verified the stability of the adsorbent and found that the reused adsorbent performed well for up to three thermal cycles. Comparative analysis revealed that the modified adsorbent outperformed previously reported adsorbents and unmodified Fe2MnO4 in terms of its partition coefficient and equilibrium adsorption capacity under different experimental conditions.

A microbiome-biochar composite synergistically eliminates the environmental risks of antibiotic mixtures and their toxic byproducts.

This study developed a continuous reactor system employing a hybrid hydrogel composite synthesized using a complex sludge microbiome and an adsorbent (HSA). This HSA-based system effectively eliminated the environmental risks associated with a mixture of the antibiotics ciprofloxacin and sulfamethoxazole, which exhibited higher toxicity in combination than individually at environmentally relevant levels. Analytical chemistry experiments revealed the in-situ generation of various byproducts (BPs) within the bioreactor system, with two of these BPs recording toxicity levels that surpassed those of their parent compound. The HSA approach successfully prevented the functional microbiome from being washed out of the reactor, while HSA efficiently removed antibiotic residues in their original and BP forms through synergistic adsorptive and biotransformation mechanisms, ultimately reducing the overall ecotoxicity. The use of HSA thus demonstrates promise not only as a mean to reduce the threat posed by toxic antibiotic residues to aquatic ecosystems but also as a practical solution to operational challenges, such as biomass loss/washout, that are frequently encountered in various environmental bioprocesses.

Machine learning surveillance of foodborne infectious diseases using wastewater microbiome, crowdsourced, and environmental data.

Clostridium perfringens (CP) is a common cause of foodborne infection, leading to significant human health risks and a high economic burden. Thus, effective CP disease surveillance is essential for preventive and therapeutic interventions; however, conventional practices often entail complex, resource-intensive, and costly procedures. This study introduced a data-driven machine learning (ML) modeling framework for CP-related disease surveillance. It leveraged an integrated dataset of municipal wastewater microbiome (e.g., CP abundance), crowdsourced (CP-related web search keywords), and environmental data. Various optimization strategies, including data integration, data normalization, model selection, and hyperparameter tuning, were implemented to improve the ML modeling performance, leading to enhanced predictions of CP cases over time. Explainable artificial intelligence methods identified CP abundance as the most reliable predictor of CP disease cases. Multi-omics subsequently revealed the presence of CP and its genotypes/toxinotypes in wastewater, validating the utility of microbiome-data-enabled ML surveillance for foodborne diseases. This ML-based framework thus exhibits significant potential for complementing and reinforcing existing disease surveillance systems.

Comprehensive assessment of chlorination disinfection on microplastic-associated biofilms.

Chlorination on microplastic (MP) biofilms was comprehensively investigated with respect to disinfection efficiency, morphology, and core microbiome. The experiments were performed under various conditions: i) MP particles; polypropylene (PP) and polystyrene (PS), ii) MP biofilms; Escherichia coli for single-species and river water microorganisms for multiple-species, iii) different chlorine concentrations, and iv) different chlorine exposure periods. As a result, chlorination effectively inactivated the MP biofilm microorganisms. The disinfection efficiency increased with increasing the free chlorination concentration and exposure periods for both single- and multiple-species MP biofilms. The multiple-species MP biofilms were inactivated 1.3-6.0 times less than single-species MP biofilms. In addition, the PP-MP biofilms were more vulnerable to chlorination than the PS-MP biofilms. Morphology analysis verified that chlorination detached most MP biofilms, while a small part still remained. Interestingly, chlorination strongly changed the biofilm microbiome on MPs; the relative abundance of some microbes increased after the chlorination, suggesting they could be regarded as chlorine-resistant bacteria. Some potential pathogens were also remained on the MP particles after the chlorination. Notably, chlorination was effective in inactivating the MP biofilms. Further research should be performed to evaluate the impacts of residual MP biofilms on the environment.

A metagenome-derived artificial intelligence modeling framework advances the predictive diagnosis and interpretation of petroleum-polluted groundwater.

Groundwater (GW) quality monitoring is vital for sustainable water resource management. The present study introduced a metagenome-derived machine learning (ML) model aimed at enhancing the predictive understanding and diagnostic interpretation of GW pollution associated with petroleum. In this framework, taxonomic and metabolic profiles derived from GW metagenomes were combined for use as the input dataset. By employing strategies that optimized data integration, model selection, and parameter tuning, we achieved a significant increase in diagnostic accuracy for petroleum-polluted GW. Explanatory artificial intelligence techniques identified petroleum degradation pathways and Rhodocyclaceae as strong predictors of a pollution diagnosis. Metagenomic analysis corroborated the presence of gene operons encoding aminobenzoate and xylene biodegradation within the de novo assembled genome of Rhodocyclaceae. Our genome-centric metagenomic analysis thus clarified the ecological interactions associated with microbiomes in breaking down petroleum contaminants, validating the ML-based diagnostic results. This metagenome-derived ML framework not only enhances the predictive diagnosis of petroleum pollution but also offers interpretable insights into the interaction between microbiomes and petroleum. The proposed ML framework demonstrates great promise for use as a science-based strategy for the on-site monitoring and remediation of GW pollution.

Microplastics in aquatic ecosystems of Africa: A comprehensive review and meta-analysis.

Microplastic pollution is a global issue of great public concern. Africa is flagged to host some of the most polluted water bodies globally, but there is no enough information on the extent of microplastic contamination and the potential risks of microplastic pollution in African aquatic ecosystems. This meta-analysis has integrated data from published articles about microplastic pollution in African aquatic ecosystems. The data on the microplastic distribution and morphological characteristics in water, sediments and biota from African rivers, lakes, oceans and seas were extracted from 75 selected studies. Multivariate statistics were used to critically analyze the effects of sampling and detection methods, ecological risks, spatial distribution and similarity of microplastics in relation to the geographical distance between sampling sites. This study found that sampling methods have significant effect on abundance and morphological characteristics of microplastics and that African aquatic ecosystems are highly contaminated with microplastics compared to global data. The most prevalent colors were white, transparent and black, the most prevalent shapes were fibres and fragments, and the most available polymers were polypropylene (PP), polystyrene (PS) and polyethene terephthalate (PET). Microplastic polymers similarity decreased with an increase in geographical distance between sites. Risk levels of microplastics in African aquatic ecosystems were comparatively high, and more than 40 % of water and sediments showed highest level of ecological risk. This review provides recent information on the prevalence, distribution and risks of microplastics in African aquatic ecosystems.

Environmental consequences of transformation products from an antibiotic mixture and their mitigation in a wastewater microbiome using an HCl-modified adsorbent.

This study enhanced our understanding of antibiotic mixtures' occurrence, transformation, toxicity, and ecological risks. The role of acid-modified biochar (BC) in treating antibiotic residues was explored, shedding light on how BC influences the fate, mobility, and environmental impact of antibiotics and transformation products (TPs) in an activated sludge (AS) microbiome. A mixture of oxytetracycline and sulfamethoxazole was found to synergistically (or additively) inhibit cell growth of AS and disrupt the microbiome structure, species richness/diversity, and function. The formation of TPs with potentially higher toxicity and persistence than the original compounds was identified, explaining the microbiome disruption. Agricultural waste-derived BC was optimized for contaminant adsorption, leading to a reduction in toxicity when added to AS by sequestering TPs on its surface. This work highlighted adsorbents as a practical engineering strategy for mitigating liquid-phase contaminants' toxicological consequences, proactively controlling the fate and effects of antibiotics and TPs.

Side effects of the addition of an adsorbent for the nitrification performance of a microbiome in the treatment of an antibiotic mixture.

This work determined the effect of biochar (BC) as an adsorbent on the nitrifying microbiome in regulating the removal, transformation, fate, toxicity, and potential environmental consequences of an antibiotic mixture containing oxytetracycline (OTC) and sulfamethoxazole (SMX). Despite the beneficial role of BC as reported in the literature, the present study revealed side effects for the nitrifying microbiome and its functioning arising from the presence of BC. Long-term monitoring revealed severe disruption to nitratation via the inhibition of both nitrite oxidizers (e.g., Nitrospira defluvii) and potential comammox species (e.g., Ca. Nitrospira nitrificans). Byproducts (BPs) more toxic than the parent compounds were found to persist at a high relative abundance, particularly in the presence of BC. Quantitative structure-activity relationship modeling determined that the physicochemical properties of the toxic BPs significantly differed from those of OTC and SMX. The results suggested that the BPs tended to mobilize and accumulate on the surface of the solids in the system (i.e., the BC and biofilm), disrupting the nitrifiers growing at the interface. Collectively, this study provides novel insights, demonstrating that the addition of adsorbents to biological systems may not necessarily be beneficial; rather, they may generate side effects for specific bacteria that have important ecosystem functions.

Water quality characteristics and reuse potential using adsorption as a post-treatment option for a full-scale hydrocyclone, coagulation, flocculation, and dissolved air flotation system.

This study established a full-scale hybrid water treatment system combining a hydrocyclone, coagulation, flocculation, and dissolved air flotation unit (HCFD) and evaluated its performance in treating anthropogenically impacted lake water. The HCFD system offered the stable and efficient treatment of fluctuating influent loadings, meeting most of the highest water reclamation quality criteria except for that of organic matter. Adsorption was subsequently examined as a post-treatment process for the HCFD effluent, which has not been examined in many previous studies. As the adsorbent for the post-treatment, pine bark, a locally available agricultural waste feedstock, was modified using H2O2 to maximize its adsorption capacity. The surface modification increased its adsorption capacity for organic matter by 53-112%. The HCFD system in conjunction with the synthesized adsorbent thus demonstrated the ability to meet the highest standards for all water quality parameters, highlighting their synergistic potential for enhancement of water treatment. Liquid chromatography-organic carbon detection and Fourier transform infrared analysis were then employed to determine the mechanisms involved in the removal of specific contaminants using the HCFD system and post-adsorption unit. While the HCFD system successfully eliminated particulate and colloidal matter (e.g., phosphorous and biopolymers with a high molecular weight) using centrifugal and floating separation with the aid of two complementary polymers, the post-adsorption unit effectively adsorbed small-sized dissolved substances (e.g., low molecular weight acids and building blocks) via surface functional groups (-CH, -OH, -CH2, C=O, C=C, and C=O) using van der Waals forces, hydrogen bonding, and π-π or n-π interactions.

Activated sludge microbiome with H2O2-modified biochar enhances the treatment resilience and detoxification of oxytetracycline and its toxic byproducts.

The widespread presence of oxytetracycline (OTC) in aquatic ecosystems poses both health risks and ecological concerns. The present study revealed the beneficial role of hydrogen peroxide (H2O2)-pretreated biochar (BC) derived from agricultural hardwood waste in an activated sludge (AS) bioprocess. The BC addition significantly enhanced the removal and detoxification of OTC and its byproducts. BC was initially modified using H2O2 to improve its OTC adsorption. Two AS reactors were then established, one with H2O2-modified BC and one without, and both were exposed to OTC. The BC-added reactor exhibited significantly higher OTC removal rates during both the start-up (0.97 d-1) and steady-state (0.98 d-1) phases than the reactor without BC (0.54 d-1 and 0.83 d-1, respectively). Two novel transformation pathways for OTC were proposed, with four byproducts originating from OTC identified, some of which were found to be more toxic than OTC itself. The BC-added reactor had significantly higher system functioning in terms of its heterotrophic activity and the reduction of the toxicity of OTC and its byproducts, as illustrated by structure-based toxicity simulations, antimicrobial susceptibility experiments, analytical chemistry, and bioinformatics analysis. Bioinformatics revealed two novel bacterial populations closely related to the known OTC-degrader Pandoraea. The ecophysiology and selective enrichment of these populations suggested their role in the enzymatic breakdown and detoxification of OTC (e.g., via demethylation and hydrogenation). Overall, the present study highlighted the beneficial role of H2O2-modified BC in combination with the AS microbiome in terms of enhancing treatment performance and resilience, reducing the toxicological disruption to biodiversity, and detoxifying micropollutants.

Nigella sativa-Manganese Ferrite-Reduced Graphene Oxide-Based Nanomaterial: A Novel Adsorbent for Water Treatment.

In this study, a novel nanohybrid composite was fabricated via the incorporation of manganese ferrite (MnFe2O4) nanoparticles into the integrated surface of reduced graphene oxide (rGO) and black cumin seeds (BC). The nanohybrid composite was prepared by a simple co-precipitation method and characterized by several spectroscopic and microscopic techniques. The characterization analysis revealed that the rGO-BC surface was decorated with the MnFe2O4. The strong chemical interaction (via electrostatic and H-bonding) between the integrated surface of rGO-BC and MnFe2O4 nanoparticles has been reported. The prepared composite was highly porous with a heterogeneous surface. The average size of the prepared composite was reported in the ranges of 2.6-7.0 nm. The specific surface area of the prepared composite was calculated to be 50.3 m2/g with a pore volume of 0.061 cc/g and a half pore width of 8.4 Å. As well, many functional sites on the nanohybrid composite surface were also found. This results in the excellent adsorption properties of nanohybrid composite and the effectual elimination of methylene blue dye from water. The nanohybrid was tested for various linear isotherms, such as Langmuir and Freundlich, for the adsorption of methylene blue dye. The Freundlich isotherm was the well-fitted model, proving the adsorption is multilayer. The maximum Langmuir adsorption capacity of nanohybrid composite for methylene blue was reported to be 74.627 mg/g at 27 °C. The adsorption kinetics followed the pseudo-second-order recommended surface interaction between the dye and nanohybrid composite. The interaction between methylene blue and the nanohybrid composite was also confirmed from the FTIR spectrum of the methylene blue-loaded adsorbent. The rate-determining step for the present study was intraparticle diffusion. Temperature-dependent studies of methylene blue adsorption were also carried out to estimate adsorption's free energy, enthalpy, and entropy. The methylene blue adsorption was feasible, spontaneous, and endothermic. A comparison study revealed that the present materials could be successfully prepared and used for wastewater treatment.

Live Biomass of Rigidoporus vinctus: A Sustainable Method for Decoloration and Detoxification of Dyes in Water.

In this study, white-rot fungus, Rigidoporus vinctus, collected from an unidentified fallen twig from Pathankot, Punjab, India, was used for biosorption of anionic Congo red and cationic Methylene blue dyes from an aqueous medium. The biosorption efficiency of the live biomass of Rigidoporus vinctus was investigated to optimize biosorbent dosage, process time, concentrations of dyes, and pH of solutions. The results indicated that Rigidoporus vinctus is more efficient than other reported bio-adsorbents for Congo red and Methylene blue dyes. The maximum biosorption activity of Rigidoporus vinctus for Congo red was found at pH 2, and that for Methylene blue was at pH 10, after 24 h of the reaction period. The process followed pseudo-second-order kinetics, which indicated that the interaction of both dyes to the adsorption sites on the surface of Rigidoporus vinctus was responsive to biosorption. The biosorption process could be well explained by the Langmuir isotherm for both dyes. The maximum monolayer biosorption capacity of Rigidoporus vinctus for Congo red and Methylene blue was observed to be 54.0 mg/g and 80.6 mg/g, respectively. The seed germination test was carried out, and it was assessed that the toxicity of dyes was reduced up to significant levels. Based on the present experimental findings, it can be concluded that biosorption using the live biomass of Rigidoporus vinctus can effectively decolorize dye-containing wastewater, thus reducing the hazardous effects of dyes on human beings.

Effect of antibiotic cocktail exposure on functional disturbance of nitrifying microbiome.

The present study quantitatively determined the degree and type of functional disturbance in the nitrifying microbiome caused by exposure to a single oxytetracycline (OTC) and a two-antibiotic mixture containing OTC and sulfamethoxazole (SMX). While the single antibiotic had a pulsed disturbance on nitritation that was recoverable within three weeks, the antibiotic mixture caused a more significant pulsed disturbance on nitritation and a potential press disturbance on nitratation that was not recoverable for over five months. Bioinformatic analysis revealed significant perturbations for both canonical nitrite-oxidizing (Nitrospira defluvii) and potential complete ammonium-oxidizing (Ca. Nitrospira nitrificans) populations that were strongly associated with the press perturbation on nitratation. In addition to this functional disturbance, the antibiotic mixture reduced the biosorption of OTC and altered its biotransformation pathways, resulting in different transformation products compared with those produced when OTC was treated as a single antibiotic. Collectively, this work elucidated how the antibiotic mixture can affect the degree, type, and duration of the functional disturbance on nitrifying microbiome and offer new insights into the environmental consequences of antibiotic residues (e.g., their fate, transformation, and ecotoxicity) when present as an antibiotic mixture rather than single antibiotics.

Biomass formation and organic carbon migration potential of microplastics from a PET recycling plant: Implication of biostability.

The growth of the polyethylene terephthalate (PET) mechanical recycling industry has resulted in the challenge of generating microplastics (MPs). However, little attention has been given to investigating the release of organic carbon from these MPs and their roles in promoting bacterial growth in aquatic environments. In this study, a comprehensive method is proposed to access the potential of organic carbon migration and biomass formation of MPs generated from a PET recycling plant, and to understand its impact on the biological systems of freshwater habitats. Various MPs sizes from a PET recycling plant were selected to conduct a series of tests, including the organic carbon migration test, biomass formation potential test, and microbial community analysis. The MPs smaller than 100 µm, which are difficult to remove from the wastewater, exhibited greater biomass in the observed samples (1.05 × 1011 bacteria per gram MPs). Moreover, PET MPs altered the microbial diversity, with Burkholderiaceae becoming the most abundant, while Rhodobacteraceae was eliminated after being incubated with MPs. This study partly revealed that organic matter adsorbed on the surface of MPs was a significant nutrient source that increased biomass formation. PET MPs acted not only as carriers for microorganisms but also for organic matter. As a result, it is crucial to develop and refine recycling methods in order to decrease the production of PET MPs and minimize their adverse effects on the environment.

Machine learning reveals the complex ecological interplay of microbiome in a full-scale membrane bioreactor wastewater treatment plant.

Membrane bioreactor (MBR) systems are one of the most widely used wastewater treatment processes for various municipal and industrial waste streams. The present study aimed to advance the understanding of ecologically important keystone taxa that play an important role in full-scale MBR systems. A machine-learning (ML) modeling framework based on microbiome data was developed to successfully predict, with an average accuracy of >91.6%, the operational characteristics of three representative full-scale wastewater systems: an MBR, a conventional activated sludge system, and a sequencing batch reactor. ML-based feature-importance analysis identified Ferruginibacter as a keystone organism in the MBR system. The phylogeny and known ecophysiology of members of Ferruginibacter supported their role in metabolizing complex organic polymers (e.g., extracellular polymeric substances) in MBR systems characterized by high concentrations of mixed liquor suspended solids and a high solid retention time. ML regression modeling also revealed temporal patterns of Ferruginibacter in response to water temperature. ML modeling was thus successfully employed in the present study to investigate complex/non-linear relationships between keystone taxa and environmental conditions that cannot be detected using conventional approaches. Overall, our microbiome-data-enabled ML modeling approach represents a methodological advance for identifying keystone taxa and their complex ecological interactions, which has implications for the sustainable and predictive management of MBR systems.

Effects of biochar addition on the fate of ciprofloxacin and its associated antibiotic tolerance in an activated sludge microbiome.

This study investigated the effects of adding biochar (BC) on the fate of ciprofloxacin (CIP) and its related antibiotic tolerance (AT) in activated sludge. Three activated sludge reactors were established with different types of BC, derived from apple, pear, and mulberry tree, respectively, and one reactor with no BC. All reactors were exposed to an environmentally relevant level of CIP that acted as a definitive selective pressure significantly promoting AT to four representative antibiotics (CIP, ampicillin, tetracycline, and polymyxin B) by up to two orders of magnitude. While CIP removal was negligible in the reactor without BC, the BC-dosed reactors effectively removed CIP (70-95% removals) through primarily adsorption by BC and biodegradation/biosorption by biomass. The AT in the BC-added reactors was suppressed by 10-99%, compared to that without BC. The BC addition played a key role in sequestering CIP, thereby decreasing the selective pressure that enabled the proactive prevention of AT increase. 16S rRNA gene sequencing analysis showed that the BC addition alleviated the CIP-mediated toxicity to community diversity and organisms related to phosphorous removal. Machine learning modeling with random forest and support vector models using AS microbiome data collectively pinpointed Achromobacter selected by CIP and strongly associated with the AT increase in activated sludge. The identification of Achromobacter as an important AT bacteria revealed by the machine learning modeling with multiple models was also validated with a linear Pearson's correlation analysis. Overall, our study highlighted Achromobacter as a potential useful sentinel for monitoring AT occurring in the environment and suggested BC as a promising additive in wastewater treatment to improve micropollutant removal, mitigate potential AT propagation, and maintain community diversity against toxic antibiotic loadings.

Anaerobic membrane bioreactor performance with varying feed concentrations of ciprofloxacin.

The anaerobic membrane bioreactor (AnMBR) has considerable potential for treating wastewater, although there is very little data on the effect of antibiotics on AnMBR performance. This study examined the effect of Ciprofloxacin (CIP) - an antibiotic that can occur at high concentrations, and has a substantial impact on ecosystems, on AnMBR performance. The long-term (44 days) presence of 0.5 mg/L CIP in the feed did not have a strong effect on COD removal, volatile fatty acid (VFA) accumulation, or methane yield, but did affect the pH, soluble microbial products (SMPs) and suspended solids. However, at 4.7 mg/L CIP, a significant effect on all the parameters tested was seen. 16S rRNA gene-based community analysis demonstrated that CIP changed the phylogenetic structure and altered the species richness and diversity. The relative abundance of various genera was also changed, and this explained much of the change in AnMBR behavior.

Machine-learning insights into nitrate-reducing communities in a full-scale municipal wastewater treatment plant.

This study carried out machine-learning (ML) modeling using activated sludge microbiome data to predict the operational characteristics of biological unit processes (i.e., anaerobic, anoxic, and aerobic) in a full-scale municipal wastewater treatment plant. An ML application pipeline with optimization strategies (e.g., model selection, input data preprocessing, and hyperparameter tuning) could significantly improve prediction performance. Comparative analysis of the ML prediction performance suggested that linear models (support vector machine and logistic regression) had a high prediction performance (93% accuracy), comparable to that of non-linear models such as random forest. Feature importance analysis using the linear ML models identified the microbial taxa that were specifically associated with anoxic processes, many of which (e.g., Ferruginibacter) were found to have ecologically important genomic and phenotypic characteristics (e.g., for nitrate reduction). Time-series microbial community dynamics demonstrated that the taxa identified using ML were frequently occurring and dominating in the anoxic process over time, thus representing the core nitrate-reducing community. Despite the general dominance of the core community over time, the analysis further revealed successional seasonal patterns of distinct sub-groups, indicating differences in the functional contribution of sub-groups by season to the overall nitrate-reducing potential of the system. Overall, the results of this study suggest that ML modeling holds great promise for the predictive identification and understanding of key microbial players governing the functioning and stability of biological wastewater systems.

Machine Learning Approach Reveals the Assembly of Activated Sludge Microbiome with Different Carbon Sources during Microcosm Startup.

Activated sludge (AS) microcosm experiments usually begin with inoculating a bioreactor with an AS mixed culture. During the bioreactor startup, AS communities undergo, to some extent, a distortion in their characteristics (e.g., loss of diversity). This work aimed to provide a predictive understanding of the dynamic changes in the community structure and diversity occurring during aerobic AS microcosm startups. AS microcosms were developed using three frequently used carbon sources: acetate (A), glucose (G), and starch (S), respectively. A mathematical modeling approach quantitatively determined that 1.7-2.4 times the solid retention time (SRT) was minimally required for the microcosm startups, during which substantial divergences in the community biomass and diversity (33-45% reduction in species richness and diversity) were observed. A machine learning modeling application using AS microbiome data could successfully (>95% accuracy) predict the assembly pattern of aerobic AS microcosm communities responsive to each carbon source. A feature importance analysis pinpointed specific taxa that were highly indicative of a microcosm feed source (A, G, or S) and significantly contributed for the ML-based predictive classification. The results of this study have important implications on the interpretation and validity of microcosm experiments using AS.