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.

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.

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.

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.

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.

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.

Ecological impact of the antibiotic ciprofloxacin on microbial community of aerobic activated sludge.

This study investigated the effects and fate of the antibiotic ciprofloxacin (CIP) at environmentally relevant levels (50-500 µg/L) in activated sludge (AS) microbial communities under aerobic conditions. Exposure to 500 µg/L of CIP decreased species diversity by about 20% and significantly altered the phylogenetic structure of AS communities compared to those of control communities (no CIP exposure), while there were no significant changes upon exposure to 50 µg/L of CIP. Analysis of community composition revealed that exposure to 500 µg/L of CIP significantly reduced the relative abundance of Rhodobacteraceae and Nakamurellaceae by more than tenfold. These species frequently occur in AS communities across many full-scale wastewater treatment plants and are involved in key ecosystem functions (i.e., organic matter and nitrogen removal). Our analyses showed that 50-500 µg/L CIP was poorly removed in AS (about 20% removal), implying that the majority of CIP from AS processes may be released with either their effluents or waste sludge. We therefore strongly recommend further research on CIP residuals and/or post-treatment processes (e.g., anaerobic digestion) for waste streams that may cause ecological risks in receiving water bodies.

Microbial Community Enhances Biodegradation of Bisphenol A Through Selection of Sphingomonadaceae.

Bisphenol A (BPA) is a common ingredient in plastic wares and epoxy resins that are essential for our daily life. Despite the obvious benefits, BPA may act as an environmental endocrine disruptor, causing metabolic, reproductive, and/or developmental consequences and diseases in humans and other organisms. Although previous studies have yielded progress toward the microbial breakdown of BPA, the work has primarily been focused on pure cultures rather than complex microbial communities. In this study, we examined microbial communities in bioreactors that control the fate of BPA at various levels (up to 5000 µg L-1). Microbial communities rapidly increased removal rates of 500-5000 µg L-1 BPA from 23-29 to 89-99% during the first 2 weeks of the acclimation period, after which > 90% stable removal rates were maintained over 3 months. Biochemical assays demonstrated that BPA was removed by biodegradation, rather than other abiotic removal routes (e.g., adsorption and volatilization). The 16S rRNA gene-based community analysis revealed that 50-5000 µg L-1 of BPA exposure systematically selected for three Sphingomonadaceae species (Sphingobium, Novosphingobium, and Sphingopyxis). The Sphingomonadaceae-enriched communities acclimated to BPA showed a 7.0-L gVSS-1 day-1 BPA degradation rate constant, which is comparable to that (4.1-6.3) of Sphingomonadaceae isolates and is higher than other potential BPA degraders. Taken together, our results advanced the understanding of how microbial communities acclimate to environmentally relevant levels of BPA, gradually enhancing BPA degradation via selective enrichment of a few Sphingomonadaceae populations with higher BPA metabolic activity.

Antiseptic chlorhexidine in activated sludge: Biosorption, antimicrobial susceptibility, and alteration of community structure.

Chlorhexidine (CHX) is a broad-spectrum antimicrobial, which may pose environmental health risks. This study examined the removal potential and the mechanisms regulating the fate of CHX in activated sludge (AS). Bioreactors inoculated with AS removed 74 ±â€¯8% and 81 ±â€¯6% of CHX at steady state while receiving 0.5 and 1 mg/L CHX, respectively. Analysis of the removal pathways showed that biosorption, rather than biological breakdown or other abiotic losses, largely (>70%) regulated the removal of CHX. 16S rRNA gene-based analysis revealed that CHX selected for Luteolibacter (4.3-10.1-fold change) and Runella (6.2-14.1-fold change) with potential multi-drug resistance mechanisms (e.g., efflux pumps). In contrast, it significantly reduced core members (Comamonadaceae and Flavobacteriaceae) of AS, playing a key role in contaminant removal and floc formation directly associated with the performance of WWTPs (e.g., wastewater effluent quality). Antimicrobial susceptibility testing showed that 0.4-1.3 mg/L of CHX can be sublethal to AS. Our work provided new insights into the fate of CHX in urban waste streams and the potential toxicity and effects on the structure and function of AS, which has practical implications for the management of biological WWTPs treating CHX.

Widely Used Benzalkonium Chloride Disinfectants Can Promote Antibiotic Resistance.

While the misuse of antibiotics has clearly contributed to the emergence and proliferation of resistant bacterial pathogens, with major health consequences, it remains less clear if the widespread use of disinfectants, such as benzalkonium chlorides (BAC), a different class of biocides than antibiotics, has contributed to this problem. Here, we provide evidence that exposure to BAC coselects for antibiotic-resistant bacteria and describe the underlying genetic mechanisms. After inoculation with river sediment, BAC-fed bioreactors selected for several bacterial taxa, including the opportunistic pathogen Pseudomonas aeruginosa, that were more resistant to several antibiotics than their counterparts in a control (no BAC) bioreactor. A metagenomic analysis of the bioreactor microbial communities, confirmed by gene cloning experiments with the derived isolates, suggested that integrative and conjugative elements encoding a BAC efflux pump together with antibiotic resistance genes were responsible for these results. Furthermore, the exposure of the P. aeruginosa isolates to increasing concentrations of BAC selected for mutations in pmrB (polymyxin resistance) and physiological adaptations that contributed to a higher tolerance to polymyxin B and other antibiotics. The physiological adaptations included the overexpression of mexCD-oprJ multidrug efflux pump genes when BAC was added in the growth medium at subinhibitory concentrations. Collectively, our results demonstrated that disinfectants promote antibiotic resistance via several mechanisms and highlight the need to remediate (degrade) disinfectants in nontarget environments to further restrain the spread of antibiotic-resistant bacteria.IMPORTANCE Benzalkonium chlorides (BAC) are biocides broadly used in disinfectant solutions. Disinfectants are widely used in food processing lines, domestic households, and pharmaceutical products and are typically designed to have a different mode of action than antibiotics to avoid interfering with the use of the latter. Whether exposure to BAC makes bacteria more resistant to antibiotics remains an unresolved issue of obvious practical consequences for public health. Using an integrated approach that combines metagenomics of natural microbial communities with gene cloning experiments with isolates and experimental evolution assays, we show that the widely used benzalkonium chloride disinfectants promote clinically relevant antibiotic resistance. Therefore, more attention should be given to the usage of these disinfectants, and their fate in nontarget environments should be monitored more tightly.

Impact of drinking water treatment and distribution on the microbiome continuum: an ecological disturbance's perspective.

While microbes are known to be present at different stages of a drinking water system, their potential functions and ability to grow in such systems are poorly understood. In this study, we demonstrated that treatment and distribution processes could be viewed as ecological disturbances exhibited over space on the microbiome continuum in a groundwater-derived system. Results from 16S rRNA gene amplicon analysis and metagenomics suggested that disturbances in the system were intense as the community diversity was substantially reduced during the treatment steps. Specifically, syntrophs and methanogens dominant in raw water (RW) disappeared after water abstraction, accompanied by a substantial decrease in both the abundance and number of functional genes related to methanogenesis. The softening effluent was dominated by an Exiguobacterium-related population, likely due to its ability to use the phosphotransferase system (PTS) as regulatory machinery to control the energy conditions of the cell. After disinfection and entering the distribution system, community-level functionality remained relatively stable, whereas the community structure differed from those taken in the treatment steps. The diversity and high abundance of some eukaryotic groups in the system suggested that predation could be a disturbance to the bacterial microbiome, which could further drive the diversification of the bacterial community.

Microbial community degradation of widely used quaternary ammonium disinfectants.

Benzalkonium chlorides (BACs) are disinfectants widely used in a variety of clinical and environmental settings to prevent microbial infections, and they are frequently detected in nontarget environments, such as aquatic and engineered biological systems, even at toxic levels. Therefore, microbial degradation of BACs has important ramifications for alleviating disinfectant toxicity in nontarget environments as well as compromising disinfectant efficacy in target environments. However, how natural microbial communities respond to BAC exposure and what genes underlie BAC biodegradation remain elusive. Our previous metagenomic analysis of a river sediment microbial community revealed that BAC exposure selected for a low-diversity community, dominated by several members of the Pseudomonas genus that quickly degraded BACs. To elucidate the genetic determinants of BAC degradation, we conducted time-series metatranscriptomic analysis of this microbial community during a complete feeding cycle with BACs as the sole carbon and energy source under aerobic conditions. Metatranscriptomic profiles revealed a candidate gene for BAC dealkylation, the first step in BAC biodegradation that results in a product 500 times less toxic. Subsequent biochemical assays and isolate characterization verified that the putative amine oxidase gene product was functionally capable of initiating BAC degradation. Our analysis also revealed cooperative interactions among community members to alleviate BAC toxicity, such as the further degradation of BAC dealkylation by-products by organisms not encoding amine oxidase. Collectively, our results advance the understanding of BAC aerobic biodegradation and provide genetic biomarkers to assess the critical first step of this process in nontarget environments.

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.

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.

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.

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.

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.

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.

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.