Heterogeneous g-C3N4/polyaniline composites enhanced the conversion of organics into methane during anaerobic wastewater treatment.

In this study, g-C3N4/PANI was prepared by in situ oxidative polymerization. Graphite-phase carbon nitride (g-C3N4) with surface defects was deposited onto the surface of conductive polyaniline (PANI) to form a p-n heterojunction. This construction aimed to create an efficient heterogeneous catalyst, increasing the surface defect level and active sites of the composite, and augmenting its capability to capture and transfer extracellular electrons under anaerobic conditions. This addresses the challenge of low efficiency in direct interspecies electron transfer between bacteria and archaea during anaerobic digestion for methane production. The results showed that the prepared g-C3N4/PANI increased the CH4 yield and CH4 production rate by 82% and 96%, respectively. Notably, the conductivity and XPS test results showed that the ratio of g-C3N4 to PANI was 0.15, and the composite exhibited favorable conductivity, with a uniform distribution of pyrrolic nitrogen, pyridinic nitrogen, and graphitic nitrogen, each accounting for approximately 30%. Furthermore, g-C3N4/PANI effectively enhanced the metabolic efficiency of intermediate products such as acetate and butyrate. Analysis of the microbial community structure revealed that g-C3N4/PANI led to a significant increase in the abundance of hydrogenotrophic methanogen Methanolinea (from 48% to 64%) and enriched Clostridium (a rise of 1%) with direct interspecies electron transfer capability. Microbial community function analysis demonstrated that the addition of g-C3N4/PANI boosted the activities of key enzymes involved in anaerobic digestion, including phosphate transacetylase (PTA), phospho-butyryl transferase (PTB), and NAD-independent lactate dehydrogenase (NNLD), by 47%, 135%, and 153%, respectively. This acceleration in enzymatic activity promoted the metabolism of acetyl-CoA, butyryl-CoA, and pyruvate. Additionally, the function of ABC transporters was enhanced, thereby improving the efficiency of material and energy exchange among microorganisms.

Green synthesis of magnetic azo-linked porous organic polymers with recyclable properties for enhanced Bisphenol-A adsorption from aqueous solutions.

Porous organic polymers (POPs) present superior adsorption performance to steroid endocrine disruptors. However, the effective recovery and high cost have been a big limitation for their large-scale applications. Herein, magnetic azo-linked porous polymers (Fe3O4@SiO2/ALP-p) were designed and prepared in a green synthesis approach using low-price materials from phloroglucinol and pararosaniline via a diazo-coupling reaction under standard temperature and pressure conditions, which embedded with Fe3O4@SiO2 nanoparticles to form three-dimensional interlayer network structure with flexible-rigid interweaving. The saturated adsorption capacity to bisphenol-A (BPA) was 485.09 mg/g at 298 K, which increased by 1.4 times compared with ALP-p of relatively smaller mass density. This enhanced adsorption was ascribed to increment from surface adsorption and pore filling with 2.3 times of specific surface area and 2.6 times of pore volume, although the total organic functional groups decreased with Fe3O4@SiO2 amendment. Also, the adsorption rate increased by about 1.1 and 1.5-fold due to enhancement in the initial stage of surface adsorption and subsequent stage pore diffusion, respectively. Moreover, this adsorbent could be used in broad pH (3.0-7.0) and salinity adaptability (<0.5 mol/L). The loss of adsorption capacity and magnetic recovery were lower than 1.1% and 0.8% in each operation cycle because of the flexible-rigid interweave. This excellent performance was contributed by synergistic effects from physisorption and chemisorption, such as pore filling, electrostatic attraction, π-π stacking, hydrogen bonding, and hydrophobic interaction. This study offered a cost-effective, high-performing, and ecologically friendly material along with a green preparation method.

Effect of different bulking agents on fed-batch composting and microbial community profile.

The current study focused on analyzing the effect of different types of bulking agents and other factors on fed-batch composting and the structure of microbial communities. The results indicated that the introduction of bulking agents to fed-batch composting significantly improved composting efficiency as well as compost product quality. In particular, using green waste as a bulking agent, the compost products would achieve good performance in the following indicators: moisture (3.16%), weight loss rate (85.26%), and C/N ratio (13.98). The significant difference in moisture of compost products (p < 0.05) was observed in different sizes of bulking agent (green waste), which was because the voids in green waste significantly affected the capacity of the water to permeate. Meanwhile, controlling the size of green waste at 3-6 mm, the following indicators would show great performance from the compost products: moisture (3.12%), organic matter content (63.93%), and electrical conductivity (EC) (5.37 mS/cm). According to 16S rRNA sequencing, the relative abundance (RA) of thermophilic microbes increased as reactor temperature rose in fed-batch composting, among which Firmicutes, Proteobacteria, Basidiomycota, and Rasamsonia were involved in cellulose and lignocellulose degradation.

An exploratory diagnosis and proposed index of technological change and sustainable industrial development in selected OECD member countries.

Industrial development has resulted in economic progress and the well-being of the society. At the same time, the impact of the industrial complex has disrupted the environment and resulted in climate change related impacts. The purpose of this study was to carry out an exploratory diagnosis and propose a technological change and sustainable industrial development index at the international level. Therefore, a network study was conducted to identify the main nodes and thematic clusters associated with cleaner production. A patent analysis was applied to technologies related three selected/relevant areas of cleaner production, i.e. carbon footprint, wastewater treatment, and renewable energy. Additionally, based on factor analysis, an index including different indicators related to scientific, technological, economic, environmental, and social issues was developed and proposed in this study.

Neuro-fuzzy modelling of a continuous stirred tank bioreactor with ceramic membrane technology for treating petroleum refinery effluent: a case study from Assam, India.

A continuous stirred tank bioreactor (CSTB) with cell recycling combined with ceramic membrane technology and inoculated with Rhodococcus opacus PD630 was employed to treat petroleum refinery wastewater for simultaneous chemical oxygen demand (COD) removal and lipid production from the retentate obtained during wastewater treatment. In the present study, the COD removal efficiency (CODRE) (%) and lipid concentration (g/L) were predicted using two artificial intelligence models, i.e., an artificial neural network (ANN) and a neuro-fuzzy neural network (NF-NN) with a network topology of 6-25-2 being the best for NF-NN. The results revealed the superiority of NF-NN over ANN in terms of determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE). Three learning algorithms were tested with NF-NN; among them, the Bayesian regularization backpropagation (BR-BP) outperformed others. The sensitivity analysis revealed that, if solid retention time and biomass concentrations were maintained between 35 and 75 h and 3.0 g/L and 3.5 g/L, respectively, high CODRE (93%) and lipid concentration (2.8 g/L) could be obtained consistently.

Improvement of biotic nitrate reduction in constructed photoautotrophic biofilm-soil microbial fuel cells.

The biotic nitrate reduction rate in freshwater ecosystems is typically constrained by the scarcity of carbon sources. In this study, 'two-chambers' - 'two-electrodes' photoautotrophic biofilm-soil microbial fuel cells (P-SMFC) was developed to accelerate nitrate reduction by activating in situ electron donors that originated from the soil organic carbon (SOC). The nitrate reduction rate of P-SMFC (0.1341 d-1) improved by âˆ¼ 1.6 times on the 28th day compared to the control photoautotrophic biofilm. The relative abundance of electroactive bacterium increased in the P-SMFC and this bacterium contributed to obtain electrons from SOC. Biochar amendment decreased the resistivity of P-SMFC, increased the electron transferring efficiency, and mitigated anodic acidification, which continuously facilitated the thriving of putative electroactive bacterium and promoted current generation. The results from physiological and ecological tests revealed that the cathodic photoautotrophic biofilm produced more extracellular protein, increased the relative abundance of Lachnospiraceae, Magnetospirillaceae, Pseudomonadaceae, and Sphingomonadaceae, and improved the activity of nitrate reductase and ATPase. Correspondingly, P-SMFC in the presence of biochar achieved the highest reaction rate constant for nitrate reduction (kobs) (0.2092 d-1) which was 2.4 times higher than the control photoautotrophic biofilm. This study provided a new strategy to vitalize in situ carbon sources in paddy soil for nitrate reduction by the construction of P-SMFC.

Emerging technology effects on combined agricultural and eco-vermicompost.

This study focused on the waste management of livestock manure and wetland plant residues and their increasing effect on terrestrial and aquatic ecosystems. The benefits of nutrient-rich plants and manures are often overlooked. By conducting a soil column experiment with a fully factorial design, this work found that adding the vermicompost amendments of wetland plants [combination of Canna indica (CiV), Cyperus alternifollius (CaV), Acorus calamus (AcV), and Hydrocotyle vulgaris (HvV) vermicompost] to agricultural wastes affected maize growth throughout its growing season. The results demonstrated that the use of combined AcV and HvV wetland plant-based vermicompost as an organic fertilizer increased the plant total nitrogen (TN: 92% increase) and soil organic matter (SOM: 192% increase) compared with those in control CK. Meanwhile, the combination of CaV with HvV increased the shoot biomass by 3.4 and 4.6 folds compared with that in NPK and CK, respectively. Overall, a new approach for transforming ecological wastes into organic fertilizers was proposed.

Occurrence, transport, and toxicity of microplastics in tropical food chains: perspectives view and way forward.

Microplastics, which have a diameter of less than 5 mm, are becoming an increasingly prevalent contaminant in terrestrial and aquatic ecosystems due to the dramatic increase in plastic production to 390.7 million tonnes in 2021. Among all the plastics produced since 1950, nearly 80% ended up in the environment or landfills and eventually reached the oceans. Currently, 82-358 trillion plastic particles, equivalent to 1.1-4.9 million tonnes by weight, are floating on the ocean's surface. The interactions between microorganisms and microplastics have led to the transportation of other associated pollutants to higher trophic levels of the food chain, where microplastics eventually reach plants, animals, and top predators. This review paper focuses on the interactions and origins of microplastics in diverse environmental compartments that involve terrestrial and aquatic food chains. The present review study also critically discusses the toxicity potential of microplastics in the food chain. This systematic review critically identified 206 publications from 2010 to 2022, specifically reported on microplastic transport and ecotoxicological impact in aquatic and terrestrial food chains. Based on the ScienceDirect database, the total number of studies with "microplastic" as the keyword in their title increased from 75 to 4813 between 2010 and 2022. Furthermore, various contaminants are discussed, including how microplastics act as a vector to reach organisms after ingestion. This review paper would provide useful perspectives in comprehending the possible effects of microplastics and associated contaminants from primary producers to the highest trophic level (i.e. human health).

Synthesis of CoWO4/g-C3N4 Z-scheme heterojunction for the efficient photodegradation of diazinon with the addition of H2O2.

Pesticides are on the list of substances that are routinely monitored by agencies and organizations in various natural environments and habitats. Diazinon (DZN) is the active ingredient in more than 20 agricultural pesticides, it causes the most damage and has been prohibited in many countries around the world. The final product CoWO4/g-C3N4 Z-scheme heterojunction was successfully synthesized in this work, where CoWO4 nanoparticles were deposited on the surface of g-C3N4. CoWO4/g-C3N4 structure allowed for the efficient separation of photo-generated electron-hole pairs, with electrons at the CoWO4 CB migrating to the g-C3N4 VB and preserving the electrons at the g-C3N4 CB and holes in the CoWO4 VB. The photodegradation efficiency of DZN using CoWO4/g-C3N4 Z-scheme heterojunction was investigated, as compared with its precursors, such as CoWO4, and g-C3N4. CoWO4/g-C3N4 Z-scheme heterojunction demonstrated the highest degradation capacity for DZN removal. Based on the results, the photocatalysis of the CoWO4/g-C3N4 Z-scheme heterojunction can be recycled for the effective removal of DZN by simple washing after three runs, proving the heterojunction's stability and suggesting CoWO4 as a promising material for the removal of DZN from contaminated water sources.

Enhanced thyroxine removal from micro-polluted drinking water resources in a bio-electrochemical reactor amended with TiO2@GAC particles: Efficiency, mechanism and energy consumption.

A three-dimensional bioelectrochemical system (3D-BES) with both electrocatalytic and biodegradation functions was designed and developed to enhance iodine-containing hormone removal from micro-polluted oligotrophic drinking water sources and to reduce energy consumption. Thyroxine (T4) removal efficiency was 99.0% in the 3D-BES amendment with TiO2@GAC as the particle electrodes, which was 20.5% higher than the total efficiency of single biodegradation (28.7%) plus electrochemical decomposition (49.8%). The high T4 removal efficiency was a result of biochemical synergistic degradation, enhancement of electron transfer and utilization, enrichment of functional microorganisms, and the expression of dehalogenation functional genes. The electron transfer was increased by 1.63 times in 3D-BES compared to the 2D-BES, which contributed to: (i) ∼17.8% enhancement of dehalogenation, (ii) 2.35 times enhancement of the attenuation rate, and (iii) 60% reduction in energy consumption. Moreover, the aggregation of microorganisms and the hydrophobic T4 onto TiO2@GAC shortened the transfer distance of matter and energy, which induced the degradation steps to be shortened and the toxic decay to be accelerated from T4 and its metabolites. These comprehensive functions also enhanced the 31.8% ATPase activity, 7.3% abundance of the functional reductive dehalogenation genera, and 52.3% dehalogenation genes expression for Pseudomonas, Ancylobacter, and Dehalogenimonas, which contributed to an increase in T4 removal. This work provides an environmental-friendly biochemical synergistic method for the detoxification of T4 polluted water.

Neural network-based performance assessment of one- and two-liquid phase biotrickling filters for the removal of a waste-gas mixture containing methanol, α-pinene, and hydrogen sulfide.

The performance of one- and two-liquid phase biotrickling filters (OLP/TLP-BTFs) treating a mixture of gas-phase methanol (M), α-pinene (P), and hydrogen sulfide (H) was assessed using artificial neural network (ANN) modeling. The best ANN models with the topologies 3-9-3 and 3-10-3 demonstrated an exceptional capacity for predicting the performance of O/TLP-BTFs, with R2 > 99%. The analysis of causal index (CI) values for the model of OLP-BTF revealed a negative impact of M on P removal (CI = -2.367), a positive influence of P and H on M removal (CI = +7.536 and CI = +3.931) and a negative effect of H on P removal (CI = -1.640). The addition of silicone oil in TLP-BTF reduced the negative impact of M and H on P degradation (CI = -1.261 and CI = -1.310, respectively) compared to the OLP-BTF. These findings suggested that silicone oil had the potential to improve P availability to the biofilm by increasing the concentration gradient of P between the air/gas and aqueous phases. Multi-objective particle swarm optimization (MOPSO) suggested an optimum operational condition, i.e. inlet M, P, and H concentrations of 1.0, 1.1, and 0.3 g m-3, respectively, with elimination capacities (ECs) of 172.1, 26.5, and 0.025 g m-3 h-1 for OLP-BTF. Likewise, one of the optimum operational conditions for TLP-BTF is achievable at inlet concentrations of 4.9, 1.7, and 0.8 g m-3, leading to the optimum ECs of 299.7, 52.9, and 0.072 g m-3 h-1 for M, P, and H, respectively. These results provide important insights into the treatment of complex waste gas mixtures, addressing the interactions between the pollutant removal characteristics in OLP/TLP-BTFs and providing novel approaches in the field of biological waste gas treatment.

E-waste management, treatment options and the impact of heavy metal extraction from e-waste on human health: Scenario in Vietnam and other countries.

Ho Chi Minh (HCM) City is the most important urban region of Vietnam, Southeast Asia. In recent times, the quantity of electronic waste (e-waste) has been growing by several thousand tonnes every year. In this research, some of the existing and developing technologies being employed for the recycling of e-waste have been reviewed. Accordingly, the paper has been divided into three sections namely, e-waste treatment technologies in Ho Chi Minh City, the effect of heavy metals on human health and the extraction of metals from e-waste using pyrolysis, hydrometallurgy, bioleaching, mechanical, and air classifier methods, respectively. The extraction of precious metals and heavy metals such as Cd, Cr, Pb, Hg, Cu, Se, and Zn from e-waste can be hazardous to human health. For example, lead causes hazards to the central and peripheral nervous systems, blood system and kidneys; copper causes liver damage; chronic exposure to cadmium ends up causing lung cancer and kidney damage, and mercury can cause brain damage. Thus, this study examines the key findings of many research and review articles published in the field of e-waste management and the health impacts of metal pollution.

Phytoremediation of nickel and zinc using Jatropha curcas and Pongamia pinnata from the soils contaminated by municipal solid wastes and paper mill wastes.

The primary source of soil pollution is a complex mixture of numerous inorganic and organic compounds (including chlorinated compounds, nutrients, and heavy metals, etc.). The presence of all of these compounds makes remediation and cleanup difficult. In this study, the phytoremediation ability of Jatropha curcas and Pongamia pinnata was tested to remove nickel (Ni) and Zinc (Zn) from paper mill and municipal landfill contaminated soils, to understand the uptake potential and to estimate the accumulation pattern of Ni and Zn in the vegetative parts of the plant. The experiments were carried out in pots (3 kg capacity) and the different combinations of soil were made by mixing the contaminated soil with a reference soil (forest soil) as T0, T25, T50, T75 and T100. The plant biomass, chlorophyll content, proline, nitrate reductase activity and metal removal efficiency (%)were determined after 120 DAS (i.e., the days after sowing). The results of the study showed that with increasing metal stress, there is a reduction in the above-ground biomass content in both the plant species with a slightly less impact on the root biomass. Over a period of 4 months, J. curcas and P. pinnata removed 82-86% and 93-90% Ni, respectively. The removal of Zn was significantly less as compared to Ni as most of the Zn remained in the belowground part (roots) and in the soil. Besides, the phytostabilization capacities of the plants were calculated on the basis of their tolerance index (TI), bioaccumulation factor (BAF) and translocation factor (TF). The low BAF and TF values with increasing heavy metals (HMs) content indicates its higher phytostabilization capacity in the root and rhizospheric region as compared to phytoaccumulation.

Artificial neural network based models for predicting the effluent quality of a combined upflow anaerobic sludge blanket and facultative pond: Performance evaluation and comparison of different algorithms.

The main objective of this work was to test different artificial neural network (ANN) based models, i.e. the ANN feed forward back propagation (ANN-FFBP), deep feed forward backpropagation (DFFBP), and deep cascade forward back propagation (DCFBP) models, for predicting the effluent quality of an upflow anaerobic sludge blanket-facultative pond (UASB-FP) system. The overall removal efficiency in the UASB-FP was >84% at organic loading rates of ∼26 kg d-1. The chemical oxygen demand (COD), ammonical nitrogen (AN), total suspended solids (TSS), biochemical oxygen demand (BOD), total Kjeldahl nitrogen (TKN), and total phosphorus (TP) were inputs to each model, while the water quality characteristics of the UASB-FP effluent was used as the output. The dataset of 180 samples, collected over a one-year period, was utilized to train, test, and validate the developed models. Compared to ANN-FFBP and DFFBP, the DCFBP network demonstrated the strongest capacity for prediction. The correlation coefficient RTrain and the root-mean-squared error (RMSE) for the selected DCFBP model (3 hidden layers and 11 neurons/layer) in the training data set were 0.997 and 6.018, respectively. The sensitivity analysis of the DCFBP model shows that the model's performance is very sensitive to BOD followed by AN, COD, TP, TSS and TKN, respectively. The results of this study will be helpful to wastewater treatment (WWTP) plant managers in their pursuit of data-driven UASB-FP based WWTP management.

Prediction of carbon emissions peak and carbon neutrality based on life cycle CO2 emissions in megacity building sector: Dynamic scenario simulations of Beijing.

In order to design an optimal carbon peak and carbon neutralization pathway for the high-density building sector, a dynamic prediction model is established using system-dynamics coupled building life cycle carbon emission model (SD-BLCA) with consideration of future evolutionary trajectory and time constraints. The model is applied in Beijing using the SD-BLCA combined with scenario analysis and Monte Carlo methods to explore optimal trajectory for its building sector under 30-year timeframe. The results indicate that by increasing the proportion of renewable energy generation by 7% and retrofitting 60 million m2 of existing buildings, these two mature measures can offset the growth of carbon emissions and achieve the peak target by 2025. However, achieving carbon neutrality necessitates a shift from isolated technologies to a comprehensive net-zero emissions strategy. The study proposes a time roadmap that integrates a zero-carbon energy supply system and the carbon reduction measures of the whole life cycle. This strategy primarily relies on renewable sources to provide heat, power, and hydrogen, resulting in estimated reductions of 29.8 Mt, 28.1 Mt, and 0.7 Mt, respectively. Zero energy buildings, green buildings, and renovated buildings can reduce carbon emissions through their own energy-saving measures by 8.4, 18.2, and 11.8 kg/m2, respectively.

Predictive modelling and optimization of an airlift bioreactor for selenite removal from wastewater using artificial neural networks and particle swarm optimization.

Selenite (Se4+) is the most toxic of all the oxyanion forms of selenium. In this study, a feed forward back propagation (BP) based artificial neural network (ANN) model was developed for a fungal pelleted airlift bioreactor (ALR) system treating selenite-laden wastewater. The performance of the bioreactor, i.e., selenite removal efficiency (REselenite) (%) was predicted through two input parameters, namely, the influent selenite concentration (ICselenite) (10 mg/L - 60 mg/L) and hydraulic retention time (HRT) (24 h - 72 h). After training and testing with 96 sets of data points using the Levenberg-Marquardt algorithm, a multi-layer perceptron model (2-10-1) was established. High values of the correlation coefficient (0.96 ≤ R ≤ 0.98), along with low root mean square error (1.72 ≤ RMSE ≤ 2.81) and mean absolute percentage error (1.67 ≤ MAPE ≤ 2.67), clearly demonstrate the accuracy of the ANN model (> 96%) when compared to the experimental data. To ensure an efficient and economically feasible operation of the ALR, the process parameters were optimized using the particle swarm optimization (PSO) algorithm coupled with the neural model. The REselenite was maximized while minimizing the HRT for a preferably higher range of ICselenite. Thus, the most favourable optimum conditions were suggested as: ICselenite - 50.45 mg/L and HRT - 24 h, resulting in REselenite of 69.4%. Overall, it can be inferred that ANN models can successfully substitute knowledge-based models to predict the REselenite in an ALR, and the process parameters can be effectively optimized using PSO.

Performance and neural modeling of a compost-based biofilter treating a gas-phase mixture of benzene and xylene.

Biofilter (BF) has been regarded as a versatile gas treatment technology for removing volatile organic compounds (VOCs) from contaminated gas streams. In order for BF to be utilized in the industrial setting, it is essential to conduct research aimed at removing VOC mixtures under different inlet loading conditions, i.e. as a function of the gas flow rate and inlet VOC concentrations. The main aim of this study was to apply artificial neural networks (ANN) and determine the relationship between flow rate (FR), pressure drop (PD), inlet concentration (C), and removal efficiency (RE) in the BF treating gas-phase benzene and xylene mixtures. The ANN model was trained and tested to assess the removal efficiency of benzene (REB) and xylene (REX) under the influence of different FR, PD and C. The model's performance was assessed using a cross-validation method. The REb varied from 20% to >60%, while the REx varied from 10% to 70% during the different experimental phases of BF operation. The causal index (CI) technique was used to determine the sensitivity of the input parameters on the output variables. The ANN model with a topology of 4-4-2 performed the best in terms of predicting the RE profiles of both the pollutants. Furthermore, the effect was more pronounced for xylene because an increase in the benzene concentration reduced xylene removal (CI = -25.7170) more severely than benzene removal. An increase in the xylene concentration had a marginally positive effect on the benzene removal (CI = +0.1178).

Hydrothermal carbonization of cow dung with human urine as a solvent for hydrochar: An experimental and kinetic study.

Hydrothermal carbonization (HTC) is the most cost-effective, environmentally friendly, and efficient physicochemical and biochemical process for converting biomass to products with added value. The objective and novelty of this work is to produce and investigate the qualities of hydrochar fuel (as a solid fuel) from cow manure using human urine as a solvent in order to find a suitable replacement for conventional fuel (i.e., coal). HTC based studies were conducted in batch, at three different reaction temperatures (180 °C, 200 °C, and 220 °C) and two different reaction periods (2 and 4 h). For kinetic analysis and reaction mechanism of the combustion behavior of the produced hydrochar, the model free kinetic methods and the z-master plot were used. From the model free kinetics methods, it was observed that the resultant optimum average activation energy and pre-exponential factor for the produced hydrochar at 180 °C and 2 h reaction period (HTC_180_2) were ∼120 kJ/mol and ∼5.59 × 1025 sec-1, respectively. In addition, the little variation between ΔEα and ΔHα (∼10 kJ/mol) suggests that the combustion of produced hydrochar (HTC_180_2) occurred with minimal energy use. Furthermore, the hydrochar exhibited its highest heating value at 200 °C for 4 h (HTC_200_4) which was 1.44 times higher than the raw dung (13.4 MJ/kg) due to the HTC process. The produced hydrochar demonstrated a significant improvement compared to the conventional solvent, i.e. water.

Process intensification approaches in wastewater and sludge treatment for the removal of pollutants.

Process Intensification (PI) is the modification or integration of conventional or novel processes within a single unit operation in order to improve product quality and reduce waste. PI offers numerous advantages, including a reduction in the initial and operational costs, an improvement in product quality/quantity, the generation of less waste, and an increase in process safety. The synergistic effect of PI in comparison to the conventional procedure ensures maximizing resource efficiency. PI can be accomplished in two ways: either by integrating various processes or by modifying the design of equipment to improve operational efficiency. In this regard, the present review provides a comprehensive insight into the application of PI in wastewater and sludge treatment methods and discusses the operational advantages. This review provides a comprehensive list of different PI approaches applied in wastewater and sludge treatment to remove pollutants and the various equipment, techniques and reactors used in PI. The second section addresses the challenges of PI in wastewater treatment that removes dyes, pesticides, organic and inorganic pollutants, micro- and nano-plastics, persistent organic pollutants, pharmaceutical and personal care pollutants.

Potential use of fungal-bacterial co-cultures for the removal of organic pollutants.

Fungi and bacteria coexist in a wide variety of natural and artificial environments which can lead to their association and interaction - ranging from antagonism to cooperation - that can affect the survival, colonization, spatial distribution and stress resistance of the interacting partners. The use of polymicrobial cultivation approaches has facilitated a more thorough understanding of microbial dynamics in mixed microbial communities, such as those composed of fungi and bacteria, and their influence on ecosystem functions. Mixed (multi-domain) microbial communities exhibit unique associations and interactions that could result in more efficient systems for the degradation and removal of organic pollutants. Several previous studies have reported enhanced biodegradation of certain pollutants when using combined fungal-bacterial treatments compared to pure cultures or communities of either fungi or bacteria (single domain systems). This article reviews: (i) the mechanisms of pollutant degradation that can occur in fungal-bacterial systems (e.g.: co-degradation, production of secondary metabolites, enhancement of degradative enzyme production, and transport of bacteria by fungal mycelia); (ii) case studies using fungal-bacterial co-cultures for the removal of various organic pollutants (synthetic dyes, polycyclic aromatic hydrocarbons, pesticides, and other trace or volatile organic compounds) in different environmental matrices (e.g. water, gas/vapors, soil); (iii) the key aspects of engineering artificial fungal-bacterial co-cultures, and (iv) the current challenges and future perspectives of using fungal-bacterial co-cultures for environmental remediation.