Enhanced degradation of bisphenol A: Influence of optimization of removal, kinetic model studies, application of machine learning and microalgae-bacteria consortia

An alternative approach to biokinetic modelling for phenol pollutant degradation and microbial growth using Genetic Programming

Biokinetic models can optimise pollutant degradation and enhance microbial growth processes, aiding to protect ecosystem protection. Traditional biokinetic approaches (such as Monod, Haldane, etc.) can be challenging, as they require detailed knowledge of the organism's metabolism and the ability to solve numerous kinetic differential equations based on the principles of micro, molecular biology and biochemistry (first engineering principles) which can lead to discrepancies between predicted and actual degradation rates. More recently, data-driven machine-learning techniques have emerged as a promising alternative for modelling microbial systems. A few machine learning models (such as ANN, SVM, RF, DT, XG BOOST, etc.) have been used recently for modelling phenol degradation, but they lack the robustness of generating mathematical models. This gap is addressed in this study using Genetic Programming (GP) as the modelling approach for modelling the phenol degradation. This study utilises the microalgae Acutodesmus Obliquus, finding that phenol degradation of 98% required 216 hours. Both the traditional kinetic approach and the Genetic Programming (GP) approach were used to determine the specific growth rate (µmax) and saturation constant (Ks). It is noted that without any a priori information on the form of the mathematical mode, GP can evolve a model which closely fits the Monod kinetics, thus demonstrating that data-driven models can bring out the first engineering principles on which biokinetic models are dependent or framed in a most swift and effective way. Performance was assessed using root mean square error (RMSE) and correlation coefficient (R), with the GP model showing superior predictive accuracy.

Machine learning in microalgae biotechnology for sustainable biofuel production: Advancements, applications, and prospects

This review explores the critical role of machine learning (ML) in enhancing microalgae bioprocesses for sustainable biofuel production. It addresses both technical and economic challenges in commercializing microalgal biofuels and examines how ML can optimize various stages, including identification, classification, cultivation, harvesting, drying, and conversion to biofuels. This review also highlights the integration of ML with technologies such as the Internet of Things (IoT) for real-time monitoring and management of bioprocesses. It discusses the adaptability and flexibility of ML in the context of microalgae biotechnology, focusing on diverse algorithms such as Artificial Neural Networks, Support Vector Machines, Decision Trees, and Random Forests, while emphasizing the importance of data collection and preparation. Additionally, current ML applications in microalgae biofuel production are reviewed, including strain selection, growth optimization, system monitoring, and lipid extraction.

A targeted review on occurrence, remediation, and risk assessments of bisphenol A in Africa

Bisphenol A (BPA) is a vital raw material used to manufacture various household and commercial goods. However, BPA is a contaminant of emerging concern (CEC) and an endocrine-disrupting chemical (EDC) capable of migrating and bio-accumulating in environmental and biological compartments. At threshold levels, they become toxic causing adverse health and environmental issues. BPA's occurrence in food, food contact materials (FCMs), beverages, water, cosmetics, consumer goods, soil, sediments, and human/biological fluids across Africa was outlined. Unlike most reviews, it further collated data on BPA remediation techniques, including the human and ecological risk assessment studies conducted across Africa. A systematic scrutiny of the major indexing databases was employed extracting relevant data for this study. Results reveal that only 10 out of 54 countries have researched BPA in Africa. BPA levels in water were the most investigated, whereas levels in cosmetics and consumer goods were the least studied. Maximum BPA concentrations found in Africa were 3,590,000 ng/g (cosmetic and consumer goods), 154,820,000 ng/g (soils), 189 ng/mL (water), 1139 ng/g (food), and 208.55 ng/mL (biological fluids). The optimum percentage removal/degradation of BPA was within 70-100%. The potential health and ecological risk levels were assessed by comparing them with recommended limits and were found to fall within safe/low risks to unsafe/high risks. In conclusion, this study revealed that there is still little research on BPA in Africa. Levels detected in some matrices call for increased research, stricter health and environmental regulations, and surveillance.

Applications of artificial intelligence for chemical analysis and monitoring of pharmaceutical and personal care products in water and wastewater: A review

Specifying and interpreting the occurrence of emerging pollutants is essential for assessing treatment processes and plants, conducting wastewater-based epidemiology, and advancing environmental toxicology research. In recent years, artificial intelligence (AI) has been increasingly applied to enhance chemical analysis and monitoring of contaminants in environmental water and wastewater. However, their specific roles targeting pharmaceuticals and personal care products (PPCPs) have not been reviewed sufficiently. This review aims to narrow the gap by highlighting, scoping, and discussing the incorporation of AI during the detection and quantification of PPCPs when utilising chemical analysis equipment and interpreting their monitoring data for the first time. In the chemical analysis of PPCPs, AI-assisted prediction of chromatographic retention times and collision cross-sections (CCS) in suspect and non-target screenings using high-resolution mass spectrometry (HRMS) enhances detection confidence, reduces analysis time, and lowers costs. AI also aids in interpreting spectroscopic analysis results. However, this approach still cannot be applied in all matrices, as it offers lower sensitivity than liquid chromatography coupled with tandem or HRMS. For the interpretation of monitoring of PPCPs, unsupervised AI methods have recently presented the capacity to survey regional or national community health and socioeconomic factors. Nevertheless, as a challenge, long-term monitoring data sources are not given in the literature, and more comparative AI studies are needed for both chemical analysis and monitoring. Finally, AI assistance anticipates more frequent applications of CCS prediction to enhance detection confidence and the use of AI methods in data processing for wastewater-based epidemiology and community health surveillance.

Microalgae-bacterial consortiums for enhanced degradation of nonylphenol: Biodegradability and kinetic analysis

The widespread use of non-ionic surfactant nonylphenol (NP) has led to significant water pollution, posing a threat to both ecological stability and human health. However, the efficient biodegradation method and system of NP-biodegradation remain complex scientific challenges. In this study, we isolated and characterized three Pseudomonas sp. strains SW-1 (Scenedesmus quadricauda-associated), ZL-2 (Ankistrodesmus acicularis-associated), XQ-3 (Chlorella vulgaris-associated), and one NP-degrading Cupriavidus sp. strain EB-4, which exhibited the ability to utilize NP as the sole carbon source. Furthermore, four consortiums of microalgae-bacterial, S. quadricauda and SW-1 (S-SW), A. acicularis and ZL-2 (A-ZL), C. vulgaris and XQ-3 (C-XQ), S. quadricauda and EB-4 (S-EB), were constructed to investigate their biodegradability and kinetic characteristics of NP degradation from water. The consortiums showed higher degradation efficiency compared to individual microalgae or bacteria. The C-XQ consortium exhibited the highest degradation rate, removing over 94% of NP within just seven days. The first-order model with the following order of degradation rate by consortiums was C-XQ (0.3960 d-1) > S-SW (0.3506 d-1) > A-ZL (0.1968 d-1) > S-EB (0.1776 d-1). Compared with the results of our previous study, the interaction between microalgae and bacteria is not a simple additive relationship. Our findings highlight the potential of an algal-bacterial consortium for the remediation of NP-contaminated environments.

A global metagenomics-based analysis of BPA degradation and its coupling with nitrogen, sulfur, and methane metabolism in landfill leachates

Microbial metabolism in landfill leachate systems is critically important in driving the degradation reactions of organic pollutants, including the emerging pollutant bisphenol A (BPA). However, little research has addressed the microbial degradation of BPA in landfill leachate and its interactions with nitrogen (N), sulfur (S), and methane (CH4) metabolism on a global scale. To this end, in this study on a global scale, an extremely high concentration of BPA was detected throughout the global landfill leachates. Subsequent reconstructive analyses of metagenomic datasets from 113 sites worldwide revealed that the predominant BPA-degrading microflora included Proteobacteria, Firmicutes, and Bacteroidota. Further metabolic analyses revealed that all four biochemical pathways involved in the degradation of BPA were achieved through biochemical cooperation between different bacterial members of the community. In addition, BPA degraders have also been found to actively collaborate synergistically with non-BPA degraders in the N and S removal as well as CH4 catabolism in landfill leachates. Collectively, this study not only provides insights into the dominant microbial communities and specific types of BPA-degrading microbial members in the community of landfill leachates worldwide, but also reveals the synergistic interactions between BPA mineralization and N, S, and CH4 metabolism. These findings offer valuable and important insights for future comprehensive and in-depth investigations into BPA metabolism in different environments.

Modelling and optimization of membrane process for removal of biologics (pathogens) from water and wastewater: Current perspectives and challenges

As one of the 17 sustainable development goals, the United Nations (UN) has prioritized "clean water and sanitation" (Goal 6) to reduce the discharge of emerging pollutants and disease-causing agents into the environment. Contamination of water by pathogenic microorganisms and their existence in treated water is a global public health concern. Under natural conditions, water is frequently prone to contamination by invasive microorganisms, such as bacteria, viruses, and protozoa. This circumstance has therefore highlighted the critical need for research techniques to prevent, treat, and get rid of pathogens in wastewater. Membrane systems have emerged as one of the effective ways of removing contaminants from water and wastewater However, few research studies have examined the synergistic or conflicting effects of operating conditions on newly developing contaminants found in wastewater. Therefore, the efficient, dependable, and expeditious examination of the pathogens in the intricate wastewater matrix remains a significant obstacle. As far as it can be ascertained, much attention has not recently been given to optimizing membrane processes to develop optimal operation design as related to pathogen removal from water and wastewater. Therefore, this state-of-the-art review aims to discuss the current trends in removing pathogens from wastewater by membrane techniques. In addition, conventional techniques of treating pathogenic-containing water and wastewater and their shortcomings were briefly discussed. Furthermore, derived mathematical models suitable for modelling, simulation, and control of membrane technologies for pathogens removal are highlighted. In conclusion, the challenges facing membrane technologies for removing pathogens were extensively discussed, and future outlooks/perspectives on optimizing and modelling membrane processes are recommended.

Integrating biosorption and machine learning for efficient remazol red removal by algae-bacteria co-culture and comparative analysis of predicted models

This research investigates into the efficacy of algae and algae-bacteria symbiosis (ABS) in efficiently decolorizing Remazol Red 5B, a prevalent dye pollutant. The investigation encompasses an exploration of the biosorption isotherm and kinetics governing the dye removal process. Additionally, various machine learning models are employed to predict the efficiency of dye removal within a co-culture system. The results demonstrate that both Desmodesmus abundans and a composite of Desmodesmus abundans and Rhodococcus pyridinivorans exhibit significant dye removal percentages of 75 ± 1% and 78 ± 1%, respectively, after 40 min. The biosorption isotherm analysis reveals a significant interaction between the adsorbate and the biosorbent, and it indicates that the Temkin model best matches the experimental data. Moreover, the Langmuir model indicates a relatively high biosorption capacity, further highlighting the potential of the algae-bacteria composite as an efficient adsorbent. Decision Trees, Random Forest, Support Vector Regression, and Artificial Neural Networks are evaluated for predicting dye removal efficiency. The Random Forest model emerges as the most accurate, exhibiting an R2 value of 0.98, while Support Vector Regression and Artificial Neural Networks also demonstrate robust predictive capabilities. This study contributes to the advancement of sustainable dye removal strategies and encourages future exploration of hybrid approaches to further enhance predictive accuracy and efficiency in wastewater treatment processes.

Machine learning modeling and additive explanation techniques for glutathione production from multiple experimental growth conditions of Saccharomyces cerevisiae

Glutathione (GSH) production is of great industrial interest due to its essential properties. This study aimed to use machine learning (ML) methods to model GSHproduction under different growth conditions of Saccharomyces cerevisiae, namely cultivation time, culture volume, pressure, and magnetic field application. Different ML and regression models were evaluated for their statistics to select the most robust model. Results showed that eXtreme Gradient Boosting (XGB) was the best predictive performance model. From the best model, additive explanation techniques were used to identify the feature importance of process. According to variable analysis, the best conditions to obtain the highest GSH concentrations would be cultivation times of 72-96 h, low magnetic field intensity (3.02 mT), low pressure (0.5 kgf.cm-2), and high culture volume (3.5 L). XGB use and additive explanation techniques proved promising for determining process optimization conditions and selecting the essential process variables.

Harnessing the potential of microalgae-bacteria interaction for eco-friendly wastewater treatment: A review on new strategies involving machine learning and artificial intelligence

In the pursuit of effective wastewater treatment and biomass generation, the symbiotic relationship between microalgae and bacteria emerges as a promising avenue. This analysis delves into recent advancements concerning the utilization of microalgae-bacteria consortia for wastewater treatment and biomass production. It examines multiple facets of this symbiosis, encompassing the judicious selection of suitable strains, optimal culture conditions, appropriate media, and operational parameters. Moreover, the exploration extends to contrasting closed and open bioreactor systems for fostering microalgae-bacteria consortia, elucidating the inherent merits and constraints of each methodology. Notably, the untapped potential of co-cultivation with diverse microorganisms, including yeast, fungi, and various microalgae species, to augment biomass output. In this context, artificial intelligence (AI) and machine learning (ML) stand out as transformative catalysts. By addressing intricate challenges in wastewater treatment and microalgae-bacteria symbiosis, AI and ML foster innovative technological solutions. These cutting-edge technologies play a pivotal role in optimizing wastewater treatment processes, enhancing biomass yield, and facilitating real-time monitoring. The synergistic integration of AI and ML instills a novel dimension, propelling the fields towards sustainable solutions. As AI and ML become integral tools in wastewater treatment and symbiotic microorganism cultivation, novel strategies emerge that harness their potential to overcome intricate challenges and revolutionize the domain.

Microalgal-based bioremediation of emerging contaminants: Mechanisms and challenges

Emerging contaminants (ECs) in different ecosystems have consistently been acknowledged as a global issue due to toxicity, human health implications, and potential role in generating and disseminating antimicrobial resistance. The existing wastewater treatment system is incompetent at eliminating ECs since the effluent water contains significant concentrations of ECs, viz., antibiotics (0.03-13.0 μg L-1), paracetamol (50 μg L-1), and many others in varying concentrations. Microalgae are considered as a prospective and sustainable candidate for mitigating of ECs owing to some peculiar features. In addition, the microalgal-based processes also offer cost and energy-efficient solutions for the bioremediation of ECs than conventional treatment systems. It is pertinent that, microalgal-based processes also provides waste valorization benefits as microalgal biomass obtained after ECs treatment can be potentially applied to generate biofuels. Moreover, microalgae can effectively utilize alternative metabolic (cometabolism) routes for enhanced degradation of ECs. Additionally, the ECs removal via the microalgal biodegradation route is highly promising as it can transform the ECs into less toxic compounds. The present review comprehensively discusses different mechanisms involved in removing ECs and various factors that affect their removal. Also, the technoeconomic feasibility of microalgae than other conventional wastewater treatment methods is summarised. The review also highlighted the different molecular and genetic tools that can augment the activity and robustness of microalgae for better removal of organic contaminants. Finally, we have summarised the challenges and future research required towards microalgal-based bioremediation of emerging contaminants (ECs) as a holistic approach.

Treatment of atrazine-containing wastewater by algae-bacteria consortia: Signal transmission and metabolic mechanism

Atrazine is a toxic endocrine disruptor. Biological treatment methods are considered to be effective. In the present study, a modified version of the algae-bacteria consortia (ABC) was established and a control was simultaneously set up to investigate the synergistic relationship between bacteria and algae and the mechanism by which atrazine is metabolized by those microorganisms. The total nitrogen (TN) removal efficiency of the ABC reached 89.24% and the atrazine concentration was reduced to below the level recommended by the Environment Protection Agency (EPA) regulatory standards within 25 days. The protein signal released from the extracellular polymeric substances (EPS) secreted by the microorganisms triggered the resistance mechanism of the algae, and the conversion of humic acid to fulvic acid and electron transfer constituted the synergistic mechanism between the bacteria and algae. The mechanism by which atrazine is metabolized by the ABC mainly consists of hydrogen bonding, H-pi interactions, and cation exchange with atzA for hydrolysis, followed by a reaction with atzC for decomposition to non-toxic cyanuric acid. Proteobacteria was the dominant phylum for bacterial community evolution under atrazine stress, and the analysis revealed that the removal of atrazine within the ABC was mainly dependent on the proportion of Proteobacteria and the expression of degradation genes (p < 0.01). EPS played a major role in the removal of atrazine within the single bacteria group (p < 0.01).

Removal of parabens from wastewater by Chlorella vulgaris-bacteria co-cultures

Anthropogenic activities have increased the dispersal of emerging contaminants (ECs), particularly of parabens, causing an escalation of their presence in wastewater (WW). Current WW technologies do not present satisfactory efficiency or sustainability in removing these contaminants. However, bioremediation with microalgae-based systems is proving to be a relevant technology for WW polishing, and the use of microalgae-bacteria consortia can improve the efficiency of WW treatment. This work aimed to study dual cultures of selected bacteria (Raoultella ornithinolytica, Acidovorax facilis, Acinetobacter calcoaceticus, Leucobacter sp. or Rhodococcus fascians) and the microalga Chlorella vulgaris in microbial growth and WW bioremediation - removal of methylparaben (MetP) and nutrients. The association with the bacteria was antagonistic for C. vulgaris biomass productivity as a result of the decreased growth kinetics in comparison to the axenic microalga. The presence of MetP did not disturb the growth of C. vulgaris under axenic or co-cultured conditions, except when associated with R. fascians, where growth enhancement was observed. The removal of MetP by the microalga was modest (circa 30 %, with a removal rate of 0.0343 mg/L.d), but increased remarkably when the consortia were used (> 50 %, with an average removal rate > 0.0779 mg/L.d), through biodegradation and photodegradation. For nutrient removal, the consortia were found to be less effective than the axenic microalga, except for nitrogen (N) removal by C. vulgaris w/ R. fascians. The overall results propose that C. vulgaris co-cultivation with bacteria can increase MetP removal, while negatively affecting the microalga growth and the consequent reduction of sludge production, highlighting the potential of microalgae-bacteria consortia for the effective polishing of WW contaminated with parabens.

Multi-omics approach reveals elevated potential of bacteria for biodegradation of imidacloprid

The residual imidacloprid, a widely used insecticide is causing serious environmental concerns. Knowledge of its biodegradation will help in assessing its residual mass in soil. In view of this, a soil microcosm-based study was performed to test the biodegradation potential of Agrobacterium sp. InxBP2. It achieved ∼88% degradation in 20 days and followed the pseudo-first-order kinetics (k = 0.0511 day^-1 and t_1/2=7 days). Whole genome sequencing of Agrobacterium sp. InxBP2 revealed a genome size of 5.44 Mbp with 5179 genes. Imidacloprid degrading genes at loci K7A42_07110 (ABC transporter substrate-binding protein), K7A42_07270 (amidohydrolase family protein), K7A42_07385 (ABC transporter ATP-binding protein), K7A42_16,845 (nitronate monooxygenase family protein), and K7A42_20,660 (FAD-dependent monooxygenase) having sequence and functional similarity with known counterparts were identified. Molecular docking of proteins encoded by identified genes with their respective degradation pathway intermediates exhibited significant binding energies (-6.56 to -4.14 kcal/mol). Molecular dynamic simulation discovered consistent interactions and binding depicting high stability of docked complexes. Proteome analysis revealed differential protein expression in imidacloprid treated versus untreated samples which corroborated with the in-silico findings. Further, the detection of metabolites proved the bacterial degradation of imidacloprid. Thus, results provided a mechanistic link between imidacloprid and associated degradative genes/enzymes of Agrobacterium sp. InxBP2. These findings will be of immense significance in carrying out the lifecycle analysis and formulating strategies for the bioremediation of soils contaminated with insecticides like imidacloprid.