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Plant-based coagulants/flocculants are foreseen to be a major progress in water treatment technology owing to their safety, biodegradability and availability, unlike synthetic chemical water refiners such as Al, Fe salts and organic polymers claimed to cause threats to our ecosystem either via their residues in the treated waters or due to their generated toxic sludge. Further, the increasing global awareness about environmental issues is acting as a driving force behind the interest toward the use of green resources as valuable products for water treatment. Substitution of synthetic coagulants/flocculants by such natural materials can not only minimize ecosystem damages and threats, but would also foster the way toward an era of clean technology and a sustainable environment. The present paper reviews works on the most efficient model plant-based coagulants/flocculants, moringa seeds, cactus pads, okra seed pods and mango kernels, via highlighting their effectiveness in treating a variety of waters. This review focuses also on the extracting processes used for their preparation, on the type of their active compounds as well as on water pollutant removal mechanisms. Among the four known coagulation–flocculation phenomenon, both polymer bridging and charge neutralization were assumed to be the main predominant mechanisms of bio-coagulants/bio-flocculants toward water contaminant removal. Further, this paper sheds light on where future works should head aiming to stress on the exploitation of green materials in water remediation. We believe that this review can provide an immediate platform for scientists to intensify their research on more efficient natural products to be used in water processing for the sake of a safer environment.Graphic abstract

Over these last years, there is no doubt that the conventional chemical flocculants commonly used for wastewater treatment have been a source of serious human health threats and environmental damage. Consequently, safe and eco-friendly substitutes are worth looking for and assessing. Within this line, flocculants derived from cactus namely, cladodes juice (CJ), powders of lyophilized (CLP) and oven-dried (CDP) cladodes, were developed as alternatives to the noxious synthetic ones. The flocculating activity of these three extracts was evaluated in treating a synthetic Disperse Blue-1 (DB-1) dye solution and a real industrial effluent loaded with heavy metals. A prominent DB-1 removal of up to 80% was achieved using CJ, CLP and CDP. Significant colour and turbidity reductions (94%) were attained using only 20 mg/L of CLP. Likewise, the cactus bio-flocculants complementing alum as a coagulant ensured an enhanced Zn removal from the industrial wastewater. For instance, both CLP and CDP allowed salient Zn uptake exceeding 99% against 69% using the CJ formula. The slight disparity in the flocculating activity between these three formulations could be ascribed to their preparation procedures affecting the integrity of their active agents (polysaccharides and chiefly polygalacturonic acid). Further, it is thought that the presence of hydroxyl (–OH) and carboxyl (–COOH) groups on this latter’s backbone confers the cactus extracts a notable flocculating ability regardless of the type of water pollutants. The plausible flocculation mechanisms for DB-1 molecules and Zn removal are assumed to be adsorption-bridging and adsorption-charge neutralization, respectively.

The global evolution of pre-hospital care systems faces dynamic challenges, particularly in multinational settings. Machine learning (ML) techniques enable the exploration of deeply embedded data patterns for improved patient care and resource optimisation. This study's objective was to accurately predict cases that necessitated transportation versus those that did not, using ML techniques, thereby facilitating efficient resource allocation. ML algorithms were utilised to predict patient transport decisions in a Middle Eastern national pre-hospital emergency medical care provider. A comprehensive dataset comprising 93,712 emergency calls from the 999-call centre was analysed using R programming language. Demographic and clinical variables were incorporated to enhance predictive accuracy. Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost) algorithms were trained and validated. All the trained algorithm models, particularly XGBoost (Accuracy = 83.1%), correctly predicted patients' transportation decisions. Further, they indicated statistically significant patterns that could be leveraged for targeted resource deployment. Moreover, the specificity rates were high; 97.96% in RF and 95.39% in XGBoost, minimising the incidence of incorrectly identified \"Transported\" cases (False Positive). The study identified the transformative potential of ML algorithms in enhancing the quality of pre-hospital care in Qatar. The high predictive accuracy of the employed models suggested actionable avenues for day and time-specific resource planning and patient triaging, thereby having potential to contribute to pre-hospital quality, safety, and value improvement. These findings pave the way for more nuanced, data-driven quality improvement interventions with significant implications for future operational strategies.

Due its high water content, sewage sludge dewatering is not just a simple operation; rather, it is a challenging process and a costly management task. Its final handling is usually preceded by several dewatering steps, and among them is the conditioning process known as the flocculation stage, which is carried out using synthetic chemical reagents. Despite the abilities of these additives to reduce sludge volume and extract its bound waters, they are suspected to cause serious environmental and health threats. Their substitution by natural and efficient additives originating from plant extracts could thus be a safe and an eco-friendly alternative, overcoming ecosystem damages. It is within this context that the present review paper critically investigates the efficacy and feasibility of plant-based flocculants, aiming to enhance sludge dewatering and dispense with environmental burdens. To do so, the types of the conventional chemical flocculants, their drawbacks, and their impacts on the ecosystem and human health were addressed. In parallel, the potential dewatering efficiency of plant extracts toward sludge treatment was compiled, and their mechanistic dewatering paths performances were thoroughly discussed. The challenges associated with dewatered sludge and its potential exploitation were also highlighted to motivate scientific communities to further explore green resources for sludge processing. It is suggested that green resources such as Moringa, Cactus, Aloe, and Okra could be used as green flocculants instead of chemical ones, which would provide a promising and eco-sustainable approach to sludge dewatering and might represent a path towards an environmentally friendly and clean technology.

Microbial extracellular polymeric substances (EPSs) are emerging as sustainable alternatives to conventional corrosion inhibitors due to their eco-friendly nature, biodegradability, and functional versatility. Secreted by diverse microorganisms including bacteria, fungi, archaea, and algae, EPSs are composed mainly of polysaccharides, proteins, lipids, and nucleic acids. These biopolymers, chiefly polysaccharides and proteins, are accountable for surface corrosion prevention through biofilm formation, allowing microbial survival and promoting their environmental adaptation. Usually, EPS-mediated corrosion inhibitions can take place via different mechanisms: protective film formation, metal ions chelation, electrochemical property alteration, and synergy with inorganic inhibitors. Even though efficacious EPS corrosion prevention has been demonstrated in several former studies, the application of such microbial inhibitors remains, so far, a controversial topic due to the variability in their composition and compatibility toward diverse metal surfaces. Thus, this review outlines the microbial origins, biochemical properties, and inhibition mechanisms of EPSs, emphasizing their advantages and challenges in industrial applications. Advances in synthetic biology, nanotechnology, and machine learning are also highlighted and could provide new opportunities to enhance EPS production and functionality. Therefore, the adoption of EPS-based corrosion inhibitors represents a promising strategy for environmentally sustainable corrosion control.

Water pollution continues to pose a critical global challenge, largely due to the unregulated discharge of industrial, agricultural, and municipal effluents. Among emerging solutions, enzymatic bioremediation stands out as a sustainable and environmentally friendly approach, offering high specificity and efficiency under mild conditions. Nonetheless, the practical application of free enzymes is hindered by their inherent instability, poor reusability, and susceptibility to denaturation. To address these limitations, the immobilization of enzymes onto solid supports, particularly clay minerals, has garnered increasing attention. This review presents a detailed analysis of clay minerals as promising carriers for enzyme immobilization in wastewater treatment. It explores their classification, structural characteristics, and physicochemical properties, highlighting key advantages such as a large surface area, cation exchange capacity, and thermal stability. Functionalization techniques, including acid/base activation, intercalation, grafting, and pillaring, are discussed in terms of improving enzyme compatibility and catalytic performance. Various immobilization methods such as physical adsorption, covalent bonding, entrapment, crosslinking, and intercalation are critically evaluated with regard to enhancing enzyme activity, stability, and recyclability. Recent case studies demonstrate the effective removal of pollutants such as dyes, pharmaceuticals, and heavy metals using enzyme–clay composites. Despite these advances, challenges such as enzyme leaching, mass transfer resistance, and variability in clay composition persist. This review concludes by outlining future prospects, including the development of hybrid and magnetic clay-based systems and their integration into advanced water treatment technologies. Overall, enzyme immobilization on clay minerals represents a promising and scalable approach for the next generation of wastewater bioremediation strategies.

Background Sfax is a very industrialized city located in the southern region of Tunisia where heavy metals (HMs) pollution is now an established matter of fact. The health of its residents mainly those engaged in industrial metals-based activities is under threat. Indeed, such workers are being exposed to a variety of HMs mixtures, and this exposure has cumulative properties. Whereas current HMs exposure assessment is mainly carried out using direct air monitoring approaches, the present study aims to assess health risks associated with chronic occupational exposure to HMs in industry, using a modeling approach that will be validated later on. Methods To this end, two questionnaires were used. The first was an identification/descriptive questionnaire aimed at identifying, for each company: the specific activities, materials used, manufactured products and number of employees exposed. The second related to the job-task of the exposed persons, workplace characteristics (dimensions, ventilation, etc.), type of metals and emission configuration in space and time. Indoor air HMs concentrations were predicted, based on the mathematical models generally used to estimate occupational exposure to volatile substances (such as solvents). Later on, and in order to validate the adopted model, air monitoring will be carried out, as well as some biological monitoring aimed at assessing HMs excretion in the urine of workers volunteering to participate. Lastly, an interaction-based hazard index HI int and a decision support tool will be used to predict the cumulative risk assessment for HMs mixtures. Discussion One hundred sixty-one persons working in the 5 participating companies have been identified. Of these, 110 are directly engaged with HMs in the course of the manufacturing process. This model-based prediction of occupational exposure represents an alternative tool that is both time-saving and cost-effective in comparison with direct air monitoring approaches. Following validation of the different models according to job processes, via comparison with direct measurements and exploration of correlations with biological monitoring, these estimates will allow a cumulative risk characterization.

Background During the COVID-19 pandemic, there was a surge in pre-hospital emergency calls due to the increased prevalence of flu-like symptoms and panic related to the pandemic. However, some patients declined transportation to hospital due to their fear of accessing healthcare facilities. This posed a significant risk to their health outcomes. This study aimed to utilise an extensive dataset, which included the period of the COVID-19 pandemic, in a modern Middle Eastern Emergency Medical Service to comprehend and predict the behaviour of non-transport decisions, a major multi-variable factor in pre-hospital emergency medicine. Methods Using Python ® programming language, this study employed various supervised machine-learning algorithms, including parametric probabilistic models, such as logistic regression, and non-parametric models, including decision trees, random forest (RF), extra trees, AdaBoost, and k-nearest neighbours (KNN), using a dataset of non-transported patients (refused transport and did not receive treatment versus those who refused transport and received treatment) between 2018 and 2022. Model performance was comprehensively evaluated using Accuracy, F1 score, Matthews correlation coefficient (MCC), receiver operating characteristic area under the curve (ROC AUC), kappa, and R-squared metrics to ensure robust model selection. Results From June 2018 to July 2022, 334,392 non-transport cases were recorded. The random forest model demonstrated the best optimised predictive performance, with accuracy = 74.78%, F1 = 0.74, MCC = 0.35, ROC AUC = 0.81, kappa = 0.34, and R-squared = 0.81. Conclusion This study indicated that predictive modelling could accurately help identify patients who refuse transport to hospital and may not require treatment on the scene. This enables them to be redirected from the call-taking phase to alternative primary healthcare facilities. This reduces the strain on emergency healthcare resources. The findings suggest that machine learning has the potential to revolutionise pre-hospital care, especially during pandemics, by improving resource allocation and patient outcomes, while highlighting the need for ongoing research to refine these models.

With the aim to establish clean and sustainable sludge treatment, green conditioning using natural flocculants has recently gained a growing interest. In this study, a variety of plant materials, namely Moringa (Moringa oleifera) seeds, Fenugreek (Trigonella foenum-graecum) seeds, Potato (Solanum tuberosum) peels, Aloe (Aloe vera) leaves, Cactus (Opuntia ficus indica) cladodes, and Phragmites (Phragmites australis) stems, were evaluated for their potential bioflocculant activity in conditioning sewage sludge. They were thoroughly characterized to determine their active flocculating compounds. Sludge dewaterability was evaluated by assessing various sludge parameters, including specific resistance to filtration (SRF), dryness of filtration cake (DC), and total suspended solid removal (TSS) from sludge filtrate. The collected results from various physicochemical characterizations of plant materials suggest that the main flocculating agents are carbohydrates in Cactus and Fenugreek and proteins in Moringa, Potato, and Phragmites. Additionally, all tested plant-based flocculants demonstrated effective dewatering performance. Interestingly, compared to the chemical flocculant polyaluminum chloride, Moringa and Cactus showed superior conditioning effects, yielding the lowest SRF values and the highest DC. As a result, the use of these natural flocculants improved sewage sludge filterability, leading to a significant removal of total suspended solids from the filtrate. The conditioning properties of Moringa and Cactus can be attributed to their high protein and sugar content, which facilitates the effective separation of bound water from solids through charge neutralization and bridging mechanisms. Thus, green conditioning using plant-based flocculants, particularly Moringa and Cactus materials, presents a promising and eco-friendly approach to enhance sewage sludge dewatering for safer disposal and valorization.

BackgroundIn pre-hospital emergency care, decisions regarding patient non-conveyance emerged as significant determinants of healthcare outcomes and resource utilization. These complex decisions became integral to the progress of emergency medical services, thus warranting an evolving exploration within the medical discourse.Objectives and methodsThis narrative review aimed to synthesize and critically evaluate various theoretical stances on patient non-conveyance in the pre-hospital emergency. The focus on studies published between January 2012 and August 2022 was intentional to capture contemporary practices and insights. PubMed and Google Scholar served as the primary databases for the investigation, while the AL-Rayyan® software facilitated a thorough screening process.Results and discussionTwenty-nine studies—encompassing articles, books, and theses—were discovered through our search, each presenting unique perspectives on patient non-transport, thus highlighting its criticality as a healthcare concern. Predominant factors influencing non-transport decisions were classified into patient-initiated refusals (PIR), clinician-initiated decisions (CID), and dispatcher-initiated decisions (DID).ConclusionsThe issue of patient non-conveyance to hospitals continues to pose a crucial challenge to the seamless operation of emergency healthcare systems, warranting increased attention from various healthcare entities. To comprehend and pinpoint potential areas of improvement, a comprehensive analysis of pre-hospital non-transport events is imperative. A well-informed, strategic approach could prevent resource waste while ensuring patients receive the required and definitive care.Key messagesWhy is this topic important?Some studies have suggested that non-transport to hospitals following emergency calls is safe. However, it is a concerning issue for health systems. It is also considered a key performance metric for health systems.What does this review attempt to show?This review aimed to map the various factors discussed in the literature regarding the decisions not to transport patients following emergency calls in a pre-hospital setting.What are the key findings?The existing theories regarding non-transport to hospitals after the provision of emergency care in the pre-hospital setting were identified. Non-transport due to non-clinical decisions jeopardizes emergency care outcomes for paediatric and elderly patients in particular. Hence, further research is required to identify and control the factors governing these decisions.How is patient care impacted?The decisions regarding patient transport following emergency calls in a pre-hospital setting are crucial for patient outcomes. They could impact the pre-hospital emergency care outcomes as well as patient safety. They can also affect the emergency services resources’ ability to respond to other critical emergencies.