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New Delhi Metallo-β-lactamase-1 (NDM-1) offers carbapenem antibiotics resistance that creates an evolving challenge in treating bacterial infections. NDM-1-bearing strains were observed in surface waters around New Delhi in 2010 and after then identified globally. The usage of antibiotics may hasten the growth of the NDM-1-producing bacteria, which pose severe hazards to human and animal health. The emergence of the NDM-1 in the aquatic environment is turning out to be a growing concern worldwide. NDM-1 gene conferring resistance to a widespread class of antibiotics has been observed in bacteria disseminated in animal production wastewaters, hospital sewage, domestic sewage, industrial effluents, wastewater treatment plants, drinking water, surface water, and even in groundwater. This review recapitulates the currently published research studies on the prevalence and geographical distribution of the NDM-1 gene in the aquatic environment, its habitats, and healthcare risk associated with NDM-1-producing bacteria, in addition to molecular techniques employed to reveal the occurrence of the NDM-1 in the aquatic environment, including conventional polymerase chain reaction, real-time qPCR, DNA hybridization, and microarray-based methods.

Contaminated sites are recognized as the “hotspot” for the development and spread of antibiotic resistance in environmental bacteria. It is very challenging to understand mechanism of development of antibiotic resistance in polluted environment in the presence of different anthropogenic pollutants. Uncertainties in the environmental processes adds complexity to the development of resistance. This study attempts to develop mathematical model by using stochastic partial differential equations for the transport of fluoroquinolone and its resistant bacteria in riverine environment. Poisson’s process is assumed for the diffusion approximation in the stochastic partial differential equations (SPDE). Sensitive analysis is performed to evaluate the parameters and variables for their influence over the model outcome. Based on their sensitivity, the model parameters and variables are chosen and classified into environmental, demographic, and anthropogenic categories to investigate the sources of stochasticity. Stochastic partial differential equations are formulated for the state variables in the model. This SPDE model is then applied to the 100 km stretch of river Musi (South India) and simulations are carried out to assess the impact of stochasticity in model variables on the resistant bacteria population in sediments. By employing the stochasticity in model variables and parameters we came to know that environmental and anthropogenic variations are not able to affect the resistance dynamics at all. Demographic variations are able to affect the distribution of resistant bacteria population uniformly with standard deviation between 0.087 and 0.084, however, is not significant to have any biological relevance to it. The outcome of the present study is helpful in simplifying the model for practical applications. This study is an ongoing effort to improve the model for the transport of antibiotics and transport of antibiotic resistant bacteria in polluted river. There is a wide gap between the knowledge of stochastic resistant bacterial growth dynamics and the knowledge of transport of antibiotic resistance in polluted aquatic environment, this study is one step towards filling up that gap.

Development of antibiotic resistance in environmental bacteria is a direct threat to public health. Therefore, it becomes necessary to understand the fate and transport of antibiotic and its resistant bacteria. This paper presents a mathematical model for spatial and temporal transport of fluoroquinolone and its resistant bacteria in the aquatic environment of the river. The model includes state variables for organic matter, fluoroquinolone, heavy metals, and susceptible and resistant bacteria in the water column and sediment bed. Resistant gene is the factor which makes bacteria resistant to a particular antibiotic and is majorly carried on plasmids. Plasmid-mediated resistance genes are transferable between different bacterial species through conjugation (horizontal resistance transfer). This model includes plasmid dynamics between susceptible and resistant bacteria by considering the rate of horizontal resistance gene transfer among bacteria and the rate of losing resistance (segregation). The model describes processes which comprise of advection, dispersion, degradation, adsorption, diffusion, settling, resuspension, microbial growth, segregation, and transfer of resistance genes. The mathematical equations were solved by using numerical methods (implicit-explicit scheme) with appropriate boundary conditions. The development of the present model was motivated by the fact that the Musi River is heavily impacted by antibiotic pollution which led to the development of antibiotic resistance in its aquatic environment. The model was simulated for hypothetical pollution scenarios to predict the future conditions under various pollution management alternatives. The simulation results of the model for different cases show that the concentration of antibiotic, the concentration of organic matter, segregation rate, and horizontal transfer rate are the governing factors in the variation of population density of resistant bacteria. The treatment of effluents for antibiotics might be costly for the bulk drug manufacturing industries, but the guidelines can be made to reduce the organic matter which can limit the growth rate of microbes and reduce the total microbial population in the river. The reduction in antibiotic concentration can reduce the selection pressure on bacteria and can limit the population of resistant culture and its influence zone in the river stretch; however, complete removal of antibiotics may not result in complete elimination of antibiotic-resistant bacteria.

Active pharmaceutical ingredients (APIs) can enter the natural environment during manufacture, use and/or disposal, and consequently public concern about their potential adverse impacts in the environment is growing. Despite the bulk of the human population living in Asia and Africa (mostly in low- or middle-income countries), limited work relating to research, development and regulations on APIs in the environment have so far been conducted in these regions. Also, the API manufacturing sector is gradually shifting to countries with lower production costs. This paper focuses mainly on APIs for human consumption and highlights key differences between the low-, middle- and high-income countries, covering factors such as population and demographics, manufacture, prescriptions, treatment, disposal and reuse of waste and wastewater. The striking differences in populations (both human and animal), urbanization, sewer connectivity and other factors have revealed that the environmental compartments receiving the bulk of API residues differ markedly between low- and high-income countries. High sewer connectivity in developed countries allows capture and treatment of the waste stream (point-source). However, in many low- or middle-income countries, sewerage connectivity is generally low and in some areas waste is collected predominantly in septic systems. Consequently, the diffuse-source impact, such as on groundwater from leaking septic systems or on land due to disposal of raw sewage or septage, may be of greater concern. A screening level assessment of potential burdens of APIs in urban and rural environments of countries representing low- and middle-income as well as high-income has been made. Implications for ecological risks of APIs used by humans in lower income countries are discussed.

In this study, we present a scenario to evaluate the backwater impacts on upstream of the Polavaram dam during floods. For this purpose, annual peak discharges across the different gauge stations in river stretch considered for flood frequency analysis. Statistical analysis is carried out for discharge data to estimate probable flood discharge values for 1000 and 10,000 years return period along with 0.1 and 0.14 million m3/s discharge. Furthermore, the resulting flood discharge values are converted to water level forecasts using a steady and unsteady flow hydraulic model, such as HEC-RAS. The water surface elevation at Bhadrachalam river stations with and without dam was estimated for 1000 and 10,000 years discharge. Unsteady 2D flow simulations with and without the dam with full closure and partial closure modes of gate operation were analysed. The results showed that with half of the gates as open and all gates closed, water surface elevation of 62.34 m and 72.34 m was obtained at Bhadrachalam for 1000 and 10,000 years. The 2D unsteady flow simulations revealed that at improper gate operations, even with a flow of 0.1 million m3/s, water levels at Bhadrachalam town will be high enough to submerge built-up areas and nearby villages.

In this study, the ability of ozone to oxidise toluene present in low levels into CO and CO2 was studied. The catalytic ozonation of toluene was carried out in a micro-fixed bed reactor. The oxidation was done in two steps: toluene adsorption on the catalyst followed by sequential ozone desorption. Toluene breakdown by ozone at low temperature and atmospheric pressure was achieved using γ-Al2O3 supported transition metal oxides impregnated with a reduced noble metal. The catalyst Ag–CoOx/γ-Al2O3 efficiently oxidised and transformed toluene into products (52.4% COx yield). This catalyst has a high surface area, more acidic sites, and lattice oxygens for better toluene oxidation. The addition of Ag to the CoOx/γ-Al2O3 catalyst surface improved toluene adsorption on the catalyst surface, resulting in improved product yield, selectivity, and carbon balance.

Zebrafish (Danio rerio) have gained prominence as a model organism in biomedical research due to its genetic accessibility, optical transparency during embryonic development, and physiological similarities to humans. These traits make zebrafish ideal for studying various human diseases, though broader insights into their diverse applications are still needed. This review explores zebrafish as a versatile model for studying both communicable and non‐communicable diseases. In communicable diseases, it has become a powerful model for studying host–pathogen interactions, immune responses, and therapeutic screening, with its transparency enabling real‐time tracking of infections. Robust models also exist for many bacterial, viral, and fungal pathogens, supported by early innate immune cell development. Additionally, microinjection techniques enable precise local or systemic infections, making zebrafish a versatile, high‐resolution model for studying disease mechanisms. For non‐communicable diseases, zebrafish support research on cardiovascular, metabolic, neurodegenerative disorders, and cancer. This review highlights recent advances in using zebrafish to study disease mechanisms, drug discovery, and therapies. It underscores the academic and translational value of zebrafish, promoting innovative strategies to improve human health outcomes. Their versatility across disciplines makes them an effective tool for both fundamental research and biomedical education, positioning them as a bridge between basic science and clinical applications. The zebrafish, with its optical transparency and genetic tractability, serves as a versatile in vivo platform for biomedical research. This review underscores its significance in modeling a wide spectrum of human diseases—from cancer to metabolic disorders—and in leveraging cutting‐edge technologies like CRISPR and TALENs. By synthesizing diverse applications, we highlight the zebrafish's crucial role in advancing translational research and therapeutic development.

Electrokinetic remediation technique is widely applied for the removal of heavy metal from contaminated soil, but the soil buffering capacity and fractionation of heavy metals mainly affect the cost and duration of the treatment. This study aims to treat heavy metal-contaminated sediments by electrokinetic remediation (EKR) technique by using various enhancing agents such as EDTA, H N O 3 , HCI, H 2 S O 4 , acetic acid and citric acid for optimizing the cost and treatment duration. The optimum molar concentration of enhancing agent for treatment was estimated by batch experiments to maximize the dissolution of target heavy metals and reduce the dissolution of earth metals (Fe, Al and Ca) to maintain soil health. The EKR experiments were performed up to 15 days with the above enhancing agents to reduce the risk associated with heavy metals and the selection of enhancing agents based on removal efficiency was found to be in an order of EDTA > citric acid > acetic acid > H N O 3 > HCl ≥ H 2 S O 4 . Also, a numerical model has been developed by incorporating main electrokinetic transport phenomena (electromigration and electroosmosis) and geochemical processes for the prediction of treatment duration and to scale up the EKR process. The model predicts well with experimental heavy metal removal with a MAPD of ≈ 2-18 %. The parametric study on electrode distance for full-scale EKR treatment was found in this study as ≈ 0.5 m.

This review highlights various experimental and mathematical modeling strategies to investigate the fate and transport of antibiotics that elucidate antimicrobial selective pressure in aquatic environments. Globally, the residual antibiotic concentrations in effluents from bulk drug manufacturing industries were 30- and 1500-fold greater than values reported in municipal and hospital effluents, respectively. The antibiotic concentration from different effluents enters the waterbodies that usually get diluted as they go downstream and undergo various abiotic and biotic reactive processes. In aquatic systems, photolysis is the predominant process for antibiotic reduction in the water matrix, while hydrolysis and sorption are frequently reported in the sediment compartment. The rate of antibiotic reduction varies widely with influencing factors such as the chemical properties of the antibiotics and hydrodynamic conditions of river streams. Among all, tetracycline was found to more unstable (log K ow  =  − 0.62 to − 1.12) that can readily undergo photolysis and hydrolysis; whereas macrolides were more stable (log K ow  = 3.06 to 4.02) that are prone to biodegradation. The processes like photolysis, hydrolysis, and biodegradation followed first-order reaction kinetics while the sorption followed a second-order kinetics for most antibiotic classes with reaction rates occurring in the decreasing order of Fluoroquinolones and Sulphonamides. The reports from various experiments on abiotic and biotic processes serve as input parameters for an integrated mathematical modeling to predict the fate of the antibiotics in the aquatic environment. Various mathematical models viz. Fugacity level IV, RSEMM, OTIS, GREAT-ER, SWAT, QWASI, and STREAM-EU are discussed for their potential capabilities. However, these models do not account for microscale interactions of the antibiotics and microbial community under real-field conditions. Also, the seasonal variations for contaminant concentrations that exert selective pressure for antimicrobial resistance has not been accounted. Addressing these aspects collectively is the key to exploring the emergence of antimicrobial resistance. Therefore, a comprehensive model involving antimicrobial resistance parameters like fitness cost, bacterial population dynamics, conjugation transfer efficiency, etc. is required to predict the fate of antibiotics.

Significance Despite growing evidence of the deleterious effects on ecological and human health, little is known regarding the global occurrence of pharmaceuticals in rivers. Studies assessing their occurrence are available for 75 of 196 countries, with most research conducted in North America and Western Europe. This leaves large geographical regions relatively unstudied. Here, we present the findings of a global reconnaissance of pharmaceutical pollution in rivers. The study monitored 1,052 sampling sites along 258 rivers in 104 countries of all continents, thus representing the pharmaceutical fingerprint of 471.4 million people. We show that the presence of these contaminants in surface water poses a threat to environmental and/or human health in more than a quarter of the studied locations globally.