Degradation of antibiotic contaminants from water by gas–liquid underwater discharge plasma

Antibiotic contamination adversely affects human health and ecological balance.In this study,gasliquid underwater discharge plasma was employed to simultaneously degrade three antibiotics,sulfadiazine(SDZ),tetracycline(TC),and norfloxacin(NOR),to address the growing problem of antibiotic contaminants in water.The effects of various parameters on the antibiotic degradation efficiency were evaluated,including the discharge gas type and flow rate,the initial concentration and pH of the solution,and the discharge voltage.Under the optimum parameter configuration,the average removal rate of the three antibiotics was 54.0% and the energy yield was 8.9 g(kW·h)-1after 5 min treatment;the removal efficiency was 96.5% and the corresponding energy yield was4.0 g(kW·h)-1 after 20 min treatment.Reactive substance capture and determination experiments indicated that ·OH and O3 played a vital role in the decomposition of SDZ and NOR,but the role of reactive substances in TC degradation was relatively less significant.

A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

Antibiotics are widely used in medicine and animal husbandry.However,due to the resistance of antibiotics to degradation,large amounts of antibiotics enter the environment,posing a potential risk to the ecosystem and public health.Therefore,the detection of antibiotics in the environment is necessary.Nevertheless,conventional detection methods usually involve complex pretreatment techniques and expensive instrumentation,which impose considerable time and economic costs.In this paper,we proposed a method for the fast detection of mixed antibiotics based on simplified pretreatment using spectral machine learning.With the help of a modified spectrometer,a large number of characteristic images were generated to map antibiotic information.The relationship between characteristic images and antibiotic concentrations was established by machine learning model.The coefficient of determination and root mean squared error were used to evaluate the prediction performance of the machine learning model.The results show that a well-trained machine learning model can accurately predict multiple antibiotic concentrations simultaneously with almost no pretreatment.The results from this study have some referential value for promoting the development of environmental detection technologies and digital environmental management strategies.

Potential for natural and enhanced attenuation of sulphanilamide in a contaminated chalk aquifer

Understanding antibiotic biodegradation is important to the appreciation of their fate and removal from the environment. In this research an Isotope Ratio Mass Spectrometry（IRMS）method was developed to evaluate the extent of biodegradation of the antibiotic,sulphanilamide, in contaminated groundwater. Results indicted an enrichment in δ^（13）C of8.44‰ from-26.56（at the contaminant source） to-18.12‰（300 m downfield of the source）.These results confirm reductions in sulphanilamide concentrations（from 650 to 10 mg/L）across the contaminant plume to be attributable to biodegradation（56%） vs. other natural attenuation processes, such as dilution or dispersion（42%）. To understand the controls on sulphanilamide degradation ex-situ microcosms assessed the influence of sulphanilamide concentration, redox conditions and an alternative carbon source. Results indicated, high levels of anaerobic capacity（~50% mineralisation） to degrade sulphanilamide under high（263 mg/L）, moderate（10 mg/L） and low（0.02 mg/L） substrate concentrations. The addition of electron acceptors; nitrate and sulphate, did not significantly enhance the capacity of the groundwater to anaerobically biodegrade sulphanilamide. Interestingly, where alternative carbon sources were present, the addition of nitrate and sulphate inhibited sulphanilamide biodegradation. These results suggest, under in-situ conditions, when a preferential carbon source was available for biodegradation, sulphanilamide could be acting as a nitrogen and/or sulphur source. These findings are important as they highlight sulphanilamide being used as a carbon and a putative nitrogen and sulphur source, under prevailing iron reducing conditions.