Antibiotic proliferation in the environment and their persistent nature is an issue of global concern as they induce antibiotic resistance threatening both human health and the ecosystem. Antibiotics have therefore been categorized as emerging pollutants. Fluoroquinolone (FQs) antibiotics are an emerging class of contaminants that are used extensively in human and veterinary medicine. The recalcitrant nature of fluoroquinolones has led to their presence in wastewater, effluents and water bodies. Even at a low concentration, FQs can stimulate antibacterial resistance. The main sources of FQ contamination include waste from pharmaceutical manufacturing industries, hospitals and households that ultimately reaches the wastewater treatment plants (WWTPs). The conventional WWTPs are unable to completely remove FQs due to their chemical stability. Therefore, the development and implementation of more efficient, economical, convenient treatment and removal technologies are needed to adequately address the issue. This review provides an overview of the technologies available for the removal of fluoroquinolone antibiotics from wastewater including adsorptive removal, advanced oxidation processes, removal using non-carbon based nanomaterials, microbial degradation and enzymatic degradation. Each treatment technology is discussed on its merits and limitations and a comparative view is presented on the choice of an advanced treatment process for future studies and implementation. A discussion on the commercialization potential and eco-friendliness of each technology is also included in the review. The importance of metabolite identification and their residual toxicity determination has been emphasized. The last section of the review provides an overview of the policy interventions and regulatory frameworks that aid in retrofitting antibiotics as a central key focus contaminant and thereby defining the discharge limits for antibiotics and establishing safe manufacturing practices.

In Southeast Asia and Vietnam, rice residues are routinely burned after the harvest to prepare fields for the next season. Specific to Vietnam, the two prevalent burning practices include: a). piling the residues after hand harvesting; b). burning the residues without piling, after machine harvesting. In this study, we synthesized field and laboratory studies from the literature on rice residue burning emission factors for PM2.5. We found significant differences in the resulting burning-practice specific emission factors, with 16.9 g kg−2(±6.9) for pile burning and 8.8 g kg−2(±3.5) for non-pile burning. We calculated burning-practice specific emissions based on rice area data, region-specific fuel-loading factors, combined emission factors, and estimates of burning from the literature. Our results for year 2015 estimate 180 Gg of PM2.5 result from the pile burning method and 130 Gg result from non-pile burning method, with the most-likely current emission scenario of 150 Gg PM2.5 emissions for Vietnam. For comparison purposes, we calculated emissions using generalized agricultural emission factors employed in global biomass burning studies. These results estimate 80 Gg PM2.5, which is only 44% of the pile burning-based estimates, suggesting underestimation in previous studies. We compare our emissions to an existing all-combustion sources inventory, results show emissions account for 14–18% of Vietnam's total PM2.5 depending on burning practice. Within the highly-urbanized and cloud-covered Hanoi Capital region (HCR), we use rice area from Sentinel-1A to derive spatially-explicit emissions and indirectly estimate residue burning dates. Results from HYSPLIT back-trajectory analysis stratified by season show autumn has most emission trajectories originating in the North, while spring has most originating in the South, suggesting the latter may have bigger impact on air quality. From these results, we highlight locations where emission mitigation efforts could be focused and suggest measures for pollutant mitigation. Our study demonstrates the need to account for emissions variation due to different burning practices.

This study investigated the year-to-year variability in the occurrence, abundance and sources of oil spills in the Eastern Arabian Sea (EAS) using sentinel-1 imagery and identified the potential oil spills vulnerable zones. The four consecutive year's data acquired from 2017 to 2020 (March–May) reveal three oil spill hot spot zones. The ship-based oil spills were dominant over zone's-1 (off Gujarat) and 3 (off Karnataka and Kerala), and the oil field based over zone-2 (off Maharashtra). The abundance of oil spills was significantly low in zone-1, only 14.30km² (1.2%) during lock-down due to the covid-19 pandemic. Whereas, the year-to-year oil spills over zone's 2 and 3 are not significantly varied (170.29 km² and 195.01 km²), further suggesting the influence of oil exploration and international tanker traffic are in operation during the lock-down. This study further recommends that manual clustering is the best method to study the distribution of unknown oil spills.

Oil spill experiment results in the coastal waters of Diu, India, with an airborne DLR ESAR sensor are discussed with reference to the SAR frequency, polarization and viewing angle. The SAR data acquired in the quad polarization of the L band and dual polarization of the C band over two spills are studied. A higher oil and water contrast is observed in the L-VV polarization than in the C-HH mode. Oil spill discrimination is possible over a wider view angle of the airborne SAR sensor data in L band than in C band. This study has also analyzed the spread and drift of oil in coastal waters.

Marine oil spills pose a significant threat to ocean and coastal ecosystems. In addition to costs incurred by response activities, an economic burden could be experienced by stakeholders dependent on coastal resources. Decision support tools for oil spill response (OSR-DSTs) have been playing an important role during oil spill response operations. This paper aims to provide an insight into the status of research on OSR-DSTs and identify future directions. Specifically, a systematic review is conducted including an examination of the advantages and limitations of currently applied and emerging decision support techniques for oil spill response. In response to elevated environmental concerns for protecting the polar ecosystem, the review includes a discussion on the use of OSR-DSTs in cold regions. Based on the analysis of information acquired, recommendations for future work on the development of OSR-DSTs to support the selection and implementation of spill response options are presented.

On June 4 and 17, 2011, separate oil spill accidents occurred at two oil platforms in the Bohai Sea, China. The oil spills were subsequently observed on different types of satellite images including SAR (Synthetic Aperture Radar), Chinese HJ-1-B CCD and NASA MODIS. To illustrate the fate of the oil spills, we performed two numerical simulations to simulate the trajectories of the oil spills with the GNOME (General NOAA Operational Modeling Environment) model. For the first time, we drive the GNOME with currents obtained from an operational ocean model (NCOM, Navy Coastal Ocean Model) and surface winds from operational scatterometer measurements (ASCAT, the Advanced Scatterometer). Both data sets are freely and openly available. The initial oil spill location inputs to the model are based on the detected oil spill locations from the SAR images acquired on June 11 and 14. Three oil slicks are tracked simultaneously and our results show good agreement between model simulations and subsequent satellite observations in the semi-enclosed shallow sea. Moreover, GNOME simulation shows that the number of ‘splots’, which denotes the extent of spilled oil, is a vital factor for GNOME running stability when the number is less than 500. Therefore, oil spill area information obtained from satellite sensors, especially SAR, is an important factor for setting up the initial model conditions.

Exploration was conducted in the study area of Coimbatore district by collecting 60 samples from the agricultural belt. The groundwater is neutral to alkaline in character with pH varying from 6.70 to 9.02 among the mean of 7.37. Elevated electrical conductivity (EC) was observed in upstream parts of the study area. Water quality for irrigation is determined by several key components like electrical conductivity (EC), total suspended solids (TDS), residual sodium carbonate (RSC), chloro alkali Indices (CA I & CAII), sodium adsorption ratio (SAR), percent sodium (%Na), Kelley’s ratio (KR), magnesium hazard (MH), permeability index (PI) and soluble sodium percent (SSP). In total, six samples were found to be with high salinity hazard. The mean value of SAR was found to be 5.24, maximum sample falls under C2S1 to C4S1 category as per USSL Salinity diagram. As per Piper diagram in the anionic regime, Cl dominates HCO3-, CO32- and SO42-. Higher magnesium hazard in the groundwater is owing to the consequence of dyeing and bleaching industries. Magnesium hazard value indicates that 51 samples out of 60 are unsuitable for irrigation. According to the study, it is revealed that groundwater in the study area is contaminated by both natural factors and anthropogenic activities.