Gravgaardmcintyre6456

Per and polyfluorinated substances (PFAS) exposure was investigated in Australian pinnipeds. Concentrations of 16 PFAS were measured in the livers of Australian sea lion (Neophoca cinerea), Australian fur seal (Arctocephalus pusillus doriferus) and a long-nosed Fur Seal (Arctocephalus forsteri) pup sampled between 2017 and 2020 from colonies in South Australia and Victoria. Findings reported in this study are the first documented PFAS concentrations in Australian pinnipeds. Median and observed range of values in ng/g wet weight were highest for perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA) and perfluorononanoic acid (PFNA) in the liver of N. cinerea (PFOS = 7.14, 1.00-16.9; PFOA = 2.73, 0.32-11.2; PFNA = 2.96, 0.61-8.22; n = 28), A. forsteri (PFOS = 15.98, PFOA = 2.02, PFNA = 7.86; n = 1) and A. p. doriferus (PFOS = 27.4, 10.5-2119; PFOA = 0.98, 0.32-52.2; PFNA = 2.50, 0.91-44.2; n = 20). PFAS concentrations in A. p. doriferus pups were significantly greater (p less then 0.05) than in N. cinerea pups for all PFAS except PFOA and were of similar magnitude to those reported in northern hemisphere marine animals. These results demonstrate exposure differences in both magnitude and PFAS profiles for N. cinerea in South Australia and A. AZD0530 in vitro p. doriferus in Victoria. This study reports detectable PFAS concentrations in Australian pinniped pups indicating the importance of maternal transfer of these toxicants. As N. cinerea are endangered and recent declines in pup production has been reported for A. p. doriferus at the colony sampled, investigation of potential health impacts of these toxicants on Australian pinnipeds is recommended.Europium (Eu) strategic importance for the manufacturing industry, high economic value and high supply risk, categorizes it as critical raw material. Due to anthropogenic contamination, Eu levels in ecosystems have been growing, which opens opportunities for innovation its recovery and recycling from contaminated water as element source - circular economy. In this pioneering study, six widely available living marine macroalgae (Ulva intestinalis, Ulva lactuca, Gracilaria sp., Osmundea pinnatifida, Fucus vesiculosus and Fucus spiralis) were characterized (water content and specific surface area) and evaluated in the pre-concentration and recovery of Eu from contaminated seawater, under different relevant contamination scenarios (10, 152 and 500 μg L-1). U. lactuca and Gracilaria sp. (3 g L-1, fresh weight) proved to be the most effective in removing Eu, reaching up to 85% in 72 h, while the highest Eu enrichment was observed in U. intestinalis biomass, up to 827 μg g-1 (bioconcentration factor of 1800), which is higher than Eu levels in common apatite ores. The effect of Eu exposure on macroalgae growth rate and organism biochemical performance (LPO, SOD, GPx and GSTs) was also evaluated for the first time, to the best of our knowledge. Although no cellular damage was recorded, findings revealed toxicity and defence mechanisms activation, emphasizing the need of further studies on the potential risks associated with the presence of this emerging contaminant in aquatic ecosystems.The effect of addition of algae to activated sludge as active biomass in membrane bioreactors (MBRs) and electro-MBRs (e-MBRs) for wastewater remediation was examined in this study. The performances of Algae-Activated Sludge Membrane Bioreactor (AAS-MBR) and electro Algae-Activated Sludge Membrane Bioreactor (e-AAS-MBR) were compared to those observed in conventional MBR and e-MBR, which were previously reported and utilized activated sludge as biomass. The effect of application of electric field was also examined by the comparison of performances of e-AAS-MBR and AAS-MBR. Similar chemical oxygen demand (COD) reduction efficiencies of AAS-MBR, e-AAS-MBR, MBR, and e-MBR (98.35 ± 0.35%, 99.12 ± 0.08%, 97.70 ± 1.10%, and 98.10 ± 1.70%, respectively) were observed. The effect of the algae-activated sludge system was significantly higher in the nutrient removals. Ammoniacal nitrogen (NH3-N) removal efficiencies of AAS-MBR and e-AAS-MBR were higher by 43.89% and 26.61% than in the conventional MBR and e-MBR, respectively. Phosphate phosphorous (PO43--P) removals were also higher in AAS-MBR and e-AAS-MBR by 6.43% and 2.66% than those in conventional MBR and e-MBR. Membrane fouling rates in AAS-MBR and e-AAS-MBR were lower by 57.30% and 61.95% than in MBR and e-MBR, respectively. Lower concentrations of fouling substances were also observed in the reactors containing algae-activated sludge biomass. Results revealed that addition of algae improved nutrient removal and membrane fouling mitigation. The study also highlighted that the application of electric field in the e-AAS-MBR enhanced organic contaminants and nutrients removal, and fouling rate reduction.Food insecurity is a growing concern due to man-made conflicts, climate change, and economic downturns. Forecasting the state of food insecurity is essential to be able to trigger early actions, for example, by humanitarian actors. To measure the actual state of food insecurity, expert and consensus-based approaches and surveys are currently used. Both require substantial manpower, time, and budget. This paper introduces an extreme gradient-boosting machine learning model to forecast monthly transitions in the state of food security in Ethiopia, at a spatial granularity of livelihood zones, and for lead times of one to 12 months, using open-source data. The transition in the state of food security, hereafter referred to as predictand, is represented by the Integrated Food Security Phase Classification Data. From 19 categories of datasets, 130 variables were derived and used as predictors of the transition in the state of food security. The predictors represent changes in climate and land, market, conflict, infrastructure, demographics and livelihood zone characteristics. The most relevant predictors are found to be food security history and surface soil moisture. Overall, the model performs best for forecasting Deteriorations and Improvements in the state of food security compared to the baselines. The proposed method performs (F1 macro score) at least twice as well as the best baseline (a dummy classifier) for a Deterioration. The model performs better when forecasting long-term (7 months; F1 macro average = 0.61) compared to short-term (3 months; F1 macro average = 0.51). Combining machine learning, Integrated Phase Classification (IPC) ratings from monitoring systems, and open data can add value to existing consensus-based forecasting approaches as this combination provides longer lead times and more regular updates. Our approach can also be transferred to other countries as most of the data on the predictors are openly available from global data repositories.