Reillylam8287

Pesticide and veterinary drug residues are one of the stress factors affecting bee health and mortality. To investigate the occurrence, the concentration and the toxicity risk to bees of pesticide residues in four different types of beeswax (brood comb wax, recycled comb wax, honey comb wax, and cappings wax), 182 samples were collected from apiaries located all over the Belgian territories, during spring 2016 and analysed by LC-MS/MS and GC-MS/MS for the presence of 294 chemical residues. The toxicity risk to bees expressed as the Hazard Quotient (HQ) was calculated for each wax sample, according to two scenarios with different tau-fluvalinate LD50 values. Residues showing the highest prevalence were correlated to bee mortality in a multivariate logistic regression model and a risk-based model was used to predict colony bee mortality. Altogether, 54 different pesticide and veterinary drug residues were found in the four types of beeswax. The residues with a higher likelihood to be retained in beeswax are applied in-hive or with a high lipophilic nature. The multivariate logistic regression model showed a statistically significant influence of chlorfenvinphos on bee mortality. All our results indicated that cappings wax was substantially less contaminated. This national survey on beeswax contamination provides guidelines on the re-use of beeswax by beekeepers and shows the necessity to introduce maximum residue levels for global trade in beeswax. An online tool was developed to enable beekeepers and wax traders to estimate the risk to honey bee health associated with contaminated wax.At the end of August 2019, in Warsaw, the sewage collection system of the Wastewater Treatment Plant malfunctioned. mTOR inhibitor During the subsequent 12 days, over 3.6 million m3 of untreated sewage was introduced from the damaged collector directly into the Vistula River. It is one of the biggest known failures of its kind in the world so far. In this study we investigated to what extent the incident contributed to the increased discharge of anti-inflammatory drugs into the environment. The study covered the section from the point of discharge to the city of Toruń (ca. 170 km downstream). It was found that in the river waters downstream of the damaged collector, the concentrations of selected pharmaceuticals increased considerably in comparison with the waters upstream of the collector. The highest concentrations were observed for paracetamol (102.9 μg/L), ibuprofen (5.3 μg/L) and diclofenac (4.8 μg/L). We also measured to what extent and at what rate these pollutants were distributed along the river. The effects of the incident were observed in further parts of the river after 16 days. In the study we also detected elevated concentrations of ibuprofen and diclofenac in the bottom sediments collected 6 weeks after the failure. Measurements of the levels of pharmaceuticals in estuarial and marine waters in the vicinity of the mouth of the Vistula River indicate that the incident did not significantly increase the load of these pollutants in the waters of the southern Baltic Sea.The discharge and consequent occurrence of antibiotics in the environment has led to increasing concerns because their presence can promote the development of resistance genes, which in turn pose a significant health risk. A key process to control the transport and risk of antibiotics is adsorption. Thus, we investigated the adsorption mechanisms of six typical antibiotics onto a MnFe2O4@cellulose activated carbon (CAC) hybrid combining batch adsorption experiments and quantum chemical calculations. In the single-adsorbate adsorption systems, the solid-phase concentrations of the adsorbates varied from 152.8 to 395.7 mg/g, which were dependent on the adsorption affinity and molecular structures or sizes of the antibiotics. Chemisorption was the main adsorption mechanism, and it was driven by p-d electronic conjugation and cation-π interactions. In the competitive adsorption systems, the solid-phase concentrations of both primary (sulfamethazine, SMT) and secondary (the other five antibiotics) adsorbates decreased significantly. The decrease ratio of SMT varied from 15.42% to 67.28% while that of the secondary adsorbates varied from 14.13% to 52.74%. The "competition" strength was depended on the adsorption energy and the overlapping of adsorption sites. We believe that these findings will provide a better understanding of the adsorption characteristics of typical antibiotics and facilitate the strategy developing for the removal of antibiotics from the aqueous phase.Bioenergetic models, and specifically dynamic energy budget (DEB) theory, are gathering a great deal of interest as a tool to predict the effects of realistically variable exposure to toxicants over time on an individual animal. Here we use aquatic ecological risk assessment (ERA) as the context for a review of the different model variants within DEB and the closely related DEBkiss theory (incl. reserves, ageing, size & maturity, starvation). We propose a coherent and unifying naming scheme for all current major DEB variants, explore the implications of each model's underlying assumptions in terms of its capability and complexity and analyse differences between the models (endpoints, mathematical differences, physiological modes of action). The results imply a hierarchy of model complexity which could be used to guide the implementation of simplified model variants. We provide a decision tree to support matching the simplest suitable model to a given research or regulatory question. We detail which new insights can be gained by using DEB in toxicokinetic-toxicodynamic modelling, both generally and for the specific example of ERA, and highlight open questions. Specifically, we outline a moving time window approach to assess time-variable exposure concentrations and discuss how to account for cross-generational exposure. Where possible, we suggest valuable topics for experimental and theoretical research.Snow avalanches can destroy lives and infrastructure and are very important phenomena in some regions of the world. This study maps snow avalanche susceptibility in Sirvan Watershed, Iran, using a new approach. Two statistical models - belief function (Bel) and probability density (PD) - are combined with two learning models - multi-layer perceptron (MLP) and logistic regression (LR) - to predict avalanche susceptibility using remote sensing data in a geographic information system (GIS). A snow avalanche inventory map was generated from Google Earth imagery, regional documentation, and field surveys. Of 101 avalanche locations, 71 (70%) were used to train the models and 30 (30%) were used to validate the resulting models. Fourteen snow avalanche conditioning factors were used as independent variables in the predictive modeling process. First, the weight of Bel and PD techniques were applied to each class of factors. Then, they were combined with two MLP and LR learning models for snow avalanche susceptibility mapping (SASM).