International audience | Antibiotics and antibiotic resistance genes have shown to be omnipresent in the environment. In this study, we investigated the effect of vancomycin (VA) on denitrifying bacteria in river sediments of a Waste Water Treatment Plant, receiving both domestic and hospital waste. We exposed these sediments continuously in flow-through reactors to different VA concentrations under denitrifying conditions (nitrate addition and anoxia) in order to determine potential nitrate reduction rates and changes in sedimentary microbial community structures. The presence of VA had no effect on sedimentary nitrate reduction rates at environmental concentrations, whereas a change in bacterial (16S rDNA) and denitrifying (nosZ) community structures was observed (determined by polymerase chain reaction-denaturing gradient gel electrophoresis). The bacterial and denitrifying community structure within the sediment changed upon VA exposure indicating a selection of a non-susceptible VA population.

Liaohe River has received significant attention in the northeast region and even in the entire country. As part of a recently completed water quality assessment, a series of water column and sediment toxicity tests was performed throughout the watershed. In the current study, we subjected sediments from the Liaohe River to toxicity identification evaluation manipulations and tests for chronic toxicity with midge (Chironomus riparius), with survival as the end point. In Phase I, the sediments were treated with zeolite, cation-exchange resin, and powdered coconut charcoal. Results confirmed that ammonia compounds were the major contaminants in terms of toxicity, although toxic effects from metals were also a concern in at least three sites. In Phase II identification, chemical analysis provided a strong evidence that the metals As and Cd are the probable causes of toxicity in the sediments, without the influence of ammonia. Temporally, ammonia is responsible for the toxicity of the selected sediments.

A fine-scale survey of δ15N, δ13C, tissue-N in seaweeds was conducted using samples from 17 sampling points at two sites (Grandcamp-Maisy (GM), Courseulles/Mer (COU)) along the French coast of the English Channel in 2012 and 2013. Partial triadic analysis was performed on the parameter data sets and revealed the functioning of three areas: one estuary (EstA) and two rocky areas (GM∗, COU∗). In contrast to oceanic and anthropogenic reference points similar temporal dynamics characterized δ15N signatures and N contents at GM∗ and COU∗. Nutrient dynamics were similar: the N-concentrations in seawater originated from the River Seine and local coastal rivers while P-concentrations mainly from these local rivers. δ15N at GM∗ were linked to turbidity suggesting inputs of autochthonous organic matter from large-scale summer seaweed beachings made up of a mixture of Rhodophyta, Phaeophyta and Chlorophyta species. This study highlights the coupling between seaweed beachings and nitrogen sources of intertidal macroalgae.

Recent gas discoveries in the eastern Mediterranean Sea led to multiple operations with substantial economic interest, and with them there is a risk of oil spills and their potential environmental impacts. To examine the potential spatial distribution of this threat, we created seasonal maps of the probability of oil spill pollution reaching an area in the Israeli coastal and exclusive economic zones, given knowledge of its initial sources. We performed simulations of virtual oil spills using realistic atmospheric and oceanic conditions. The resulting maps show dominance of the alongshore northerly current, which causes the high probability areas to be stretched parallel to the coast, increasing contamination probability downstream of source points. The seasonal westerly wind forcing determines how wide the high probability areas are, and may also restrict these to a small coastal region near source points. Seasonal variability in probability distribution, oil state, and pollution time is also discussed.

In 2010, a massive bloom of the raphidophycean flagellate Chattonella occurred in the Ariake Sea and Tachibana Bay. Bloom dynamics and hydrographical conditions were examined by field survey. The development and decline of the bloom occurred three times in Tachibana Bay. First and third bloom developments synchronized with precipitation, and the second bloom developed in synchronization with a salinity decrease which occurred in relation to an increase of river discharge from the Chikugo River which takes several days to flow from the Ariake Sea. These results imply that the bloom was transported with the low salinity water from the Ariake Sea to Tachibana Bay. During blooms along the northern coast of Shimabara Peninsula, the predominant phytoplankton species changed from Chattonella to Skeletonema. Low salinity water intrusion induced an interregional difference of the Chattonella and Skeletonema bloom spatially-differentiated by the salinity in the Ariake Sea and Tachibana Bay.

Previous models on simulating gas releases in deepwater were not focused on the dissolved component and its impact on water quality. This paper presents a new model developed for simulating the transport/spread of dissolved methane from an underwater release and its impact on dissolved oxygen in ambient water. Methane dissolves into ambient water from gas phase, direct from hydrate phase, and from dissociating hydrates formed earlier. Dissolved methane affects the dissolved oxygen levels in ambient water due to microbial interaction and possible direct absorption of oxygen into methane bubbles. We use new model simulations of Deepspill field experiments to compare with instantaneous profiles which were unpublished until now. The comparisons are very good with a short time lag, but are within the acceptable discrepancy for models for emergency response and contingency planning. Scenario simulations show the effect on dissolved oxygen due to different methane release situations.

We present a simple neural network and data pre–selection framework, discriminating the most essential input data for accurately forecasting the concentrations of PM10, based on observations for the years between 2002 and 2006 in the metropolitan region of Lisbon, Portugal. Starting from a broad panoply of different data sets collected at several air quality and meteorological stations, a forward stepwise regression procedure is applied enabling to automatically identify the most important variables for predicting the pollutant and also to rank them in order of importance. The importance of this variable ranking is discussed, showing that it is very sensitive to the urban location where measurements are obtained. Additionally, the importance of Circulation Weather Types is highlighted, characterizing synoptic scale circulation patterns and the concentration of pollutants. We then quantify the performance of linear and non–linear neural network models when applied to PM10 concentrations. In the light of contradictory results of previous studies, our results show no clear superiority for the case studied of non–linear models over linear models. While all models show similar predictive performances, we find important differences in false alarm rates and demonstrate the importance of removing weekly cycles from input variables.