Distribution and transport of microplastics in the upper 1150 m of the water column at the Eastern North Atlantic Subtropical Gyre, Canary Islands, Spain

Nowadays it is widely known that pollution by microplastics (MP) at the open ocean covers immense areas. Buoyant plastics tend to accumulate in areas of convergence at the sea surface such as subtropical gyres, while non-buoyant plastics accumulate at the seafloor. However, previous studies have revealed that the total amount of plastic in the different oceans is not well correlated with the concentrations measured at the sea surface and the sea floor, evidencing a significant amount of missing plastic in the oceans. This deviation could be related to an underestimation of the role played by small fragments of plastic and fibers in the oceans. Furthermore, microplastic fragments with a density lower than the density of seawater have been gathered hundreds of meters below the sea surface in the Pacific Ocean due to their size and shape. The main objective of this study is to carry out, for the first time, an equivalent analysis along the water column for the Atlantic Ocean. In that sense, a total number of 51 samples were collected during four different oceanographic cruises between February and December 2019, from the sea surface down to 1150 m depth at the open ocean waters of the Canary Islands region (Spain). For each sample, 72 l of seawater were filtered on board with a mesh size of 100 μm, where the presence of microplastics has been clearly observed. Our results reveal the presence of microplastics at least up to 1150 m depth, at the Northeastern Atlantic Subtropical Gyre with noticeable seasonal differences. The spatial distribution of these small fragments and fibers at the water column is mainly related to the oceanic dynamics and mesoscale convective flows, overcoming the MP motion induced by their own buoyancy. Moreover, these microplastics have being transported by the ocean dynamics as passive drifters.

A prediction model using machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose during cesarean section

Background

The intrathecal hyperbaric bupivacaine dosage for cesarean section is difficult to predetermine. This study aimed to develop a decision-support model using a machine-learning algorithm for assessing intrathecal hyperbaric bupivacaine dose based on physical variables during cesarean section.

Methods

Term parturients presenting for elective cesarean section under spinal anaesthesia were enrolled. Spinal anesthesia was performed at the L3/4 interspace with 0.5% hyperbaric bupivacaine at dosages determined by the anesthesiologist. A spinal spread level between T4-T6 was considered the appropriate block level. We used a machine-learning algorithm to identify relevant parameters. The dataset was split into derivation (80%) and validation (20%) cohorts. A decision-support model was developed for obtaining the regression equation between optimized intrathecal 0.5% hyperbaric bupivacaine volume and physical variables.

Results

A total of 684 parturients were included, of whom 516 (75.44%) and 168 (24.56%) had block levels between T4 and T6, and less than T6 or higher than T4, respectively. The appropriate block level rate was 75.44%, with the mean bupivacaine volume [1.965, 95%CI (1.945,1.984)]ml. In lasso regression, based on the principle of predicting a reasonable dose of intrathecal bupivacaine with fewer physical variables, the model is “Y=0.5922+ 0.055117* X₁-0.017599*X₂” (Y: bupivacaine volume; X₁: vertebral column length; X₂: abdominal girth), with λ 0.055, MSE 0.0087, and R² 0.807.

Conclusions

After applying a machine-learning algorithm, we developed a decision model with R² 0.8070 and MSE due to error 0.0087 using abdominal girth and vertebral column length for predicting the optimized intrathecal 0.5% hyperbaric bupivacaine dosage during term cesarean sections.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-021-01331-8.

Changes in the distribution of intertidal macroalgae along a longitudinal gradient in the northern coast of Spain

The distribution of macroalgal species along the north and northwest coast of the Iberian Peninsula is in a period of change, during which mostly cold-temperate species have decreased in cover and others have disappeared. On the other hand, other species have increased in abundance. These shifts could be related with the modification of environmental factors that determine species distribution caused by climate change. A standardised sampling methodology was applied at 18 sites along the north coast of Spain in 2011 and 2017. The relationship between the coverage of intertidal macroalgal species and abiotic variables (sea surface temperature, air temperature and significant wave height) was analysed in order to detect possible changes in the historical data. Results suggest a modification in the communities in the inner part of the Bay of Biscay, mostly related to an increase in water and air temperature. Each seaweed group (warm-temperate, cold-temperate, opportunistic and exotic species) showed a different pattern of modification. Coralline algae, Bifurcaria bifurcata and Cystoseira baccata have increased, which may be related to the warming trend. The exotic species Asparagopsis armata has also increased, particularly in the Lower Rias. On the other hand, there was a drastic decrease of the cold-temperate species Himanthalia elongata. Data obtained support the relationship of macroalgae species communities and environment in the context of climate change in this particular area.

Spatio-temporal benthic biodiversity patterns and pollution pressure in three Mediterranean touristic ports

The Mediterranean Sea is one of the busiest areas worldwide in terms of maritime activity, facing considerable anthropogenic disturbance, such as pollution by hydrocarbons and heavy metals. The present study has evaluated the environmental and benthic biodiversity characteristics of three touristic ports, Cagliari (Sardinia, Italy), Heraklion (Crete, Greece) and El Kantaoui (Tunisia), based on the combined assessment of physical parameters, chemical variables (i.e. nutrients, pigments), sediment pollution and macrobenthic biodiversity. Different port sectors (leisure, fishing, passenger, cargo, shipyard) and different seasons (winter, before touristic period, after touristic period) were compared. Salinity and sediment concentration of copper and antimony were the three environmental parameters most highly correlated with benthic species composition and diversity. Both the environmental variables and the benthic biodiversity patterns were significantly different between the three ports (i.e. different geographical locations). Heraklion port was heavily polluted by AHs in surface and anoxic sediments and had the highest percentage of opportunistic species, while Cagliari had the highest levels of PAHs and UCM and low species richness. El Kantaoui port was less polluted and characterised by a richer biodiversity. The shipyard sector in Heraklion port was significantly different from all other sectors in terms of abiotic and biotic parameters. Physico-chemical and pollution variables recorded during the period after tourism (late summer) were significantly different from the ones recorded in winter. Seasonal differences were not significant between benthic species diversity patterns, but were revealed when the patterns derived from the aggregation of higher taxonomic levels were compared. The present study indicates that a regular-basis monitoring plan including evaluation of environmental health based on benthic biodiversity, can provide a basis for perceiving changes and reveal the degree of anthropogenic disturbance in port environments.

Variability of chlorophyll-a concentration in the Gulf of Guinea and its relation to physical oceanographic variables

The Gulf of Guinea represents a wide tract of the African coast with complex and rich coastal ecosystems undergoing various pressures. The seasonal variations of chlorophyll-a concentration (Chla) along the Gulf of Guinea (GoG) and their relations with physical oceanographic variables were analyzed using satellite observations covering the period 2002-2012. The effects of sea surface temperature (SST), sea level anomalies (SLA), winds, geostrophic currents, eddy kinetic energy (EKE), mesoscale eddies and fronts were considered on a monthly time scale. The analysis for each unit area was carried out on a chlorophyll index (IChla) computed as the product of the mean distance from the coast to the eutrophic threshold (1 mg m^-3 isoline) and the average Chla in the eutrophic area. The study, based on satellite-derived Chla, was allowed by the unprecedented coverage given by the products distributed by the ESA Ocean Colour Climate Change Initiative (OC_CCI) resulting from the merging of data from several satellite missions. The physical variables served as potential predictors in a statistical Boosted Regression Tree (BRT) model. To account for the heterogeneous nature of the GoG, the analysis was conducted on eight systems that made up a partition of the whole region defined on the basis of the BRT model results and climatological properties. The western-most domain, from Guinea-Bissau to Sierra Leone, was associated with upwelling properties in boreal winter and appeared to share some characteristics with the overall Northwest African upwelling system. The region of Ivory Coast and Ghana also had upwelling properties but the main upwelling season was in boreal summer. In general upwelling conditions with cold SST, negative SLA, fairly strong frontal activity, and moderate winds, appeared as the environmental window most favorable to high IChla values. For these systems, the BRT model fitted the IChla data well with a percentage of explained total deviance [Formula: see text] between 70% and 91% when using only physical oceanographic variables. Finally, the systems associated with the coasts of Nigeria to Gabon showed some mixed properties, with [Formula: see text] values of 54-60%. Among these systems, a common feature seemed to be the importance of river discharge to explain IChla variations. Where possible (for the Niger River in the Nigeria system), the addition of river data as predictor in the BRT model resulted in a significant increase of [Formula: see text] to 75%. Further progress is needed to understand the observed relationships and to predict how they can evolve in the face of climate change.