Quantifying the topographical structure of rocky and coral seabeds.

Describing the structural complexity of seabeds is of primary importance for a number of geomorphological, hydrodynamical and ecological issues. Aiming to bring a decisive insight on the long-term development of a unified view, the present study reports on a comparative multi-site analysis of high resolution topography surveys in rough nearshore environments. The nine study sites have been selected to cover a wide variety of topographical features, including rocky and coral seabeds. The topography data has been processed to separate roughness and bathymetry-related terrain features, allowing to perform a comprehensive spectral and statistical analysis of each site. A series of roughness metrics have been tested to identify the most relevant estimators of the bottom roughness at each site. The spectral analysis highlights the systematic presence of a self-affine range of variable extension and spectral slope. The standard deviation of the seabed elevation varies from 0.04 to 0.77 m. The statistical and multi-scale analysis performed on the whole set of roughness metrics allows to identify connection between metrics and therefore to propose a reduced set of relevant roughness estimators. A more general emphasis is placed on the need to properly define a unified framework when reconstructing roughness statistics and bathymetry from fine seabed topographical data.

SMOTE-CD: SMOTE for compositional data.

Compositional data are a special kind of data, represented as a proportion carrying relative information. Although this type of data is widely spread, no solution exists to deal with the cases where the classes are not well balanced. After describing compositional data imbalance, this paper proposes an adaptation of the original Synthetic Minority Oversampling TEchnique (SMOTE) to deal with compositional data imbalance. The new approach, called SMOTE for Compositional Data (SMOTE-CD), generates synthetic examples by computing a linear combination of selected existing data points, using compositional data operations. The performance of the SMOTE-CD is tested with three different regressors (Gradient Boosting tree, Neural Networks, Dirichlet regressor) applied to two real datasets and to synthetic generated data, and the performance is evaluated using accuracy, cross-entropy, F1-score, R2 score and RMSE. The results show improvements across all metrics, but the impact of oversampling on performance varies depending on the model and the data. In some cases, oversampling may lead to a decrease in performance for the majority class. However, for the real data, the best performance across all models is achieved when oversampling is used. Notably, the F1-score is consistently increased with oversampling. Unlike the original technique, the performance is not improved when combining oversampling of the minority classes and undersampling of the majority class. The Python package smote-cd implements the method and is available online.

Effects of Biofilms and Particle Physical Properties on the Rising and Settling Velocities of Microplastic Fibers and Sheets.

Vertical dynamics of microplastics (MPs) in the water column are complex and not fully understood due to the diversity of environmental MPs and the impact of weathering and biofouling on their dynamical properties. In this study, we investigate the effects of the particle properties and biofilm on the vertical (settling or rising) velocity of microplastic sheets and fibers under laboratory conditions. The experiments focus on three types of MPs (polyester PES fibers, polyethylene terephthalate PET sheets, and polypropylene PP sheets) of nine sizes and two degrees of biological colonization. Even though pristine PES fibers and PET sheets had a similar density, the sinking velocity of fibers was much smaller and independent of their length. The settling or rising velocity of sheets increased with the particle size up to a threshold and then decreased in the wake of horizontal oscillations in large particles. Biofilms had unexpected effects on vertical velocities. Irregular biofilm distributions can trigger motion instabilities that decrease settling velocities of sheets despite the increase in density. Biofilms can also modify the orientation of fibers, which may increase their settling velocity. Finally, we selected the most performant theoretical formulation for each type of particle and proposed modifications to consider the effect of biofilm distribution.

Microplastics in a salt-wedge estuary: Vertical structure and tidal dynamics.

The abundance and distribution of microplastics in estuaries have been barely documented, and generally without accounting for the vertical structure in the water column. This study presents the very first data on the occurrence and distribution of microplastics in the Adour Estuary, SW France. The experimental data set was complemented by numerical simulations to gain understanding of the behaviour of suspended microplastics. Microplastics were found throughout the water column with a mean abundance of 1.13 part/m3. Films and fragments were the most abundant types of particles collected. Numerical simulations demonstrated that vertical distribution of microplastics in the water column is highly dependent on particle characteristics and on the local hydrodynamics. The main trend is that neutrally-buoyant microplastics are easily flushed out while heavier microplastics are prone to entrapment in the estuary, in particular under low discharge conditions. The present study suggest that estuaries could be a sink of microplastics.

Detecting Technical Anomalies in High-Frequency Water-Quality Data Using Artificial Neural Networks.

Anomaly detection (AD) in high-volume environmental data requires one to tackle a series of challenges associated with the typical low frequency of anomalous events, the broad-range of possible anomaly types, and local nonstationary environmental conditions, suggesting the need for flexible statistical methods that are able to cope with unbalanced high-volume data problems. Here, we aimed to detect anomalies caused by technical errors in water-quality (turbidity and conductivity) data collected by automated in situ sensors deployed in contrasting riverine and estuarine environments. We first applied a range of artificial neural networks that differed in both learning method and hyperparameter values, then calibrated models using a Bayesian multiobjective optimization procedure, and selected and evaluated the "best" model for each water-quality variable, environment, and anomaly type. We found that semi-supervised classification was better able to detect sudden spikes, sudden shifts, and small sudden spikes, whereas supervised classification had higher accuracy for predicting long-term anomalies associated with drifts and periods of otherwise unexplained high variability.

Behaviour of plastic litter in nearshore waters: First insights from wind and wave laboratory experiments.

Plastic litter in nearshore waters is an environmental pollutant with increasing impact on coastal environments. At present, knowledge on basic plastic particle dynamics and the interaction with complex hydrodynamics is lacking. The present laboratory study, performed under controlled wave and wind conditions, demonstrates the dispersion of plastics in shallow waters. The study presents a simple case looking solely at cross-shore particle transport. The results show that both wind and waves as well as plastic properties (shape and density) govern the behaviour of plastic litter in the nearshore zone. Heavy particles behave like natural sand with accumulation in the wave breaking zone. Light particles have varying accumulation along the coastal profile depending on the wind, waves and particle shapes. More extensive characterization remains to be done in future studies.