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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.

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.