Platform of integrated tools to support environmental studies and management of dredging activities.

Dredging activities can cause environmental impacts due to, among other, the increase of the Suspended Solid Concentration (SSC) and their subsequent dispersion and deposition (DEP) far from the dredging point. The dynamics of the resulting dredging plume can strongly differ in spatial and temporal evolution. This evolution, for both conventional mechanical and hydraulic dredges, depends on the different mechanisms of sediment release in water column and the site-specific environmental conditions. Several numerical models are currently in use to simulate the dredging plume dynamics. Model results can be analysed to study dispersion and advection processes at different depths and distances from the dredging source. Usually, scenarios with frequent and extreme meteomarine conditions are chosen and extreme values of parameters (i.e. maximum intensity or total duration) are evaluated for environmental assessment. This paper presents a flexible, consistent and integrated methodological approach. Statistical parameters and indexes are derived from the analysis of SSC and DEP simulated time-series to numerically estimate their spatial (vertical and horizontal) and seasonal variability, thereby allowing a comparison of the effects of hydraulic and mechanical dredges. Events that exceed defined thresholds are described in term of magnitude, duration and frequency. A new integrated index combining these parameters, SSCnum, is proposed for environmental assessment. Maps representing the proposed parameters allow direct comparison of effects due to different (mechanical and hydraulic) dredges at progressive distances from the dredging zone. Results can contribute towards identification and assessment of the potential environmental effects of a proposed dredging project. A suitable evaluation of alternative technical choices, appropriate mitigation, management and monitoring measure is allowed in this framework. Environmental Risk Assessment and Decision Support Systems (DSS) may take advantage of the proposed tool. The approach is applied to a hypothetical dredging project in the Augusta Harbour (Eastern coast of Sicily Island-Italy).

Management of harmful benthic dinoflagellates requires targeted sampling methods and alarm thresholds.

Concern regarding Benthic Harmful Algal Blooms (BHABs) is increasing since some harmful benthic species have been identified in new areas. In the Mediterranean basin, the most common harmful benthic microalgae are Ostreopsis cf. ovata and Prorocentrum lima, which produce palytoxin-like compounds and okadaic acid respectively, and the need to implement monitoring activities has increased. However, a general agreement on appropriate strategies (e.g. sampling season, definition of alarm thresholds, etc.) is still lagging behind, especially for P. lima, whose proliferation dynamics are still poorly known.

A novel application of an adaptable modeling approach to the management of toxic microalgal bloom events in coastal areas.

Harmful algal blooms have been increasing in frequency in recent years, and attention has shifted from describing to modeling and trying to predict these phenomena, since in many cases they pose a risk to human health and coastal activities. Predicting ecological phenomena is often time and resource consuming, since a large number of field collected data are required. We propose a novel approach that involves the use of modeled meteorological data as input features to predict the concentration of the toxic benthic dinoflagellate Ostreopsis cf. ovata in seawater. Ten meteorological features were used to train a Quantile Random Forests model, which was then validated using field collected concentration data over the course of a summer sampling season. The proposed model was able to accurately describe Ostreopsis abundance in the water column in response to meteorological variables. Furthermore, the predictive power of this model appears good, as indicated by the validation results, especially when the quantile for predictions is tuned to match management requirements. The Quantile Random Forests method was selected, as it allows for greater flexibility in the generated predictions, thus making this model suitable as a tool for coastal management. The application of this approach is novel, as no other models or tools that are adaptable to this degree are currently available. The model presented here was developed for a single species over a limited geographical extension, but its methodological basis appears flexible enough to be applied to the prediction of HABs in general and it could also be extended to the case of other ecological phenomena that are strongly dependent on meteorological drivers, that can be independently modeled and potentially globally available.