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The understanding of the coastal environment is fundamental for efficiently and effectively facing the pollution phenomena as expected by the Marine Strategy Framework Directive, and for limiting the conflicts between anthropic activities and sensitivity areas, as stated by Maritime Spatial Planning Directive. To address this, the Laboratory of Experimental Oceanology and Marine Ecology developed a multi-platform observing network that has been in operation since 2005 in the coastal marine area of Civitavecchia (Latium, Italy) where multiple uses and high ecological values closely coexist. The Civitavecchia Coastal Environment Monitoring System (C-CEMS), implemented in the current configuration, includes various components allowing one to analyze the coastal conflicts by an ecosystem-based approach. The long-term observations acquired by the fixed stations are integrated with in situ data collected for the analysis of the physical, chemical and biological parameters of the water column, sea bottom and pollution sources detected along the coast. The in situ data, integrated with satellite observations (e.g., temperature, chlorophyll a and TSM), are used to feed and validate the numerical models, which allow the analysis and forecasting of the dynamics of pollutant dispersion under different conditions. To test the potential capabilities of C-CEMS, two case studies are reported here: (1) the analysis of fecal bacteria dispersion for bathing water quality assessment, and (2) the evaluation of the effects of the dredged activities on Posidonia meadows, which make up most of the two sites of community importance located along the Civitavecchia coastal zone. The simulation outputs are overlapped by the thematic maps showing bathing areas and Posidonia oceanica distribution, thus giving a first practical tool that could improve the resolution of the conflicts between coastal uses (in terms of stress produced by anthropic activities) and sensitivity areas.

Among marine ecosystems globally, those in the Mediterranean Sea, are facing many threats. New technologies are crucial for enhancing our understanding of marine habitats and ecosystems, which can be complex and resource-intensive to analyse using traditional techniques. We tested, for the first time, an integrated multi-platform approach for mapping the coastal benthic habitat in the Civitavecchia (northern Latium, Italy) coastal area. This approach includes the use of an Unmanned Surface Vehicle (USV), a Remote Operated Vehicle (ROV), and in situ measurements of ecosystem functionality. The echosounder data allowed us to reconstruct the distribution of bottom types, as well as the canopy height and coverage of the seagrass Posidonia oceanica. Our study further involved assessing the respiration (Rd) and net primary production (NCP) rates of P. oceanica and its associated community through in situ benthic chamber incubation. By combining these findings with the results of USV surveys, we were able to develop a preliminary spatial distribution model for P. oceanica primary production (PP-SDM). The P. oceanica PP-SDM was applied between the depths of 8 and 10 m in the studied area and the obtained results showed similarities with other sites in the Mediterranean Sea. Though in the early stages, our results highlight the significance of multi-platform observation data for a thorough exploration of marine ecosystems, emphasizing their utility in forecasting biogeochemical processes in the marine environment., (© 2024. The Author(s).)

Underwater noise assessment is particularly important in coastal areas where a wide range of natural and anthropogenic sounds generate complex and variable soundscapes. In the last century, the number and size of noise sources has increased significantly, thereby increasing the ocean's background noise. Shipping is the main source of lower-frequency underwater noises (<500 Hz). This research aimed to provide an initial assessment of underwater noise levels in a coastal area of the northern Tyrrhenian Sea (Italy) using short-term recordings. Spatial and temporal variations in the noise level, and the type and number of ships sailing through the port were recorded. A significant correlation was found between ferry boats and sound pressure levels, indicating their role as a prevalent source of low frequency underwater noise in the project area. This research could provide the baseline for implementation of distribution and point-source underwater noise models that are required for sustainable coastal management., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The Arctic region is known to be severely affected by climate change, with evident alterations in both physical and biological processes. Monitoring the Arctic Ocean ecosystem is key to understanding the impact of natural and human-induced change on the environment. Large data sets are required to monitor the Arctic marine ecosystem and validate high-resolution satellite observations (e.g., Sentinel), which are necessary to feed climatic and biogeochemical forecasting models. However, the Global Observing System needs to complete its geographic coverage, particularly for the harsh, extreme environment of the Arctic Region. In this scenario, autonomous systems are proving to be valuable tools for increasing the resolution of existing data. To this end, a low-cost, miniaturized and flexible probe, ArLoC (Arctic Low-Cost probe), was designed, built and installed on an innovative unmanned marine vehicle, the PROTEUS (Portable RObotic TEchnology for Unmanned Surveys), during a preliminary scientific campaign in the Svalbard Archipelago within the UVASS project. This study outlines the instrumentation used and its design features, its preliminary integration on PROTEUS and its test results.

Coastal areas are strongly affected by episodes of fecal contamination due to polluted water inflows from inadequately treated sewages. The present study aims to investigate the dispersion of Escherichia coli in the artificial semi-enclosed bathing area of Santa Marinella (Latium, Italy) through in situ samplings carried out in summer 2012 and the application of a dynamic model. Collected samples were analyzed by the Culture-Based technique and the Fluorescent Antibody method in order to estimate both the viable culturable cells and the total E. coli population, respectively. The in situ datasets were used to test the proposed modeling approach and simulate the behavior of bacteria as particles subjected, or not, to decay. Next, the flushing time and the computation of the Microbiological Potential Risk Area allowed the evaluation of the contribution of physical and biological processes to coliform dispersion and the related potential risk for bathers., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

The development of low-cost instrumentation plays a key role in marine environmental studies and represents one of the most innovative aspects of current oceanographic research. These kinds of devices can be used for several applications, ranging from vertical profilers to stand-alone systems, and can be installed on different platforms (buoys, Voluntary Observing Ships, underwater vehicles, etc.). The availability of low-cost technologies enables the realization of extended observatory networks for the study of marine physical and biological processes through an integrated approach merging in situ observations, forecasting models and remotely sensed data. We present new low-cost sensors and probes developed to measure marine temperature, conductivity, chlorophyll a and Chromophoric Dissolved Organic Matter fluorescence, focusing on sensing strategies, general architecture, laboratory trials, in situ tests and comparison with standard instruments. Furthermore, we report the expendable (New T-FLaP), vertical profiler (T-FLaPpro) and stand-alone (Spectra) applications of these technological developments that were tested during several oceanographic surveys in the Mediterranean Sea.

This methodology assesses the accuracy with which remote data characterizes a surface, as a function of Full Width at Half Maximum (FWHM). The purpose is to identify the best remote data that improves the characterization of a surface, evaluating the number of bands in the spectral range. The first step creates an accurate dataset of remote simulated data, using in situ hyperspectral reflectances. The second step evaluates the capability of remote simulated data to characterize this surface. The spectral similarity measurements, which are obtained using classifiers, provide this capability. The third step examines the precision of this capability. The assumption is that in situ hyperspectral reflectances are considered the "real" reflectances. They are resized with the same spectral range of the remote data. The spectral similarity measurements which are obtained from "real" resized reflectances, are considered "real" measurements. Therefore, the quantity and magnitude of "errors" (i.e., differences between spectral similarity measurements obtained from "real" resized reflectances and from remote data) provide the accuracy as a function of FWHM. This methodology was applied to evaluate the accuracy with which CHRIS-mode1, CHRIS-mode2, Landsat5-TM, MIVIS and PRISMA data characterize three coastal waters. Their mean values of uncertainty are 1.59%, 3.79%, 7.75%, 3.15% and 1.18%, respectively.