Giuseppe Masetti

Design of a standardized geo-database for risk monitoring of potentially polluting marine sites

An increasing availability of geospatial marine data provides an opportunity for hydrographic agencies to contribute to the identification of potentially polluting marine sites (PPMS). This new acronym has been created not only to refer to shipwrecks of modern vessels, but also for other types of marine sites such as dumping areas, pipelines, etc. Independent of the specific type, a PPMS represents a potential source of pollution for the marine environment. Although several type-specific databases are available worldwide (from local to global scale), there is an evident lack of uniformity (e.g., different aims of data collection). A common approach description of these sites at local single-site level may permit aggregation for multi-scale decisions, e.g., for remediation and incident response. To adequately manage these sites, a standardized PPMS geospatial database (GeoDB) application has been designed to collect relevant information suitable for site inventory and geo-spatial analysis. In particular, benefits in structuring the data in conformance with the Universal Hydrographic Data Model (IHO S-100) and encoding using the Geographic Markup Language (GML) are presented. A possible practical storage solution is proposed using a GML-enabled spatial relational database management system. Finally, a Web GIS deployment is illustrated, being the simplest way to communicate to the public the collected information, with the related possibility of using the data as a Web Map Service in almost any GIS, allowing for better development and integration with other available datasets. The adoption of the PPMS GeoDB product specification as part of the IHO S-100 series would represent an innovative and important contribution from the hydrographic community to reduce, or at least better manage, environmental and economic risks related to PPMSs.

A risk index methodology for potentially polluting marine sites

Attempting to assess the risk of a release from a potentially polluting marine site (PPMS) can be a very subjective process. The Marine Site Risk Index (MaSiRI) is designed to provide a more objective approach to this process by adopting a table-based evaluation scheme, while still allowing for the inevitable unknown conditions by including a subjective ‘expert correction’ in a suitably controlled manner. Building on a geographic database of PPMS records, the MaSiRI algorithm applies data filters to remove PPMS records for which it is not applicable and then estimates a basic risk index based on core data that almost all sites would contain. It can then refine the results for those sites that have auxiliary data, varying the assessed risk as appropriate, according to standard rule-sets. A risk level of confidence is computed and adjusted to express dynamic confidence in the risk value (e.g., due to reliance on estimates rather than measured values), and where appropriate an upper and lower bound of risk can be used to assess the range of values associated with an estimated parameter. This information can be visualized by a composite quality symbol proposed here. MaSiRI is demonstrated on three illustrative shipwrecks and then compared against the DEvelopment of European guidelines for Potentially Polluting (DEEPP) project database from the Pelagos Sanctuary in the western Mediterranean. The aggregate results of the comparison are broadly similar to DEEPP, within the limits of the comparison, but provide a more detailed analysis in the case of estimated pollutant volume and ubiquitous assessment of levels of confidence.

A Ray-Tracing Uncertainty Estimation Tool for Ocean Mapping

A tool to estimate the ray-tracing component of the surveyed depth uncertainty was created and made publicly available through Web services and a Web geographic information system. The estimation is based on a spatial variability analysis at the time of validity of two popular, global-scope sources of oceanographic environmental data. The tool has potential applications in all the phases of ocean mapping, from survey planning to data collection and processing.