Author(s)

M. Lega, M. Casazza, R. Teta & C.J. Zappa

Abstract

In any process of Environmental Impact Assessment (EIA) a key role is played by the action of monitoring. Indeed, the acquisition of real field data provides the evidence of the environmental status and identifies hazards and sources of pollution. When environmental pollution is revealed, it is important to identify the source following the source-path-target model. However, when monitoring operations are planned, often the three-dimensional (3D) nature of monitored hotspots is neglected. Instead, information can be gathered through a multi-parametric, multi-level framework, which combines multiple disciplines and generates correlations between several data sets acquired in the analysed scenario. This novel new framework is named MuM3, meaning that the proposed Monitoring (M) is Multi- Disciplinary, Multi-level and Multi-parametric (i.e. Mu) and it is developed in all the three dimensions of physical space (the superscript ‘3’). This paper outlines the implementation of this framework. In particular, monitoring polluted coastal waters refers to one of the critical areas identified by EIA regu- lations. The framework incorporates different spatial scales of observation (Levels) and the potential sensors that can be used at each Level. A three-step work-flow model describes the raw data acquisition and the transformation and integration of different indicators into useful information for EIA. A schematic flow chart describes the approach to developing multi-level, multi-parameter connections. Extension of this framework can be applied to any EIA, especially in the case of critical areas that are identified by the regulations as: (i) Wetlands, riparian areas, river mouths; (ii) Mountain and forest areas; (iii) Nature reserves and parks; (iv) Densely populated areas; (v) Landscapes and sites of histori- cal, cultural or archaeological significance.

Keywords

coastal waters, EIA, environmental impact assessment, environmental monitoring, environ- mental pollution, framework, multi-level, multi-parameter, MuM3.