Managing an ocean of information to monitor coastal environments

Europe’s coastlines are exposed to risk of pollution. I-MARQ’s prototype Geographical Information System (GIS) delivers detailed information on coastal water quality, helping decision makers shore up defences by taking appropriate action against contamination.

A recent Communication from the European Commission highlights some of the issues the IST-funded i-MARQ project aims to solve: “Our coastal zones are facing serious problems of habitat destruction, water contamination, coastal erosion and resource depletion,” states the European Commission’s report on ‘Integrated Coastal Zone Management: a Strategy for Europe’. “Given the coast’s critical value and its potential, these problems must be solved. And, as many of the problems of the coastal zone have a European dimension, the response must include action at the European level,” it continues.

This is where I-MARQ steps in.

As project coordinator Dr Jonathan Williams of Marinetech explains: “These trends create a rapidly growing demand for solutions, comprising the ability to process high volumes of raw data; to extract high quality information; and to present it in a form which maximises usability, understanding and decision-making.”

I-MARQ will estimate and forecast several important factors affecting water quality. These variables include the amount of suspended sediment in water (often accompanied by waste), water temperature, run-off from land that may bring pollution, salinity, dissolved oxygen, nitrate levels, chlorophyll (algae) and microbial risk.

A tide of information

Growing numbers of distributed sensor networks (DSNs) will create problems of data management: such networks can be used to create a spatially rich data resource. In a marine context, large numbers of drifting buoys have been deployed in major programmes such as the international Global Ocean Observing System (GOOS). Further developments in low-cost sensor technologies (e.g. biosensors) and durable fixed sensor platforms will stimulate a proliferation of DSNs for management of marine and coastal waters. Converting large data volumes into succinct information within a decision support context is thus becoming a definite challenge.

A critical feature of i-MARQ is its ability to integrate water quality data from a multitude of different data sources, using a technique called ‘data fusion’. “This technique allows different types of data, ranging from those sensed by buoys or hand-held instruments to data obtained from satellite images, to be used in estimating water quality,” says Williams. “In light of the new European Bathing Water Directive, such a system is paramount, since public administrations need to be able to forecast water quality with greater accuracy so that action can be taken to ensure publically-accessible water quality remains above a certain level at all times,” he adds.

“The [I-MARQ] system could be used for compliance monitoring of coastal and estuarine waters,” explains Williams. “For example, a civil servant in a national agency concerned with water quality might notice that sensors reported continuing abnormal fluctuation in dissolved oxygen levels in an estuary that do not correlate with natural land run-off. This would enable local management teams to mobilise in order to test the area of concern. Once tested and having confirmed the i-MARQ GIS system’s findings, the inspection team could then focus their effort on local industrial processes which could be responsible for the run-off.”

I-MARQ explained

The i-MARQ system is composed of two sub-systems. The Meta Information System is the infrastructure which communicates with the data sources to find out what inputs they have for the GIS system. It is exceptional in its field since it is fully scaleable, and after launch, users will be able to freely add new data sources and processing models to amend the network of sensors and therefore improve data quality.

The other part of the system is the Fusion Engine which updates the GIS and information on water quality. The data then feeds a dynamic GIS configured to supply a variety of end-user markets. The application of data fusion techniques permits forecasting of parameters, by combining latest measured values with the statistical characteristics of historic datasets. It also tracks the uncertainty of those forecasts. This determination of information robustness is considered an essential ingredient of advanced environmental decision support systems.

The UK’s Environment agency and the Portuguese Instituto da Água (INAG) are planning to trial a cut-down system, and i-MARQ is currently validating the full version using historic data. The i-MARQ team will then pilot the full system in the Solent and at two locations on the Cote D’Azur in the New Year.

In addition the project team have been contacted by other possible users. Says Williams: “The system can be used by a range of other users such as beach-side hotels to predict bathing water quality and diving schools to forecast underwater visibility.”