DS0104 - Innovations scientifiques et technologiques pour anticiper ou remédier les risques environnementaux

Coastal waters quality surveillance using bivalve mollusk-based sensors – WaQMoS

WaQMoS : Coastal waters quality surveillance using bivalve mollusk-based sensors

Coastal waters quality surveillance using bivalve mollusk-based sensors

Design of an intelligent biosensor, which measures and estimates mollusks’ behavior, for detection of coastal water pollution and climate changes

Monitoring the quality of ocean water is a major challenge with various social, economic and ecological implications. Traditional monitoring systems of water quality in the aquatic environments are rather expensive and based on an intensive exploitation of human resources (for sampling collection or chemical analysis). Then, a desirable solution is to develop unmanned systems, able to work as early warning detectors to trigger a sampling campaign. A particular difficulty of coastal monitoring lies in severe environmental conditions, where existing technological instruments may not provide a reliable solution. On another side, wildlife may react quite rapidly to changes in its environment, so its behavioral and physiological responses to environmental changes can be used for ecological monitoring. However, online and continual physiological monitoring of wildlife requires transmitting a huge volume of data, whose analysis needs specific algorithmic and software tools. The ANR project WaQMoS has addressed all these questions. The main difficulties lie in the fact that regular environmental changes (e.g., human activity, blooming) heavily influenced animals and their internal rhythms (e.g., feeding, breathing, spawning).

WaQMoS was an interdisciplinary project that joined our knowledge of marine biology & electronics on one side, with applied mathematics & software development on another side. Systems or control theory deals with different issues related to designing observation and detection systems: from the placement of sensors (i.e., which variables are necessary to measure) and the model identification (i.e., the system structure and parameters) to estimator syntheses (i.e., processing measurements to evaluate internal variables). Mainly all these approaches are developed for technical systems, and it was a challenge addressed in the project for adapting them to animals using marine biology skills. In particular, the influences of internal circadian/circatidal rhythms were taken into account. Furthermore, a dynamic model of mollusk’s population was used to predict the process state in the future and raise alarms in advance (a model accumulates a lot of information about the monitoring system). Moreover, the developed algorithms are oriented on low-data transmission and low-energy consumption of computation units to increase the autonomy time.

The main outcome of WaQMoS is the development of a biosensor for remote detection of coastal water pollution and climate changes based on the EA team’s technology of noninvasive valvometry (see MolluSCAN Eye project) and by applying the systems and control theory methods mastered by Non-A team of Inria. The developed monitoring system has intelligent capabilities possessing more than one year of autonomous work without human intervention. It has been tested for climate change investigation in the Arctic (for C. islandica at Svalbard, Norway), where a relationship has been observed between the amount of animal movements and the growth of the water temperature, as well as the influence of moon cycles on internal rhythms of animals.

Any public or private organization interested in this smart biosensor use can ask EA team for its installation in case of acceptable scientific cooperation. A spin-off startup can be launched in the case of a large number of demands in order to finalize the technology transfer.

WaQMoS’s obtained scientific results have been published in both domains, in control theory and marine biology. A Ph.D. student who participated in the project (H. Ahmed) got 2 Ph.D. thesis awards for the development of modeling and identification tools for marine animals. In addition, several scientific vulgarization papers were prepared, and these communications have attracted a wide attention of the general public.

The main goal of the project is to develop a biosensor, based on measurements and interpretation of bivalves mollusks behavior, for remote on-line detection of coastal water pollution and climate change consequences. The biosensor development is based on EA team's (CNRS UMR 5805 EPOC, Arcachon) technology of high-frequency noninvasive valvometry for mollusks and by applying the systems and control theory methods of Non-A team (Inria, Lille). The monitoring system should have intelligent capabilities and possess long term autonomous work without human intervention. This is an interdisciplinary collaborative project (marine biology -- electronics -- applied mathematics). One of the main potential applications of the monitoring technique is water quality surveillance along coastlines and ports (harmful algal bloom events, noise pollution), and around oil or wind energy production offshore platforms. Another one is monitoring changing ecosystem due to global warming in sensitive areas such as the Arctic zone.

Project coordination

Denis Efimov (Inria Lille - Nord Europe)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.

Partner

Inria Inria Lille - Nord Europe
CNRS CNRS - EPOC laboratory

Help of the ANR 393,848 euros
Beginning and duration of the scientific project: - 48 Months

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