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pH2Otable – Small Sensor System Network to self-monitor and manage waste water pollution


Abstract

The aim of our project is to create a network of Small Sensors Systems (S3) to detect water microbiological and chemical pollution through their gas phase.S3 will be based on an array of semi-selective sensors and pattern-recognition software.It will be a flexible system suitable for real-time analysis (response in a few minutes), able to work in different contexts.


Description of Projectidea

Water pollution is the leading worldwide cause of deaths and diseases, i.e. more than 14000 people every day. The most recent United States national report on water quality shows that 45% of assessed stream miles, 45% of assessed lake and 32% of assessed bays are classified as polluted.
Water pollution affects the entire biosphere, with dangerous consequences on the individual species, the population and the natural biological communities.
Due to the different pollution sources, monitoring and improving water quality is a challenging task requiring a flexible and multidisciplinary approach. Three main classes of water pollution can be identified:
microbiological, chemical and physical. In particular, the first and the second are the subject of this project, since these are related to substances that may be injested by humans or living beings. Moreover, both chemical and biological pollution involve compounds that would develop a detectable gas phase through their evaporation from water (i.e. the chemical pollutants in the first case and compounds produced by microbiota activities in the latter case).
The aim of our project is to create a network of Small Sensors Systems (S3) suitable to self-monitor and manage water pollution. The S3 network will be based on an array of metal oxide semi-selective gas sensors and pattern-recognition methods for complex volatile mixtures detection. Metal oxide gas sensors will be fabricated using state-of-the-art metal oxide nanotechnology developed in recent EU-founded projects focused on materials science, namely the FP6 project N. 001528 and the FP7 project N. 247768. There, improved sensitivity and long time stability have been achieved with respect to traditional sensing technologies, and these sensors are now mature for exploitation in specific applications.
The S3 system will be able to detect and identify, once trained on the specific types of contamination taken in exam, the nature of the pollutant in a few minutes. It will consists of a network of small sensors able to work in different contexts of water production/distribution/use chain, such as water treatment plants or zootecnic fields.
Fast response times, flexibility to work directly on-site in different contexts and easy to use (without the requirement of high-skilled personnel) represent an important progress with respect of the state of the art techniques actually in use. Just to mention a few examples, microbiological and chemical analysis requires long-operation times (one week and around 2 hours respectively) and highly trained personnel as well as well-equipped labs with expensive instruments and consumables.
As for chemical pollution, studies carried out for example in Lombardy Region (Italy) have underlined the magnitude of the phenomenon: organic halogenated compounds (ex: tetrachloroethylene) in waters for human consumption are present in 510 wells over 92 townships, affecting a population of 1,934,133 equivalent to 20% of the total resident population. The low miscibility of such chemicals with water cause a strong evaporation, suitable for a detection method based on the gas phase, such as the S3 system.
Another target scenario to develop and test S3 system is represented by implants for water purification. Chemicals added to clean water is actually carried out without a proper feedback due to the lack of real time technologies. The S3 system, suitable for in-situ and real time diagnosis, will allow to trigger actions improving both safety and security of citizens, thus improving the quality of life, as well as the cost of exercise and maintenance of implants (for example optimizing parameters such as the purification time or the dose of chemical additives and thus costs).


Consortium of partners, already involved

The consortium is based on the synergic collaboration between experience in water analysis and the competences needed to develop the S3 system, namely the development of sensors, pattern recognition software and sensor networking.
- CNR-INO SENSOR Lab will prepare and calibrate sensors ad hoc for the selected scenarios/applications of water pollution detection. SENSOR lab will exploit it s experience developed through European (FP6 project N. 001528 and the FP7 project N. 247768) and national projects (SUSBIOREM) for the development of innovative metal oxide nanotechnologies and their use in safety and security applications.


Partners sought to complete the consortium

Company with the activity focussed on water quality monitoring not from Italy and Portugal .
Partner with great experience on data analysis

Partner with great experience on electronics for sensors network.


Topics

CIRC-02a-2016:
Water in the context of the circular economy (a) Demonstrating the potential of efficient nutrient recovery from water (IA)
 
CIRC-02b-2017:
Water in the context of the circular economy (b) Towards the next generation of water systems and services– large scale demonstration projects (IA)
 
SC5-12-2016:
Food systems and water resources for the development of inclusive, sustainable and healthy Euro-Mediterranean societies (CSA)
 


Contact

FRANCESCA BARISANI
 
UniversitÓ degli Studi di Brescia
 
Via Branze 38
25123 Brescia
Italy
 
Homepage: http://
Email: francesca.barisani@unibs.it
Phone: francesca.barisani@unibs.it
Organisation: Research Organization

Responsible NCP

NPC SC5 Matteo Di Rosa
APRE
dirosa@apre.it
Telephone:+39 0648939993
Italy