SANY Applications

Use of SANY Based on the Service Oriented Architecture and the ORCHESTRA Reference Model, SANY offers the flexibility to tailor the implementation of SANY to the context of the user, ranging from the invocation of a web service up to the creation of an open platform enabling the trade of information and added value services. Consequently, SANY results are applicable to a great range of environmental risk management applications, and beyond (e.g. for keeping track of the natural resources, climate change, or as a part of a traffic management system.).

Within the project, several prototype applications of interest to GMES were developed and deployed at locations in Austria, France, Hungary, Poland, Spain, and United Kingdom. Short description of the SANY applications is given below. More details canbe foundin the SANY book and in the public deliverables.

Air Quality

Air quality is one of the most important indicators for the sustainable development. The air quality monitoring is therefore required and regulated by the law in all European states. In addition, to existing reporting obligations the
EU-wide initiatives such as INSPIRE and CAFE are gradually introducing the need for the pan-European interoperability and real time exchange of data.
SP4 overview The SANY ‘Air Quality’ pilot is used to validated the usability of the SensorSA based air quality management networks for three main groups of users: network operators, national environmental agencies, and for the European Environmental agency. The SANY Air Quality Management Pilot focuses on the following topics:

Providing uniform access to data from air quality monitoring systems of France, Belgium and Austria. The Air Quality Pilot also showcases the feasibility of serving the INSPIRE-relevant meta information over the standardized OGC Sensor Observation Service interface
Aiding the domain experts in performing the routine Quality Assurance of the data. This is achieved by mean of the state space fusion service. This service continuously monitors all available air quality (immission) observations and publishes the now-casts and confidence intervals at 17 measurement locations using the data model similar to the original
immission data model. The combination of the data from both servers, presented side-by side provides a very effective help in finding suspicious measurements.
Identifying the impact of the known pollution sources to actually measured immission, and providing an indication for the relative importance of the unknown (unaccounted for) sources of pollution at the selected positions. This is achieved by comparing the immission measurements with the prediction based on real-time emissions from major industrial plants in the Linz area.
Illustrating the feasibility of the automatic report generation. This use case is limited to automatic generation of the data required for reporting in the CAFE

INSPIRE Meta-Information

SANY Air Quality Pilot demonstrates the feasibility of building ‘INSPIRE-ready’ service networks based on SensorSA components deployed in Austria. This use case integrates the data from all Austrian provinces and the background measurements from the measurement network of the Austrian Environmental Agency.

Contours

In order to demonstrate the strengths of a decentralized system, the data from two provinces as well as additional background data are provided through separate SOS service instances. All retrieved data is annotated on-the-fly according to INSPIRE rules for meta-information using the Cascading SOS service. Finally, the relevant meta-information is harvested by special instance of SensorSA catalogue, and published through INSPIRE compliant catalogue service interface.

CAFE Report Generation

CAFE Reporting Use Cases In addition to providing the INSPIRE-ready metadata model, the SANY Air Quality pilot also implemented functionality to automate the report generation in order to accommodate periodic national and European reporting obligations. an open service architecture for sensor networks.

The retrieval and submission process is the same for all reports, but a parametrized data download service has to be provided to support each individual report. The raw report data is automatically generated from the observations by Formula 3 time series processor embedded in the SensorSA Cascading SOS.

This offers a number of advantages over manual report generation:

The relevant reporting indicators can be easily reproduced at any time with a minimal effort. This eliminates the main source of errors in report generation.
The Map and Diagram Service provides a convenient way for automatic generation of maps and diagrams based on the data generated by the Cascading SOS.
The Cascading SOS and the Map and Diagram Service can be easily used as a back-end for fully automated report generator. (not in SANY scope)

Data Plausability

Data quality assurance can be a tedious, expensive and sometimes also error-prone process that requires continuous supervision of the highly qualified domain experts. Rather than attempting to completely replace the work of domain experts by automatic quality control procedures, SANY looked into options to support domain experts in their work by automatically identifying suspicious measurements.

In order to achieve this goal, a state-space fusion service has been developed and deployed in the region of Linz. This service continuously monitors all available immission observations and publishes the nowcasts and 24 hours forecasts at 17 measurement locations using the data model similar to the one used by original immission SOS.

In addition to the nowcasts and forecasts, the state-space fusion also provides the confidence intervals for all estimated values. This allows easy identification of the ‘suspicious’ measurement: a measurement can be automatically declared ‘suspicious’ when the difference between data nowcast and actual measurement is larger than the confidence interval advertised by the fusion service.

Air Quality Data Inspection

In SANY, the nowcasting data and the confidence level are visualized by Data Inspection Tool (based on GTV), thus providing the visual aid for experts performing the quality assurance task. The suspicious data can be presented in form of colour-coded tables, special symbols on a map, or by overlapping the time series as shown above.

Impact of Known Pollution Sources

Identifying the impact of known pollution sources on actually measured immission provides an indication for the relative importance of pollution sources at selected positions, which are either not known or not taken into account.

In other words: whilst the major immission sources tend to be known, additional immisions from background sources will lead to higher measurement levels.

SANY implemented a dispersion modelling service, which takes the real time emission data from all major industrial sites in the city of Linz and meteorological data as input. It calculates the dispersion of the emissions, and produces the estimate of the contamination load at the positions of the immission measurement stations. Thus the estimated immission from known sources are correlated to the actual immission measurements.

The estimated Immision are published using the SOS service for a number of points corresponding to existing air quality measurement stations, and the output data model is similar to the one used by immission SOS. This allows easy comparison of the predicted and measured values of immission, e.g. using the Data Inspection Tool (GTV).

In addition, the estimates are also published in form of colour-coded maps using the Map and Diagram Service.

Marine risk

SP5 prototype can be explored at SP5 application web page. In case the demonstration is currently offline, please contact BMT.

Marine risks arise from a number of sources including natural events, anthropogenic causes and a combination of both. In almost all cases, marine risks have an economic, human and environmental impact. In SANY, short term microbial contamination of both Bathing Waters and Shellfish Waters has been targeted. These designated water areas are subject to extensive regulatory standards, established via EU Directives.

SANY marine pilot overview In the case of Bathing Waters, failure to meet regulatory standards can have significant impacts on public health and tourism revenue. Similarly, short term microbial pollution events in Shellfish Waters can have serious consequences for consumer health and cause a reduction in revenue for the local aquaculture industry. Presently, microbial levels in the selected water areas are assessed using laboratory testing. These tests often have a turnaround time of more than 24
hours and, as such, can only determine whether a contamination event has occurred. Improvement in the ability to forecast the risk of short term microbial pollution in designated waters could reduce both the human and economic
impact of such events. The SANY marine risk applications use a number of services developed within the SANY project to access data from sensor networks and assess the likelihood of a contamination event occurring. The use of SANY
Sensor Service Architecture enables:

Access to third-party sensor networks and phenomenological models, to create cost-effective access to measured or modelled data streams and equally to allow operators of such networks/models to valorise their investments;
The use of web-based services to provide high-value data processing (eg for spatial fusion, temporal fusion and modelling) that will enable users to get enhanced information about parameters of interest;
Rapid deployment of additional in-situ sensors on both fixed and mobile platforms. These will acquire data on key water quality parameters, to fill gaps in spatial and temporal data coverage, and thereby permit improved quality of risk now-casting and forecasting;
Provision of alerts and alarm systems to raise the awareness on a possible hazard and support preventative measures;
Remote configuration of smart sensors and, if possible, adaptive tuning of stochastic models to allow ‘on the fly’ enhancement of risk forecasting through incorporation of recent data within the forecasting algorithm.

Marine Risk Application Tutorial

Following tutorial explains how to use the SANY Marine Risk Application Prototype:

Urban Geo Hazards

Geo-hazards may be caused by human activities or natural events. Whether those hazards are induced by human activity or natural hazards, they have an economic, human and environmental impact, which cannot be neglected. As an example, landslides are among the most widespread hazards on Earth causing billions of dollars in damage and thousands of deaths and injuries each year around the world, and Europe has the second highest incidence of landslide casualties of any other continent. As well, recent accidents in European cities induced by construction works raised the awareness for a better control of monitoring data, and enhanced services for decision support.

In SANY, the geo-hazards pilot focuses on hazards related to construction works in dense urban areas. Indeed, with the expansion of urban areas and the densification of population and transport networks of those areas, construction and rehabilitation works on structures have become more frequent, thus the population is exposed to higher risks. There is therefore a critical need for a better management of geotechnical risk in such a context.

Moreover, monitoring systems and sensor management software installed on a construction site are usually proprietary, and vary from one provider to the other, thus multiplying data sources and information. With respect to those limitations, the Geo-hazard application intends to provide an easy and fast access to sensor data, independently from the sources, and the possibility to merge that information through fusion and modelling services, in order to offer synthetic and comprehensive information to the end-user.

Geo Hazards Pilot Demonstration

SANY Geo-hazard application validates the usability of ad-hoc sensor networks, SensorSA/SWE services and SANY Decision Support infrastructure in the context of urban geo hazards. The final goal of this application is improved risk management in areas affected by construction works. The SP6 pilot has been developed and tested at metro building sites in Barcelona, Budapest and Toulon. The video embedded at this page illustrates the main features of the final SP6 application demonstration in Barcelona (November 2009).

Most of the services used and implemented for SP6 Pilot may be transposable and used in other contexts (landslides, structural health monitoring, etc). The use of SANY Sensor Service Architecture enables:

A common and interoperable access to third party in-situ, EO data, and wireless smart sensors data for a more comprehensive and global information;
A compliance of information between different systems using well-define resources identifiers, as well as a standard description of sensors and sensor systems;
The provision of alerts when alarms conditions are met, and a customised notification of such alerts by the user for a better awareness on a possible hazard and support preventative measures;
The remote configuration and management of wireless smart sensor networks;
The Fusion of distributed in-situ measurements of geophysical parameters with other relevant data (e.g. EO data, topographic data, …) in order to generate more accurate information;
The provision of an early risk awareness information using predictive services (temporal fusion and the use of geotechnical models) to predict alarms and ensure a faster response to a potential risk;
The possibility to have additional information where no sensor measurement is available through spatial fusion services or through the rapid deployment of additional in-situ sensors;
The use of a Services platform onto which the SANY services are grafted. The services are chained to one another, using a workflow engine that triggers the services and passes the information in a standardised way from one service element to the other, in order to create new applications that will be used for monitoring and forecasting environmental geophysical phenomena.