Jana Klánová

Luděk Bláha

Jiří Damborský

Petr Klán

Ladislav Dušek
email: 4dusek@iba.muni.cz

Ivan Holoubek
email: holoubek@recetox.muni.cz2jiri@chemi.muni

CETOCOEN Project: From the Laboratory to the Field and Beyond

Keywords: Persistent organic pollutants, POPs, ambient air, monitoring, Stockholm Convention, Global Monitoring Plan, CETOCOEN, MONET

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Environmental problems including their social and economical impacts have to be set into the context of the overall development of society, as a good management of natural resources is one of the key aspects of sustainable development. Such management requires sufficient information on the key factors affecting environment, data on occurrence, fate, persistence and long-range transport of various chemicals, their accumulation in biota and food chains as well as on mechanisms of their toxicity, and related ecological and human health risks. Only such complex information can provide sufficient support for decision making and development of strategies related to protection of natural environment and human health. International cooperation in this field is crucial as the environmental pollution goes beyond the boundaries.

Growing demand for such information coming from legal and regional authorities as well as from private subjects, professionals and public resulted in initiation of the CETOCOEN (CEntre for TOxic COmpounds in the ENvironment) [START_REF] Dvorska | Košetice, Czech Republic -ten years of air pollution monitoring and four years of evaluating the origin of persistent organic pollutants[END_REF] project of Operational Program Research and Development for Innovations2 supported from European Union (EU) Structural funds. The CETOCOEN project is coordinated by the Research centre for toxic compounds in the environment (RECETOX) [START_REF] Holoubek | Trends in background levels of persistent organic pollutants at Kosetice observatory, Czech Republic. Part II. Aquatic and terrestric environments 1988-2005[END_REF] of the Masaryk University in Brno.

Background

The centre addressing problems related to persistent organic pollutants (POPs) was established in 1996 and from the beginning it has been involved in a number of international networks and projects. In cooperation with the Czech Hydrometeorological Institute, the centre established the programme of integrated monitoring of POPs in all environmental matrices at the central European background station in Košetice in 1996. Unique data on long-term trends in environmental levels of polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) at European background are reported to the European Monitoring and Evaluation Programme (EMEP) [START_REF] Lammel | Observation and origin of organochlorine pesticides, polychlorinated biphenyls and polycyclic aromatic hydrocarbons in the free troposphere over central Europe[END_REF] , a collaborative project addressing long-range transport of the air-born pollutants in Europe and supporting international Convention on Long-range Transboundary Air Pollution (CLRTAP) [START_REF] Lammel | Polycyclic aromatic hydrocarbons on small spatial and temporal scales -I. Levels and variabilities[END_REF] [1], [2], [3]. In the last decade, interactions among chemicals, environment and biological systems and their consequences at local, regional and global levels were the main focus of the centre. New approaches and experimental techniques were developed in photochemistry, toxicology, risk assessment, biostatistics, modelling and environmental informatics. Interdisciplinary links were established when addressing effects of the climate change on the contaminant cycling in the atmosphere [4 -7]. In 2001-4, the centre coordinated the project funded by United Nations Industrial Development Organization (UNIDO) [START_REF] Lammel | Polycyclic aromatic hydrocarbons on small spatial and temporal scales -II. Mass size distributions and gas-particle partitioning[END_REF] focused on implementation of the Stockholm Convention on Persistent Organic Pollutants (SC) [START_REF] Gasić | Quantification of sources of PCBs to the atmosphere in urban areas: A comparison of cities in North America, Western Europe and former Yugoslavia[END_REF] in the Czech Republic. Consequently, a National centre for POPs [START_REF] Harner | Passive air sampling for persistent organic pollutants: introductory remarks to the Special Issue[END_REF] , was established as a part of RECETOX, an expert body focused on chemical and toxicological assessments of the chemicals being a subject of the international conventions ratified by the Czech Republic. The centre serves the needs of government, ministries, regional authorities and industry, and provides new information on levels and fate of these chemicals, their toxicological properties and risks, and developing new methods of sampling, chemical analyses and effective toxicological testing. In 2009, the Conference of the Parties of the Stockholm Convention of United Nations Environmental Programme (UNEP) [START_REF] Klánová | Passive air sampler as a tool for long-term air pollution monitoring: Part 1. Performance assessment for seasonal and spatial variations[END_REF] officially endorsed RECETOX as a Σ PAHs (Gas Phase)

Regional Centre of the Stockholm Convention for capacity building and technology transfer in the region of Central and Eastern Europe [START_REF] Čupr | Passive air sampler as a tool for long-term air pollution monitoring: Part 2. Air genotoxic potency screening assessment[END_REF] . In addition to above mentioned integrated POPs monitoring at the Košetice station, the centre has been developing the national network for monitoring of POPs in ambient air called MONET CZ (MOnitoring NETwork) [START_REF] Klánová | Are the residents of former Yugoslavia still exposed to elevated PCB levels due to the Balkan wars? Part 1: Air sampling in Croatia, Serbia, Bosnia & Hercegovina[END_REF] since 2003 [8-10]. Vast number of samples collected in frames of such long-term monitoring programs introduced a problem of efficient data management. Robust environmental database was urgently needed as well as interactive tools permitting the user to validate, aggregate, analyze, and visualize data. Due to the international and multilateral character of the MONET project, the centre faced a task to develop a web portal allowing professionals as well as general public to assess available environmental data providing them at the same time with the advanced analytical and modelling tools based on the Geographical information system (GIS).

CETOCOEN project and its goals

In 2009, the research team of the centre prepared a successful project for the EU Structural funds, Operational programme Research and Development for Innovations, in the priority axis 2, Regional centres of applied research. The CETOCOEN project stemmed from the previous successful history of the centre and set new and ambitious goals. Following substantial enhancement of the laboratory capacities and human resources, the centre is expanding from the original fields of research to new directions including development of sampling and analytical methods for emerging environmental pollutants, laboratory and field studies of processes affecting the fate of chemical substances in the environment, development of biosensors and natural biocatalyzers, and assessment of toxic effects of chemical compounds and natural toxins on organisms. Major contribution of the CETOCOEN project is improved understanding to the environmental fate of toxic compounds, their distribution among matrices, long range transport and bioaccumulation, degradation mechanisms of pollutants in various climatic regions with respect to increasing risks of their re-release due to the climate change, responsible assessment of toxic effects, and quantification of ecological and human health risks. The project will help to identify new classes of toxic chemicals suspected of persistence, bioaccumulation and long-range transport, which are major criteria for their listing in the global conventions on chemical safety. The centre is also involved in the Global monitoring plan (GMP) [START_REF] Růžičková | An assessment of air-soil exchange of polychlorinated biphenyls and organochlorine pesticides across Central and Southern Europe[END_REF] . GMP is an activity developed under the Stockholm Convention with a purpose of providing a tool for effectiveness evaluation of the Stockholm Convention measures. First global assessment of current baseline levels of POPs in core matrices (ambient air and human milk) was completed in 2008. Majority of data on ambient air levels of POPs was collected using the passive air samplers (PAS) with a significant contribution of data from the MONET networks. In 2009, the Conference of the Parties of the SC decided that levels of POPs in core matrices will be assessed every six years assuming that such period will be sufficient for establishment of the temporal trends. As the Košetice station is the only site worldwide where active and passive samplers have been co-employed for full six years so far, results from this station have an important role in the intercalibration of both techniques, comparison of trends derived from both datasets, and development of future global monitoring programmes [17-19]. Long-term sustainability of such monitoring programmes is of a great importance for a success of the Global monitoring plan. To integrate available monitoring data reporting environmental levels of toxic chemicals with newly acquired knowledge on their behaviour and effects in a single database available to public and widely utilized requires a new type of software for simultaneous visualization and analyses of various types of data. Development of the mathematical models for quantification of mass transfers from primary and secondary sources, exposure of ecosystems and human, model simulations of distribution, transformation, and long range transport of POPs, as well as designing the software tools allowing the use of the whole database as expert system, is the most important task of the CETOCOEN project. This unique environmental data system should be further linked to existing epidemiological databases in order to enhance our understanding to environmentally induced diseases and their triggering mechanisms. While epidemiological data are currently well organized, data from the environmental monitoring programmes are rather fractionated. This problem has been also recognized on the international level.

Project deliverables and impacts

Successful implementation of the CETOCOEN project, namely development of Global environmental assessment and information system (GENASIS) [START_REF] Bartoš | Which compounds are most responsible for elevated human health risks in the Western Balkans?[END_REF] would provide professional platform and decision making tool supporting implementation of European legislation and international conventions on environment protection. At the same time, the GENASIS should function as a clearing house mechanism transferring know-how between experts, institutions, countries and regions. The outcomes of the CETOCOEN project will serve to the government and national ministries by providing information services necessary to control compliance with international conventions, to update inventories, toxicological databases and risk profiles, to develop of monitoring networks and expert systems for interpretation of environmental data. They will support the regional state authorities coordinating studies of environmental quality, toxicological investigations and risk assessments. Impact assessments of industrial technologies, data on contamination of the working environment and assessment of related risks will be useful for industrial partners. Availability of such data may trigger transfer of environment-friendly technologies, development of new remediation methods and better education of experts. The project reduces a problem of insufficient links between scientific and applied sector and, at the same time, it establishes the links between the applied research and higher education. It is closely related to the capacity building and technology transfer, supports development of the human resources and training of interdisciplinary educated experts for industrial innovations as well as public authorities. Beside a scientific quality, the CETOCOEN project has also an important socialeconomical impact on the local and regional levels. It increases attractiveness of the region for the scientists and in so doing prevents a brain drain of young scientists, supports underdeveloped congress tourism, and enhances environmental education and public awareness in the region.

Fig. 1 .

 1 Fig. 1. Network of EMEP stations monitoring POPs.

Fig. 2 .

 2 Fig. 2. Long-term trends of PAHs at the Košetice EMEP station.

Fig. 3 .

 3 Fig. 3. Long term measurement of DDT and metabolites in ambient air at background stations of MONET network. Since 2004, MONET CZ has expanded to all Central and Eastern European (CEE) countries as MONET CEE 12 and became a successful example of know-how and technology transfer in the region [11-15]. Passive air sampling technique was selected as an official tool for global monitoring of POPs, and RECETOX became a strategic partner for many world regions (CEE, Africa, Asia and Pacific) [16].Currently, the centre operates the long-term air monitoring programme (MONET EU) at the EMEP stations in all European countries and executes the air monitoring programme in Africa (MONET AFRICA) supported from the EU . With a help of the Global Environmental Facility (GEF)[START_REF] Škarek | A combined approach to the evaluation of organic air pollution -A case study of urban air in Sarajevo and Tuzla (Bosnia and Herzegovina)[END_REF] the centre also contributes to the development of analytical capacities in participating countries.

Fig. 4 .

 4 Fig. 4. Measurement of DDT and metabolites at the background stations of the MONET networks in Europe, Asia and Africa.

Fig. 5

 5 Fig. 5 Global distribution of the sampling sites with on-going air monitoring. MONET sampling sites are coloured blue.

  [START_REF] Lammel | Polycyclic aromatic hydrocarbons on small spatial and temporal scales -I. Levels and variabilities[END_REF] http://www.unece.org/env/lrtap/ 6 http://www.unido.org/ 7 http://chm.pops.int/default.aspx 8 http://www.recetox.muni.cz/index-en.php?pg=national-pops-centre 9 http://hqweb.unep.org/

										PAHs in Ambient Air -Košetice 1996-2008
													Weekly Sampling
		140
									Σ PAHs (Aerosol)
		120
		100
	PAHs [ng.m-3]	60 80
		40
		20
		0
		3.1.1996	3.7.1996	1.1.1997	2.7.1997	31.12.1997	1.7.1998	30.12.1998	30.6.1999	29.12.1999	28.6.2000	27.12.2000	27.6.2001	26.12.2001	26.6.02	25.12.02	25.6.03	24.12.03	23.6.04	22.12.04	22.6.05	21.12.05	21.6.06	20.12.06	20.6.07	19.12.07	18.6.08	19.12.08
														Sampling Date

http://www.cetocoen.cz/

http://www.strukturalni-fondy.cz/getdoc/977e2e36-937e-4432-afe7-165afd87e676/OP-Vyzkum-a-vyvoj-pro-inovace

3 http://www.recetox.muni.cz/index-en.

php 4 http://www.emep.int/

http://www.recetox.muni.cz/index-en.php?pg=regional-pops-center

http://www.monet-cz.cz/

http://www.recetox.muni.cz/index-en.php?pg=regional-pops-center--scope-of-sc-rcactivities--monitoring-of-pops-in-the-cee-countries-and-other-regions

[START_REF] Škarek | A combined approach to the evaluation of organic air pollution -A case study of urban air in Sarajevo and Tuzla (Bosnia and Herzegovina)[END_REF] http://www.thegef.org/gef/home

http://chm.pops.int/Programmes/Global%20Monitoring%20Plan/Overview/tabid/83/languag e/en-GB/Default.aspx

http://www.genasis.cz/main-index/en/

Acknowledgments. This research received financial support from the CETOCOEN project of the European Structural Funds (CZ.1.05/2.1.00/01.0001), the Ministry of Education (MSM 0021622412) and the Ministry of Environment (SP1a3/29/07) of the Czech Republic.


Stuart E Middleton

Zoheir A Sabeur

Knowledge-based service architecture for multi-risk environmental decision support applications

Keywords: OGC, SWE, SOA, semantics, data fusion, environmental risk management, crisis management

This paper describes our work to date on knowledge-based service architecture implementations for multi-risk environmental decision-support. The work described spans two research projects, SANY and TRIDEC, and covers application domains where very large, high report frequency real-time information sources must be processed in challenging timescales to support multi-risk decision support in evolving crises. We describe how OGC and W3C standards can be used to support semantic interoperability, and how contextware information filtering can reduce the amount of processed data to manageable levels. We separate our data mining and data fusion processing into distinct pipelines, each supporting JDL inspired semantic levels of data processing. We conclude by outlining the challenges ahead and our vision for how knowledge-based service architectures can address these challenges.

Introduction

Multiple environmental risks, including those leading to crisis events, require fast and intelligent access to relevant spatial-temporal environmental information with meaningful thematic context by decision makers. The potentially very large and heterogeneous information generated from such data sources should be critically integrated and coherently presented alongside uncertainty information such as sensor accuracy or modelling error estimations. The handling of such information complexity requires agile and information channelling, supported by intelligent data filtering, mining and fusion.

This paper describes our work to date on environmental service oriented architecture implementations for multi-risk environmental management and decisionsupport. The work described spans two projects, SANY [START_REF]Sensors Anywhere[END_REF] and TRIDEC [START_REF]Collaborative Complex and Critical Decision Support in Evolving Crises[END_REF], and covers application domains where very large, high report frequency information sources are processed in challenging timescales in order to support multi-risk decision making in evolving crises.

Our approach to implementing knowledge-based service architectures is to use standards from the Open Geospatial Consortium (OGC) and World Wide Web Consortium (W3C). These support semantic interoperability, allowing metadata driven automation when integrating new data sources. We also propose the use of the Business Processing Execution Language (BPEL) to achieve agility for our data processing services, and the identification of clear semantic levels for each data fusion & mining processes under a structured Joint Directors of Laboratories (JDL) type data fusion framework.

Finally, we propose context-ware information filtering methods to intelligently filter and index raw information events and tailor processing.

Problem statement

We now live in an information age with increasing volumes of information from affordable means of communication, monitoring and observation systems. This information is more accessible to much larger communities of multi-disciplinary users than ever before. Such large volumes of data, and associated high reporting event frequencies, require data to be stored, intelligently retrieved, analysed and efficiently distributed to groups of collaborating users.

The domain of air quality monitoring, marine risk management and geo-hazard risk management in urban areas has been examined in the SANY project [START_REF]Sensors Anywhere[END_REF]. These applications areas are characterised by their use of in-situ sensors & sensor networks, remote sensing data and contextual information. Decision timescales range from minutes to days, with tens of sensor stations reporting measurements and dataset sizes in the gigabyte range. For example, in the area of bathing water quality risk management, beach attendants need to receive alerts about microbial contamination risks levels of exceedance under the EC bathing water directives. Exceedence levels are predicted from a fusion of meteorological and hydrological sensor measurements fed into simulation data models.

The domains of natural crisis management and industrial drilling operations are being examined within the on-going TRIDEC project [START_REF]Collaborative Complex and Critical Decision Support in Evolving Crises[END_REF]. These domains are characterised by having survey data sizes in the terabyte range, which are likely to increase to petabytes of data in the next decade. These application domain areas use a variety of information and data sources, such as sensor networks with hundreds of sensors, camera streams, textual data and social networking web 2.0 sites.

While oil drilling operation decision-making timescales range from hours, for planning decisions, to few seconds for detecting drilling operation system anomalies, the intergovernmental agencies decision timescales, for tsunami warning, range from an hour down to few minutes, depending on the distance a tsunami will travel before impacting on the coastline.

Developing a knowledge-based service architecture

Semantic interoperability within communities, and also between traditionally disparate communities is a major challenge to overcome. The W3C community has developed standards for the semantic web such as XML, RDF and OWL. The OGC [START_REF]Open Geospatial Consortium[END_REF] SWE standard set [START_REF]Sensor Web Enablement (SWE)[END_REF] has also been developed to handle different sensor types (in-situ, remote sensors, video, models, etc.) from a variety of different disciplines such as those in environmental sciences, defence, crisis management, and spatial application domains covering marine, atmospheric, terrestrial biodiversity and so forth.

SWE is a suite of OGC standards consisting of three standard XML encodings (SensorML, O&M, SWE Common) and four standard web service interfaces (SOS, SAS, SPS, WNS). The SWE standards are predicated on a service oriented architecture (SOA) approach. UncertML is an emerging standard [START_REF]Uncertainty Markup Language: UncertML[END_REF] for representing statistics and different mathematical distribution types that the SWE working group are currently discussing with a view to integration into their next generation of standards. It can be seen in Figure 1 that standards now exist to support knowledgebased services working in a variety of different domain layers. As sensor systems become ever larger and more inter-connected we are seeing the volumes of data being shared moving from gigabytes to petabytes (especially in satellite systems [START_REF] Gehrz | The NASA Spitzer Space Telescope[END_REF]). In these circumstances, the need for context-aware data filtering and processing becomes important for achieving critical information management and decision-support. By using different types of context (e.g. a decision support task context or trust model in a data source) intelligent information filtering algorithms can reduce the volume of relevant data which needs to be processed. Context-aware algorithms can also improve the effectiveness of data mining and data fusion approaches by helping to steer algorithms according to the temporally evolving needs of decision makers in a crisis.

The underlying work on knowledge-based service architecture, through projects such as SANY and TRIDEC, consists of identifying semantically well-defined levels of data fusion, motivated by JDL data fusion information model [START_REF] Lambert | A blueprint for higher-level fusion systems[END_REF]. The separation of the semantic layers involved in data processing allows us to implement structured and specialized processing units that can be orchestrated into agile processing pipelines, which could be well suited to respond to the constantly changing requirements of multi-risk environmental decision support applications. These semantic layers can be seen in Figure 2.

Fig. 2. Mapping semantic layers to structured levels of data fusion

Existing environmental information systems, such as those implemented in the OSIRIS [START_REF]Open architecture for Smart and Interoperable networks in Risk management based on In-situ Sensors[END_REF] and ORCHESTRA [START_REF]RM-OA: reference model for the orchestra architecture, OGC bestpractice document[END_REF] projects, make use of OGC standards and service orchestrated architectures. Other relevant projects like GITEWS [START_REF] Rudloff | The GITEWS Project (The German-Indonesian Tsunami Early Warning System)[END_REF], and its follow-on DEWS [START_REF] Sabeur | Distant Early Warning System[END_REF], provide examples of open sensor platforms, using OGC standards to integrate sensor networks. These projects use OGC semantic metadata to assist semantic interoperability, however, they do not address the issues of handling large volumes of data. Also, they provide limited agility in response to the changing processing requirements of dynamic decision support situations that occur in risk management. The novel approach which we aim to adopt is the coupling of de-facto data fusion methodologies, state of the art scalable processing architectures and semantic technology in order to overcome the above mentioned shortcomings.

Knowledge-based services for in-situ sensors & sensor networks

The SANY project [START_REF]Sensors Anywhere[END_REF] focused on interoperability of in-situ sensors and sensor networks, assuring sensor data could be easily processed and used as a basis for decision support. The sensor service architecture (SensorSA) is the fundamental architectural framework of the SANY project for the design of sensor-based environmental applications and their supporting service infrastructure.

The SensorSA is a service-oriented architecture (SOA) with support for event processing and a particular focus on the access, management and processing of information provided by sensors and sensor networks. The foundation for the SensorSA is the ORCHESTRA project architecture (RM-OA) [START_REF]RM-OA: reference model for the orchestra architecture, OGC bestpractice document[END_REF] and the OGC SWE architecture. The knowledge-based service architecture in SANY is a concrete example implementation [START_REF]SANY fusion and modelling architecture, OGC discussion paper[END_REF] of the SensorSA architecture principles. This is shown in Figure 3.

Fig. 3. Knowledge-based service architecture [SANY]

All sensor data in SANY was obtained directly from a sensor observation service (SOS). In SANY SOS's provided access to both sensor measurement datasets and fusion processing result sets. Sensor datasets contained measurements for air pollution, meteorology, ground displacement and more. Whenever a web processing service (WPS) or sensor planning service (SPS) needed data it used a SOS client.

Because sensor data was represented using both the SWE observation and measurement (O&M) model and UncertML, sensor metadata (units, measurement types, sensor accuracy etc.) was directly available to be used by the SANY processing services. Much of SANY's data fusion pre-processing was thus automated, using metadata to aggregate data from separate distributed SOS's. Examples of this are the use of metadata to identify identical phenomenon for dataset merging and the use of unit metadata to validate data value ranges. In SANY new sensor datasets from SOS's could just be 'plugged in'.

An example of the type of data processing work performed in SANY is spatial interpolation of meteorological data, for subsequent input into a bathing water quality data-driven model. The semantic level of fusion in SANY was mostly a combination of pre-processing (level 0) and impact assessment (level 3).

For complex multi-service workflows in SANY a BPEL orchestrator service was used to execute workflows involving SOS's, WPS's and SPS's. The results of these workflows would be sent to the decision support services, making use of geospatial mapping services and advanced visualizations (e.g. Google Earth 3D visualizations). In this way, raw sensor data, data fusion results and the uncertainty context associated with this data could be made available to decision makers.

Knowledge-based services for real-time & multi-modal data

In the on-going TRIDEC project [START_REF]Collaborative Complex and Critical Decision Support in Evolving Crises[END_REF] we are building on the approaches developed within in SANY, focussing on the challenges associated with handling very large scale multi-modal data sources in real-time. In common with SANY we are using OGC and W3C standards to achieve semantic interoperability. To achieve scalability and performance we will employ a message oriented middleware (MOM), allowing sensor measurements to be sent as events on a message bus. An example of an event is a set of measurements taken over a sample period. Using a message bus allows us to employ complex event routing, and dynamically configure this routing based on the current decision support requirements and context. Context-aware filtering of events, as described in figure 4, is essential as we are planning to handle up to a thousand multi-modal data sources, each source measuring in real-time tens of properties with sampling at periods down to the millisecond range. It is simply not possible to naively receive and process this amount of unfiltered data within the decision making time window of our applications. Because crisis situations are dynamically changing, the decision making task context is modelled in real-time. Up-to-date task context allows the knowledge-based services' processing framework to re-configure itself in an agile fashion, through refocussing of processing services to answer the questions required by decision makers at any given evolutionary stage of a crisis. OGC SWE services, such as sensor planning services (SPS's) and web processing services (WPS's), can be used to control the specific processing steps in a number of processing pipelines. A BPEL orchestrator can enact specific pipeline workflows. However, it will need to be controlled by a choreography component dynamically linking the questions decision makers need to answer to known pipelines capable of generating the answers. Each processing step provides result data at a different semantic level. An example of a processing step would be feature extraction, annotating a fused multi-sensor data feed with metadata to describe temporally correlated patterns. Each processing pipeline provides the processing steps needed to build up evidence that is able to answer a specific 'question' of interest to a decision maker. An example of a pipeline would be a set of several processing steps generating reports, in real-time, on sensor correlations matching conditions where a drilling 'kick' looks likely to occur.

The use of multi-modal data sources is a challenge in itself. Sensor data, text, video and image data all present their own processing requirements. Our agile processing framework allows us to include semantic enrichment processing steps into our pipelines, facilitating annotation of real-time information feeds with metadata suitable for multi-level semantic processing.

Environmental information systems are becoming more and more complex as they increase in scale and scope. As sensor systems become more advanced, the data volumes also increase, both in terms of data sizes (petabyte datasets) and message throughput (hundreds of sensors reporting measurements multiple times per second). These data volumes are too large to be processed naively, and are forcing us to develop context-aware information filtering capabilities into our knowledge-based service architecture.

We are starting to see federated approaches involving the integration of legacy sensor networks and processing capabilities, coupled together in system of systems type architectures. Such federated systems increasingly require the use of standards, such as the OGC and W3C standards sets, to facilitate semantic interoperability and allow seamless automated data processing across aggregated distributed sensor networks.

We are also seeing time-critical decision support environments, such as natural crisis management and industrial drilling operations, which present challenging decision time scales ranging from hours to seconds. This type of multi-risk decision support requires an agile real-time processing architecture which is capable of responding to changing decision support requirements as the crisis unfolds over time.

Our work in projects like SANY and TRIDEC is allowing us to experiment with architectures that couple context-aware information filtering, agile processing and context-aware data fusion and mining in a structure framework.

Fig. 1 .

 1 Fig. 1. Relationships between different domain layers and available standards.

Fig. 4 .

 4 Fig. 4. Illustration of intelligent context-aware information filtering [TRIDEC]

Fig. 5 .

 5 Fig. 5. Illustration of an agile processing framework for knowledge-based services [TRIDEC]

Acknowledgments. Both authors acknowledge the continuing research collaboration with partners from the SANY consortium, in particular the Fraunhofer Institute IOSB. Also with GFZ in the TRIDEC project. This research work was funded by the European Commission FP6 IST Programme under contract SANY IP FP6-0033564 and is currently funded under contract TRIDEC IP FP7-258723.


Dumitru Roman
email: dumitru.roman@sintef.no

Sven Schade
email: sven.schade@jrc.ec.europa.eu

Arne J Berre
email: arne.j.berre@sintef.no

Open Environmental Platforms: Top-Level Components and Relevant Standards

Keywords: open environmental platform, infrastructure, architecture, standards

We present our ideas of an open Information and Communication Technology (ICT) platform for monitoring, mapping and managing our environment. The envisioned solution bridges the gap between the Internet of Things, Content and Services, and highly specific applications, such as oil spill detection or marine monitoring. On the one hand, this environmental platform should be open to new technologies; on the other hand, it has to provide open standard interfaces to various application domains. We identify core components, standards, and needs for new standard development in ICT for environment. We briefly outline how our past and present activities contribute to the development of the desired open environmental platform. Future implementations shall contribute to sustainable developments in the environmental domain.

Introduction

The Shared Environmental Information System (SEIS) 1 is one of three recent major initiatives along with the INSPIRE Directive (European Parliament and Council 2007) and the Global Monitoring for Environment and Security (GMES) 2 undertaken by Europe to collect and share environmental information for the benefit of the global society. ICT have an essential role to play in the context of environmental information systems as they provide the necessary support in terms of tools, systems and protocols to establish a dynamic environmental space of collaboration in a more and more sophisticated digital world. Core challenges are not only related to providing seamless environmental data access to public authorities, businesses and the public at large, but also to allowing for interoperable environmental services based on Web technologies, and stimulating new market opportunities 3 . ICT for environmental collaboration is widely recognised as a major step for addressing complex management issues including adaptation to climate change and sustainable management of urban environment. The European Commission recently funded several projects4 in the area of ICT for Sustainable Growth, with a core focus on ICT for Environmental Services and Climate Change aiming at providing the foundations for an infrastructure for monitoring, predicting and managing the environment and its natural resources.

Current research problems addressed by such projects are centred on frameworks, methods, concepts, models, languages and technologies that enable enhanced environmental service infrastructures and platforms (e.g. the ENVISION project [START_REF] Roman | Environmental Services Infrastructure with Ontologies -A Decision Support Framework[END_REF]). Environmental Information Systems are migrating towards being provided as Software as a Service (SaaS) and will benefit from the utilization and specialization of emerging Infrastructures as a Service (IaaS) and Platforms as a Service (PaaS) as this is emerging under the umbrella of Cloud computing as well as the evolution of the Future Internet5 with its three core pillars: an Internet of Services, where applications live in the network, and data becomes an active entity, an Internet of Content, where most of the contents are generated by end-users, and an Internet of Things (IoT), where every electronic device will be an active participant in the network [START_REF] Vermesan | Internet of Things Strategic Research Roadmap[END_REF]. Cost effective design, development and deployment of environmental models and applications in this context requires novel platforms to emerge, and inter-connect, inter-operate and inter-work with each other and with platforms for other verticals. Of particular interest are the architectural foundations of infrastructures and platforms supporting flexible discovery and chaining of distributed environmental services and content, and showing how they combine synergistically to enable better collaborations on the scale required by Future Internet connected environments. This will foster a greater understanding of how open environmental service infrastructures and platforms can enable enhanced collaboration between public authorities, businesses and the general public for a better management of the environment and its natural resources. In this paper, we look at the architectural foundations of open environmental infrastructures and platforms, and propose top-level components of an open architecture (Section 2). We also identify standards relevant to architectures of environmental infrastructures and platforms (Section 3). Standards of ICT for environment are addressed in most detail. We conclude this paper with some challenges (Section 4).

Top-level Components of the Environmental Monitoring Platform

The basic architecture of the suggested open environmental platform is depicted in Figure 1. The core platform (three layers in the middle) bridges the gap between the Internet of Things and end-user applications, such as marine monitoring or air quality monitoring and reporting. A relation of the suggested components with the Internet of Services and Internet of Content is depicted in addition. In the remainder of this section, we detail each of these layers (from bottom to the top). Notably, the complete environmental platform may be offered as a service in itself and by this mean implement the Platform as a Service (PaaS) concept.

Service and Content Models

On the bottom level of the platform (Service and Content Models), the diverse resources that are available from the IoT are aggregated into domain specific components, which encapsulate content, as well as access and processing services that are adopted to domain specific needs. We put particular emphasis on the use of open standards and flexibility of the proposed platform. We have to account for new technologies that need to be integrated (technology-push).

Functionalities, non-functional aspects, as well as behaviours of the objects provided by the IoT need to be encapsulated and properly described in order to create a certain level of abstraction on top of which other added-value services can be created. At this level, depending on the types of objects that need to be abstracted, various types of services are created that hide the technicalities and access details of the IoT objects. Examples of such services include sensor services (accessing real-time sensor data well defined interfaces), data services (providing access to relatively static data such as geographical features), or processing services (providing access to geospatial processing, simulations, and other resource-intensive processes). An important challenge here is the development of proper conceptual models for services, that can be customized and extended for different domains.

In respect to content, we particularly distinguish content based on physical measurements by sensors, content based on environmental simulation/models, and content based on citizen contributed information, especially on Volunteered Geographic information (VGI) [START_REF] Goodchild | Citizens as sensors: the world of volunteered geography[END_REF]. Taking air quality as an example, current values for CO2 concentration on a particular measurement station are usually measured by physical sensors, simulations result in an areal coverage of CO2 or in temporal forecasts of the CO2 concentration, while reports, such as 'it's smelling badly' or 'today it is hard to breath normally in Milan', are part of VGI. For us, this content triangle (Figure 2) serves the basis for all environmental information in the Web. An extension to a rectangle, including literature based content, could be discussed.

simulation based content sensor based content citizen based content

Figure 2. Triangle of Geospatial Content (adopted from [START_REF] Schade | A Future Sensor Web for the Environment in Europe[END_REF]).

Specific content models rely on vocabulary generation, conceptual data modelling, and encodings that have to be developed in each specific domain. Inputs from the IoT are wrapped according to each (domain) specific model including required adapters on data types and values. In order to ease interoperability within and across domains, it is desirable to establish a common generic model for integrating content coming from each of the identified kinds of sources (sensor, citizen and simulation) more easily. Nevertheless, the actual integration task is part of the next layer.

Semantically Enhanced Infrastructure

At its intermediate layer in Figure 1 (Semantically Enhanced Infrastructure), the Open Environmental Platform provides harmonized access to the IoT. It facilitates semantic discovery based on annotations. Harmonized access to the IoT is provided in form of mediators that provide encapsulating services.

Semantic annotation should be used to describe the intended interpretation of services and content. Resource descriptions shall make use of existing domain vocabularies and ontologies. This requires (i) tools for creating vocabularies, ontologies and resource descriptions, and (ii) according storage facilities. Discovery mechanisms shall facilitate these descriptions and provide human users and machines with the capabilities to search for desired services or content using their own expressions or a predefined set of domain terms/concepts. For example, users may be provided with terms in English, French and German, whereas the initial resources are only described in English. Once a resource or a set of resources has been discovered, users have to be able to retrieve it. This process may include mediation, i.e. the translation of the initial domain model of a provided resource into that of the consumer. Apart from natural language translations, this may include unit conversions or more sophisticated schema mapping rules [START_REF] Wache | Ontology-Based Integration of Information -A Survey of Existing Approaches[END_REF].

Value-Added Services and Content

The third layer of the architecture (Value-Added Services and Content) supports the creation of added value products by offering methodologies and facilities for content fusion and service composition. This layer makes use of the semantic discovery and mediation mechanisms provided by the semantically enhanced infrastructure and to access services or content coming from physical sensors, (environmental) simulations and citizen, as well as contextual information on the targeted added value chain.

The design of service compositions for creation of added-value services on top of atomic services encapsulating the different object provided by the IoT, requires the use a graphical language to allow for user-friendly specification of service chains by domain experts as well as users with little technical skills. The composition language needs to provide a sufficient level of abstraction, while still enabling sufficient level of detail so that the graphical service compositions can be used to generate complete executable service descriptions. The composition language must be able to compose different kinds of services including sensor, data, and processing services, as well as to express control and data flow dependencies between such services. A language based on (a subset of) the emerging Business Process Modelling Notation (BPMN) standard [START_REF] Omg | Business Process Modeling Notation (BPMN) Version Beta 1 for version 2.0 -OMG Document[END_REF], extended with declarative features for flexible modelling of compositions and explicit treatment of data mediation and uncertainty would be a proper candidate for addressing the representational needs of environmental service compositions.

Given the harmonised access to data and the specified context, for example using air quality data for calculating individual exposure indexes, Content Fusion components provide the means for streamlining the available data to a particular use.

Sabeur et al. proposed four fusion levels [8]:

o Fusion level 0 provides prepossessing of data, such as smoothing, interpolation, and re-formatting; o Fusion level 1 offers algorithms for auto-correlation and Fourier analysis and the like, in order to identify objects or trends in the incoming data; o Fusion level 2 includes phenomenology models for estimating and refining previous fusion results; and o Fusion level 3 provides prediction and correction information, for example based on Kalman filtering.

The value added content can be exposed after each of the fusion steps and may serve input to multiple further processing steps. In this way, it provides manifold contributions to the Internet of Content. Notably, the modelling and propagation of uncertainties should be considered in the complete fusion process. This will require a quantification of initial errors and vagueness in the raw data coming from the lower levels of the proposed platform.

End-User Applications

Complementary to the technology-push, novel developments have to account for arising user requirements (application-pull). Otherwise, implementing sophisticated technology solutions, which are not used in the mid-and long-term, might be exciting, but remain a unprofitable exercise. In the following we outline a subset of the manifold applications, which shall be supported by open environmental platforms. Their specific technical and non-technical requirements will help to sharpen the specifications of the components on the lower layers of the proposed solution and thereby to develop a flexible solution for information systems that account for all environmental spheres, including the atmospheric, terrestrial and marine domains.

Ground instabilities caused by mud flows, landslides or ground failure may cause significant physical and socio-economic damage in many regions of the world. Being able to correctly forecast such phenomena for proper prevention and mitigation implies the availability of and access to relevant environmental models, data, and services, and the ability of combining them in meaningful ways. Issues such as finding a landslide risk model with specific characteristics, which produces the required output data that fits given input data, chaining models for creating value added services such as landslide risk assessment models and making them accessible for the wider community pose great challenges for discovery and composition of both services and content.

Biodiversity related applications add user-contributed content as an important aspect to data acquisition. While many environmental monitoring applications reply on classical sensors, and environmental simulation and modelling, the biodiversity community retrieves large amounts of information from volunteers, which post their observations on species occurrences on diverse networking platforms. Birdwatch6 provides one of the many impressive examples, in this case related to the occurrence of birds. The handling, quality assurance, and integration of such information is public authority data, such as protected sites, particularly challenges content fusion and semantic content descriptions.

Among others, air quality monitoring bear the challenge of assessing individualized exposure. Today, we have easy access to a great deal of information via television, radio and the World Wide Web. This includes pollution, pollen and meteorological data which are all relatively easily accessed in one or more dissemination channels. All this data contributes to a common sense, but it is not tailored to individual user needs. Relevancy of data and interpreting it are key issues for users today, especially with regards to pollen and pollution. The envisioned platform shall therefore aid the users in tailoring information relevant to their individual requirements, which again poses requirements on the functionalities for implementing the added value chain from general purpose to individualised information.

For the marine domain, the challenge is to create synergies with the market and with policy needs that are necessary to deliver significant value added to Europe from its vast marine resources. Enabling technology platforms are currently deployed across a range of existing marine related sectors including shipping, security and logistics, environmental monitoring and offshore energy. Next generation platforms have to dissolve national borders. They shall address these developments in respect to distributed sensing, and wireless and cable communications. In the marine domain, oil spills are an extremely sensitive topic. Accidental oil releases to the sea may have severe environmental, social and economic consequences, with vital natural resources and human enterprises being at risk. For oil spill decision making, it is essential to be able to predict the fate and effects of the spilled oil. Fate prediction requires data on the spill (location, time, amount, oil type), the environmental conditions (wind, current), and geography (sea depths, coast line). Effects prediction is based on data about natural resources (e.g. fish populations). Being able to combine such data (possibly made available through service) in real-time for prediction of the fate of the oil spill implies sophisticated discovery, composition, and integration mechanisms.

Relevant Standards

Open standards play a key role for interoperability between emerging environmental platforms. By only briefly pointing to standards etc. which have to be considered at the surrounding levels, we particularly focus on required standards on ICT for environment.

Depending on the exact field, end-user applications have to account for a large series of legal documents and standards. The UN Convention on Biodiversity, Clean Air for Europe, and the Marine Strategy Framework Directive are only some examples of these European and global Directives, Regulations and Strategies effecting particular environmental thematic areas. In total several hundred of similar texts apply to environmental monitoring on European and national level.

Value added services and content partially involve mainstream ICT standards, such as Business Process Modelling Notation (BPMN) [START_REF] Omg | Business Process Modeling Notation (BPMN) Version Beta 1 for version 2.0 -OMG Document[END_REF] or Business Process Execution Language (BPEL) [START_REF]Web Services Business Process Execution Language Version 2[END_REF] for service composition. However, special standards and best practises exist for the environmental (and wider geospatial) community. Most notably, these include:

o ISO 19119 (Geographic information -Services) [4] is a standard defined by the Technical Committee 211 (TC211) of the International Standardisation Organization (ISO). It defines the architecture patterns for service interfaces used for geographic information and presents a geographic services taxonomy which includes human interaction services, model/information management services, geographic processing services (spatial, thematic, temporal) and communication services. The standard includes guidelines for the selection and specification of geographic services from both platformneutral and platform-specific perspectives. In fact, the various types of services proposed by this standard are relevant for all layers in Figure 1 where spatiotemporal aspects are relevant. o The Sensor Web Enablement (SWE) of the Open Geospatial Consortium (OGC) [START_REF] Botts | OGC Sensor Web Enablement: Overview and High Level Architecture[END_REF] provides standardized interfaces and metadata encodings that aim to enable real time integration of heterogeneous sensor webs into the information infrastructure. Incubator Group7 developed ontologies that define the capabilities of sensors and sensor networks, and provides semantic annotations of a key language used by services based sensor networks. On top of its Semantic Web standards (RDF, OWL), W3C is setting the standards foundations for semantic interoperability (see also below). The Semantically Enhanced Infrastructure layer serves the building blocks to the higher-level value-added components. Here, standards of the W3C Semantic Web community, such as the Resource Description Framework (RDF) 8 and the Web Ontology Language (OWL)9 should be applied.

Challenges and Outlook

The

Figure 1 .

 1 Figure 1. Top-level Components of the Open Environmental Platform.

  realization of the desired open platform faces several challenges. For us, these include (ordered from the bottom layer of the platform to the top): o Interoperability in a heterogeneous environment: Dealing with resource discovery, processing support, service interfacing, and intermodal operation between heterogeneous types of resources. All of the above required semantically rich and extendible data models. o Advanced data mining: Including the handling of large volumes of data, pattern recognition depending on context, and data flow processing. o Data fusion with uncertainty analysis: Involving uncertainty and the combination of data and processing, but also the economic value of information from source to consumer. Security mechanisms and errorpropagation are some of the required tools. o Dynamic service composition: Considering run-time identification of suitable service components, their bindings and execution. o User support: Covering generation of templates for end-user applications, best practices for application development and training.

  Sensor Model Language (SensorML), Observations & Measurements (O&M), Sensor Observations Service (SOS) are the most important specifications provided by SWE. These standards become highly relevant in relation to value added content and services. SWE standards currently undergo a phase of broad acceptance. o The Worl Wide Web Consortium (W3C) Semantic Sensor Network

http://ec.europa.eu/environment/seis/, last accessed 11th January

2 http://www.gmes.info/, last accessed 11th January 2011.

http://ec.europa.eu/information_society/digital-agenda/, last accessed 11th January 2011

http://cordis.europa.eu/fp7/ict/sustainable-growth/environment_en.html, last accessed 11th January 2011.

http://www.future-internet.eu/

http://www.birdwatch.co.uk/

http://www.w3.org/2005/Incubator/ssn/wiki/Main_Page, last accessed 11th January 2011.

http://www.w3.org/RDF/ http://www.w3.org/RDF/

http://www.w3.org/TR/owl2-overview/

As many prototype solutions already exist, issues of scalability, robustness, and quality of service are central to all of the listed issues [9]. An integrated approach, which is easy to instantiate, remains to be developed. The required balance between application-pull and technology-push can only be reached if the gap between platform developers and intended users can be bridged. This aspect is the most crucial for successful implementation.

We are currently addressing these issues within numerous de-(or loosely) coupled research projects. The Europe 2020 strategy 10 and two of its seven flagship initiatives (the Digital Agenda and Innovation Union) provide possibilities for long term funding.


Leo Wanner
email: leo.wanner@upf.edu

Stefanos Vrochidis
email: stefanos@iti.gr

Sara Tonelli
email: satonelli@fbk.eu

Jürgen Moßgraber
email: juergen.mossgraber@iosb.fraunhofer.de

Harald Bosch
email: harald.bosch@vis.uni-stuttgart.de

Ari Karppinen
email: ari.karppinen@fmi.fi

Maria Myllynen
email: maria.myllynen@hsy.fi

Marco Rospocher
email: rospocher@fbk.eu

Nadjet Bouayad- Agha

Ulrich Bügel
email: ulrich.buegel@iosb.fraunhofer.de

Gerard Casamayor
email: gerard.casamayor@upf.edu

Thomas Ertl
email: thomas.ertl@vis.uni-stuttgart.de

Ioannis Kompatsiaris

Tarja Koskentalo
email: tarja.koskentalo@hsy.fi

Simon Mille
email: simon.mille@upf.edu

Anastasia Moumtzidou
email: moumtzid@iti.gr

Emanuele Pianta
email: pianta@fbk.eu

Horacio Saggion
email: horacio.saggion@upf.edu

Luciano Serafini
email: serafini@fbk.eu

Virpi Tarvainen
email: virpi.tarvainen@fmi.fi.

Kessler

Building an Environmental Information System for Personalized Content Delivery

Keywords: environmental information service, environmental node discovery, knowledge, personalization, infrastructure, services

Citizens are increasingly aware of the influence of environmental and meteorological conditions on the quality of their life. This results in an increasing demand for personalized environmental information, i.e., information that is tailored to citizens' specific context and background. In this work we describe the development of an environmental information system that addresses this demand in its full complexity. Specifically, we aim at developing a system that supports submission of user generated queries related to environmental conditions. From the technical point of view, the system is tuned to discover reliable data in the web and to process these data in order to convert them into knowledge, which is stored in a dedicated repository. At run time, this information is transferred into an ontology-structured knowledge base, from which then information relevant to the specific user is deduced and communicated in the language of their preference.

Introduction

Citizens are increasingly aware of the influence of environmental and meteorological conditions on the quality of their life. One of the consequences of this awareness is the demand for high quality environmental information that is tailored to one's specific context and background (e.g. health conditions, travel preferences, etc.), i.e., which is personalized. Personalized environmental information may need to cover a variety of aspects (such as meteorology, air quality, pollen, and traffic) and take into account a number of specific personal attributes (health, age, etc.) of the user, as well as the intended use of the information. So far, only a few approaches have been proposed with a view of how this information can be facilitated in technical terms. All of these approaches focus on one environmental aspect and only very few of them address the problem of information personalization [START_REF] Karatzas | State-of-the-art in the dissemination of AQ information to the general public[END_REF], [START_REF] Peinel | Customized Information Services for Environmental Awareness in Urban Areas[END_REF], [START_REF] Wanner | MARQUIS: Generation of User-Tailored Multilingual Air Quality Bulletins[END_REF]. We aim to address the above task in its full complexity.

In this work, we take advantage of the fact that nowadays, the World Wide Web already hosts a great range of services (i.e. websites, which provide environmental information) that offer data on each of the above aspects, such that, in principle, the required basic data are available. The challenge is threefold: first, to discover and orchestrate these services, second, to process the obtained data in accordance with the needs of the user, and, third, to communicate the gained information in the user's preferred mode. To address this problem, we need to involve a considerable number of rather heterogeneous applications and thus an infrastructure that is flexible and stable enough to support a potentially distributed architecture. In what follows, we first outline the process of the discovery of the environmental services (also referred to as nodes) in the Web. This is considered as the prerequisite step for enable the retrieval capabilities of the system. Then, we describe briefly the tasks involved in the processing of the data obtained from the environmental nodes until their delivery to the user, and finally present the infrastructure designed to accommodate for both the discovery itself and the posterior tasks.

Discovery of Environmental Nodes

As already pointed out above, the web hosts a large amount of environmental (meteorological, air quality, traffic, pollen, etc.) services, which include both (static or dynamic) public webpages that offer environmental information worldwide, as well as dedicated environmental web services with free access. Especially the number of meteorological services that cover each major location is impressive. However, the fact that environmental information is highly distributed and available in heterogeneous forms and formats makes the problem of the discovery and extraction of information from webpages that provide environmental information a serious challenge. Still, it can be considered to be a problem of domain-specific web search, such that methodologies from this area can be applied to implement a node discovery framework.

We apply two types of methodologies of domain search: (a) the use of existing search engines for the submission of domain-specific automatically generated queries, and (b) focused crawling of predetermined websites [START_REF] Wöber | Domain Specific Search Engines[END_REF]. To perform the queries generated by combining domain information from ontologies and geographical input obtained by geographical web services, we use a web search API (e.g., as offered by Yahoo). The queries are expanded by keyword spices [START_REF] Oyama | Domain-Specific Web Search with Keyword Spices Awareness in Urban Areas[END_REF], which are domain specific keywords extracted with the aid of machine learning techniques from environmental websites. In parallel, a set of predefined environmental websites is further enriched using a focused crawler, which is capable of exploring the web in a directed fashion in order to collect other nodes that satisfy specific criteria related to the content of the source pages and the link structure of the web. The output of the search is post-processed in order to: (i) separate relevant from irrelevant nodes; (ii) categorize and further filter the relevant nodes with respect to the types of environmental data they provide (air quality, pollen, weather, etc.);

(iii) parse the body and the metadata of the relevant webpages in order to extract the structure and the clues that reveal the information presented;

(iv) identify internal and external links of the retrieved webpages, which can be further explored by crawlers. The determination of the relevance of the nodes and their categorization is done using a classifier that operates on a weight-based vector of key phrases and concepts from the metadata and content of the webpages. The procedure of the exploration of the external links is recursive and terminated by a manually-set threshold. The information obtained with respect to each relevant node is indexed in a repository as its finger print, which can be accessed and retrieved by the system through the data retrieval service.

The whole discovery procedure is automatic, however an administrative user could intervene through an interactive user interface, in order to select geographic regions of interest to perform the discovery, optimize the selection of keyword spices and parameterize the training of the classifiers. Figure 1 shows the architecture of the discovery of environmental service nodes.

Processing, Orchestration and Retrieval of Environmental Nodes

Once the environmental nodes have been detected and indexed, they are available as data sources or as active data consuming services (if they require external data and are accessible via a web service).

The following tasks still need to be resolved, in order to be able to offer a usertailored environmental information service.

1. Orchestration of environmental service nodes: Environmental service nodes encountered in the web may require input data provided by other service nodes. In order to obtain all necessary data, the environmental service nodes must thus be -orchestrated‖, i.e., selected and chained. This presupposes the selection of appropriate protocols and the use of appropriate data interchange formats. To decide which nodes are to be selected over which other nodes, or which nodes fit best together, node quality criteria must be taken into account that are measured by data uncertainty and service confidence metrics derived by using statistical measures, machine learning and visual analytics techniques.

Identification of user relevant service nodes:

The process of user-tailored information delivery consists of the identification of environmental service nodes in the compiled repository that are relevant to the query of the user, their profile and their context. This is not trivial, given that a user may be moving, be located in an area which is not directly covered by any node, etc.

Extraction and distillation of the data from the webpages of the nodes:

To distill the data from webpages, advanced natural language processing techniques are needed for webpage parsing, information extraction and text mining. Although these techniques can be tuned to deal with the presentation mode of environmental (i.e., air quality, meteorological, traffic, etc.) data and information, the task of webpage scraping remains a very challenging task. In particular, given a service node, all and only the relevant data (e.g. all the temperature measurements for a city reported in a weather forecast website, but not advertisements) must be extracted.

Given the fact that much information in environmental websites is encoded as images and maps, we also plan to employ image analysis, with the goal to extract information from them.

Converting the data into content:

In order to guarantee a motivated orchestration of heterogeneous environmental service nodes and offer user-tailored decision support services and environmental information production, we need to convert the data into structured unified content, which will allow for application of intelligent reasoning algorithms. To this end, the extracted environmental information is integrated into an environmental knowledge base (KB).

Our KB, which is codified in the standard semantic web ontology language OWL [START_REF]World Wide Web Consortium: OWL Web Ontology Language Reference[END_REF], covers environmental content such as meteorological conditions and phenomena, air quality, and pollen, as well as other relevant environment-related content essential for the targeted user-tailored service: travel and traffic information, human diseases, geographical data, monitoring station details, user profile details, etc. In addition, the KB is also capable of formally representing the description of the user's inquiry.

The current version of the KB contains around 202 classes, 143 attributes and properties, 463 individuals 1

. Its Description Logic (DL) expressivity is ALCHOIQ(D).

The KB has been obtained by (i) including customized version of currently available ontologies (e.g., parts of the SWEET ontology), (ii) automatically extracting key concepts from domain relevant text sources, and (iii) manually adding additional properties and attributes.

Fusion of environmental content:

Environmental service nodes may provide competing or complementary data on the same or related aspect for the same or the neighboring location. To ensure the availability of a most reliable and comprehensive content as basis for further processing stages, the content proceeding from these nodes must be fused. As already in the case of node orchestration, this implies an assessment of the quality of the contributing services and data.

Assessment of the content with respect to the needs of the user:

Once the data from the nodes have been incorporated into the KB, they need to be evaluated and reasoned about in order to infer how they affect the addressee, given his/her personal health and life circumstances and the purpose of the request of the information. For instance, a citizen may request information because he/she wants to decide upon a planned action, be aware of extreme episodes or monitor the environmental conditions in a location.

Selection of user-relevant content and its delivery:

Not all content in the KB is apt to be communicated to the addressee: some of it would sound trivial or irrelevant. Intelligent content selection strategies that take into account the background of the user and the intended use of the information are thus needed to decide which elements of the content are worth and meaningful to be communicated. To deliver the selected content, techniques are required that present the content in a suitable mode (text, graphic and/or table) in the language of the preference of the addressee.

Interaction with the user:

The interaction between the system and the user is also an important aspect of this work. The user must be able to formulate the problem in a simple and intuitive format -be it based on natural language or on graphical building blocks. The user should also receive the generated information in a suitable form and, as already mentioned above, in the language of his/her preference. In order to accommodate for all tasks described above, we opt for a service-based architecture. This architecture is based on a methodology which has been developed in ORCHESTRA [START_REF]Reference Model for the ORCHESTRA Architecture Version 2.1[END_REF] for risk management, and which has been extended in SANY [START_REF] Usländer | Specification of the Sensor Service Architecture[END_REF] to cover the domain of sensor networks and standard-based sensor web enablement. The focus of this methodology is on a platform neutral specification. In other words, it aims to provide the basic concepts and their interrelationships (conceptual models) as abstract specifications. The design is guided by the methodology developed in the ISO/IEC Reference Model for Open Distributed Processing (RM-ODP), which explicitly foresees an engineering step that maps solution types, such as information models, services and interfaces specified in information and service viewpoints, respectively, to distributed system technologies.

We defined application-specific major tasks and actions as abstract service specifications, which can be implemented as service instances on a specific platform. Web service instances for these services are currently being developed, which can be redefined and substituted as needed.

Figure 2 displays a simplified sample workflow with the major application services in action. Two services are not cited in Figure 2 since they are consulted by nearly all other services: the Knowledge Base Access Service and the User Profile Management Service. The figure also does not include the services related to data discovery (Figure 1) and information distillation from webpages.

A main dispatcher service (called Answer Service, AS) controls the workflow and the execution of the services. First, the user interacts with the system via the User Interaction Service (UIS). In the case that the user is unsure with respect to the types of information they can ask for, he/she can inquire this information by requesting it from the Problem Description Service (PDS).

To ensure a full comprehension of the problem or user generated question, we decided to operate with controlled graphical and natural language input formats. Once the user has chosen what kind of question he wants to submit to the system, the UIS provides the user the corresponding formats. Thereupon, the user can formulate his/her query, which is subsequently translated by the PDS into a formal ontologybased representation understood by the system. After the problem description is generated, this is passed by the UIS to the AS as a -Request Answer‖ inquiry. Then, the AS assesses what kinds of data beyond environmental data are required to answer the query of the user and solicits these data from the Auxiliary Services (AuxS). For instance, such services can provide travel route information in the case that the user's query concerns the environmental conditions for a bicycle tour from A to B.

After having acquired the complementary data, the AS can request from the Data Retrieval Service (DRS) the environmental data needed to answer the user query. The DRS solicits these data from the environmental nodes that were identified by the Data Node Retrieval Service (DNRS) as relevant to the user's query and the complementary data. The DNRS retrieves this information from the data node repository after the node discovery phase has taken place.

As already mentioned, the retrieved nodes may deliver complementary or competing data of varying quality (to keep the presentation simple, we dispense with the illustration of the orchestration of service nodes). The Fusion Service (FS) applies uncertainty metrics to obtain the optimal and maximally complete data set, which is passed by the AS to the Decision Service (DS). The DS converts the data set into knowledge, or content, in that it relates it to the knowledge in our KB, reasons about it, and assesses it from the perspective of its relevance to the user. From this content, the Content Selection Service (CSS) compiles a content plan, which contains the knowledge to be communicated to the user as the answer. The Information Production Service (IPS) takes the content plan as input and generates information in the language and mode (text, table, or graphic) of the preference of the user, which then is passed to the user.

Outlook

We are currently implementing the described service infrastructure -including the environmental node discovery. The first operational prototype of the service will be available for demonstration by early summer 2011.

Fig. 1 .

 1 Fig. 1. Architecture for the discovery of environmental service nodes.

Fig. 2 .

 2 Fig. 2. Sequence diagram for the execution of the services for delivery of environmental information

These data refer to the -empty‖ KB, i.e. without considering any environmental data coming from the nodes.

Acknowledgments. This work is partially funded by the European Commission under the contract number FP7-248594 -Personalized Environmental Service Configuration and Delivery Orchestration‖ (PESCaDO).


Toshitaka Fukiharu
email: fukiharu@si.aoyama.ac.jp

The Global Warming, Sustainability, and Environmental Tax: Dynamic General Equilibrium Model

Keywords: general equilibrium, simulation, population, sustainability, environmental tax

A primitive economic model with classical population theory is constructed in order to examine the greenhouse effect on the sustainability of human population as well as the environmental tax when the sustainability is in danger. The conclusion of this paper is that when the negative effect is small, the tax can guarantee the sustainability, where the effective tax rate interval for the sustainability shrinks as the negative effect rises. When the negative effect exceeds the critical level, however, the environmental tax cannot guarantee the sustainability of human population. Thus, the remedial measure to reduce the greenhouse effect other than the environmental tax is needed for the sustainability.

Introduction

The argument on the global warming through greenhouse effect gained momentum in the 1980s. Brown [START_REF] Brown | State of the World[END_REF] warned the decline of food production due to the greenhouse effect. One of the main concerns of The Earth Summit in Rio de Janeiro in 1992 was whether the participants could agree to adopt the environmental tax on CO 2 emission globally. The agreement was not reached. In 1995 the IPCC concluded that global warming is taking place due to human activity: greenhouse effect. The main aim of the 1997 Kyoto Protocol was to reach agreement for each country to reduce the global warming gases. Stern [START_REF] Stern | The Economics of Climate Change: The Stern Review[END_REF] still warned the decline of food production due to the greenhouse effect, while CO 2 has the carbon fertilization effect. In this paper, a primitive economic model is constructed in order to examine this greenhouse effect on the sustainability of human population, as well as the economic policies when the sustainability is in danger. This model is constructed in terms of dynamic general equilibrium (GE) approach.

Economic Model and Short-Run Equilibrium

In this model, there are two industries: food industry and energy industry. For simplicity the capital accumulation is omitted from the model. The food industry produces output (food), using labor and energy. The energy industry produces output (energy), using only labor. Two industries maximize profit. The aggregate household maximizes utility subject to income constraint, where the utility is a function of food and energy.

In the short run, the population (working hours) and the CO 2 level are assumed to be constant. Given the population and CO 2 level, it is guaranteed that general equilibrium prices exist which equate demand and supply in food and energy markets. Under these prices, energy is consumed, expanding CO 2 level in the atmosphere. In the long run, the expanded CO 2 level affects food production. Furthermore, population varies in the long run according to classical population theory, propounded by Malthus and Verhulst. We start with the formal model and the short run equilibrium.

Formal Model

A primitive general equilibrium (GE) model is constructed, for the purpose of examining the greenhouse effect. Suppose that there are two industries (firms). The first firm is a farm which produces food; Z f . Whereas food is produced by labor: L 1 , and energy: H f1 , the output is affected by CO 2 level: Y, in the atmosphere. Thus, this firm has the production function:

Z f =g 1 [L 1 , H f1 , Y] g 11 >0, g 12 >0
where g ij is the partial derivative of g i with respect to the j th variable. It aims at the profit maximization:

max π 1 =pZ f -wL 1 -p H H f1
where p is the wheat price, w is the wage rate, and p H is the energy price. The second firm is the energy industry which produces energy: H f2 , using only labor: L 2 . It has the production function

H f2 =g 2 [L 2 ] g 21 >0 .
It aims at the profit maximization:

max π 2 =p H H f2 -wL 2
There is only one (representative) household, which consumes food: Z h , and energy: H h . Household maximizes utility subject to budget constraint:

max u[Z h , H h ] s.t. pZ h +p H H h =wN+π 1 +π 2
where u[Z h , H h ] is the utility function, and N is the initial leisure hours (population), and π i is the profit from the i th firm (i=1, 2). For the sake of simplification, in this model, leisure consumption is excluded from the utility function.

Short-Run GE and Specification of Functions

Given CO 2 level: Y, and N, the short-run General Equilibrium (SGE) is defined, which satisfies

H f1 d + H h d = H f2 s (energy market) Z h d = Z f s (wheat market) L 1 d + L 2 d =N (labor market)
where superscript d(s) implies "demand" ("supply").

We assume that the production and utility functions are stipulated by the following.

g 1 [L 1 , H f1 , Y]= L 1 α1 H f1 α2 A[Y, x 0 ] α3 , α 1 +α 2 +α 3 ≤1, 0≤ x 0 ≤1 g 2 [L 2 ]= L 2 u[Z h , H h ]= Z h γ H h 1-γ , 0<γ<1
The function, A[Y, x 0 ], is a negative factor from the greenhouse effect. Suppose that Y[0]=1000 is the present level of CO 2 in the atmosphere. We specify that

A[Y, x 0 ]=Min [1, 1-(1-e -Y/1000 ) 5 x 0 ]
When x 0 =1/10, for example, A[Y, 1/10] is depicted as in Figure 1.

p H /p*=((1-γ(1-α 1 -α 2 ))/ γN) 1-α1-α2 α 1 α1 α 2 α2 A[Y, x 0 ] α3
The SGE labor input for wheat, L 1 *, is given as in what follows.

L 1 *=α 1 γN/(1-γ(1-α 1 -α 2 )) (1)
The SGE energy input for wheat, H f1 *, is given as in what follows.

H f1 *=α 2 γN/(1-γ(1-α 1 -α 2 )) (2)
The SGE energy consumption of household, H h *, is given as in what follows.

H h *=(1-γ)N/(1-γ(1-α 1 -α 2 )) (3)
Also for the later use, the SGE wheat output, g 1 *, is computed as in what follows.

g 1 *=α 1 α1 (α 1 +α 2 ) α2 (γN) α1+α2 A[Y, x 0 ] α3 /((1-γ(1-α 1 -α 2 )) α1+α2 . ( 4
)

Long-Run General Equilibrium Dynamics

The analysis in the previous section is called the short-run general equilibrium model, SGE, since two variables were fixed by the assumption. First, CO 2 in the atmosphere: Y, was fixed. In fact, CO 2 in the atmosphere increases through the use of energy in the household's direct consumption and farm's use of energy in the wheat production, while it decreases thanks to the absorption by the working of sea and the photosynthetic function of food. The variation of CO 2 in the atmosphere, in turn, causes the variation of food output. Thus, the economic analysis of greenhouse effect must be constructed in terms of dynamic system. Second, population was assumed to be constant in the previous section. In this section this assumption is relaxed. In "On Population", T.R.Malthus argued that population growth is expressed as the geometric progression; 1, 2 2 , 3 2 , ..., n 2 , ..., while food production as the natural progression; 1, 2, 3, ... , n, ..., since the latter is under diminishing marginal productivity. It was asserted that in the long run, a society's population growth is restricted by food production. In this section, this assertion is incorporated. However, the population cannot become infinite due to the capacity limit of the earth. Considering this limit, P.f. Verhulst proposed a different type of population growth model. In this section, population growth has two factors, the first of which is the one stemming from the Verhulst model, while the second is the one stemming from the Malthus model.

If we admit the variation of two variables, the previous model must be reformulated in terms of dynamic system, which is called the long-run general equilibrium dynamics (LGED). In this section, an extension of this sort is attempted.

The Variation of CO 2 in the Atmosphere

Formally, as energy is produced, Y: a in the atmosphere, increases by the amount of F 1 [H] where H=H f1 + H h , while Y decreases, first, by the amount of F 2 [Y], thanks to the activity of the sea and forest, second, F 3 [g 1 ], thanks to the photosynthetic function of wheat. Thus, we have a dynamic system

dY [t]/dt=μ 1 H[t]-μ 2 Y[t]-μ 3 g 1 [t] (5)
where t stands for time and μ i s are all positive constant (i=1,2 3).

3.2

The Variation of Population P.f. Verhulst assumed that the variation of population follows the differential equation:

dN [t ]/dt =εN [t ](1-N [t ]/K ) where N [t )]
is the population at time t , K is the capacity limit for the population, and ε is a positive parameter. In view of the Malthus' argument it is assumed that K depends on the per-capita consumption of food when we reach K. In this process, N [t ] monotonically converges to K . Taking account of the Malthus argument, we assume the variation of population as in what follows where β is a positive parameter.

dN [t ]/dt =εN [t ](1-N [t ]/K )+ β(d(g 1 [L 1 [t], H f1 [t], Y[t]]/N [t])/dt)N (t ) (6)
It is assumed that if the per capita consumption of wheat decreases the population growth decreases: growth adjustment, and vice versa. The dynamic analysis in terms of differential equations, derived from ( 1), ( 2), ( 3), ( 4), ( 5), and ( 6), is called the longrun general equilibrium dynamics.

Simulations

In this section, simulations are attempted by specifying parameters. When x 0 =0 or α 3 =0, the greenhouse effect is zero, which is the starting point of the present section. As the greenhouse effect is intensified, x 0 rises. Assuming α 3 >0, we conduct the simulations by raising x 0 from zero. In this simulation, the parameters are specified as in what follows. Traditionally, Pigou proposed the Pigouvian tax in order to alleviate pollutions. In the present days, this tax on the consumption of energy is called the environmental tax. In this subsection we examine the effect of environmental tax. Let τ be the tax rate on the consumption of energy. In other words, for the demanders of energy, they must pay p H (1+τ), although the tax receipt, p H τ, is distributed to the (aggregate) household. Depending on τ, the convergent per capita food production, (g 1 /N)*[1/10, τ], can be computed utilizing the simulation for LGED. By definition we have (g 1 /N)*[1/10, 0]= (g 1 /N)*[1/10].

In Figure 3, (g 1 /N)*[1/10, τ] is depicted as a function of τ. It is ascertained that (g 1 /N)*[1/10, τ]≥(g 1 /N )* for τ belonging to the interval [0.18, 22.21], T[1/10], which is called the sustainability tax range. Note that too high tax rate cannot guarantee the sustainability, whereas too low tax rate cannot either.

Active Greenhouse Effect Case: α 3 =1/4 and x 0 >1/10

As in 4.1, when α 3 =1/4 and 1/10<x 0 <5/10, in the long run, K is not sustainable when the greenhouse effect is active without remedial measures.

As one of the remedial measures, the environmental tax is required to guarantee the sustainability. For x 0 =1/10, 2/10, 3/10, and 4/10, corresponding (g 1 /N)*[x 0 ] and sustainable tax range T[x 0 ] are shown in Table 1. As in the previous subsections, in the long run, K is not sustainable when the greenhouse effect is active without remedial measures. In Figure 4, (g 1 /N)*[5/10, τ] is depicted as a function of τ. It is ascertained that (g 1 /N)*[5/10, τ]<(g 1 /N )* for all τ>0. Thus, when x 0 =5/10, it is shown that the environmental tax cannot guarantee the sustainability

Conclusions

The aim of this paper is to examine the economic consequences of greenhouse effect, constructing a primitive general equilibrium model by simulation approach. In Section 2, short-run general equilibrium model is constructed, with population and CO 2 level assumed constant. In Section 3, the assumption of constant population and CO 2 level is relaxed, where the theory of population by Verhulst and Malthus is utilized. In Section 4 by simulation approach on the long run general equilibrium dynamics it was shown that CO 2 level converges, while the population converges to the capacity limit, K. The converging per capita food consumption, however, might not allow the sustainability of K. It is shown that the environmental tax on the energy consumption raises the converging per capita food consumption and it can guarantee the sustainability of K when the greenhouse effect on food production is small. It is shown, furthermore, that the sustainable tax range, which guarantee the sustainability, shrinks as the effect on food production becomes more serious. Finally, it is shown that the sustainable tax range disappears when the effect exceeds a critical level. Thus, we may conclude that the remedial measure to reduce the greenhouse effect other than the environmental tax is needed for the sustainability.

Figure 1 .

 1 Figure 1. A[Y, 1/10] : Greenhouse Effect Factor In this formulation, the relative price of energy, p H /p*, is determined at SGE, as in what follows.

α 1

 1 =1/4, α 2 =1/4, ε=1/1000, γ=1/2, μ 1 =1/5, μ 2 =1/100, μ 3 =1/100, K=1000000, β=14.1 Zero Greenhouse Effect CaseWhen x 0 =0 or α 3 =0 (the case of no greenhouse effect), the trajectory path of {N[t], Y[t]} on LGED, starting from the initial position, {N[0], Y[0]}={1000, 1000}, converges to {N*, Y*}, where N*=1000000 and Y*= 832573.39699090708710. The trajectory path of per capita food production, g 1 [t]/N[t] on LGED, starting from the initial position, {N[0]), Y[0]}={1000, 1000} is depicted in Figure 2. It converges to (g 1 /N )*= 0.000408248.

Figure 2 .

 2 Figure 2. Trajectory of Per Capita Consumption: x 0 =0It must be noted that population capacity, K=1000000, is sustainable only when the per capita consumption of wheat is at least (g 1 /N )*

Figure 3 .

 3 Figure 3. The Effect of Tax: x 0 =1/10

4. 3

 3 Active Greenhouse Effect Case: α 3 =1/4 and x 0 =5/10

Figure 4 .

 4 Figure 4. The Effect of Tax: x 0 =5/10

Table 1 .

 1 The Sustainable Tax Range

	x 0	(g 1 /N)*[x 0 ]	T[x 0 ]
	0	0.000408248
	1/10	0.000397635364383525	[0.18, 22.21]
	2/10	0.000386097395096089	[0.45, 16.35]
	3/10	0.000373421126552421	[0.88, 12.32]
	4/10	0.000359304111963084	[1.87, 7.73]

Acknowledgements

The author appreciates the financial support provided by the Grants-in-Aid for Science Research (Contract Number: 22530181).