Katharina Feiden

Fred Kruse

Tomas Reznik

Petr Kubicek

Herbert Schentz

Einar Eberhardt

Rainer Baritz

Best Practice Network GS SOIL promoting access to European, interoperable and INSPIRE compliant soil information

Keywords: INSPIRE, spatial data infrastructure, geoportal, soil data, metadata, data quality, thesaurus, controlled vocabulary, application schema, data specification

INSPIRE provides the framework for the establishment of a European Spatial Data Infrastructure. The cross-border use and applicability of data requires that specific standards and rules are fulfilled by data providers. Such rules are currently being developed as data specifications. Soil as a theme in the INSPIRE annex III is included in this process, and was selected as the target theme for the EU best practice network GS SOIL "Assessment and strategic development of INSPIRE compliant Geodata-Services for European soil data". The project contributes to the harmonization and provision of interoperable soil geodata in Europe. The main deliverable of the project is the web portal http://gssoil-portal.eu/, which provides information, data management tools and links to data sources. Examples are the soil specific multilingual thesaurus, a metadata editor and catalogue service, provision of WMS and prototype WFS.

Introduction

The best practice network is co-funded by the European Community programme eContentplus, by the European Commission DG Information Society and Media in the duration from June 2009 -May 2012.

2 Katharina Feiden1, Fred Kruse1, Tomas Reznik2, Petr Kubicek2, Herbert Schentz3, Einar Eberhardt4, Rainer Baritz4

The network consortium comprises 34 partners from 18 EU member states. Project Coordinator is the Coordination Center PortalU at the Lower Saxony Ministry of Environment and Climate Protection (Germany). Overall 24 partners out of the consortium are soil data providers and will make the data available for the project. Hence, a complex and high quality data basis in a European context is assured. Beyond that, European institutions are also involved via the advisory board, as the European Environment Agency and the Joint Research Center of the EC.

The partners will establish and operate a network of services for spatial datasets and metadata. This network includes distributed services for data transformation, discovery, view and download. The final result of the project will be a central GS SOIL Portal, where European soil data from heterogeneous sources will be bundled. In order to ensure cross-border usability of the portal and related services, aspects of multilingualism and data interpretation will be considered thoroughly.

The project will extensively support the implementation of the INSPIRE requirements on basis of available experience in selected European countries and regions on different organizational levels. The planned results of the project are [START_REF] Feiden | Progress of the transnational cooperation in building up a SDI for European soil data (eContentplus-project GS SOIL)[END_REF]: -A consolidated soil-related theme catalogue and consolidated soil-related theme content-framework standards, -An INSPIRE compatible metadata profile for spatial soil datasets, dataset series and services, -Generic application schemes for soil information [START_REF] Gs Soil Consortium | D3.4 Final best practice guidelines for Creating and Maintaining Metadata for Soil Database[END_REF], -A web portal (GS SOIL Portal) which provides access to all project soil data, including [START_REF]D4.1 Theme specific test suite for developing data specifications[END_REF], [START_REF]GS SOIL consortium: D5.1 Design specifications of the GS SOIL Portal and its network[END_REF], [10] a view service which provides access to spatial soil data, discovery and view of the INSPIRE conform metadata for the provided soil maps [5], [START_REF]D3.1 Metadata profile for soil geographic datasets and dataset series[END_REF], interoperable spatial soil datasets (for exemplary soil products), case studies on cross-boarder delivery of harmonised soil data access [START_REF]GS SOIL consortium: D3.2 Metadata profile for soil geographic data services[END_REF], -Best practise guidelines for creating and maintaining metadata for soil database [START_REF] Gs Soil Consortium | D3.4 Final best practice guidelines for Creating and Maintaining Metadata for Soil Database[END_REF], and for data harmonization.

The GS SOIL Portal

The GS SOIL community provides a centralized web access point for standardized, interoperable and INSPIRE compliant European soil information. In the GS SOIL Portal http://gssoil-portal.eu all soil related information from web pages, over databases to data catalogues will be made available and accessible [START_REF] Feiden | The GS SOIL portal prototype and its integrated network[END_REF]. Search results will be ranked and listed in shared result lists. Spatial soil data from OGC compatible Web Mapping Services (WMS) and Web Feature Services (WFS) will be visualized in a map viewer. For all tasks within the project the GS SOIL Portal will be used as a platform for an improved access to soil data. GS SOIL Portal has been built in an iterative cycle, adopting the relevant INSPIRE Implementing Rules (Network Services and related) and on the basis of the InGrid software designed for the German

Best Practice Network GS SOIL promoting access to European, interoperable and INSPIRE compliant soil information 3

Environment Information Portal (PortalU) [START_REF]Soil Quality Vocabulary[END_REF]. Also general open tools and services will be provided for re-use by the project partners (data / service providers) and later technical integration of services and underlying geospatial data sets. Particular focus will be placed on mutual harvesting (CSW) with external systems. The current version of the GS SOIL Portal is already available in 13 project languages. The product harvesting, as well as the development of so-called content-framework standards (terminology, reference material and definitions to compare soil data), are based on a substantial search for existing soil data in European countries. The result has been implemented into a catalogue of soil data existing in Europe with a specific focus on products provided through web-services.

Data quality, data management and metadata

In GS SOIL two soil data relevant metadata profiles (the first one for datasets and dataset series while the second is intended for describing a service) compliant to INSPIRE metadata and ISO standards were developed in 2010. Subsequently, metadata proposed by the GS SOIL community was delivered as reference material to the Thematic Working Group for the sections dealing with metadata and data quality in the first version of INSPIRE Annex III Data Specification on soil. INSPIRE Data Specifications contain requirements (that are legally mandated) and recommendations 4 Katharina Feiden1, Fred Kruse1, Tomas Reznik2, Petr Kubicek2, Herbert Schentz3, Einar Eberhardt4, Rainer Baritz4 [START_REF]D4.2 Generic application schemes for soil information[END_REF], [START_REF]Drafting Team] Data Specification. Definition of Annex Themes and Scope[END_REF]. On the other hand, INSPIRE Data Specifications may be hardly readable by data providers that are not aware of all underlying standards. Thus, it complicated to get an overall picture. GS SOIL Best Practice Guidelines for creating and maintaining metadata for soil databases are intended to overcome this insufficiency.

Proposed Best Practice Guidelines are covering both -metadata and data quality aspects. This approach consists of several steps, including a review of the GS SOIL metadata profile with respect to newly published INSPIRE documents (e.g. Commission Regulation 1089/2010 on interoperability of spatial data sets and services or new version 1.2 of Technical Guidelines on INSPIRE metadata), connections between dataset metadata and feature-level metadata through a feature catalogue, user-friendly guidelines for data providers who would like to describe their soil-related databases, the GS SOIL metadata profile applied on two real soil datasets as examples, guidelines on how to manage soil metadata in two or more languages, brief information about the development of the soil specific multilingual thesaurus (which is in detail written in the following section) and examples on usage of soil metadata within INSPIRE, i.e. spatial data infrastructure framework (e.g. how to get from dataset metadata to view/download service or dataset itself as well as description of related aspects). Best Practice Guidelines are established as a recommended set of steps to obtain the right value that should be reported within INSPIRE compliant metadata. All steps are written in user-friendly way while an end user does not have to be aware of underlying standardization documents to successfully create INSPIRE valid metadata with GS SOIL profile. The consortium partners decide for a selected and open tool to integrate the metadata into the GS SOIL portal (e.g. GeoNetwork, InGrid Editor etc.). These open tools were adopted to the defined metadata structure and are INSPIRE compliant.

Another specific part of Best Practice Guidelines is related to data quality issues that are currently divided into two main streams. The first is focused on analyzing the existing quality specifications and measures of compliancy specified within the data themes of Annex I as well as data quality evaluation according to the ISO standards (like ISO 19157, 15158, 19113, 19114). The second activity is focused on the discussion paper "Data quality in INSPIRE: from requirements to metadata" (from October 2010). This document [15], although only on the level of discussion paper, could seriously change the demands and also the viewpoints for defining the data quality for all INSPIRE spatial data themes, including soils.

Best Practice Network GS SOIL promoting access to

Soil specific multilingual thesaurus

Sharing metadata and/or data generally leads to the need of common semantics, a common set of concepts and a common controlled vocabulary. This is a fact wellknown long before the existence of IT and IT networks, and has lead to controlled lists of physical units, chemical elements, species lists or even to complete vocabularies like the medical or pharmaceutics vocabularies. Today thesauri and/or ontologies have become state of the art within communities and international projects even when the participants do not want to share data but "just" want to exchange their 6 Katharina Feiden1, Fred Kruse1, Tomas Reznik2, Petr Kubicek2, Herbert Schentz3, Einar Eberhardt4, Rainer Baritz4 knowledge. Public institutions who collect data/information/knowledge from various parties meet this well-known need establishing controlled vocabulary and making it accessible for their data providers. The European Environmental Agency e.g. uses and publishes GEMET, the GEneral Multilingual Environmental Thesaurus, Which is provided in 26 languages, as multilingualism is a need for any European collaboration, not only in the environmental sector. Referencing concepts of GEMET [START_REF] Gs Soil Consortium | D3.4 Final best practice guidelines for Creating and Maintaining Metadata for Soil Database[END_REF] is requested by the EU INSPIRE directive.

As GS SOIL aims to be INSPIRE compliant, GEMET has to be at least a part of the controlled vocabulary of that project. But GEMET represents a very general environmental vocabulary and needs more specific extensions for special domains, such as water, protected areas, air and -soil.

The GS SOIL Portal has a reference to a service for GEMET but for the reasons explained above, in addition to the preliminarily planned tasks, the GS SOIL community decided to establish a soil specific multilingual thesaurus in addition to the preliminarily planned tasks. This thesaurus shall be created as an extension of GEMET and shall contain the soil vocabulary of ISO11074 [START_REF]Data Specification. Data quality in INSPIRE: from requirements to metadata[END_REF], the concepts of WRB [START_REF] Deckers | World References Base for Soil Resources[END_REF] and concepts created and/or defined during the GS SOIL project.

Concerning the architectural aspects the thesaurus has to be linkable to the GS SOIL Portal and must be free and easily accessible. In order to make referencing GEMET as easy to handle as possible, it was decided to make the soil specific thesaurus not only semantically but also syntactically interoperable with this controlled vocabulary demanded by INSPIRE.

The architectural decisions and the willingness to stick to widely accepted standards lead to the decision to use SKOS/ RDF [20], a W3C standard, as formal language. Therefore an SKOS/RDF compatible thesaurus editor, which allows collaborative work, is chosen as tool for the establishment. Several tools for that standard are available, but so far the only freeware tool is iQvoc [START_REF] Rüther | Workshop des Arbeitskreises "Umweltinformationssysteme" der Fachgruppe[END_REF].

One benefit of the chosen formal language and tool is that content can also be presented as linked data, thus allowing interconnections with other controlled vocabulary, which will help to establish cross -domain semantic interoperability [START_REF] Kruse | InGrid® -eine Software zum Aufbau von Umweltinformationssystemen[END_REF]. Moreover even links to concepts, which are defined for data and metadata transfer, could be established as soon as those models are transformed from UML to OWL.

The guidance and rules for the establishment of the multilingual content is set up in March and the continuous maintenance beyond the end of the project will be topic of the planning of GS SOIL.

Application schema, data harmonization and interoperability

Data can be exchanged in many different formats and structures. To ensure that the receiver does interpret the data in a proper way, a common or standardised application schema for soil data is needed. Because INSPIRE is determined to use already existing standards, GS SOIL adopted the ideas of the upcoming ISO 28258 Digital Exchange of Soil-related Data (i.e. the working draft available end of July 2010), developed it further and produced a practical implementation in two XML schema Best Practice Network GS SOIL promoting access to European, interoperable and INSPIRE compliant soil information 7 files and a set of rules how data providers set up a further, extended XML schema definition and define properties and produce data files in an interoperable way.

ISO 28258 is basically an extension of ISO 19156 Observations and measurements for soil data. The core of the application schema is the feature catalogue. It lists all objects that can be described in the soil universe. The feature types and their relationships have been modeled using the Universal Modelling Language (UML). To ensure the interoperability of the data, this feature catalogue is complete and cannot be extended. The application schema is nonetheless generic in that it leaves the properties that can be described for each feature with the user of the standard (i.e. the data providers).

Fig. 3. Implementation of the application schema using XML and XML schema definitions

The application schema has been implemented in XML (Fig. 3). The feature types are defined in the DataFile_core.xsd. In order to have a mechanism to check XML data files against an xsd, the data file definition has to be extended for each use with the properties that shall be exchanged in the data file. In an additional PropertyFile.xml (that itself can be checked against the PropertyFile.xsd provided with the application schema) further property definitions are given that cannot be put into the DataFile_ext.xsd due to the impossibility to define more than the codes itself in xsd code list definitions. For interoperability, often further information is needed, e.g. the class boundaries of classified numerical values, or the link to the multilingual thesaurus.

The application schema and its implementation serve as reference material for the INSPIRE process of data specification development (e.g. proposal for a soil metadata profile). The GS SOIL amendments to the former ISO 28258 Working Draft were fed 8 Katharina Feiden1, Fred Kruse1, Tomas Reznik2, Petr Kubicek2, Herbert Schentz3, Einar Eberhardt4, Rainer Baritz4 back into the ISO process. The application schema is tested with data from the GS SOIL data providing partners using a hands-on testing procedure description. The GS SOIL SoilML is compared with the currently developed INSPIRE application schema for soil data.

Interoperability includes harmonization to varying degrees, depending on domainspecific agreements. A closer look to the descriptions and scope of the INSPIRE Annex Themes shows that soil data include a wide variety of data content. The historic development of soil data, especially high-resolution maps, has caused substantial regional differences in resolution, terminology, and taxonomy. The feasibility to harmonize the content of such products has not yet been systematically investigated. Independently of the agreements under INSPIRE, the array of possible harmonization methodologies is being investigated in detail in GS SOIL test cases. This includes high and low-resolution soil maps, as well as thematic maps, data from soil inventories such as long-term field experiments, and national soil monitoring. Thematic maps are applications of basic soil maps, for example susceptibility maps, very often combined with ancillary data such as climate and land cover. In the future, web processing services will allow such applications on the fly, using data and methodologies shared among distributed information systems. Given these frame conditions and outlook, semantic aspects of harmonization of soil information play an important role to the soil data user community.

Outlook

Until end of May 2012 the best practice network will finalize the self defined work plan. Technically, the GS SOIL Portal is already online and will be further improved. The soil thesaurus with the set of minimum terms will be realized. All data providers in the project and the extended network will make the interoperable data accessible and will also create metadata.. The Best Practice Guidelines for creating and maintaining metadata for soil databases were prepared to answer opened questions that data providers may have. At the same time, it serves as the "cookbook" where the process of metadata creation and management is described step by step to get to INSPIRE valid metadata with added relevant information on soil data. These Guidelines may be used in other application fields as well and in the full scope may be used for metadata according to the Commission Regulation No 1205/2008 (on INSPIRE metadata). Second benefit represents explicit guidelines on data quality including quantitative methodologies on specific measurements that are suitable for soil data. As a result, metadata and data quality information is well defined and contains values that are comparable.

In terms of harmonization GS SOIL first focus on the content framework. Concepts are being exchanged (for example, about map stratification, or about the definition of soil map units), and definitions agreed upon. This will allow focusing test cases on a common vocabulary. Recommendations will be provided as "Best practice guidelines for developing a content framework for interoperable soil data in Europe". Because the World Reference Base for Soil Resources plays an important Best Practice Network GS SOIL promoting access to European, interoperable and INSPIRE compliant soil information 9 role in international data exchange, many GS SOIL partners will investigate the possibility of applying this reference system for identifying the taxonomic units of their products as the basic classification system. The next steps will be concentrated on comparing and adapting existing data sets across borders. Further harmonization steps will then look at the soil maps in detail. The geometric data content will be analysed against independent GIS-based criteria for comparing the structure and content of maps. The GS SOIL test cases will thus allow estimating the possibilities and effort to fully harmonize soil data. The results will be provided as "Data Harmonization Best Practice Guidelines".

Acknowledgments. The GS SOIL best practice network "Assessment and strategic development of INSPIRE compliant Geodata-Services for European Soil Data" is cofunded by the European Community programme eContentplus. The work presented in this paper is the joint effort of the GS SOIL consortium. The authors are representatives of project partners. They are solely responsible for the content. The text does not represent the opinion of the European Community and the European Community is not responsible for any use that might be made of information contained therein. For further information on the project please visit the website: http://www.gssoil.eu/.

Fig. 1 .

 1 Fig. 1. The GS SOIL Portal (www.gssoil-portal.eu) and its advanced search function. The interface is available in 13 languages

Fig. 2 .

 2 Fig. 2.Example of (data quality) metadata element structure as a part of GS SOIL Best Practice Guidelines for creating and maintaining metadata for soil databases

  European, interoperable and INSPIRE compliant soil information 5 Reported should be only one number expressing the positional accuracy according to the number of measured dimensions of soil dataset. Explicit formulas are defined in section 6.2. This result value is usually designated as Root Mean Square Error (RMSE) within GIS software. This is the only result to be reported within this metadata element. Following steps are recommended: 1. Recalculate resulting RMSE into metres, in case when resulting RMSE is in different units (for example GIS has calculated RMSE as 6 cm, i.e. recalculated RMSE is 0.06 m). 2. Write recalculated RMSE into the value metadata subelement as a real number with a precision of 2 decimals (e.g. 1.85).Usage of decimal point is recommended. 3. Write "m" into the valueType metadata subelement

	Metadata element name	Data Quality -Positional accuracy -Absolute or external accuracy
	Definition	Closeness of reported coordinate values to values accepted as being true.
	ISO 19115 number and name	18. dataQualityInfo
	ISO/TS 19139 path	dataQualityInfo
	INSPIRE obligation / condition	Optional
	INSPIRE multiplicity	0..*
	Data type (and ISO 19115 no.)	117. DQ_AbsoluteExternalPositionalAccuracy
	Domain	Lines 100-107 from ISO 19115
		This measure is for expressing of positional accuracy of point
		data (boreholes) only.
	Implementing guidelines
	Example	value: 2.00 valueType: m
		<gmd:MD_Metadata ..
		..
		<gmd:dataQualityInfo>
	Example XML encoding	..	<gmd:report>
			<gmd:DQ_AbsoluteExternalPositionalAccuracy>
			<gmd:result>
		[…]


Clemens Düpmeier
email: clemens.duepmeier@kit.edu

Werner Geiger
email: werner.geiger@kit.edu

Thorsten Schlachter
email: thorsten.schlachter@kit.edu

Rainer Weidemann
email: rainer.weidemann@kit.edu

Renate Ebel
email: renate.ebel@lubw.bwl.de

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

Towards a Universal Search in Environmental Information Systems

Keywords: Public Search Portals, Semantic Technologies, Result Mash-up, Search Broker, OpenSearch Description, Dark Web

Full-text search functions in environmental portals make a large amount of environmental data accessible. Many data sources, however, are not suited for indexing by search machines or the data themselves are not suited for access by fulltext search. A possibility to make such data of the "dark web" accessible consists in addressing the data sources in the environmental portal directly. The procedure presented here starts with a formal description of data sources (e.g. from the point of view of the portal, these are the target systems). Based on this description, a special component of full-text search, the so-called search broker, can extend and detail a search query, such that all necessary parameters (if possible) are compiled to address these systems and to guide the user directly to the data desired. The presentation component of the environmental portal is responsible for the adequate compilation and display of these data, the so-called result mash-up.

Introduction

Since the adoption of the EU directives on the citizens' access to environmental data in 1990 and 2003 at least, active dissemination of this information has been a duty of environmental administrations [5], [6]. Many authorities provide central environmental portals via which the citizen is given access to data that are often distributed over many individual environmental information systems.

Unfortunately, these portals often provide links to the start or search pages of the target systems only. While integrated full-text search engines offer access to all text information, other types of data like database tables, geographic information systems, and multi-media files often are not covered by these full-text search engines or they cannot be represented adequately in the list of results.

Huge parts of many environmental information systems have to be connected by specialized adaptors or remain hidden to the users of environmental portals [START_REF] Schlachter | Erschließen von Datenbank-Inhalten durch die Volltextsuche in Landes-Umweltportalen, Umweltinformationssysteme: Suchmaschinen und Wissensmanagement -Methoden und Instrumente, Workshop 'Umweltdatenbanken/Umweltinformationssysteme[END_REF].

In practice, very few systems offer a real semantic description or links of data in terms of the semantic web [START_REF] Berners-Lee | Semantic Web Road Map[END_REF]. A "real" semantic search is not yet possible. Analysis of environmental information systems in the state of Baden-Württemberg has even revealed differences in the semantics of individual terms in several systems of a single authority, although this semantics is never expressed explicitly.

The idea of the approach presented here is to deposit some semantics in the description of systems and, thus, to make queries that are as intelligent as possible.

Search requests to environmental web portals typically consist of the following three components:

1. Subject 2. Spatial reference (e.g. coordinate, administrative unit, professional object) 3. Temporal reference (e.g. point or period of time) Very often, queries refer to one or more topics alone or in combination with a spatial reference. Queries relating to temporal references, also in combination with a subject or spatial reference, are rare.

Mobile end devices, such as smart phones or tablet PCs often allow for a more or less precise determination of the own location (and, hence, of the location of the user) with components like GPS receivers, WLAN, and mobile radio communication devices. This location information is available in principle as a context of the search query, even though these data are not input explicitly by the user. Within the project "Semantic Search for Environmental Information (SUI)" [START_REF] Abecker | SUI -Ein Demonstrator zur semantischen Suche im Umweltportal Baden-Württemberg[END_REF], [START_REF] Bügel | SUI II -Weiterentwicklung der diensteorientierten Infrastruktur des Umweltinformationssystems Baden-Württemberg für die semantische Suche nach Umweltinformationen[END_REF], an architecture was developed (Figure 1), which ─ provides and processes descriptions of various target systems, ─ delegates preprocessing of search terms to specialized components for environmental issues, spatial, and temporal references, and ─ presents multiple data formats in an integrated view (mash-up).

The core of this architecture is the "SearchBroker" that acts as a search engine for the environment portal.

Target System Descriptions

By a description, the individual target systems are made known to the SearchBroker (Figure 2). An extension of the OpenSearch description XML format [START_REF] Opensearch | OpenSearch description document[END_REF] is used as a vehicle for these descriptions. This format is based on URL patterns 1 which may contain wild cards for all necessary parameters (see Listing 1).

<Url type="text/html" template= "http://foo.org/?q={searchTerms}&pw={startPage?}"/> <Url type="application/atom+xml" template= "http://foo.org/atom?q={searchTerms}&pw={startPage?}"/> Listing 1. OpenSearch URL templates Generally, several URL patterns may be contained in an OpenSearch description. The patterns are distinguished by giving the reply format (type). If several reply formats are available, the application may decide which to use and select the corresponding URL pattern.

URL patterns in OpenSearch descriptions normally refer to the addressing of search engines. This mechanism can be extended easily by the admission of any parameters 1 Addressing systems via URL indeed excludes some systems (e.g. such as those based on web services). If required, however, these can be integrated via appropriate adapters.

Search Broker

Target Descriptions instead of the standard parameter set. While the semantics of standard parameters is set already, the "free" parameters have to be explained more explicitly. Again, the extension of an OpenSearch description element is used, as shown in Listing 2.

<Url type="text/html" template= "http://lubw.de/dienst?repId=biotope&value={commune}"/> <Query role="substitution" ui:name="commune" ui:type="geo:commune:id"/> Listing 2. URL template and corresponding semantic description

The "query" element is extended by two attributes containing the name of the variable ("ui: name", here: "commune") and a unique identifier for the semantics of this variable ("ui: type", here: "geo: commune: id").

The unique identifier has to be known to the search broker and to be used by the associated plug-ins for preprocessing of the query.

An OpenSearch description may contain multiple URL templates that may differ in the target format given by the "type" attribute. This enables the environmental portal to retrieve data from a target system in different formats, e.g. HTML, XML, GeoRSS, or Atom.

In this sense, even a full-text search engine can be treated as a target system. It can also be described by an OpenSearch description and provides a corresponding hit list, e.g. "Application/atom+xml" or "application/rss+xml". In practice, many websites will not be described as specific target systems, but they will be indexed and queried by a full-text search engine.

In the case of the state environmental portals [START_REF] Schlachter | LUPO -Ausbau der Suchfunktionalität der Landesumweltportale und Vernetzung mit dem Umweltportal Deutschland[END_REF], it may be reasonable to define certain tasks previously executed by the full-text search machine as separate target systems. The Google Search appliance [START_REF]Google Search Appliance[END_REF] used as full-text search machine is capable of submitting queries to other systems parallel to the search in the own index. This mechanism called "OneBox" delegates the search query to other systems. If they reply within a defined period of time, these results are delivered in addition to the search results from full-text index and can be represented near them by the portal (Figure 3).

If these OneBoxes are addressed by the search broker or the mash-up component of the environmental portal and not by the search machine, many unnecessary queries can be avoided, as it is possible to decide which queries are promising or not after preprocessing the search query already (Section 3). for search terms "schutzgebiet hockenheim" ("nature reserve hockenheim"): search results from environmental databases, air quality index, images of nature reserves

Preprocessing

The search broker knows the descriptions of all systems connected to the environmental portal and, hence, the syntax and semantics of their calls. Upon receipt of a query by a user from the environmental portal, the search broker has to provide for all relevant parameters being available. Otherwise, target systems cannot be called up.

To identify the semantics of a given query, the SearchBroker is assisted by a series of specialized plug-ins. The plug-ins analyze each constituent (e.g. single words or a series of words) of the query and try to allocate an explicit semantics to them.

In this way, one or more gazetteer plug-ins can resolve the spatial reference in a query. Many internet search engines use gazetteer services available online from different vendors. Some environmental agencies also offer specialized gazetteer services which, for example, can resolve field names, names of water bodies or names of natural areas in addition to place names.

In the above example, a gazetteer plug-in can recognize a place name, e.g. "Karlsruhe", and allocate several properties to it, such as: ─ geo:commune:name = Karlsruhe ─ geo:commune:id = 08212000

The latter can now serve as a parameter for addressing the target system shown in Listing 2.

Another gazetteer plug-in may supply further information about the same place name "Karlsruhe": Apart from spatial and temporal references, resolving of environmental issues is the biggest challenge in the preprocessing of search terms. Resolution aims at mapping search terms onto one or more elements of a well-defined vocabulary, as it is used in the connected target systems. For the purpose of restricting or expanding a query, also the neighborhood of each issue is supplied to the SearchBroker, e.g. synonyms as well as superordinate and subordinate concepts.

The environmental issues are modeled in an ontology. This ontology initially is restricted to the terms of a certain domain (here: "Umwelt" (environment)) that reflects the contents of the portal. The ontology consists of several partial ontologies. The backbone is the GEMET environmental thesaurus [START_REF] Gemet | [END_REF]. It is extended by a thematic catalog, the entries of which contain further metadata, e.g. frequently used key values. This structure is covered by another partial ontology that contains so-called "life situations". This corresponds to the approach of many portals to meet the citizen in a life situation and to guide him from this situation to the relevant services and data [12]. These partial ontologies are linked by mapping. Due to this mapping, the environmental issues found generally contain entries from all partial ontologies. Hence, a maximum of information is available for queries by target systems. A more detailed description of this semantic approach is given in [START_REF] Bügel | Leveraging Ontologies for Environmental Information Systems[END_REF].

Presenting Search Results (Mash-up)

After the completion of preprocessing, the SearchBroker can decide which target systems are available and how to request them. The SearchBroker can now query the data by itself or return the full addresses to the environmental portal. Currently, the latter approach is being used.

Within the environmental portal, a mash-up component is responsible for the presentation of search results. Depending on the results supplied, it can decide how these will be presented.

Essentially, the following target formats are distinguished:

─ spatial data, e.g. displayed by a web map client ─ links, e.g. in the form of link lists ─ tabulated data and charts that may be converted into HTML ─ multi-media contents, for example in the form of a gallery view ─ text messages (e.g. RSS), for example in the form of summary lists ─ HTML pages, HTML fragments and microformats displayed at certain points in the layout ─ results of a full-text search, for example in the form of hit lists The target formats are assigned using the MIME types given in the target system descriptions. In case more than one target format are available, the mash-up component can decide how the hits will be displayed. The same data may be displayed at several places, e.g. within a hit list and a map view.

Furthermore, the gathered information may be used to provide the user with additional navigation steps in the portal. These include, for example, the restriction or expansion of the query based on subordinate or superordinate concepts (Figure 4). It is also possible to resolve ambiguities resulting from the preprocessing of place names.

Conclusion and Outlook

The architecture proposed provides environmental portals with the opportunity to access different target systems with different target formats. The description of the target systems and their addresses are stored in an XML document that is based on and extends the OpenSearch description format. The parameters necessary for addressing the target systems are obtained from the search query by various specialized plug-ins during preprocessing. An ontology component is included for the resolution and explanation of the thematic reference.

The complete addresses for target system requests are supplied to the environmental portals together with format information. Depending on the formats supplied, the portals display an integrated results page.

Thus, a broad variety of formats can be represented adequately within the search facility of an environmental portal, e.g. map layers with nature reserves, geo-point objects, such as measuring points, full-text search results, tables, or multi-media data.

This "universal search" approach offers a significant value added compared to a conventional full text search.

Fig.

  Fig. 1. Architecture of the Environmental Portal with SearchBroker, Semantic Component (Ontology), and Target Systems

Fig. 2 .

 2 Fig. 2. Architecture of the SearchBroker with Plug-ins and Semantic Component (Ontology)

Fig. 3 .

 3 Fig. 3. Environmental Portal of Baden-Württemberg showing 3 OneBoxes (right column)for search terms "schutzgebiet hockenheim" ("nature reserve hockenheim"): search results from environmental databases, air quality index, images of nature reserves

─

  geo:lon = 8.4037563 ─ geo:lat = 49.0080848 ─ geo:bbox = 8.2756969,48.9494975 8.5318157,49.0666033 Similarly, another plug-in can explain temporal terms. For a search, including "summer 2010", the explicit start and end dates are assigned: ─ datetime:calendar:day:first = 2010/06/21 ─ datetime:calendar:day:last = 2010/09/22

Fig. 4 .

 4 Fig. 4. Screenshot of the SUI II prototype: Subjects gathered from the ontology component for the provision of additional navigation steps within the full-text search results.


Joachim Ungar
email: joachim.ungar@gmail.com

Jan Peters-Anders

Wolfgang Loibl

Climate Twins -An Attempt to Quantify Climatological Similarities

Keywords: Climate Change, Similarity Measures, Web Mapping

As climate change appears, strategies and actions will be necessary to cope with its effects on environment and society in the coming decades. Current climate conditions can be observed everywhere in the world but future climate conditions can only be estimated through climate simulations which produce huge amounts of quantitative data. This data leads to statements like "temperature increase is expected to exceed 2.6 • C" or similar and remain fuzzy to non-experts in climate research. The Climate Twins application is designed to communicate climate changes in an intuitive and understandable way by showing regions which have now similar climate conditions according to a given Point of Interest (POI) in the future. This paper explains how the application seeks for locations with similar climatological patterns according to the POI. To achieve this goal a method has been developed to quantify similarity between two locations' climate data.

Introduction

To allow "real world insights" about future climate impact and appropriate adaptation, one can look at model regions, where the current climate appears similar to an expected future climate of a POI. We call such region pairs with similar climate conditions (at different times) "Climate Twins". From these (remote) current Climate Twin region parts we can learn "hands on" how future climate impacts may be experienced in the POI and how to adapt there to the changing climate conditions, expected in the future.

The idea of Climate Twins is to identify regions whose current climate conditions show high similarity to the expected future climate in the POI. The Climate Twins search tool is a web-based graphical user interface (GUI) allowing to explore climate change effects based on maps of current and future climate.

To identify climatological similarity seems to be a simple exercise but the accuracy and validity of the result strongly depends on the indicators used and the similarity thresholds defined. A huge number of indicators in combination with narrow threshold ranges will reduce the number of matching regions significantly as well as few indicators combined with wide thresholds will show a big number of matching regions.

The climate indicators used here are daily mean temperatures and daily precipitation because they are seen as the most important ones and provide sufficient input for proving the concept's applicability. The most important part was to find a suitable matching method which strongly depends on the quantification of similarity between any two data vectors.

This matching method now provides a "unit-less" similarity value able to be combined with similarity values of other indicators, -information of the degree of similarity to derive statements like "more similar than" or "less similar than", and a consideration of many statistical properties because whole statistical distributions are being compared.

2 Theory and Methodology

Climate Data

Climate can be seen as a statistical collection of various climate variables. These variables are either measured or modeled in various time steps and therefore come as a list of values. A statistical distribution of these values can be described by three main attributes: dispersion (measure of variability), skewness (measure of asymmetry) and kurtosis (measure of peakedness) or by aggregations like mean, median or range. There are various ways to quantify these properties but especially measuring skewness and kurtosis is challenging and the results are not always satisfactory. By using conventional methods major problems have to be faced as climate data is rather not normally distributed. Furthermore the results have to be combined to a single attribute afterwards which also leads to problems in weighting them in an appropriate manner.

Figure 1 shows frequency distributions of modelled daily temperature means. A quick visual interpretation shows that the more values are located on the right hand side, the warmer the location (e.g. Rome). The annual temperature amplitude equals the value range. As Vienna and Munich have a higher value range, the annual temperature range is wider due to their rather continental location. On the other hand Rome and Copenhagen, wich are located in maritime locations, show a narrow value range. Bipolar distributions indicate strong and distinct seasons like winter and summer with short and alternating changeovers in spring and autumn (Rome) whereas Gauss-like distributions indicate a more homogeneous climate (Vienna, Copenhagen, Munich) and so on.

Similarity Measures

A similarity measure in this context should define and quantify the similarity between two statistical distributions-i.e. the numerical attributes we used to describe climate. Vegelius et al. [START_REF] Vegelius | Measures of similarity between distributions[END_REF] did a comparative analysis on various similarity measures. His research group tested measures according to predefined criteria. The three main criteria being:

1. The result of the measurement (r) has to be a value between 0 and 1, 2. between two identical distributions r has to be 1 and 3. r has to be equal when measuring in both directions.

After the analysis the group pointed out two measures which fit to all criterias given. These two were the Proportional Similarity (P D, 1) and the Hellinger Coefficient (r H , 2).

P D(U, V ) = C i=1 min(f Ui , f Vi ) (1) r H (U, V ) = C i=1 f Ui * f Vi (2)
U , V are two distributions, r similarity value, C being "category". Both measures use relative frequencies to measure similarity. Having data on a categorical or ordinal scale these categories are already defined. Given for examples two farms with cattle, chickens and sheep it is possible to quantify the relative similarity by using the frequencies of the three species living in each farm. In this example each species is a category.

Application

Problem: Climate data though occurs on an interval (temperature) or ratio (precipitation) scale where the borders-value ranges-that enclose the categories have to be defined manually in order to use these similarity measures.

Solution:

The number of categories a distribution is split into determines the resolution of the measure (and therefore the accuracy of the measurement). If there is only one category describing each distribution, this category contains 100% of the values which leads to a r value of 1. The more categories are introduced, the more convincing the similarity measurement gets but for the Climate Twins application a meaningful number of categories had to be found where on the one hand the r value provides a valid similarity indicator and computer ressources are used effectively on the other hand. In Figure 2 the behaviour of Proportional Similarity values when increasing the category number is shown. Visually the resulting curve can be divided into three parts: (1) when having one category the r values have-as expecteda value of 1. When increasing the number the curve shows major fluctuations until it (2) stabilizes after approximately 30 categories. The r values stay (3) constant when having approximately 700 categories and more.

It is important to specify an amount of categories where valid results are possible on the one hand and the computing effort is as low as possible on the other hand. Therefore the number of categories should be a lower value of part two of the curve. Due to other reasons like the total possible value range, the categories for the Climate Twins application were set to 37 for temperature (every 2 • C between -30 • C and 40 • C and one category each below and above that range) and 29 categories for precipitation (1 mm width each category from 0 to 10 mm, 5 mm width from 10 to 100 mm and one category for 100 mm and more).

Problem: A major problem in applying this method on climate data was losing all temporal information. A pure frequency distribution does not contain the chronological information of the distribution values anymore. Therefore two locations where the precipitation peaks occur at location A in spring and at location B in autumn will show erroniously high similarity.

Solution:

The data is split up in seasonal data by aggregating to spring (MAM), summer (JJA), autumn (SON) and winter (DJF). After computing the respective seasonal r values they are recombined by averaging to an annual value.

Recombination can be done easily at least in combining seasonal r values to an annual value as the r value is "unit-less". To asses the problem in combining the similarity values of two different indicators a slider was implemented in the web application to interactively change the weighting. However, the results showed no significant change in Climate Twin result regions whilst variing the indicator weighting.

Figure 3 shows the logic behind the combination of single r values to an overall simlarity value. The most important part is that every single seasonal r value has to exeed a certain similarity threshold so that every overall similarity is mapped. If two locations match perfectly in three of four seasons but not in the fourth season, there is no point in declaring the two locations similar to each other.

Problem: As the declaration of similarity is a subjective one and up to some point an arbitrary process, so is the definition of the similarity thresholds. The thresholds should of course be tight eneough to provide a reliable result but on the other hand wide enough so that an acceptable amount of Climate Twin regions can be found. Furthermore an applicable threshold also depends on the indicator used and the category number as it can be seen in Figure 2.

Solution: Until now no satisfying validation method or data could be found to compare the Climate Twin results with. Therefore the fictive line between "good" and "bad" results can only be drawn subjectively by visual interpretations of result maps while variing the thresholds. In the web application the user is enabled and encouraged to influence this parameter interactively through a slider. The problem of thresholds has to be faced in order to further development of this method.

3 Technical Infrastructure

Input Data

The input data is from the COSMO-CLM (COnsortium for Small-scale MOdelling -Climate Local Model) model 2.4.11 which is embedded into the ECHAM5/MPIOM global model. The model results are climate data on an hourly basis from 1960 to 2100 in a raster with a resolution of 0.165 • (approx. 18 to 20 km) [START_REF] Lautenschlager | Climate simulation with CLM, scenario A1B run no.1[END_REF].

The input data for the Climate Twins exploration have to be prepared and "condensed" in advance for fast data retrieval and comparison. The data actually stored in the data base are the absolute frequencies of daily data aggregated seasonally and in fourteen blocks of ten years each.

Interactive Map Application

The Climate Twins map functions have been built on open source software making use of UMN map server's [START_REF]UMN Mapserver[END_REF] capabilities of displaying file based geographical data and spatially enabled data through PostGIS [START_REF]PostGIS[END_REF] layers stored in a PostGreSQL [10] database while using JSP (Java Server Pages) technology to conduct the grid cell queries. On the client side the highly configurable Flamingo Viewer [START_REF]Flamingo Mapcomponents[END_REF] is used to display the maps served by the map server and to communicate with the Climate Twins data cube.

Each click on a location in the interactive map triggers a database query and array comparison of future with current climate indicators from the selected grid cell related to the respective municipality. The comparison results of the query are stored in a 2 nd map view of the database, displayed in the application's second window.

Summary and Outlook

This paper showed the advanced matching method of Climate Twins v3 to identify locations or regions with similar climate conditions. By applying a similarity measure and configuring it for a use on interval and ratio scaled data, it was possible to build this prototype with a completely new matching method. The main advantages of this method is its scalability towards adding any number of further climate indicators and the representation of similarity's spatial fuzziness. The definition and quantification of similarity thresholds still is a challenge and not solved, yet.

The current Climate Twins application's climate data is based on the "business as usual" green house gas (GHG) increase scenario IS92b [START_REF]Climate Change[END_REF]. For the future, a set of climate scenarios of different GHG increase rates will be applied in order to show the "movement" of the Climate Twins areas over Europe (to be expected in south and southeast direction) with respect to larger time steps into future climate.

Semantic web technologies (developed in the EU FP7 project TaToo) may allow direct access to web sites related to Climate Twins areas in order to identify adaptation measures to cope better with further climate conditions.

Fig. 1 .

 1 Fig. 1. Frequency distributions of daily mean temperatures 2001 to 2010

Fig. 2 .

 2 Fig. 2. Influence of category number on similarity measures (daily mean temperatures 2001-2010).

Fig. 3 .

 3 Fig.3. Logical structure of combining r-values while define similarity between two locations. The structure is scalable, i.e. any number of indicators can be implemented as long as respective similarity thresholds are applied.

Fig. 4 .

 4 Fig. 4. Screenshot of the current version Climate Twins Viewer 3 beta


Magnuz Engardt

Christer Johansson
email: 3christer.johansson@itm.su.se

Lars Gidhagen
email: lars.gidhagen@smhi.se

Web Services for Incorporation of Air Quality and Climate Change in

Keywords: climate change, climate services, climate scenario, air quality modeling, urban planning

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

Introduction

The FP7 ICT-2009-6.4 project SUDPLAN [START_REF] Gidhagen | Sustainable Urban Development Planner for Climate Change Adaptation (SUDPLAN)[END_REF], [START_REF] Denzer | A Decision Support System for Urban Climate Change Adapatation[END_REF] includes the development of a webbased ICT tool that will assist urban planners to incorporate future environment factors in the decision process. Due to its strong impact on citizens' health, comfort, safety and quality life it is important to also consider air quality in urban planning. For European cities, the future urban air quality will be determined by processes and decisions on very different scales.

Climate change will alter the global background of pollutants and it will also affect the way the atmosphere reacts to air pollution emissions over Europe. Actions takene.g. in form of emission ceilings for EU member states -may reduce emissions of both air pollutants and climate forcing agents. These efforts will, over decadal periods, contribute to changes in the levels of long range transported air pollution reaching European cities. On the local scale, city populations will grow and planners may opt for spatial expansion or for concentration. This, together with the design and technical developments of transportation and energy production systems, will determine how much local emissions will influence the future air quality.

Through the SUDPLAN web based user interface, named Scenario Management System (SMS), end-users in an arbitrary European city will be able to use a service for climate and environmental information: the Common Services (CS). CS offers as one component the visualization of how temperature and precipitation will evolve over Europe during the coming decades. The information consists of gridded 10-yearaverages for a certain period, currently the years of 1960-2100 output from SMHI's regional climate model RCA3 [START_REF] Kjellström | 21 st century changes in the European climate: uncertainties derived from an ensemble of regional climate model simulations[END_REF]. Available for SUDPLAN visualization is currently RCA3 downscaled results using the Global Climate Models (GCMs) ECHAM5 A1B [START_REF] Roeckner | Sensitivity of simulated climated to horizontal and vertical resolution in the ECHAM5 atmosphere model[END_REF], [START_REF] Jungclaus | Ocean cireculation and tropical variability in the coupled ECHAM5/MPI-OM[END_REF] and HADLEY A1B [START_REF] Gordon | The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments[END_REF] as boundary conditions. An important aspect in SUDPLAN use of climate scenarios is an ensemble approach, i.e. to use various scenario results to illustrate the uncertainty in future climate.

SUDPLAN visualization on the European scale also includes scenarios of the evolution of NOx, NO 2 , O 3 and PM10. These air quality results stem from simulations with the regional dispersion model MATCH [START_REF] Robertson | An Eulerian limited-area atmospheric transport model[END_REF], forced by meteorological data from RCA3 simulating different climate change realizations. In order to include a realistic temporal evolution of European primary and precursor emissions, the MATCH model has used the RCP4.5 emission scenario [START_REF] Moss | The next generation of scenarios for climate change research and assessment[END_REF] to assess air quality impact of the ECHAM5 A1B and HADLEY A1B climate scenarios. SUDPLAN will, in the near future, replace the current GCM and RCA3 results from the IPPC fourth assessment's A1B scenario with the new results based on RCP emissions also on the global scale.

SUDPLAN offer European cities downscaling of future rainfall, hydrological conditions and air quality. The focus of this paper is the urban air quality downscaling that can be performed with the European scale climate and air pollution as input. In what follows the downscaling procedure is described, how this is performed by the end-user and how the results can be presented. We will also evaluate the downscaling model results against measured air quality. All results are taken from Stockholm, one of four SUDPLAN pilot cities.

Methods

The following sections describe the chemistry transport model MATCH used for the downscaling and how it is used in the SUDPLAN tool. To determine future air quality over a city we use a dedicated version of the threedimensional off-line CTM MATCH [START_REF] Robertson | An Eulerian limited-area atmospheric transport model[END_REF], operated over the city of concern. The domain is flexible and set by the usertypically 100×100 km 2 with 50-100 grids in each horizontal direction. This model is driven by high-resolution tracer emissions from a local emission inventory and interpolated meteorology from a regional climate model. Local emission scenarios should represent both present (reference) and future conditions, the latter based on assumptions about traffic solutions and future population densities, etc. In the SMS it will be possible to operate the urban-scale model with different local emission scenarios, thus executing the model a number of times. As outlined in the introduction, the meteorology from the regional climate model is also driving a version of MATCH operating on courser resolution to calculate concentrations of tracers in Europe, following climate change and variations of tracer emissions.

Air quality downscaling model

The SUDPLAN SMS user interface lists an ensemble of climate simulations and European-scale air quality simulations representing different climate change realizations and different European tracer emission scenarios. These results are available as boundary conditions to the urban air quality downscaling.

Air quality downscaling in Common Services

The basic principle for SUDPLAN Common Services (CS) is that the end-user provides local data to improve the downscaling of environmental information reflecting a future climate change. This means that the end-user operating the SUDPLAN user interface, the Scenario Management System (SMS), will transfer data both to and from CS, and they will also operate models inside CS. For air quality downscaling the transfer of time series or gridded data is through Sensor Observation Services (SOS) and the model execution takes place through the Sensor Planning Services (SPS), see Table 1. The use of these two standard interfaces will allow also external users to access CS information, without using the SMS user interface. Fig. 2 illustrates the data flows and user options while doing an air quality downscaling. Local emissions may be uploaded to CS as spatial grids of annual averaged emissions for NOx, SOx, CO, VOC, NH3 and PM10. Temporal variations for each grid are described as tables for monthly and daily variations. Uploaded emissions are stored in a CS emission database, so that upload of a specific emission scenario is only made once.

Fig. 2. Work flows in Common Services

Other options for the end-user is defining grid size and simulation area, emission database, simulation period and which European scale result to use as forcing and boundary conditions (see preceding section of details TO USER = SMS = CS Airviro Airviro will be status messages sent to the SMS user interface, notifying when the simulation is completed. Downscaled results are available as 10-year, yearly, monthly and daily averaged grids. For shorter simulation periods up to one year, also hourly grids will be available (allowing extreme values to be visualized statistically compared to relevant limit values).

Air quality downscaling results

To demonstrate the performance of the urban downscaling Fig. 3 shows observed concentrations of NOX, NO 2 , O 3 and PM10 at Torkel Knutssonsgatan and Norr Malma during 2009 and 2010 together with calculated concentrations of these species. The calculations were all done on the same domain covering Stockholm and its surroundings, utilizing identical emission inventory but meteorology and tracer boundaries concentrations from different sources. Fig. 3 shows that the observed variability between two consecutive years is as large as the year-to-year variability of the simulations driven by the European climate data. The results show that MATCH reproduces NOx, NO 2 and O 3 well, at both sites but underestimate PM10 at both sites. The simulations utilizing meteorology from a climate model performs equally reasonable as the simulations using "real" meteorology. The underestimation of PM10 is expected due to omissions of certain sources in the present set-up. Omitted sources include non-tailpipe emissions, which contribute significantly to PM10 in the Stockholm region [START_REF] Norman | Studies of some measures to reduce road dust emissions from paved roads in Scandinavia[END_REF], [START_REF] Johansson | Spatial & temporal variations of PM10 and particle number concentrations in urban air[END_REF], [START_REF] Omstedt | A Model for vehicle Induced Non-tailpipe Emissions of Particles Along Swedish Roads[END_REF]. Fig. 4 and 5 show 3-year averaged concentrations of NO 2 and O 3 centered in 2010 and 2030. In this example, the change between the two periods is only brought about by changes in European tracer emissions and changes in climate. Note that the data is the result of a chain of three-dimensional meteorological and air quality models, operating with hourly resolution over multi-year periods. The figures demonstrate that the background concentration of NO 2 and O 3 will decrease in the future -mainly as a result of reduced precursor emissions in Europe. Other studies have shown that climate change may, to some extent and in particular in continental Europe, counteract the positive effects on ozone levels achieved by reduced precursor emissions [START_REF] Engardt | Climate and Emission Changes Contributing to Changes in Near-surface Ozone in Europe over the Coming Decades: Results from Model Studies[END_REF]. In Stockholm it seems that the effect of reduced precursor emissions in Europe will have a dominating positive effect on average ozone levels.

Conclusions

The SUDPLAN air quality downscaling model system has been executed over the Stockholm region and reproduces observed concentration of NOx, NO 2 , and O 3 well. The average concentration and variability of PM10 is underestimated, however this is expected since all sources of PM10 are not included in the simulations. From comparisons between simulations with "observed" meteorology and simulations with meteorology from a climate model represented "current climate" it is deduced that the climate data is well suited for describing current climate. Using one particular climate realization (ECHAM5 A1B) together with one tracer emission scenario (RCP4.5) result in decreasing concentrations of air pollutants NO2 and O3 in Stockholm from 2010 to 2030 -mostly a result of decreasing precursor emissions. Other studies have shown that climate change may counteract the positive impact resulting from emission reductions over Europe.

The SUDPLAN scenario management system (SMS), together with the Common Services (CS), are developed to investigate the effect of different climate scenarios and different tracer emissions over Europe. The Sudplan tool also permits the utilization of different emissions scenarios in specific cities. In the present study we investigated the result of changing tracer emissions in Europe together with changing climate, but maintaining local city emissions at a fix (today's) level. The next step will be to include also the effect of different political decisions taken in a particular city, decisions that will alter local emissions. For example, what will be the effect of a certain city bypass, or a general increase of the vehicle fleet.

Fig. 1 .

 1 Fig. 1. SUDPLAN models. Linking global climate change to future air quality. A sequential approach.

Fig. 3 .

 3 Fig. 3. Annual average concentration of NOX, NO 2 , O 3 and PM10 at the urban background site Torkel Knutssonsgatan (left), and a rural site Norr Malma (right). Black dots are measurements collected during 2009 and 2010. Blue dot is model simulations for the year 2010. Red dots are model simulations using climate data representative of two years close to 2010. Red rings are model simulations using climate data representative of two years close to 2030. Bars represent ± one standard deviation of hourly mean values. All concentrations are given in µg m -3 .

Fig. 4 .

 4 Fig. 4. 3-year average NO 2 concentration (ca. 2010 left, ca. 2030 right). The simulations depict a situation where the local emissions were kept at the levels of 2010, but meteorology and climate were changing according to the ECHAM A1B scenario and European tracer emissions following RCP4.5.

Fig. 5 .

 5 Fig. 5. 3-year average O 3 concentration (ca. 2010 left, ca. 2030 right). The simulations depict a situation where the local emissions were kept at the levels of 2010, but meteorology and climate were changing according to the ECHAM A1B scenario and European tracer emissions following RCP4.5.

Table 1 .

 1 Overview of standardized interfaces (OGC) used to access Common Services

	Model	SOS SPS WMS	WFS
	European scale visualisation	X	X
	Rainfall downscaling	X	X
	Hydrological downscaling	X	X	X
	Air quality downscaling	X	X

Acknowledgments. SUDPLAN is a Collaborative Project (contract number 247708) co-funded by the Information Society and Media DG of the European Commission within the RTD activities of the Thematic Priority Information Society Technologies.