Austrian AQD e-Reporting via INSPIRE Services

Abstract

To comply with the recently issued AQD e-Reporting data standards by the European Commission (EC) and the European Environment Agency (EEA), the Austrian Environment Agency (EAA) has, together with the Austrian Institute of Technology (AIT), started the development of INSPIRE compliant download services supporting the full requirements of air quality reporting under European Air Quality Directive 2008/50/EC (AQD). Thus, the Austrian air quality data measured under legal requirements, together with the corresponding measurement metadata and reporting relevant information, will soon be available via real time web-services. One difficult question encountered was which INSPIRE download services to use for which of the features encompassed by the reported datasets. For data flow on air quality zones (B) as well as on air quality assessment metadata (D) it was clear that we would provide these data via a Web Feature Service (WFS), namely the OGC compliant implementation GeoServer. For the data flows C (assessment regime) and G (attainment,) which are purely reporting relevant data, we chose to also use the WFS option for simplicity. For the primary air quality data provided under data flow E we decided to use a Sensor Observation Service (SOS), as this service is far better suited for the provision of time series data. However, there is an area of overlap between the two services, pertaining to the measurement metadata. As the features provided by both services are identical, and the only difference in the response being the service response wrapper, the SOS forwards the request to a coupled WFS, and re-wraps the response before providing it to the client. The SOS used in this solution is a new implementation based on the openUwedat-Framework developed by AIT [1]. This framework provides a harmonized way to wrap virtually any source of time series data by configuring a data handler in a documented way. In addition, the framework is able to deal with semantic information pertaining to individual time series [2] to dynamically influence the fields that should be included in the SOS output such as data quality.