Assessing Health Effects of Water Pollution Using a Semantic Water Quality Portal

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Abstract:

We demonstrate a semantically enabled approach for environmental monitoring as embodied in our semantic water quality portal. The portal assesses water quality utilizing two data sources, the United States Environmental Protection Agency (EPA) and the United States Geological Survey (USGS), by the user’s choice from a number of regulations, e.g. federal level regulations established by the EPA as well as state departments of environmental protection. The portal identifies pollution events using an OWL-based reasoning system and provides browsing facets generated from provenance data encoded using the Proof Markup Language (PML). We show how exposing these measurements and their provenance as semantic data enables them to be combined with additional external data sources to look for correlations between pollution levels and health effects seen in nearby populations. This submission highlights the interactive demonstration aspects of the portal and augments the more detailed technical description of the semantic infrastructure, reasoning, and benefits of the approach that has been accepted for presentation in the Semantic Web In Use track.

History

DateCreated ByLink
October 16, 2011
14:28:46
Evan W. PattonDownload

Related Projects:

SemantAQUA LogoSemantic Water Quality Portal (SemantAQUA)
Principal Investigator: Deborah L. McGuinness
Co Investigator: Joanne S. Luciano
Description: We present a semantic technology-based approach to emerging environmental information systems. We used our linked data approach in the Tetherless World Constellation Semantic Water Quality Portal (TWC-SWQP). Our integration scheme uses a core domain ontology and integrates water data from different authoritative sources along with multiple regulation ontologies to enable pollution detection and monitoring. An OWL-based reasoning scheme identifies pollution events relative to user chosen regulations. Our approach also captures and leverages provenance to improve transparency. In addition, semantic water quality portal features provenance-based facet generation, query answering and data validation over the integrated data via SPARQL. We introduce the approach and the water portal, and highlight some of its potential impacts for the future of environmental monitoring systems.

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Description: Inference And Trust
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