M10 PM Chemical Hazard Identification, Classification and Communication
Monday, 14 November 2005: 1:50 PM - 5:30 PM in 341-342

(DRA-1117-839606) Integrated Data Management in Environmental Investigations.

Dragos, P¹, Baptiste-Carpenter, E¹, Gulbransen, T¹, ¹ Battelle, Duxbury, MA, USA

ABSTRACT- Large environmental investigations generate a significant volume of data in a wide range of disciplines including chemistry, toxicology, biology, geophysics, and health science among others. These data may be generated in a range of environmental studies including site investigations, environmental monitoring programs, and risk assessments all of which frequently require monitoring of both short term and long-term changes within the environment. The data may be used in scientific research and/or for environmental management decision making. Ultimately, data need to be synthesized into focused information products such as trend analyses, fate and transport analyses, biological abundance, and habitat maps, to name a few. The effectiveness of these investigations depends on the system of data management implemented to assimilate, manage, archive, and disseminate the environmental data and information. Through more than 20 years of experience in the management of data in large environmental investigations ranging from the Exxon Valdez oil spill through ongoing superfund remediations, methods to facilitate effective environmental information management have been developed and refined. These include electronic data capture in the field using mobile information technology, assimilation of in situ sensor data through remote communications technology, assimilation of analytical data through LIMS, database, and web technologies, dissemination of data using web technologies, and quality control methodologies implemented throughout. The methods described provide for rapid flow of quality-controlled data, data documentation, and derived data products to scientific and management users and increase the long-term viability of data through reduced risk of data entropy.

Key words: environmental data management