2007 GSA Denver Annual Meeting (28–31 October 2007)

Paper No. 9
Presentation Time: 1:30 PM-5:30 PM


PORTA, Lisa1, ILLANGASEKARE, Tissa H.1, LODEN, Philip2 and HAN, Qi2, (1)Environmental Sciences and Engineering, Colorado School of Mines, 1500 Illinois Street, Golden, CO 80401, (2)Mathematical and Computer Sciences, Colorado School of Mines, 1500 Illinois Street, Golden, CO 80401, lporta@mines.edu

The current practice for monitoring subsurface plumes involves the laboratory analysis of water samples from monitoring wells to determine chemical concentrations. This data is used in modeling and in site management. Cost and time constraints limit the collection and analysis of numerous samples; hence, this approach becomes impractical for continuous monitoring of large, transient plumes. With the expected advances made in new sensor technologies and wireless sensor networks (WSNs), the potential exists to develop new and efficient subsurface data collection to monitor plumes. The goal is to automatically collect data from the sensors and wirelessly transmit them to a computer for continuous plume monitoring and for dynamic groundwater model calibration. Many technological and operational challenges related to sensor calibration, placement and distribution, automation of real-time data collection, wireless communication, and modeling have to be overcome before the implementation of this technology in the field. This preliminary proof of concept demonstration study assesses this technology using a physical aquifer test bed constructed in an intermediate scale tank. The tank was packed using three well-characterized silica sands to represent a heterogeneous aquifer. Bromide tracer was injected as a slug into a steady flow field and concentration at different points in the tank was measured with ten calibrated electrical conductivity sensors attached to five different motes connected wirelessly to the computer. The accuracy of the sensor-measured concentrations was tested against traditional grab samples analyzed using an ion chromatograph. Results show that good quantitative and qualitative correlations can be found between sensing data and chemical analysis data. These data were used to calibrate a groundwater flow and transport model and to determine subsurface parameters needed for predictive modeling. A closed-loop model calibration system was developed to dynamically recalibrate the groundwater model with new sets of wirelessly sensed data. This preliminary study is the initial step in the development of a more complex wireless communication system to be used in field applications involving remediation design, performance assessment, risk analy-sis, and exposure assessment.