Continuous Plume Monitoring Using Wireless Sensors: Proof of Concept in Intermediate Scale Tank

Abstract

The current practice for monitoring of subsurface plumes involves the collection of water samples from sparsely distributed monitoring wells and laboratory analysis to determine chemical concentrations. In most field situations, cost and time constraints limit the number of samples that could be collected and analyzed for continuous monitoring of large, transient plumes. With the development of wireless sensor networks ͑WSNs͒, that allow sensors to be incorporated into a distributed wireless communication and processing system, the potential exists to develop new, efficient, economical, large-scale subsurface data collection and monitoring methods. This paper presents a proof-of-concept study conducted in a two-dimensional synthetic aquifer constructed in an intermediate scale test tank to demonstrate the feasibility of using WSN for subsurface plume monitoring. The tank was packed to represent a heterogeneous aquifer, and a sodium bromide tracer was used to create a plume. A set of ten wireless sensor nodes ͑motes͒ equipped with conductivity probes to measure electrical conductivity formed the network. Software for automated data acquisition was developed and tested. Results of two experiments conducted using this test system are presented. The lessons learned from the first experiment were used to make modifications to the way the sensors were placed, how they were calibrated and how the sensors were interfaced with the data acquisition system. The findings are used to identify future research directions and issues that need to be addressed before field implementations of a WSN based data collection system for plume monitoring.

Inverse Dispersion for an Unknown Number of Sources: Model Selection and Uncertainty Analysis

“…Although there are various alternatives leading to many different combinations that could be used potentially to draw samples from p θ | m N , D, I and to evaluate Z m 1 and Z m 1 , we have used instead a single methodology to do both. The methodology that is used in this paper to address both these problems is nested sampling developed by Skilling 28 for the efficient evaluation of the evidence Z for the general case.…”

“…2.2 or Z m 1 Step 5, cf. The are a number of options that are available for the computation of the evidences Z m 1 and Z m 1 .…”

“…There are two major computational problems that need to be addressed in order to fully specify the algorithm summarized in Section 2, namely, 1 specification of a method to draw samples from the posterior distribution p θ | m N , D, I Steps 2 and 6 and 2 specification of a method for computation of the evidence Z m 1 Step 3, cf. 2.2 or Z m 1 Step 5, cf.…”

“…Furthermore, it is assumed that the expectation value e i of the composite error is zero, and the variance e 2 i is assumed for now to be known and given by σ 2 i . Hence, it is assumed that we are given the concentration data D ≡ {d 1 …”

Quality-aware sensor data collection

“…Our experience collaborating with experts from several disciplines such as environmental science and engineering [Porta et al (2009)], hydrology, and mining has indicated that many applications are interested in raw data from sensor nodes, instead of aggregated data. This motivates us to focus on a common service that can be applied to many sensor applications: raw sensor data collection from all nodes to one centralized server.…”