The microelectronic wireless nitrate sensor network for environmental water monitoring.

Quantitative monitoring of water conditions in a field is a critical ability for environmental science studies. We report the design, fabrication and testing of a low cost, miniaturized and sensitive electrochemical based nitrate sensor for quantitative determination of nitrate concentrations in water samples. We have presented detailed analysis for the nitrate detection results using the miniaturized sensor. We have also demonstrated the integration of the sensor to a wireless network and carried out field water testing using the sensor. We envision that the field implementation of the wireless water sensor network will enable "smart farming" and "smart environmental monitoring".

Monitoring network design for phytoremediation systems using primary and secondary data sources.

Phytoremediation, or contaminant removal using plants, has been deployed at many sites to remediate contaminated soil and groundwater. Research has shown that trees are low-cost, rapid, and relatively simple-to-use monitoring systems as well as inexpensive alternatives to traditional pump-and-treat systems. However, tree monitoring is also an indirect measure of subsurface contamination and inherently more uncertain than conventional techniques such as wells or soil borings that measure contaminant concentrations directly. This study explores the implications for monitoring network design at real-world sites where scarce primary data such as monitoring wells or soil borings are supplemented by extensive secondary data such as trees. In this study, we combined secondary and primary data into a composite data set using models to transform secondary data to primary, as primary data were too sparse to attempt cokriging. Optimal monitoring networks using both trees and conventional techniques were determined using genetic algorithms, and trade-off curves between cost and uncertainty are presented for a phytoremediation system at Argonne National Laboratory. Optimal solutions found at this site indicate that increasing the number of secondary data sampled resulted in a significant decrease in global uncertainty with a minimal increase in cost. The choice of the data transformation model had an impact on the optimal designs and uncertainty estimated at the site. Using a data transformation model with a higher error resulted in monitoring network designs where primary data were favored over colocated secondary data. The spatial configuration of the monitoring network design was similar with regard to the areas sampled, irrespective of the data transformation model used. Overall, this study shows that using a composite data set, with primary and secondary data, results in effective monitoring designs, even at sites where the only data transformation model available is one with significant error.

Spatial interpolation methods for nonstationary plume data.

Plume interpolation consists of estimating contaminant concentrations at unsampled locations using the available contaminant data surrounding those locations. The goal of ground water plume interpolation is to maximize the accuracy in estimating the spatial distribution of the contaminant plume given the data limitations associated with sparse monitoring networks with irregular geometries. Beyond data limitations, contaminant plume interpolation is a difficult task because contaminant concentration fields are highly heterogeneous, anisotropic, and nonstationary phenomena. This study provides a comprehensive performance analysis of six interpolation methods for scatter-point concentration data, ranging in complexity from intrinsic kriging based on intrinsic random function theory to a traditional implementation of inverse-distance weighting. High resolution simulation data of perchloroethylene (PCE) contamination in a highly heterogeneous alluvial aquifer were used to generate three test cases, which vary in the size and complexity of their contaminant plumes as well as the number of data available to support interpolation. Overall, the variability of PCE samples and preferential sampling controlled how well each of the interpolation schemes performed. Quantile kriging was the most robust of the interpolation methods, showing the least bias from both of these factors. This study provides guidance to practitioners balancing opposing theoretical perspectives, ease-of-implementation, and effectiveness when choosing a plume interpolation method.