Cost/benefit assessment of green infrastructure: Spatial scale effects on uncertainty and sensitivity.

Green infrastructure (GI) is becoming a common solution to mitigate stormwater-related problems. Given the uncertain costs of GI relative to other stormwater management strategies, stakeholders investing in GI need performance-analysis tools that consider the full suite of benefits and the impacts of uncertainty to help justify GI expenditures. This study provides a quantitative and comparative analysis of GI benefits, including nutrient uptake from stormwater and air pollutant deposition. Economic costs and benefits of GI are assessed using two metrics, benefit-cost ratios (BCRs) and nutrient removal costs, at three scales: household, subwatershed, and watershed scale. Results from a case study in the state of Maryland show that the costs of nutrient uptake at the subwatershed scale can be lower than those at either the watershed or household scales. Moreover, rain gardens are far more efficient in stormwater treatment at the household scale in comparison to watershed scale, for which large-scale dry or wet basins are more efficient. Using a BCR metric, smaller subwatersheds show more promise, while using a nutrient removal cost metric indicates that upstream subwatersheds are more suitable for stormwater treatment. The results also show that implementation of GI at all potential pervious locations does not necessarily increase nutrient removal costs and that self-installation of rain gardens greatly reduces nutrient removal costs. Finally, the results show that using numerous small-sized rain garden practices in front of residential buildings yields lower nutrient removal costs in comparison to permeable pavements placed in parking lots and commercial buildings.

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.

Reducing long-term remedial costs by transport modeling optimization.

The Department of Defense (DoD) Environmental Security Technology Certification Program and the Environmental Protection Agency sponsored a project to evaluate the benefits and utility of contaminant transport simulation-optimization algorithms against traditional (trial and error) modeling approaches. Three pump-and-treat facilities operated by the DoD were selected for inclusion in the project. Three optimization formulations were developed for each facility and solved independently by three modeling teams (two using simulation-optimization algorithms and one applying trial-and-error methods). The results clearly indicate that simulation-optimization methods are able to search a wider range of well locations and flow rates and identify better solutions than current trial-and-error approaches. The solutions found were 5% to 50% better than those obtained using trial-and-error (measured using optimal objective function values), with an average improvement of approximately 20%. This translated into potential savings ranging from 600,000 dollars to 10,000,000 dollars for the three sites. In nearly all cases, the cost savings easily outweighed the costs of the optimization. To reduce computational requirements, in some cases the simulation-optimization groups applied multiple mathematical algorithms, solved a series of modified subproblems, and/or fit "meta-models" such as neural networks or regression models to replace time-consuming simulation models in the optimization algorithm. The optimal solutions did not account for the uncertainties inherent in the modeling process. This project illustrates that transport simulation-optimization techniques are practical for real problems. However, applying the techniques in an efficient manner requires expertise and should involve iterative modification to the formulations based on interim results.

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.