The Value of Information for Managing Contaminated Sediments

Effective management of contaminated sediments is important for long-term human and environmental health, but site-management decisions are often made under high uncertainty and without the help of structured decision support tools. Potential trade-offs between remedial costs, environmental effects, human health risks, and societal benefits, as well as fundamental differences in stakeholder priorities, complicate decision making. Formal decision-analytic tools such as multicriteria decision analysis (MCDA) move beyond ad hoc decision support to quantitatively and holistically rank management alternatives and add transparency and replicability to the evaluation process. However, even the best decisions made under uncertainty may be found suboptimal in hindsight, once additional scientific, social, economic, or other details become known. Value of information (VoI) analysis extends MCDA by systematically evaluating the impact of uncertainty on a decision. VoI prioritizes future research in terms of expected decision relevance by helping decision makers estimate the likelihood that additional information will improve decision confidence or change their selection of a management plan. In this study, VoI analysis evaluates uncertainty, estimates decision confidence, and prioritizes research to inform selection of a sediment capping strategy for the dibenzo-<i>p</i>-dioxin and -furan contaminated Grenland fjord system in southern Norway. The VoI model extends stochastic MCDA to model decisions with and without simulated new information and compares decision confidence across scenarios with different degrees of remaining uncertainty. Results highlight opportunities for decision makers to benefit from additional information by anticipating the improved decision confidence (or lack thereof) expected from reducing uncertainties for each criterion or combination of criteria. This case study demonstrates the usefulness of VoI analysis for environmental decisions by predicting when decisions can be made confidently, for prioritizing areas of research to pursue to improve decision confidence, and for differentiating between decision-relevant and decision-irrelevant differences in evaluation perspectives, all of which help guide meaningful deliberation toward effective consensus solutions

Initial performance of each remedial alternative on the evaluation criteria.

<p>Initial performance of each remedial alternative on the evaluation criteria.</p

The decision model utilized in the EAM approach for remediation of legacy Hg in the South River.

<p>The remedial alternatives need to be specified in terms of their efficiency in reducing the THg loading rate expected in each compartment (orange boxes). This local efficiency in THg loading reduction together with initial river flow rates and water column THg concentrations are utilized in a mass-balance calculation (river schematic) which determines the anticipated change in water column THg loading and concentration in various specified river reaches. The new water column THg concentration is then compared to empirical data (inserted graph) to predict of the smallmouth bass tissue MeHg concentration anticipated at steady state after implementation. The effectiveness of the remedial alternative, as indicated by the anticipated reduction in smallmouth bass MeHg, is combined with the implementability, ecological effects and estimated cost of that alternative to calculate the relative value associated with that specific remedial approach.</p

A basic sensitivity analysis of the effect of the criteria weights on the performance.

<p>The remedial alternatives are ranked according to their mean value score. For each alternative, black boxes indicate the mean and standard deviation of 1000 simulations across the range of probable values. The green box shows the highest and lowest possible scores achieved with the maximum and minimum probable values for that alternative, respectively. The blue boxes show the range of scores between the probable value and the possible value for each alternative; above the mean is the range of scores between the maximum probable and the maximum possible while the minimum range is below. (A.) The original weighting scheme of 40% effectiveness, 25% ecological effect, 25% implementability and 10% cost results in Alternatives 1 and 3 providing the highest. (B.) For this calculation, 100% of the criteria weight was placed on the effectiveness of the alternative in reduction in smallmouth bass MeHg concentration. The most aggressive alternative, Alternative 4 which includes both upstream sources control and extensive bank stabilization, has the highest mean value. (C.) For this calculation, 50% of the criteria weight was effectiveness and 50% was ecological effects. Again, Alternative 4 has the highest mean value, and the reduction in uncertain associated with the inclusion of ecological effect allows it to outperform Alternatives 4 and 5. (D.) The case with 40% of the criteria weight on effectiveness, 30% on implementability and 30% on cost also results in Alternatives 1 and 4 outperforming the other alternatives.</p

The process for EAM as demonstrated by the case study.

<p>Beginning at the top, the river conditions and change in total Hg loading in response to each alternative are estimated based on monitoring and pilot study results. These are then input into the mass balance to calculate the water column total Hg concentration and the smallmouth bass tissue MeHg concentration both initially and following alternative implementation. The smallmouth bass tissue concentrations are then entered into the decision model along with performance scores for the other criteria and preference weights and a relative ranking of alternatives is calculated. Based on the outcome of the decision model, an alternative is selected and implemented. Metrics that relate to parameters in the decision model are monitored according to the monitoring plan and updated as necessary. The process then repeats with the latest information gathered through monitoring and research.</p

THg concentration predictions from the mass-balance model for the initial alternatives.

<p>(A.) The mean predicted water column concentrations in ng/L are reported for each alternative for each reach of the river as calculated by the mass balance model. Alternatives 1 and 5 result in higher predicted THg concentrations then the other alternatives along the length of the river. (B.) The uncertainty in anticipated water column THg concentration at relative river mile 6 is shown as predicted by the mass balance model. Below the graphical display are the calculated values for the maximum, minimum and mean concentration of THg (ng/L). Alternatives 1 and 5 are anticipated to result in higher mercury levels in this stretch of the river. The other alternatives cannot be distinguished because of the uncertainty in the loading multiplied by the anticipated rate of reduction. (C.) The mean predicted concentrations of MeHg in smallmouth bass tissue (mg/kg) are reported for each alternative in each river reach as calculated by the mass balance model. Following from the water concentrations, Alternatives 1 and 5 result in higher fish tissue concentrations than the other options.</p

An Enhanced Adaptive Management Approach for Remediation of Legacy Mercury in the South River

<div><p>Uncertainties about future conditions and the effects of chosen actions, as well as increasing resource scarcity, have been driving forces in the utilization of adaptive management strategies. However, many applications of adaptive management have been criticized for a number of shortcomings, including a limited ability to learn from actions and a lack of consideration of stakeholder objectives. To address these criticisms, we supplement existing adaptive management approaches with a decision-analytical approach that first informs the initial selection of management alternatives and then allows for periodic re-evaluation or phased implementation of management alternatives based on monitoring information and incorporation of stakeholder values. We describe the application of this enhanced adaptive management (EAM) framework to compare remedial alternatives for mercury in the South River, based on an understanding of the loading and behavior of mercury in the South River near Waynesboro, VA. The outcomes show that the ranking of remedial alternatives is influenced by uncertainty in the mercury loading model, by the relative importance placed on different criteria, and by cost estimates. The process itself demonstrates that a decision model can link project performance criteria, decision-maker preferences, environmental models, and short- and long-term monitoring information with management choices to help shape a remediation approach that provides useful information for adaptive, incremental implementation.</p></div

Use of Stochastic Multi-Criteria Decision Analysis to Support Sustainable Management of Contaminated Sediments

Sustainable management of contaminated sediments requires careful prioritization of available resources and focuses on efforts to optimize decisions that consider environmental, economic, and societal aspects simultaneously. This may be achieved by combining different analytical approaches such as risk analysis (RA), life cycle analysis (LCA), multicriteria decision analysis (MCDA), and economic valuation methods. We propose the use of stochastic MCDA based on outranking algorithms to implement integrative sustainability strategies for sediment management. In this paper we use the method to select the best sediment management alternatives for the dibenzo-<i>p</i>-dioxin and -furan (PCDD/F) contaminated Grenland fjord in Norway. In the analysis, the benefits of health risk reductions and socio-economic benefits from removing seafood health advisories are evaluated against the detriments of remedial costs and life cycle environmental impacts. A value-plural based weighing of criteria is compared to criteria weights mimicking traditional cost–effectiveness (CEA) and cost–benefit (CBA) analyses. Capping highly contaminated areas in the inner or outer fjord is identified as the most preferable remediation alternative under all criteria schemes and the results are confirmed by a probabilistic sensitivity analysis. The proposed methodology can serve as a flexible framework for future decision support and can be a step toward more sustainable decision making for contaminated sediment management. It may be applicable to the broader field of ecosystem restoration for trade-off analysis between ecosystem services and restoration costs

Inferring Species Richness and Turnover by Statistical Multiresolution Texture Analysis of Satellite Imagery

<div><h3>Background</h3><p>The quantification of species-richness and species-turnover is essential to effective monitoring of ecosystems. Wetland ecosystems are particularly in need of such monitoring due to their sensitivity to rainfall, water management and other external factors that affect hydrology, soil, and species patterns. A key challenge for environmental scientists is determining the linkage between natural and human stressors, and the effect of that linkage at the species level in space and time. We propose pixel intensity based Shannon entropy for estimating species-richness, and introduce a method based on statistical wavelet multiresolution texture analysis to quantitatively assess interseasonal and interannual species turnover.</p> <h3>Methodology/Principal Findings</h3><p>We model satellite images of regions of interest as textures. We define a texture in an image as a spatial domain where the variations in pixel intensity across the image are both stochastic and multiscale. To compare two textures quantitatively, we first obtain a multiresolution wavelet decomposition of each. Either an appropriate probability density function (pdf) model for the coefficients at each subband is selected, and its parameters estimated, or, a non-parametric approach using histograms is adopted. We choose the former, where the wavelet coefficients of the multiresolution decomposition at each subband are modeled as samples from the generalized Gaussian pdf. We then obtain the joint pdf for the coefficients for all subbands, assuming independence across subbands; an approximation that simplifies the computational burden significantly without sacrificing the ability to statistically distinguish textures. We measure the difference between two textures' representative pdf's via the Kullback-Leibler divergence (KL). Species turnover, or diversity, is estimated using both this KL divergence and the difference in Shannon entropy. Additionally, we predict species richness, or diversity, based on the Shannon entropy of pixel intensity.To test our approach, we specifically use the green band of Landsat images for a water conservation area in the Florida Everglades. We validate our predictions against data of species occurrences for a twenty-eight years long period for both wet and dry seasons. Our method correctly predicts 73% of species richness. For species turnover, the newly proposed KL divergence prediction performance is near 100% accurate. This represents a significant improvement over the more conventional Shannon entropy difference, which provides 85% accuracy. Furthermore, we find that changes in soil and water patterns, as measured by fluctuations of the Shannon entropy for the red and blue bands respectively, are positively correlated with changes in vegetation. The fluctuations are smaller in the wet season when compared to the dry season.</p> <h3>Conclusions/Significance</h3><p>Texture-based statistical multiresolution image analysis is a promising method for quantifying interseasonal differences and, consequently, the degree to which vegetation, soil, and water patterns vary. The proposed automated method for quantifying species richness and turnover can also provide analysis at higher spatial and temporal resolution than is currently obtainable from expensive monitoring campaigns, thus enabling more prompt, more cost effective inference and decision making support regarding anomalous variations in biodiversity. Additionally, a matrix-based visualization of the statistical multiresolution analysis is presented to facilitate both insight and quick recognition of anomalous data.</p> </div

Remedial alternatives considered in the case study.

<p>The alternatives are different combinations of measures including vegetative bank stabilization, monitored natural recovery (MNR) in the reaches closest to the outfall, and outflow source control.</p