Ungtae Kim

Cost optimization of DNAPL source and plume remediation under uncertainty using a semi-analytic model

Dense non-aqueous phase liquid (DNAPL) spills represent a potential long-term source of aquifer contamination, and successful low-cost remediation may require a combination of both plume management and source treatment. In addition, substantial uncertainty exists in many of the parameters that control field-scale behavior of DNAPL sources and plumes. For these reasons, cost optimization of DNAPL cleanup needs to consider multiple treatment options and their associated costs while also gauging the influence of prediction uncertainty on expected costs. In this paper, we present a management methodology for field-scale DNAPL source and plume management under uncertainty. Using probabilistic methods, historical data and prior information are combined to produce a set of equally likely realizations of true field conditions (i.e., parameter sets). These parameter sets are then used in a simulation-optimization framework to produce DNAPL cleanup solutions that have the lowest possible expected net present value (ENPV) cost and that are suitably cautious in the presence of high uncertainty. For simulation, we utilize a fast-running semi-analytic field-scale model of DNAPL source and plume evolution that also approximates the effects of remedial actions. The degree of model prediction uncertainty is gauged using a restricted maximum likelihood method, which helps to produce suitably cautious remediation strategies. We test our methodology on a synthetic field-scale problem with multiple source architectures, for which source zone thermal treatment and electron donor injection are considered as remedial actions. The lowest cost solution found utilizes a combination of source and plume remediation methods, and is able to successfully meet remediation constraints for a majority of possible scenarios. Comparisons with deterministic optimization results show that not taking into account uncertainty can result in optimization strategies that are not aggressive enough and result in greater overall total cost.

Value of Information as a Context-Specific Measure of Uncertainty in Groundwater Remediation

The remediation of groundwater sites has been recognized as a difficult and expensive task for years. One of the challenges is that the success of remediation is usually contingent upon an appropriate level of characterization of the physical, chemical, and biological site properties. For example, thermal treatment cannot be economically applied if the location of a non-aqueous phase liquid (NAPL) source is unknown. Both characterization and remediation are expensive. Thus, efforts need to be prioritized and optimized taking effects of uncertainty into consideration. Traditional measures of uncertainty, such as variance and correlation coefficients, do not fully depict the significance of uncertainty. For example, a small error in a parameter to which performance is sensitive may affect the prospect for remediation success much more than a large error in a parameter that has minor influence. In this paper, we quantify uncertainty as the expected increase in the cost of achieving clean-up objectives that is associated with uncertainty in performance prediction models, i.e., the minimum expected cost attainable with the present state of uncertainty minus the expected cost achievable if uncertainty were fully or partially removed. This measure, a.k.a., the value of information (VOI), is context-specific, i.e., it is dependent on site conditions and remediation strategies as well as specific remediation objectives and unit costs. We consider clean-up objectives, cost formulations, and sensitivity of costs to uncertainty in parameters, measurements, and the model itself and seek to minimize expected cost under conditions of incomplete information. We present results from a synthetic case study of dense non-aqueous phase liquid (DNAPL) plume treatment. The results quantify the cost attributable to uncertainty, thus setting an upper limit on how much one should pay for characterization, and helping decision makers to decide whether the data should be collected or not.

Effects of Climate Change on the Streamflow for the Daechung Dam Watershed

Climate change mainly due to the increase of green house gases cause different patterns of water cycle within the basin. However, it is common that current planning and management practices do not consider the effect of the climate change. So, this study evaluated the effect of climate change on the water circulation within the watershed. This study used several GCM simulations for the double condition for the generation of temperature and rainfall series using the Markov chain. Daily runoff series for 100 years were generated using a rainfall-runoff model. As results. annual temperature increase by ＋3.2 ∼＋4.6, annual precipitation change －7 ∼ ＋8 %, annual runoff change －14 ∼ ＋7 %, and potential evapotranspiration amount change ＋3 ∼＋4 % for the change of 1 are found to be expected depending on GCM simulations. Even though the simulation results are very dependent on the GCM predictions considered, overall variability of runoff is expected to become higher than the current state.

Bivariate drought frequency curves and confidence intervals: a case study using monthly rainfall generation

Although water resources management practices recently use bivariate distribution functions to assess drought severity and its frequency, the lack of systematic measurements is the major hindrance in achieving quantitative results. This study aims to suggest a statistical scheme for the bivariate drought frequency analysis to provide comprehensive and consistent drought severities using observed rainfalls and their uncertainty using synthesized rainfalls. First, this study developed a multi-variate regression model to generate synthetic monthly rainfalls using climate variables as causative variables. The causative variables were generated to preserve their correlations using copula functions. This study then focused on constructing bivariate drought frequency curves using bivariate kernel functions and estimating their confidence intervals from 1,000 likely replica sets of drought frequency curves. The confidence intervals achieved in this study may be useful for making a comprehensive drought management plan through providing feasible ranges of drought severity.

A Data Mining Approach to Predict In Situ Detoxification Potential of Chlorinated Ethenes

Despite advances in physicochemical remediation technologies, in situ bioremediation treatment based on Dehalococcoides mccartyi (Dhc) reductive dechlorination activity remains a cornerstone approach to remedy sites impacted with chlorinated ethenes. Selecting the best remedial strategy is challenging due to uncertainties and complexity associated with biological and geochemical factors influencing Dhc activity. Guidelines based on measurable biogeochemical parameters have been proposed, but contemporary efforts fall short of meaningfully integrating the available information. Extensive groundwater monitoring data sets have been collected for decades, but have not been systematically analyzed and used for developing tools to guide decision-making. In the present study, geochemical and microbial data sets collected from 35 wells at five contaminated sites were used to demonstrate that a data mining prediction model using the classification and regression tree (CART) algorithm can provide improved predictive understanding of a sites reductive dechlorination potential. The CART model successfully predicted the 3-month-ahead reductive dechlorination potential with 75.8% and 69.5% true positive rate (i.e., sensitivity) for the training set and the test set, respectively. The machine learning algorithm ranked parameters by relative importance for assessing in situ reductive dechlorination potential. The abundance of Dhc 16S rRNA genes, CH4, Fe(2+), NO3(-), NO2(-), and SO4(2-) concentrations, total organic carbon (TOC) amounts, and oxidation-reduction potential (ORP) displayed significant correlations (p < 0.01) with dechlorination potential, with NO3(-), NO2(-), and Fe(2+) concentrations exhibiting precedence over other parameters. Contrary to prior efforts, the power of data mining approaches lies in the ability to discern synergetic effects between multiple parameters that affect reductive dechlorination activity. Overall, these findings demonstrate that data mining techniques (e.g., machine learning algorithms) effectively utilize groundwater monitoring data to derive predictive understanding of contaminant degradation, and thus have great potential for improving decision-making tools. A major need for realizing the predictive capabilities of data mining approaches is a curated, open-access, up-to-date and comprehensive collection of biogeochemical groundwater monitoring data.

Climate Change Impacts on Meteorological Drought and Flood

Recent increase of green house gases may increase the frequency of meteorological extremes. In this study, using the index and meteorological data generated by the Markov chain model under the condition of GCM predictions, the possible width of variability of flood and drought occurrences were predicted. As results, we could find that the frequency of both floods and droughts would be increased to make the water resources planning and management more difficult. Thus, it is recommended to include the effect of climate change on water resources in the related policy making.

Development and Application of Urban Landslide Vulnerability Assessment Methodology Reflecting Social and Economic Variables

An urban landslide vulnerability assessment methodology is proposed with major focus on considering urban social and economic aspects. The proposed methodology was developed based on the landslide susceptibility maps that Korean Forest Service utilizes to identify landslide source areas. Frist, debris flows are propagated to urban areas from such source areas by Flow-R (flow path assessment of gravitational hazards at a regional scale), and then urban vulnerability is assessed by two categories: physical and socioeconomic aspect. The physical vulnerability is related to buildings that can be impacted by a landslide event. This study considered two popular building structure types, reinforced-concrete frame and nonreinforced-concrete frame, to assess the physical vulnerability. The socioeconomic vulnerability is considered a function of the resistant levels of the vulnerable people, trigger factor of secondary damage, and preparedness level of the local government. An index-based model is developed to evaluate the life and indirect damage under landslide as well as the resilience ability against disasters. To illustrate the validity of the proposed methodology, physical and socioeconomic vulnerability levels are analyzed for Seoul, Korea, using the suggested approach. The general trend found in this study indicates that the higher population density areas under a weaker fiscal condition that are located at the downstream of mountainous areas are more vulnerable than the areas in opposite conditions.

Stochastic cost optimization of DNAPL remediation - Field application

A stochastic remediation design optimization methodology implemented in the program Stochastic Cost Optimization Toolkit (SCOToolkit) was successfully applied to evaluate remediation options at the East Gate Disposal Yard (EGDY) at the former Fort Lewis, now Joint Base Lewis-McChord (JBLM), Washington. Non-optimized forward simulations based on calibrated parameters and their uncertainty inferred from data prior to actual thermal source remediation system implementation at the site indicated a low probability of the actual thermal system design meeting remediation criteria in a reasonable time frame. Calibration using additional data collected during thermal treatment reduced prediction uncertainty, but still predicted a high probability of taking more than 100 years to reach compliance criteria using the actual thermal treatment design with no additional remedial action. Stochastic optimization of the thermal treatment design indicated larger treatment areas were needed to capture source mass due to uncertainty in source delineation. The expected cost for the enlarged thermal treatment system was estimated to be

Application of parameter estimation and regionalization methodologies to ungauged basins of the Upper Blue Nile River Basin, Ethiopia

22M, which is nearly twice that of the actual system, suggesting that additional characterization to reduce source delineation uncertainty or consideration of an alternative strategy that is less sensitive to delineation uncertainty may be warranted. Stochastic optimization of whey injection was investigated to accelerate source zone dense nonaqueous phase liquid (DNAPL) dissolution and enhance dissolved plume biodecay. The optimized design indicated a 93% probability of meeting compliance criteria by 2100 with an expected net present value cost of

CLIMATE CHANGE IMPACTS ON WATER RESOURCES IN THE UPPER BLUE NILE RIVER BASIN, ETHIOPIA

4.7M. Whey injection substantially shortened the remediation time compared to no whey injection. The results indicate that the proposed stochastic cost optimization approach is able to reduce expected remediation costs, increase the probability of achieving remediation objectives, and identify data characterization needs.