Colloid-Associated Groundwater Contaminant Transport in Homogeneous Saturated Porous Media: Mathematical and Numerical Modeling

During the past two decades, significant efforts have been made to study contaminant transport in the presence of colloids. Several researchers reported that colloidal particles could enhance the migration of contaminants in groundwater by reducing retardation factor. When the colloidal particles are present in the aquifer, the subsurface system can be considered as a three-phase system with two solid phases and an aqueous phase. The interaction between contaminants, colloids, and solid matrix should be considered in assessing the fate and transport of the contaminant in the groundwater flow system. In this study, a one-dimensional numerical model is developed by employing a fully implicit finite difference method. This model is based on mass balance equations and mass partition mechanisms between the carriers and solid matrix, as well as between the carriers and contaminants in a saturated homogeneous porous medium. This phenomenon is presented by two approaches: equilibrium approach and fully kinetic first-order approach. The formulation of the model can be simplified by employing equilibrium partitioning of particles. However, contaminant transport can be predicted more accurately in realistic situations by kinetic modeling. To test the sensitivity of the model, the effect of the various chemical and physical coefficients on the migration of contaminant was investigated. The results of numerical modeling matched favorably with experimental data reported in the literature.     

Bioremediation With Oxygen-Releasing Solids: A Mathematical Model

The use of oxygen releasing solids in passive wells has become of considerable interest in the bioremediation of groundwater contaminated with hydrocarbons and other biodegradable contaminants. Labor, operating, and maintenance costs of this technology are quite competitive with more conventional techniques in many instances. Because of the small rates of mass transport by transverse dispersion, however, a pin-stripe pattern of alternating contaminant and oxygen distribution is expected downgradient of a line of wells which is unacceptable if it extends beyond the point of compliance downgradient. A mathematical model is presented which permits determination of the maximum well spacing in an array of wells which will yield effective remediation within a specified distance of a plume of contaminated groundwater passing through the array. Transverse dispersion of both oxygen and the contaminant is assumed, and biodegradation is modeled by means of Monod kinetics. Longitudinal numerical dispersion is minimized by the use of a asymmetrical upwind algorithm for modeling advection. Modeling results are presented showing the dependence of model behavior on input parameters.     

Estimating contaminant attenuation half-lives in alluvial groundwater systems

One aspect of describing contamination in an alluvial aquifer is estimating changes in concentrations over time. A variety of statistical methods are available for assessing trends in contaminant concentrations. We present a method that extends trend analysis to include estimating the coefficients for the exponential decay equation and calculating contaminant attenuation half-lives. The conceptual model for this approach assumes that the rate of decline is proportional to the contaminant concentration in an aquifer. Consequently, the amount of time to remove a unit quantity of the contaminant inventory from an aquifer lengthens as the concentration decreases. Support for this conceptual model is demonstrated empirically with log-transformed time series of contaminant data. Equations are provided for calculating system attenuation half-lives for non-radioactive contaminants. For radioactive contaminants, the system attenuation half-life is partitioned into the intrinsic radioactive decay and the concentration reduction caused by aquifer processes. Examples are presented that provide the details of this approach. In addition to gaining an understanding of aquifer characteristics and changes in constituent concentrations, this method can be used to assess compliance with regulatory standards and to estimate the time to compliance when natural attenuation is being considered as a remediation strategy. A special application of this method is also provided that estimates the half-life of the residence time for groundwater in the aquifer by estimating the half life for a conservative contaminant that is no longer being released into the aquifer. Finally, the ratio of the half-life for groundwater residence time to the attenuation half-life for a contaminant is discussed as a system-scale retardation factor which can be used in analytical and numerical modeling.     

An Interval-Parameter Fuzzy-Stochastic Programming Approach for Municipal Solid Waste Management and Planning

In this study, an integrated fuzzy-stochastic linear programming model is developed and applied to municipal solid waste management. Methods of chance-constrained programming and fuzzy linear programming are incorporated within a general interval-parameter mixed-integer linear programming framework. It improves upon the existing optimization methods with advantages in uncertainty reflection, data availability, and computational requirement. The model can be used for answering questions related to types, times and sites of solid waste management practices, with the objective of minimizing system costs over the planning horizon. The model can effectively reflect dynamic, interactive, and uncertain characteristics of municipal waste management systems. In its solution process, the model is transformed into two deterministic submodels, corresponding to upper and lower bounds of the desired objective function values under a given significance level, based on an interactive algorithm. Results of the method's application to a hypothetical case indicate that reasonable outputs have been obtained. It demonstrates the practical applicability of the proposed methodology.     

Experimental Methodology to Assess Contaminant Diffusion in Rock Mass

Monitoring changes in electrical conductivity (EC) of aqueous phase, due to contaminant diffusion through porous media, is one of the techniques followed by researchers to understand the migration mechanism. However, the contaminant diffusion in the rock mass is a slow process and hence detecting small changes in conductivity with the help of conventional laboratory conductivity meters is quite difficult. With this in view, an experimental methodology to monitor diffusion of contaminant(s) through the intact and fractured rock mass, with the help of a USDTS (Ultra-Sensitive Devices and Technical Services) conductivity meter, has been developed. Results have been validated with those obtained from the ion chromatograph (IC) technique and a good agreement has been noted. The study demonstrates usefulness of the proposed methodology for online monitoring of contaminant migration through the porous media.     

A Method for Designing Upgradient Groundwater Monitoring Networks

A graphical heuristic was devised for locating upgradient groundwater monitoring wells near landfills. Utilizing computer-simulated contaminant plumes, the heuristic considers the direction of groundwater flow relative to the shape of a landfill, the location of the downgradient migration boundary used for configuring detection wells, and uniformity of spatial coverage. The heuristic positions upgradient wells far enough from a landfill to avoid contamination, but close enough to measure ambient water quality near the landfill. It can be adapted to nonuniform flow fields, nonlinear migration boundaries, and irregularly shaped landfills. An application to a rectangular landfill, oriented at various angles to the direction of groundwater flow, demonstrates the utility of the approach.     

Uncertainty based optimal monitoring network design for a chlorinated hydrocarbon contaminated site

An application of a newly developed optimal monitoring network for the delineation of contaminants in groundwater is demonstrated in this study. Designing a monitoring network in an optimal manner helps to delineate the contaminant plume with a minimum number of monitoring wells at optimal locations at a contaminated site. The basic principle used in this study is that the wells are installed where the measurement uncertainties are minimum at the potential monitoring locations. The development of the optimal monitoring network is based on the utilization of contaminant concentration data from an existing initial arbitrary monitoring network. The concentrations at the locations that were not sampled in the study area are estimated using geostatistical tools. The uncertainty in estimating the contaminant concentrations at such locations is used as design criteria for the optimal monitoring network. The uncertainty in the study area was quantified by using the concentration estimation variances at all the potential monitoring locations. The objective function for the monitoring network design minimizes the spatial concentration estimation variances at all potential monitoring well locations where a monitoring well is not to be installed as per the design criteria. In the proposed methodology, the optimal monitoring network is designed for the current management period and the contaminant concentration data estimated at the potential observation locations are then used as the input to the network design model. The optimal monitoring network is designed for the consideration of two different cases by assuming different initial arbitrary existing data. Three different scenarios depending on the limit of the maximum number of monitoring wells that can be allowed at any period are considered for each case. In order to estimate the efficiency of the developed optimal monitoring networks, mass estimation errors are compared for all the three different scenarios of the two different cases. The developed methodology is useful in coming up with an optimal number of monitoring wells within the budgetary limitations. The methodology also addresses the issue of redundancy, as it refines the existing monitoring network without losing much information of the network. The concept of uncertainty-based network design model is useful in various stages of a potentially contaminated site management such as delineation of contaminant plume and long-term monitoring of the remediation process.     

Cost-Benefit Analysis and the environment: The time horizon is of the essence

Cost-Benefit Analysis is a key tool for evaluating welfare gains or losses from an investment. It is now well established that environmental impacts are crucial to consider the full welfare implications of a project. Debate has focussed on approaches to improve the valuation of environmental impacts, and controversy in the discounting of future impacts to present values. The issue of the time horizon of analysis is frequently overlooked. The framing of the time horizon has major implications, as environmental costs and benefits often accrue in the long-term. The technical aspects of setting the time horizon are reviewed, along with updates to practice guidance, noting the longer time horizons now becoming typical. It is demonstrated that the time horizon can have a considerable impact on results, even more substantial than the discount rate. While uncertainty is noted as a technical challenge to longer-term analysis, the use of scenarios and sensitivity testing are noted as an appropriate response. For projects with long-term environmental effects, such as those related to air pollution, climate change and ecosystem damages, it is recommended to use timescales of 100+ years for economic evaluation of the impact. Failing to fully capture these long-term welfare gains and losses will distort analysis with a bias towards those projects that are more carbon-intensive, or environmentally damaging. Such a bias would undermine not only the evaluation, but welfare and sustainable development in general.     

The design of long-term air quality monitoring networks in urban areas using a spatiotemporal approach

The implementation and maintenance of an air pollution monitoring program can be expensive and time consuming, especially when the aim is for long-term monitoring over a significant area. Consequently, it is essential that sites are optimized to provide the best representative cover while minimizing costs. In the past, there has been a tendency to locate sampling stations at pollution hot-spots. While this is acceptable for determining a maximum potential exposure or identifying the extent of a risk, there are limitations to this approach when assessing the potential impact of any future abatement strategies or determining the level of exposure outside the vicinity. This paper presents an approach in which representative air quality assessments can be undertaken for an urban area using the minimum number of measurement sites. A novel methodology is described that involves site selection to capture the maximum variance in measured pollutants, while minimizing spatiotemporal autocorrelation between the selected sites. A case study is presented for Yazd, Iran. Overall, the results show that the proposed methodology can be effective and enable the long-term monitoring of air pollution to be undertaken on a cost-effective basis in urban areas. In addition, there is the potential for the methodology to be utilized for other forms of pollution (e.g., water, soil, and noise).     

A Water Quality Monitoring Network Design Methodology for the Selection of Critical Sampling Points: Part I

The principal instrument to temporally and spatially manage water resources is a water quality monitoring network. However, to date in most cases, there is a clear absence of a concise strategy or methodology for designing monitoring networks, especially when deciding upon the placement of sampling stations. Since water quality monitoring networks can be quite costly, it is very important to properly design the monitoring network so that maximum information extraction can be accomplished, which in turn is vital when informing decision-makers. This paper presents the development of a methodology for identifying the critical sampling locations within a watershed. Hence, it embodies the spatial component in the design of a water quality monitoring network by designating the critical stream locations that should ideally be sampled. For illustration purposes, the methodology focuses on a single contaminant, namely total phosphorus, and is applicable to small, upland, predominantly agricultural-forested watersheds. It takes a number of hydrologic, topographic, soils, vegetative, and land use factors into account. In addition, it includes an economic as well as logistical component in order to approximate the number of sampling points required for a given budget and to only consider the logistically accessible stream reaches in the analysis, respectively. The methodology utilizes a geographic information system (GIS), hydrologic simulation model, and fuzzy logic.     

Comparison of field-observed and model-predicted plume trends at fuel-contaminated sites: implications for natural attenuation rates

Subsequent to modeling of natural attenuation processes to predict contaminant trends and plume dynamics, monitoring data were used to evaluate the effectiveness of natural attenuation at reducing contaminant concentrations in groundwater at seven fuel-contaminated sites. Predicted and observed contaminant trends at seven sites were compared in order to empirically assess the accuracy of some fundamental model input parameters and assumptions. Most of the models developed for the study sites tended to overestimate plume migration distance, source persistence, and/or the time required for the benzene, toluene, ethyl benzene, and xylenes (BTEX) plumes to attenuate. Discrepancies between observed and predicted contaminant trends and plume behavior suggested that the influence of natural attenuation process may not have been accurately simulated. The conservatism of model simulations may be attributed to underestimation of natural source weathering rates, overestimation of the mass of contaminant present in the source area, and/or use of overly conservative first-order solute decay rates.     

Energy Security: A Robust Optimization Approach to Design a Robust European Energy Supply via TIAM-WORLD

Energy supply routes to a given region are subject to random events, resulting in partial or total closure of a route (corridor). For instance, a pipeline may be subject to technical problems that reduce its capacity. Or, oil supply by tanker may be reduced for political reasons or because of equipment mishaps at the point of origin or again, by a conscious decision by the supplier in order to obtain economic benefits. The purpose of this article is to formulate a simplified version of the above issue that mainly addresses long-term uncertainties. The formulation is done via a version of the TIAM-WORLD Integrated Model, modified to implement the approach of robust optimization. In our case, the approach can be interpreted as a revival of chance-constrained programming under the name of distributionally robust, or ambiguous, chance-constrained programming. We apply the approach to improve the security of supply to the European Energy system. The resulting formulation provides several interesting features regarding the security of EU energy supply and has also the advantage to be numerically tractable.     

A quantitative methodology for indexing environmental sensitivity and pollution potential

A methodology for rating the suitability of sites forthe location of industrial facilities is formulated and appliedto the case of a coal-fired power plant location. The methodologycomprises two major interlinked components: the environmentalplant location indexing component, which involves theidentification, scaling and weighting of environmentalsensitivity factors; and the impact analysis component, whichinvolves the superimposition of the pollution generation impactsof an industrial facility on spatially gridded zones of variousenvironmental sensitivities. For each rectangular areal unitdefined by a square grid, the Unit Pollution Potential Index isdetermined by the severity and distribution of key environmentalsensitivity factors and the coverage of superimposed pollutanteffects as determined by contaminant fate and transport models.For any alternative site of a planned facility, the summation ofthe unit indices over the area of influence of the facilityprovides the quantitative Pollution Index, which can be used as abasis for comparison of alternative sites for planned facilities.For this paper, this methodology is applied to the hypotheticalcase of the siting of a coal-fired power plant in thenortheastern region of the United States, in which threealternative sites are considered. The three sites: A, B and Cyielded indices of 47.83, 47.91 and 47.6, respectively, indicatingthat site C is the most suitable for siting the power plant.     

Normalization procedures for sediment contaminants in spatial and temporal trend monitoring

Rational pollution, or the effectiveness of natural attenuation assessments based upon estimating the degree of contamination, critically depends on the basis of a sound normalization to take into account heterogeneous sedimentary environments. By normalizing the measured contaminant concentration patterns for the sediment characteristics, the inherent variability can be reduced and so allow a more meaningful assessment of both the spatial distributions and the temporal trends. A brief overview and guidance in the methodology available for choosing an appropriate site-specific normalization approach is presented. This is followed by general recommendations with respect to the choice of normalizer and the necessary geochemical and statistical quality assurance methods, with support from the results of recent international intercomparison exercises within the QUASH (Quality Assurance of Sample Handling) programme, as well as discussions within the International Commission on the Exploration of the Sea (ICES) working groups. The most important of these recommendations is the use of a two-tiered normalization approach including wet sieving (<63 microm), followed by an additional geochemical co-factor normalization.     

Simulation of the migration and transformation of petroleum pollutants in the soils of the Loess plateau: a case study in the Maling oil field of northwestern China

We developed a coupled water–oil simulation model to simulate the migration and transformation of petroleum-derived contaminants in the soil of the Xifeng oil field. To do so, we used the HYDRUS-2D model, which simulates the diffusion, adsorption or desorption, and microbial degradation of petroleum-derived hydrocarbons in the soil–water system. The saturated soil hydraulic conductivity of petroleum-derived pollutants was 0.05 cm?day^?1, which is about 1 to 2 % of the soil moisture permeability coefficient. Our numerical simulation results show that spilled crude oil was mainly concentrated in the surface horizons of the soil. The organic pollutant concentration tended to be highest nearest to the pollution source. The pollutant migration was generally concentrated within the top 20 to 30 cm of the soil, with the maximum concentration in the top 5 cm of the soil. With passing time, the pollutant accumulation increased and the adsorption and degradation functions reached a dynamic balance with the input rate at depths greater than 30 cm below the soil surface. The oil-derived pollutants totaled 50 to 100 mg?kg^?1 under the dynamic balance condition, which occurred after 20 to 30 years. The petroleum-derived pollutant concentration in the loess soil was inversely correlated with the horizontal distance from the oil well, and the concentration decreased greatly at a distance greater than 40 m from the well.     

Designing a conservation reserve network with minimal fragmentation: A linear integer programming approach

In the biological conservation literature, the optimum reserve site selection problem has often been addressed by using the prototype set covering and maximal covering formulations, assuming that representation of species is the only criterion in site selection. This approach usually results in a small but highly fragmented reserve, which is not useful for practical conservation planning. To improve the chances of species' persistence, it may be desirable to reduce habitat fragmentation. This paper presents a linear integer programming formulation to minimize spatial gaps between selected sites in a reserve network, which is applied to a data set on breeding birds. The authors express their willingness to share the database used in this study. Those readers who wish to have access to the data may contact Robert A. Briers at r.briers@napier.ac.uk.     

Applying mathematical programming to reserve selection

During the last 10–15 years heuristic methods have been developed for problems in optimal reserve selection. Unfortunately, there is no guarantee that heuristics will find optimal solutions. In recognizing this limitation, analysts have formulated reserve selection problems as set covering problems, for which matrix reduction and integer (0/1) programming can be used to find optimal solutions. In this paper we restate the set covering formulation and review solution techniques. A new 0/1 programming model, which is a generalization of the set covering model, is then presented and applied to a hypothetical reserve selection problem. Objectives of minimizing the number of sites selected and maximizing the number of species represented are addressed. Solutions which characterize the tradeoffs between these objectives provide a rich set of information for planners and decision makers. Applications of mathematical programming to related problems in land use planning and forestry are also discussed.     

Acid soil indicators in forest soils of the Cherry River Watershed, West Virginia

Declining forest health has been observed during the past several decades in several areas of the eastern USA, and some of this decline is attributed to acid deposition. Decreases in soil pH and increases in soil acidity are indicators of potential impacts on tree growth due to acid inputs and Al toxicity. The Cherry River watershed, which lies within the Monongahela National Forest in West Virginia, has some of the highest rates of acid deposition in Appalachia. East and West areas within the watershed, which showed differences in precipitation, stream chemistry, and vegetation composition, were compared to evaluate soil acidity conditions and to assess their degree of risk on tree growth. Thirty-one soil pits in the West area and 36 pits in the East area were dug and described, and soil samples from each horizon were analyzed for chemical parameters. In A horizons, East area soils averaged 3.7 pH with 9.4 cmol_c kg^???1 of acidity compared to pH 4.0 and 6.2 cmol_c kg^???1 of acidity in West area soils. Extractable cations (Ca, Mg, and Al) were significantly higher in the A, transition, and upper B horizons of East versus West soils. However, even with differences in cation concentrations, Ca/Al molar ratios were similar for East and West soils. For both sites using the Ca/Al ratio, a 50% risk of impaired tree growth was found for A horizons, while a 75% risk was found for deeper horizons. Low concentrations of base cations and high extractable Al in these soils translate into a high degree of risk for forest regeneration and tree growth after conventional tree harvesting.     

Interval-parameter Fuzzy-stochastic Semi-infinite Mixed-integer Linear Programming for Waste Management under Uncertainty

An interval-parameter fuzzy-stochastic semi-infinite mixed-integer linear programming (IFSSIP) method is developed for waste management under uncertainties. The IFSSIP method integrates the fuzzy programming, chance-constrained programming, integer programming and interval semi-infinite programming within a general optimization framework. The model is applied to a waste management system with three disposal facilities, three municipalities, and three periods. Compared with the previous methods, IFSSIP have two major advantages. One is that it can help generate solutions for the stable ranges of the decision variables and objective function value under fuzzy satisfaction degree and different levels of probability of violating constraints, which are informative and flexible for solution users to interpret/justify. The other is that IFSSIP can not only handle uncertainties through constructing fuzzy and random parameter, but also reflect dynamic features of the system conditions through interval function of time over the planning horizon. By comparing IFSSIP with interval-parameter mixed-integer linear semi-infinite programming and parametric programming, the IFSSIP method is more reasonable than others.     

Using a relative health indicator (RHI) metric to estimate health risk reductions in drinking water

When a new drinking water regulation is being developed, the USEPA conducts a health risk reduction and cost analysis to, in part, estimate quantifiable and non-quantifiable cost and benefits of the various regulatory alternatives. Numerous methodologies are available for cumulative risk assessment ranging from primarily qualitative to primarily quantitative. This research developed a summary metric of relative cumulative health impacts resulting from drinking water, the relative health indicator (RHI). An intermediate level of quantification and modeling was chosen, one which retains the concept of an aggregated metric of public health impact and hence allows for comparisons to be made across “cups of water,” but avoids the need for development and use of complex models that are beyond the existing state of the science. Using the USEPA Six-Year Review data and available national occurrence surveys of drinking water contaminants, the metric is used to test risk reduction as it pertains to the implementation of the arsenic and uranium maximum contaminant levels and quantify “meaningful” risk reduction. Uranium represented the threshold risk reduction against which national non-compliance risk reduction was compared for arsenic, nitrate, and radium. Arsenic non-compliance is most significant and efforts focused on bringing those non-compliant utilities into compliance with the 10 μg/L maximum contaminant level would meet the threshold for meaningful risk reduction.