Worldwide Residential Soil Regulatory Guidance Values for Chlorinated Methanes

In many environmental jurisdictions worldwide, surface soil contamination is regulated using guidance values that specify the maximum amount of pollutant that can be present without prompting a regulatory response. Three of the four chlorinated methanes are among the seven most frequently regulated synthetic surface soil contaminants. There are at least 80 U.S. regulatory jurisdictions and at least 30 international jurisdictions with guidance values for at least one of the chlorinated methanes, yielding as many as 106 values (for dichloromethane) for the same regulatory consideration. This analysis explores the variability of these values. Results indicate that the values span at least 5 orders of magnitude and are distributed in patterns similar to a lognormal random variable, but fit distributions that are statistically distinct. The distributions also contain value clusters that may imply emerging consensus about appropriate regulatory levels or demonstrate the impact of regulatory leadership to suppress variability. Simulation based on the current U.S. Environmental Protection Agency risk analysis model are used to estimate the degree to which value ranges may be attributed to uncertainty in exposure models. Approximately 50% of guidance values fall within the 95% confidence interval uncertainty bounds of risk model results.     

Worldwide Residential Soil Regulatory Guidance Values for Chlorinated Ethanes

Surface soil contamination is often regulated using guidance values that specify the maximum amount of pollutant that can be present without prompting a regulatory response. In the United States, there are at least 89 value sets (and worldwide there are another 29) that provide guidance for at least one of the nine chlorinated ethanes. The most commonly regulated chlorinated ethane is 1,2-dichloroethane (108 values), and it is the third most commonly regulated synthetic organic surface soil contaminant. Pentachloroethane (17 values) is the least regulated chlorinated ethane. Overall, there are at least 690 guidance values for chlorinated ethanes. This analysis explores the origin, magnitude, and form of the variability of these values. Results indicate that the values span from 3.7 to 7.6 orders of magnitude and are distributed in patterns similar to log-normal random variables. Less than 20% of these values are similar to those of national regulatory agencies such as the U.S. EPA or the Canadian Council of Ministers of the Environment, but more than 60% of the values fall within the 95% confidence interval bounds of the uncertainty in U.S. EPA risk model calculations.     

Worldwide Residential Soil Regulatory Guidance Values for Chlorinated Ethenes

Surface soil contamination is often regulated by using guidance values that specify the maximum amount of pollutant that can be present without prompting a regulatory response. In the United States, there are at least 88 value sets, and another 35 worldwide, that provide guidance for at least one chlorinated ethene. Trichloroethene is the most commonly regulated chlorinated ethene (118 values) and may be the most commonly regulated synthetic organic surface soil contaminant. Cis- and trans-1,2-dichloroethene are the least regulated chlorinated ethenes. Overall, there are 617 guidance values for specific chlorinated ethenes plus another 32 for mixed isomers of dichlorethene. This analysis explores the origin, magnitude, and form of the variability of these values. Results indicate that values span from 4.9 to 6.6 orders of magnitude and follow distributions similar to lognormal random variables. However, distributions include value clusters similar to values advocated by the U.S. Environmental Protection Agency (USEPA) or the Canadian Council of Ministers of the Environment (CCME). Although only 9.5% of the regulatory guidance values (RGVs) are identical to USEPA or CCME values, 55% of these fall within the uncertainty bounds estimated for USEPA risk models. Results suggest that stronger national leadership and reduced risk model uncertainty could be effective in reducing the RGV variability of chlorinated ethenes.     

Statistical Analysis of BTEX Surface Soil Regulatory Guidance Values

Residential surface soil regulatory guidance values (RGVs) specify the threshold at which soil contamination requires action. Usually, these are risk-based values based on child ingestion, inhalation, and dermal exposure. Benzene, toluene, ethylbenzene, and xylenes (BTEX) are among the five most commonly regulated soil contaminants in the United States and worldwide. More than 100 regulatory jurisdictions have established surface soil RGVs for BTEX compounds. Analysis of these values indicates that they vary by several orders of magnitude and appear to fit a lognormal random variable model with values well dispersed across the number spans. The RGVs applied to benzene are statistically distinct from those applied to TEX contamination, but the TEX values appear to be statistically indistinguishable. The magnitude of difference between TEX RGVs of different jurisdictions appears to be more significant than differences in the T, E, and X values specified by any one jurisdiction. Although value distributions are dominated by randomness, some contain clusters of points that are unlikely to be random and may represent consensus on appropriate values. Where “consensus clusters” exist, they should be identified and explored. The mechanistic explanations for cluster values may yield methods of reducing RGV variability.     

Identification of Statistically Homogeneous Soil Layers Using Modified Bartlett Statistics

Stationarity or statistical homogeneity is an important prerequisite for subsequent statistical analysis on a given section of a soil profile to be valid. The estimation of important soil statistics such as the variance is likely to be biased if the profile is not properly demarcated into stationary sections. Existing classical statistical tests are inadequate even for simple identification of stationarity in the variance because the spatial variations of soil properties are generally correlated with each other. In this paper, a modified Bartlett statistical test is proposed to provide a more rational basis for rejecting the null hypothesis of stationarity in the correlated case. The accompanying rejection criteria are determined from simulated correlated sample functions and summarized into a convenient form for practical use. A statistical-based soil boundary identification procedure is then developed using the modified Bartlett test statistic. Based on the analysis of a piezocone sounding record, two advantages can be discerned. First, the proposed procedure provides a useful supplement to existing empirical soil classification charts, especially in situations where inherent variability tends to complicate interpretation of soil layers. Second, various key assumptions in geostatistical analysis such as stationarity and choice of trend function can be verified more rigorously using the framework of hypothesis testing.     

Undrained Bearing Capacity of Two-Strip Footings on Spatially Random Soil

A probabilistic study on the interference of two parallel rough rigid strip footings on a weightless soil with a randomly distributed undrained shear strength performed. The problem is studied using the random finite element method, where nonlinear finite element analysis is merged with random field theory within a Monte Carlo framework. The variability of undrained shear strength is characterized by a lognormal distribution and an exponentially decaying spatial correlation length. The estimated bearing capacity statistics of isolated and two footings cases are compared and the effect of footing interference discussed. Although interference between footings on frictionless materials is not very great, the effect is shown to be increased by soil variability and spatial correlation length.     

Reliability Analysis Using Parabolic Failure Surface Approximation

In the second-order reliability method the failure surface is approximated by a general quadratic surface in the neighborhood of the design point. In this paper this general quadratic surface is further approximated by a parabolic surface. Several methods are proposed to obtain the probability content associated with this parabolic failure surface. It is assumed that the basic random variables are Gaussian. The proposed methods can be broadly grouped into: (1) nonasymptotic approximate methods, (2) exact methods, and (3) asymptotic distribution methods. Most of these methods result in a closed-form expression for the failure probability. For nonasymptotic approximations, a least-square approach and an optimal point expansion method using approximate probability density functions of a quadratic form in Gaussian random variables have been proposed. It is shown that such approximations give accurate results without significant numerical effort. Exact results, however, require greater numerical effort. The new asymptotic result is derived for the case when the number of random variables approaches infinity. Several numerical examples are provided to compare the proposed results with existing equivalent results and Monte Carlo simulations.     

Probabilistic Assessment of Slope Stability That Considers the Spatial Variability of Soil Properties

In this paper, a numerical procedure for probabilistic slope stability analysis is presented. This procedure extends the traditional limit equilibrium method of slices to a probabilistic approach that accounts for the uncertainties and spatial variation of the soil strength parameters. In this study, two-dimensional random fields were generated based on a Karhunen-Loève expansion in a fashion consistent with a specified marginal distribution function and an autocorrelation function. A Monte Carlo simulation was then used to determine the statistical response based on the generated random fields. This approach makes no assumption about the critical failure surface. Rather, the critical failure surface corresponding to the input random fields of soil properties is searched during the process of analysis. A series of analyses was performed to verify the application potential of the proposed method and to study the effects of uncertainty due to the spatial heterogeneity on the stability of slope. The results show that the proposed method can efficiently consider the various failure mechanisms caused by the spatial variability of soil property in the probabilistic slope stability assessment.     

Quasi-Monte Carlo Simulation with Low-Discrepancy Sequence for Reinforced Soil Slopes

This paper describes a precise numerical technique to compute the limit state exceedance probability of geosynthetic reinforced soil (GRS) slopes with normally distributed backfill and foundation soils by using the low-discrepancy sequence Monte Carlo (LDSMC) and importance sampling with LDSMC (ISLDSMC) methods. The LDSMC and ISLDSMC methods can effectively compute an accurate limit state exceedance probability of GRS slopes with a limited number of simulations. By using importance sampling, random variables can be generated in an expected failure region, thereby enabling enumeration by the Monte Carlo simulation. The failure region can be searched by the conventional first-order reliability method. To increase the computational efficiency, a low-discrepancy sequence, which is a sequence of quasi-random numbers with uniform distribution, is adopted in this study. The numerical simulation in this study revealed that the LDSMC and ISLDSMC methods can effectively compute an accurate limit state exceedance probability of GRS slopes by performing comparatively fewer simulations than the conventional crude Monte Carlo simulation.     

Reliability Analysis of Laterally Loaded Piles Involving Nonlinear Soil and Pile Behavior

An alternative approach of analyzing laterally loaded piles in the ubiquitous spreadsheet platform is presented. The numerical procedure couples nonlinear pile flexural rigidity (EpIp) with nonlinear p-y analysis. The deterministic study is then extended to carry out reliability analysis, which reflects the uncertainties and correlation structure of the underlying parameters. The reliability index is evaluated based on the alternative intuitive perspective of an expanding equivalent ellipsoid in the original space of the random variables. This paper investigates two modes of failure: deflection and bending moment, and considers non-normal random variables. Spatial variability of the soil medium is accounted for by incorporating an autocorrelation model. The spreadsheet-based reliability approach can also be coupled with stand-alone programs via the response surface method. The probabilities of failure inferred from reliability indices agree well with Monte Carlo simulations. Simple reliability-based design is demonstrated, in which the appropriate pile section or length that satisfies target reliability in one or more limit states is sought.     

Cost of Risk Transfer: Pricing Agreements in Residential Supply Chains

Theoretical and computational studies of supply chains are confined mainly to buyer-supplier dyads. Evaluation of more tiers in the construction sector specifically is also rare, perhaps in part because short-term partnerships are typical. However, supplier selection in residential construction is often conducted in support of multiple subdivision tracts over a comparatively long time-horizon. This paper describes the lumber supply chain for residential construction, extending from the homebuyer to the lumber company. A particular case for a real builder is examined in which a builder adopted a pricing strategy to control their lumber cost risk. The strategy included minimum time periods of fixed pricing, insulating the builder from price fluctuations during those periods. Consideration of supply-chain lead times allows financial risk modeling for the builder-framer/lumber yard–lumber company portion of the supply chain in order to evaluate the cost-effectiveness of this strategy. Historical records of lumber prices were used to conduct Monte Carlo simulations of three tiers of the supply chain. The pricing strategy is shown to result in a risk premium generally in excess of the true commodity price risk.     

Stochastic Free Vibration Response of Soft Core Sandwich Plates Using an Improved Higher-Order Zigzag Theory

In this paper, an improved higher-order zigzag theory for vibration of soft core sandwich plates with random material properties is proposed. The theory satisfies the condition of continuity in transverse shear stresses at all the layer interfaces and transverse shear stress free condition at the top and bottom of the plate, including the transverse flexibility effect of the core. The variation of in-plane displacements through thickness is assumed to be cubic while transverse displacement varies quadratically within the core and constant throughout the faces. The core is modeled as a 3D elastic continuum. An efficient C0 finite element in conjunction with a first-order perturbation approach is developed for the implementation of the proposed plate theory in a random environment and is employed to evaluate the second-order statistics of the eigensolutions by modeling lamina material properties as basic random variables. The mean and standard deviations of natural frequencies and their mode shapes are computed and validated with Monte Carlo simulation.     

Enhanced Soil Flushing for Simultaneous Removal of PAHs and Heavy Metals from Industrial Contaminated Soil

Polycyclic aromatic hydrocarbons (PAHs) and heavy metals are environmental concerns and must be removed to acceptable levels. This paper evaluates different flushing agents to enhance the remediation of soil contaminated with PAHs and heavy metals at a former manufactured gas plant site. Four flushing column tests at a constant hydraulic gradient of 1.2 were conducted using four different flushing agents, which included deionized water, chelant (0.2?M EDTA), surfactant (5% Igepal CA-720), and cyclodextrin (10% hydroxypropyl-β-cyclodextrin or HPCD). Additional column tests using Igepal and HPCD at a lower hydraulic gradient of 0.2 were conducted to investigate the effects of rate-limited desorption or solubilization of PAHs. The results showed that the EDTA produced the maximum metal removal from the soil compared with deionized water, Igepal, and HPCD under different hydraulic gradient conditions. The 0.2?M EDTA flushing solution removed approximately 25–75% of the toxic heavy metals found in the soil. None of the PAHs were removed from the soil when deionized water and EDTA were the flushing solutions. The PAHs removal efficiencies in the Igepal and HPCD systems decreased as the hydraulic gradient decreased. However, the surfactant-enhanced systems were more efficient in removing PAHs from the soil than the HPCD systems under high- and low-hydraulic gradients. The results also demonstrated that the removal of PAHs in surfactant-enhanced systems depended upon the micelles formation, whereas in the HPCD-enhanced systems, it depended upon the sterioselective diffusion of the PAHs to the nonpolar cavity of the HPCD. Overall, this study showed that the contaminant removal in soil flushing systems depends on the flushing solution affinity and selectivity toward the target contaminant and the existing hydraulic gradient condition.     

GaN MOCVD生长速率及表面形貌随生长参数的变化

根据现有氮化镓(GaN)金属有机物化学气相淀积(Metalorganic Chemical Vapor Deposotion)生长动力学理论,结合具体的MOCVD反应腔体的构造,用计算流体力学和动力学蒙特卡罗方法对GaN MOcvD生长过程中的生长速率和表面形貌演变进行了计算机模拟.结果表明,在950～1350 K的温度范围内反应气体充分热分解,是适合GaN外延生长的温度区间;温度低于950 K,反应气体未能充分地分解,导致较低的生长速率;而温度高于1350 K则Ga组分的脱附现象开始变得严重,从而抑制GaN的生长速率.另一方面,较高的v/Ⅲ也会抑制GaN的生长速率.生长过程中表面形貌随时间的演变结果显示,GsN薄膜在高温下(1073～1473 K)为2D层状生长,在1373 K的温度下生长的GaN薄膜表面最为平整.     

Statistical Modeling of Coupled Shear-Moment Resistance for RC Bridge Girders

Many reinforced concrete bridges are posted or restricted to traffic, and repair or replacement decisions for these bridges involves both economical and safety considerations. To avoid the high costs of unnecessary replacement or repair, safety evaluation should be done with the most accurate methods available. Due to variability in material properties, geometrical properties, and methods of analysis, load carrying capacity evaluation may lead to uncertain outcomes. This paper presents a statistical model for combined shear-moment resistance of conventionally reinforced concrete bridge girders with common vintage design details and properties. New statistical data on stirrup spacing variability were developed from field measurements on in-service deck-girder bridges and these were combined with available data in the literature to model resistance uncertainty. The model offers bias factor and coefficient of variation for combined moment and shear carrying capacity per modified compression field theory. AASHTO-LRFD and ACI-318 were utilized to calculate capacity of the selected sections and strength reduction factors in AASHTO-LRFD and ACI-318 were compared using the obtained statistical parameters.     

Finite Difference Method for Computation of 1D Pollutant Migration through Saturated Homogeneous Soil Media

The physical processes such as advection, dispersion, and diffusion and interaction between the solution and the soil solids such as sorption, biodegradation, and retention processes have been considered in the governing equation used in the present study. Finite difference method has been adopted herein to solve the one-dimensional contaminant transport model to predict the pollutant migration through soil in waste landfill. In the finite difference technique, the velocity field is first determined within a hydrologic system, and these velocities are then used to calculate the rate of contaminant migration by solving the governing equation. A total of seven contaminants have been chosen for analysis to represent a wide variety of wastes both organic and inorganic. A computer software CONTAMINATE has been developed for solution of the contaminant transport model. Results of this study have been compared with existing analytical solution for validation of the proposed solution technique. Design charts for liners have also been developed to facilitate the designers. The liner thickness has been optimized by considering the effect of velocity of advection, dispersion coefficient, and geochemical reactions for all the contaminants of this study.     

Monte Carlo Methods for Estimating the Extreme Response of Dynamical Systems

The development of simple accurate, and efficient methods for estimation of the extreme response of dynamical systems subjected to random excitations is discussed in the present paper. The key quantity for calculating the statistical distribution of extreme response is the mean level upcrossing rate function. By exploiting the regularity of the tail behavior of this function, an efficient simulation based methodology for estimating the extreme response distribution function is developed. This makes it possible to avoid the commonly adopted assumption that the extreme value data follow an appropriate asymptotic extreme value distribution, which would be a Gumbel distribution for the models considered in this paper. It is demonstrated that the commonly quoted obstacle against using the standard Monte Carlo method for estimating extreme responses, i.e., excessive CPU time, can be circumvented, bringing the computational efforts down to quite acceptable levels.     

Probabilistic Analysis of Coupled Soil Consolidation

Coupled Biot consolidation theory was combined with the random finite-element method to investigate the consolidation behavior of soil deposits with spatially variable properties in one-dimensional (1D) and two-dimensional (2D) spaces. The coefficient of volume compressibility (mv) and the soil permeability (k) are assumed to be lognormally distributed random variables. The random fields of mv and k are generated by the local average subdivision method which fully takes account of spatial correlation, local averaging, and cross correlations. The generated random variables are mapped onto a finite-element mesh and Monte Carlo finite-element simulations follow. The results of parametric studies are presented, which describe the effect of the standard deviation, spatial correlation length, and cross correlation coefficient on output statistics relating to the overall “equivalent” coefficient of consolidation. It is shown that the average degree of consolidation defined by excess pore pressure and settlement are different in heterogeneous soils. The dimensional effect on the soil consolidation behaviors is also investigated by comparing the 1D and 2D results.     

Field and Laboratory Evaluation of the Impact of Tall Fescue on Polyaromatic Hydrocarbon Degradation in an Aged Creosote-Contaminated Surface Soil

A field study was initiated in 1997 to assess the ability of tall fescue grass to remediate an aged creosote-contaminated surface soil. Field monitoring was combined with aerobic microcosm experiments, microbial enumerations, and plant tissue analysis to determine the impact of tall fescue on the degradation of six polycyclic aromatic hydrocarbons (PAHs), acenaphthene, fluorene, phenanthrene, fluoranthene, pyrene, and chrysene, and to elucidate the mechanisms of remediation. Fescue grass had a beneficial impact on the degradation of all PAHs except phenanthrene. Mean concentrations of the three-ring PAHs, acenaphthene and fluorene, were lower in fescue cells compared to unvegetated cells after 36 months. In microcosms with soil from fescue cells, acenaphthene had a significantly higher degradation rate and lower final concentration after 180 days than in microcosms prepared with soil from unvegetated cells. Mean concentrations of the four-ring PAHs, fluoranthene, pyrene, and chrysene, were statistically similar in the field study; however, the 10th and 20th percentile concentrations were lower in fescue cells during all sampling periods. Microcosm studies showed increased degradation of fluoranthene and pyrene in soil samples taken from tall fescue rhizosphere compared to unvegetated soil and abiotic controls. Degradation of four-ring PAHs was enhanced in the shallow zones (10–15 cm below ground surface) of vegetated cells. The root mass was approximately 35% greater in shallow zones than in medium depth zones (15–21 cm below ground surface). Microbial populations on solid mineral media plates with pyrene and chrysene as the sole carbon source were two times higher in soils from tall fescue plots than from unvegetated soils, suggesting that the increased PAH degradation was a result of increased microbial activity in the rhizosphere. Gas chromatography/mass spectrometry analysis of fescue shoots indicated that no uptake or translocation of PAHs or PAH degradation intermediates into the shoots was occurring.     

Practical Assessment of Real-Time Impact Point Estimators for Smart Weapons

There are numerous ways to estimate the trajectory and subsequent impact point of a projectile. Some complex methods are highly accurate and require a lot of input data while others are fairly trivial and less accurate but require minimal input data. Projectile impact point predictors (IPPs) have three primary error sources: model error, parameter error, and initial state error. While model error typically shrinks as model complexity increases, parameter and initial state errors grow with increasing model complexity. Since all input data feeding an IPP are uncertain to some level, the ideal IPP for an overall situation is not clear cut by any means. This paper examines several different projectile IPPs that span the range of complex nonlinear rigid projectile models to simple vacuum point mass models with the intent to better understand relative merits of each algorithm in relation to the other algorithms and as a function of parameter uncertainty and initial state error. Monte Carlo simulation is employed to compute impact point statistics as a function of the range to the target for an indirect fire 155-mm spin stabilized round. For this specific scenario, results indicated neglecting physical phenomena in the formulation of the equations of motion can degrade impact point prediction, especially early in the flight. Adding uncertainty to the parameters and states induces impact point errors that dominate model error contributions. Impact point prediction errors scaled linearly with parameter and state errors. All IPPs investigated converged to the actual impact point as the time at which the estimate took place approached the time of impact.