Estimation of effective cleanup radius for soil‐vapor extraction systems

Soil‐vapor extraction (SVE) is a standard and effective in situ treatment for the removal of volatile contaminants from vadose‐zone soil. The duration of SVE operation required to reach site closure is quite variable, however, ranging up to several years or more. An understanding of the contaminant recovery rate as a function of distance from each vapor‐extraction well allows SVE systems to be designed so that cleanup goals can be achieved within a specified time frame.

A simple one‐dimensional model has been developed that provides a rough estimate of the effective cleanup radius (defined as “the maximum distance from a vapor extraction point through which sufficient air is drawn to remove the required fraction of contamination in the desired time") for SVE systems. Because the model uses analytical rather than numerical methods, it has advantages over more sophisticated, multidimensional models, including simplicity, speed, versatility, and robustness.

The contaminant removal rate at a given distance from the vapor‐extraction point is assumed to be a function of the local rate of soil‐gas flow, the contaminant soil concentration, and the contaminant volatility. Soil‐gas flow rate as a function of distance from the vapor‐extraction point is estimated from pilot test data by assuming that the infiltration of atmospheric air through the soil surface is related to the vacuum in the soil. Although widely applicable, the model should be used with some caution when the vadose zone is highly stratified or when venting contaminated soil greater than 30 ft below grade. Since 1992, Groundwater Technology, Inc. has been using this model routinely as a design tool for SVE systems.     

Indicator plant species selection for monitoring the impact of climate change based on prediction uncertainty

To develop a long-term volunteer-based system for monitoring the impacts of climate change on plant distributions, potential indicator plants and monitoring sites were assessed considering habitat prediction uncertainty. We used species distribution models (SDMs) to project potential habitats for 19 popular edible wild plants in Japan. Prediction uncertainties of SDMs were assessed using three high-performance modeling algorithms and 19 simulated future climate data. SDMs were developed using presence/absence records, four climatic variables, and five non-climatic variables. The results showed that prediction uncertainties for future climate simulations were greater than those from the three different modeling algorithms. Among the 19 edible wild plant species, six had highly accurate SDMs and greater changes in occurrence probabilities between current and future climate conditions. The potential habitats of these six plants under future climate simulations tended to shift northward and upward, with predicted losses in potential southern habitats. These results suggest that these six plants are candidate indicators for long-term biological monitoring of the impacts of climate change. If temperature continuously increases as predicted, natural populations of these plants will decline in Kyushu, Chugoku and Shikoku districts, and in low altitudes of Chubu and Tohoku districts. These results also indicate the importance of occurrence probability and prediction uncertainty of SDMs for selecting target species and site locations for monitoring programs. Sasa kurilensis, a very popular and widespread dominant scrub bamboo in the cool-temperate regions of Japan, was found to be the most effective plant for monitoring.     

A fugacity model for soil vapor extraction

A relatively simple fugacity‐based model is developed for predicting the effectiveness of soil vapor extraction (SVE), an in situ soil remediation technique used for removing volatile organic chemicals from unsaturated soils. The model accounts for the natural processes of volatilization, degradation, and leaching, as well as gas‐phase advection due to SVE. Model predictions are compared with published data for a field‐scale SVE operation. An exponentially declining sweep efficiency for SVE is introduced to improve the fit between simulated and measured soil extraction gas concentrations. The model permits the magnitudes of the various processes affecting the fate and transport of 1,1,1‐trichloroethane (TCA) and perchloroethylene (PCE) in soils to be evaluated. Without SVE, the dominant removal process is biodegradation, but the rate of degradation is low, requiring more than 9 years for soil gas concentrations from a spill of about 13 kg of TCA to be reduced to a concentration of 0. 001 μg/l. The removal time may be reduced to only about 2 years if SVE is used. Moreover, substantially less chemical leaches into the underlying groundwater, greatly reducing the potential extent of ground water contamination.     

Simplified methods for monitoring petroleum‐contaminated ground water and soil vapor

Portable meters and simplified gas Chromatographic (GC) techniques were investigated for monitoring volatile hydrocarbon (HC), CO₂, and O₂, concentrations in groundwater, exhaust gases, and soil vapor during in situ remediation using soil vapor extraction (SVE) and air sparging (AS). Results of groundwater samples analyzed in‐house using a headspace technique compared well to split samples analyzed by a certified analytical laboratory (r² = 0.94). SVE exhaust gas HC and CO₂ concentrations measured using a GT201 portable HC/O₂ meter and a RA‐411A meter (GasTech), respectively, were highly correlated with in‐house laboratory GC analyses (r² = 0.91). O₂ concentrations fell in a small range and meter analyses were not well correlated with laboratory analyses. Results of soil gas monitoring were not as well correlated as those for exhaust gases for HC, CO₂, or O₂, perhaps due to environmental conditions such as changes in relative humidity or the wider range of soil gas values. Overall, the meters were good indicators of vapor contamination, they greatly simplified estimates of total HC mass removal, and they allowed estimates of the biological contribution to contaminant removal during the remediation process.     

Characterization and Simulation of Uncertain Frequency Distributions: Effects of Distribution Choice,Variability, Uncertainty,and Parameter Dependence

We use bootstrap simulation to characterize uncertainty in parametric distributions, including Normal, Lognormal, Gamma, Weibull, and Beta, commonly used to represent variability in probabilistic assessments. Bootstrap simulation enables one to estimate sampling distributions for sample statistics, such as distribution parameters, even when analytical solutions are not available. Using a two-dimensional framework for both uncertainty and variability, uncertainties in cumulative distribution functions were simulated. The mathematical properties of uncertain frequency distributions were evaluated in a series of case studies during which the parameters of each type of distribution were varied for sample sizes of 5, 10, and 20. For positively skewed distributions such as Lognormal, Weibull, and Gamma, the range of uncertainty is widest at the upper tail of the distribution. For symmetric unbounded distributions, such as Normal, the uncertainties are widest at both tails of the distribution. For bounded distributions, such as Beta, the uncertainties are typically widest in the central portions of the distribution. Bootstrap simulation enables complex dependencies between sampling distributions to be captured. The effects of uncertainty, variability, and parameter dependencies were studied for several generic functional forms of models, including models in which two-dimensional random variables are added, multiplied, and divided, to show the sensitivity of model results to different assumptions regarding model input distributions, ranges of variability, and ranges of uncertainty and to show the types of errors that may be obtained from mis-specification of parameter dependence. A total of 1,098 case studies were simulated. In some cases, counter-intuitive results were obtained. For example, the point value of the 95th percentile of uncertainty for the 95th percentile of variability of the product of four Gamma or Weibull distributions decreases as the coefficient of variation of each model input increases and, therefore, may not provide a conservative estimate. Failure to properly characterize parameter uncertainties and their dependencies can lead to orders-of-magnitude mis-estimates of both variability and uncertainty. In many cases, the numerical stability of two-dimensional simulation results was found to decrease as the coefficient of variation of the inputs increases. We discuss the strengths and limitations of bootstrap simulation as a method for quantifying uncertainty due to random sampling error.     

Comparison,Validation, and Use of Models for Predicting Indoor Air Quality from Soil and Groundwater Contamination

The use of models to predict indoor air quality and health risk for the soil vapor transport to indoor air pathway is commonplace; however, there is significant uncertainty surrounding processes and factors affecting this pathway, and the accuracy of models used. Available screening models were evaluated through a review of model characteristics and sensitivity, and through comparisons to measured conditions at field sites. Model simulations and comparisons to field data indicate that the vapor attenuation ratio (α) is highly sensitive to certain processes (e.g., biodegradation and ad-vection) and input parameters. Comparisons of model predicted to measured a values indicate that models based on the Johnson and Ettinger (1991) framework in most cases result in predictions that are conservative by up to one to two orders of magnitude for field sites that were assessed, providing that appropriate input parameters are used. However, for sites where the advection potential is high, these models may not be conservative. The potential for advective transport of vapors into building may be significant for sites with shallow contamination, high permeability soil and foundation and high building underpressurization. The paper concludes with possible tiered management framework for the soil vapor pathway.     

Helium Tracer Tests for Assessing Contaminant Vapor Recovery and Air Distribution During In Situ Air Sparging

Helium tracer tests are used as an alternative to soil-gas pressure measurements to assess the effectiveness of soil vapor extraction (SVE) systems for capturing contaminant vapors liberated by in situ air sparging (IAS). The tracer approach is simple to conduct and provides more direct and reliable measures than the soil-gas pressure approach. The tracer test described here can be used to both determine SVE system capture efficiency and to evaluate air distribution during IAS pilot tests. The tests can also be conducted on operating, full-scale systems to confirm system performance. In addition, the tests can be easily repeated, which allows system parameters to be modified and the impact of those modifications to be quickly assessed. Whether used alone or in conjunction with other diagnostic tools, helium tracer tests provide an important measure of IAS system performance.     

Phytoremediation removal rates of benzene,toluene, and chlorobenzene

Phytoremediation is a sustainable remedial approach, although performance efficacy is rarely reported. In this study, we assessed a phytoremediation plot treating benzene, toluene, and chlorobenzene. A comparison of the calculated phytoremediation removal rate with estimates of onsite contaminant mass was used to forecast cleanup periods. The investigation demonstrated that substantial microbial degradation was occurring in the subsurface. Estimates of transpiration indicated that the trees planted were removing approximately 240,000 L of water per year. This large quantity of water removal implies substantial removal of contaminant due to large amounts of contaminants in the groundwater; however, these contaminants extensively sorb to the soil, resulting in large quantities of contaminant mass in the subsurface. The total estimate of subsurface contaminant mass was also complicated by the presence of non-aqueous phase liquids (NAPL), additional contaminant masses that were difficult to quantify. These uncertainties of initial contaminant mass at the site result in large uncertainty in the cleanup period, although mean estimates are on the order of decades. Collectively, the model indicates contaminant removal rates on the order of 10^?2–10⁰ kg/tree/year. The benefit of the phytoremediation system is relatively sustainable cleanup over the long periods necessary due to the presence of NAPL.     

Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks

Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is “non-intrusive” and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design.     

Vapor Extraction/Bioventing Sequential Treatment of Soil Contaminated with Volatile and SemiVolatile Hydrocarbon Mixtures

A cost-effective removal strategy was studied in bench-scale columns that involved vapor extraction (SVE) and bioventing (SBV) sequential treatment of toluene- and decane-contaminated soil. By using GC analysis to measure hydrocarbon concentrations, CO₂, and O₂ content values in the outlet gas, the removal kinetics were determined as was the contribution of evaporation and biodegradation to the removal of contaminants from soil. The effect of operating mode on treatment performance was studied at a continuous air flow and consecutively at two different flow rates, and compared with an intermittent (pulse) flow rate. The two-rate flow was required due to the inhibitory effect of toluene on indigenous microorganisms at above 75% of the toluene saturation concentrations in the gas phase. The intermittent flow was controlled by the O₂ content values in the soil gas, which at above 4% did not limit biodegradation. To reach comparable removal efficiency at the constant flow, about three times less air was required for toluene than for decane. This air volume could be reduced, in the case of decane, by a factor of 1.6 and 2.9, at the two-rate and intermittent flow, respectively. A higher contribution of biodegradation to the overall removal of hydrocarbon will lower hydrocarbon concentrations in the off-gases to be treated. Together with the decreased amount of air used, this can reduce the overall remediation costs. The overall process can be better understood by determining the degree of contaminants removal by evaporation and biodegradation in the experimental set up.     

Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change

Forecasts of species range shifts under climate change are fraught with uncertainties and ensemble forecasting may provide a framework to deal with such uncertainties. Here, a novel approach to partition the variance among modeled attributes, such as richness or turnover, and map sources of uncertainty in ensembles of forecasts is presented. We model the distributions of 3837 New World birds and project them into 2080. We then quantify and map the relative contribution of different sources of uncertainty from alternative methods for niche modeling, general circulation models (AOGCM), and emission scenarios. The greatest source of uncertainty in forecasts of species range shifts arises from using alternative methods for niche modeling, followed by AOGCM, and their interaction. Our results concur with previous studies that discovered that projections from alternative models can be extremely varied, but we provide a new analytical framework to examine uncertainties in models by quantifying their importance and mapping their patterns.     

Parametric studies on bioengineering effects of tree root-based suction on ground behaviour

Using native vegetation to improve soil stiffness, stabilise slopes and control erosion is a rapidly evolving process. A theoretical model previously developed by the authors for the rate of tree root water uptake together with an associated numerical simulation is used to study the effects of a wide range of soil, tree, and atmospheric parameters on partially saturated ground. The influence of different parameters on the maximum initial rate of root water uptake is investigated through parametric and sensitivity analyses. Field measurements taken from previously published literature are compared with numerical predictions for validation. The rate of selected parameters such as potential transpiration and its distribution, suction at wilting point, the coefficient of permeability and the distribution of root length density are studied in detail. The analysis shows that the rate of potential transpiration increases the soil matric suction and ground settlement, while the potential transpiration rate has an insignificant effect on the distribution of soil suction. Root density distribution factors affect the size of the influence zone. Suction at the wilting point increases the soil matric suction and ground settlement, whereas the saturation permeability decreases the maximum soil matric suction generated. The analysis confirms that the most sensitive parameters, including the coefficients of the tree root system, the transpiration rate, the permeability of the soil and its suction at the wilting point should be measured or estimated accurately for an acceptable prediction of ground conditions in the vicinity of trees.     

Modelling of landfill gas generation: a review

Landfill gas generation models apply utilizes several different parameters to project Methane (CH₄) generation from a specific mass of disposed waste over a time period in a landfill site. These models are used for better estimating the size of landfill gas (LFG) collection systems, monitoring objectives, assessments, and prognostications. Compared to other options to estimate and control LFG production (such as the application of the test wells), models offer advantages due to the relatively prompt results and cost-effectiveness. Over the recent years, developing LFG generation models has become precedence for the LFG industry. The main trouble in designing and operating an LFG collection system is the uncertainty of LFG generation rates. The LFG generation rates are presently measured via models associated with the waste disposal history, moisture content, gas collection systems, and cover type, which have significant uncertainties. From literature studies, there is not sufficient data regarding the comparison between different models or calibrating them with CH₄-filled landfill data and the CH₄ generation and recovery models are not adequately progressive. The purpose of this study is to provide a comprehensive review of the commonly used models that predict LFG generated in landfill sites as well as discuss their specific characteristics. As a result, these numerical models are categorized in mathematical, numerical, and zero-, first-, and second-order decay models. Since first-order decay models are extensively used throughout the world, their consideration as multiphase models to make more appropriate projections is discussed in more detail.     

Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions

Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi‐year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO₂ concentration [CO₂]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model‐based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well‐controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO₂] and temperature.     

Carbon cycle confidence and uncertainty: Exploring variation among soil biogeochemical models

Emerging insights into factors responsible for soil organic matter stabilization and decomposition are being applied in a variety of contexts, but new tools are needed to facilitate the understanding, evaluation, and improvement of soil biogeochemical theory and models at regional to global scales. To isolate the effects of model structural uncertainty on the global distribution of soil carbon stocks and turnover times we developed a soil biogeochemical testbed that forces three different soil models with consistent climate and plant productivity inputs. The models tested here include a first‐order, microbial implicit approach (CASA‐CNP), and two recently developed microbially explicit models that can be run at global scales (MIMICS and CORPSE). When forced with common environmental drivers, the soil models generated similar estimates of initial soil carbon stocks (roughly 1,400 Pg C globally, 0–100 cm), but each model shows a different functional relationship between mean annual temperature and inferred turnover times. Subsequently, the models made divergent projections about the fate of these soil carbon stocks over the 20^th century, with models either gaining or losing over 20 Pg C globally between 1901 and 2010. Single‐forcing experiments with changed inputs, temperature, and moisture suggest that uncertainty associated with freeze‐thaw processes as well as soil textural effects on soil carbon stabilization were larger than direct temperature uncertainties among models. Finally, the models generated distinct projections about the timing and magnitude of seasonal heterotrophic respiration rates, again reflecting structural uncertainties that were related to environmental sensitivities and assumptions about physicochemical stabilization of soil organic matter. By providing a computationally tractable and numerically consistent framework to evaluate models we aim to better understand uncertainties among models and generate insights about factors regulating the turnover of soil organic matter.     

Environmental Risk Controllability and Management of VOCs during Remediation of Contaminated Sites

The environmental risk controllability assessment system and its method of controlling volatile organic compounds (VOCs) during remediation of contaminated sites are established in this article based on soil vapor extraction (SVE) technology. According to the properties of VOCs and the technical and operational characteristics of the site remediation process, the environmental risk controllability index system includes environmental risk identification, risk source analysis, and risk assessment. Environmental risk management during site remediation was focused on technical control methods and engineering control technologies. Specifically, acceptance based on risk management was suitable for low-risk levels such as RRI3 and RRI4. Furthermore, control methods for high-level risk (RRI1 or RRI2) could be developed along with transformation and control, combined with the necessary emergency risk plan.     

A methodology for performing global uncertainty and sensitivity analysis in systems biology

Accuracy of results from mathematical and computer models of biological systems is often complicated by the presence of uncertainties in experimental data that are used to estimate parameter values. Current mathematical modeling approaches typically use either single-parameter or local sensitivity analyses. However, these methods do not accurately assess uncertainty and sensitivity in the system as, by default, they hold all other parameters fixed at baseline values. Using techniques described within we demonstrate how a multi-dimensional parameter space can be studied globally so all uncertainties can be identified. Further, uncertainty and sensitivity analysis techniques can help to identify and ultimately control uncertainties. In this work we develop methods for applying existing analytical tools to perform analyses on a variety of mathematical and computer models. We compare two specific types of global sensitivity analysis indexes that have proven to be among the most robust and efficient. Through familiar and new examples of mathematical and computer models, we provide a complete methodology for performing these analyses, in both deterministic and stochastic settings, and propose novel techniques to handle problems encountered during these types of analyses.     

A Bayesian framework for parameter estimation in dynamical models

Mathematical models in biology are powerful tools for the study and exploration of complex dynamics. Nevertheless, bringing theoretical results to an agreement with experimental observations involves acknowledging a great deal of uncertainty intrinsic to our theoretical representation of a real system. Proper handling of such uncertainties is key to the successful usage of models to predict experimental or field observations. This problem has been addressed over the years by many tools for model calibration and parameter estimation. In this article we present a general framework for uncertainty analysis and parameter estimation that is designed to handle uncertainties associated with the modeling of dynamic biological systems while remaining agnostic as to the type of model used. We apply the framework to fit an SIR-like influenza transmission model to 7 years of incidence data in three European countries: Belgium, the Netherlands and Portugal.     

Toluene biodegradation rates in unsaturated soil systems versus liquid batches and their relevance to field conditions

Contaminant biodegradation in unsaturated soils may reduce the risks of vapor intrusion. However, the reported rates show large variability and are often derived from slurry experiments that are not representative of unsaturated conditions. Here, different laboratory setups are used to derive the biodegradation capacity of an unsaturated soil layer through which gaseous toluene migrates from the water table upwards. Experiments in static unsaturated soil microcosms at 6–30 % water-filled porosity (WFP) and unsaturated soil columns at 9, 14, and 27 % WFP were compared with liquid batches containing the same culture of Alicycliphilus denitrificans. The biodegradation rates for the liquid batches were orders of magnitude lower than for the other setups. Hence, liquid batches do not necessarily reflect optimal conditions for bacteria; either oxygen or toluene mass transfer at the cell scale or the absence of soil–water–air interfaces seemed to be limiting bacterial activity. For the column setup, the rates were limited by mass supply. The microcosm results could be described by apparent first-order biodegradation constants that increased with WFP or through a numerical model that included biodegradation as a first-order process taking place in the liquid phase only. The model liquid phase first-order rates varied between 6.25 and 20 h^?1 and were not related to the water content. Substrate availability was the primary factor limiting bioactivity, with evidence for physiological stress at the lowest water-filled porosity. The presented approach is useful to derive liquid phase biodegradation rates from experimental data and to include biodegradation in vapor intrusion models.     

Consensus Forecasting of Species Distributions: The Effects of Niche Model Performance and Niche Properties

Ensemble forecasting is advocated as a way of reducing uncertainty in species distribution modeling (SDM). This is because it is expected to balance accuracy and robustness of SDM models. However, there are little available data regarding the spatial similarity of the combined distribution maps generated by different consensus approaches. Here, using eight niche-based models, nine split-sample calibration bouts (or nine random model-training subsets), and nine climate change scenarios, the distributions of 32 forest tree species in China were simulated under current and future climate conditions. The forecasting ensembles were combined to determine final consensual prediction maps for target species using three simple consensus approaches (average, frequency, and median [PCA]). Species’ geographic ranges changed (area change and shifting distance) in response to climate change, but the three consensual projections did not differ significantly with respect to how much or in which direction, but they did differ with respect to the spatial similarity of the three consensual predictions. Incongruent areas were observed primarily at the edges of species’ ranges. Multiple stepwise regression models showed the three factors (niche marginality and specialization, and niche model accuracy) to be related to the observed variations in consensual prediction maps among consensus approaches. Spatial correspondence among prediction maps was the highest when niche model accuracy was high and marginality and specialization were low. The difference in spatial predictions suggested that more attention should be paid to the range of spatial uncertainty before any decisions regarding specialist species can be made based on map outputs. The niche properties and single-model predictive performance provide promising insights that may further understanding of uncertainties in SDM.