Development of a screening relationship to describe migration of contaminant vapors into buildings

The relationship between subsurface contaminant concentrations and indoor air concentrations, arising from the migration of contaminant vapors into buildings, is affected by a number of complex processes and parameters, many of which are subject to uncertainty. A study was undertaken to develop a simplified relationship between subsurface contaminant concentrations and indoor air concentrations. This relationship is intended for use as a screening tool to determine the relative significance of vapor transport and inhalation as an exposure scenario in the establishment of soil quality guidelines. The relationship was developed using a proprietary model to analyze the infiltration of subsurface vapors into buildings. A probabilistic analysis of the relationship, using a form of Monte Carlo simulation, was undertaken to estimate the dilution of contaminant concentrations between the source (soil gas) and point of exposure (indoor air). Using standardized values for certain parameters and generic distributions for key variables, probability distributions were generated for the dilution factor as a function of contaminant depth and soil type.     

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.     

Effect of Precipitation Variability on Pollutant Concentrations in Soil: Implications for Exposure and Risk Assessments

Concentrations of toxic pollutants in surface soils must be predicted in order to assess exposures and risks that may arise from emissions at incinerators and other air pollution sources. At present, concentrations are predicted using deterministic models and time-averaged values of input parameters. This steady-state equilibrium approach does not address variability in the underlying transport and fate processes. This paper explores the variability of pollutant concentrations in surface soils that arises from precipitation, an intermittent process that governs wet deposition and leaching processes. Using long-term (45 to 50 years) historical records at 6 climato-logically diverse sites, concentrations predicted using the steady-state approach are compared to those predicted using a dynamic numerical model that simulates dry and wet deposition, leaching, and pollutant accumulation in the surface layer of soil using a daily time step. The models are compared for pollutants of low, medium, and high water solubility. Both models show that predictions depend strongly on the pollutant solubility and the precipitation pattern at the location. Average concentrations differed between locations by a factor of up to 4 due to precipitation patterns; the solubility of the pollutant had a much more pronounced effect. Both models produced similar long-term trends, for example, the duration of the period needed to achieve a quasi-steady-state pollutant concentration. However, for soluble pollutants, the dynamic model produced maximum 24-hr average concentrations that exceeded long-term averages by 4 to 8 times, and long-term predictions of the dynamic model exceeded predictions of the steady-state model by 1.9 to 3.6 times (depending on the site). These differences are caused by the steady-state assumption that deposition and leaching occur continuously. While the steady-state model can be used to estimate long-term trends of moderately to highly insoluble pollutants, the dynamic model should be used to predict short-term, maximum concentrations and both short- and long-term averages of soluble pollutants. Site-specific exposure and risk assessments should consider temporal variation and the use of a dynamic model if concentrations of soluble pollutants approach risk-based target levels.     

VIRTUS, a model of virus transport in unsaturated soils.

As a result of the recently proposed mandatory groundwater disinfection requirements to inactivate viruses in potable water supplies, there has been increasing interest in virus fate and transport in the subsurface. Several models have been developed to predict the fate of viruses in groundwater, but few include transport in the unsaturated zone and all require a constant virus inactivation rate. These are serious limitations in the models, as it has been well documented that considerable virus removal occurs in the unsaturated zone and that the inactivation rate of viruses is dependent on environmental conditions. The purpose of this research was to develop a predictive model of virus fate and transport in unsaturated soils that allows the virus inactivation rate to vary on the basis of changes in soil temperature. The model was developed on the basis of the law of mass conservation of a contaminant in porous media and couples the flows of water, viruses, and heat through the soil. Model predictions were compared with measured data of virus transport in laboratory column studies and, with the exception of one point, were within the 95% confidence limits of the measured concentrations. The model should be a useful tool for anyone wishing to estimate the number of viruses entering groundwater after traveling through the soil from a contamination source. In addition, model simulations were performed to identify parameters that have a large effect on the results. This information can be used to help design experiments so that important variables are measured accurately.     

VIRTUS, a model of virus transport in unsaturated soils.

As a result of the recently proposed mandatory groundwater disinfection requirements to inactivate viruses in potable water supplies, there has been increasing interest in virus fate and transport in the subsurface. Several models have been developed to predict the fate of viruses in groundwater, but few include transport in the unsaturated zone and all require a constant virus inactivation rate. These are serious limitations in the models, as it has been well documented that considerable virus removal occurs in the unsaturated zone and that the inactivation rate of viruses is dependent on environmental conditions. The purpose of this research was to develop a predictive model of virus fate and transport in unsaturated soils that allows the virus inactivation rate to vary on the basis of changes in soil temperature. The model was developed on the basis of the law of mass conservation of a contaminant in porous media and couples the flows of water, viruses, and heat through the soil. Model predictions were compared with measured data of virus transport in laboratory column studies and, with the exception of one point, were within the 95% confidence limits of the measured concentrations. The model should be a useful tool for anyone wishing to estimate the number of viruses entering groundwater after traveling through the soil from a contamination source. In addition, model simulations were performed to identify parameters that have a large effect on the results. This information can be used to help design experiments so that important variables are measured accurately.     

A parametric distribution for the fraction of outdoor soil in indoor dust

For a chemical that does not have a source inside a house, the ratio of its dust concentration indoors to its soil concentration outdoors is equal to the fraction of house dust that is composed of soil. To estimate the fraction of soil in house dust, we compiled ratios of the concentrations of a chemical in dust and soil from the scientific literature. We find that a lognormal distribution fits the data extremely well. This distribution is suitable for use in public health risk assessments for single‐family homes in temperate climates.     

Spatial Modeling of Air Pollution in Studies of Its Short‐Term Health Effects

Summary In studies that estimate the short‐term effects of air pollution on health, daily measurements of pollution concentrations are often available from a number of monitoring locations within the study area. However, the health data are typically only available in the form of daily counts for the entire area, meaning that a corresponding single daily measure of pollution is required. The standard approach is to average the observed measurements at the monitoring locations, and use this in a log‐linear health model. However, as the pollution surface is spatially variable this simple summary is unlikely to be an accurate estimate of the average pollution concentration across the region, which may lead to bias in the resulting health effects. In this article, we propose an alternative approach that jointly models the pollution concentrations and their relationship with the health data using a Bayesian spatio‐temporal model. We compare this approach with the simple spatial average using a simulation study, by investigating the impact of spatial variation, monitor placement, and measurement error in the pollution data. An epidemiological study from Greater London is then presented, which estimates the relationship between respiratory mortality and four different pollutants.     

Predicting the likely response of data‐poor ecosystems to climate change using space‐for‐time substitution across domains

Predicting ecological response to climate change is often limited by a lack of relevant local data from which directly applicable mechanistic models can be developed. This limits predictions to qualitative assessments or simplistic rules of thumb in data‐poor regions, making management of the relevant systems difficult. We demonstrate a method for developing quantitative predictions of ecological response in data‐poor ecosystems based on a space‐for‐time substitution, using distant, well‐studied systems across an inherent climatic gradient to predict ecological response. Changes in biophysical data across the spatial gradient are used to generate quantitative hypotheses of temporal ecological responses that are then tested in a target region. Transferability of predictions among distant locations, the novel outcome of this method, is demonstrated via simple quantitative relationships that identify direct and indirect impacts of climate change on physical, chemical and ecological variables using commonly available data sources. Based on a limited subset of data, these relationships were demonstrably plausible in similar yet distant (>2000 km) ecosystems. Quantitative forecasts of ecological change based on climate‐ecosystem relationships from distant regions provides a basis for research planning and informed management decisions, especially in the many ecosystems for which there are few data. This application of gradient studies across domains – to investigate ecological response to climate change – allows for the quantification of effects on potentially numerous, interacting and complex ecosystem components and how they may vary, especially over long time periods (e.g. decades). These quantitative and integrated long‐term predictions will be of significant value to natural resource practitioners attempting to manage data‐poor ecosystems to prevent or limit the loss of ecological value. The method is likely to be applicable to many ecosystem types, providing a robust scientific basis for estimating likely impacts of future climate change in ecosystems where no such method currently exists.     

Probability and Persistence of High Pollutant Concentrations in Soils: A Modeling Study and Implications for Exposure and Risk Assessment

Risk assessments for environmental pollutants have relied upon steady-state models that do not represent the variability of pollutant transport and fate processes, thus predictions are unlikely to reflect the true variability in pollutant concentrations. Such models cannot be used to estimate the probability, magnitude and duration of short- to intermediate-term and high-concentration events that might lead to adverse acute impacts. In this study, a numerical model is used to simulate pollutant accumulation in surface soils at six U.S. locations that result from atmospheric deposition and leaching. Historical (50 year) precipitation data drive the model. Model predictions are filtered and analyzed to identify high pollution events (exceeding specific concentration thresholds) and their occurrence probability and duration. Predicted concentrations at each site varied by a factor of 100 over time and by a factor of five among the six locations. The frequency and duration of high pollution events also differed by locations and concentration threshold. In general, larger thresholds lead to less frequent events and shorter durations. The proposed method allows estimates of the probability of occurrence and duration of high pollution events, providing information that complements the steady-state methods.     

Stream isotherm shifts from climate change and implications for distributions of ectothermic organisms

Stream ecosystems are especially vulnerable to climate warming because most aquatic organisms are ectothermic and live in dendritic networks that are easily fragmented. Many bioclimatic models predict significant range contractions in stream biotas, but subsequent biological assessments have rarely been done to determine the accuracy of these predictions. Assessments are difficult because model predictions are either untestable or so imprecise that definitive answers may not be obtained within timespans relevant for effective conservation. Here, we develop the equations for calculating isotherm shift rates (ISRs) in streams that can be used to represent historic or future warming scenarios and be calibrated to individual streams using local measurements of stream temperature and slope. A set of reference equations and formulas are provided for application to most streams. Example calculations for streams with lapse rates of 0.8 °C/100 m and long‐term warming rates of 0.1–0.2 °C decade^?1 indicate that isotherms shift upstream at 0.13–1.3 km decade^?1 in steep streams (2–10% slope) and 1.3–25 km decade^?1 in flat streams (0.1–1% slope). Used more generally with global scenarios, the equations predict isotherms shifted 1.5–43 km in many streams during the 20th Century as air temperatures increased by 0.6 °C and would shift another 5–143 km in the first half of the 21st Century if midrange projections of a 2 °C air temperature increase occur. Variability analysis suggests that short‐term variation associated with interannual stream temperature changes will mask long‐term isotherm shifts for several decades in most locations, so extended biological monitoring efforts are required to document anticipated distribution shifts. Resampling of historical sites could yield estimates of biological responses in the short term and should be prioritized to validate bioclimatic models and develop a better understanding about the effects of temperature increases on stream biotas.     

An Evaluation of Vapor Intrusion Into Buildings through a Study of Field Data

A systematic examination of cases on file with the Massachusetts Department of Environmental Protection was undertaken to identify a universe of sites with volatile organic compound (VOC) contamination in groundwater in close proximity to buildings. Such locations were grouped according to site variables, such as contaminants of concern and concentrations in various media; soil type; depth to groundwater; distance to building; and building construction. Indoor air, soil gas, and/or groundwater field data collected from these sites was then assembled and used to: (1) evaluate available transport models which describe the intrusion of vapors into buildings and predict indoor air contaminant concentrations resulting from the volatilization of VOC s in the subsurface; (2) examine the validity of established regulatory criteria; (3) identify specific trends and field conditions which appear to most influence vapor phase contaminant migration and intrusion processes; and (4) evaluate the possibility of vapor migration being inhibited by a "freshwater lens".     

A new net mineralizable nitrogen assay improves predictions of floristic composition

Question: Can a simple measurement of nitrogen (N) availability be related to an ecologically relevant response, i.e. mean Ellenberg N indicator value (E_N)? Location: UK (England, Wales and Scotland). Methods: Soil cores from a stratified sample of UK habitats were analysed for mineralizable N with a conventional incubation and a new flushing method, which uses a single mineral N extraction. Predictions of mean E_N using mineralizable N and other soil measurements were assessed by fitting linear mixed‐effect models, using the Akaike information criterion (AIC) as a measure of model parsimony. Results: Mineralizable N measurements using the flushing method described a component of the variation in mean E_N that was more orthogonal to bulk soil properties such as moisture content, total N/C ratio and pH than that described by conventionally measured mineralizable N. Mineralizable N as measured using the flushing method improved the accuracy of predictions obtained using only bulk soil measurements, and appeared in the best two‐term and three‐term models. Conclusions: Much of the variation in mean E_N can be related to soil N/C ratio, pH or moisture content, but mineralizable N distinguishes variation in mean E_N that is independent of these bulk soil properties. The new measure will be useful for studies of the exposure of plants to N, in particular when assessing N pollution effects on plant species composition.     

Faster turnover of new soil carbon inputs under increased atmospheric CO2

Rising levels of atmospheric CO₂ frequently stimulate plant inputs to soil, but the consequences of these changes for soil carbon (C) dynamics are poorly understood. Plant‐derived inputs can accumulate in the soil and become part of the soil C pool (“new soil C”), or accelerate losses of pre‐existing (“old”) soil C. The dynamics of the new and old pools will likely differ and alter the long‐term fate of soil C, but these separate pools, which can be distinguished through isotopic labeling, have not been considered in past syntheses. Using meta‐analysis, we found that while elevated CO₂ (ranging from 550 to 800 parts per million by volume) stimulates the accumulation of new soil C in the short term (<1 year), these effects do not persist in the longer term (1–4 years). Elevated CO₂ does not affect the decomposition or the size of the old soil C pool over either temporal scale. Our results are inconsistent with predictions of conventional soil C models and suggest that elevated CO₂ might increase turnover rates of new soil C. Because increased turnover rates of new soil C limit the potential for additional soil C sequestration, the capacity of land ecosystems to slow the rise in atmospheric CO₂ concentrations may be smaller than previously assumed.     

Prevalence of dermal pathway dominance in risk assessment of contaminated soils: A survey of superfund risk assessments, 1989–1992

Exposure to soil‐borne contaminants can occur through ingestion, inhalation and/or dermal absorption. A study was undertaken to assess the relative frequency with which dermal exposures are predicted to pose the greatest risk attributable to contaminated soils in Superfund risk assessments. Screening of over 200 risk assessments from the period 1989–1992 resulted in identification of 37 sites at which projected lifetime excess cancer risks attributed to dermal contact with soil were greater than the nominal regulatory threshold of 1.10^‐4. At 19 of these sites, the dermal/soil pathway is estimated to contribute the largest carcinogenic risk associated with surface soil contamination, and may therefore drive cleanup of that medium. At 9 of the sites, the dermal/soil pathway is predicted to present a higher carcinogenic risk than any other pathway. Chemical contaminant type and estimates of soil adherence and surface area exposed appear to be the primary factors that distinguish sites at which dermal/soil pathway carcinogenic risk estimates are elevated relative to other exposure pathways. Quantification of exposure parameters, especially those related to behavior, remains a significant need.     

地表水热通量研究进展

介绍了当前国内外地表水热通景观测研究的进展及3种不同类型的土壤-植被-大气传输模型（SVAT）：单层模型、双层模型和多层模型。遥感手段常用于监测大面积地表水热通量。基于地表能量平衡方程,现已建立了许多遥感模型以估算水热通量（如简化模型、单层模型、附加阻抗模型、作物缺水指数模型和二源阻抗模型等）,并对这些模型复杂程度及应用范围进行了分析。     

Soil carbon loss with warming: New evidence from carbon‐degrading enzymes

Climate warming affects soil carbon (C) dynamics, with possible serious consequences for soil C stocks and atmospheric CO₂ concentrations. However, the mechanisms underlying changes in soil C storage are not well understood, hampering long‐term predictions of climate C‐feedbacks. The activity of the extracellular enzymes ligninase and cellulase can be used to track changes in the predominant C sources of soil microbes and can thus provide mechanistic insights into soil C loss pathways. Here we show, using meta‐analysis, that reductions in soil C stocks with warming are associated with increased ratios of ligninase to cellulase activity. Furthermore, whereas long‐term (≥5 years) warming reduced the soil recalcitrant C pool by 14%, short‐term warming had no significant effect. Together, these results suggest that warming stimulates microbial utilization of recalcitrant C pools, possibly exacerbating long‐term climate‐C feedbacks.     

Proliferation and differentiation potential of cryopreserved human skin-derived precursors

Objectives: Skin‐derived precursors are recognized to be a potentially autologous and accessible source of neural precursor cells for drug screening or cell‐based treatments, in many neurological disorders. Thus, it is necessary to investigate appropriate methods for cryopreservation of such human skin‐derived precursors (hSKPs). The aim of this study was to evaluate different cryopreservation techniques for retention of hSKPs to discover an optimized protocol. Materials and methods: We cryopreserved hSKPs treated with 0%, 10%, 20%, 30% and 40% foetal bovine serum (FBS) and three concentrations of dimethylsulphoxide (DMSO) 5%, 10% and 15%, with two different storage periods in liquid nitrogen (2 days: short‐term storage; and 2 months: long‐term storage). Then, we assessed survival and proliferation levels of the cells after freeze–thaw processes, by viability measurement and colony‐forming assay. For detecting hSKPs, we used immunocytochemistry and RT‐PCR assessments. Results: Our findings indicated that hSKPs cryopreserved in 5% DMSO without FBS, had better survival and proliferation potentials compared to other working formulations. With various concentrations of cryoprotectants over different time periods, hSKPs retained their differentiation potentiality and were able to differentiate into neurons (NFM and βΙΙΙ tubulin‐positive), glial cells (GFAP‐positive) and smooth muscle cells (SMA‐positive). Conclusions: Results revealed that in only 5% DMSO, hSKPs could be cryopreserved for long‐term storage with considerable survival and proliferation levels, without losing multipotency.     

Chemical Vapor Intrusion from Soil or Groundwater to Indoor Air: Significance of Unsaturated Zone Biodegradation of Aromatic Hydrocarbons

The soil vapor to indoor air exposure pathway is considered in a wide number of risk-based site management programs. In screening-level assessments of this exposure pathway, models are typically used to estimate the transport of vapors from either subsurface soils or groundwater to indoor air. Published studies indicate that the simple models used to evaluate this exposure pathway often over estimate the impact for aromatic hydrocarbons (e.g., benzene, toluene, ethylbenzene, and xy-lene or BTEX), while showing reasonable agreement for estimates of chlorinated hydrocarbon impacts (e.g., PCE, TCE, DCE). Aerobic biodegradation of the petroleum hydrocarbons is most often attributed as the source of this disparity in the model/ data comparisons. This paper looks at the significance of aerobic biodegradation of aromatic hydrocarbons as part of the assessment of chemical vapor intrusion from soil or groundwater to indoor air. A review of relevant literature summarizing the available field data as well as various modeling approaches that include biodegradation is presented. This is followed by a simple modeling analysis that demonstrates the potential importance of biodegradation in the assessment of the soil vapor to indoor air exposure pathway. The paper concludes with brief discussions of other model considerations that are often not included in simple models but may have a significant impact on the intrusion of vapors into indoor air.