Investigating the role of vadose zone breathing in vapor intrusion from contaminated groundwater

Machine Learning-Enhanced Prediction for Soil-to-Air VOC Emission and Environmental Impact Pertaining Contaminated Fractured Aquifers

How to scientifically and efficiently quantify the impact and hazards of volatile organic compounds (VOCs) pollution and volatilization from complex groundwater systems on surface air environments is a critical environmental issue. This paper employed an integrated modeling approach, incorporating numerical simulations, statistical analyses, and machine learning to address this issue. We comprehensively accounted for the different driving mechanisms, along with the various migration and transformation processes of groundwater VOCs. This investigation identified 11 key factors influencing surface pollutant flux. The data-enhanced statistical surrogate models and sampling-fusion-based support vector machine (SVM) surrogate models were established for appropriate generic modeling applications in which the high computation burden and difficulty could be avoided of the complicated numerical modeling. Those models would enable accurate prediction of surface fluxes and reliable classification of environmental risks. Notably, the pollutant fluxes through the soil-air interface over a short period could be sufficient to cause slow-airflow space air concentrations to exceed acceptable levels. Particularly, the established generic statistical surrogate models and SVM surrogate models have significant implications in efficiently and rapidly assessing the VOCs surface fluxes and environmental risk with meaningful quantified uncertainties for specific site conditions.

Short-term temporal variability of volatile contaminant concentrations in soil gas related to soil-atmosphere interface dynamics: Two case studies in the Veneto region (Italy)

The study of the variability of soil gas concentrations is crucial for defining effective monitoring and remediation strategies and for the risk assessment related to the emission of vapors from the subsurface. The traditional soil gas monitoring strategy consists of seasonal surveys based on short-time-averaged sampling. Soil gas monitoring results are often used to assess the risk associated with the emission of volatile contaminants from the subsurface, using models mainly based on molecular diffusion and therefore assuming continuous emission from the soil. At two contaminated sites located in the Veneto region (Italy), continuous monitoring using a photoionization detector, pressure gauges, and an ultrasonic anemometer was used to relate soil gas variability to surface and subsurface physical parameters. At both sites a cyclic diurnal variation of volatile organic compounds concentration in soil gas was observed, correlated with the variation of several meteorological parameters and in particular with the variation of the differential pressure between soil and atmosphere and the buoyancy vertical flux. These findings question the reliability of the conventional methodology employed in the collection and assessment of soil gas data. Integr Environ Assess Manag 2024;20:2023-2032. © 2024 SETAC.

A Critical Review of Groundwater Table Fluctuation: Formation, Effects on Multifields, and Contaminant Behaviors in a Soil and Aquifer System

The groundwater table fluctuation (GTF) zone is an important medium for the hydrologic cycle between unsaturated soil and saturated aquifers, which accelerates the migration, transformation, and redistribution of contaminants and further poses a potential environmental risk to humans. In this review, we clarify the key processes in the generation of the GTF zone and examine its links with the variation of the hydrodynamic and hydrochemistry field, colloid mobilization, and contaminant migration and transformation. Driven by groundwater recharge and discharge, GTF regulates water flow and the movement of the capillary fringe, which further control the advection and dispersion of contaminants in soil and groundwater. In addition, the formation and variation of the reactive oxygen species (ROS) waterfall are impacted by GTF. The changing ROS components partially determine the characteristic transformation of solutes and the dynamic redistribution of the microbial population. GTF facilitates the migration and transformation of contaminants (such as nitrogen, heavy metals, non-aqueous phase liquids, and volatile organic compounds) through colloid mobilization, the co-migration effect, and variation of the hydrodynamic and hydrochemistry fields. In conclusion, this review illustrates the limitations of the current literature on GTF, and the significance of GTF zones in the underground environment is underscored by expounding on the future directions and prospects.

Field investigation of temporal variation and diffusion of hydrogen sulfide on waste working face and intermediate landfill cover

This paper presents the study on the variation, influencing factors and diffusion regularity of hydrogen sulfide (H2S) concentration and surface flux on the working face and intermediate geomembrane cover of a landfill. Field investigations were conducted using static chambers at a large-scale municipal solid waste landfill in Hangzhou, China, from January 2019 to June 2021. The analytical models of H2S transport in the working face and intermediate cover were developed to investigate the surface flux under various conditions. The CALPUFF model was used to demonstrate the diffusion path. The H2S surface flux on the working face ranged from 7.1 × 10-3 to 1.7 mg/m2/h, whereas the range was found to be 1.5 × 10-4 to 0.9 mg/m2/h on the intermediate geomembrane cover. This observation indicated that the geomembrane can reduce H2S emissions. In addition, the H2S surface fluxes at the HDPE GMB seams and near the gas collecting wells were generally 1-2 orders of magnitude larger than that in the intact GMB. The analytical model estimates that the intact GMB exhibits a diffusion coefficient of H2S ranging from 2.7 × 10-11 to 2.2 × 10-10 m2/s. However, the diffusion coefficient increases significantly to a range of 3.3 × 10-11-9.8 × 10-7 m2/s on the GMB seams. According to CALPUFF results, only the H2S diffusion from the working face had areas exceeding the standard concentration.

Influence of advection on the soil gas radon deficit technique for the quantification of LNAPL

The Radon (Rn) deficit technique is a rapid, low-cost, and non-invasive method to identify and quantify light non-aqueous phase liquids (LNAPL) in the soil. LNAPL saturation is typically estimated from Rn deficit using Rn partition coefficients, assuming equilibrium conditions. This work examines the applicability of this method in the presence of local advective fluxes that can be generated by groundwater fluctuations or biodegradation processes in the source zone. To this end, a one-dimensional analytical model was developed to simulate the steady-state diffusive-advective transport of soil gas Rn in the presence of LNAPL. The analytical solution was first validated against an existing numerical model adapted to include advection. Then a series of simulations to study the effect of advection on Rn profiles were carried out. It was found that in high-permeability soils (such as sandy soils), advective phenomena can significantly affect Rn deficit curves in the subsurface compared with those expected, assuming either equilibrium conditions or a diffusion-dominated transport. Namely, in the presence of pressure gradients generated by groundwater fluctuations, applying the traditional Rn deficit technique (assuming equilibrium conditions) can lead to an underestimation of LNAPL saturation. Furthermore, in the presence of methanogenesis processes (e.g., in the case of a fresh LNAPL of petroleum hydrocarbons), local advective fluxes can be expected above the source zone. In such cases, Rn concentrations above the source zone can be higher than those above background areas without advective phenomena, resulting in Rn deficits higher than 1 (i.e., Rn excess), and thus leading to a wrong interpretation regarding the presence of LNAPL in the subsurface if advection is not considered. Overall, the results obtained suggest that advection should be considered in the presence of pressure gradients in the subsurface to ensure an effective application of the soil gas Rn-deficit technique for quantitative estimation of LNAPL saturation.

Identification and Risk Assessment of Priority Control Organic Pollutants in Groundwater in the Junggar Basin in Xinjiang, P.R. China

The Junggar Basin in Xinjiang is located in the hinterland of Eurasia, where the groundwater is a significant resource and has important ecological functions. The introduction of harmful organic pollutants into groundwater from increasing human activities and rapid socioeconomic development may lead to groundwater pollution at various levels. Therefore, to develop an effective regulatory framework, establishing a list of priority control organic pollutants (PCOPs) is in urgent need. In this study, a method of ranking the priority of pollutants based on their prevalence (Pv), occurrence (O) and persistent bioaccumulative toxicity (PBT) has been developed. PvOPBT in the environment was applied in the screening of PCOPs among 34 organic pollutants and the risk assessment of screened PCOPs in groundwater in the Junggar Basin. The results show that the PCOPs in groundwater were benzo[a]pyrene, 1,2-dichloroethane, trichloromethane and DDT. Among the pollutants, benzo[a]pyrene, 1,2-dichloroethane and DDT showed high potential ecological risk, whilst trichloromethane represented low potential ecological risk. With the exception of benzo[a]pyrene, which had high potential health risks, the other screened PCOPs had low potential health risks. Unlike the scatter distribution of groundwater benzo[a]pyrene, the 1,2-dichloroethane and trichloromethane in groundwater were mainly concentrated in the central part of the southern margin and the northern margin of the Junggar Basin, while the DDT in groundwater was only distributed in Jinghe County (in the southwest) and Beitun City (in the north). Industrial and agricultural activities were the main controlling factors that affected the distribution of PCOPs.

Database examination, multivariate analysis, and machine learning: Predictions of vapor intrusion attenuation factors

Traditional soil vapor intrusion (VI) models usually rely on preset conceptual scenarios, simplifying the influences of limiting environmental covariates in determining indoor attenuation factors relative to subsurface sources. This study proposed a technical framework and applied it to predict VI attenuation factors based on site-specific parameters recorded in the United States Environmental Protection Agency (USEPA)'s and the California Environmental Protection Agency (CalEPA)'s VI databases, which can overcome the limitations of traditional VI models. We examined the databases with multivariate analysis of variance to identify effective covariates, which were then employed to develop VI models with three machine learning algorithms. The results of multivariate analysis show that the effective covariates include soil texture, source depth, foundation type, lateral separation, surface cover, and land use. Based on these covariates, the predicted attenuation factors by these new models are generally within one order of magnitude of the observations recorded in the databases. Then the developed models were employed to generate the generic indoor attenuation factors to subsurface vapor (i.e., the 95th percentile of selected dataset), the values of which are different between the USEPA's and CalEPA's databases by one order of magnitude, although comparable to recommendations by the USEPA and literature, respectively. Such a difference may reflect the significant regional disparity in factors such as building structures or operational conditions (e.g., indoor air exchange rates), which necessitates generating generic VI attenuation factors on a state-specific basis. This study provides an alternative for VI risk screens on a site-specific basis, especially in states with a good collection of datasets. Although the proposed technical framework is used for the VI databases, it can be equally applied to other environmental science problems.

Approximate analytical model for transient transport and oxygen-limited biodegradation of vapor-phase petroleum hydrocarbon compound in soil

Volatile organic compounds (VOCs) contamination may occur in subsurface soil due to various reasons and pose great threat to people. Petroleum hydrocarbon compound (PHC) is a typical kind of VOC, which can readily biodegrade in an aerobic environment. The biodegradation of vapor-phase PHC in the vadose zone consumes oxygen in the soil, which leads to the change in aerobic and anaerobic zones but has not been studied by the existing analytical models. In this study, a one-dimensional analytical model is developed to simulate the transient diffusion and oxygen-limited biodegradation of PHC vapor in homogeneous soil. Laplace transformation and Laplace inversion of the Talbot method are adopted to derive the solution. At any given time, the thickness of aerobic zone is determined by the dichotomy method. The analytical model is verified against numerical simulation and experimental results first and parametric study is then conducted. The transient migration of PHC vapor can be divided into three stages including the pure aerobic zone stage (Stage I), aerobic-anaerobic zones co-existence stage (Stage II), and steady-state stage (Stage III). The proposed analytical model should be adopted to accommodate scenarios where the transient effect is significant (Stage II), including high source concentration, deep contaminant source, high biodegradation capacity, and high water saturation. The applicability of this model to determine the breakthrough time for better vapor intrusion assessment is also evaluated. Lower first-order biodegradation rate, higher source concentration, and shallower source depth all lead to smaller breakthrough time.

Characterization of vapor intrusion sites with a deep learning-based data assimilation method

Appropriate characterization of site soils is essential for accurate risk assessment of soil vapor intrusion (VI). In this study, we develop a data assimilation method based on deep learning (i.e., ES_(DL)) to estimate the distribution of soil properties with limited measurements. Two hypothetical VI scenarios are employed to demonstrate site characterization using the ES_(DL) method, followed by validation with a laboratory sandbox experiment and then one practical site application. The results show that the ES_(DL) method can provide reasonable estimates of the effective diffusion coefficient distributions and corresponding emission rates (into the building) in all four cases. The spatial heterogeneity of site soils can be characterized by considerably enough measurements (i.e., 15 sampling points in the first hypothetical case); otherwise, layered characterization is recommended at the cost of neglecting horizontal heterogeneity of site soils. This new method provides an alternative to characterize VI sites with relatively fewer sampling efforts.