Importance of Infiltration Rates for Fate and Transport of Benzene in High-Tiered Risk-Based Assessment Considering Korean Site-Specific Factors at Contaminated Sites

Widely used conservative approaches for risk-based assessments of the subsurface transport processes have been calculated using simple analytical equations or general default values. Higher-tier risk assessment of contaminated sites requires the numerical models or additional site-specific values for input parameters. Previous studies have focused on the development of sophisticated models fit into risk-based frameworks. Our study mainly aims to explore the applicability of site-specific parameters and to modify the risk-based fate and transport model according to the types of the site-specific parameters. To apply the modified fate and transport equation and the site-specific default infiltration range, this study assessed the source depletion, leachate concentrations, and exposure concentration of benzene, which is a representative organic contaminant. The numerical models consist of two continuous processes, the fate and transport of contaminants from (1) the soil to the groundwater table in the vadose zone and subsequently (2) from the groundwater table to exposure wells in the saturated zone. Spatially varied Korean domestic recharge data were successfully incorporated into site-specific infiltration parameters in the models. The numerical simulation results were expressed as transient time series of concentrations over time. Results presented the narrow range of predicted concentrations at the groundwater table when site-specific infiltration was applied, and the dilution–attenuation factors for the unsaturated zone (DAFunsat) were derived based on the prediction. When a contaminant travels to the longest path length of 10 m with a source depth of 1 m in the vadoze zone, the simulated DAFunsat ranged from 3 to 4. The highest DAFunsat simulation results are close to 1 when contaminants travel to a source depth of 5 m and the shortest path length of 1 m. In the saturated aquifer below the contaminated sites, the variation in exposure concentration with time at monitoring wells is detected differently depending on the depth of the saturated zone.

In-stream <i>Escherichia coli</i> modeling using high-temporal-resolution data with deep learning and process-based models

Contamination of surface waters with microbiological pollutants is a major concern to public health. Although long-term and high-frequency Escherichia coli (E. coli) monitoring can help prevent diseases from fecal pathogenic microorganisms, such monitoring is time-consuming and expensive. Process-driven models are an alternative means for estimating concentrations of fecal pathogens. However, process-based modeling still has limitations in improving the model accuracy because of the complexity of relationships among hydrological and environmental variables. With the rise of data availability and computation power, the use of data-driven models is increasing. In this study, we simulated fate and transport of E. coli in a 0.6 km2 tropical headwater catchment located in the Lao People's Democratic Republic (Lao PDR) using a deep-learning model and a process-based model. The deep learning model was built using the long short-term memory (LSTM) methodology, whereas the process-based model was constructed using the Hydrological Simulation Program–FORTRAN (HSPF). First, we calibrated both models for surface as well as for subsurface flow. Then, we simulated the E. coli transport with 6 min time steps with both the HSPF and LSTM models. The LSTM provided accurate results for surface and subsurface flow with 0.51 and 0.64 of the Nash–Sutcliffe efficiency (NSE) values, respectively. In contrast, the NSE values yielded by the HSPF were −0.7 and 0.59 for surface and subsurface flow. The simulated E. coli concentrations from LSTM provided the NSE of 0.35, whereas the HSPF gave an unacceptable performance with an NSE value of −3.01 due to the limitations of HSPF in capturing the dynamics of E. coli with land-use change. The simulated E. coli concentration showed the rise and drop patterns corresponding to annual changes in land use. This study showcases the application of deep-learning-based models as an efficient alternative to process-based models for E. coli fate and transport simulation at the catchment scale.