Journal of contaminant hydrology最新文献

Microbial reductive dechlorination is a key process in aquifers contaminated with chlorinated ethenes and results in a net mass reduction of organic pollutants. Biodegradation rates in the subsurface are temperature-dependent and may be enhanced by increased groundwater temperatures. This study explores the potential of combining the temperature increase from low-temperature Aquifer Thermal Energy Storage with In Situ Bioremediation (ATES-ISB). The effects of highly dynamic groundwater flow and heat transport on microbial degradation rates were examined in a contaminated aquifer based on a pilot-scale experiment and a comprehensive process-based modeling analysis. The low-temperature ATES-ISB pilot test was carried out in Birkerød (Denmark), in an aquifer contaminated with trichloroethene by implementing a groundwater flow dipole, injecting heated groundwater, biostimulating the system with lactate and bioaugmenting it with a Dehalococcoides containing culture. Solute concentrations were monitored in four observation wells over the course of the test and a non-isothermal reactive transport model, solved in a two-dimensional heterogeneous domain, was developed to quantitatively interpret the experimental observations. The process-based numerical model also allowed evaluating the evolution of chlorinated ethenes concentrations considering different hydraulic, thermal, and operational scenarios. The results demonstrate the beneficial combination of ATES with in situ contaminant bioremediation, showing enhancement of contaminant mass reduction and more complete reductive dechlorination. The developed process-based model can be instrumental for the design and parameterization of pilot and full scale low-temperature ATES-ISB remediation in shallow aquifer systems.

The transport of per- and polyfluoroalkyl substances (PFASs) through unsaturated source-zone soils is a critical yet poorly understood aspect of their environmental behavior. To date, most experimental studies have only focused on the equilibrium or non-equilibrium partitioning of PFASs to the air-water interface, or solid-phase based equilibrium or non-equilibrium transport. Currently, there are discrepancies between air-water interfacial partitioning (K_ia) results measured using a drainage-based column method (which supports a Langmuir isotherm) when compared to measurements from alternative experimental methods (which support a Freundlich isotherm). We hypothesize that this discrepancy is the result of non-Fickian transport conditions developing during column tests using the drainage method, which reduces the magnitude of the apparent K_ia (K_ia,app) when estimated using the retardation factor correlation from breakthrough curve experiments. To test the validity of this hypothesis, the drainage method was implemented using PFOS in a sand column and compared with prior data collected using a quasi-saturated column method. Results demonstrate that the apparent K_ia was reduced by 3 to 123-fold, resulting in up to 123-fold faster breakthrough of PFOS than predicted with the assumption of equilibrium adsorption to the air-water interface. A novel mobile-immobile model (MIM) of PFAS fate and transport was developed, incorporating a term for anomalously adsorbed solute in the mobile zone to explain highly anomalous data. The modelling results using a modified HYDRUS-1D software show that anomalous air-water interfacial adsorption and/or flowpath channelization are plausible mechanisms for accelerated transport of PFOS and support the application of a Freundlich isotherm for PFOS. Overall, non-Fickian transport mechanisms demonstrate the potential to accelerate PFOS transport through the vadose zone by up to a factor of 123 under specific circumstances. This work demonstrates the assumption of equilibrium adsorption to air-water interfaces, even for homogeneous laboratory experiments, is not necessarily valid.

At present, as the problem of water shortage and pollution is growing serious, it is particularly important to understand the recycling and treatment of wastewater. Artificial intelligence (AI) technology is characterized by reliable mapping of nonlinear behaviors between input and output of experimental data, and thus single/integrated AI model algorithms for predicting different pollutants or water quality parameters have become a popular method for simulating the process of wastewater treatment. Many AI models have successfully predicted the removal effects of pollutants in different wastewater treatment processes. Therefore, this paper reviews the applications of artificial intelligence technologies such as artificial neural networks (ANN), adaptive network-based fuzzy inference system (ANFIS) and support vector machine (SVM). Meanwhile, this review mainly introduces the effectiveness and limitations of artificial intelligence technology in predicting different pollutants (dyes, heavy metal ions, antibiotics, etc.) and different water quality parameters such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) in wastewater treatment process, involving single AI model and integrated AI model. Finally, the problems that need further research together with challenges ahead in the application of artificial intelligence models in the field of environment are discussed and presented.

This study applied electrokinetic (EK) in situ soil remediation for perfluorooctanoic acid (PFOA) removal from kaolinite soil. The kaolinite soil was spiked with 10 mg/kg PFOA for the EK treatment using Sodium Cholate bio-surfactant coupled with Activated Carbon (AC) or iron-coated Activated Carbon (FeAC) permeable reactive barrier (PRB). The study also evaluated the impact of AC and FeAC PRBs' position on the EK process performance. In the EK with the PRB in the middle section, PFOA removal from kaolinite was 52.35 % in the AC-EK tests and 59.55 % in the FeAC-EK. Experimental results showed the accumulation of PFOA near the cathode region in FeAC PRB tests, hypothesising that Fe from the PRB formed a complex with PFOA ions and transported it to the cathode region. Spent PRBs were regenerated with methanol for PFOA extraction and reuse in the EK experiments. Although FeAC PRB achieved better PFOA removal than AC PRB, the EK tests with regenerated AC-EK and FeAC-EK PRBs achieved 40.37 % and 20.62 % PFOA removal. For EK with FeAC PRB near the anode, PFOA removal was 21.96 %. Overall, using PRB in conjunction with the EK process can further enhance the removal efficiency. This concept could be applied to enhance the removal of various PFAS compounds from contaminated soils by combining a suitable PRB with the EK process. It also emphasizes the feasibility of in-situ soil remediation technologies for forever chemical treatment.

Mine waste rock poses significant environmental challenges. Evaluating management and reclamation options is particularly complex because of the wide particle size distribution, the non-uniform distribution of acid-generating and buffering minerals, and the variable contribution of the different particle size fractions to acid mine drainage (AMD) generation. Reactive transport simulations can be useful to complement and overcome the limitations of laboratory and field experiments. However, predicting field-scale and long-term geochemical behavior of waste rock requires a better understanding of numerical parameters scale-up. In this study, three waste rocks, with different mineral composition and particle size distribution, were separated into different fractions and tested in the laboratory. Kinetic tests were used to calibrate numerical models and adjust minerals' effective kinetic rate constants to match measured pH and metal concentrations. Calibrated reactive transport simulations were able to reproduce accurately the effect of particle size on pH and sulfate and calcium production rates. Experimental and numerical results confirmed that waste rock oxidation and neutralization rates tended to decrease with increasing particle sizes. Several models were tested and the weighted geometric mean of the effective kinetic rate constants as a function of the proportion of each fraction provided the most accurate estimation of the whole specimen kinetic rate constants. A novel approach to predict waste rock geochemical behavior from a single laboratory test also showed promising results. Overall, these results should contribute to improving the extrapolation of laboratory kinetic test results to field predictions.

The contaminant mass discharge is a relevant metric to evaluate the risk that a groundwater plume poses to water resources. However, this assessment is often vitiated by a high uncertainty inherent to the assessment method and often limited number of measurement points to carry out the assessment. Direct-Push techniques in combination with profiling tools and dedicated sampling can be an interesting alternative to increase the measurement point density and hence reduce the mass discharge uncertainty. The main objective of our study was to assess if DP logging and sampling could be employed to get a reasonable estimate of contaminant mass discharge in a large sulfonamide contaminant plume (> 1500 m wide), compared to a more traditional approach based on monitoring wells. To do so, an Hydraulic Profiling Tool (HPT) logging with a dedicated site calibration was used to estimate the hydraulic conductivity field. The sulfonamide concentrations were inferred from the compound fluorescence properties measured by laboratory spectrofluorometry (λ_Ex / λ_Em = 255/340 nm) and a dedicated log-log linear regression model. Our results show that HPT-derived hydraulic conductivity values are in good agreement with the monitoring well results, and within the order of magnitude reported in similar studies or indirect geophysical techniques. Fluorescence appears as a powerful proxy for the sulfonamide concentration levels. Ultimately, the contaminant mass discharge estimate from HPT and fluorescence techniques lies within a factor 2 from the estimate by monitoring wells, with 549 [274–668] and 776 [695–879] kg/yr respectively. Overall, this study highlights that DP logging tools combined with indirect methods (correlation with fluorescence) could provide a relevant contaminant mass discharge estimate for some optically active substances, given that a proper calibration phase is carried out.

Large-scale open-pit combined underground mining activities (OUM) not only reshape the original topography, geomorphology, and hydrogeochemical environment of the mining area, but also alter the regional water cycle conditions. However, due to the complexity arising from the coexistence of two coal mining technologies (open-pit and underground mining), the hydrological environmental effects remain unclear. Here, we selected the Pingshuo Mining Area in China, one of the most modernized open-pit combined underground mining regions, as the focus of our research. We comprehensively employed mathematical statistics, Piper diagram, Gibbs model, ion combination ratio, principal component analysis and other methods to compare the hydrochemistry and isotope data of different water bodies before (2006) and after (2021) large-scale mining. The changing patterns of hydrochemical characteristics of different water bodies and their main controlling factors in mining area driven by OUM were analyzed and identified, revealing the water circulation mechanism under the background of long-term coal mining. The results showed that: (1) The chemical composition of water has changed greatly due to large-scale coal mining. The hydrochemical types of Quaternary and Permian-Carboniferous aquifers shifted from predominantly HCO₃-Ca·Mg before intensive mining to primarily HCO₃·SO₄-Ca·Mg, HCO₃-Na, HCO₃·SO₄-Na·Mg, and HCO₃·SO₄-Ca·Mg, HCO₃-Ca·Na, HCO₃·SO₄-Mg·Ca post-mining. Variations in the hydrochemical types of surface water were found to be complex and diverse. (2) Coal mining activities promote the dissolution of silicate rock and sodium-bearing evaporites, enhancing the strength and scale of positive alternating adsorption of cations. The oxidation of pyrite, dissolution of silicate weathering, and the leaching of coal gangue were identified as the main reasons for the significant increase of SO₄²⁻, while decarbonation in confined aquifers led to a decrease in HCO₃⁻. (3) Results from the principal component analysis and stable isotopes demonstrated the hydraulic connection among surface water, Quaternary aquifers, and Permian-Carboniferous aquifers induced by long-term OUM. The research findings provide a reference basis for the coordinated development of coal and water in the Pingshuo Mining Area and other open-pit combined underground mining areas.

Scarcity of stream salinity data poses a challenge to understanding salinity dynamics and its implications for water supply management in water-scarce salt-prone regions around the world. This paper introduces a framework for generating continuous daily stream salinity estimates using instance-based transfer learning (TL) and assessing the reliability of the synthetic salinity data through uncertainty quantification via prediction intervals (PIs). The framework was developed using two temporally distinct specific conductance (SC) datasets from the Upper Red River Basin (URRB) located in southwestern Oklahoma and Texas Panhandle, United States. The instance-based TL approach was implemented by calibrating Feedforward Neural Networks (FFNNs) on a source SC dataset of around 1200 instantaneous grab samples collected by United States Geological Survey (USGS) from 1959 to 1993. The trained FFNNs were subsequently tested on a target dataset (1998-present) of 220 instantaneous grab samples collected by the Oklahoma Water Resources Board (OWRB). The framework's generalizability was assessed in the data-rich Bird Creek watershed in Oklahoma by manipulating continuous SC data to simulate data-scarce conditions for training the models and using the complete Bird Creek dataset for model evaluation. The Lower Upper Bound Estimation (LUBE) method was used with FFNNs to estimate PIs for uncertainty quantification. Autoregressive SC prediction methods via FFNN were found to be reliable with Nash Sutcliffe Efficiency (NSE) values of 0.65 and 0.45 on in-sample and out-of-sample test data, respectively. The same modeling scenario resulted in an NSE of 0.54 for the Bird Creek data using a similar missing data ratio, whereas a higher ratio of observed data increased the accuracy (NSE = 0.84). The relatively narrow estimated PIs for the North Fork Red River in the URRB indicated satisfactory stream salinity predictions, showing an average width equivalent to 25 % of the observed range and a confidence level of 70 %.