Analysis of the association between vestibular schwannoma and hearing status using a newly developed radiomics technique.

PURPOSE: Vestibular schwannoma is a benign tumor originating from Schwann cells surrounding the eighth cranial nerve and can cause hearing loss, tinnitus, balance problems, and facial nerve disorders. Because of the slow growth of the tumor, predicting the hearing function of patients with vestibular schwannoma's is important to obtain information that would be useful for deciding the treatment modality. This study aimed to analyze the association between magnetic resonance imaging features and hearing status using a new radiomics technique. METHODS: We retrospectively analyzed 115 magnetic resonance images and hearing results from 73 patients with vestibular schwannoma. A total of 70 radiomics features from each tumor volume were calculated using T1-weighted magnetic resonance imaging. Radiomics features were classified as histogram-based, shape-based, texture-based, and filter-based. The least absolute shrinkage and selection operator method was used to select the radiomics features among the 70 features that best predicted the hearing test. To ensure the stability of the selected features, the least absolute shrinkage and selection operator method was repeated 10 times. Finally, features set five or more times were selected as radiomics signatures. RESULTS: The radiomics signatures selected using the least absolute shrinkage and selection operator method were: minimum, variance, maximum 3D diameter, size zone variance, log skewness, skewness slope, and kurtosis slope. In random forest, the mean performance was 0.66 (0.63-0.77), and the most important feature was Log skewness. CONCLUSIONS: Newly developed radiomics features are associated with hearing status in patients with vestibular schwannoma and could provide information when deciding the treatment modality.

A Potential Radiomics-Clinical Model for Predicting Failure of Lymph Node Control after Definite Radiotherapy in Locally Advanced Head and Neck Cancer.

Background and Objectives: To optimally predict lymph node (LN) failure after definite radiotherapy (RT) in head and neck cancer (HNC) with LN metastases, this study examined radiomics models extracted from CT images of different periods during RT. Materials and Methods: This study retrospectively collected radiologic and clinical information from patients undergoing definite RT over 60 Gy for HNC with LN metastases from January 2010 to August 2021. The same largest LNs in each patient from the initial simulation CT (CTpre) and the following simulation CT (CTmid) at approximately 40 Gy were indicated as regions of interest. LN failure was defined as residual or recurrent LN within 3 years after the end of RT. After the radiomics features were extracted, the radiomics alone model and the radiomics plus clinical parameters model from the set of CTpre and CTmid were compared. The LASSO method was applied to select features associated with LN failure. Results: Among 66 patients, 17 LN failures were observed. In the radiomics alone model, CTpre and CTmid had similar mean accuracies (0.681 and 0.697, respectively) and mean areas under the curve (AUC) (0.521 and 0.568, respectively). Radiomics features of spherical disproportion, size zone variance, and log minimum 2 were selected for CTpre plus clinical parameters. Volume, energy, homogeneity, and log minimum 1 were selected for CTmid plus clinical parameters. Clinical parameters including smoking, T-stage, ECE, and regression rate of LN were important for both CTpre and CTmid. In the radiomics plus clinical parameters models, the mean accuracy and mean AUC of CTmid (0.790 and 0.662, respectively) were more improved than those of CTpre (0.731 and 0.582, respectively). Conclusions: Both models using CTpre and CTmid were improved by adding clinical parameters. The radiomics model using CTmid plus clinical parameters was the best in predicting LN failure in our preliminary analyses.

Incremental benefits of size-zone matrix-based radiomics features for the prognosis of lung adenocarcinoma: advantage of spatial partitioning on tumor evaluation.

OBJECTIVES: Prognostic models of lung adenocarcinoma (ADC) can be built using radiomics features from various categories. The size-zone matrix (SZM) features have a strong biological basis related to tumor partitioning, but their incremental benefits have not been fully explored. In our study, we aimed to evaluate the incremental benefits of SZM features for the prognosis of lung ADC. METHODS: A total of 298 patients were included and their pretreatment computed tomography images were analyzed in fivefold cross-validation. We built a risk model of overall survival using SZM features and compared it with a conventional radiomics risk model and a clinical variable-based risk model. We also compared it with other models incorporating various combinations of SZM features, other radiomics features, and clinical variables. A total of seven risk models were compared and evaluated using the hazard ratio (HR) on the left-out test fold. RESULTS: As a baseline, the clinical variable risk model showed an HR of 2.739. Combining the radiomics signature with SZM feature was better (HR 4.034) than using radiomics signature alone (HR 3.439). Combining radiomics signature, SZM feature, and clinical variable (HR 6.524) fared better than just combining radiomics signature and clinical variables (HR 4.202). These results confirmed the added benefits of SZM features for prognosis in lung ADC. CONCLUSION: Combining SZM feature with the radiomics signature was better than using the radiomics signature alone and the benefits of SZM features were maintained when clinical variables were added confirming the incremental benefits of SZM features for lung ADC prognosis. KEY POINTS: • Size-zone matrix (SZM) features provide incremental benefits for the prognosis of lung adenocarcinoma. • Combining the radiomics signature with SZM features performed better than using a radiomics signature alone.

Influence of inter-aquifer leakage on well-injection capacity: Theory and aquifer-scale mapping for artificial recharge.

Aquifer storage and recovery (ASR) is an important water resources management technique that involves the injection of a large volume of water underground. For the successful implementation of an ASR project, a target aquifer should have a sufficient injection capacity, which is the maximum volume of water that can be safely injected. In nature, no aquitard is perfectly impermeable, and inter-aquifer leakage may have a major impact on injection capacity. Despite the importance of determining the injection capacity for ASR planning, there is no quantitative methodology that estimates the injection capacity of leaky aquifers. In this study, we first develop a solution for injection capacity with inter-aquifer leakage based on the Hantush - Jacob solution, and conduct a comprehensive sensitivity analysis to elucidate the influence of inter-aquifer leakage on injection capacity. From the sensitivity analysis, we show that inter-aquifer leakage can impact injection capacity by more than one order of magnitude, depending on the hydrogeological and operational parameters. We then develop a practical mapping methodology that estimates the injection capacity of leaky aquifers. We demonstrate the proposed methodology by applying it to a potential ASR site in Minnesota, USA, where ASR is considered as a solution to alleviate groundwater contamination by PFAS chemicals. The case study results reveal significant spatial variability in injection capacity over the study area and show an average increase in the injection capacity of about 26% compared to that in the nonleaky scenario. We also analyze the uncertainty in the estimated injection capacity due to the variability of aquitard properties and show that the variability of aquitard vertical hydraulic conductivity leads to a larger uncertainty in the estimated injection capacity than does the variability of aquitard thickness. This study elucidates the effects of inter-aquifer leakage on injection capacity and provides a practical methodology for injection capacity mapping.

Biogeochemical Alteration of an Aquifer Soil during In Situ Chemical Oxidation by Hydrogen Peroxide and Peroxymonosulfate.

In this study, the effects of in situ chemical oxidation (ISCO) on the biogeochemical properties of an aquifer soil were evaluated. Microcosms packed with an aquifer soil were investigated for 4 months in two phases including oxidant exposure (phase I) and biostimulation involving acetate addition (phase II). The geochemical and microbial alterations from different concentrations (0.2 and 50 mM) of hydrogen peroxide (HP) and peroxymonosulfate (PMS) were assessed. The 50 mM PMS-treated sample exhibited the most significant geochemical changes, characterized by the decrease in pH and the presence of more crystalline phases. Microbial activity decreased for all ISCO-treated microcosms compared to the controls; particularly, the activity was severely inhibited at high PMS concentration exposure. The soil microbial community structures were shifted after the ISCO treatment, with the high PMS causing the most distinct changes. Microbes such as the Azotobacter chroococcum and Gerobacter spp. increased during phase II of the ISCO treatment, indicating these bacterial communities can promote organic degradation despite the oxidants exposure. The HP (low and high concentrations) and low concentration PMS exposure temporarily impacted the microbial activity, with recovery after some duration, whereas the microbial activity was less recovered after the high concentration PMS exposure. These results suggest that the use of HP and low concentration PMS are suitable ISCO strategies for aquifer soil bioattenuation.

Identification of iron and sulfate release processes during riverbank filtration using chemical mass balance modeling.

Various hydrogeochemical processes can modify the quality of river water during riverbank filtration (RBF). Identifying the subsurface processes responsible for the bank-filtered water quality is challenging, but essential for predicting water quality changes and determining the necessity of post-treatment. However, no systematic approach for this has been proposed yet. In this study, the subsurface hydrogeochemical processes that caused the high concentrations of total iron (Fe) and sulfate (SO42-) in the bank-filtered water were investigated at a pilot-scale RBF site in South Korea. For this purpose, water quality variations were monitored in both the extraction well and the adjacent river over five months. The volumetric mixing ratio between the river water and the native groundwater in the RBF well was calculated to understand the effect of mixing on the quality of water from the well and to assess the potential contribution of subsurface reactions to water quality changes. To identify the subsurface processes responsible for the evolution of Fe and SO42- during RBF, an inverse modeling based on the chemical mass balance was conducted using the water quality data and the calculated volumetric mixing ratio. The modeling results suggest that pyrite oxidation by abundant O2 present in an unsaturated zone could be a primary process explaining the evolution of total Fe and SO42- during RBF at the study site. The presence of pyrite in the aquifer was indirectly supported by iron sulfate hydroxide (Fe(SO4)(OH)) detected in oxidized aquifer sediments.

Identification of individuals by trait prediction using whole-genome sequencing data.

Prediction of human physical traits and demographic information from genomic data challenges privacy and data deidentification in personalized medicine. To explore the current capabilities of phenotype-based genomic identification, we applied whole-genome sequencing, detailed phenotyping, and statistical modeling to predict biometric traits in a cohort of 1,061 participants of diverse ancestry. Individually, for a large fraction of the traits, their predictive accuracy beyond ancestry and demographic information is limited. However, we have developed a maximum entropy algorithm that integrates multiple predictions to determine which genomic samples and phenotype measurements originate from the same person. Using this algorithm, we have reidentified an average of >8 of 10 held-out individuals in an ethnically mixed cohort and an average of 5 of either 10 African Americans or 10 Europeans. This work challenges current conceptions of personal privacy and may have far-reaching ethical and legal implications.

Clustering approach to identify intratumour heterogeneity combining FDG PET and diffusion-weighted MRI in lung adenocarcinoma.

OBJECTIVES: Malignant tumours consist of biologically heterogeneous components; identifying and stratifying those various subregions is an important research topic. We aimed to show the effectiveness of an intratumour partitioning method using clustering to identify highly aggressive tumour subregions, determining prognosis based on pre-treatment PET and DWI in stage IV lung adenocarcinoma. METHODS: Eighteen patients who underwent both baseline PET and DWI were recruited. Pre-treatment imaging of SUV and ADC values were used to form intensity vectors within manually specified ROIs. We applied k-means clustering to intensity vectors to yield distinct subregions, then chose the subregion that best matched the criteria for high SUV and low ADC to identify tumour subregions with high aggressiveness. We stratified patients into high- and low-risk groups based on subregion volume with high aggressiveness and conducted survival analyses. This approach is referred to as the partitioning approach. For comparison, we computed tumour subregions with high aggressiveness without clustering and repeated the described procedure; this is referred to as the voxel-wise approach. RESULTS: The partitioning approach led to high-risk (median SUVmax = 14.25 and median ADC = 1.26x10-3 mm2/s) and low-risk (median SUVmax = 14.64 and median ADC = 1.09x10-3 mm2/s) subgroups. Our partitioning approach identified significant differences in survival between high- and low-risk subgroups (hazard ratio, 4.062, 95% confidence interval, 1.21 - 13.58, p-value: 0.035). The voxel-wise approach did not identify significant differences in survival between high- and low-risk subgroups (p-value: 0.325). CONCLUSION: Our partitioning approach identified intratumour subregions that were predictors of survival. KEY POINTS: • Multimodal imaging of PET and DWI is useful for assessing intratumour heterogeneity. • Data-driven clustering identified subregions which might be highly aggressive for lung adenocarcinoma. • The data-driven partitioning results might be predictors of survival.

Reductive dissolution and sequestration of arsenic by microbial iron and thiosulfate reduction.

Iron oxide and oxy-hydroxide are commonly used for remediation and rehabilitation of arsenic (As)-contaminated soil and water. However, the stability of As sequestered by iron oxide and oxy-hydroxide under anaerobic conditions is still uncertain. Geochemical properties influence the behavior of As; in addition, microbial activities affect the mobility of sequestered As in soil and water. Microbial-mediated iron reduction can increase the mobility of As by reductive dissolution of Fe oxide; however, microbial-mediated sulfate reduction can decrease the mobility of As by sulfide mineral precipitation. This study investigated the geomicrobial impact on the behavior of As and stability of sequestered As in iron-rich sediment under anaerobic conditions. Increase in Fe(II) concentrations in water was evidence of microbial-mediated iron reduction. Arsenic concentrations increased with Fe(II) concentration; however, the thiosulfate reduction process also induced immobilization of As through the precipitation of AsFeS. Therefore, microbial-mediated iron reduction and thiosulfate reduction have opposite influences on the mobility of As under anaerobic condition.

Comprehensive Computed Tomography Radiomics Analysis of Lung Adenocarcinoma for Prognostication.

BACKGROUND: In this era of personalized medicine, there is an expanded demand for advanced imaging biomarkers that reflect the biology of the whole tumor. Therefore, we investigated a large number of computed tomography-derived radiomics features along with demographics and pathology-related variables in patients with lung adenocarcinoma, correlating them with overall survival. MATERIALS AND METHODS: Three hundred thirty-nine patients who underwent operation for lung adenocarcinoma were included. Analysis was performed using 161 radiomics features, demographic, and pathologic variables and correlated each with patient survival. Prognostic performance for survival was compared among three models: (a) using only clinicopathological data; (b) using only selected radiomics features; and (c) using both clinicopathological data and selected radiomics features. RESULTS: At multivariate analysis, age, pN, tumor size, type of operation, histologic grade, maximum value of the outer 1/3 of the tumor, and size zone variance were statistically significant variables. In particular, maximum value of outer 1/3 of the tumor reflected tumor microenvironment, and size zone variance represented intratumor heterogeneity. Integration of 31 selected radiomics features with clinicopathological variables led to better discrimination performance. CONCLUSION: Radiomics approach in lung adenocarcinoma enables utilization of the full potential of medical imaging and has potential to improve prognosis assessment in clinical oncology. IMPLICATIONS FOR PRACTICE: Two radiomics features were prognostic for lung cancer survival at multivariate analysis: (a) maximum value of the outer one third of the tumor reflects the tumor microenvironment and (b) size zone variance represents the intratumor heterogeneity. Therefore, a radiomics approach in lung adenocarcinoma enables utilization of the full potential of medical imaging and could play a larger role in clinical oncology.

Predicting Survival Using Pretreatment CT for Patients With Hepatocellular Carcinoma Treated With Transarterial Chemoembolization: Comparison of Models Using Radiomics.

OBJECTIVE: The purpose of this study was to investigate the use of radiomics features as prognostic biomarkers for predicting the survival of patients treated with transarterial chemoembolization (TACE) for hepatocellular carcinoma (HCC). MATERIALS AND METHODS: We retrospectively analyzed 88 patients with HCC treated with TACE. High-dimensional quantitative feature analysis was applied to extract 116 radiomics features of pretreatment CT. A radiomics score model was constructed from these features with the use of least absolute shrinkage and selection operator Cox regression. A clinical score model was constructed from clinical variables with the use of multivariate Cox regression. A combined score model was constructed using the radiomics and clinical models. We compared the three models (the radiomics score, clinical score, and combined score models) for predicting overall survival, using Kaplan-Meier analysis and the log-rank test. RESULTS: The following radiomics features were selected for the radiomics score model: histogram-based features (median, kurtosis, and energy), shape-based features (spherical disproportion and surface-to-volume ratio), gray-level co-occurrence matrix (GLCM)-based features (energy, informational measure of correlation, maximum probability, contrast, and sum average), and intensity size zone matrix-based features (size zone variability). For the clinical score model, the Child-Pugh score, α-fetoprotein level, and HCC size were included. The combined score model included five radiomics features (surface area-to-volume ratio, kurtosis, median, gray-level co-occurrence matrix contrast, and size zone variability) and three clinical factors (Child-Pugh score, α-fetoprotein level, and HCC size). The combined model was a better predictor of survival (hazard ratio, 19.88; p < 0.0001) than the clinical score model or the radiomics score model. CONCLUSION: A radiomics approach combined with conventional clinical variables could be effective in predicting the survival of patients with HCC treated with TACE.

Backward genotype-transcript-phenotype association mapping.

Genome-wide association studies have discovered a large number of genetic variants associated with complex diseases such as Alzheimer's disease. However, the genetic background of such diseases is largely unknown due to the complex mechanisms underlying genetic effects on traits, as well as a small sample size (e.g., 1000) and a large number of genetic variants (e.g., 1 million). Fortunately, datasets that contain genotypes, transcripts, and phenotypes are becoming more readily available, creating new opportunities for detecting disease-associated genetic variants. In this paper, we present a novel approach called "Backward Three-way Association Mapping" (BTAM) for detecting three-way associations among genotypes, transcripts, and phenotypes. Assuming that genotypes affect transcript levels, which in turn affect phenotypes, we first find transcripts associated with the phenotypes, and then find genotypes associated with the chosen transcripts. The backward ordering of association mappings allows us to avoid a large number of association testings between all genotypes and all transcripts, making it possible to identify three-way associations with a small computational cost. In our simulation study, we demonstrate that BTAM significantly improves the statistical power over "forward" three-way association mapping that finds genotypes associated with both transcripts and phenotypes and genotype-phenotype association mapping. Furthermore, we apply BTAM on an Alzheimer's disease dataset and report top 10 genotype-transcript-phenotype associations.

A network-driven approach for genome-wide association mapping.

MOTIVATION: It remains a challenge to detect associations between genotypes and phenotypes because of insufficient sample sizes and complex underlying mechanisms involved in associations. Fortunately, it is becoming more feasible to obtain gene expression data in addition to genotypes and phenotypes, giving us new opportunities to detect true genotype-phenotype associations while unveiling their association mechanisms. RESULTS: In this article, we propose a novel method, NETAM, that accurately detects associations between SNPs and phenotypes, as well as gene traits involved in such associations. We take a network-driven approach: NETAM first constructs an association network, where nodes represent SNPs, gene traits or phenotypes, and edges represent the strength of association between two nodes. NETAM assigns a score to each path from an SNP to a phenotype, and then identifies significant paths based on the scores. In our simulation study, we show that NETAM finds significantly more phenotype-associated SNPs than traditional genotype-phenotype association analysis under false positive control, taking advantage of gene expression data. Furthermore, we applied NETAM on late-onset Alzheimer's disease data and identified 477 significant path associations, among which we analyzed paths related to beta-amyloid, estrogen, and nicotine pathways. We also provide hypothetical biological pathways to explain our findings. AVAILABILITY AND IMPLEMENTATION: Software is available at http://www.sailing.cs.cmu.edu/ CONTACT: : epxing@cs.cmu.edu.

Oxidizing capacity of periodate activated with iron-based bimetallic nanoparticles.

Nanosized zerovalent iron (nFe0) loaded with a secondary metal such as Ni or Cu on its surface was demonstrated to effectively activate periodate (IO4-) and degrade selected organic compounds at neutral pH. The degradation was accompanied by a stoichiometric conversion of IO4- to iodate (IO3-). nFe0 without bimetallic loading led to similar IO4- reduction but no organic degradation, suggesting the production of reactive iodine intermediate only when IO4- is activated by bimetallic nFe0 (e.g., nFe0-Ni and nFe0-Cu). The organic degradation kinetics in the nFe0-Ni(or Cu)/IO4- system was substrate dependent: 4-chlorophenol, phenol, and bisphenol A were effectively degraded, whereas little or no degradation was observed with benzoic acid, carbamazepine, and 2,4,6-trichlorophenol. The substrate specificity, further confirmed by little kinetic inhibition with background organic matter, implies the selective nature of oxidant in the nFe0-Ni(or Cu)/IO4- system. The comparison with the photoactivated IO4- system, in which iodyl radical (IO3•) is a predominant oxidant in the presence of methanol, suggests IO3• also as primary oxidant in the nFe0-Ni(or Cu)/IO4- system.

Development of a predictive model for hypoxia due to sedatives in gastrointestinal endoscopy: a prospective clinical study in Korea.

BACKGROUND/AIMS: Sedation has become a standard practice for patients undergoing gastrointestinal (GI) endoscopy. However, considering the serious cardiopulmonary adverse events associated with sedatives, it is important to identify patients at high risk. Machine learning can generate reasonable prediction for a wide range of medical conditions. This study aimed to evaluate the risk factors associated with sedation during GI endoscopy and develop a predictive model for hypoxia during endoscopy under sedation. METHODS: This prospective observational study enrolled 446 patients who underwent sedative endoscopy at the Korea University Ansan Hospital. Clinical data were used as predictor variables to construct predictive models using the random forest method that is a machine learning algorithm. RESULTS: Seventy-two of the 446 patients (16.1%) experienced life-threatening hypoxia requiring immediate medical intervention. Patients who developed hypoxia had higher body weight, body mass index (BMI), neck circumference, and Mallampati scores. Propofol alone and higher initial and total dose of propofol were significantly associated with hypoxia during sedative endoscopy. Among these variables, high BMI, neck circumference, and Mallampati score were independent risk factors for hypoxia. The area under the receiver operating characteristic curve for the random forest-based predictive model for hypoxia during sedative endoscopy was 0.82 (95% confidence interval, 0.79-0.86) and displayed a moderate discriminatory power. CONCLUSIONS: High BMI, neck circumference, and Mallampati score were independently associated with hypoxia during sedative endoscopy. We constructed a model with acceptable performance for predicting hypoxia during sedative endoscopy.

Prediction of arsenic retention in vadose zone based on empirical relationship between soil properties and segmented retardation factors.

Arsenic (As) is a widespread environmental contaminant that poses a significant threat to ecosystems and human health. Although previous studies have qualitatively revealed the effects of individual soil properties on the transport and fate of As in the vadose zone, their integrated impacts remain obscure. Moreover, studies investigating the retardation factor therein, which is a key parameter for comprehending As transport in the vadose zone, are extremely limited. In this study, we investigated the interplay of soil properties with As transport and retention within the vadose zone, while focusing on the retardation factor of As. We employed steady-state unsaturated water-flow soil column experiments coupled with a mobile-immobile model and multiple linear regression analysis to elucidate the dependence of As retardation factors on the soil properties. In the mobile water zone, iron and organic matter contents emerged as the two most influential properties that impedes As mobility. Whereas, in the immobile water zone, the coefficient of uniformity and bulk density were the most influential factors that enhanced As retention. Finally, we derived an empirical equation for calculating the As retardation factors in each zone, offering a valuable tool for describing and predicting As behavior to protect the groundwater resources underneath.

Influence of iron (hydr)oxide mineralogy and contents in aquifer sediments on dissolved organic carbon attenuations during aquifer storage and recovery.

Aquifer storage and recovery (ASR) is a promising approach for managing water resources that enhances water quality through biogeochemical reactions occurring within aquifers. Iron (hydr)oxides, which are the predominant metallic oxides in soil, play a crucial role in degrading dissolved organic carbon (DOC), primarily through a process known as dissimilatory iron reduction (DIR). However, the efficiency of this reaction varies depending on the mineralogy and composition of the aquifer, and this understanding is essential for adequate water quality in ASR. The objective of this study is to investigate the impact of iron (hydr)oxide on acetate, as an organic carbon source, attenuation during the ASR. To achieve this, three sets of laboratory sediment columns were prepared, each containing a different type of iron (hydr)oxide minerals: ferrihydrite, goethite, and hematite. Following an acclimation period of 28 days to simulate the microcosm within an aquifer, the columns were continuously supplied with the simulated river water spiked with acetate (DOC 40-60 mg L-1), and the acetate concentration in the effluent was monitored. The result revealed that the column containing ferrihydrite achieved 97% acetate attenuation through DIR with anoxic conditions (DO < 0.1 mg L-1), while the goethite and hematite columns exhibited limited attenuation rates of 40 and 50%, respectively. Furthermore, the efficiency of acetate attenuation in the ferrihydrite columns increased with the content of ferrihydrite but experienced a rapidly declined at higher contents (3-4%), possibly due to the partial conversion of ferrihydrite to goethite as a result of the interaction between ferrihydrite and the Fe(II) produced during DIR. Additionally, an analysis of the microbial community demonstrated that microorganisms known to possess the ability to reduce iron (hydr)oxides under anaerobic conditions were abundant in the ferrihydrite columns.

Synergetic effect of nitrate on dissolved organic carbon attenuation through dissimilatory iron reduction during aquifer storage and recovery.

Aquifer storage and recovery (ASR) is a promising water management technique in terms of quantity and quality. During ASR, iron (Fe) (hydr)oxides contained in the aquifer play a crucial role as electron acceptors in attenuating dissolved organic carbon (DOC) in recharging water through dissimilatory iron reduction (DIR). Considering the preference of electron acceptors, nitrate (NO3⁻), possibly coexisting with DOC as the prior electron acceptor to Fe (hydr)oxides, might influence DIR by interrupting electron transfer. However, this phenomenon is yet to be clarified. In this study, we systematically investigated the potential effect of NO3⁻ on DOC attenuation during ASR using a series of sediment columns representing typical aquifer conditions. The results suggest that DOC attenuation could be enhanced by the presence of NO3⁻. Specifically, total DOC attenuation was notably higher than that from the stoichiometric calculation simply employing NO3⁻ as the additional electron acceptor to Fe (hydr)oxides, implying a synergetic effect of NO3⁻ in the overall reactions. X-ray photoelectron spectroscopy analyzes revealed that the Fe(II) ions released from DIR transformed the Fe (hydr)oxides into a less bioavailable form, inhibiting further DIR. In the presence of NO3⁻, however, no aqueous Fe(II) was detected, and another form of Fe (hydr)oxide appeared on the sediment surface. This may be attributed to nitrate-dependent Fe(II) oxidation (NDFO), in which Fe(II) is (re)oxidized into Fe (hydr)oxide, which is available for the subsequent DOC attenuation. These mechanisms were supported by the dominance of DIR-relevant bacteria and the growth of NDFO-related bacteria in the presence of NO3⁻.

Empirical relationship between vadose zone properties and diesel attenuation capacity: A complement for intrinsic vulnerability models.

Evaluating and predicting the natural attenuation capacity (AC) of a vadose zone is essential for determining groundwater vulnerability to contamination from upper sources. However, it remains unclear how the physicochemical properties of vadose zone soils affect AC owing to their complexity and spatial heterogeneity. In this study, we developed a regression model for estimating the AC of a vadose zone against diesel using datasets from different soils with a wide range of physicochemical properties. Among the 17 properties, six (i.e., organic matter (OM), total phosphorous (TP), coefficient of uniformity, particle size (D30), van Genuchten's n, saturation degree (SD)) were selected as primary regressors. The results indicate that biogeochemical factors, including OM and TP, have decisive effects on the AC. Finally, the regression model was expanded to a GIS-based spatial model and applied to Namyangju, Korea using the index-overlay method. The produced AC map showed a nonmonotonic decrease along the depth, and the areas closer to the water bodies generally represented low AC values, most likely due to the lower OM, TP, and higher SD. This study provides an empirical basis for future research initiatives for spatial and temporal AC dynamics, which complements conventional intrinsic groundwater vulnerability models such as DRASTIC.

Unveiling interfacial interaction between antimony oxyanions and boehmite nanorods: Spectroscopic evidence and density functional theory analysis.

In natural environments, the fate and migratory behavior of metalloid contaminants such as antimony (Sb) significantly depend on the interfacial reactivity of mineral surfaces. Although boehmite (γ-AlOOH) is widely observed in (sub)surface environments, its underlying interaction mechanism with Sb oxyanions at the molecular scale remains unclear. Considering Sb-contaminated environmental conditions in this study, we prepared boehmite under weakly acidic conditions for use in the systematic investigation of interfacial interactions with Sb(III) and Sb(V). The as-synthesized boehmite showed a nanorod morphology and comprised four crystal facets in the following order: 48.4% (010), 27.1% (101), 15.0% (001), and 9.5% (100). The combined results of spectroscopic analyses and theoretical calculations revealed that Sb(III) formed hydrogen bonding outer-sphere complexation on the (100), (010), and (001) facets and that Sb(V) preferred to form bidentate inner-sphere complexation via mononuclear edge-sharing configuration on the (100), (001), and (101) facets and binuclear corner-sharing configuration on the (010) facet. These findings indicate that the facet-mediated thermodynamic stability of the surface complexation determines the interaction affinity toward the Sb species. This work is the first to document the contribution of boehmite to (sub)surface media, improving the ability to forecast the fate and behavior of Sb oxyanions at mineral-water interfaces.