A Review on Applications of Time-Lapse Electrical Resistivity Tomography Over the Last 30 Years : Perspectives for Mining Waste Monitoring.

Mining operations generate large amounts of wastes which are usually stored into large-scale storage facilities which pose major environmental concerns and must be properly monitored to manage the risk of catastrophic failures and also to control the generation of contaminated mine drainage. In this context, non-invasive monitoring techniques such as time-lapse electrical resistivity tomography (TL-ERT) are promising since they provide large-scale subsurface information that complements surface observations (walkover, aerial photogrammetry or remote sensing) and traditional monitoring tools, which often sample a tiny proportion of the mining waste storage facilities. The purposes of this review are as follows: (i) to understand the current state of research on TL-ERT for various applications; (ii) to create a reference library for future research on TL-ERT and geoelectrical monitoring mining waste; and (iii) to identify promising areas of development and future research needs on this issue according to our experience. This review describes the theoretical basis of geoelectrical monitoring and provides an overview of TL-ERT applications and developments over the last 30 years from a database of over 650 case studies, not limited to mining operations (e.g., landslide, permafrost). In particular, the review focuses on the applications of ERT for mining waste characterization and monitoring and a database of 150 case studies is used to identify promising applications for long-term autonomous geoelectrical monitoring of the geotechnical and geochemical stability of mining wastes. Potential challenges that could emerge from a broader adoption of TL-ERT monitoring for mining wastes are discussed. The review also considers recent advances in instrumentation, data acquisition, processing and interpretation for long-term monitoring and draws future research perspectives and promising avenues which could help improve the design and accuracy of future geoelectric monitoring programs in mining wastes.

PyLEnM: A Machine Learning Framework for Long-Term Groundwater Contamination Monitoring Strategies.

In this study, we have developed a comprehensive machine learning (ML) framework for long-term groundwater contamination monitoring as the Python package PyLEnM (Python for Long-term Environmental Monitoring). PyLEnM aims to establish the seamless data-to-ML pipeline with various utility functions, such as quality assurance and quality control (QA/QC), coincident/colocated data identification, the automated ingestion and processing of publicly available spatial data layers, and novel data summarization/visualization. The key ML innovations include (1) time series/multianalyte clustering to find the well groups that have similar groundwater dynamics and to inform spatial interpolation and well optimization, (2) the automated model selection and parameter tuning, comparing multiple regression models for spatial interpolation, (3) the proxy-based spatial interpolation method by including spatial data layers or in situ measurable variables as predictors for contaminant concentrations and groundwater levels, and (4) the new well optimization algorithm to identify the most effective subset of wells for maintaining the spatial interpolation ability for long-term monitoring. We demonstrate our methodology using the monitoring data at the Savannah River Site F-Area. Through this open-source PyLEnM package, we aim to improve the transparency of data analytics at contaminated sites, empowering concerned citizens as well as improving public relations.

Low-Power, Flexible Sensor Arrays with Solderless Board-to-Board Connectors for Monitoring Soil Deformation and Temperature.

Landslides are a global and frequent natural hazard, affecting many communities and infrastructure networks. Technological solutions are needed for long-term, large-scale condition monitoring of infrastructure earthworks or natural slopes. However, current instruments for slope stability monitoring are often costly, require a complex installation process and/or data processing schemes, or have poor resolution. Wireless sensor networks comprising low-power, low-cost sensors have been shown to be a crucial part of landslide early warning systems. Here, we present the development of a novel sensing approach that uses linear arrays of three-axis accelerometers for monitoring changes in sensor inclination, and thus the surrounding soil's deformation. By combining these deformation measurements with depth-resolved temperature measurements, we can link our data to subsurface thermal-hydrological regimes where relevant. In this research, we present a configuration of cascaded I2C sensors that (i) have ultra-low power consumption and (ii) enable an adjustable probe length. From an electromechanical perspective, we developed a novel board-to-board connection method that enables narrow, semi-flexible sensor arrays and a streamlined assembly process. The low-cost connection method relies on a specific FR4 printed circuit board design that allows board-to-board press fitting without using electromechanical components or solder connections. The sensor assembly is placed in a thin, semi-flexible tube (inner diameter 6.35 mm) that is filled with an epoxy compound. The resulting sensor probe is connected to an AA-battery-powered data logger with wireless connectivity. We characterize the system's electromechanical properties and investigate the accuracy of deformation measurements. Our experiments, performed with probes up to 1.8 m long, demonstrate long-term connector stability, as well as probe mechanical flexibility. Furthermore, our accuracy analysis indicates that deformation measurements can be performed with a 0.390 mm resolution and a 95% confidence interval of ±0.73 mm per meter of probe length. This research shows the suitability of low-cost accelerometer arrays for distributed soil stability monitoring. In comparison with emerging low-cost measurements of surface displacement, our approach provides depth-resolved deformation, which can inform about shallow sliding surfaces.

Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics.

Bedrock property quantification is critical for predicting the hydrological response of watersheds to climate disturbances. Estimating bedrock hydraulic properties over watershed scales is inherently difficult, particularly in fracture-dominated regions. Our analysis tests the covariability of above- and belowground features on a watershed scale, by linking borehole geophysical data, near-surface geophysics, and remote sensing data. We use machine learning to quantify the relationships between bedrock geophysical/hydrological properties and geomorphological/vegetation indices and show that machine learning relationships can estimate most of their covariability. Although we can predict the electrical resistivity variation across the watershed, regions of lower variability in the input parameters are shown to provide better estimates, indicating a limitation of commonly applied geomorphological models. Our results emphasize that such an integrated approach can be used to derive detailed bedrock characteristics, allowing for identification of small-scale variations across an entire watershed that may be critical to assess the impact of disturbances on hydrological systems.

Integrated hydrogeophysical modelling and data assimilation for geoelectrical leak detection.

Time-lapse electrical resistivity tomography (ERT) measurements provide indirectobservations of hydrological processes in the Earth's shallow subsurface at high spatial and temporal resolution. ERT has been used in the past decades to detect leaks and monitor the evolution of associated contaminant plumes. Specifically, inverted resistivity images allow visualization of the dynamic changes in the structure of the plume. However, existing methods do not allow the direct estimation of leak parameters (e.g. leak rate, location, etc.) and their uncertainties. We propose an ensemble-based data assimilation framework that evaluates proposed hydrological models against observed time-lapse ERT measurements without directly inverting for the resistivities. Each proposed hydrological model is run through the parallel coupled hydro-geophysical simulation code PFLOTRAN-E4D to obtain simulated ERT measurements. The ensemble of model proposals is then updated using an iterative ensemble smoother. We demonstrate the proposed framework on synthetic and field ERT data from controlled tracer injection experiments. Our results show that the approach allows joint identification of contaminant source location, initial release time, and solute loading from the cross-borehole time-lapse ERT data, alongside with an assessment of uncertainties in these estimates. We demonstrate a reduction in site-wide uncertainty by comparing the prior and posterior plume mass discharges at a selected image plane. This framework is particularly attractive to sites that have previously undergone extensive geological investigation (e.g., nuclear sites). It is well suited to complement ERT imaging and we discuss practical issues in its application to field problems.

Dual in-aquifer and near surface processes drive arsenic mobilization in Cambodian groundwaters.

Millions of people globally, and particularly in South and Southeast Asia, face chronic exposure to arsenic from reducing groundwaters in which. Arsenic release to is widely attributed largely to reductive dissolution of arsenic-bearing iron minerals, driven by metal reducing bacteria using bioavailable organic matter as an electron donor. However, the nature of the organic matter implicated in arsenic mobilization, and the location within the subsurface where these processes occur, remains debated. In a high resolution study of a largely pristine, shallow aquifer in Kandal Province, Cambodia, we have used a complementary suite of geochemical tracers (including 14C, 3H, 3He, 4He, Ne, δ18O, δD, CFCs and SF6) to study the evolution in arsenic-prone shallow reducing groundwaters along dominant flow paths. The observation of widespread apparent 3H-3He ages of <55years fundamentally challenges some previous models which concluded that groundwater residence times were on the order of hundreds of years. Surface-derived organic matter is transported to depths of >30m, and the relationships between age-related tracers and arsenic suggest that this surface-derived organic matter is likely to contribute to in-aquifer arsenic mobilization. A strong relationship between 3H-3He age and depth suggests the dominance of a vertical hydrological control with an overall vertical flow velocity of ~0.4±0.1m·yr-1 across the field area. A calculated overall groundwater arsenic accumulation rate of ~0.08±0.03µM·yr-1 is broadly comparable to previous estimates from other researchers for similar reducing aquifers in Bangladesh. Although apparent arsenic groundwater accumulation rates varied significantly with site (e.g. between sand versus clay dominated sequences), rates are generally highest near the surface, perhaps reflecting the proximity to the redox cline and/or depth-dependent characteristics of the OM pool, and confounded by localized processes such as continued in-aquifer mobilization, sorption/desorption, and methanogenesis.

High resolution profile of inorganic aqueous geochemistry and key redox zones in an arsenic bearing aquifer in Cambodia.

Arsenic contamination of groundwaters in South and Southeast Asia is a major threat to public health. In order to better understand the geochemical controls on the mobility of arsenic in a heavily arsenic-affected aquifer in northern Kandal Province, Cambodia, key changes in inorganic aqueous geochemistry have been monitored at high vertical and lateral resolution along dominant groundwater flow paths along two distinct transects. The two transects are characterized by differing geochemical, hydrological and lithological conditions. Arsenic concentrations in groundwater are highly heterogenous, and are broadly positively associated with iron and negatively associated with sulfate and dissolved oxygen. The observed correlations are generally consistent with arsenic mobilization by reductive-dissolution of iron (hydr)oxides. Key redox zones, as identified using groupings of the PHREEQC model equilibrium electron activity of major redox couples (notably ammonium/nitrite; ammonium/nitrate; nitrite/nitrate; dissolved oxygen/water) have been identified and vary with depth, site and season. Mineral saturation is also characterized. Seasonal changes in groundwater chemistry were observed in areas which were (i) sandy and of high permeability; (ii) in close proximity to rivers; and/or (iii) in close proximity to ponds. Such changes are attributed to monsoonal-driven surface-groundwater interactions and are consistent with the separate provenance of recharge sources as identified using stable isotope mixing models.

Geoelectrical monitoring of simulated subsurface leakage to support high-hazard nuclear decommissioning at the Sellafield Site, UK.

A full-scale field experiment applying 4D (3D time-lapse) cross-borehole Electrical Resistivity Tomography (ERT) to the monitoring of simulated subsurface leakage was undertaken at a legacy nuclear waste silo at the Sellafield Site, UK. The experiment constituted the first application of geoelectrical monitoring in support of decommissioning work at a UK nuclear licensed site. Images of resistivity changes occurring since a baseline date prior to the simulated leaks revealed likely preferential pathways of silo liquor simulant flow in the vadose zone and upper groundwater system. Geophysical evidence was found to be compatible with historic contamination detected in permeable facies in sediment cores retrieved from the ERT boreholes. Results indicate that laterally discontinuous till units forming localized hydraulic barriers substantially affect flow patterns and contaminant transport in the shallow subsurface at Sellafield. We conclude that only geophysical imaging of the kind presented here has the potential to provide the detailed spatial and temporal information at the (sub-)meter scale needed to reduce the uncertainty in models of subsurface processes at nuclear sites.

Parathyroid hormone-related protein confers chemoresistance by blocking apoptosis signaling via death receptors and mitochondria.

Parathyroid hormone (PTH) has a central role in the regulation of serum calcium and phosphate, whereas parathyroid hormone-related peptide (PTHrP) has important developmental roles. In addition, PTHrP has been discovered as a causative agent of hypercalcemia of malignancy. PTHrP is also expressed in many tumors, and expression often correlates with unfavorable prognosis. We have investigated the effects of PTHrP on apoptosis signaling pathways initiated by DNA damaging chemotherapeutic drugs. Stimulation experiments of the CD95-, the TNF-R-, and the TRAIL-R-death receptor systems in Saos human osteosarcoma cells revealed that PTHrP can block signaling via each of these death receptors. Furthermore, our findings demonstrate a link between PTHrP and the mitochondrial apoptosis pathway. PTHrP down-regulates expression of pro-apoptotic Bcl-2 family members like Bax and PUMA and up-regulates expression of antiapoptotic molecules like Bcl-2 and Bcl-xl. It is of clinical relevance that PTHrP and anticancer drugs show opposing interactions on death receptor-triggered as well as on mitochondrial apoptosis pathways. In addition, PTHrP induces chemoresistance by interference with p53 family-dependent apoptosis signaling pathways and p53-mediated transactivation of apoptosis target genes. Inhibition of CD95- and Bax gene transactivation is a mechanism by which PTHrP reduced the apoptosis response and treatment sensitivity of tumor cells. Our data indicate that PTHrP inhibits major apoptosis signaling pathways by blocking signaling via p53, death receptors and mitochondria and, consequently, confers chemoresistance of cancer cells. Thus, beyond its importance in development and differentiation, we describe an important role for PTHrP in tumorigenesis.