Matthew Eyre

Water contamination poses threats not only to aquatic life but also to human health. Understanding mining-related potentially ecotoxic metal(loid) (PEM) contamination in a tropical environment is vital for regulation and management. The study aimed to examine the concentrations, sources, and environmental and human health risks of PEMs in tropical river systems. Using the mining-impacted River Nyamwamba in Southwestern Uganda as an exemplar case study, 19 water samples along the River Nyamwamba and its tributaries, and Lake George were collected. The samples were analysed with ICP-MS. Concentrations decreased in the order Co > Mn > Fe > Cu > Ni > Al > Zn > Mo > As > Pb > Cd = Cr. Cobalt, Mn, and Ni concentrations exceeded safe drinking water standards. Multivariate statistical analysis revealed that mining contributed to the presence of As, Co, Cu, Mn, Mo, Ni, and Zn. Pollution load index and potential ecological risk index indicated severe ecological risks. Health risk assessment showed that both carcinogenic and non-carcinogenic risks were posed to human health, with children being the most vulnerable. Up to 2 in 10 children and 3 in 100 adults were at risk of developing excess cancer from PEMs exposure in the river water. The study highlights the importance of preventing untreated aqueous and mine waste discharge into tropical river systems, and recommends that the local government sensitise the community and restrict the use of River Nyamwamba water in favour of other sources (shallow wells, boreholes, springs, and rainwater harvesting), while regularly monitoring water quality.

Legacy colliery spoil tip failures pose a significant hazard that can result in harm to persons or damage to property and infrastructure. In this research, the 2020 Wattstown tip landslide caused by heavy rainfall was examined to investigate the likely mechanisms and developmental factors contributing to colliery spoil tip failures in Welsh coalfields. To achieve this, an integrated method was proposed through the combination of remote sensing mapping, stability chart analysis, 2D limit equilibrium (LE) modelling, and 3D finite difference method (FDM) analysis. Various water table geometries were incorporated into these models to ascertain the specific groundwater condition that triggered the occurrence of the 2020 landslide. In addition, sensitivity analyses were carried out to assess the influence of the colliery spoil properties (i.e., cohesion, friction angle, and porosity) on the slope stability analysis. The results indicate that the landslide was characterised by a shallow rotational failure mode and spatially constrained by the critical water table and an underlying geological interface. In addition, the results also imply that the landslide was triggered by the rise of water table associated with heavy rainfall. Through sensitivity analysis, it was found that the properties of the colliery spoil played an important role in confining the extent of the landslide and controlling the process of its development. The findings underscore the detrimental effects of increased pore pressures, induced by heavy rainfall, on the stability of colliery tips, highlighting the urgent needs for local government to enhance water management strategies for this region.

Semi-automated algorithms incorporating multi-sourced datasets into a single analysis are increasingly common, but until now operate at a fixed pixel resolution resulting in multi-sourced methods being limited by the largest input pixel size. Multi-scale lineament detection circumvents this issue and allows increased levels of detail to be captured. We present a semi-automated method using a bottom-up Object-Based Image Analysis approach to map regional lineaments to a high level of detail. The method is applied to onshore light detection and ranging (LiDAR) data and offshore bathymetry around the Land's End Granite (Cornwall, UK). The method uses three different pixel resolutions to extract detailed lineaments across a 700 km ² area. The granite displays large-scale NW–SE fault zones that are considered analogous to those being targeted as onshore deep geothermal reservoirs (2–5 km in depth). Investigation of the lineaments derived from this study shows along-strike variations from NW–SE orientations within granite to NNW–SSE within slate and reflects structural inheritance of early Variscan structures within Devonian slates. This is furthered by analysing these major structures for reservoir potential. Lineaments proximal to these broadly NW–SE features indicate that a damage zone c. 100–200 m wide is present. These observations provide a preliminary understanding of reservoir characteristics for fault-hosted geothermal systems. Thematic collection: This article is part of the Remote sensing for site investigations on Earth and other planets collection available at: https://www.lyellcollection.org/topic/collections/remote-sensing-for-site-investigations-on-earth-and-other-planets Supplementary material: A description of the OBAI method and additional figures are available at https://doi.org/10.6084/m9.figshare.c.6309629

The increased accessibility of drone technology and structure from motion 3D scene reconstruction have transformed the approach for mapping inaccessible slopes undergoing active rockfalls and generating virtual outcrop models (VOM). The Poggio Baldi landslide (Central Italy) and its natural laboratory offers the possibility to monitor and characterise the slope to define a workflow for rockfall hazard analysis. In this study, the analysis of multitemporal VOM (2016–2019) informed a rockfall trajectory analysis that was carried out with a physical-characteristic-based GIS model. The rockfall scenarios were reconstructed and then tested based on the remote sensing observations of the rock mass characteristics of both the main scarp and the rockfall fragment inventory deposited on the slope. The highest concentration of trajectory endpoints occurred at the very top of the debris talus, which was constrained by a narrow channel, while longer horizontal travel distances were allowed on the lower portion of the slope. To further improve the understanding of the Poggio Baldi landslide, a time-independent rockfall hazard analysis aiming to define the potential runout associated with several rock block volumetric classes is a critical component to any subsequent risk analysis in similar mountainous settings featuring marly–arenaceous multilayer sedimentary successions and reactivated main landslide scarps.

Globally, many mines emit acid mine drainage (AMD) during and after their operational life cycle. AMD can affect large and often inaccessible areas. This leads to expensive monitoring via conventional ground-based sampling. Recent advances in remote sensing which are both non-intrusive and less time-consuming hold the potential to unlock a new paradigm of automated AMD analysis. Herein, we test the feasibility of remote sensing as a standalone tool to map AMD at various spatial resolutions and altitudes in water-impacted mining environments. This was achieved through the same-day collection of satellite-based simulated Sentinel-2 (S2) and PlanetScope (PS2.SD) imagery and drone-based UAV Nano-Hyperspec (UAV) imagery, in tandem with ground-based visible and short-wave infrared analysis. The study site was a historic tin and copper mine in Cornwall, UK. The ground-based data collection took place on the 30 July 2020. Ferric (Fe(III) iron) band ratio (665/560 nm wavelength) was used as an AMD proxy to map AMD pixel distribution. The relationship between remote-sensed Fe(III) iron reflectance values and ground-based Fe(III) iron reflectance values deteriorated as sensor spatial resolution decreased from high-resolution UAV imagery (2 per pixel; r² = 0.78) to medium-resolution PlanetScope Dove-R (3 m² per pixel; r² = 0.51) and low-resolution simulated Sentinel-2 (10 m² per pixel; r² = 0.23). A fractioned water pixel (FWP) analysis was used to identify mixed pixels between land and the nearby waterbody, which lowered spectral reflectance. Increases in total mixed pixels were observed as the spatial resolution of sensors decreased (UAV: 2.4%, PS: 3.7%, S2: 8.5%). This study demonstrates that remote sensing is a non-intrusive AMD surveying tool with varying degrees of effectiveness relative to sensor spatial resolution. This was achieved by identifying and successfully mapping a cross-sensor Fe(III) iron band ratio whilst recognizing water bodies as reflectance inhibitors for passive sensors.

The increased accessibility of drone technology and the wide use of Structure from Motion 3D scene reconstruction have transformed the approach for mapping inaccessible slopes undergoing active rockfalls. The Poggio Baldi landslide offers the possibility for many of these techniques to be deployed and integrated with the aim of defining a suitable workflow for the analysis of hazards in mountainous regions. The generation of multitemporal digital slope twins (2016 – 2019), informed a rockfall trajectory analysis that was carried out with a physical-based GIS model. We tested the rockfall scenario reconstructed and calibrated on the analysis of the rock mass characteristics and the geometrical and physical constraints given by the multi-temporal analysis of the SfM point clouds. This time-independent rockfall hazard analysis is a critical component to any subsequent holistic risk analysis on this case study, and any potential similar mountainous setting.

This paper focuses on the back analysis of an ancient, catastrophic rock avalanche located in the small city of Lettopalena (Chieti, Italy). The integrated use of various investigation methods was employed for landslide analysis, including the use of traditional manual surveys and remote sensing (RS) mapping for the identification of geological structures. The outputs of the manual and RS surveys were then utilised to numerically model the landslide using a 2D distinct element method. A series of numerical simulations were undertaken to perform a sensitivity analysis to investigate the uncertainty of discontinuity properties on the slope stability analysis and provide further insight into the landslide failure mechanism. Both numerical modelling and field investigations indicate that the landslide was controlled by translational sliding along a folded bedding plane, with toe removal because of river erosion. This generated daylighting of the bedding plane, creating kinematic freedom for the landslide. The formation of lateral and rear release surfaces was influenced by the orientation of the discrete fracture network. Due to the presence of an anticline, the landslide region was constrained in the middle-lower section of the slope, where the higher inclination of the bedding plane was detected. The landslide is characterized by a step-path slip surface at the toe of the slope, which was observed both in the modelling and the field. This paper highlights the combined use of a geological model and numerical modelling to provide an improved understanding of the origin and development of rock avalanches under the influence of river erosion, anticline structures, and related faults and fractures.

This paper proposes a novel method to incorporate unfavorable orientations of discontinuities into machine learning (ML) landslide prediction by using GIS-based kinematic analysis. Discontinuities, detected from photogrammetric and aerial LiDAR surveys, were included in the assessment of potential rock slope instability through GIS-based kinematic analysis. Results from the kinematic analysis, coupled with several commonly used landslide influencing factors, were adopted as input variables in ML models to predict landslides. In this paper, various ML models, such as random forest (RF), support vector machine (SVM), multilayer perceptron (MLP) and deep learning neural network (DLNN) models were evaluated. Results of two validation methods (confusion matrix and ROC curve) show that the involvement of discontinuity-related variables significantly improved the landslide predictive capability of these four models. Their addition demonstrated a minimum of 6% and 4% increase in the overall prediction accuracy and the area under curve (AUC), respectively. In addition, frequency ratio (FR) analysis showed good consistency between landslide probability that was characterized by FR values and discontinuity-related variables, indicating a high correlation. Both results of model validation and FR analysis highlight that inclusion of discontinuities into ML models can improve landslide prediction accuracy.

This paper presents a novel technique to improve geological understanding in regions of historic mining activity. This is achieved through inferring the orientations of geological structures from the imprints left on the landscape by past mining activities. Open source high resolution LiDAR datasets are used to fine-tune a deep convolutional neural network designed initially for Lunar LiDAR crater identification. By using a transfer learning approach between these two very similar domains, high accuracy predictions of pit locations can be generated in the form of a raster mask of pit location probabilities. Taking the raster of the predicted pit location centres as an input, a Hough transformation is used to fit lines through the centres of the detected pits. The results demonstrate that these lines follow the patterns of known mineralised veins in the area, alongside highlighting veins which are below the scale of the published geological maps.