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The commented paper uses arbitrary and unsubstantiated hypotheses to attribute land subsidence phenomena in the Amyntaion basin to the operations of the Public Power Corporation (PPC) surface coal mine, disregarding, or at least grossly underestimating, the effect of about 600 pumped deep wells for irrigation purposes all over the basin. In addition to the huge difference in the pumped quantities of water from the aquifer, ground water table lowering due to the PPC mine has negligible influence at distances over 500 m from the edge of the mine, while the areas examined in the paper are at distances of several kilometers from the edge of the mine. Furthermore, the authors attribute the landslide that occurred in the mine in 2017 to the steep excavation slopes of the mine and the increased groundwater pore pressure due to reduced peripheral pumping, which is completely inaccurate. To build their case, the authors of the commented paper disregard multiple references in research publications on the above issues, as explained in the main text of this discussion.

Planned decommissioning of coal-fired plants in Europe requires innovative technical and economic strategies to support coal regions on their path towards a climate-resilient future. The repurposing of open pit mines into hybrid pumped hydro power storage (HPHS) of excess energy from the electric grid, and renewable sources will contribute to the EU Green Deal, increase the economic value, stabilize the regional job market and contribute to the EU energy supply security. This study aims to present a preliminary phase of a geospatial workflow used to evaluate land suitability by implementing a multi-criteria decision making (MCDM) technique with an advanced geographic information system (GIS) in the context of an interdisciplinary feasibility study on HPHS in the Kardia lignite open pit mine (Western Macedonia, Greece). The introduced geospatial analysis is based on the utilization of the constraints and ranking criteria within the boundaries of the abandoned mine regarding specific topographic and proximity criteria. The applied criteria were selected from the literature, while for their weights, the experts’ judgement was introduced by implementing the analytic hierarchy process (AHP), in the framework of the ATLANTIS research program. According to the results, seven regions were recognized as suitable, with a potential energy storage capacity from 1.09 to 5.16 GWh. Particularly, the present study’s results reveal that 9.27% (212,884 m2) of the area had a very low suitability, 15.83% (363,599 m2) had a low suitability, 23.99% (550,998 m2) had a moderate suitability, 24.99% (573,813 m2) had a high suitability, and 25.92% (595,125 m2) had a very high suitability for the construction of the upper reservoir. The proposed semi-automatic geospatial workflow introduces an innovative tool that can be applied to open pit mines globally to identify the optimum design for an HPHS system depending on the existing lower reservoir.

The development of three-dimensional geological models has proven to be critical for conceptualizing complex subsurface environments. This is crucial for mining areas due to their various hazards and unstable conditions. Furthermore, three-dimensional (3D) models can be the initial step for the development of numerical models in order to support critical decisions and sustainable mining planning. This paper illustrates the results and the development phases of a 3D geological model within the boundaries of the Kardia lignite deposit in western Macedonia, Greece. It also highlights the usefulness of a Geographic Information System (GIS) methodology in the subsurface geological and hydrogeological analysis regarding the Underground Coal Gasification (UCG) methodology. In addition, the work focuses on the integrated geospatial framework that was developed to support the Coal-to-Liquids Supply Chain (CLSC) integration in unfavorable geological settings. A 3D subsurface geological model of the study area was developed to identify a suitable area for in situ coal conversion and UCG considering criteria related to specific coal thickness and depth. In this context, the suggested integrated geomodelling workflow can positively contribute to the implementation of conventional and innovative mining, saving time and reducing the cost to improve the quality of information needed to support decisions related to UCG implementation.

In the coal phase-out era, achieving sustainable mine closure is significant and prioritizes targets for the mining industry. In this study, the already closed lignite mine of Kardia, North Greece, is investigated, where the mine void left is naturally filled with water. The viability of different repurposing land uses is evaluated, and the natural water level development inside the mine pit is investigated concerning its future uses. The potential for solar photovoltaic (PV) panels developed on mining land and its surrounding area is evaluated in combination with the application of pumped hydro storage (PHS) technology, utilizing pit lake water. Except for electricity system planning, other end-uses that offer multiple, mutually reinforcing and lasting benefits are investigated, such as recreation parks, terrestrial wildlife, aquaculture and agriculture. All repurposing scenarios are evaluated with regard to the spatiotemporal evolution of the lake, by generating forecasts of the dependent variables (rainfall and temperature) via linear (autoregressive integrated moving average) and non-linear (artificial neural network) models. The prediction of pit lake natural development redefines the new land use layout and the land repurposing decisions. This is essential for strategic planning, considering the Greek lignite mining industry’s priority regarding transitioning from the current coal-based electricity to renewable energy sources (RES) technology.

Entering the coal phase-out era, major priorities of the Greek lignite mining industry are sustainable mine closure, land reclamation, and planning for long-term and sustainable land repurposing scenarios. A holistic and digitally enabled approach to water management could lead to effective strategic planning in the coal phase-out era. This research proposes an integrated approach to evaluate the hydrological profile of a large and complex open pit lignite mining area based on remote sensing data. This framework included processing of satellite images in different bands and an evaluated digital elevation model of an ≈170 km 2 area within the Ptolemais Basin, northern Greece, that has extensive surface lignite mining. Several spectral ( SMI, RIP, NDVI, NDWI, MNDWI ) and topographic ( SPI, TWI, LS ) indices were investigated, providing details of spatial and temporal variations in the terrestrial water cycle. The analysis permitted the hydrological characteristics of the study area to be mapped with sufficient accuracy. Water bodies, low-flowing streams, water flow direction, vegetation cover, erodible areas, locations with water accumulation, and areas with high soil moisture were identified. A major additional outcome of this research was the construction of flood-susceptibility maps introducing several hydrological parameters in a fuzzy model. The results indicated several areas inside the mining area with high flood susceptibility. These results were subsequently validated through PPC flood event reports during heavy rainfalls and events reported in the National River Basin Management plans. Using high-resolution digital elevation models and remote sensing data processing to identify hazards, like flooding, could improve land use planning and water management strategies.

Sustainable mine closure is one of the main priorities of the mining industry. This aim of this research was to predict the spatiotemporal development of water levels in a mined-out pit by generating forecasts of the dependent variables (rainfall and temperature) via linear (autoregressive integrated moving average) and non-linear (artificial neural network) models. We investigated natural water level development in one mined-out pit of the closed lignite mines in Amynteon, north Greece, with no artificial recharge. The forecasted rate of water level increase was estimated to be ≈ 10 m per year in the ‘early’ stage of pit lake spatiotemporal evolution (first 10 years), and 0.1 m per year in the ‘last’ stage of potential lake development (after year 2060). Also, the optimum lake surface (i.e. the level where no significant increase in water level rate appears) was estimated at + 520 m, which was predicted to occur in ≈ 40 years. The proposed methodology was validated via water level measurements performed during the first year of lake development, where field measurements of water elevations closely followed predictions. Forecasting pit lake water levels is essential for strategic planning, examining pit lake repurposing options, and informing decisions about post-mining futures and economic transitions.

PurposeAn overview of the current use of handwritten text recognition (HTR) on archival manuscript material, as provided by the EU H2020 funded Transkribus platform. It explains HTR, demonstrates Transkribus, gives examples of use cases, highlights the affect HTR may have on scholarship, and evidences this turning point of the advanced use of digitised heritage content. The paper aims to discuss these issues.Design/methodology/approachThis paper adopts a case study approach, using the development and delivery of the one openly available HTR platform for manuscript material.FindingsTranskribus has demonstrated that HTR is now a useable technology that can be employed in conjunction with mass digitisation to generate accurate transcripts of archival material. Use cases are demonstrated, and a cooperative model is suggested as a way to ensure sustainability and scaling of the platform. However, funding and resourcing issues are identified.Research limitations/implicationsThe paper presents results from projects: further user studies could be undertaken involving interviews, surveys, etc.Practical implicationsOnly HTR provided via Transkribus is covered: however, this is the only publicly available platform for HTR on individual collections of historical documents at time of writing and it represents the current state-of-the-art in this field.Social implicationsThe increased access to information contained within historical texts has the potential to be transformational for both institutions and individuals.Originality/valueThis is the first published overview of how HTR is used by a wide archival studies community, reporting and showcasing current application of handwriting technology in the cultural heritage sector.

Spatial modeling and evaluation is a critical step for planning the exploitation of mineral deposits. In this work, a methodology for the investigation of a multi-seam coal deposit spatial variability is proposed. The study area includes the Klidi (Florina, Greece) multi-seam lignite deposit which is suitable for surface mining. The analysis is based on the original data of 76 exploratory drill-holes in an area of 10 km2, in conjunction with the geological and geomorphological data of the deposit. The analytical methods include drill-hole data analysis and evaluation based on an appropriate algorithm, principal component analysis and geographic information techniques. The results proved to be very satisfactory for the explanation of the maximum variance of the initial data values as well as the identification of the deposit structure and the optimum planning of mine development. The proposed analysis can be also helpful for minimizing cost and optimizing efficiency of surface mining operations. Furthermore, the provided methods could be applied in other areas of geosciences, indicating the theoretical value as well as the important practical implications of the analysis.

Quantifying impacts on the environment and human health is a critical requirement for geological subsurface utilisation projects. In practice, an easily accessible interface for operators and regulators is needed so that risks can be monitored, managed, and mitigated. The primary goal of this work was to create an environmental hazards quantification toolkit as part of a risk assessment for in-situ coal conversion at two European study areas: the Kardia lignite mine in Greece and the Máza-Váralja hard coal deposit in Hungary, with complex geological settings. A substantial rock volume is extracted during this operation, and a contaminant pool is potentially left behind, which may put the freshwater aquifers and existing infrastructure at the surface at risk. The data-driven, predictive tool is outlined exemplary in this paper for the Kardia contaminant transport model. Three input parameters were varied in a previous scenario analysis: the hydraulic conductivity, as well as the solute dispersivity and retardation coefficient. Numerical models are computationally intensive, so the number of simulations that can be performed for scenario analyses is limited. The presented approach overcomes these limitations by instead using surrogate models to determine the probability and severity of each hazard. Different surrogates based on look-up tables or machine learning algorithms were tested for their simplicity, goodness of fit, and efficiency. The best performing surrogate was then used to develop an interactive dashboard for visualising the hazard probability distributions. The machine learning surrogates performed best on the data with coefficients of determination R2>0.98, and were able to make the predictions quasi-instantaneously. The retardation coefficient was identified as the most influential parameter, which was also visualised using the toolkit dashboard. It showed that the median values for the contaminant concentrations in the nearby aquifer varied by five orders of magnitude depending on whether the lower or upper retardation range was chosen. The flexibility of this approach to update parameter uncertainties as needed can significantly increase the quality of predictions and the value of risk assessments. In principle, this newly developed tool can be used as a basis for similar hazard quantification activities.

Purpose An overview of the current use of handwritten text recognition (HTR) on archival manuscript material, as provided by the EU H2020 funded Transkribus platform. It explains HTR, demonstrates Transkribus, gives examples of use cases, highlights the affect HTR may have on scholarship, and evidences this turning point of the advanced use of digitised heritage content. The paper aims to discuss these issues. Design/methodology/approach This paper adopts a case study approach, using the development and delivery of the one openly available HTR platform for manuscript material. Findings Transkribus has demonstrated that HTR is now a useable technology that can be employed in conjunction with mass digitisation to generate accurate transcripts of archival material. Use cases are demonstrated, and a cooperative model is suggested as a way to ensure sustainability and scaling of the platform. However, funding and resourcing issues are identified. Research limitations/implications The paper presents results from projects: further user studies could be undertaken involving interviews, surveys, etc. Practical implications Only HTR provided via Transkribus is covered: however, this is the only publicly available platform for HTR on individual collections of historical documents at time of writing and it represents the current state-of-the-art in this field. Social implications The increased access to information contained within historical texts has the potential to be transformational for both institutions and individuals. Originality/value This is the first published overview of how HTR is used by a wide archival studies community, reporting and showcasing current application of handwriting technology in the cultural heritage sector.