Results

The concentrations of arsenic (As) and sulphate (SO4) are important indicators in the assessment of surface water quality, particularly in regions with active mining operations. This study investigate…

The concentrations of arsenic (As) and sulphate (SO4) are important indicators in the assessment of surface water quality, particularly in regions with active mining operations. This study investigates the application of remote sensing techniques for the detection of As and SO4 in water bodies using Earth Observation data. We present a case study conducted on the Chalkidiki peninsula in Greece, encompassing three active surface mines—Olympias, Stratoni, and Skouries—operated by Hellas Gold, a subsidiary of Eldorado Gold Corporation. For this investigation, we employed a small and lightweight VNIR hyperspectral sensor (STS Spectrometer by Ocean Optics, with a spectral range of 337 – 823 nm) mounted on a DJI Phantom 3 drone to capture spectral data at 15 selected locations covering three streams related to the each of the studied mines. The hyperspectral data collection was complemented by in situ surface water sampling, followed by comprehensive laboratory analyses performed at Hellas Gold’s facilities. Due to the common limitations in direct identification of As and SO4 using imaging spectroscopy methods, the focus of our study was on indirect detection of the optically active water substances. For such purpose we utilized in situ measured parameters which showed strong correlation with the presence of these substances and could also be identified spectrally at the same time. Specifically, we targeted Total Suspended Solids (TSS) which strongly correlates with the stream flow. TSS exhibited statistically significant correlation with As concentration (in situ sampling data: R2 = 0.434) and at selected sites with SO4 concentrations (long term water quality measuring: R2 = 0.471 or higher). All these correlations were validated using the Hellas Gold’s regular water quality monitoring data, publicly accessible for the Chalkidiki peninsula (https://environmental.hellas-gold.com/). To estimate TSS (direct estimation) and As, SO4 (indirect estimation) using the acquired hyperspectral UAV spectral data we employed spectral indices (NDVI, NDWI1 and WRI1) adjusted for our spectral data characteristics (e.g., wavelengths of local spectral max/min). These unique spectral features were found between 580-590 nm (green-band), 670-680 nm (red-band) and 708-715 nm (NIR-band). The preliminary results indicate that the proposed spectral ratios, which are sensitive to spectral changes in water caused by TSS, could serve as proxies for estimating concentrations of As and SO4 in surface waters. This study demonstrates the potential of integrating drone-based hyperspectral imaging with in situ measurements for water-quality monitoring in mining-impacted regions, contributing to more effective environmental management practices. Acknowledgments: The presented analysis was conducted under the support of the EC grant MultiMiner. The MultiMiner project is funded by the European Union’s Horizon Europe research and innovation actions programme under Grant Agreement No. 10109137474.

Surface mining can have a significant impact on various aspects of the environment, including human health. One of the most harmful effects on the human body comes from particulate pollution, which is…

Surface mining can have a significant impact on various aspects of the environment, including human health. One of the most harmful effects on the human body comes from particulate pollution, which is dispersed into the air through mining processes. These pollutants, known as particulate matter (PM10), are particles smaller than 10 micrometers that can be inhaled and are associated with respiratory and cardiovascular illnesses. Monitoring this type of pollution is challenging due to its spatial distribution. The atmosphere is a three-dimensional space, and it is impossible to cover an entire locality with in-situ monitoring stations. However, remote sensing satellite data offers an effective alternative for monitoring airborne dust. Although satellite-based data may have limitations, they provide stable, spatial, and temporal coverage that is valuable for long-term monitoring and offers advantages in monitoring over large, inaccessible areas. This study has been carried out in Chalkidiki peninsula, Greece encompassing three active surface mines—Olympias, Stratoni, and Skouries—operated by Hellas Gold, a subsidiary of Eldorado Gold Corporation. Three Earth Observation (EO) datasets were used: the PM10 variable from the CAMS European air quality analysis dataset provided by the Copernicus Atmosphere Monitoring Service (CAMS), Aerosol Optical Depth (AOD) data at 0.55 micrometers from NASA’s MODIS Terra and Aqua satellites, and data from NASA’s SEVIRI sensor, where the Dust product was used for visual evaluation and the creation of a dust index. In-situ measurements (2015-2023) from Hellas-Gold dust monitoring stations (https://environmental.hellas-gold.com/) were used for building and validating spatial and temporal models. CAMS and MODIS data were aggregated into daily values for further evaluation. The CAMS data showed strong correlations with in-situ data, making them a valuable resource for monitoring, with correlation coefficients (R) around 0.8, depending on the locality. In contrast, MODIS data presented a different scenario, with correlations peaking at 0.5 and in some cases turning negative. This incompatibility may be attributed to long periods of missing data due to cloud coverage, so different MODIS preprocessing levels will be further examined. CAMS data was also aggregated into monthly and yearly values to observe trends, which indicated a decreasing tendency in concentrations, suggesting an improvement in air quality over the years. Additionally, machine learning methods, including Random Forest, LSTM, and XGBoost, were applied for data modeling and prediction. The XGBoost model produced the best results, with R values above 0,65 slightly varying within the sites of the interest. Future steps in this research will include further data modeling and prediction methods, with an emphasis on integrating artificial intelligence to develop an optimal workflow for this use case. Additionally, this methodology will be tested on another locality in Finland. ACKNOWLEDGEMENTS The presented analysis was conducted under the support of the EC grant MultiMiner. The MultiMiner project is funded by the European Union’s Horizon Europe research and innovation actions programme under Grant Agreement No. 10109137474.

This study focuses on advancing the development of effective monitoring systems Acid Mine Drainage (AMD) by leveraging Machine Learning techniques on optical multi and hyperspectral data. More specifi…

This study focuses on advancing the development of effective monitoring systems Acid Mine Drainage (AMD) by leveraging Machine Learning techniques on optical multi and hyperspectral data. More specifically, the research investigates the utilization of hyperspectral data (PIKA L) acquired through Unmanned Aerial Vehicles (UAV) and multi-temporal data sets from the Sentinel-2 satellite. The results of ML classifications have been validated using ground truth, and it has been determined that the Radial Basis Function Support Vector Machine (RBF SVM) and Random Forest (RF) performs better than other tested ML approaches demonstrating especially effectiveness in handling high-dimensional spaces, which is crucial for hyperspectral data. Future work will focus on testing machine learning techniques on extended multi-temporal data sets, expanding the training and validation data sets to validate results across all scales and evaluating the transferability of the model to other geographical locations.  Acknowledgments: The presented analysis was conducted under the support of the EC grant MultiMiner. The MultiMiner project is funded by the European Union’s Horizon Europe research and innovation actions programme under Grant Agreement No. 10109137474.

This study addresses the monitoring of water quality in mining regions, focusing on arsenic (As) concentration as a critical parameter and total suspended solids (TSS) as its proxy. We investigate the…

This study addresses the monitoring of water quality in mining regions, focusing on arsenic (As) concentration as a critical parameter and total suspended solids (TSS) as its proxy. We investigate the integration of multi-source and multi-scale Earth Observation (EO) techniques by combining diverse spectral and spatial resolutions from several platforms: a small-scale OceanOptics STS-VIS spectroradiometer mounted on a DJI Phantom 3 UAV; a Parrot Sequoia 4-band multispectral camera deployed on a DJI Phantom 4 Pro UAV; and 8-band multispectral satellite imagery from Planet Labs with a spatial resolution of 3 meters. The spectral datasets were analyzed alongside in situ surface water samples collected during UAV-flight campaigns and from long-term monitoring conducted by Hellas Gold (HG). The findings highlight the potential of synergistic use of these datasets for improved water quality assessment in mining-impacted environments. Future research will focus on refining the temporal analysis and developing predictive models using the complete PlanetScope time series in combination with HG’s open-access long-term monitoring data. Acknowledgments: The presented analysis was conducted under the support of the EC grant MultiMiner. The MultiMiner project is funded by the European Union’s Horizon Europe research and innovation actions programme under Grant Agreement No. 10109137474.

This study investigates the application of multisource and multiscale Earth Observation (EO) technologies for monitoring water quality in the Kassandra Mining District, Chalkidiki, Greece. We integrat…

This study investigates the application of multisource and multiscale Earth Observation (EO) technologies for monitoring water quality in the Kassandra Mining District, Chalkidiki, Greece. We integrate spectral data from various platforms: high-resolution water spectral libraries from an OceanOptics STS-VIS-VIS spectrometer (337–823 nm) on a DJI Phantom 3 UAV, multispectral imagery from a Parrot Sequoia camera (4 bands) on a DJI Phantom 4 Pro UAV, and 8-band multispectral Planet Labs satellite data at 3-meter resolution. These datasets are combined with in situ surface water samples from f ield campaigns and long-term monitoring by Hellas Gold (HG). Initial results indicate that Total Suspended Solids (TSS), serving as a proxy for estimating arsenic high/ medium/low (As) levels, can be predicted at high accuracy using Partial Least Square Regression (PLSR) on the water spectral libraries (R² = 0.85). Modified spectral indices based on the most important wavelengths identified by PLSR achieved an R² of 0.82, while original multispectral indices from PlanetScope imagery yielded an R² of 0.78. T hese findings suggest strong potential for utilizing these data and methods in water quality monitoring at local and regional scales. Acknowledgments: The presented analysis was conducted under the support of the EC grant MultiMiner. The MultiMiner project is funded by the European Union’s Horizon Europe research and innovation actions programme under Grant Agreement No. 10109137474.

Acid Mine Drainage (AMD), a significant water pollutant from sulfide mineral oxidation in mining areas, generates sulfuric acid and harmful substances. Effective AMD management involves careful materi…

Acid Mine Drainage (AMD), a significant water pollutant from sulfide mineral oxidation in mining areas, generates sulfuric acid and harmful substances. Effective AMD management involves careful material handling, treatment, and water management. Imaging spectroscopy provides a practical alternative to traditional chemical analysis, identifying pyrite oxidation "hot spots" and sulfate mineral formation. This research develops robust AMD monitoring systems using machine learning techniques on optical multi- and hyperspectral data. We use hyperspectral (PIKA L camera, UAV) and multispectral (Sentinel-2, orbital) datasets at the Lítov post-mine dump (Sokolov lignite basin, Czech Republic). Validated against XRD mineralogy and Google Earthidentified hotspots, Radial Basis Function Support Vector Machine (RBF SVM) outperforms other ML methods in identifying AMD hot spots and class separation. RBF SVM effectively detects AMD discharge and distinguishes mineral mixtures (oxy-hydroxides and oxides) using both Pika L and Sentinel-2 data. Acknowledgments: The presented analysis was conducted under the support of the EC grant MultiMiner. The MultiMiner project is funded by the European Union’s Horizon Europe research and innovation actions programme under Grant Agreement No. 10109137474.

Surface mining can have a significant impact on various aspects of the environment, including human health. One of the most harmful effects on the human body has the particulate pollution that can be …

Surface mining can have a significant impact on various aspects of the environment, including human health. One of the most harmful effects on the human body has the particulate pollution that can be released into the air during mining operations. Particles smaller than 10 micrometres, known as PM10, can be inhaled and linked to respiratory and cardiovascular diseases. Monitoring this type of pollution is challenging because of its spatial distribution. The atmosphere is a three-dimensional space, and it is impossible to cover an entire area with in-situ monitoring stations. However, remote sensing satellite data provides an effective alternative for monitoring airborne dust. Although satellite-based data have limitations, they provide stable spatial and temporal coverage that is valuable for long-term monitoring and offers advantages for monitoring large, inaccessible areas. This study was carried out at two sites, on the Chalkidiki peninsula in Greece and in the Finnish inland near the city of Lappeenranta, between years 2015-2024 and 2013-2024, respectively. To achieve the objectives of this work, local in-situ measurements of PM10 pollution provided by the Finnish Meteorological Institute and the Hellas-Gold environmental platform and two remote sensing data products were used: the CAMS European air quality re/analysis dataset provided by the Copernicus Atmosphere Monitoring Service (CAMS) and the Aerosol Optical Depth (AOD) data at 0.55 micrometres from NASA's MODIS Terra and Aqua satellites at 10, 3 and 1 km resolution. All data were aggregated to daily values for further evaluation, from which CAMS data showed strong correlations with in-situ data, making it a valuable resource for monitoring, with correlation coefficients (rs) around 0.8, depending on the site. In contrast, MODIS data presented a different scenario, with correlations peaking at 0.5 and in some cases becoming negative. This incompatibility may be due to long periods of missing data because of cloud cover. CAMS data were also aggregated into monthly and annual values to observe trends and seasonality. These showed a decreasing trend in concentrations, suggesting an improvement in air quality over the years at both locations. Machine learning techniques were also used, including Prophet and BSTS for trend and seasonality prediction, and XGBoost and Random Forest for short-term prediction. Prophet and XGBoost were found to be more accurate with this type of data. Future steps in this research will include further data modelling and prediction methods, with a focus on integrating other machine learning methods, and exploring additional remote sensing datasets suitable for developing an optimal workflow for this use case. Acknowledgments: The presented analysis was conducted under the support of the EC grant MultiMiner. The MultiMiner project is funded by the European Union’s Horizon Europe research and innovation actions programme under Grant Agreement No. 10109137474.

Monitoring water quality in areas affected by mining activities requires high-spatial and temporal resolution data, which remains a challenge for traditional satellite and ground-based methods. We pre…

Monitoring water quality in areas affected by mining activities requires high-spatial and temporal resolution data, which remains a challenge for traditional satellite and ground-based methods. We present a novel, cost-effective instrumental setup for water surface reflectance measurements using a miniature light-weight Ocean Optics STS-VIS microspectrometer (40 x 42 x 24 mm; 337–823 nm; 1.2 nm spectral resolution; 1024 bands). The sensor was mounted on the DJI Phantom 3 Advanced UAV using a custom-developed, 3D-printed holder to ensure stability and precise nadir orientation. With a field of view (FOV) of 25° and an operational flight altitude of 3 m, the system achieved a spatial resolution (ground footprint) of 1.2 m per measurement point, allowing for precise targeting of narrow water bodies. The system was used across three diverse mining regions: the Chalkidiki Peninsula and Kirki (Greece), and Andalusia (Spain). To derive accurate reflectance from raw intensity data, a standardized calibration protocol was established, involving dark spectrum subtraction and reference measurements using a Spectralon panel (Spectral Evolution; 100% reflectance). Flights were conducted manually to minimize propeller propeller-induced surface turbulence, following standardized patterns (longitudinal and diagonal for streams; sun-relative for water bodies). The processing workflow addresses the high volume of raw data (up to 400 spectra per site). We implemented a smoothing pipeline and filtration: (1) application of Savitzky-Golay filters (SGF) with a 2nd-degree polynomial and varying window sizes (66, 99, 132), (2) statistical outlier removal based on +/-1.5 standard deviations, (3) visual inspection eliminating interference from bank vegetation or rocks above the water. Based on our analysis, the SGF window size of 99 was selected as optimal. While the window size 66 left significant residual noise and the window size of 132 caused the loss of critical spectral absorption features, the window size of 99 provided sufficient noise reduction while preserving the integrity of the spectral signal. The final averaged spectra were correlated with water sample laboratory analyses using Partial Least Squares Regression (PLSR). Our results identified key wavelengths sensitive to specific mining-related water quality parameters. This study demonstrates that the proposed UAV-spectrometer integration provides a robust, flexible, and high-precision alternative for monitoring contaminated aquatic systems in logistically challenging environments. The presented analysis was conducted under the support of the EC through the MultiMiner project, funded under the European Union’s Horizon Europe research and innovation programme (Grant Agreement No. 10109137474), and under the support of the MINEYE project, funded under the European Union’s Horizon Europe research and innovation programme (Grant Agreement No. 101138456).