Abandoned mine lands inventory

Abandoned mine lands (AMLs) denote mining areas that have ceased production for an extended period. The exploration and mining operations for minerals like gold (Au), silver (Ag), zinc (Zn), lead (Pb) and copper (Cu) often endure for prolonged durations. These operations carry inherent environmental risks that can have far-reaching impacts on both the environment and society as a whole. The materials utilized in mineral extraction, the extraction processes themselves, the generation of tailings and waste rock, and the alteration of physical features that reshape landscapes are all potential sources of environmental risk. For instance, mining operations have the potential to adversely affect aquatic ecosystems, wildlife habitats, species distribution, vegetation cover and human health. Despite these potential risks, AMLs had historically received limited attention until recent years. The growing awareness of their environmental impact has prompted national and state agencies in the United States to raise concerns about the hazards posed by these sites. Consequently, numerous initiatives and projects have been initiated to address and mitigate the problems associated with AMLs. The diversity of features, from surface to underground openings to processing facilities and waste sites, poses a challenge given what are at best fragmentary records and the elusive natures of many sites, particularly those with underground development. While there are existing resources in the United States that compile information on these sites, such as the Mineral Resources Data System (MRDS) and other U.S. Geological Survey (USGS) databases, they are often incomplete. These databases typically lack comprehensive details about the nature of AMLs, the minerals extracted, historical context, related alteration and various other critical aspects. Additionally, these databases tend to be sporadic and lack the depth needed for a thorough examination; they are not just incomplete but are sometimes at odds with one another due to compilation by different agencies (for example, MRDS is a mash-up of USGS and U.S. Bureau of Mines records and is not internally coherent). Recognizing the limitations of existing resources, there is a pressing need for each state to develop and maintain a comprehensive database specifically dedicated to AMLs. Such state-specific databases would not only serve as valuable tools for public awareness, they would additionally enable a more in-depth analysis of AMLs, facilitating informed decision-making and effective mitigation strategies. Efforts toward compiling detailed and comprehensive databases for AMLs across states are essential to safeguard the environment, protect ecosystems and ensure the well-being of local communities. Arizona AML project In spite of more than two centuries of mining history, it has only been with the establishment of reclamation laws that environmental agencies and mining inspectors began evaluating the risks associated with historical mining activities lacking environmental considerations. The Arizona Department of Environmental Quality (ADEQ) and Arizona Mine Inspector’s Office initiated efforts to assess the environmental health and physical hazards caused by AMLs in the surrounding areas. A request from ADEQ led the University of Arizona to spearhead a multiuniversity project in collaboration with Northern Arizona University (NAU) and Arizona State University (ASU). Each university focused on specific environmental and physical aspects resulting from AMLs. NAU has concentrated on human and wildlife health in these areas, while ASU has been engaged in risk assessment using environmental factors related to AMLs. The project aims at providing a state-of-theart, Arizona-wide database of the distribution and types of abandoned-miningrelated features, coupled with a first-pass assessment of their associated risks (such as environmental and safety) and opportunities (such as reprocessing of waste solids and liquids, and the development or redevelopment of mine lands). The University of Arizona team concentrates on gathering mine-siterelated information, encompassing opportunities, risks and mitigation aspects. This effort complements the emphasis of ASU and NAU on health and environmental risk assessment, along with the development of mitigation strategies. All investigators collaborate closely to coordinate every facet of the project, ensuring efficiency and productivity in generating and delivering project deliverables. The team holds regular meetings, typically once per quarter, which are joined by representatives from relevant agencies to discuss project progress. Table 1 Public data in Arizona AML database. There is a dearth of information on AML sites, their activities, commodities, mineral alterations, waste rocks, tailings and physical features left behind by mining activities. The University of Arizona has prioritized the creation of an AML inventory to establish a comprehensive database encompassing active and abandoned mine sites, their geology, alterations, and associated waste and tailings. In response to the increasing demand for energy transition and the significance of copper, the reprocessing of tailings to extract critical minerals has become imperative. The AML database holds potential for assessing various mines, their environmental impacts and their potential for critical minerals. Supported by the University of Arizona Board of Regents over a three-year period, the AML project is advancing into its second year. Efforts have expanded significantly beyond the initial scope, with the University of Arizona now spearheading a specialized project that leverages remote sensing technology for mapping minerals associated with acid mine drainage. Moreover, the potential of LiDAR data to accurately identify mine features across Arizona is being explored. Given the challenging accessibility of many sites, the adoption of cutting-edge data analysis and technological solutions are crucial. This innovative approach enables the team to have a more comprehensive understanding of AMLs in the region, thereby fulfilling the project’s objectives. The subsequent section will delve into the University of Arizona’s efforts in assessing AML sites, geological and mineral settings and environmental hazards. Data The development of an AML database in Arizona has relied on diverse data sources, primarily drawn from public resources. Access to private-sector data has been limited due to confidentiality constraints and the scarcity of such datasets in private domains. Throughout the project, the team endeavored to amass data from a variety of sources, including USGS, MRDS, U.S. Forest Service (USFS), Arizona mining sector, ADEQ and numerous other potential resources. A key stakeholder in the project, ADEQ played a pivotal role by furnishing an array of data encompassing contamination sites, watershed bodies and hydrological information. Table 1 shows the available public data and their sources. The compiled database features data in two formats: vector-based and raster-based. Notably, various environmental datasets, such as Shuttle Radar Topography Mission (SRTM) data, elevation data and slope data, were extracted from the state’s digital elevation model (DEM). Additionally, a land-cover map from the USGS was incorporated into the database. Recognizing the significance of AMLs in Arizona, especially in remote and challenging-to-access areas, remote sensing data was harnessed to assess and characterize these sites. LiDAR point cloud data, boasting a remarkable high spatial resolution in LAZ format, was acquired from the USGS Library (https://apps.nationalmap.gov/lidar-explorer/#/). The LiDAR data form a crucial component for the machine-learning approach being developed to predict AML sites using known AML locations. As the demand for comprehensive knowledge about minerals in AML sites grows, coupled with the recognition that many of these areas are remote and difficult to physically visit, hyperspectral data were procured for mineral mapping. Detailed methodologies for utilizing spaceborne hyperspectral data, such as the Environmental Mapping and Analysis Program (EnMAP) and Environmental Materials Information Technology (EMIT), will be covered in subsequent sections, shedding light on the strategies employed to enhance the understanding of minerals within AML sites. Methods The geographic information system (GIS) has proven instrumental in aggregating data and conducting intricate spatial analyses for a comprehensive understanding of mining sites (Choi, Baek and Park, 2020). Utilizing various techniques within ArcGIS, our approach encompasses the creation of databases, generation of shapefiles, implementation of spatial joins, overlaying diverse layers and crafting insightful maps. This GIS-driven methodology facilitates the exploration of spatial relationships among different datasets. The application of ArcGIS techniques spans the spectrum from the establishment of databases to the meticulous creation of shapefiles. Spatial joins have been executed to interlink disparate datasets, enabling a more nuanced evaluation of the intricate connections between them. Overlaying diverse layers has been a pivotal step, allowing us to discern patterns and correlations within the spatial data. Maps, a key product of this GIS-centric endeavor, serve as powerful tools to visually comprehend the distribution of mine sites with respect to critical spatial parameters such as elevation, slope and land cover. These maps provide a dynamic representation of the intricate relationships and variations in mining site characteristics across the geographic landscape. Integrating mine site data with maps detailing geology, topography, vegetation and land use yields invaluable insights for responsible environmental stewardship, effective mining practices and conservation efforts. This holistic approach uncovers the underlying geological context and mineral presence at mine sites and evaluates the potential for erosion and its impact on water flow, including the risk of contaminant leaching into ground water. It also maps the extent of ecological disruption, gauging the impact on wildlife habitats and biodiversity. Furthermore, this method assists in planning for infrastructure and access, identifies areas suitable for ecological restoration and sets a foundation for ongoing environmental surveillance. Such comprehensive analysis is pivotal in shaping strategies for mine planning, enhancing ecological sustainability, pinpointing ecological sensitivities to guide conservation, and managing risks related to environmental health and safety, thereby aiding in the reduction of mining’s ecological impacts and fostering sustainable resource management. A selection of these maps will be presented, offering insights into the spatial distribution of mine sites and their interplay with elevation, slope and land cover. The GIS-driven spatial analysis not only enhances the understanding of the geographic complexities surrounding mining activities but also serves as a valuable resource for informed decision-making and environmental management strategies. The Mineral Districts GIS database, originally published by Keith et al. (1983) and digitized recently by the Arizona Geological Survey (AZGS), have been updated to include mineral deposit types and geoenvironmental models based on the USGS classification scheme (Richard, 2002). The mineral deposit models were originally classified by Cox and Singer (1996) and have been updated frequently since then. The geoenvironmental models are based on those of the mineral deposits and aims to classify them based on their environmental characteristics (du Bray, 1995). A critical component of these models is a section that outlines the metals that are typically associated with them. This list equates to the types of contaminants that may be found in an area containing previously mined, actively mined or unmined mineral resources. Combining this geospatial knowledge with other datasets, such as watershed area and drainage configuration, can be used by those investigating the effect of mining on ground water contamination (such as ADEQ). Future work might include a more holistic study of these districts that would integrate local geology into our understanding of how a mineral deposit might affect its surroundings: for example, how might the presence of a limestone proximal to a major mine influence how well a contaminant is buffered in solution, especially compared to other lithologies? The answer to questions like this lies in how well the mineral districts of Arizona are characterized. LiDAR data from the USGS Library offers a high spatial resolution that is crucial for detailed landscape analysis, enabling the study of variations and relationships between different features like intensity and classifications (such as vegetation and buildings). This facilitates the creation of 3D visualizations and highly precise DEMs, enhancing landscape analysis. Our research utilizes this data through machine learning to predict AMLs with techniques such as random forest and neural networks, using known mine sites for calibration. The project involves analyzing approximately 650 LiDAR cloud point records of the Patagonia area in Arizona, processed using the high-performance computing facilities at the University of Arizona. Although the research is in its early stages without finalized results, it holds promise for improving AML prediction and understanding landscape dynamics through the integration of LiDAR data and machine learning. Future updates will reveal more about our findings and their implications for environmental management. The EnMAP hyperspectral imaging technology has been deployed to meticulously map minerals associated with acid mine drainage in Pinal County, an area renowned for its historic and present-day metal production. Acid mine drainage stands as a pressing environmental issue intricately linked to mining activities, posing the potential for water contamination and ecosystem degradation. In response to these environmental concerns, this study leverages the advanced capabilities of EnMAP hyperspectral imaging to scrutinize the presence and distribution of minerals linked to acid mine drainage. Adiverse selection of hyperspectral analysis algorithms to map the spatial distribution of the minerals associated with acid mine drainage is being applied. These algorithms and analytic methodology play a crucial role in extracting meaningful information from the hyperspectral images, allowing for precise mineral identification and mapping. Through these methodologies, the team aims to amplify the accuracy and clarity of mineral identification related to acid mine drainage (Cramer et al., 2021), unraveling the intricate tapestry of mineral distribution in the study area. This multifaceted approach not only bolsters detection capabilities but also deepens the understanding of the environmental impacts of acid mine drainage in mining-affected regions. The outcomes of this study are expected to assist in environmental monitoring, resource management and the formulation of informed decisions for mitigating the impacts of mining activities, guiding efforts by the ADEQ in prioritizing remediation actions for AML sites to protect surface water quality. This comprehensive approach, combining EnMAP hyperspectral imaging with advanced remote sensing techniques and sophisticated software tools, not only advances our ability to detect minerals associated with acid mine drainage but also contributes to a deeper comprehension of the environmental dynamics in mining-affected regions. The results of this study hold significance for environmental monitoring, resource management and informed decision-making in mitigating the impact of mining activities on local ecosystems. ADEQ specifically hopes to use this information to prioritize cleanup of AML sites that threaten surface water contamination. As our analysis progresses, we anticipate uncovering valuable insights into the mineral composition of Pinal County and its implications for environmental sustainability. Figure 1 Distribution of MRDS dataset on (a) geological map, (b) land cover, (c) elevation and (d) slope. Results and applications Deliverables. The project is committed to fulfilling the ADEQ requirements by providing a state-of-the-art database, conducting comprehensive data analyses and offering strategic recommendations. The longevity and enhancement of these results will be ensured by a state agency, potentially the AZGS within the University of Arizona, ensuring that our contributions are not only preserved but also advanced over time. Moreover, the outcomes will be designed to align with and support national initiatives, aiding efforts by the USGS and other land-use agencies. Additionally, this project promises to refine the methods used for identifying and characterizing AMLs through the integration of advanced remote sensing technologies with traditional field verification, enhancing the effectiveness and accuracy of environmental assessments. ArcGIS generated maps. The study employs a layered mapping approach to explore the environmental and geological contexts of AML sites across Arizona, integrating these sites with a series of detailed maps. By juxtaposing AML locations with geological, slope, elevation and land-cover maps, a nuanced perspective on how these historical mining operations intersect with key environmental features is offered. The purpose of this comprehensive mapping is to discern patterns, assess potential impacts, and provide a holistic overview of the mining landscape’s influence on the region. Figure 1 is composed of multiple panels with each AML site superimposed over different backgrounds, illustrating the dynamic relationship between mining activities and their surrounding ecosystems and geology. This comparative visualization enables a deeper understanding of the complexities at play, including the geological formations and mineral distributions across Arizona, how AML sites correlate with the state’s varying topography, and the ecological consequences of mining operations as reflected in changes to land cover and vegetation. This approach not only facilitates a detailed analysis of the environmental and operational challenges posed by mining in diverse topographical regions but also highlights the ecological impacts, critical for evaluating changes in land-use patterns such as deforestation and urban encroachment. To complement this visual analysis, we provide an overview of the AML dataset, encompassing a range of mining features from open shafts to tailings, each annotated to reflect their distribution and environmental implications. Figure 2 (a) Geological map of the study area, (b) RGB image of EnMAP (R: 525 nm [B23], G: 2113 nm [B183], B: 2330 nm [B209]) and (c) PCA false color composite (R: PCA4, G: PCA3, B:PCA2). Presenting these maps together in a single figure aims to underscore the multifaceted nature of AML impacts, offering readers a clear, comprehensive view of how abandoned mines affect various aspects of Arizona’s environment and geology. This structured visual representation enhances the accessibility and depth of the findings, making it easier for readers to grasp the interrelated effects of mining across different environmental dimensions. Moreover, recognizing the dynamic nature of land cover, a temporal analysis would be a valuable addition to the toolkit. Such an analysis could provide insights into the temporal evolution of land cover, offering a nuanced understanding of how AMLs influence and potentially alter their surroundings over time. This approach becomes particularly relevant for assessing the long-term impact of mining activities on the environment, helping to guide sustainable land-management practices and urban-planning strategies. EnMAP. Figure 2 offers a comprehensive visual representation, juxtaposing the geological map, a false color composite (RGB) from bands 23, 183 and 209 respectively, and the outcomes of principal component analysis (PCA). This visual comparison aims to assess the agreement between various lithological units extracted from remote sensing data and the geological map of Pinal County, obtained from the state geological map. The geological map lays the foundation for understanding the lithological composition of Pinal County, revealing insights into the distribution and characteristics of different rock formations. In tandem, the color composite (RGB) utilizes spectral features from remote sensing data, presenting a vibrant depiction of the landscape and aiding in visually distinguishing lithological variations (Fig. 2b). PCA (Plaza et al., 2009) orthogonalizes the data maximizing the variance into the earliest components. This orthogonalizing of the data allows for data dimensionality reduction but also for the application of machine learning methods for mapping acid mine drainage minerals, emphasizing key spectral features associated with lithological units. By superimposing PCA results (false color composite of PCA4, PCA3 and PCA2) (Fig. 2c) onto the geological map (Fig. 2a), Fig. 2 enables a detailed examination of how well remote sensing data capture the geological intricacies within Pinal County. This integrated representation is a valuable tool for researchers and geologists, allowing for an assessment of the accuracy and reliability of extracting lithological units from remote sensing data. The approach assists in validating information derived from remote sensing techniques against the geological ground truth provided by the map. Through this comparative analysis, Fig. 2 enhances our understanding of the geological landscape, contributing to more informed geological interpretations and resource management strategies specific to Pinal County. Spectral feature fitting (SFF) (Clark et al., 2003) was employed using the USGS Library to map minerals of interest accurately. This method involved comparing the fit of image spectra to reference spectra using a least-square fit, allowing for the identification and quantification of specific minerals present in the study area. The spectral feature fitting was conducted in ENV 5.7, and the results are visualized by a stretching method (Median +/-3 * MAD) in HypPy3 (Bakker, 2023). Figure 3 is an RGB false color composite image for the SFF analysis. Figure 3 The RGB of SFF analysis. Red corresponds to jarosite, green corresponds to calcite, and blue corresponds to pyrite. Minerals have different chemical compositions, which influence their spectral characteristics. Two wavelength maps were generated to enhance our understanding of mineral composition. The first map covers the visible regions (1 μm), focusing on minerals with electronic absorption features, primarily iron-bearing minerals (Fig. 4). The second map spans the 2-μm range (Fig. 5), highlighting minerals with vibrational absorptions (due to OH, H2O and CO3). These results demonstrate the reliability of EnMAP data for mapping minerals such as calcite, iron-bearing minerals, hydrous minerals, chlorite and muscovite. The map provides valuable insights into the mineral variation within the Ray area of Pinal County, utilizing EnMAP hyperspectral imaging. The integration of geological maps, spectral libraries, and wavelength mapping techniques enhances our ability to identify and map minerals related to acid mine drainage, contributing to a comprehensive understanding of the environmental impact of mining activities in the region. These findings lay the foundation for future fieldwork and continued research in this dynamic geological and environmental context. Conclusions The project has reached a significant milestone as we enter the second year of funding. Notably, our colleagues at NAU have contributed to the project’s success by submitting a review paper on the distribution of bats around abandoned mine lands. This valuable research adds a unique dimension to our understanding of the ecological aspects associated with these areas. The University of Arizona team has been actively engaged in fostering collaboration with various stakeholders, including ADEQ, the mining sector, USFS, USGS and other relevant organizations and agencies. An exciting development is the integration of our GIS-based database into the national AML inventory project led by the USGS. This inclusion not only validates the significance of our work but also contributes to a broader national initiative. Moving forward, the University of Arizona is set to embark on new ventures, building upon the significant achievements during the initial one and a half years of our The RGB of SFF analysis. Red corresponds to jarosite, green corresponds to calcite, and blue corresponds to pyrite. Figure 3 project. Our journey thus far has been marked by notable accomplishments, including the comprehensive evaluation of diverse data sources and establishing productive dialogues with essential stakeholders such as the ADEQ, USGS, the Arizona Mine Inspector’s Office and the USFS. Engaging in consistent discussions with our academic collaborators at ASU and NAU, we have navigated through the project’s advancements and hurdles, fostering a synergistic atmosphere that encourages innovative solutions. Figure 4 Map of spectral wavelength in 1,000-nanometer regions. Blue and green colors indicate iron-bearing minerals. One of the project’s cornerstone achievements is the creation of a GIS-based database, incorporating publicly available data on Arizona’s AMLs. This critical tool is intended for integration with the national AML inventory by the USGS, representing a significant step toward streamlining AML data management. Figure 5 Wavelength map of minerals in the 2,100- to 2,400-nanometer range. Yellow to red areas indicate the presence of mafic silicate and carbonate minerals, and greenish areas indicate the presence of Al-OH-bearing silicates. Our biweekly sessions with ADEQ have been instrumental in aligning our research objectives with their environmental analysis and remediation needs, ensuring that our efforts are both relevant and impactful. Our project has been characterized by the strategic application of state-of-the-art technology. Through the adoption of advanced data sources, such as LiDAR and hyperspectral EnMAP data, we have conducted in-depth analyses in regions like Patagonia and Pima County in southern Arizona. These efforts have enabled us to map mineral distributions and their influence on stream impairment, shedding light on their contributions to acid mine drainage issues. This innovative approach not only enhances our understanding of AML-associated risks but also places us at the cutting edge of applying technology to environmental research. As we look to the future, our agenda includes developing machine-learning algorithms to process LiDAR data, analyzing hyperspectral data (EnMAP), and compiling comprehensive data sets to support ADEQ’s use cases through our GIS-based database. Our commitment to leveraging advanced technologies for a deeper insight into AMLs and their risks continues to drive our project forward. Highlighting our progress, we have shared our findings at international conferences such as the MINEXCHANGE SME Annual Conference & Expo, successfully meeting the project’s milestones for its first and second years. This path of innovation and progress underscores our dedication to employing technological advancements in promoting environmental protection and sustainable mining practices. While our project has made significant strides, it is essential to acknowledge its limitations. Some organizations possessing AML databases are reluctant to share them due to confidentiality constraints. Our analyses and findings are primarily based on public data, mostly sourced from USGS, which, despite its wealth of information, has limitations concerning completeness, lack of geological details, and historical mine information. Recognizing these constraints, we continue to navigate challenges to provide robust insights and build a foundation for future programs aimed at addressing the complexities surrounding AMLs. References Bakker W (2023) https://blog.utwente.nl/bakker/hyppy/ Choi Y, Baek J, Park S (2020) Review of GISbase applications for mining: planning, operation, and environmental management. Journal of applied sciences 10:2266. http://dx.doi.org/10.3390/app10072266 Cox DP, Singer DA (1986) Mineral deposit models, Bulletin 1693 Cramer AS, Calvin WM, McCoy SW, Breitmeyer RJ, Haagsma M, Kratt C (2021) Mapping potentially acid generating material on abandoned mine lands using remotely piloted aerial systems. Minerals 11:365. https://doi. org/10.3390/min11040365 du Bray EA (ed.) (1995) Preliminary compilation of descriptive geoenvironmental mineral deposit models. U.S. Geological Survey Open-File Report 95-831. 272 pp Harris Geospatial Solutions. ENVI version 5.7. http:// www.harrisgeospatial.com Clark RN, Swayze GA, Livo KE, Kokaly RF, Sutley SJ, Dalton JB, McDougal RR, Gent CA (2003) Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems. Journal of Geophysical Research: Planets 108(E12):5131 https://doi. org/10.1029/2002JE001847 Keith S, Stanley B., Gest, DE, DeWitt E (1983) Metallic mineral districts of Arizona: Arizona Bureau of Geology and Mineral Technology Map 18 Plaza A, Benediktsson JA, Boardman JW, Brazile J, Bruzzone L, Camps-Valls G, Chanussot J, Fauvel M, Gamba P, Gualtieri A, Marconcini M, Tilton JC, Trianni G (2009) Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment 113(Supplement 1):S110-S122 Richard SM (2002) Database for mineral districts in the state of Arizona – Based on metallic mineral districts of Arizona: Arizona Geological Survey Digital Information Series DI-23 http://hdl.handle.net/10150/630074 USGS MRDS database. https://mrdata.usgs.gov/mrds/ USGS topographic mining-related database. https:// mrdata.usgs.gov/usmin/

Abandoned mine lands inventory

Authored by:
Mark D. Barton, Julia W. Neilson, Eytan Bos Orent, Carson A. Richardson, Dean N. Riley, Sanaz Salati

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To view the past issues of the UCA magazine you must be a member of SME/UCA.

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