harper medium salt mineral processing production line sell at a loss

production line, mineral processing, concentration of ore - xinhai

Xinhai has been committed to providing customers with more professional services in the turnkey solutions for mineral processing plant, optimized its services continually, and formed its own set of service system, besides, Xinhai set up Mining Research and Design Institute, ensuring the smooth operation in plant service. The following is the detailed flowchart of Xinhai mineral processing plant services. Xinhai proceed from every detail, creating the comprehensive green and efficient mineral processing plant for all customers.

Xinhai has been committed to providing customers with more professional services in the turnkey solutions for mineral processing plant, optimized its services continually, and formed its own set of service system, besides, Xinhai set up Mining Research and Design Institute, ensuring the smooth operation in plant service. The following is the detailed flowchart of Xinhai mineral processing plant services. Xinhai proceed from every detail, creating the comprehensive green and efficient mineral processing plant for all customers.

Engineering consulting can allow customers to have an overall concept of dressing plant, , including mining value, useful mineral elements, available mineral technology, mineral plant scale, equipment required, project duration, making customer know fairly well.

mineral processing, equipment manufacturers, ball mills, flotation, thickener - xinhai

Xinhai devotes to providing Turn-key Solutions for Mineral Processing Plant (EPC+M+O), namely design and research - complete equipment manufacturing and procurement - commissioning and delivery - mine management - mine operation. The essence of EPC+M+O Service is to ensure sound work in every link. The model is suitable for most of the mines in the world.

Focusing on the research and development and innovation of mineral processing equipment, Xinhai has won more than 100 national patents, strives for perfection, strives to complete the combination of equipment and technology, improve productivity, reduce energy consumption, extend equipment stable operation time, and provide cost-effective services.

With Class B design qualifications in the metallurgical industry, rich in ore mining, beneficiation, smelting technology and experience, completed more than 2,000 mine design and research, not only can provide customers with a reasonable process, but also can provide customized equipment configuration.

The precious metal minerals are mainly gold and silver mines. Xinhai Mining has more than 20 years of experience in beneficiation for gold and silver mines, especially gold ore beneficiation technology. Gold craft and placer gold selection craft etc.

With Class B design qualification, it can provide accurate tests for more than 70 kinds of minerals and design a reasonable beneficiation process. In addition, it can also provide customized complete set of mineral processing equipment and auxiliary parts.

Xinhai can provide the all-round and one-stop mineral processing plant service for clients, solving all the mine construction, operation, management problems, devoting to provide modern, high-efficiency.

Through mineral processing experiment, the mineral processing flow is customized. Multiple tests are carried out in every link, and make sure the final processing flow to guarantee the successful mineral processing plant construction.

According to tailing processing technology, Xinhai has tailings reprocessing technology and tailings dry stacking. Tailings dry stacking is the self-launched tailings dewatering technology, which is the effective technology in green mine construction.

More than 2,000 mine design and research, equipment supply projects, more than 500 mining industry chain services (EPC+M+O) projects in more than 90 countries and regions around the world, we are always committed to providing you with one-stop, customized Chemical mine solution!

salt production and processing - morton salt

This is the oldest method of salt production. It has been used since salt crystals were first noticed in trapped pools of sea water. Its use is practical only in warm climates where the evaporation rate exceeds the precipitation rate, either annually or for extended periods, and ideally, where there are steady prevailing winds. Solar salt production is, typically, the capturing of salt water in shallow ponds where the sun evaporates most of the water. The concentrated brine precipitates the salt which is then gathered by mechanical harvesting machines. Any impurities that may be present in the brine are drained off and discarded prior to harvesting.

Usually two types of ponds are used. First is the concentrating pond, where the salty water from the ocean or salt lake is concentrated. The second is called the crystallizing pond, where the salt is actually produced.

Crystallizing ponds range from to 40 to 200 acres with a foot-thick floor of salt resulting from years of depositions. During the salt-making season of four to five months, brine flows continuously through these ponds. This is a saturated brine solution, containing as much salt as it can hold, so pure salt crystallizes out of the solution as the water evaporates. Natural chemical impurities are returned to the salt water source.

Morton also uses the second oldest method of producing salt underground mining. This is probably the most dramatic method of gathering salt. Large machines travel through vast cave-like passageways performing various operations.

Salt may appear in veins, as does coal. Veins are the original bedded salt deposits. Salt also may be found in domes, which were formed when Earth pressures forced salt up through cracks in the bedrock from depths as great as 30,000 or 40,000 feet; they resemble plugs of almost-circular shape a few hundred yards to a mile across. Some domes occur close to the surface. Both domes and veins are mined in a similar way. Most domes in North America are located in the south from Alabama to Texas with many out under water in the Gulf of Mexico.

To enter a salt mine, miners go down a shaft from the Earths surface to the salt bed. There are two shafts in each Morton mine one for personnel and one to lower materials and equipment into the mine, as well as to hoist the mined rock salt to the surface. The shafts also are used to deliver a constant supply of fresh air to the miners while they work hundreds to thousands of feet below the surface. Most mine shafts are lined with a concrete wall called a shaft liner.

Salt is mined by the room and pillar method. It is removed in a checkerboard pattern to leave permanent, solid salt pillars for mine roof support. Usually 45 to 65 percent of the salt is removed. The room height may average 18 feet in a bedded deposit to 100 feet in a dome mine.

Normally, the first operation is undercutting. Large machines cut a slot 10 or more feet in depth across the bottom of a solid salt wall. This leaves a smooth floor for picking up the salt after blasting.

Next, small holes are drilled into the salt wall to a depth of 10 or more feet and explosives are loaded into the drilled holes. After the work shift, the explosives are set off electrically. Several hundred to several thousand tons of rock salt are blasted and fall onto the mine floor.

Equipment is used to load and haul the salt to machines that crush and feed the salt onto a conveyor belt. The lumps are conveyed to a series of stations for crushing and additional sizing of the lumps. The salt is then placed in a storage bin to await hoisting to the surface.

The above ground processing of the rock salt consists of screening the mined salt into various marketable sizes by sorting through mechanically operated screens. When separated, each size is conveyed to its individual storage bin to await packaging for shipment or to be loaded as bulk salt into railroad cars, trucks, river barges or lake boats for shipment to customers.

Another method of salt production used by Morton Salt is the evaporation of salt brine by steam heat in large commercial evaporators, called vacuum pans. This method yields a very high purity salt, fine in texture, and principally used in those applications requiring the highest quality salt.

The first part of the operation is known as solution mining. Wells are drilled from several hundred to 1,000 feet apart into the salt deposit. These wells are connected via lateral drilling, a recently developed technology. Once the wells are connected, the solution mining operation begins: water is pumped down one well, the salt below is dissolved, and the resulting brine is forced to the surface through the other well. It is then piped into large tanks for storage.

Next, the brine is pumped into vacuum pans. These are huge closed vessels under vacuum about three stories high. They are normally arranged in a series of three, four or five, with each one in the line under greater vacuum than the preceding one. This series of vacuum pans operates on a very simple principle: Whenever pressure is lowered, the temperature at which water will boil is also lowered. For instance, under normal air pressure at sea level, water boils at 212F. But at ten thousand feet above sea level, where air pressure is much less, water boils at 194F. Vacuum pans may operate at as low as 100F.

In the vacuum pan process, steam is fed to the first pan. This causes the brine in the pan to boil. The steam from the boiling brine is then used to heat the brine in the second pan. The pressure in the second pan is lower, allowing the steam made by the boiling in the first pan to boil the brine in the second pan. The pressure is reduced still further in each succeeding pan. This allows the steam made by the boiling brine in the previous pan to boil the brine in the next pan. While the boiling operation could be done with just one pan, several pans in a row produce more salt per pound of steam, thus allowing greater energy efficiency.

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list of minerals in sea salt | healthy eating | sf gate

Sea salt and table salt have the same basic nutritional value, according to MayoClinic.com. Table salt comes from salt mines and goes through extensive processing that strips it of many minerals. In contrast, sea salt comes from evaporated sea water. As a result, the two types of salt differ in their components, taste and texture. The specific elements of sea salt vary slightly depending on the geographic area of origin, but its mineral profile is a strong marketing point. According to Western Analysis, Inc., sea salt can boast of as many as 75 minerals and trace elements.

Sodium and chloride are the most abundant ions in sea salt, representing about 33 and 50.9 percent of total minerals, respectively. They are both essential substances your body needs for normal function and nutrient absorption. Chloride specifically helps with muscle and nerve function. Sodium also acts in muscle function and helps regulate your blood volume and pressure. Potassium is another important macro-mineral that works with chloride to help regulate acid levels in your body. A quarter-teaspoon of Celtic sea salt contains 601.25 milligrams of chloride, 460 milligrams of sodium and 2.7 milligrams of potassium, according to CureZone.com.

Calcium and magnesium play essential roles in several chemical reactions in your body. Magnesium, for example, intervenes in energy production and the synthesis of RNA and DNA. Calcium helps give structure to your bones and teeth, in addition to regulating your heartbeat and normal muscle and nerve function. Both are present in sea salt at the approximate concentrations of 1.5 milligrams and 5.2 milligrams per 1/4 teaspoon, respectively.

With about 9.7 milligrams per quarter-teaspoon of sea salt, sulfur is the third most common mineral in sea salt, says Western Analysis, Inc.. Even though it is not an essential mineral, sulfur plays an important role in your immune system and the detoxification of your body. Every cell in your body contains it, and it helps give structure to two amino acids. According to researcher Stephanie Seneff, Ph.D., sulfur is the eighth most common element in the human body and is important for normal metabolism and heart health.

Sea salt can also contain numerous trace elements. These elements exist in minute concentrations but work with other minerals to maintain optimal function in your body. Trace minerals you may find in sea salt include phosphorus, bromine, boron, zinc, iron, manganese, copper and silicon. Your body uses some of these minerals, such as iron and zinc, to make enzymes involved in metabolism. Although phosphorus typically occurs in trace amounts in sea salt, it is actually an essential macro-mineral. Your body uses it as a structural component of bones, teeth and cell membranes, as well as for energy production.

Suzanne Fantar has been writing online since 2009 as an outlet for her passion for fitness, nutrition and health. She enjoys researching and writing about health, but also takes interest in family issues, poetry, music, Christ, nature and learning. She holds a bachelor's degree in biological sciences from Goucher College and a MBA in healthcare management from the University of Baltimore.

dense-heavy medium separation hms / dms process

HMS and DMS are acronyms for Heavy (Dense)Medium Separation and is applied to the process of pre-concentration of minerals mainly the production of a high weight, low assay product, which may be rejected as waste. In principle it is the simplest of all gravity processes and is a standard laboratory method for separating minerals of different specific gravity. Fluids of suitable density are used so that the minerals lighter than the fluid float and those denser sink.

Closely sized samples are required for optimum separation. The ore sample to be tested is crushed to the required top-size and screened at at appropriate sizes (we and dry screening is carried out at 38-75 microns if these sizes are to be examined). A typical size distribution on crushed drill core would be 2440, 850, 300, 106 and 38 microns from a top-size of 3350 microns.

Dense medium separation (or heavy medium separation (HMS), or the sink-and-float process) is applied to the pre-concentration of minerals, i.e. the rejection of gangue prior to grinding for final liberation. It is also used in coal preparation to produce a commercially graded end-product, clean coal being separated from the heavier shale or high-ash coal.

In principle, it is the simplest of all gravity processes and has long been a standard laboratory method for separating minerals of different specific gravity. Heavy liquids of suitable density are used, so that those minerals lighter than the liquid float, while those denser than it sink (Figure 11.1).

Since most of the liquids used in the laboratory are expensive or toxic, the dense medium used in industrial separations is a thick suspension, or pulp, of some heavy solid in water, which behaves as a heavy liquid.

The process offers some advantages over other gravity processes. It has the ability to make sharp separations at any required density, with a high degree of efficiency even in the presence of high percentages of near-density material. The density of separation can be closely controlled, within a relative density of 0.005 kg l -1 and can be maintained, under normal conditions, for indefinite periods. The separating density can, however, be changed at will and fairly quickly, to meet varying requirements. The process is, however, rather expensive, mainly due to the ancillary equipment needed to clean the medium and the cost.


metallurgist & mineral processing engineer

For its extensive practical experience, 911 Metallurgisthas a clear understanding of what successful mineral processing engineering is and how to go about achieving it. Your goal is the production of a material that is marketable and returns you and your investors sustainable revenues.

Although improvements to the metallurgical processes have been made over the years the fact is that the unit operations, the machines, those too often called black boxes involved have not evolved or changed much since inception. Ore is reduced in size, chemicals are added and minerals separated and upgraded to produce a marketable product. Much of this process is mechanical and generally mistaken for some dark alchemy.We are the Anti-Alchemists.

Our vast experience has been gained through operation and start-up of both small and large scale mining/metallurgical operations in a range of commodities in thebase metals (Cu, Pb, Zn) and theprecious metals (Au, Ag,)

A solid metallurgist understands, the most important aspect of an operating process is its stability. Simple to say, but generally the most ignored in mineral processing. Linked unit operations require each to be stable, and each contains a different set of variables that have to be contended with. Thanks to some degree of stability: operating changes can be made and evaluated; increases in throughput can be made; and equipment performance improved. The more complicated the processes become, the more difficult it is to achieve and maintain stability. In mineral processing, unlike most processing operations, we have limited control of the main input, the feed ore. In most cases this inherently is variable and usually outside of the processors control.

Because you are too close to your own story, you might not see the forest for the trees and have chaos mistaken for stability. We, you, and your group have been battling plant problems for weeks, you start to accept chaos as a daily state of affair and consider it your new stability.

Each mineral processing plant is different: with varied ore types, mining equipment, and management (operating) philosophy. The evaluation and prioritisation of variables that affect the plant performance is the primary function. Implementing changes within the constraints imposed can be difficult, as resources may be limited.

Invariably the ability to solve problems can be confusing due the large numbers of variables that may impact the processes. In most cases problems are not metallurgical in nature but rather operational and mechanical. Problem solving is a process and in many operations this ability is absent. All too often many changes are made together without a solution resulting, on more confusion. Most plants learn to live or survive their problems, not to solve them.

Our engineering team has a global experience in the mining industry across all facets of the mine life-cycle. Our focus is to add value to your project and company by understanding your needs, employing innovative ideas and applying sound engineering while maintaining an economically driven approach. We have a combination of senior level professionals, experienced project managers, and technical staff to execute projects efficiently. We work in a partnership with our clients to achieve their company goals and operational milestones in a timely and cost effective manner.

resilience in the tantalum supply chain - sciencedirect

4 resilience-promoting mechanisms are analyzed for disruptions in the tantalum supply chain.Increased recycling, fast substitution, and supply from illegal artisanal and small-scale mining have kept tantalum prices low.250 percent increase in unaccounted African production 20042014.Supply not constrained by geological availability.

Tantalum, considered one of the critical elements by many countries, is a widely used metal in industries such as electronics, aerospace and automotive. The tantalum market has experienced several disruptions and subsequent price swings in the past, implying problems with its supply chain resilience and stability. In this study, we trace the entire value chain of the tantalum industry from mining to the intermediate and the downstream industries. Our interest is to see how dependent the tantalum supply chain is on specific countries and regions, how exposed primary production is to disruptions, and what mechanism counteracts disruption. This study assesses the tantalum supply chain from a resilience perspective rather than an investigation of any specific disruption in the system. We analyze several resilience-promoting mechanisms such as: (a) diversity of supply, (b) material substitution, (c) recycling and (d) stockpiling. We evaluate each of these mechanisms, and find that even though diversity of supply and stockpiling mechanisms have been decreasing for years, the tantalum supply chain has been flexible in its response to disruption. We find a much larger supply from unaccounted artisanal and small mining sources than expected based on official statistics, and estimate the unaccounted production in Africa, which shows an almost 250 percent increase from around 600 tons in 2004 to more than 2000 tons in 2014.. Besides flexible primary production from small-scale mining, we identfy rapid material substitution and increasing availability of waste and scrap as the main reasons behind the observed supply chain resilience.

a review on sustainable recycling technologies for lithium-ion batteries | springerlink

Due to increasing environmental awareness, tightening regulations and the need to meet the climate obligations under the Paris Agreement, the production and use of electric vehicles has grown greatly. This growth has two significant impacts on the environment, with the increased depletion of natural resources used for the production of the lithium-ion batteries for these electric vehicles and disposal of end-of-life lithium-ion batteries. In particular, when end-of-life lithium-ion batteries are incorrectly landfilled, pollution to groundwater and soil occurs. Therefore, sustainable recycling technologies must be implemented to construct a cyclic economy for the lithium-ion battery market and help alleviate the severity of these environmental consequences. The majority of current recycling methods involve energy-intensive pyrometallurgy, whereas hydrometallurgy techniques pose a viable alternative with promising advances at lab scale that can adapt with the evolution of new mixed cathode chemistries. As reviewed in this work, a combination of pre-treatment and hydrometallurgical processes was identified as a potential mechanism that could meet this criterion, which focuses on the recovered economic value and cumulative environmental benefits. Furthermore, automation of the pre-treatment process and mechanisms for electrolyte recovery were identified as potential opportunities for future works. Here, we evaluate the opportunities for sustainable recycling technologies for lithium-ion batteries.

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K. Kaviyarasu, E. Manikandan, J. Kennedy, M. Jayachandran, M. Maaza, Rice Husks As A Sustainable Source Of High Quality Nanostructured Silica For High Performance Li-Ion Battery Requital By Sol-Gel Method A Review. Adv. Mater. Lett. 7(9), 684696 (2016)

S. Kim, M. Hankel, W. Cha, G. Singh, J.M. Lee, I.Y. Kim, A. Vinu, Theoretical and experimental investigations of mesoporous C3N5/MoS2 hybrid for lithium and sodium ion batteries. Nano Energy 72, 104702 (2020)

T. Kesavan, T. Partheeban, M. Vivekanantha, N. Prabu, M. Kundu, P. Selvarajan, S. Umapathy, A. Vinu, M. Sasidharan, Design of P-Doped Mesoporous Carbon Nitrides as High-Performance Anode Materials for Li-Ion Battery. ACS Appl. Mater. Interfaces 12(21), 2400724018 (2020)

P.S. Murphin Kumar, A.H. Al-Muhtaseb, G. Kumar, A. Vinu, W. Cha, et al., Piper longumExtract-Mediated Green Synthesis of Porous Cu2O:Mo Microspheres and Their Superior Performance as Active Anode Material in Lithium-Ion Batteries. ACS Sustain. Chem. Eng. 8(38), 1455714567 (2020)

B. Oberle, S. Bringezu, S. Hatfield-Dodds, S. Hellweg, H. Schandl, J. Clement, et al., Global resources outlook 2019: natural resources for the future we want. https://www.resourcepanel.org/reports/global-resources-outlook. Accessed 10 December 2020

D. Bernhardt, I. Reilly, Mineral commodity summaries (US Geological Survey, Reston, 2016), pp. 4243 https://prd-wret.s3-us-west-.amazonaws.com/assets/palladium/production/atoms/files/mcs2019_all.pdf. Accessed 10 December 2020

P. Meshram, B. Pandey, T. Mankhand, Hydrometallurgical processing of spent lithium ion batteries (LIBs) in the presence of a reducing agent with emphasis on kinetics of leaching. Chem. Eng. J. 281, 418427 (2015)

C. Herrmann, A. Raatz, M. Mennenga, J. Schmitt, S. Andrew, Assessment of automation potentials for the disassembly of automotive lithium ion battery systems. Leveraging Technology for a Sustainable (World: Springer); pp. 149-54 (2012)

M. Grtzke, X. Mnnighoff, F. Horsthemke, V. Kraft, M. Winter, S. Nowak, Extraction of lithium-ion battery electrolytes with liquid and supercritical carbon dioxide and additional solvents. RSC Adv. 5(54), 4320943217 (2015)

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J. Yu, Y. He, Z. Ge, H. Li, W. Xie, S. Wang, A promising physical method for recovery of LiCoO 2 and graphite from spent lithium-ion batteries: Grinding flotation. Sep. Purif. Technol. 190, 4552 (2018)

L. Li, E. Fan, Y. Guan, X. Zhang, Q. Xue, L. Wei, F. Wu, R. Chen, Sustainable Recovery of Cathode Materials from Spent Lithium-Ion Batteries Using Lactic Acid Leaching System. ACS Sustain. Chem. Eng. 5(6), 52245233 (2017)

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a global-scale data set of mining areas | scientific data

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The area used for mineral extraction is a key indicator for understanding and mitigating the environmental impacts caused by the extractive sector. To date, worldwide data products on mineral extraction do not report the area used by mining activities. In this paper, we contribute to filling this gap by presenting a new data set of mining extents derived by visual interpretation of satellite images. We delineated mining areas within a 10km buffer from the approximate geographical coordinates of more than six thousand active mining sites across the globe. The result is a global-scale data set consisting of 21,060 polygons that add up to 57,277km2. The polygons cover all mining above-ground features that could be identified from the satellite images, including open cuts, tailings dams, waste rock dumps, water ponds, and processing infrastructure. The data set is available for download from https://doi.org/10.1594/PANGAEA.910894 and visualization at www.fineprint.global/viewer.

Global extraction of minerals grew at an unprecedented pace in the past decades, causing a wide range of social and environmental impacts around the world1,2,3. Growing demand for essential minerals and declining quality of ores4,5,6 lead to larger volumes of unused material extracted and disposed7, increasing appropriation of land8,9. The direct land used by mining is a crucial indicator of environmental pressure, which is closely associated with a range of negative impacts, including fragmentation and degradation of ecosystems and biodiversity loss10,11,12,13,14. Such an indicator supports the implementation and monitoring of several Sustainable Development Goals (SDGs), as mining impacts on biodiversity and ecosystem services can be reduced by limiting mining areas15. Data on land use of mining is also important to further develop land footprint indicators that inform about land required along global supply chains to satisfy final consumption of products16,17. Yet, to date information about mining areas worldwide is not available.

Databases on the global mining sector are regularly updated by national geological services, mining industries, associations, and information services18,19. These databases, however, focus on commodities production, not on land use or other environmental aspects. They include, for example, commodity classifications, produced volumes, and approximate location of the sites, but not their geographic extents. These data sources alone are therefore not sufficient for a comprehensive assessment of the impacts related to the direct land use of global mining.

Satellite images are an important source of information on mining extents complementing surveys and statistics. Visual interpretation of satellite images9, for example, has been applied to map the 295 most relevant mining sites in terms of commodities production across the world20,21. This approach is effective and precise but can be costly and time-intensive, therefore, posing challenges to producing comprehensive accounts of global mining areas. Alternatively, automated classification algorithms to monitor land-use changes have rapidly advanced due to the increasing availability of satellite images and computational infrastructure22,23,24,25,26. These developments have helped to map mining extents in many regions27,28,29,30,31. However, scaling automated classification is difficult, as current state-of-the-art algorithms require a large amount of labeled examples32, which are usually not available.

In this work, we contribute to filling this knowledge gap by presenting a new data set of mining extents derived by visual interpretation of satellite images. Our data set covers more than six thousand mining sites distributed across the entire globe. These mining sites have reported mineral extraction or activities between the years 2000 and 2017, according to the SNL Metals and Mining database19. Within these regions, we delineated the mining areas (i.e., drew polygons) by visual interpretation of several satellite data sources, including Google Satellite, Microsoft Bing Imagery and Sentinel-2 cloudless33. As a result, we derived a set of 21,060 polygons globally, covering a total area of 57,277km2. The overall accuracy, calculated from 1,000 stratified random points is 88.4% (for details see the section on Technical Validation).

This novel data set can help improving environmental impact assessments of the global mining sector, for example, regarding mining-induced deforestation or fragmentation and degradation of ecosystems. It can also serve as a benchmark for further monitoring the temporal evolution of mining sites around the world and astraining and validation data to support automated classification of mines using satellite images.

We produced the global-scale data set on mining areas by visual interpretation of satellite images. This remote sensing technique is precise but also costly and time-intensive. To make the visual interpretation viable on a global scale, we defined regions of interest (ROI) based on the SNL Metals and Mining database19. This was important to reduce the time spent inspecting the satellite images and delineating the mining extents. Automated post-processing was also applied to check and correct possible invalid polygon geometries34, for instance polygons with self-intersections.

We defined our ROI as a buffer around the geographical coordinates (georeferenced points) of active mines reported in the SNL Metals and Mining database19. The SNL database provides production information on more than 35,000 mines across the globe. Among many other variables, SNL reports the approximate geographic coordinates of the extraction sites, from which we selected all mines reporting activity (i.e., actual production or active status) at any time between the years 2000 and 2017. This subset added up to 6,021 mining locations extracting 76 different commodities, with a focus on coal, metal ores and industrial minerals. Note that many mines, particularly regarding metal ore extraction, report more than one commodity in the SNL database (see full list in Table1).

The buffer around the selected SNL mines was necessary to increase the efficiency and systematize the interpretation of the satellite images. The radius of the buffer should be as small as possible and cover all mining ground features, including open cuts, tailings dams, waste rock piles, water ponds, and processing infrastructure. Besides, the size of the buffer should consider that the geographical coordinates reported in the SNL database can differ between 1km and 3km from the mines identified in satellite images10,14.

After inspecting a random selection of mines we found that a 10km radius was adequate for our propose, i.e., covering all ground features related to the mines while minimizing the time spent on the visual interpretation of the images. The 10km buffer was sufficient to cover most of the mining complexes spreading over several kilometers, including the largest mines in the world, which have an open cut extending over 4km diameter.

The polygons were delineated by two trained experts using an open-source web application35 developed for this specific purpose. The web interface systematically displays buffers and markers with information about the mines. As background, the app offers three options of satellite layers: Google Satellite, Microsoft Bing Imagery, and Sentinel-2 cloudless33. Google Satellite and Microsoft Bing provide images with a spatial resolution finer than 5m for many regions of the world. These images allow identifying ground features related to mines with high confidence9. However, these data sources do not cover the whole globe with the same spatial resolution and contain out-of-date images for some regions36. To fill this gap, we used the Sentinel-2 cloudless data product with a 10m spatial resolution provided by EOX33. The Sentinel-2 cloudless provides a mosaic built from Sentinel-2 images taken during the years 2017 and 2018. Combining these data layers, the experts identified and delineated the ground features related to mining.

All three satellite data sources were visually inspected before delineating the polygons. The majority of the inspected locations had at least two sources of clear images (e.g., no cloud cover) and sufficient spatial resolution to identify mining features. Only very few locations lacked images with sufficient quality to draw the polygons, for example, due to cloud cover or low spatial resolution.

We used the source showing the largest mining extent for the delineation of the areas. This premise was taken because the largest extent of a mine is usually stable for several years as a long lifespan is intended due to economic reasons. Besides, mining areas generally increase and could only reduce through ecological restoration, which can take a long time37. These conjectures do not ensure the temporal consistency of all delineated extents but helped to capture the largest and most up-to-date extent of the mines according to the available satellite images within our ROI.

In some cases,the mining polygons can also extend beyond the ROI. Mining features intersecting the buffer borders were delineated to account for their full extent, even if they extend beyond the bufferlimits. Moreover, the mining polygons can contain isolated patches with forest or other land covers, which do not necessarily represent any mining feature on the ground. These patches were included because we aim at accounting for the total area used by mining, including isolated spare areas that most probably cannot have other uses. The delineated polygons do not distinguish the different ground features within the mines, i.e., each polygon can cover several mining features (open cuts, tailings dams, waste rock dumps, etc). As a final product from the delineation we obtained a set of polygons covering the total land used by mining within the ROI.

We applied geospatial and geometric operations to check and correct the raw data collection. This geoprocessing was performed to avoid double counting of mining areas, correct invalid geometries, and add attributes (variables) to the polygons. To avoid double-counting, we dissolved polygons that possibly overlapped or shared a common boundary, i.e., we merged them to form a single polygon. After that, we removed sliver polygons (unwanted small polygons) and invalid polygon geometries, producing a consistent set of polygons.

From this set of preprocessed polygons, we calculated the area of each feature and added information on the country where each polygon is located. We calculated the area in square kilometers by projecting each polygon to its respective Universal Transverse Mercator (UTM) zone. After that, a spatial join query acquired country name and ISO 3166-1 alpha-3 code from countrys administrative units geometries available from EUROSTAT38. The final set of polygons thus includes the geometries (polygons) covering the mining areas, their respective areas in square kilometers, country name, and ISO 3166-1 alpha-3 code of the corresponding country.

From the mining polygons we derived global grid data sets with the mining area at 30 arcsecond, 5 arcminute and 30 arcminute spatial resolution (approximately 11km, 1010km and 5050km at the equator). This is useful because many modeling applications require standardized grid data39. The 30 arcsecond grid was derived from the percentage of area of the geometric intersection between each cell and the geometries of the mining polygons. These percentages were rounded to zero decimal digits to reduce the size of the data set. Therefore, the percentage of the cell covered by mine should be greater than 0.5% to be considered, i.e., approximately 0.5 ha at the equator. To obtain the gridded mining area, we estimated the area of each cell in square kilometers and multiplied with the percentage of mining cover per cell, resulting in a 30 arcsecond global grid indicating the mining area within each cell. The 5 arcminute and 30 arcminute grid resolutions were downsampled form the 30 arcsecond grid. All scripts used in the geoprocessing of data records are available with our open-source web application tool35.

Our data records provide spatially explicit information on the direct land use of mining activities. The main data set consists of 21,060 mining polygons covering the extents of mining sites worldwide40. Grid data derived from the polygons is available at 30 arcsecond, 5 arcminute, and 30 arcminute spatial resolution, providing a ready-to-use data set for modeling purposes with the mining area in square kilometers per grid cell. All data records are available for download from PANGAEA (Data Publisher for Earth & Environmental Science) at https://doi.org/10.1594/PANGAEA.910894 and for visualization at https://www.fineprint.global/viewer.

Figure1 illustrates how the satellite images were used to delineate the mining extent. In this example, the area is used for coal mining in Mackenzie River, Queensland, Australia. The polygon in Fig.1a was derived from the Sentinel-2 cloudless mosaic (Fig.1b), which shows the largest extent of the mine among all three images sources. The Sentinel-2 cloudless mosaic is composed by images from the years 2017 and 201833 while Microsoft Bing (Fig.1c) and Google Satellite (Fig.1d) only offered out-of-date images for that location, respectively taken in July 2011 and December 2007. Nevertheless, all three data sources contributed to providing pieces of evidence of mining in the mapped area.

An example polygon delineated over a coal mine in Mackenzie River, Queensland, Australia. (a) Shows the delineated polygon in purple and (b) shows the Sentinel-2 cloudless mosaic composed by images from the year 201833 used to delineate the mining extent. (c) Shows a Microsoft Bing image from July 2011 and (d) a Google Satellite image from December 2007.

The delineated polygons cover all infrastructure and land cover types directly related to mining activities. This can produce large polygons, such as in the case of the Salar de Atacama, Chile. In that area, we delineated a polygon of approximately 1,354km2, covering almost the whole nucleus of the salt flat, which extends over 1,360km2 and is used as a source to extract lithium, boron, potassium, iodine, sodium chloride, and bischofite41. Figure2 shows the delineated polygon extent and a detailed view of one of the mining plants. Some pipelines and wells are more than 10km away from the core infrastructure of the mine. We decided to map the whole area because the mining plants, in fact, have brine pumping and monitoring wells spreading over the entire salt flat far beyond the actual evaporation ponds41. Alternative assumptions mapping only the evaporation ponds estimated an area of only 80.53km2 in 201742. However, it is important to note that the case of Salar de Atacama was rather isolated; in most cases, no features such as pipelines and wells outside the main mining sites could be identified from the available satellite images.

Mine on the Salar de Atacama salt flat, Chile. The purple polygon on the left side was derived from the Sentinel-2 images shown in the background. The polygon covers all infrastructure spread over the salt flat, including water pipelines, wells, and the actual mining plants. The zoom boxes on the right side show Google Satellite images with a detailed view of water pipelines and wells over the salt flat as well as one of the mining plants.

In many cases, mines are located following the structure of mineral deposits, making it easy to map them from satellite images. We selected three mines to illustrate these large-scale concentrated activities (Fig.3). The first example (Fig.3a) shows the main open cut of the Carajs iron ore mine complex in the Brazilian Amazon, which is among the worlds largest iron ore mining operations43. Figure3b shows the Batu Hijau copper-gold mine. Despite its large open cut, this mine does not use much area for unused material, as its tailings disposal takes place in the ocean44. The third example is the Super Pit gold mine in Australia, Fig.3c. This mine is located in one of the largest gold producing regions in the world. In the case of these large mines, coordinates reported in the SNL database were accurate.

Contrasting to the above examples, in other regions the reported coordinates were of lower accuracy. Figure4, for example, shows a large area with widely spread coal mining activities in East Kalimantan, Indonesia. The SNL database reports some mining locations in this region, however, they do not always spatially intersect the mining areas mapped from the satellite images. In these cases the predefined ROI (10km buffer around the coordinates) was crucial to systematically map the extents of the mines.

Figure5 shows an overview of the geographical distribution of our mapped mining area across the globe. The map in the figure is projected to equal area Interrupted Goode Homolosine and resampled to a 5050km grid to facilitate visualization. From this figure we can see concentrations of mining areas in many regions, for example, in northern Chile mainly due to copper extraction and northeastern Australia and East Kalimantan in Indonesia because of coal mining.

Mining area aggregated to 50km grid cells projected to Interrupted Goode Homolosine. The map at the top shows the global distribution of the mapped mining area. The maps at the bottom are zoomed to South America, Australia, and parts of South-East Asia.

A summary of our data aggregated by country shows that 51% of the mapped mining area is concentrated in only five countries: China, Australia, the United States, Russia, and Chile. Another ten countries account for 30%, and the remaining countries add up to 19% of the total mapped mining area (Fig.6). These results show that mining areas are highly concentrated in only a few countries. However, it is worth mentioning that our polygons could be biased by the activities reported in the SNL database and could mask countries and commodities that are poorly reported. For instance, SNL data underestimates the quantities extracted in China for most metals and minerals compared to national accounts according to UNEPs Global Material Flows Database2. For most African countries, however, SNL extraction of metals compares well to the national aggregates. One of the few exceptions is gold from the DR Congo, where SNL data sums up to less than 6mt in the year 2017, while UNEP reports more than 10mt of gold ore extraction.

Countries have different profiles regarding the spatial distribution of the mines. For example, China and Australia have similar figures on the mapped mining area, 6,567km2, and 6,470km2. However, they vary with respect to the number of identified polygons, 5,557 and 1,797, respectively. This discrepancy in the number of mining locations can be related to the high importance of the small-scale mining industry in China45,46, while Australia is characterized by fewer, large-scale mines19.

Figure7 displays the relationship between the mapped area and the number of polygons on a country level. Most of the variation in mining area can be explained by a linear relationship to the number of polygons. Excluding China from the data set, a simple linear regression model reaches r2=0.90 (dashed line in Fig.7). However, r2 drops to = 0.71 for the full data set including China (solid line in Fig.7). A complete summary of the mining area mapped per county is shown in Table2 and available from download with our data records40.

Relationship between the mapped mining area and the number of features (polygons) on a country level. The solid line summarizes the relationship between area and number of features for the complete data set, the dashed line excludes China.

Our mining data set accounts for all land cover types related to mining that could be identified from the satellite images. However, it does not distinguish the different features within the polygons. For example, we could not separate mining from quarry, because this would require additional information other than the satellite images. Although our data set does not cover all existing mines, to date, it is the most comprehensive database on mining extents openly available. The data set can help filling existing gaps for spatially explicit mineral extraction assessments on a global scale. It opens up opportunities to improve environmental pressure and impact indicators of the mining sector and can support the development of automated systems to monitor mining sites worldwide.

The mapped mining extents presented in this work can be subject to many sources of error, ranging from experts interpretation to the temporal availability and precision of the satellite images. The precision of the delineated mining borders can vary according to the satellite data source and the location. In general, the satellite sources used in this work provide sufficient spatial resolution and georeferencing accuracy to map mining areas9. Images available from Google Earth, for instance, have an overall positional root mean squared error (RMSE) of 39.7m related to the reality on the ground47. Sentinel-2, on the other hand, has a RMSE below its pixel size (1010m)48. These errors are acceptable for global scale environmental assessments.

The visual interpretation of satellite images depends on the previous knowledge of the perceiving person. The ground features related to mining are not always easy to identify in the satellite images and can be subject to the judgment of the person that delineates a particular mine. For that reason, we obtained a second independent classification for a set of random points. We drew a set of 1,000 random points stratified49 between the area mapped as mine and those not mapped as mine (no-mine) within the region of interest (10km buffer from the geographical coordinates). These validation points were inspected independently by experts that did not participate in the delineation of the mines. They classified these validation points as mine or no-mine based on the three satellite data sources without information whether or not the points were originally mapped as part of a mining areas. The validation points are also part of our data records40.

The overall agreement between the mapped areas and the validation points was 88.4%. Assuming that the validation points consist of a reference data set, we derived Users (commission errors) and Producers (omission errors) accuracy (see Table3). The Users accuracy tells how well the classes in the map represent the reality on the ground; the Producers accuracy points how well a class has been mapped50. In our case the mapped mining areas have 97.5% Users accuracy and 78.8% Producers accuracy, meaning that the mapped areas are highly reliable (less than 3% was incorrectly mapped as mining), but we missed some mining areas (the omission of mines was around 21.2%). The omission of mines also reflects a lower Users accuracy of the no-mine class (82.2%).

An alternative way to visualize the accuracy of our data set is the Receiver Operating Characteristic (ROC probability curve). The graph in Fig.8 displays the classification performance in terms of true positive and false positive. A discrete classifier (mine/no-mine) produces a point in the ROC curve. For our classification, the point is near the upper-left corner of the ROC curve, meaning that the classification performs well (a perfect classifier would reach the point 0, 1). Besides, the area under the curve (AUC) in Fig.8 shows that our classification has 89.9% probability of correctly distinguishing between mine and no-mine.

Receiver Operating Characteristic (ROC) derived from 1,000 random points equally allocated between the mapped classes mine and no-mine. The point in the ROC curve shows the performance of our binary (mine/non-mine) classification and the shade shows the area under the ROC curve (AUC).

Looking at the spatial distribution of the validation points, we found that half of the points with disagreement (i.e., 58 points) are located less than 50m from the borders of the delineated polygons. On the other hand, of the points with an agreement (i.e., 884 points) only 16% are located closer than 50m to the polygons borders. This shows that higher uncertainty lies on the borders of the delineated extents as it can be expected due to the use of several satellite data sources with different precision. These results also indicate that we have high confidence in the existence of mines within the mapped polygons.

The global mining data set described here is available from PANGAEA under the license Creative Commons Attribution-ShareAlike 4.0 International (CC-BY-SA). The data records include the mining polygons, validation points, mining area grid, and a summary of the mining area per country.

2. The mining grids include a single layer (one band raster) encoded in Geographic Tagged Image File Format (GeoTIFF)52. Each grid cell over land has a float number (data type Float32) greater than or equal to zero representing the mining area in square kilometers; grid cells over water have no-data values. The grid is available in three spatial resolutions, 30 arcsecond, 5 arcminute, and 30 arcminute, extending from the longitude 180 to 180 degrees and from the latitude 90 to 90 degrees in the geographical reference system WGS84.

Our spatially explicit data records can be combined with other geographical data to perform further statistical analysis, for example, to test spatially stratified heterogeneity54 and non-stationarity of variables55,56. For that, users can open the data records using software that support Geographic Information System (GIS), including, QGIS57, R58, and Python59. Besides, we also provide a tool for visual analysis of the geographical data records at www.fineprint.global/viewer and a Web Map Service (WMS)60 accessible from www.fineprint.global/geoserver/wms.

All the code and geoprocessing scripts used to produce the results of this paper are distributed under the GNU General Public License v3.0 (GPL-v3)61 from the repository www.github.com/fineprint-global/app-mining-area-polygonization35. The processing scripts were written in R58, Python59, and GDAL (Geospatial Data Abstraction Library62). The web application to delineate the polygons was written in R Shiny63 using a PostgreSQL64 database with PostGIS65 extension for storage. The full app setup uses Docker65 containers to facilitate management, portability, and reproducibility.

The web application supports the delineation of areas from the satellite images layers. It systematically displays the regions of interest (e.g., buffer around the mines) and several background options of satellite images, which the users can take into account to draw and edit polygons. Note that mining coordinates are not part of the web application and must be fed into the database by the user. To learn more about the application setup see www.github.com/fineprint-global/app-mining-area-polygonization. The current version of app provides image layers from Sentinel-2 Cloudless33, Google Satellite, and Microsoft Bing Imagery. Further sources of satellite images can be added to the application via WMS.

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Victor Maus conceptualization, experiment design, data collection, data revision, and writing the manuscript. Stefan Giljum conceptualization, data validation, and writing the manuscript. Jakob Gutschlhofer experiment design, scripting and web application development. Dieison Morozoli da Silva data collection and revision. Michael Probst data collection and revision. Sidnei Lus Bohn Gass data validation. Sebastian Luckeneder compiling mining data and data validation. Mirko Lieber experiment design and data revision. Ian McCallum data validation and accuracy assessment.

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