For the open-file report we collected new coking coal samples from Alabama, Kentucky, Oklahoma and Arkansas and analyzed them for proximate and ultimate analyses; calorific value; sulfur forms; major-, minor-, and trace-element abundances; free swelling indices; Gieseler plasticity; ASTM dilatation; coal petrography, and several other tests [air-dry loss (ADL), residual moisture (RM), equilibrium moisture (EQM), and true specific gravity (TSG)]. Coal Stability Factor (CSF) and Coal Strength after Reaction with CO2 (CSR) were predicted using chemical, rheological, and petrographic data (pCSF and pCSR, respectively). In addition, data from previously analyzed samples in Pennsylvania, West Virginia, Virginia, and Kentucky were shared with us by three companies, including results from the tests listed above, plus oxidation, Hardgrove Grindability Index (HGI), and ash fusion temperatures. These data are the contents of appendices 2-8 of the open-file report and this data release. In addition, appendices 20 and 21 of the open-file report and this data release include data previously published by the U.S. Bureau of Mines (USBM) in their Minerals Yearbooks listing the annual amounts of coal purchased (in short tons) for manufacturing oven-coke in six coal districts in Pennsylvania from 1942-1965 (in appendix 20), and the annual amounts of coal received by oven-coke plants (in short tons) in 17 Pennsylvania counties from 1966-1976 (in appendix 21). These previously published data have been included in this data release because they are currently not available online and the original USBM paper publications are not available in most libraries.

India’s Coal Production

Investigating how socio-economic factors influence coal production levels

By Andy Kriebel [source]

About this dataset

This dataset contains information on coal mines in India, including production data, mine ownership, location, and type of mine. It can be used to analyse sub-national socioeconomic developments in the Indian coal industry

How to use the dataset

India is the world’s third-largest producer of coal with production reaching 688.7 million metric tons in 2019/20. Indian coal production has continued to grow in recent years, despite concerns about environmental damage and air pollution from burning coal.

This dataset on India's top coal producing states provides information on which states produce the most coal, how much they produce, mine ownership, and location. The data can be used to track changes in coal production over time and to compare production levels across states

Research Ideas

• Examining the relationship between coal production and socioeconomic development in different states of India
• Investigating the impact of privatization of the coal industry on production levels
• Analyzing the effect of geography on coal production levels in India

Acknowledgements

Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Indian Coal Mines Dataset_January 2021-1.csv | Column name | Description | |:----------------------------------------------|:---------------------------------------------------------------------------| | SL No. | Serial number of the dataset. (Numeric) | | State/UT Name | Name of the state or union territory. (String) | | District Name | Name of the district. (String) | | Mine Name | Name of the mine. (String) | | Coal/ Lignite Production (MT) (2019-2020) | Coal/lignite production in metric tonnes for the year 2019-2020. (Numeric) | | Coal Mine Owner Name | Name of the coal mine owner. (String) | | Coal Mine Owner Full Name | Full name of the coal mine owner. (String) | | Coal/Lignite | Type of coal/lignite. (String) | | Govt Owned/Private | Whether the mine is owned by the government or privately. (String) | | Type of Mine (OC/UG/Mixed) | Type of mine (opencast/underground/mixed). (String) | | **Latitude ** | Latitude of the mine. (Numeric) | | **Longitude ** | Longitude of the mine. (Numeric) | | Source | Source of the data. (String) | | Accuracy (exact vs approximate) | Accuracy of the data. (String) |

Acknowledgements

If you use this dataset in your research, please credit Andy Kriebel.

Estimated Alaska coal resources are largely in Cretaceous and Tertiary rocks distributed in three major provinces. Northern Alaska-Slope, Central Alaska-Nenana, and Southern Alaska-Cook Inlet. Cretaceous resources, predominantly bituminous coal and lignite, are in the Northern Alaska-Slope coal province. Most of the Tertiary resources, mainly lignite to subbituminous coal with minor amounts of bituminous and semianthracite coals, are in the other two provinces. The combined measured, indicated, inferred, and hypothetical coal resources in the three areas are estimated to be 5,526 billion short tons (5,012 billion metric tons), which constitutes about 87 percent of Alaska's coal and surpasses the total coal resources of the conterminous United States by 40 percent. Coal mining has been intermittent in the Central Alaskan-Nenana and Southern Alaska-Cook Inlet coal provinces, with only a small fraction of the identified coal resource having been produced from some dozen underground and strip mines in these two provinces. Alaskan coal resources have a lower sulfur content (averaging 0.3 percent) than most coals in the conterminous United States are within or below the minimum sulfur value mandated by the 1990 Clean Air Act amendments. The identified resources are near existing and planned infrastructure to promote development, transportation, and marketing of this low-sulfur coal. The relatively short distances to countries in the west Pacific Rim make them more exportable to these countries than to the lower 48 States of the United States. Another untapped but potential resource of large magnitude is coalbed methane, which has been estimated to total 1,000 trillion cubic feet (28 trillion cubic meters) by T.N. Smith 1995, Coalbed methane potential for Alaska and drilling results for the upper Cook Inlet Basin: Intergas, May 15 - 19, 1995, Tuscaloosa, University of Alabama, p. 1 - 21.

This dataset is a polygon shapefile representing the most recent update of the coal fields of the conterminous United States. Scale of data is 1:5,000,000. This publication is based on a USGS paper map that was a representation of the coal fields and major regions of the time (Trumbull, 1960). Trumbull's 1960 map was digitized and coal fields from the Gulf Coast were added to create USGS OFR 96-92, Coal Fields of the Conterminous United States (Tully, 1996). Tully's (1996) publication consisted of a map in pdf format that could be printed, and an ArcInfo coverage of the coal fields, attributed with rank and potential economic use (minability) of the coal. This new dataset includes a pdf showing updated coal fields and a shapefile that contains attributes on coal rank (without regard to outdated economic standards), province, name, and age. The data used to update Tully's (1996) digital map was collected from the National Coal Resource Assessment (NCRA) regional Professional Papers produced by the USGS and from AAPG Discovery Series 14/Studies in Geology 62, all of which were conducted by USGS geologists and professional staff. A small number of field names were added and or updated in the western states of Washington, Oregon, California, Utah, Colorado and New Mexico using additional coal resource literature.The full study is available from USGS: https://doi.org/10.3133/ofr20121205

This data set shows the coal fields of Alaska and the conterminous United States. Most of the material for the conterminous United States was collected from James Trumbull's 'Coal Fields of the United States, Conterminous United States' map (sheet 1, 1960). The Gulf Coast region was updated using generalized, coal-bearing geology obtained from State geologic maps. The Alaska coal fields were collected from Farrell Barnes's 'Coal Fields of the United States, Alaska' map (sheet 2, 1961).

This data set was extracted from an original set that shows the coal fields of Alaska and the conterminous United States. Most of the material for the conterminous United States was collected from James Trumbull's "Coal Fields of the United States, Conterminous United States" map (sheet 1, 1960). The Gulf Coast region was updated using generalized, coal-bearing geology obtained from State geologic maps. The Alaska coal fields were collected from Farrell Barnes's "Coal Fields of the United States, Alaska" map (sheet 2, 1961). (National Atlas of the United States, 2002) Purpose: These data are intended for geographic display and analysis at the National level, and for large regional areas. The data should be displayed and analyzed at scales appropriate for 1:5,000,000-scale data. No responsibility is assumed by the U.S. Geological Survey in the use of these data. Shapefiles were obtained from the National Atlas of the United States web site. See the Full Metadata page for process step information pertaining to the creation of the original data.

As described by ASTM D7780-12: This feature class contains polygons depicting boundaries of surface Coal Mining Operations and surface disturbance due to underground Coal Mining Operations. This dataset consists of coalmining related features as described by ASTM D7780-12, "Standard Practice for Geospatial Data for Representing Coal Mining Features". These data are gathered using automated processes from participating coalmining regulatory authorities, which are generally state government agencies. The data from the various sources are transformed into common schemas as described by the ASTM Standard above. The resultant feature classes represent seamless information covering the coal producing areas of the United States. Development of these data are ongoing and will become more complete as more cooperating regulatory authorities are added to the GeoMine system.

As described by ASTM: D7780-12: This feature class contains points that depict the location of coal preparation plants on a given Coal Mining Operation. This dataset consists of coalmining related features as described by ASTM D7780-12, "Standard Practice for Geospatial Data for Representing Coal Mining Features". These data are gathered using automated processes from participating coalmining regulatory authorities, which are generally state government agencies. The data from the various sources are transformed into common schemas as described by the ASTM Standard above. The resultant feature classes represent seamless information covering the coal producing areas of the United States. Development of these data are ongoing and will become more complete as more cooperating regulatory authorities are added to the GeoMine system.

As described by ASTM D7780-12: This feature class contains polygons that depict the location of coal refuse disposal areas. They include, but are not limited to, slurry impoundments (including cross valley, diked, and incised impoundments), slurry cell structures, and coarse coal refuse structures. This dataset consists of coalmining related features as described by ASTM D7780-12, "Standard Practice for Geospatial Data for Representing Coal Mining Features". These data are gathered using automated processes from participating coalmining regulatory authorities, which are generally state government agencies. The data from the various sources are transformed into common schemas as described by the ASTM Standard above. The resultant feature classes represent seamless information covering the coal producing areas of the United States. Development of these data are ongoing and will become more complete as more cooperating regulatory authorities are added to the GeoMine system.

In March 2013, the LADWP Board approved a contract that will enable LADWP to completely transition out of coal power. Through these actions, the City of Los Angeles became the first major city in the United States to commit to becoming coal free. More information: https://www.ladwp.com/ladwp/faces/ladwp/aboutus/a-power/a-p-renewableenergy?_adf.ctrl-state=1cnzdaglow_191&_afrLoop=46545720898498

The purpose of this data set is to allow exploration between various types of data that is commonly collected by the US government across the states and the USA as a whole. The data set consists of three different types of data:

• Census and Geographic Data;
• Energy Data; and
• Economic Data.

When creating the data set, I combined data from many different types of sources, all of which are cited below. I have also provided the fields included in the data set and what they represent below. I have not performed any research on the data yet, but am going to dive in soon. I am particularly interested in the relationships between various types of data (i.e. GDP or birth rate) in prediction algorithms. Given that I have compiled 5 years’ worth of data, this data set was primarily constructed with predictive algorithms in mind.

An additional note before you delve into the fields: * There could have been many more variables added across many different fields of metrics. I have stopped here, but it could potentially be beneficial to observe the interaction of these variables with others (i.e. the GDP of certain industries, the average age in a state, the male/female gender ratio, etc.) to attempt to find additional trends.

Census and Geographic Data

• StateCodes: The state 2-letter abbreviations. Note that I added "US" for the United States.
• Region: The number corresponding to the region the state lies within, according to the 2010 census. (1 = Northeast, 2 = Midwest, 3 = South, 4 = West)
• Division: The number corresponding to the division the state lies within, according to the 2010 census. (1 = New England, 2 = Middle Atlantic, 3 = East North Central, 4 = West North Central, 5 = South Atlantic, 6 = East South Central, 7 = West South Central, 8 = Mountain, 9 = Pacific)
• Coast: Whether the state shares a border with an ocean. (1 = Yes, 0 = No)
• Great Lakes: Whether the state shares a border with a great lake. (1 = Yes, 0 = No
• CENSUS2010POP: 4/1/2010 resident total Census 2010 population
• POPESTIMATE{year}: 7/1/{year} resident total population estimate
• RBIRTH{year}: Birth rate in period 7/1/{year - 1} to 6/30/
• RDEATH{year}: Death rate in period 7/1/{year - 1} to 6/30/
• RNATURALINC{year}: Natural increase rate in period 7/1/{year - 1} to 6/30/
• RINTERNATIONALMIG{year}: Net international migration rate in period 7/1/{year - 1} to 6/30/
• RDOMESTICMIG{year}: Net domestic migration rate in period 7/1/{year - 1} to 6/30/
• RNETMIG{year}: Net migration rate in period 7/1/{year - 1} to 6/30/

As noted from the census:

Net international migration for the United States includes the international migration of both native and foreign-born populations. Specifically, it includes: (a) the net international migration of the foreign born, (b) the net migration between the United States and Puerto Rico, (c) the net migration of natives to and from the United States, and (d) the net movement of the Armed Forces population between the United States and overseas. Net international migration for Puerto Rico includes the migration of native and foreign-born populations between the United States and Puerto Rico.

Codes for most of the data, information about the geographic terms and coditions, and more information about the methodology behind the population estimates can be found on the US Census website.

Energy Data

• TotalC{year}: Total energy consumption in billion BTU in given year.
• TotalP{year}: Total energy production in billion BTU in given year.
• TotalE{year}: Total Energy expenditures in million USD in given year.
• TotalPrice{year}: Total energy average price in USD/million BTU in given year.
• TotalC{first year}–{second year}: The first year’s total energy consumption divided by the second year’s total energy consumption, times 100. (The percent change between years in total energy consumption.)
• TotalP{first year}–{second year}: The first year’s total energy production divided by the second year’s total energy production, times 100. (The percent change between years in total energy production.)
• TotalE{first year}–{second year}: The first year’s total energy expenditure divided by the second year’s total energy expenditure, times 100. (The percent change between years in total energy expenditure.)
• TotalPrice{first year}–{second year}: The first year’s total energy average price divided by the second year’s total energy average price, times 100. (The percent change between years in total energy average price.)
• BiomassC{year}: Biomass total consumption in billion BTU in given year.
• CoalC{year}: Coal total consumption in billion BTU in given year.
• CoalP{year}: Coal total production in billion BTU in given year.
• CoalE{year}: Coal total expenditures in million USD in given year.
• CoalPrice{year}:...

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The Coal Basin Outlines dataset …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The Coal Basin Outlines dataset is a combination of data from various sources displaying the best available (7 March 2013) coal basin extents for all of Australia. These extents were taken predominantly from the Australian Geological Provinces Database(extract from 4 November 2012 - see metadata statement below) with additional data coming from the Australian Sedimentary Basins Database (27 August 2012 update), State (South Australia) basin databases and hydrogeological basin databases. The Australian Geological Provinces Database contains descriptions and spatial extents of the fundamental geological elements of the Australian continent and immediate surrounds. Captured province types include sedimentary basins, tectonic provinces such as cratons and orogens, igneous provinces, and metallogenic provinces. Spatial data has been captured largely at approximately 1:1M scale for best use at between 1:2M to 1:5M scale. Where possible, provinces have been attributed with their age, contained lithostratigraphic units, relationships to other provinces, and geological history. The geological definition of some provinces, in particular certain sedimentary basins and orogens, is contentious. While every effort has been made to achieve a consensus interpretation of each province, scientific debate may still occur about the nature and extent of some provinces. The total 2D spatial extent of most provinces in the database has been captured (ie, the full extent of a province under any overlying cover). The extent of outcrop of some provinces has also been captured. Where possible, the full extent outlines of provinces have been attributed with information about the source, accuracy, and observation method of those lines. Purpose Note that these datasets are pre-release data. While every effort has been made to maintain accuracy and precision during data compilation, the data have not been fully QA checked for public release. This was extracted from the database on 01/11/2012. Dataset History The following list shows which database each basin was derived from: Australian Geological Provinces Database (4 November 2012): Arckaringa, Bowen, Clarence-Moreton, Cooper, Eucla, Galilee, Gippsland, Gloucester, Gunnedah, Marybourough, Mulgildie, Murray, Otway, Pedirka, Perth, Polda, Styx, Surat, Sydney and Tasmania basins Australian Sedimentary Basins Database (27 August 2012): Canning Basin (Fitroy Trough), Eromanga, Oaklands, Collie, Boyup, Wilga and Leigh Creek basins State provided data - South Australia: St Vincents Basin Hydrogeological Basin Data: Laura Basin Feature Classes: ProvinceFullExtent - polygons showing the full 2D spatial extent of a province. This includes non-outcropping regions of a province that may be concealed by regolith or other overlying provinces. The ProvinceFullExtent feature class has two related tables, linked through relationship classes: ProvinceRelations - this table lists relationships between provinces (eg, the Surat Basin overlies the Bowen Basin) ProvinceStratigraphy - this table list the stratigraphic units contained within a province. Full details of each stratigraphic unit are available at http://dbforms.ga.gov.au/www/geodx.strat_units.int ProvinceOutcrop - polygons showing the outcropping extent of a province. Note that not all provinces have this data available. ProvinceFullExtentBdy - lines overlying the margins of province full extent polygons showing the nature of contacts between provinces, data source, and spatial accuracy. Note that not all provinces have this data available. Further information can be found at http://www.ga.gov.au/metadata-gateway/metadata/record/gcat_c008f6a4-6b89-6c99-e044-00144fdd4fa6/Australian+In+Situ+Coal+Resources%2C+2012 Dataset Citation Geoscience Australia (2013) Australian Coal Basins. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/9d5a8d74-a201-42bd-9d49-3c392244be16.

These data accompany the 2018 manuscript published in PLOS One titled "Mapping the yearly extent of surface coal mining in Central Appalachia using Landsat and Google Earth Engine". In this manuscript, researchers used the Google Earth Engine platform and freely-accessible Landsat imagery to create a yearly dataset (1985 through 2015) of surface coal mining in the Appalachian region of the United States of America.This specific dataset is a GeoTIFF file depicting when an area was most recently mined, from the period 1985 through 2015. The raster values depict the year that mining was most recently detected by the paper's processing model. A year of "1984" indicates mining that likely was most recently mined at some point prior to 1985. These pre-1985 mining data are derived from a prior study; see https://skytruth.org/wp/wp-content/uploads/2017/03/SkyTruth-MTR-methodology.pdf for more information. This dataset does not indicate for how long an area was a mine or when mining began in a given area.

Pacific Island states are some of the most vulnerable nations in the world when it comes to the impact of climate change. As yet, none of the Pacific Island States have any operational coal mines or coal-fired power stations. However, this could all soon change.

As described by ASTM D7780-12: This feature class contains polygons that depict post mining land use for a reclaimed Coal Mining Operation. This dataset consists of coalmining related features as described by ASTM D7780-12, "Standard Practice for Geospatial Data for Representing Coal Mining Features". These data are gathered using automated processes from participating coalmining regulatory authorities, which are generally state government agencies. The data from the various sources are transformed into common schemas as described by the ASTM Standard above. The resultant feature classes represent seamless information covering the coal producing areas of the United States. Development of these data are ongoing and will become more complete as more cooperating regulatory authorities are added to the GeoMine system.

This dataset (located by latitude and longitude) is a subset of the geochemical dataset found in Chap. H, Appendix 2, Disc 1, and used in this study of the Pocahontas No. 3 coal bed. That dataset is a compilation of data from the U.S. Geological Survey's (USGS) National Coal Resources Data System (NCRDS) USCHEM (U.S. geoCHEMical), The Pennsylvania State University (PSU), and the West Virginia Economic and Geological Survey (WVGES) coal quality databases. The metadata file for the complete dataset is found in Chap. H, Appendix 3, Disc 1 (please see it for more detailed information on this geochemical dataset). This subset of the geochemical data for the Pocahontas No. 3 coal bed includes ash yield, sulfur content, SO2 value, gross calorific value, arsenic content and mercury content for these records, as well as the ranking of these values, which is described later under the attributes in this metadata file. Analytical techniques are described in the references in Chap. H, Appendix 4, Disc 1. The analytical data are stored as text fields because many of the parameters contain letter qualifiers appearing after the numerical data values. The following is a list of the possible qualifier values: L - less than, G - greater than, N - not detected, or H - interference that cannot be easily resolved. Not all of these codes may be in this database.

This dataset (located by latitude and longitude) is a subset of the geochemical dataset found in Chap. C, Appendix 8, Disc 1, and used in this study of the Pittsburgh coal bed. That dataset is a compilation of data from the U.S. Geological Survey's (USGS) National Coal Resources Data System (NCRDS) USCHEM (U.S. geoCHEMical), The Pennsylvania State University (PSU), the West Virginia Economic and Geological Survey (WVGES), and the Ohio Division of Geological Survey (OHGS) coal quality databases as well as published U.S. Bureau of Mines (USBM) data. The metadata file for the complete dataset is found in Chap. C, Appendix 9, Disc 1 (please see it for more detailed information on this geochemical dataset). This subset of the geochemical data for the Pittsburgh coal bed includes ash yield, sulfur content, SO2 value, gross calorific value, arsenic content and mercury content for these records, as well as the ranking of these values, which is described later under the attributes in this metadata file. Analytical techniques are described in the references in Chap. C, Appendix 10, Disc 1. The analytical data are stored as text fields because many of the parameters contain letter qualifiers appearing after the numerical data values. The following is a list of the possible qualifier values: L - less than, G - greater than, N - not detected, or H - interference that cannot be easily resolved. Not all of these codes may be in this database.

This dataset (located by latitude and longitude) is a subset of the geochemical dataset found in Chap. H, Appendix 2, Disc 1, and used in this study of the Pocahontas No. 3 coal bed. That dataset is a compilation of data from the U.S. Geological Survey's (USGS) National Coal Resources Data System (NCRDS) USCHEM (U.S. geoCHEMical), The Pennsylvania State University (PSU), and the West Virginia Economic and Geological Survey (WVGES) coal quality databases. The metadata file for the complete dataset is found in Chap. H, Appendix 3, Disc 1 (please see it for more detailed information on this geochemical dataset). This subset of the geochemical data for the Pocahontas No. 3 coal bed includes ash yield, sulfur content, SO2 value, gross calorific value, arsenic content and mercury content for these records, as well as the ranking of these values, which is described later under the attributes in this metadata file. Analytical techniques are described in the references in Chap. H, Appendix 4, Disc 1. The analytical data are stored as text fields because many of the parameters contain letter qualifiers appearing after the numerical data values. The following is a list of the possible qualifier values: L - less than, G - greater than, N - not detected, or H - interference that cannot be easily resolved. Not all of these codes may be in this database.

About this dataset

Context

This list ranks the 7 cities in the Coal County, OK by Non-Hispanic Asian population, as estimated by the United States Census Bureau. It also highlights population changes in each cities over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Non-Hispanic Asian Population: This column displays the rank of cities in the Coal County, OK by their Non-Hispanic Asian population, using the most recent ACS data available.
• cities: The cities for which the rank is shown in the previous column.
• Non-Hispanic Asian Population: The Non-Hispanic Asian population of the cities is shown in this column.
• % of Total cities Population: This shows what percentage of the total cities population identifies as Non-Hispanic Asian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Coal County Non-Hispanic Asian Population: This tells us how much of the entire Coal County, OK Non-Hispanic Asian population lives in that cities. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

This dataset (located by latitude and longitude) is a subset of the geochemical dataset found in Chap. H, Appendix 2, Disc 1, and used in this study of the Pocahontas No. 3 coal bed. That dataset is a compilation of data from the U.S. Geological Survey's (USGS) National Coal Resources Data System (NCRDS) USCHEM (U.S. geoCHEMical), The Pennsylvania State University (PSU), and the West Virginia Economic and Geological Survey (WVGES) coal quality databases. The metadata file for the complete dataset is found in Chap. H, Appendix 3, Disc 1 (please see it for more detailed information on this geochemical dataset). This subset of the geochemical data for the Pocahontas No. 3 coal bed includes ash yield, sulfur content, SO2 value, gross calorific value, arsenic content and mercury content for these records, as well as the ranking of these values, which is described later under the attributes in this metadata file. Analytical techniques are described in the references in Chap. H, Appendix 4, Disc 1. The analytical data are stored as text fields because many of the parameters contain letter qualifiers appearing after the numerical data values. The following is a list of the possible qualifier values: L - less than, G - greater than, N - not detected, or H - interference that cannot be easily resolved. Not all of these codes may be in this database.