Exports of Coal in the United States increased to 1455 USD Million in February from 1244 USD Million in January of 2024. This dataset includes a chart with historical data for the United States Exports of Coal.

Coal rose to 112 USD/T on July 11, 2025, up 0.90% from the previous day. Over the past month, Coal's price has risen 7.07%, but it is still 16.32% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Coal - values, historical data, forecasts and news - updated on July of 2025.

"Coal Fields of the Conterminous United States" is a digital representation of James Trumbull's "Coal Fields of the United States" (sheet 1, 1960), which is an adaptation of previous maps by Averitt (1942) and Campbell(1908). It is intended to be the first in a series of open file reports that will eventually result in an I-series map that conforms to the U.S. Geological Survey mapping standards. For this edition, coal boundaries were digitized from Trumbull and plotted to represent as closely as possible the original map. In addition, the Gulf Province was updated using generalized boundaries of coal bearing formations digitized from various state geological maps.

About this dataset

Context

The dataset tabulates the Coal Center population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Coal Center. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 105 (67.74% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Coal Center population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Coal Center is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Coal Center is shown in the following column.

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/.

Recommended for further research

This dataset is a part of the main dataset for Coal Center Population by Age. You can refer the same here

New York Energy Prices presents retail energy price data. Energy prices are provided by fuel type in nominal dollars per million Btu for the residential, commercial, industrial, and transportation sectors. This section includes a column in the price table displaying gross domestic product (GDP) price deflators for converting nominal (current year) dollars to constant (real) dollars. To convert nominal to constant dollars, divide the nominal energy price by the GDP price deflator for that particular year. Historical petroleum, electricity, coal, and natural gas prices were compiled primarily from the Energy Information Administration. How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

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}:...

Worldwide coal consumption and international coal trade are projected to increase in the next several decades (Energy Information Administration, 2007). A search of existing literature indicates that in the Western Hemisphere, coal resources are known to occur in about 30 countries. The need exists to be able to depict these areas in a digital format for use in Geographic Information System (GIS) applications at small scales (large areas) and in visual presentations.

Existing surficial geology GIS layers of the appropriate geologic age have been used as an approximation to depict the extent of coal-bearing areas in North, Central, and South America, as well as Greenland. Global surficial geology GIS data were created by the U.S. Geological Survey (USGS) for use in world petroleum assessments (Hearn and others, 2003). These USGS publications served as the major sources for the selection and creation of polygons to represent coal-bearing areas. Additional publications and maps by various countries and agencies were also used as sources of coal locations. GIS geologic polygons were truncated where literature or hardcopy maps did not indicate the presence of coal.

The depicted areas are not adequate for use in coal resource calculations, as they were not adjusted for geologic structure and do not include coal at depth. Additionally, some coal areas in Central America could not be represented by the mapped surficial geology and are shown only as points based on descriptions or depictions from scientific publications or available maps. The provided GIS files are intended to serve as a backdrop for display of coal information. Three attributes of the coal that are represented by the polygons or points include geologic age (or range of ages), published rank (or range of ranks), and information source (published sources for age, rank, or physical location, or GIS geology base).

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Coal Center. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Coal Center. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Coal Center, the median household income stands at $93,750 for householders within the 45 to 64 years age group, followed by $39,313 for the 25 to 44 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $27,000.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

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/.

Recommended for further research

This dataset is a part of the main dataset for Coal Center median household income by age. You can refer the same here

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.

Coal mining has occurred in Pennsylvania for over a century. This dataset tries to identify the areas of the various coal seams in Pennsylvania that have been extracted by various underground mining techniques. This information can be used for many environmental related issues, including mine land reclamation and determination of needs for Mine Subsidence Insurance. The information in this dataset was gathered from digitizing the area of extracted coal identified on historic and modern underground mine maps. The maps to these coal mines are stored at many various public and private locations (if they still exist at all) throughout the commonwealth, they have been scanned to create a digital archive, and georeferenced to their approximate location for use in a geographic information system (GIS). The dataset is continuously updated as new maps are processed and is not considered â completedâ , i.e. just because an area in Pennsylvania is not identified in this dataset as mined, does not mean the area was not mined.

This dataset represents coal mine density and storage volumes within individual local and accumulated upstream catchments for NHDPlusV2 Waterbodies based on the National Coal Resource Dataset System (NCRDS). Catchment boundaries in LakeCat are defined in one of two ways, on-network or off-network. The on-network catchment boundaries follow the catchments provided in the NHDPlusV2 and the metrics for these lakes mirror metrics from StreamCat, but will substitute the COMID of the NHDWaterbody for that of the NHDFlowline. The off-network catchment framework uses the NHDPlusV2 flow direction rasters to define non-overlapping lake-catchment boundaries and then links them through an off-network flow table. The National Coal Resources Data System (NCRDS) began as a cooperative venture between the USGS and State geological agencies in 1975 and focused on the stratigraphy and chemistry of coal. Web pages have been developed to query data within both the USCOAL database and a subset of the USCHEM database. The USTRAT database, due to its size and complexity, was first made available in 2011 for direct query through web pages. The (coal mine sites/AreaSqKm) were summarized and accumulated into watersheds to produce local catchment-level and watershed-level metrics as a point data type.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Coal County, OK, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 12,679, while the mean income for the highest quintile (20% of households with the highest income) is 166,423. This indicates that the top earners earn 13 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 294,155, which is 176.75% higher compared to the highest quintile, and 2320.02% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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/.

Recommended for further research

This dataset is a part of the main dataset for Coal County median household income. You can refer the same here

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. E, Appendix 2, Disc 1, and used in this study of the Lower Kittanning 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. E, Appendix 3, Disc 1 (please see it for more detailed information on this geochemical dataset). This subset of the geochemical data for the Lower Kittanning 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. E, 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

The dataset presents the distribution of median household income among distinct age brackets of householders in Coal Valley. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Coal Valley. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Coal Valley, the median household income stands at $146,250 for householders within the 25 to 44 years age group, followed by $135,727 for the 45 to 64 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $64,500.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

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/.

Recommended for further research

This dataset is a part of the main dataset for Coal Valley median household income by age. You can refer the same here

This dataset (located by latitude and longitude) is a subset of the geochemical dataset found in Chap. F, Appendix 7, Disc 1, and used in this study of the Fire Clay coal zone. 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), and the Kentucky Geological Survey (KGS) Kentucky Coal Resources Information System (KCRIS) databases. The metadata file for the complete dataset is found in Chap. F, Appendix 8, Disc 1 (please see it for more detailed information on this geochemical dataset). This subset of the geochemical data for the Fire Clay coal zone 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. F, Appendix 9, 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 provides an archive of the US EPA AMPD data used for exposure assignment as well as code written for analyses related to the peer-reviewed published manuscript: Wilkie, Adrien A; Richardson, David B; Luben, Thomas J; Serre, Marc L; Woods, Courtney G; Daniels, Julie L. Sulfur dioxide reduction at coal-fired power plants in North Carolina and associations with preterm birth among surrounding residents. Environmental Epidemiology 7(2):p e241, April 2023. | DOI: 10.1097/EE9.0000000000000241 Exposure data is publicly available from the United States (US) Environmental Protection Agency (EPA) Air Markets Program Data (AMPD), which has been updated to the Clean Air Markets Program Data (CAMPD) available at https://campd.epa.gov/. Births data has identifiable information so is not available unless formally requested from Birth Defects Monitoring Program within the State Center for Health Statistics of the North Carolina Department of Health and Human Services. Portions of this dataset are inaccessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. They can be accessed through the following means: These data can be requested from the North Carolina State Center for Vital Statistics, Birth Defects Monitoring Program by experienced researchers with an approved IRB. Input data for unit and facility level data for coal-fired power plants in North Carolina from 2002-2015 were downloaded from US EPA AMPD (see attached data files). After data files were downloaded, this publicly available database/dashboard was updated to US EPA CAMPD (Clean Air Markets Program Data), which is also publicly available for download at https://campd.epa.gov/. Format: We received birth certificate records linked with birth defects monitoring program data from the NC State Center for Vital Statistics for all births in North Carolina between 2003 and 2015. These data include identifying information, including birth date and residential address, which was used to assign exposure to polluted sites. This dataset is associated with the following publication: Wilkie, A., D. Richardson, T. Luben, M. Serre, C. Woods, and J. Daniels. Sulfur dioxide reduction at coal-fired power plants in North Carolina and associations with preterm birth among surrounding residents. Environmental Epidemiology. Wolters Kluwer, Alphen aan den Rijn, NETHERLANDS, 7(2): e241, (2023).

This dataset (located by latitude and longitude) is a subset of the geochemical dataset found in Chap. D, Appendix 8, Disc 1, and used in this study of the Upper Freeport 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. D, Appendix 9, Disc 1 (please see it for more detailed information on this geochemical dataset). This subset of the geochemical data for the Upper Freeport 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. D, 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.