The service displays data in the form of an indicator for emissions related to the macro-sector “Non-industrial combustion” of the Regional Inventory of Atmospheric Emissions (IREA).Estimates made are calculated on the basis of the INEMAR system (air emissions inventory) based on the EMEP — CORINAIR methodology and relate to sources classified according to the SNAP (Selected Nomenclature for Air Pollution) nomenclature. They are classified according to the following parameters: reference year, province and municipality, reference activities according to the SNAP methodology (macrosector, sector and emissive activity), fuel used and pollutant emitted. The main pollutants exposed are: CH4 (tonnes/year); Co (t/year); CO2 (kt/year); N2O (t/year); NH3 (t/year); NMVOC (t/year); NOx (tonnes/year); PM10 (tonnes/year); PM2.5 (tonnes/year); PTS (t/year); SO2 (tonnes/year). The data shall be rounded to the fourth decimal place. The service exposes the data in four different spatial resolutions: Municipalities, Provinces, Region, Air Quality Zones.By a special function in the Environmental Knowledge System it is possible to view the themed inventory data according to three different types of statistical classification (Jenks, Equal Interval, Quantile).The WFS service can also be used in any GIS desktop (e.g. QGIS).

The service shall display data in the form of an indicator for emissions related to the macro-sector “Other sources and removals” of the Regional Inventory of Atmospheric Emissions (IREA).Estimates made are calculated on the basis of the INEMAR (air emission inventory) system based on the EMEP — CORINAIR methodology and relate to sources classified according to the SNAP (Selected Nomenclature for Air Pollution) nomenclature. They are classified according to the following parameters: reference year, province and municipality, reference activities according to the SNAP methodology (macrosector, sector and emissive activity), fuel used and pollutant emitted. The main pollutants exposed are: CH4 (tonnes/year); Co (t/year); CO2 (kt/year); N2O (t/year); NH3 (t/year); NMVOC (t/year); NOx (tonnes/year); PM10 (tonnes/year); PM2.5 (tonnes/year); PTS (t/year); SO2 (tonnes/year). The data shall be rounded to the fourth decimal place. The service exposes the data in four different spatial resolutions: Municipalities, Provinces, Region, Air Quality Zones.By a special function in the Environmental Knowledge System it is possible to view the themed inventory data according to three different types of statistical classification (Jenks, Equal Interval, Quantile).The WFS service can also be used in any GIS desktop (e.g. QGIS).

The service displays data in the form of an indicator for emissions relating to the "Fuel extraction and distribution" macro-sector of the Regional Inventory of Atmospheric Emissions (IREA). The estimates made are calculated on the basis of the INEMAR system (INventario EMissioni ARia) on the basis of EMEP - CORINAIR methodology and concern sources classified according to the SNAP nomenclature (Selected Nomenclature for Air Pollution). They are classified according to the following parameters: reference year, province and municipality, reference activity according to the SNAP methodology (macro-sector, sector and activity emissions), fuel used and pollutant emitted. The main pollutants exposed are: CH4 (t/year); CO (t/year); CO2 (kt/year); N2O (t/year); NH3 (t/year); NMVOC (t/year); NOX (t/year); PM10 (t/year); PM2.5 (t/year); PTS (t/year); SO2 (t/year). Data is rounded to four decimal places. The service displays the data in four different spatial resolutions: Municipalities, Provinces, Regions, Air quality zones. Through a special function in the Environmental Knowledge System it is possible to view the inventory data according to three different types of classification statistics (Jenks, Equal Interval, Quantile). The WFS service can also be used in any GIS desktop (e.g. QGIS).

The service displays data in the form of an indicator for point emissions from production plants and landfills of the Regional Inventory of Atmospheric Emissions (IREA).The estimates made are calculated according to the INEMAR system (air emissions inventory) based on the EMEP — CORINAIR methodology and relate to sources classified according to the SNAP nomenclature (Selected Nomenclature for Air Pollution).These are classified according to the following parameters: reference year, province and municipality, reference activities according to the SNAP methodology (macrosector, sector and emissive activity), fuel used and pollutant emitted. The main pollutants exposed are: CH4 (t/year); Co (t/year); CO2 (kt/year); N2O (t/year); NH3 (t/year); NMVOC (t/year); NOx (t/year); PM10 (t/year); PM2.5 (t/year); PTS (t/year); SO2 (t/year). The data shall be rounded to the fourth decimal place. The service exposes the data in four different spatial resolutions: Municipalities, Provinces, Region, Air Quality Zones.Through a specific function in Environmental Knowledge System, you can view the thematic inventory data based on three different types of statistical classification (Jenks, Equal Interval, Quantum).The WFS service can also be used in any GIS desktop (e.g. QGIS).

This dataset uses Census Data following published social vulnerability index literature to provide an index at the Place level.

The Corps of Engineers has chosen SoVI as the “foundational SVA (Social Vulnerability Analysis) method for characterizing social vulnerability….” (Dunning and Durden 2013) The University of South Carolina has provided extensive and historic data for this methodology. Susan Cutter and her team have published their methodology and continue to maintain their database. Thus it was chosen as the “primary tool for [Army] Corps SVA applications.” (ibid) The downside is that this method is complex and hard to communicate and understand at times. (S. Cutter, Boruff, and Shirley 2003) The Social Vulnerability Index (SoVI) for this study was constructed at the U.S. Census Place level for the state of Utah. We utilized the conventions put forth by Cutter (2011) as closely as possible using the five-year American Community Survey (ACS) data from 2008 to 2012. The ACS collects a different, more expansive set of variables than the Census Long Form utilized in Cutter et al. (2003), which required some deviation in variable selection from the original method. However, Holand and Lujala (2013) demonstrated that the SoVI could be constructed using regional contextually appropriate variables rather than the specific variables presented by Cutter et al. (2003). Where possible, variables were selected which matched with the Cutter et al. (2003) work. The Principle Components Analysis was conducted using the statistical software R version 3.2.3 (R 2015) and the prcomp function. Using the Cutter (2011) conventions for component selection, we chose to use the first ten principle components which explained 76% of the variance in the data. Once the components were selected, we assessed the correlation coefficients for each component and determined the tendency (how it increases or decreases) of each component for calculating the final index values. With the component tendencies assessed, we created an arithmetic function to calculate the final index scores in ESRI’s ArcGIS software (ESRI 2014). The scores were then classified using an equal interval classification in ArcGIS to produce five classes of vulnerability, ranging from very low to very high. The SoVI constructed for our study is largely consistent with previous indices published by Susan Cutter at a macro scale, which were used as a crude validation for the analysis. The pattern of vulnerability in the state is clustered, with the lowest vulnerability in the most densely populated area of the state, centered on Salt Lake City (see Figure [UT_SoVI.png]). Most of the state falls in the moderate vulnerability class, which is to be expected.

The service displays data in the form of an indicator for the emissions relating to the "Non-industrial combustion" macro-sector of the Regional Inventory of Atmospheric Emissions (IREA). The estimates made are calculated on the basis of the INEMAR system (INventario EMissioni ARia) on the basis of the methodology EMEP - CORINAIR and concern sources classified according to the SNAP nomenclature (Selected Nomenclature for Air Pollution). They are classified according to the following parameters: reference year, province and municipality, reference activity according to the SNAP methodology (macro-sector, sector and emission activity ), fuel used and pollutant emitted. The main pollutants exposed are: CH4 (t/year); CO (t/year); CO2 (kt/year); N2O (t/year); NH3 (t/year); NMVOC (t/year); NOX (t/year); PM10 (t/year); PM2.5 (t/year); PTS (t/year); SO2 (t/year). Data is rounded to four decimal places. The service displays the data in four different spatial resolutions: Municipalities, Provinces, Regions, Air quality zones. Through a special function in the Environmental Knowledge System it is possible to view the inventory data according to three different types of classification statistics (Jenks, Equal Interval, Quantile). The WFS service can also be used in any GIS desktop (e.g. QGIS).

The service displays data in the form of an indicator for the emissions relating to the "Other mobile sources and machinery" macro-sector of the Regional Inventory of Atmospheric Emissions (IREA). The estimates made are calculated on the basis of the INEMAR system (INventario EMissioni ARia) on the basis of the EMEP - CORINAIR methodology and concern sources classified according to the SNAP nomenclature (Selected Nomenclature for Air Pollution). They are classified according to the following parameters: reference year, province and municipality, reference activity according to the SNAP methodology (macro-sector, sector and emission activity), fuel used and pollutant emitted. The main pollutants exposed are: CH4 (t/year); CO (t/year); CO2 (kt/year); N2O (t/year); NH3 (t/year); NMVOC (t/year); NOX (t/year); PM10 (t/year); PM2.5 (t/year); PTS (t/year); SO2 (t/year). Data is rounded to four decimal places. The service displays the data in four different spatial resolutions: Municipalities, Provinces, Regions, Air quality zones. Through a special function in the Environmental Knowledge System it is possible to view the inventory data according to three different types of classification statistics (Jenks, Equal Interval, Quantile). The WFS service can also be used in any GIS desktop (e.g. QGIS).

Classification performance achieved by the classification models that are trained using the loss functions discussed in this study.

Bathymetric data from Welsh lakes collated from documentary and electronic sources and re-formatted into a consistent and accessible form and structure. The data includes transformed raw coordinate and depth data to bathymetric surface rasters at 0.5 m interval polygon shapefiles that can be placed into GIS software with volume/depth and area/depth data for the lakes at the same 0.5 m interval. The data supplied collates existing bathymetric data to produce a series of GIS layers that display detailed bathymetric maps of Welsh lakes.

This grid is an interpolation of maximum depositional age point data held in the Isotopic Atlas as of February 2021. Data added to the Isotopic Atlas after February 2021 is not represented in this grid. The grid was generated with Esri's ArcMap Spatial Analyst interpolation tool using the Natural Neighbor algorithm and 0.01 degree cell size. Two classifications are provided for this grid; a Jenks natural breaks classification with ten classes (classification 1) as per Champion (2013) http://pid.geoscience.gov.au/dataset/ga/77772, and; the same seven-class interval binning and colour scheme applied to the source point data in the Sedimentary Processes Age Data layer (classification 2).

The service displays data in the form of an indicator for emissions related to the “Non-industrial combustion — Wood heating” of the Regional Inventory of Atmospheric Emissions (IREA), i.e. generated by all residential and commercial heating activities using wood or similar (pellets) as fuel.Estimates made are calculated on the basis of the INEMAR system (air emissions inventory) on the basis of the EMEP — CORINAIR methodology and relate to sources classified according to the SNAP nomenclature (Selected Nomenclature for Air Pollution).These are classified according to the following parameters: reference year, province and municipality, reference activities according to the SNAP methodology (macrosector, sector and emissive activity), fuel used and pollutant emitted. The main pollutants exposed are: CH4 (tonnes/year); Co (t/year); N2O (t/year); NH3 (t/year); NMVOC (t/year); NOx (tonnes/year); PM10 (tonnes/year); PM2.5 (tonnes/year); PTS (t/year); SO2 (tonnes/year). The data shall be rounded to the fourth decimal place. The service exposes the data in four different spatial resolutions: Municipalities, Provinces, Region, Air Quality Zones.By a special function in the Environmental Knowledge System it is possible to view the themed inventory data according to three different types of statistical classification (Jenks, Equal Interval, Quantile).The WFS service can also be used in any GIS desktop (e.g. QGIS).

The service presents data in the form of an indicator for emissions related to the macro-sector “Agriculture” of the Regional Inventory of Atmospheric Emissions (IREA).Estimates made are calculated on the basis of the INEMAR system (air emissions inventory) based on the EMEP — CORINAIR methodology and relate to sources classified according to the SNAP nomenclature (Selected Nomenclature for Air Pollution).They are classified according to the following parameters: reference year, province and municipality, reference activities according to the SNAP methodology (macrosector, sector and emissive activity), fuel used and pollutant emitted. The main pollutants exposed are: CH4 (tonnes/year); Co (t/year); CO2 (kt/year); N2O (t/year); NH3 (t/year); NMVOC (t/year); NOx (tonnes/year); PM10 (tonnes/year); PM2.5 (tonnes/year); PTS (t/year); SO2 (tonnes/year). The data shall be rounded to the fourth decimal place. The service exposes the data in four different spatial resolutions: Municipalities, Provinces, Region, Air Quality Zones.By a special function in the Environmental Knowledge System it is possible to view the themed inventory data according to three different types of statistical classification (Jenks, Equal Interval, Quantile).The WFS service can also be used in any GIS desktop (e.g. QGIS).

This is an interpolated grid of Pb-Pb ω (omega, 232Th/204Pb) data from primarily ore-related rocks and minerals. The point data used for the interpolation is from the Pb-Pb dataset held in the Isotopic Atlas as of February 2021. Point data added to the Isotopic Atlas after February 2021 is not represented in this grid. The grid was generated with Esri's ArcMap Spatial Analyst interpolation tool using the Natural Neighbor algorithm and 0.01 degree cell size. Two classifications are provided for this grid; a Jenks natural breaks classification with ten classes (classification 1) as per Champion (2013) http://pid.geoscience.gov.au/dataset/ga/77772, and; the same seven-class interval binning and colour scheme applied to the source point data in the Pb-Pb Isotope Data layer (classification 2).

Classification performance achieved by model-level ensembles.

This is an interpolated grid of Pb-Pb model age data from primarily ore-related rocks and minerals. The point data used for the interpolation is from the Pb-Pb dataset held in the Isotopic Atlas as of February 2021, and the model ages are calculated using Stacey and Kramers (1975) Pb-evolution model. Point data added to the Isotopic Atlas after February 2021 is not represented in this grid. The grid was generated with Esri's ArcMap Spatial Analyst interpolation tool using the Natural Neighbor algorithm and 0.01 degree cell size. Two classifications are provided for this grid; a Jenks natural breaks classification with ten classes (classification 1) as per Champion (2013) http://pid.geoscience.gov.au/dataset/ga/77772, and; the same six-class interval binning and colour scheme applied to the source point data in the Pb-Pb Isotope Data layer (classification 2).

The service displays data in the form of an indicator for emissions relating to the "Other sources and absorption" macro-sector of the Regional Inventory of Atmospheric Emissions (IREA). The estimates made are calculated on the basis of the INEMAR system (INventario EMissioni ARia) on the basis of EMEP - CORINAIR methodology and concern sources classified according to the SNAP nomenclature (Selected Nomenclature for Air Pollution). They are classified according to the following parameters: reference year, province and municipality, reference activity according to the SNAP methodology (macro-sector, sector and activity emissions), fuel used and pollutant emitted. The main pollutants exposed are: CH4 (t/year); CO (t/year); CO2 (kt/year); N2O (t/year); NH3 (t/year); NMVOC (t/year); NOX (t/year); PM10 (t/year); PM2.5 (t/year); PTS (t/year); SO2 (t/year). Data is rounded to four decimal places. The service displays the data in four different spatial resolutions: Municipalities, Provinces, Regions, Air quality zones. Through a special function in the Environmental Knowledge System it is possible to view the inventory data according to three different types of classification statistics (Jenks, Equal Interval, Quantile). The WFS service can also be used in any GIS desktop (e.g. QGIS).

The service shall display data in the form of an indicator for emissions relating to the macro-sector “Other mobile sources and machinery” of the Regional Inventory of Atmospheric Emissions (IREA).Estimates made are calculated on the basis of the INEMAR (air emission inventory) system based on the EMEP — CORINAIR methodology and relate to sources classified according to the SNAP (Selected Nomenclature for Air Pollution) nomenclature. They are classified according to the following parameters: reference year, province and municipality, reference activities according to the SNAP methodology (macrosector, sector and emissive activity), fuel used and pollutant emitted. The main pollutants exposed are: CH4 (tonnes/year); Co (t/year); CO2 (kt/year); N2O (t/year); NH3 (t/year); NMVOC (t/year); NOx (tonnes/year); PM10 (tonnes/year); PM2.5 (tonnes/year); PTS (t/year); SO2 (tonnes/year). The data shall be rounded to the fourth decimal place. The service exposes the data in four different spatial resolutions: Municipalities, Provinces, Region, Air Quality Zones.By a special function in the Environmental Knowledge System it is possible to view the themed inventory data according to three different types of statistical classification (Jenks, Equal Interval, Quantile).The WFS service can also be used in any GIS desktop (e.g. QGIS).

This grid is an interpolation of deformation/metamorphism/alteration age point data held in the Isotopic Atlas as of February 2021. Data added to the Isotopic Atlas after February 2021 is not represented in this grid. The grid was generated with Esri's ArcMap Spatial Analyst interpolation tool using the Natural Neighbor algorithm and 0.01 degree cell size. Two classifications are provided for this grid; a Jenks natural breaks classification with ten classes (classification 1) as per Champion (2013) http://pid.geoscience.gov.au/dataset/ga/77772, and; the same seven-class interval binning and colour scheme applied to the source point data in the Deformation/Metamorphism/Alteration Age Data layer (classification 2).

The service displays data in the form of an indicator for emissions relating to the "Road transport" macro-sector of the Regional Inventory of Atmospheric Emissions (IREA). The estimates made are calculated on the basis of the INEMAR system (INventario EMissioni ARia) on the basis of the methodology EMEP - CORINAIR and concern sources classified according to the SNAP nomenclature (Selected Nomenclature for Air Pollution). They are classified according to the following parameters: reference year, province and municipality, reference activity according to the SNAP methodology (macro-sector, sector and emission activity ), fuel used and pollutant emitted. The main pollutants exposed are: CH4 (t/year); CO (t/year); CO2 (kt/year); N2O (t/year); NH3 (t/year); NMVOC (t/year); NOX (t/year); PM10 (t/year); PM2.5 (t/year); PTS (t/year); SO2 (t/year). Data is rounded to four decimal places. The service displays the data in four different spatial resolutions: Municipalities, Provinces, Regions, Air quality zones. Through a special function in the Environmental Knowledge System it is possible to view the inventory data according to three different types of classification statistics (Jenks, Equal Interval, Quantile). The WFS service can also be used in any GIS desktop (e.g. QGIS).

The service displays data in the form of an indicator for emissions related to the macro-sector “Production processes” of the Regional Inventory of Atmospheric Emissions (IREA).Estimates made are calculated on the basis of the INEMAR system (air emission inventory) on the basis of the EMEP — CORINAIR methodology and relate to sources classified according to the SNAP (Selected Nomenclature for Air Pollution) nomenclature. They are classified according to the following parameters: reference year, province and municipality, reference activities according to the SNAP methodology (macrosector, sector and emissive activity), fuel used and pollutant emitted. The main pollutants exposed are: CH4 (tonnes/year); Co (t/year); CO2 (kt/year); N2O (t/year); NH3 (t/year); NMVOC (t/year); NOx (tonnes/year); PM10 (tonnes/year); PM2.5 (tonnes/year); PTS (t/year); SO2 (tonnes/year). The data shall be rounded to the fourth decimal place. The service exposes the data in four different spatial resolutions: Municipalities, Provinces, Region, Air Quality Zones.By a special function in the Environmental Knowledge System it is possible to view the themed inventory data according to three different types of statistical classification (Jenks, Equal Interval, Quantile).The WFS service can also be used in any GIS desktop (e.g. QGIS).