Class 1 - EDGAR Atmospheric Acid Gas Burden

Interactive map with scientific data analysis, point lookup, and detailed environmental information

Map Information

This dataset represents the global EDGAR Acid Gas Atmospheric Burden, a normalized indicator of long-term anthropogenic acid gas influence derived from the Emissions Database for Global Atmospheric Research (EDGAR) v8 emissions inventory for the period 2020–2022.

Data Source:
Environmental Data
Units:
Acid Gas Burden Index (0-1)
Coverage:
GLOBAL
Citation:
Mazzella, J. (2026). EDGAR Acid Gas Atmospheric Burden (2020–2022). AtmosphericIQ LLC / Engineering Director, Inc.
Data Legend
Values are displayed with colors from lowest (left) to highest (right)
Interactive Environmental Data Map
Click anywhere on the map to get data values for that location
Location Analysis
Important Disclaimers
Represents relative persistent anthropogenic acid gas atmospheric burden derived from EDGAR emissions analytics and atmospheric normalization workflows. This raster is intended for atmospheric corrosion interpretation and engineering contextualization, and does not represent direct pollutant concentration measurements or regulatory air quality conditions.
Technical Specifications

EDGAR Acid Gas Atmospheric Burden (2020–2022)

Overview

This dataset represents the global EDGAR Acid Gas Atmospheric Burden, a normalized indicator of long-term anthropogenic acid gas influence derived from the Emissions Database for Global Atmospheric Research (EDGAR) v8 emissions inventory for the period 2020–2022.

The raster was developed to characterize persistent atmospheric acidification potential associated with human activities and is intended to support:

  • Atmospheric corrosion assessment
  • Industrial environmental exposure analysis
  • Atmospheric chemistry interpretation
  • Environmental severity mapping
  • Localized Emissions (LE) modeling
  • GIS-based environmental screening

The framework integrates emissions associated with:

  • Sulfur Dioxide (SO₂)
  • Nitrogen Oxides (NOₓ)

to estimate persistent atmospheric acid gas burden associated with industrial, transportation, power generation, and combustion-related activities.

The dataset is expressed as a continuous normalized index ranging from 0.00 to 1.00, where higher values indicate greater long-term anthropogenic acid gas influence.

Units:

  • Normalized Acid Gas Burden Index (0–1)

Background

Anthropogenic acid gas emissions can significantly influence atmospheric chemistry and environmental corrosivity.

Acid gas burden is commonly associated with:

  • Fossil fuel combustion
  • Power generation
  • Refinery operations
  • Industrial manufacturing
  • Transportation corridors
  • Petrochemical facilities
  • Smelting operations
  • Urban atmospheric pollution

Persistent acid gas emissions may contribute to:

  • Atmospheric acidity
  • Sulfate formation
  • Nitrate formation
  • Acid deposition
  • Electrochemical corrosion acceleration
  • Environmental degradation processes

Unlike real-time air quality measurements, this dataset is intended to represent long-term atmospheric burden conditions rather than transient pollution events.

The EDGAR framework consists of four atmospheric burden layers:

  • Total Burden
  • Acid Gas Burden
  • Particulate Burden
  • Chemistry Burden

and one interpretation layer:

  • Dominant Component

The Dominant Component layer identifies which atmospheric burden category contributes the greatest normalized influence at each location, allowing rapid interpretation of the primary emissions driver affecting environmental exposure conditions.


Modeling Methodology

The Acid Gas Burden framework incorporates anthropogenic emissions data derived from the EDGAR v8 global atmospheric emissions inventory.

Primary pollutants include:

  • Sulfur Dioxide (SO₂)
  • Nitrogen Oxides (NOₓ)

The modeling framework incorporates:

Emissions Integration

  • Multi-pollutant emissions aggregation
  • Atmospheric burden normalization
  • Pollutant weighting methodologies
  • Long-term emissions characterization

Atmospheric Burden Scaling

  • Logarithmic burden normalization
  • Continuous scaling workflows
  • Relative burden interpretation
  • Global consistency adjustments

Environmental Integration

The resulting burden framework supports:

  • Atmospheric corrosion modeling
  • Localized emissions enhancement workflows
  • Environmental severity assessment
  • Industrial exposure characterization

The final raster is normalized to a continuous scale ranging from 0.00 to 1.00.


Interpretation Guidelines

Acid Gas Burden Interpretation
0.00–0.10 Minimal Burden
>0.10–0.25 Low Burden
>0.25–0.45 Moderate Burden
>0.45–0.65 High Burden
>0.65–1.00 Very High Burden

Higher values generally indicate greater long-term atmospheric influence from combustion-related emissions sources.


Spatial Resolution

Property Value
Coverage Global
Resolution ~1 km
Coordinate System WGS 84
EPSG Code 4326
Temporal Coverage 2020–2022

Data Sources

Primary environmental inputs include:

Primary pollutants incorporated:

  • Sulfur Dioxide (SO₂)
  • Nitrogen Oxides (NOₓ)

Derived environmental layers supported by this dataset include:

  • Localized Emissions (LE) Modeling
  • Environmental Severity Mapping
  • Atmospheric Corrosion Modeling
  • Industrial Exposure Analysis

Intended Applications

This dataset may be used for:

  • Atmospheric corrosion assessment
  • Industrial exposure analysis
  • Environmental severity mapping
  • Atmospheric chemistry studies
  • Localized emissions modeling
  • GIS visualization
  • Environmental screening
  • Infrastructure risk assessment
  • Enterprise API workflows

Related Datasets

EDGAR Atmospheric Burden Layers

Dominant Component Layer

LE Corrosion Layers


Validation

The Localized Emissions enhancement framework was evaluated using:

  • CORRAG atmospheric corrosion datasets
  • MICAT atmospheric exposure datasets
  • ASTM STP1239 atmospheric corrosion datasets
  • Historical emissions reconstruction analytics

Representative Leave-One-Out (LOO) model performance from the ISO Classic + EDGAR Random Forest framework:

Metal LOO R² LOO MAE (µm/year) LOO RMSE (µm/year)
Steel 0.864 12.72 27.99
Zinc 0.839 0.42 0.92
Aluminum 0.897 0.26 0.39
Copper 0.900 0.34 0.50

These results supported incorporation of EDGAR-derived atmospheric burden analytics into the Localized Emissions corrosion framework.


Attribution

Joseph Mazzella
AtmosphericIQ LLC
Engineering Director, Inc.


Dataset Citation

Mazzella, J. (2026). EDGAR Acid Gas Atmospheric Burden (2020–2022). AtmosphericIQ LLC / Engineering Director, Inc.


Supporting Dataset Citations

EDGAR v8

Crippa, M., Guizzardi, D., Solazzo, E., et al. EDGAR v8 Global Air Pollutant Emissions Database. European Commission Joint Research Centre (JRC).
https://edgar.jrc.ec.europa.eu/

NASA MERRA-2

NASA Global Modeling and Assimilation Office (GMAO). MERRA-2 Atmospheric Reanalysis Dataset.
https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/

ERA5 Reanalysis

Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049.
https://doi.org/10.1002/qj.3803


Version Information

Property Value
Dataset Name EDGAR Acid Gas Atmospheric Burden
Dataset Version 1.0
Publication Year 2026
Author Joseph Mazzella
Organization AtmosphericIQ LLC / Engineering Director, Inc.
Temporal Coverage 2020–2022
Resolution ~1 km
Coordinate System WGS 84 (EPSG:4326)
Units Normalized Index (0–1)
Data Type Continuous Raster
Primary Pollutants SO₂, NOₓ
Source Dataset EDGAR v8

Data Distribution Analysis

These histograms show the distribution of pixel values across the entire raster dataset, helping you understand the range and frequency of different measurements.

Linear Scale Distribution
Shows the actual frequency distribution of values using a standard linear scale.
Logarithmic Scale Distribution
Shows the same data using a logarithmic scale, making it easier to see patterns in data with large value ranges.