Class 3 - EDGAR Atmospheric Chemistry Burden
Interactive map with scientific data analysis, point lookup, and detailed environmental information
Map Information
This dataset represents the global EDGAR Atmospheric Chemistry Burden, a normalized indicator of long-term anthropogenic atmospheric chemistry influence derived from the Emissions Database for Global Atmospheric Research (EDGAR) v8 emissions inventory for the period 2020–2022.
Data Legend
Location Analysis
Important Disclaimers
Technical Specifications
EDGAR Atmospheric Chemistry Burden (2020–2022)
Overview
This dataset represents the global EDGAR Atmospheric Chemistry Burden, a normalized indicator of long-term anthropogenic atmospheric chemistry 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 chemical influence 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:
- Ammonia (NH₃)
- Non-Methane Volatile Organic Compounds (NMVOC)
- Carbon Monoxide (CO)
to estimate persistent atmospheric chemistry burden associated with industrial activity, petrochemical operations, transportation emissions, agricultural influence, chemical manufacturing, and mixed atmospheric chemistry environments. :contentReference[oaicite:0]{index=0}
The dataset is expressed as a continuous normalized index ranging from 0.00 to 1.00, where higher values indicate greater long-term anthropogenic atmospheric chemistry influence. :contentReference[oaicite:1]{index=1}
Units:
- Normalized Atmospheric Chemistry Burden Index (0–1)
Background
Anthropogenic atmospheric chemistry can significantly influence atmospheric pollutant interactions, deposition behavior, environmental exposure conditions, and atmospheric corrosivity.
Atmospheric chemistry burden is commonly associated with:
- Petrochemical environments
- Industrial processing regions
- Urban atmospheric pollution
- Transportation corridors
- Agricultural ammonia emissions
- Refinery operations
- Chemical manufacturing facilities
Persistent atmospheric chemistry influence may contribute to:
- Secondary aerosol formation
- Pollutant interaction processes
- Atmospheric deposition behavior
- Environmental degradation processes
- Atmospheric corrosion acceleration
- Industrial environmental severity
Unlike real-time air quality measurements, this dataset is intended to represent long-term atmospheric burden conditions rather than transient pollution events. :contentReference[oaicite:2]{index=2}
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 Atmospheric Chemistry Burden framework incorporates anthropogenic emissions data derived from the EDGAR v8 global atmospheric emissions inventory.
Primary pollutants include:
- Ammonia (NH₃)
- Non-Methane Volatile Organic Compounds (NMVOC)
- Carbon Monoxide (CO)
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. :contentReference[oaicite:3]{index=3}
Interpretation Guidelines
| Atmospheric Chemistry 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 industrial, petrochemical, agricultural, and mixed atmospheric chemistry emissions sources. :contentReference[oaicite:4]{index=4}
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:
- EDGAR v8 Global Air Pollutant Emissions Database
- NASA MERRA-2 Atmospheric Reanalysis
- ERA5 Reanalysis Dataset
Primary pollutants incorporated:
- Ammonia (NH₃)
- Non-Methane Volatile Organic Compounds (NMVOC)
- Carbon Monoxide (CO)
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
- Petrochemical exposure assessment
- Localized emissions modeling
- GIS visualization
- Environmental screening
- Infrastructure risk assessment
- Enterprise API workflows
Related Datasets
EDGAR Atmospheric Burden Layers
Dominant Component Layer
Localized Emissions Layers
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: :contentReference[oaicite:5]{index=5}
| 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. :contentReference[oaicite:6]{index=6}
Attribution
Joseph Mazzella
AtmosphericIQ LLC
Engineering Director, Inc.
Dataset Citation
Mazzella, J. (2026). EDGAR Atmospheric Chemistry 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 Atmospheric Chemistry 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 | NH₃, NMVOC, CO |
| 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.