EDGAR Dominant Atmospheric Component (2020–2022)

Overview

This dataset represents the global EDGAR Dominant Atmospheric Component, a categorical classification layer derived from the EDGAR v8 atmospheric burden framework for the period 2020–2022.

The raster identifies the atmospheric burden category that exerts the greatest normalized influence at each location and serves as an interpretation layer for the Localized Emissions (LE) atmospheric corrosion framework.

The dataset was developed to support:

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

Unlike the EDGAR burden rasters, which quantify the magnitude of atmospheric influence, the Dominant Component raster identifies the primary emissions category responsible for that influence. :contentReference[oaicite:0]{index=0}

The classification framework evaluates:

• Acid Gas Burden
• Particulate Burden
• Atmospheric Chemistry Burden
• Mixed Atmospheric Burden Conditions

to determine the dominant atmospheric emissions influence at each location. :contentReference[oaicite:1]{index=1}

Background

Anthropogenic atmospheric emissions can originate from a wide range of sources and pollutant classes. Different pollutant groups often dominate different environmental settings and may influence atmospheric chemistry, deposition behavior, and corrosion environments in distinct ways.

The Dominant Atmospheric Component raster was developed to identify the atmospheric burden category that most strongly characterizes a location. :contentReference[oaicite:2]{index=2}

The raster classifies each location as:

• Minimal Burden
• Acid Gas Dominant
• Particulate Dominant
• Chemistry Dominant
• Mixed Burden

The classification is intended to provide atmospheric context for environmental interpretation and should not be interpreted as:

• A corrosion rate raster
• A pollutant concentration raster
• An air quality index
• A real-time monitoring layer
• An atmospheric dispersion model

Modeling Methodology

The Dominant Component framework was derived from normalized comparisons of:

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

The methodology incorporates:

Low-Burden Screening

Locations exhibiting minimal anthropogenic atmospheric influence are assigned to the Minimal Burden category.

Dominant Component Selection

The atmospheric burden category exhibiting the highest normalized influence is identified.

Mixed-Burden Classification

Locations where multiple burden categories exhibit similar influence are classified as Mixed Burden environments.

The generalized classification logic follows: :contentReference[oaicite:3]{index=3}

```text If Total_Burden < 0.10: Class = 0

Else:

Max_Component = max(AcidGas, Particulate, Chemistry)

If (Max_Component - Second_Highest_Component) < 0.03:
    Class = 4

Else:
    Class = Dominant Component

```

Classification Categories

Interpretation Guidelines

Class 0 — Minimal Burden

Typically associated with:

• Remote environments
• Rural regions
• Low industrial activity
• Limited anthropogenic influence

Class 1 — Acid Gas Dominant

Typically associated with:

• Fossil fuel combustion
• Industrial combustion regions
• Refinery districts
• Sulfur dioxide influence
• Nitrogen oxide emissions
• Acidifying atmospheric environments

Class 2 — Particulate Dominant

Typically associated with:

• Urban corridors
• Transportation emissions
• Combustion-heavy environments
• Black carbon influence
• PM10 and PM2.5 loading
• Deposition-intensive environments

Class 3 — Chemistry Dominant

Typically associated with:

• Agricultural emissions
• Ammonia-rich environments
• VOC emissions
• Carbon monoxide burden
• Industrial atmospheric chemistry
• Secondary atmospheric chemistry processes

Class 4 — Mixed Burden

Typically associated with:

• Industrial corridors
• Petrochemical complexes
• Urban-industrial regions
• Mixed pollutant source environments
• Locations with multiple elevated burden categories

Spatial Resolution

Data Sources

Primary environmental inputs include:

• EDGAR v8 Global Air Pollutant Emissions Database
• NASA MERRA-2 Atmospheric Reanalysis
• ERA5 Reanalysis Dataset

The classification framework incorporates atmospheric influence from:

Acid Gas Components

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

Particulate Components

• PM10
• PM2.5
• Black Carbon (BC)
• Organic Carbon (OC)

Atmospheric Chemistry Components

• Ammonia (NH₃)
• Carbon Monoxide (CO)
• Non-Methane Volatile Organic Compounds (NMVOC)

Intended Applications

This dataset may be used for:

• Atmospheric corrosion assessment
• Industrial exposure analysis
• Atmospheric chemistry interpretation
• Environmental severity mapping
• Pollution source characterization
• GIS visualization
• Environmental screening
• Infrastructure risk assessment
• Enterprise API workflows

Related Datasets

EDGAR Atmospheric Burden Layers

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

Dominant Component Layer

• EDGAR Dominant Atmospheric Component

LE Corrosion Layers

• ISO 9223 Steel LE
• ISO 9223 Zinc LE
• ISO 9223 Aluminum LE
• ISO 9223 Copper LE

Validation

The Dominant Component framework was developed using normalized atmospheric burden comparisons and evaluated as part of the broader LE atmospheric corrosion framework. The classification logic supports interpretation of atmospheric emissions influence and provides contextual information for atmospheric corrosion modeling. :contentReference[oaicite:4]{index=4}

The underlying LE 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}

Attribution

Joseph Mazzella
AtmosphericIQ LLC
Engineering Director, Inc.

Dataset Citation

Mazzella, J. (2026). EDGAR Dominant Atmospheric Component Classification (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

ISO 9223

ISO 9223:2012. Corrosion of metals and alloys — Corrosivity of atmospheres — Classification, determination and estimation. International Organization for Standardization (ISO).
https://www.iso.org/standard/53499.html

Version Information