# EDGAR - Atmospheric Dominant Component Class (`EDGAR_Dominant_Component_V4_2020_2022_1km`) — EDIS AI Agent Dataset View

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## 1. Dataset Metadata
- **Raster Name**: EDGAR - Atmospheric Dominant Component Class
- **Slug**: `EDGAR_Dominant_Component_V4_2020_2022_1km`
- **Units**: `Dominant LE Class (0-4)`
- **Spatial Extent**: UL(83.8671659, -179.9999948) to LR(-89.9838547, 179.9999871)
- **Summary**: 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.
- **Included in Flagship Map Package**: `Global_ISO9223_2020_2024_LE_V4` (`GET https://secure.engineeringdirector.com/api/bundles/Global_ISO9223_2020_2024_LE_V4/point_query`)

### Color Table & Value Breaks
- `(0.0, (34.0, 139.0, 34.0))`
- `(0.05, (35.6, 145.6, 35.6))`
- `(0.1, (37.2, 152.2, 37.2))`
- `(0.15000000000000002, (38.8, 158.8, 38.8))`
- `(0.2, (40.4, 165.4, 40.4))`
- `(0.25, (42.0, 172.0, 42.0))`
- `(0.30000000000000004, (43.6, 178.6, 43.6))`
- `(0.35000000000000003, (45.2, 185.2, 45.2))`
- `(0.4, (46.8, 191.8, 46.8))`
- `(0.45, (48.4, 198.4, 48.4))`
- `(0.5, (50.0, 205.0, 50.0))`
- `(0.5, (50.0, 205.0, 50.0))`

# 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

| Class | Category | Interpretation |
|---:|---|---|
| 0 | Minimal Burden | No significant anthropogenic atmospheric influence |
| 1 | Acid Gas Dominant | Acid gas emissions are the primary atmospheric burden |
| 2 | Particulate Dominant | Particulate emissions are the primary atmospheric burden |
| 3 | Chemistry Dominant | Atmospheric chemistry emissions are the primary atmospheric burden |
| 4 | Mixed Burden | Multiple burden categories exhibit similar influence |

---

## 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

| 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](https://edgar.jrc.ec.europa.eu/)
- [NASA MERRA-2 Atmospheric Reanalysis](https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/)
- [ERA5 Reanalysis Dataset](https://cds.climate.copernicus.eu/)

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](https://secure.engineeringdirector.com/public/map/EDGAR_Total_Burden_2020_2022_mean_1km)
- [EDGAR Acid Gas Burden](https://secure.engineeringdirector.com/public/map/EDGAR_AcidGas_Burden_2020_2022_mean_1km)
- [EDGAR Particulate Burden](https://secure.engineeringdirector.com/public/map/EDGAR_Particulate_Burden_2020_2022_mean_1km)
- [EDGAR Chemistry Burden](https://secure.engineeringdirector.com/public/map/EDGAR_Chemistry_Burden_2020_2022_mean_1km)

### Dominant Component Layer

- [EDGAR Dominant Atmospheric Component](https://secure.engineeringdirector.com/public/map/EDGAR_Dominant_Component_V4_2020_2022_1km)

### LE Corrosion Layers

- [ISO 9223 Steel LE](https://secure.engineeringdirector.com/public/map/steel_ISO9223_LE_V4_Enhanced_2020_2024_1km)
- [ISO 9223 Zinc LE](https://secure.engineeringdirector.com/public/map/zinc_ISO9223_LE_V4_Enhanced_2020_2024_1km)
- [ISO 9223 Aluminum LE](https://secure.engineeringdirector.com/public/map/aluminum_ISO9223_LE_V4_Enhanced_2020_2024_1km)
- [ISO 9223 Copper LE](https://secure.engineeringdirector.com/public/map/copper_ISO9223_LE_V4_Enhanced_2020_2024_1km)

---

## 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}

| 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 |

---

## 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

| Property | Value |
|---|---|
| Dataset Name | EDGAR Dominant Atmospheric Component |
| Dataset Version | 4.0 |
| 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) |
| Classification Range | 0–4 |
| Data Type | Categorical Raster |
| Source Dataset | EDGAR v8 |
| Classification Type | Dominant Atmospheric Burden Category |

---

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