Mean Sulfate Deposition

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Map Information

This dataset represents modeled global atmospheric sulfate deposition developed to support ISO 9223 atmospheric corrosivity modeling for the period 2020–2024.

Data Source:
Environmental Data
Units:
mg/m²·d
Coverage:
CONTINENTAL
Citation:
Mazzella, J., Hayden, T. (2026). Mean Sulfate Deposition Raster (2020–2024). AtmosphericIQ LLC / Engineering Director, Inc.
Data Legend
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Interactive Environmental Data Map
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Location Analysis
Technical Specifications

Mean Sulfate Deposition (2020–2024)

Overview

This dataset represents modeled global atmospheric sulfate deposition developed to support ISO 9223 atmospheric corrosivity modeling for the period 2020–2024.

Atmospheric sulfate deposition is one of the primary environmental variables influencing:

  • Atmospheric corrosion
  • Acid deposition chemistry
  • Electrochemical corrosion processes
  • Protective coating degradation
  • Industrial atmospheric exposure

The raster represents estimated long-term ambient atmospheric sulfate deposition behavior at approximately 1 km spatial resolution.

Units:

  • Milligrams per square meter per day (mg/m²/day)

Background

Atmospheric sulfate deposition is a core environmental driver within the ISO 9223 atmospheric corrosivity framework and is closely associated with sulfur-containing atmospheric pollutants.

Sulfate deposition is primarily associated with:

  • Industrial emissions
  • Fossil fuel combustion
  • Urban atmospheric pollution
  • Atmospheric sulfur transport
  • Regional acid deposition processes

Higher sulfate deposition environments generally result in increased atmospheric acidity, enhanced electrolyte chemistry, corrosion acceleration, and coating degradation.

This dataset was developed to provide global sulfate deposition estimates suitable for corrosion engineering, environmental modeling, and GIS-based exposure assessment.


Modeling Methodology

The sulfate deposition framework integrates atmospheric chemistry products, climate reanalysis data, observational datasets, and regional interpolation methods.

Primary sulfate inputs include:

  • MERRA-2 sulfate aerosol products
  • Atmospheric sulfur transport variables
  • Sulfate deposition products
  • Atmospheric chemistry reanalysis datasets

The modeling framework incorporates:

Atmospheric Chemistry

  • Sulfate aerosol transport
  • Sulfur deposition behavior
  • Regional atmospheric chemistry gradients

Climate Integration

  • Temperature
  • Humidity
  • Atmospheric circulation patterns
  • Long-term climatological averages

Spatial Modeling

  • Kriged observational datasets
  • Atmospheric interpolation methods
  • Regional environmental gradients

Industrial Influence

  • Urban atmospheric influence
  • Industrial emissions patterns
  • Regional acid deposition behavior

The resulting framework was designed to improve representation of ambient atmospheric sulfate deposition behavior within industrial, urban, rural, and remote environments.


Interpretation Guidelines

Sulfate Deposition (mg/m²/day) Interpretation
0–2 Very Low Sulfate Exposure
2–10 Low Atmospheric Sulfate
10–25 Moderate Sulfate Influence
25–75 Industrial / Urban Sulfate Exposure
>75 High Sulfate Deposition Environment

Higher sulfate deposition values generally indicate increased atmospheric acidity and elevated atmospheric corrosion potential.


Spatial Resolution

Property Value
Coverage Global
Resolution ~1 km
Coordinate System WGS 84
EPSG Code 4326
Temporal Coverage 2020–2024

Data Sources

Primary environmental inputs include:

Derived environmental layers include:

  • Temperature
  • Relative Humidity
  • Wind Speed
  • Wind Direction
  • Atmospheric Corrosion Layers

Intended Applications

This dataset may be used for:

  • Atmospheric corrosion assessment
  • ISO 9223 modeling
  • Acid deposition analysis
  • Industrial exposure assessment
  • Corrosion engineering
  • GIS visualization
  • Environmental modeling
  • Enterprise API workflows

Related Datasets

Corrosion Layers

Supporting Atmospheric Layers

Supporting Coastal & Terrain Layers


Attribution

Joseph Mazzella
AtmosphericIQ LLC
Engineering Director, Inc.


Dataset Citation

Mazzella, J. (2026). Mean Sulfate Deposition Raster (2020–2024). AtmosphericIQ LLC / Engineering Director, Inc.


Supporting Dataset Citations

NASA MERRA-2

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

NASA Giovanni

NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). NASA Giovanni System.
https://giovanni.gsfc.nasa.gov/giovanni/

NOAA ISD

NOAA National Centers for Environmental Information (NCEI). Integrated Surface Database (ISD).
https://www.ncei.noaa.gov/products/land-based-station/integrated-surface-database

National Atmospheric Deposition Program

National Atmospheric Deposition Program (NADP). National Atmospheric Deposition Program.
https://nadp.slh.wisc.edu/

ISO 9223 Standard

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


Version Information

Property Value
Dataset Name Mean Sulfate Deposition
Dataset Version 1.0
Publication Year 2026
Author Joseph Mazzella
Organization AtmosphericIQ LLC / Engineering Director, Inc.
Temporal Coverage 2020–2024
Resolution ~1 km
Units mg/m²/day
Coordinate System WGS 84 (EPSG:4326)
Coverage Global

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.