Distance to the Nearest Coast (km)
Interactive map with scientific data analysis, point lookup, and detailed environmental information
Map Information
This dataset represents the modeled global distance from each raster cell to the nearest marine coastline.
Location Analysis
Technical Specifications
Distance to Coast (1 km)
Overview
This dataset represents the modeled global distance from each raster cell to the nearest marine coastline.
Distance to Coast is one of the most important environmental variables influencing:
- Atmospheric chloride deposition
- Marine aerosol transport
- Coastal exposure severity
- Atmospheric corrosion potential
- Chloride persistence
- Coastal environmental gradients
The raster represents the shortest horizontal distance from each location to the nearest marine coastline and is used extensively within ISO 9223 atmospheric corrosion workflows.
Units:
- Kilometers (km)
Background
Distance to Coast is a primary environmental variable used in atmospheric corrosion modeling because marine aerosols and chloride deposition generally decrease with increasing distance from the ocean.
Distance to Coast directly influences:
- Marine aerosol transport
- Atmospheric chloride deposition
- Coastal corrosion severity
- Atmospheric conductivity
- Environmental exposure classification
- Coastal infrastructure risk
Nearshore environments typically experience elevated chloride deposition and greater atmospheric corrosion potential compared to inland continental environments.
This dataset was developed to provide globally consistent coastal proximity information suitable for corrosion engineering, environmental modeling, and GIS-based exposure assessment.
Modeling Methodology
The distance framework utilizes global coastline datasets and marine boundary processing techniques to calculate the shortest distance to the nearest marine shoreline.
Primary inputs include:
- Global coastline vectors
- Marine boundary datasets
- GMT coastal proximity products
- Coastal geometry datasets
The modeling framework incorporates:
Coastal Geometry Processing
- Global shoreline extraction
- Marine boundary identification
- Coastal topology correction
- Coastline continuity verification
Distance Analysis
- Euclidean distance calculations
- Global proximity analysis
- Coastal distance raster generation
- Spatial interpolation workflows
Atmospheric Corrosion Integration
- Chloride transport modeling
- Inland aerosol decay functions
- Coastal exposure weighting
- Environmental severity mapping
Marine Aerosol Support
- Coastal proximity assessment
- Chloride persistence modeling
- Offshore-to-onshore transition analysis
- Marine source region identification
The resulting framework provides globally consistent coastal distance estimates suitable for atmospheric corrosion and environmental exposure applications.
Interpretation Guidelines
| Distance to Coast (km) | Interpretation |
|---|---|
| 0–5 | Immediate Marine Coastal Exposure |
| 5–25 | Strong Marine Influence |
| 25–100 | Moderate Coastal Influence |
| 100–250 | Weak Marine Influence |
| >250 | Inland Continental Environment |
Shorter distances generally indicate increased marine aerosol influence and elevated atmospheric corrosion potential.
Spatial Resolution
| Property | Value |
|---|---|
| Coverage | Global |
| Resolution | ~1 km |
| Coordinate System | WGS 84 |
| EPSG Code | 4326 |
| Units | Kilometers (km) |
Data Sources
Primary environmental inputs include:
- GMT Intermediate Coast Distance Dataset
- Global coastline datasets
- Generic Mapping Tools (GMT) coastal products
- Supplemental marine boundary datasets
Derived environmental layers supported by this dataset include:
- Chloride Deposition
- Bathymetry and Terrain Elevation
- Wind Exposure Modeling
- Atmospheric Corrosion Layers
Intended Applications
This dataset may be used for:
- Atmospheric corrosion assessment
- Chloride deposition modeling
- Coastal exposure analysis
- Marine aerosol transport studies
- Environmental exposure assessment
- GIS visualization
- Environmental modeling
- Enterprise API workflows
Related Datasets
Corrosion Layers
- ISO 9223 Steel Corrosion Rate
- ISO 9223 Zinc Corrosion Rate
- ISO 9223 Aluminum Corrosion Rate
- ISO 9223 Copper Corrosion Rate
Supporting Atmospheric Layers
- Mean Chloride Deposition
- Mean Sulfate Deposition
- Mean Annual Temperature
- Mean Relative Humidity
- Time of Wetness (TOW)
Supporting Coastal & Terrain Layers
- Distance to Coast
- Bathymetry 2024 – Terrain Elevation
- WindRIX Terrain–Wind Exposure Index
- Wind Resultant Direction (0–360°)
- Wind Speed
Attribution
Joseph Mazzella
AtmosphericIQ LLC
Engineering Director, Inc.
Dataset Citation
Mazzella, J. (2026). Distance to Coast Raster (1 km). AtmosphericIQ LLC / Engineering Director, Inc.
Supporting Dataset Citations
Generic Mapping Tools (GMT)
Wessel, P., Smith, W.H.F., Scharroo, R., Luis, J., & Wobbe, F. (2013). Generic Mapping Tools: Improved Version Released. EOS, Transactions American Geophysical Union, 94(45), 409–410.
https://doi.org/10.1002/2013EO450001
GMT Coastal Proximity Dataset
GMT Intermediate Coast Distance Dataset. Global Coastal Proximity Data Derived from Generic Mapping Tools (GMT) Coastline Processing Workflows.
NASA MERRA-2
NASA Global Modeling and Assimilation Office (GMAO). MERRA-2 Atmospheric Reanalysis Dataset.
https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/
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 | Distance to Coast |
| Dataset Version | 1.0 |
| Publication Year | 2026 |
| Author | Joseph Mazzella |
| Organization | AtmosphericIQ LLC / Engineering Director, Inc. |
| Coverage | Global |
| Resolution | ~1 km |
| Units | Kilometers (km) |
| Coordinate System | WGS 84 (EPSG:4326) |
| Data Type | Continuous Raster |
| Measurement | Euclidean Distance to Marine Coastline |
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.