Global Agricultural Ammonia Emissions Model from 1990-2019

Ammonia emissions from agricultural soils are characterized by the NH3_STAT statistical model, which uses a multiple linear regression between NH3 emission and physicochemical variables. The model provides a global estimate of 13.9 teragrams of nitrogen per year for 2012, which is 57% lower than the EDGAR inventory estimate. Developed and hosted by NOAA NCEI, the dataset allows comparison with other major models like EDGAR, MASAGE, MIX, and U.S. EPA.

Use Cases

• Comparing regional NH3 emission estimates for the U.S., India, and China against benchmarks like the U.S. EPA inventory.
• Analyzing the spatial distribution of global agricultural ammonia emissions using the model's geospatial outputs.
• Projecting future ammonia emissions under different agricultural and climate scenarios based on the statistical relationship.
• Benchmarking the NH3_STAT model's simplified approach against more complex process-based models like EDGAR.

Strengths

• Model provides a specific global estimate of 13.9 Tg N yr-1 for 2012, enabling quantitative comparison.
• Dataset includes region-based analysis for three major agricultural countries: the U.S., India, and China.
• Covers a significant time range from 1990 to 2019, supporting trend analysis.
• Presence on multiple authoritative platforms (NASA EarthData, Data.gov) signals peer-reviewed importance.

Limitations

• Core metadata conflicts: last updated date is listed as both 2019-01-01 and 2026-03 05 across sources.
• Essential technical details like row count, column names, file size, and specific license are unavailable from all sources.
• The model's estimates for key regions differ significantly from other inventories, indicating methodological variance.

Provenance

Source

NOAA NCEI (National Centers for Environmental Information)

Collection Method

Statistical model (NH3_STAT) developed via multiple linear regression between NH3 emission and physicochemical variables.

Time Range

1990-01-01 to 2019-01-01

Freshness

2026-03-05 23:27:32.357503

Geography

Global, with specific analysis for the U.S., India, and China.