We've updated our Privacy Policy to make it clearer how we use your personal data. We use cookies to provide you with a better experience. You can read our Cookie Policy here.


AI-Aided Map Shows Global Ammonia Pollution

A map of the world.
Credit: Andrew Stutesman/ Unsplash
Listen with
Register for free to listen to this article
Thank you. Listen to this article using the player above.

Want to listen to this article for FREE?

Complete the form below to unlock access to ALL audio articles.

Read time: Less than a minute

A new computational framework developed in collaboration with Oak Ridge National Laboratory scientist Jiafu Mao provides a detailed assessment of ammonia emissions from global croplands and identifies practices that could curb release of the gas.

Croplands are the largest single source of atmospheric ammonia, emitted from fields treated with nitrogen fertilizer. Ammonia can harm human health, acidify soil and waterways and contribute to biodiversity loss, food insecurity and climate change. However, the international study found that emissions could be cut by 38% without altering total fertilizer inputs, as detailed in Nature.

Want more breaking news?

Subscribe to Technology Networks’ daily newsletter, delivering breaking science news straight to your inbox every day.

Subscribe for FREE
Mao helped devise a machine learning approach to improve ammonia emission estimates from wheat, corn and rice fields. The model enabled the identification of local best practices that could mitigate emissions, even in a warming climate.

“This valuable model, backed by artificial intelligence tools, can also fine-tune biogeochemical cycling and greenhouse gas emissions in the Department of Energy’s Earth system model,” Mao said. —Stephanie Seay 

Reference: Xu P, Li G, Zheng Y, et al. Fertilizer management for global ammonia emission reduction. Nature. 2024. doi: 10.1038/s41586-024-07020-z

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.