Reactive Nitrogen from Agriculture: A Review of Emissions, Air Quality, and Climate Impacts

Abstract

Purpose of Review

As fossil fuel–related emissions gradually decline, agriculture has become a major source of reactive nitrogen (Nr) in regions such as China, the USA, and Europe, significantly contributing to air pollution, including particulate matter (PM) and surface ozone (O₃), as well as climate change. Despite this, agriculture has historically been underrepresented in air quality management and climate policy. Without effective mitigation, agricultural Nr emissions are expected to rise, driven by growing food demand and climate change, further exacerbating their negative impacts on air quality and the climate. This review provides a process-level overview of the current understanding of agricultural Nr emissions and their effects on atmospheric chemistry, with a focus on the underlying mechanisms, and also highlights research gaps and proposes future research directions.

Recent Findings

Agricultural Nr emissions are influenced by a variety of factors and released through multiple biotic and abiotic pathways, resulting in significant spatial and temporal variability. Our understanding of the underlying mechanisms driving agricultural Nr emissions remains incomplete, and current emission estimates are associated with substantial uncertainties. Agriculture contributes to ambient PM pollution primarily through ammonia (NH₃) emissions and to surface O₃ pollution via oxidized Nr species, including nitrous acid (HONO) and nitrogen oxides (NO_x). Although the chemistry of PM and surface O₃ is highly nonlinear, with sensitivities to their precursors varying widely, agricultural Nr is gradually becoming a key contributor, particularly in regions where fossil fuel emissions are declining, such as China, the USA, and Europe. Agricultural Nr is estimated to exert a net cooling effect, with warming contributions from nitrous oxide (N₂O) emissions and cooling from aerosols generated by Nr, although this estimate remains highly uncertain.

Summary

Our understanding of the underlying mechanisms driving agricultural Nr emissions remains limited, particularly when it comes to episodic pulses during extreme weather events. A knowledge-guided machine learning approach that integrates ground and airborne observations with process-based agroecosystem models could offer new opportunities for more accurate emission estimations. Further research is essential to fully understand the role of both reduced and oxidized reactive nitrogen in influencing air quality and climate.