Characterization of the Global Sources of Atmospheric Ammonia from Agricultural Soils. Issue 3 (8th February 2020)

• Record Type: Journal Article
• Authors: Aneja, Viney P.; Schlesinger, William H.; Li, Qi; Nahas, Alberth; Battye, William H.
• Is Part Of: Journal of geophysical research. Volume 125:Issue 3(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-02-08
• Subjects: ammonia -- agricultural soils -- emissions -- statistical model; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2019JD031684
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Global ammonia (NH3 ) emissions to the atmosphere are projected to increase in the coming years with the increased use of synthetic nitrogen fertilizers and cultivation of nitrogen‐fixing crops. A statistical model (NH3 _STAT) is developed for characterizing atmospheric NH3 emissions from agricultural soils and compared to the performance of other global and regional NH3 models (e.g., Emission Database for Global Atmospheric Research, Magnitude and Seasonality of Agricultural Emissions, MIX, and U.S. Environmental Protection Agency). The statistical model was developed from a multiple linear regression between NH3 emission and the physicochemical variables. The model was evaluated for 2012 NH3 emissions. The results indicate that, in comparison to other data sets, the model provides a lower global NH3 estimate by 58%, (NH3 _STAT: 13.9 Tg N yr −1 ; Emission Database for Global Atmospheric Research: 33.0 Tg N yr −1 ). We also performed a region‐based analysis (United States, India, and China) using the NH3 _STAT model. For the United States, our model produces an estimate that is a ~1.4 times higher in comparison to the Environmental Protection Agency. Meanwhile, the NH3 _STAT estimate for India shows NH3 emissions between 0.8 and 1.4 times lower when compared to other data sets. A lower estimate is also seen for China, where the model estimates NH3 emissions 0.4 to 5 times lower than other data sets. The difference in the global estimates is attributed to the lower estimates in major agricultural countries like China and India. The statistical model captures the spatial distribution of global NH3 emissions by utilizing a simplified approach compared to other readily available data sets. Moreover, the NH3 _STAT model provides an opportunity to predict future NH3 emissions in a changing world. Key Points: Estimates global ammonia emissions from agricultural soils Predict future ammonia emissions from agricultural soils in a changing world Develops a statistical model for ammonia emissions from agricultural soil