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Updated: Dec 21, 2025

Calibrated Passive Sampling - Multi-plot Field Measurements of NH3 Emissions with a Combination of Dynamic Tube Method and Passive Samplers
Published on: March 21, 2016
Global estimates of dry ammonia deposition inferred from space-measurements
Lei Liu1, Xiuying Zhang2, Wen Xu3
1College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China.
Abstract:
Ammonia (NH3), as an alkaline gas, contributes substantially to atmospheric nitrogen deposition, which can cause biodiversity loss, water eutrophication and soil acidification. Advances in the application of satellite observations allow us to gain deeper insights into atmospheric NH3 concentrations at large spatial scales. A new satellite-based methodology is proposed for estimating dry NH3 deposition with consideration of bi-directional NH3 exchange. We estimate the global dry NH3 deposition for nine years (2008-2016) by using the Infrared Atmospheric Sounding Interferometer Instrument (IASI) NH3 retrievals. Satellite-based dry NH3 deposition is in general consistent with measured dry NH3 deposition over the monitoring sites (R2 = 0.65). Global dry NH3 deposition over 8 kg N ha-1 is mainly distributed in the Eastern China, Northern and Central Pakistan, and Northern India. An annual increase rate of 0.27 and 0.13 kg N ha-1 y-1 in dry NH3 deposition during 2008-2016 occurs in Eastern China and Sichuan Basin, which are the major Chinese agricultural regions. The NH3 compensation point is high during warm months, and can be above 1 μg m-3 such as in Eastern China, implying the importance of considering the NH3 compensation points for estimating dry NH3 deposition. We find, if the upward NH3 flux was ignored, it will cause 11%, 17%, 5% and 3% overestimation in dry NH3 deposition in Eastern China, Northern India, Eastern United States and Western Europe, respectively. This study presents the potential of using the satellite retrievals to estimate the large-scale dry NH3 deposition, and the methodology is able to provide temporally continuous and spatially complete fine-resolution datasets.
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