Literature DB >> 32388359

Global estimates of dry ammonia deposition inferred from space-measurements.

Lei Liu1, Xiuying Zhang2, Wen Xu3, Xuejun Liu3, Jing Wei4, Zhen Wang5, Yuyu Yang6.   

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.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Air-surface exchange; Ammonia; Dry deposition; Satellite observation

Year:  2020        PMID: 32388359     DOI: 10.1016/j.scitotenv.2020.139189

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  1 in total

1.  Improving Estimates of Sulfur, Nitrogen, and Ozone Total Deposition through Multi-Model and Measurement-Model Fusion Approaches.

Authors:  Joshua S Fu; Gregory R Carmichael; Frank Dentener; Wenche Aas; Camilla Andersson; Leonard A Barrie; Amanda Cole; Corinne Galy-Lacaux; Jeffrey Geddes; Syuichi Itahashi; Maria Kanakidou; Lorenzo Labrador; Fabien Paulot; Donna Schwede; Jiani Tan; Robert Vet
Journal:  Environ Sci Technol       Date:  2022-01-26       Impact factor: 9.028

  1 in total

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