Journal article
Improved Inversion of Monthly Ammonia Emissions in China Based on the Chinese Ammonia Monitoring Network and Ensemble Kalman Filter
ENVIRONMENTAL SCIENCE & TECHNOLOGY, Vol.53(21), pp.12529-12538
2019
DOI: 10.1021/acs.est.9b02701
Abstract
Ammonia (NH3) emission inventories are an essential input in chemical transport models and are helpful for policy-makers to refine mitigation strategies. However, current estimates of Chinese NH3 emissions still have large uncertainties. In this study, an improved inversion estimation of NH3 emissions in China has been made using an ensemble Kalman filter and the Nested Air Quality Prediction Modeling System. By first assimilating the surface NH3 observations from the Ammonia Monitoring Network in China at a high resolution of 15 km, our inversion results have provided new insights into the spatial and temporal patterns of Chinese NH3 emissions. More enhanced NH3 emission hotspots, likely associated with industrial or agricultural sources, were captured in northwest China, where the a posteriori NH3 emissions were more than twice the a priori emissions. Monthly variations of NH3 emissions were optimized in different regions of China and exhibited a more distinct seasonality, with the emissions in summer being twice those in winter. The inversion results were well-validated by several independent datasets that traced gaseous NH3 and related atmospheric processes. These findings highlighted that the improved inversion estimation can be used to advance our understanding of NH3 emissions in China and their environmental impacts.
Details
- Title: Subtitle
- Improved Inversion of Monthly Ammonia Emissions in China Based on the Chinese Ammonia Monitoring Network and Ensemble Kalman Filter
- Creators
- Lei Kong - Chinese Academy of SciencesXiao Tang - Chinese Academy of SciencesJiang Zhu - Chinese Academy of SciencesZifa Wang - Chinese Academy of SciencesYuepeng Pan - Chinese Academy of SciencesHuangjian Wu - Chinese Academy of SciencesLin Wu - Chinese Academy of SciencesQizhong Wu - Beijing Normal UniversityYuexin He - Chinese Academy of SciencesShili Tian - Chinese Academy of SciencesYuzhu Xie - Chinese Academy of SciencesZ R Liu - Chinese Academy of SciencesWenxuan Sui - Chinese Academy of SciencesLina Han - Chengdu University of Information TechnologyGreg Carmichael - University of Iowa
- Resource Type
- Journal article
- Publication Details
- ENVIRONMENTAL SCIENCE & TECHNOLOGY, Vol.53(21), pp.12529-12538
- DOI
- 10.1021/acs.est.9b02701
- ISSN
- 1520-5851
- Grant note
- DOI: 10.13039/501100002855, name: Ministry of Science and Technology of the People's Republic of China, award: 2016YFC0201802, 2017YFC0210103; DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 41405144, 41575128, 41875164, 91644216; name: Guangdong Province, award: 2017B020216007
- Language
- English
- Date published
- 2019
- Academic Unit
- Nursing; Civil and Environmental Engineering; Chemical and Biochemical Engineering
- Record Identifier
- 9984231920002771
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