Improved Inversion of Monthly Ammonia Emissions in China Based on the Chinese Ammonia Monitoring Network and Ensemble Kalman Filter
- 1Institute of Atmospheric Physics, Chinese Academy of Sciences, LAPC, Beijing, China (tangxiao@mail.iap.ac.cn)
- 2CAS-TWAS Center of Excellence for Climate and Environment Sciences (ICCES), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
- 3University of Chinese Academy of Sciences, Beijing 100049, China
- 4Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
- 5College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
- 6Chengdu University of Information Technology, Chengdu 610225, China
- 7Center for Global and Regional Environmental Research, University of Iowa, Iowa City, Iowa 52242, United States
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.
How to cite: Tang, X., Kong, L., Zhu, J., Wang, Z., Pan, Y., Wu, H., Wu, L., Wu, Q., He, Y., Tian, S., Xie, Y., Liu, Z., Sui, W., Han, L., and Carmichael, G.: Improved Inversion of Monthly Ammonia Emissions in China Based on the Chinese Ammonia Monitoring Network and Ensemble Kalman Filter, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13077, https://doi.org/10.5194/egusphere-egu2020-13077, 2020.