4DEnVar-based inversion system for ammonia emission estimation in China through assimilating IASI ammonia retrievals

Abstract

Atmospheric ammonia has been hazardous to the environment and human health for decades. Current inventories are usually constructed in a bottom-up manner and subject to uncertainties and incapable of reproducing the spatiotemporal characteristics of ammonia emission. Satellite measurements, for example, Infrared Atmospheric Sounder Interferometer (IASI) and Cross-Track Infrared Sounder (CrIS), which provide global coverage of ammonia distribution, have gained popularity in ammonia emission estimation through data assimilation methods. However, satellite-based emission inversion studies on China are limited. In this study, we propose a four-dimensional ensemble variational -based ammonia emission inversion system to optimize ammonia emissions in China. It was developed by assimilating the IASI ammonia retrievals onboard Meteorological Operational satellite A and B (Metop-A/B) into a chemical transport model Goddard Earth Observing System Chemical model (GEOS-Chem). Monthly inversion experiments were conducted in April, July, and October 2016 to test performance. The inversion result indicated that the prior inventory from the MEIC model captured ammonia spreads in general; however, it heterogeneously underrated the emission intensity. The increments obtained in the assimilation were as high as 50% in North, East, and Northwest China. The posterior emission inventory presented a regional emission flux consistent with relevant studies. Driven by the optimized source estimate, GEOS-Chem provides superior results than using the prior in the evaluation of the assimilated IASI retrievals and the surface ammonia concentration measured by the ground-based Ammonia Monitoring Network (AMoN) in China.

Significant Increase in Ammonia Emissions in China: Considering Nonagricultural Sectors Based on Isotopic Source Apportionment

“…In contrast, most other studies demonstrated a clearer seasonality in NH 3 emissions, with summer and winter seeing higher and lower emissions, respectively. ,,,,,− Regarding nonagricultural sectors, in contrast to what the original EI could not adequately capture, isotopic results showed that the relative contributions of nonagricultural sectors also showed clear seasonal variations. , Consequently, the simulations driven by the MEIC would lead to a relative underestimation (overestimation) of NH 3 concentration during the summer (winter) season in this study. Zhang et al and Jin et al . also demonstrated that the MEIC underestimated and overestimated NH 3 emissions in the summer and winter seasons in the BTH region and China, respectively.…”

“…36,82 Consequently, the simulations driven by the MEIC would lead to a relative underestimation (overestimation) of NH 3 concentration during the summer (winter) season in this study. Zhang et al 83 and Jin et al 84 also demonstrated that the MEIC underestimated and overestimated NH 3 emissions in the summer and winter seasons in the BTH region and China, respectively. To characterize the NH 3 EI more accurately in the future, the monthly profile of NH 3 emissions in the MEIC must be significantly improved.…”

Observational operator for fair model calibration with ground NO2 measurements

“…These ground observations provide valuable insights on air pollution conditions and are widely used for model calibrations (Zhu et al, 2021), and their distributions are presented in Supplement Figure S3. Concurrently, the rapid advancements in satellite remote sensing and other technologies have made it possible to observe near-surface air pollutant abundances from space (Zhang et al, 2020;Jin et al, 2023a). For example, satellite onboard instruments such as the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI) can facilitate the measurement of nitrogen dioxide (NO 2 ) with extensive coverage (van Geffen et al, 2022).…”