Estimation of Ammonia Emissions over China Using IASI Satellite-Derived Surface Observations

An accurate ammonia (NH<sub>3</sub>) emission inventory is crucial for policymakers developing air pollution mitigation strategies. Both satellite observations and bottom-up estimates identify significant NH<sub>3</sub> emission hotspots in China. However, bottom-up NH<sub>3</sub> emission inventories are highly uncertain due to the lack of localized emission factors, while large and uncertain errors in IASI satellite NH<sub>3</sub> columns have hindered their direct application in top-down emission inversion methods. In this study, we perform a top-down optimization of monthly NH<sub>3</sub> emissions over China using IASI-derived surface NH<sub>3</sub> concentrations with well-evaluated error estimates, combined with the CAMx model at a 36 km resolution. Our posterior NH<sub>3</sub> emissions for 2020 (12.3 [10.9-13.6] Tg N yr<sup>-1</sup>) are significantly higher than prior estimates from the MEIC inventory (7.6 Tg N yr<sup>-1</sup>), which primarily underestimates emissions during the warm months in hotspot areas (e.g., NCP and MLYR). We employ multiple approaches to comprehensively evaluate our inversion results. Our study highlights that error estimates for low-value observations are a particularly critical factor in the inversion setup, significantly influencing the reliability of emission optimization.

Authors

Institutions

Publication Details

Journal
Environmental Science & Technology
Published
2025-05-15
DOI
https://doi.org/10.1021/acs.est.4c10878
Citations
5
Primary Topic
Atmospheric chemistry and aerosols
Type
article
Field-Weighted Citation Impact
6.50

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Estimation of Ammonia Emissions over China Using IASI Satellite-Derived Surface Observations

Maarten Krol, Wen Hai Xu, Jianan Chen, Xuejun Liu et al.
5 citations
Environmental Science & Technology
Atmospheric chemistry and aerosols
6.50
article

Estimation of Ammonia Emissions over China Using IASI Satellite-Derived Surface Observations

Maarten Krol, Wen Hai Xu, Jianan Chen, Xuejun Liu, Xiaohui Du
article en
5 citations

Abstract

An accurate ammonia (NH<sub>3</sub>) emission inventory is crucial for policymakers developing air pollution mitigation strategies. Both satellite observations and bottom-up estimates identify significant NH<sub>3</sub> emission hotspots in China. However, bottom-up NH<sub>3</sub> emission inventories are highly uncertain due to the lack of localized emission factors, while large and uncertain errors in IASI satellite NH<sub>3</sub> columns have hindered their direct application in top-down emission inversion methods. In this study, we perform a top-down optimization of monthly NH<sub>3</sub> emissions over China using IASI-derived surface NH<sub>3</sub> concentrations with well-evaluated error estimates, combined with the CAMx model at a 36 km resolution. Our posterior NH<sub>3</sub> emissions for 2020 (12.3 [10.9-13.6] Tg N yr<sup>-1</sup>) are significantly higher than prior estimates from the MEIC inventory (7.6 Tg N yr<sup>-1</sup>), which primarily underestimates emissions during the warm months in hotspot areas (e.g., NCP and MLYR). We employ multiple approaches to comprehensively evaluate our inversion results. Our study highlights that error estimates for low-value observations are a particularly critical factor in the inversion setup, significantly influencing the reliability of emission optimization.

Environmental Science & TechnologyVol. 59(20)
Utrecht University (NL), Chinese Research Academy of Environmental Sciences (CN), China Agricultural University (CN), Wageningen University & Research (NL)
National Natural Science Foundation of China, China Scholarship Council, National Key Research and Development Program of China
Climate action
Openalex Percentile: Top 3%
Atmospheric chemistry and aerosols
6.50
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.