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.