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Excessive anthropogenic activities have led to high-level ammonia loss and volatilization, which is regarded as a key factor in Chinese haze formation. In this study, a comprehensive analysis of ammonia emission estimations is accomplished at both temporal (1980–2016) and spatial (provincial) scales using a mass-balanced model, and emission projections through 2030 are also studied in different development scenarios. The results show that the ammonia emissions increased from 4.7 Tg N yr−1 in 1980 to 11 Tg N yr−1 in 2016, which is an approximately 2.4-fold increase. The cropland and livestock emissions are the largest contributors, as most reports show approximately 80% contributions; however, nonagriculture sources of fuel combustion, waste treatment and ammonia escape have grown rapidly in recent years, accounting for 14% in 2016. The spatial differences also reveal the complex heterogeneity in Chinese provinces. In addition, the emission intensities of major agriculture and non-agriculture sources are 0–80 kg N ha−1 yr−1 and over 100 kg N ha−1 yr−1, respectively, indicating a higher degree of ammonia concentration from non-agriculture emissions, which should attract wide concern. In terms of scenario analysis, emissions would reach 12.8 Tg N yr−1 in 2030 under the currently developed model and 7.3 Tg N yr−1 under a series of reduction policies; the spatial analysis also shows that the North China Plain has a 2.1 Tg N yr−1 reduction potential. The results of this study provide new insights into ammonia emission estimations and a better understanding of the environmental impacts of ammonia emitted from different sources. Excessive anthropogenic activities have led to high-level ammonia loss and volatilization, which is regarded as a key factor in Chinese haze formation. In this study, a comprehensive analysis of ammonia emission estimations is accomplished at both temporal (1980–2016) and spatial (provincial) scales using a mass-balanced model, revealing the temporal-spatial differences of the complex heterogeneity in China. In addition, the high-resolution sectorial emission pattern is also revealed by land-use model, indicating a higher degree of ammonia concentration from non-agriculture emissions, which should attract wider concern. Scenario analyses are also studied to reveal the emission tendency in different development scenario. The results of this study provide new insights into ammonia emission estimations and a better understanding of the environmental impacts of ammonia emitted from different sources. Unlabelled Image • Temporal and spatial variations in the ammonia emission were quantified. • The nonagriculture emissions show higher emission intensity with agriculture. • The ammonia emission to 2030 show different trends in scenario analysis. [ABSTRACT FROM AUTHOR]