Bottom-up emission inventories can provide valuable information for understanding emission status and are needed as input datasets to drive chemical transport models. However, this type of inventory has the disadvantage of taking several years to be compiled because it relies on a statistical dataset. Top-down approaches use satellite data as a constraint and overcome this disadvantage. We have developed an immediate inversion system to estimate anthropogenic NO _x emissions with NO₂ column density constrained by satellite observations. The proposed method allows quick emission updates and considers model and observation errors by applying linear unbiased optimum estimations. We used this inversion system to estimate the variation of anthropogenic NO _x emissions from China and India from 2005 to 2016. On the one hand, NO _x emissions from China increased, reaching a peak in 2011 with 29.5 Tg yr⁻¹, and subsequently decreased to 25.2 Tg yr⁻¹ in 2016. On the other hand, NO _x emissions from India showed a continuous increase from 2005 to 2016, reaching 13.9 Tg yr⁻¹ in 2016. These opposing trends from 2011 to 2016 were −0.83 and +0.76 Tg yr⁻¹ over China and India, respectively, and correspond to strictly regulated and unregulated future scenarios. Assuming these trends continue after 2016, we expect NO _x emissions from China and India will be similar in 2023, with India becoming the world’s largest NO _x emissions source in 2024.

A model intercomparison of the atmospheric dispersion of cesium‐137 (¹³⁷Cs) emitted after the Fukushima Daiichi Nuclear Power Plant accident in Japan was conducted to understand the behavior of atmospheric ¹³⁷Cs in greater detail. The same meteorological data with a fine spatiotemporal resolution and an emission inventory were applied to all models to exclude the differences among the models originating from differences in meteorological and emission data. The meteorological data were used for initial, boundary, and nudging data or offline meteorological field. Furthermore, a horizontal grid with the same resolution as that of the meteorological data was adopted for all models. This setup enabled us to focus on model variability originating from the processes included in each model, for example, physical processes. The multimodel ensemble captured 40% of the atmospheric ¹³⁷Cs events observed by measurements, and the figure of merit in space for the total deposition of ¹³⁷Cs exceeded 80. The lower score of the atmospheric ¹³⁷Cs than that of the deposition originated from the difference in timing between observed and simulated atmospheric ¹³⁷Cs. Our analyses indicated that meteorological data were most critical for reproducing the atmospheric ¹³⁷Cs events. The results further revealed that differences in ¹³⁷Cs concentrations among the models originated from deposition and diffusion processes when the meteorological field was simulated reasonably well. The models with small deposition fluxes produced higher scores for atmospheric ¹³⁷Cs, and those with strong diffusion succeeded in capturing the high ¹³⁷Cs concentrations observed; however, they also tended to overestimate the concentrations.