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.
All Science Journal Classification (ASJC) codes
- Renewable Energy, Sustainability and the Environment
- Environmental Science(all)
- Public Health, Environmental and Occupational Health