We use a chemical transport model to examine the change of sulfate-nitrate-ammonium (SNA) aerosols over China due to anthropogenic emission changes of their precursors (SO₂, NO_x and NH₃) from 2000 to 2015. From 2000 to 2006, annual mean SNA concentrations increased by about 60% over China as a result of the 60% and 80% increases in SO₂ and NO_x emissions. During this period, sulfate is the dominant component of SNA over South China (SC) and Sichuan Basin (SCB), while nitrate and sulfate contribute equally over North China (NC). Based on emission reduction targets in the 12th (2011–2015) Five-Year Plan (FYP), China's total SO₂ and NO_x emissions are projected to change by −16% and +16% from 2006 to 2015, respectively. The amount of NH₃ emissions in 2015 is uncertain, given the lack of sufficient information on the past and present levels of NH₃ emissions in China. With no change in NH₃ emissions, SNA mass concentrations in 2015 will decrease over SCB and SC compared to their 2006 levels, but increase over NC where the magnitude of nitrate increase exceeds that of sulfate reduction. This suggests that the SO₂ emission reduction target set by the 12th FYP, although effective in reducing SNA over SC and SCB, will not be successful over NC, for which NO_x emission control needs to be strengthened. If NH₃ emissions are allowed to keep their recent growth rate and increase by +16% from 2006 to 2015, the benefit of SO₂ reduction will be completely offset over all of China due to the significant increase of nitrate, demonstrating the critical role of NH₃ in regulating nitrate. The effective strategy to control SNA and hence PM_2.5 pollution over China should thus be based on improving understanding of current NH₃ emissions and putting more emphasis on controlling NH₃ emissions in the future.

Quantification and control of NO_x and CO₂ emissions are essential across the world to limit adverse climate change and improve air quality. We present a new top-down method, an improved superposition column model to estimate day-to-day NO_x and CO₂ emissions from the large city of Wuhan, China, located in a polluted background. The latest released version 2.3.1 TROPOMI (TROPOspheric Monitoring Instrument) NO₂ columns and version 10r of the Orbiting Carbon Observatory-2 (OCO-2)-observed CO₂ mixing ratio are employed. We quantified daily NO_x and CO₂ emissions from Wuhan between September 2019 and October 2020 with an uncertainty of 31 % and 43 %, compared to 39 % and 49 % with the earlier v1.3 TROPOMI data, respectively. Our estimated NO_x and CO₂ emissions are verified against bottom-up inventories with minor deviations (<3 % for the 2019 mean, ranging from −20 % to 48 % on a daily basis). Based on the estimated CO₂ emissions, we also predicted daily CO₂ column mixing ratio enhancements, which match well with OCO-2 observations (<5 % bias, within ±0.3 ppm). We capture the day-to-day variation of NO_x and CO₂ emissions from Wuhan in 2019–2020, which does not reveal a substantial “weekend reduction” but does show a clear “holiday reduction” in the NO_x and CO₂ emissions. Our method also quantifies the abrupt decrease and slow NO_x and CO₂ emissions rebound due to the Wuhan lockdown in early 2020. This work demonstrates the improved superposition model to be a promising new tool for the quantification of city NO_x and CO₂ emissions, allowing policymakers to gain real-time information on spatial–temporal emission patterns and the effectiveness of carbon and nitrogen regulation in urban environments.

SO₂ emission control has been one of the most important air pollution policies in China since 2000. In this study, we assess regional differences in SO₂ emission control efficiencies in China through the modeling analysis of four scenarios of SO₂ emissions, all of which aim to reduce the national total SO₂ emissions by 8% or 2.3 Tg below the 2010 emissions level, the target set by the current twelfth Five-Year Plan (FYP; 2011–2015), but differ in spatial implementation. The GEOS-Chem chemical transport model is used to evaluate the efficiency of each scenario on the basis of four impact metrics: surface SO₂ and sulfate concentrations, population-weighted sulfate concentration (PWC), and sulfur export flux from China to the western Pacific. The efficiency of SO₂ control (β) is defined as the relative change of each impact metric to a 1% reduction in SO₂ emissions from the 2010 baseline. The S1 scenario, which adopts a spatially uniform reduction in SO₂ emissions in China, gives a β of 0.99, 0.71, 0.83, and 0.67 for SO₂ and sulfate concentrations, PWC, and export flux, respectively. By comparison, the S2 scenario, which implements all the SO₂ emissions reduction over North China (NC), is found most effective in reducing national mean surface SO₂ and sulfate concentrations and sulfur export fluxes, with β being 1.0, 0.76, and 0.95 respectively. The S3 scenario of implementing all the SO₂ emission reduction over South China (SC) has the highest β in reducing PWC (β = 0.98) because SC has the highest correlation between population density and sulfate concentration. Reducing SO₂ emissions over Southwest China (SWC) is found to be least efficient on the national scale, albeit with large benefits within the region. The difference in β by scenario is attributable to the regional difference in SO₂ oxidation pathways and the source–receptor relationship. Among the three regions examined here, NC shows the largest proportion of sulfate formation through gas-phase oxidation, which is more sensitive to SO₂ emissions change than aqueous oxidation. In addition, NC makes the largest contribution to inter-regional transport of sulfur within China and to the transport fluxes to the western Pacific. The policy implication of this is that China needs to carefully design a regionally specific implementation plan of realizing its SO₂ emissions reduction target in order to maximize the resulting air quality benefits, not only for China but for the downwind regions, with emphasis on reducing emissions from NC, where SO₂ emissions have decreased at a slower rate than national total emissions in the previous FYP period.