Extreme particulate matter (PM) air pollution of January 2013 in China was found to be associated with an anomalous eastward extension of the Siberian High (SH). We developed a Siberian High position index (SHPI), which depicts the mean longitudinal position of the SH, as a new indicator of the large-scale circulation pattern that controls wintertime air quality in China. This SHPI explains 58 % (correlation coefficient of 0.76) of the interannual variability of wintertime aerosol optical depth (AOD) retrieved by MODIS over North China (NC) during 2001–2013. By contrast, the intensity-based conventional Siberian High index (SHI) shows essentially no skill in predicting this AOD variability. On the monthly scale, some high-AOD months for NC are accompanied with extremely high SHPIs; notably, extreme PM pollution of January 2013 can be explained by the SHPI value exceeding 2.6 times the standard deviation of the 2001–2013 January mean. When the SH extends eastward, thus higher SHPI, prevailing northwesterly winds over NC are suppressed not only in the lower troposphere but also in the middle troposphere, leading to reduced southward transport of pollution from NC to South China (SC). The SHPI hence exhibits a significantly negative correlation of −0.82 with MODIS AOD over SC during 2001–2013, although the robustness of this correlation depends on that of satellite-derived AOD. The suppressed northwesterly winds during high-SHPI winters also lead to increased relative humidity (RH) over NC. Both the wind and RH changes are responsible for enhanced PM pollution over NC during the high-SHPI winters.

Calculations of the aerosol direct effect on climate rely on simulated aerosol fields. The model representation of aerosol mixing state potentially introduces large uncertainties into these calculations, since the simulated aerosol optical properties are sensitive to mixing state. In this study, we systematically quantified the impact of aerosol mixing state on aerosol optical properties using an ensemble of 1800 aerosol populations from particle-resolved simulations as a basis for Mie calculations for optical properties. Assuming the aerosol to be internally mixed within prescribed size bins caused overestimations of aerosol absorptivity and underestimations of aerosol scattering. Together, these led to errors in the populations' single scattering albedo of up to −22.3 % with a median of −0.9 %. The mixing state metric χ proved useful in relating errors in the volume absorption coefficient, the volume scattering coefficient and the single scattering albedo to the degree of internally mixing of the aerosol, with larger errors being associated with more external mixtures. At the same time, a range of errors existed for any given value of χ. We attributed this range to the extent to which the internal mixture assumption distorted the particles' black carbon content and the refractive index of the particle coatings. Both can vary for populations with the same value of χ. These results are further evidence of the important yet complicated role of mixing state in calculating aerosol optical properties.

This study integrates machine learning and particle‐resolved aerosol simulations to develop emulators that predict submicron aerosol mixing state indices from the Earth system model (ESM) simulations. The emulators predict aerosol mixing state using only quantities that are predicted by the ESM, including bulk aerosol species concentrations, which do not by themselves carry mixing state information. We used PartMC‐MOSAIC as the particle‐resolved model and NCAR's CESM as the ESM. We trained emulators for three different mixing state indices for submicron aerosol in terms of chemical species abundance (χ_a), the mixing of optically absorbing and nonabsorbing species (χ_o), and the mixing of hygroscopic and nonhygroscopic species (χ_h). Our global mixing state maps show considerable spatial and seasonal variability unique to each mixing state index. Seasonal averages varied spatially between 13% and 94% for χ_a, between 38% and 94% for χ_o, and between 20% and 87% for χ_h with global annual averages of 67%, 68%, and 56%, respectively. High values in one index can be consistent with low values in another index depending on the grouping of species and their relative abundance, meaning that each mixing state index captures different aspects of the population mixing state. Although a direct validation with observational data has not been possible yet, our results are consistent with mixing state index values derived from ambient observations. This work is a prototypical example of using machine learning emulators to add information to ESM simulations.

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.

Aerosol particles undergo physical and chemical changes during cloud processes. In this work, we quantified the changes in aerosol mixing state using a particle‐resolved model. To this end, we coupled the particle‐resolved aerosol model PartMC‐MOSAIC with the aqueous chemistry module CAPRAM 2.4 and designed cloud parcel simulations that mimicked several cloud cycles that a particle population may be exposed to in polluted urban environments. With ammonium nitrate and ammonium sulfate added to the activated particles, after the cloud evaporated, the activation potential of the resuspended aerosol particles increased for supersaturation thresholds lower than the maximum supersaturation attained in the cloud. Formation of sulfate and nitrate increased the internally mixed state of all particle populations, quantified by the mixing state index χ. The change of aerosol mixing state due to aqueous‐phase chemistry was related to the fraction of activated particles. For a case with low aerosol number concentration, where the activated fraction was up to 60%, χ increased by up to 50 percentage points after cloud processing, reaching an almost completely internal mixture. In contrast, for a case with high aerosol emissions and activated fraction of less than 20%, the increase in χ was less than 20 percentage points, and χ remained below 80% after cloud processing. The change in aerosol mixing state caused by coagulation within the cloud parcel was negligible. These findings highlight the complex influence of cloud processing on particle properties.

The authors evaluated and compared the behavior of PM_2.5 with respect to NO_x and NH₃ emission changes in high (the year 2013) and low (the year 2018) SO₂ emission cases. Two groups of simulations were conducted based on anthropogenic emissions from China in 2013 and 2018, respectively. In each group of simulations, a respective 25% reduction in NO_x and NH₃ emissions were assumed. A sensitivity factor (β) was defined as the relative change in PM_2.5 concentration due to 1% change in NO_x or NH₃ emissions. In the high SO₂ emissions case, PM_2.5 was more sensitive to NH₃ (0.31) emissions change than NO_x (0.21). Due to the significant decrease in SO₂ emissions from the high to low SO₂ emissions case, the sensitivity of PM_2.5 to NO_x increased to 0.33, while its sensitivity to NH₃ decreased to 0.22. The result implies that now and in the future, PM_2.5 is/will be less sensitive to NH₃ emissions change, while NO_x emissions control is more effective in reducing the surface PM_2.5 concentration. Seasonally, in the low SO₂ emissions case, the sensitivities of PM_2.5 to NO_x and NH₃ in winter were higher than those in summer, indicating that to deal with severe winter haze more attention should be paid to the emissions control of inorganic PM_2.5 precursors, especially NO_x. Graphical abstract