Mingxinyu Lu

The internal mixing state of aerosol particles governs their hygroscopicity, removal and activation to cloud droplets, yet assessments of emission reductions rarely resolve it. When emissions of sulfur dioxide and black carbon (BC) are reduced simultaneously, the remaining sulfate may either concentrate on the surviving BC-containing particles or redistribute to BC-free particles, with different consequences for coating thickness and cloud condensation nuclei. We examine this response with GISS ModelE2.1–MATRIX-VBS in two simulations with identical meteorology differing only in anthropogenic emissions over China, where SO₂, BC and organic carbon emissions are lower by 86, 83 and 66 % over 2031–2050. The sulfate column over China decreases by 24 %, because the region changes from a net exporter to a net importer of sulfate, and the decrease extends across the North Pacific to the Arctic. Sulfate is removed about three times faster from BC-containing than from BC-free particles, at the source and downwind, and the share of column sulfate on BC-containing particles over China decreases from 35 to 28 %. A closed process budget attributes this to the reduced number of BC cores available to receive sulfate: condensation and coagulation onto them weaken by about 70 %. Boundary-layer particle number over China decreases by 26 % and BC-containing particle number by 65 %, whereas the remaining coated particles grow. Cloud droplet number decreases by 29 %, mainly through loss of BC–organic-carbon particles. Particle number, size and mixing state therefore change more than bulk mass, and mass-based assessments do not resolve them.

The internal mixing state of aerosol particles governs their hygroscopicity, removal and activation to cloud droplets, yet assessments of emission reductions rarely resolve it. When emissions of sulfur dioxide and black carbon (BC) are reduced simultaneously, the remaining sulfate may either concentrate on the surviving BC-containing particles or redistribute to BC-free particles, with different consequences for coating thickness and cloud condensation nuclei. We examine this response with GISS ModelE2.1–MATRIX-VBS in two simulations with identical meteorology differing only in anthropogenic emissions over China, where SO₂, BC and organic carbon emissions are lower by 86, 83 and 66 % over 2031–2050. The sulfate column over China decreases by 24 %, because the region changes from a net exporter to a net importer of sulfate, and the decrease extends across the North Pacific to the Arctic. Sulfate is removed about three times faster from BC-containing than from BC-free particles, at the source and downwind, and the share of column sulfate on BC-containing particles over China decreases from 35 to 28 %. A closed process budget attributes this to the reduced number of BC cores available to receive sulfate: condensation and coagulation onto them weaken by about 70 %. Boundary-layer particle number over China decreases by 26 % and BC-containing particle number by 65 %, whereas the remaining coated particles grow. Cloud droplet number decreases by 29 %, mainly through loss of BC–organic-carbon particles. Particle number, size and mixing state therefore change more than bulk mass, and mass-based assessments do not resolve them.

Organic aerosols (OA) are a major component of fine particulate matter that affects air quality and climate, yet atmospheric models often underestimate their concentrations, partly reflecting uncertainties in the assumed volatility distribution of primary organic aerosol emissions. Using the NASA GISS ModelE Earth system model, we test four hypotheses: OA biases decrease when volatility distributions are differentiated by (H1) emission source, (H2) anthropogenic sector, (H3) geographic region, and (H4) VBS volatility bin. Simulations are evaluated against IMPROVE over the Continental US, ACTRIS over Europe, and the Chen et al. (2024, https://doi.org/10.1038/s41467‐024‐48902‐0 ) field‐campaign data set over China; for Africa and India, where direct OA observations are sparse, modeled OA is compared with MERRA‐2 reanalysis and complemented by MODIS/CALIPSO aerosol optical depth (AOD). Optimized volatility distributions reduce OA biases by up to 35% relative to the baseline, with optimal profiles varying across regions, seasons, and sectors. To extend these gains to data‐limited regions, we train Random Forest, XGBoost, and a back‐propagation neural network to construct spatiotemporally variable distributions. Although this improves performance, purely statistical predictions produce unrealistic summertime overestimations because they neglect the underlying chemistry. We therefore impose a physicochemical constraint based on RO ₂ + NO chemistry, activated where exceeds a piecewise‐regression threshold, and anthropogenic plus biomass‐burning OA emissions exceed the monthly 75th percentile, which reduces biases by up to 77% in the most overestimated regions. Aerosol‐population diagnostics show that volatility changes redistribute organic mass among primary‐organic, black‐carbon–organic, and organic–sulfate populations, providing a physically consistent pathway for improving OA representation.

High-quality observational data play a crucial role in deepening the investigation of the Tibetan Plateau’s influence on the Asian climate. This study employs eight machine learning models (support vector regression (SVR), k-nearest neighbors (KNN), extreme gradient boosting (XGBoost), random forest (RF), long short-term memory (LSTM), gated recurrent unit (GRU), Transformer, and Transformer–convolutional neural network (Transformer-CNN)) to interpolate missing observational data on surface net radiation (Rn), soil surface temperature (Ts), soil water content (SWC), air temperature (Ta), relative humidity (RH), and wind speed (WS) from the QOMS observation site. The data covers the period from 1 January 2007 through to 31 December 2016. A comparative evaluation of these models shows that the Transformer-CNN model consistently outperforms the other models in terms of prediction accuracy. On the test dataset, the coefficients of determination for the interpolated results of Ta, RH, WS, SWC, Ts, and Rn were 0.97, 0.92, 0.97, 0.79, 0.93, and 0.98, respectively. Secondly, the Transformer-CNN model was then applied to generate a complete meteorological dataset for the full period. A time series analysis of this dataset reveals statistically significant trends over the past decade: air temperature (Ta) increased by 0.60 °C (p = 0.022) and soil temperature (Ts) by 1.85 °C (p = 1.37 × 10⁻⁵). Meanwhile, wind speed (WS), soil water content (SWC), and net radiation (Rn) declined by 0.42 m/s (p = 1.18 × 10⁻¹²), 1.24% (p < 0.001), and 9.21 W/m² (p = 8.81 × 10⁻⁶), respectively.

Global aerosol models often underestimate the mass concentration of aerosols in the remote troposphere, as evidenced by aircraft measurements. This study leveraged data from the NASA Atmospheric Tomography Mission (ATom), which provides remote aerosol concentrations, to refine algorithms for simulating these concentrations. Using the GEOS-Chem model, we simulate five fine aerosol types and enhance the simulation results using five machine-learning algorithms: Random Forest, XGBoost, SVM, KNN, and LightGBM, and compare the performance of these algorithms. Additionally, we evaluate the refinement effect of algorithms based on decision trees on a validation dataset. The results demonstrate that GEOS-Chem generally underestimated aerosol mass concentration. Among the tested algorithms, algorithms based on decision trees, particularly the Random Forest algorithm and the LightGBM algorithm, exhibited a superior performance, significantly improving prediction accuracy and computational efficiency in both the training and testing phases, as well as on the validation dataset.