Atmospheric Brown Carbon (BrC) with strong wavelength-dependence light-absorption ability can significantly affect radiative forcing. Highly resolved emission inventories with lower uncertainties are important premise and essential in scientifically evaluating impacts of emissions on air quality, human health and climate change. This study developed a bottom-up inventory of primary BrC from combustion sources in China from 1960 to 2016 with a spatial resolution at 0.1 degrees x 0.1 degrees, based on compiled emission factors and detailed activity data. The primary BrC emission in China was about 593 Gg (500-735 Gg as interquartile range) in 2016, contributing to 7% (5%-8%) of a previously estimated global total BrC emission. Residential fuel combustion was the largest source of primary BrC in China, with the contribution of 67% as the national average but ranging from 25% to 99% among different provincial regions. Significant spatial disparities were also observed in the relative shares of different fuel types. Coal combustion contribution varied from 8% to 99% across different regions. Heilongjiang and North China Plain had high emissions of primary BrC. Generally, on the national scale, spatial distribution of BrC emission density per area was aligned with the population distribution. Primary BrC emission from combustion sources in China have been declined since a peak of similar to 1300 Gg in 1980, but the temporal trends were distinct in different sectors. The high-resolution inventory developed here enables radiative forcing simulations in future atmospheric models so as to promote better understanding of carbonaceous aerosol impacts in the Earth's climate system and to develop strategies achieving co-benefits of human health protection and climate change.

Two extremely devastating super dust storms (SDS) hit Mongolia and Northern China in March 2021, causing many deaths and substantial economic damage. Accurate forecasting of dust storms is of great importance for avoiding or mitigating their effects. One of the most critical factors affecting dust emissions is soil moisture, but its value in desert exhibits significant uncertainty. In this study, model experiments were conducted to simulate dust emissions using four soil moisture datasets. The results were compared with observations to assess the effects of soil moisture on the dust emission strength. The Integrated Source Apportionment Method (ISAM) was used to track the dust sources and quantify the contribution from each source region to the dust load over the North China Plain (NCP), Korea peninsula, and western Japan. The results show large differences in the dust load depending on the soil moisture datasets used. The high soil moisture in the NCEP dataset results in substantial underestimation of the dust emission flux and PM10 10 concentration. Despite a minor overestimation of PM10 10 concentrations in many Northern China cities, the ERA5 dataset yields the best simulation performance. During the two SDS events, about 7.5 Mt dust was released from the deserts in Mongolia and 2.8 Mt from the deserts in China. Source apportionment indicates that the Mongolian Gobi Desert is the dominant source of PM10 10 in the NCP, Korea peninsula, and western Japan, accounting for 60 %-80 %, while Inner Mongolia contributed 10 %- 20 %.

Understanding the origins of Tibetan Plateau (TP) glacier dust is vital for glacier dynamics and regional climate understanding. In May 2016, snow pit samples were collected from glaciers on the TP: Qiyi (QY) in the north, Yuzhufeng (YZF) in the center, and Xiaodongkemadi (XDK) in the south. Rare earth element (REE) concentrations were analyzed using inductively coupled plasma mass spectrometry (ICP-MS), and near-surface PM10 concentrations were extracted from a dataset of Chinese near-surface PM10. Two tracing approaches were used: direct REE tracing and an indirect approach combining potential source contribution function (PSCF) and concentration-weighted trajectory (CWT). Both methods yielded consistent results. Pre-monsoon, TP surface soils, Taklimakan Desert, and Qaidam Basin contributed to glacier dust. Notably, central and southern glaciers showed Thar Desert influence, unlike the northern ones. Taklimakan and Thar Deserts were major contributors due to their substantial contribution and high dust concentration. Taklimakan dust, influenced by terrain and westerly winds, affected central and southern glaciers more than northern ones. Westerlies carried Thar Desert dust to the TP after it was uplifted by updrafts in northwest India, significantly affecting southern glaciers. Furthermore, comparing the two tracer methods, the indirect approach combining PSCF and CWT proved more effective for short-term dust source tracing.