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Dust emitted from arid and semi-arid areas of China is a main contributor to the global atmospheric aerosols. However, the long-term spatial and temporal variations in dust concentrations in China is still unknown. Here, we simulated the spatial and temporal variations in spring dust concentrations in China from 2000 to 2020 using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). The results showed that the configured WRF-Chem model in this study reproduced the spatial patterns and temporal variations of dust aerosols. The annual mean spring dust concentration at the country level was 26.95 g kg−1-dry air and showed a slightly increasing trend in China during 2000–2020. There were clear spatial differences and inter-annual variations in dust concentrations. The dust concentration generally decreased from the dust source regions of the northwest to the southeast regions of China. Obvious increasing and decreasing trends in spring dust concentrations were identified in the regions of northern Xinjiang and Gansu and in the regions of southern Xinjiang and western Inner Mongolia, respectively. In May, the dust concentration showed an increasing trend in most regions of northwestern China. This provided the basic information for insight into the long-term spatial and temporal variations in spring dust concentrations in China. [ABSTRACT FROM AUTHOR]

Fine particulate matter with an aerodynamic diameter less than 2.5 μm (PM 2.5) is one of the major air pollutants risks to human health worldwide. Satellite-based aerosol optical depth (AOD) products are an effective metric for acquiring PM 2.5 information, featuring broad coverage and high resolution, which compensate for the sparse and uneven distribution of existing monitoring stations. In this study, a gradient boosting decision tree (GBDT) model for estimating ground PM 2.5 concentration directly from AOD products across China in 2017, integrating human activities and various natural variables was proposed. The GBDT model performed well in estimating temporal variability and spatial contrasts in daily PM 2.5 concentrations, with relatively high fitted model (10-fold cross-validation) coefficients of determination of 0.98 (0.81), low root mean square errors of 3.82 (11.57) μg/m3, and mean absolute error of 1.44 (7.45) μg/m3. Seasonal examinations revealed that summer had the cleanest air with the highest estimation accuracies, whereas winter had the most polluted air with the lowest estimation accuracies. The model successfully captured the PM 2.5 distribution pattern across China in 2017, showing high levels in southwest Xinjiang, the North China Plain, and the Sichuan Basin, especially in winter. Compared with other models, the GBDT model showed the highest performance in the estimation of PM 2.5 with a 3-km resolution. This algorithm can be adopted to improve the accuracy of PM 2.5 estimation with higher spatial resolution, especially in summer. In general, this study provided a potential method of improving the accuracy of satellite-based ground PM 2.5 estimation. Image 1 • Satellite AOD, human activities, and various natural variables were predictors. • The GBDT successfully captured the PM 2.5 distribution pattern across China. • This model had high accuracy in PM 2.5 estimation with a 3-km resolution. • The GBDT allowed to improve the accuracy of PM 2.5 estimation with higher resolution. [ABSTRACT FROM AUTHOR]