Aerosol optical depth (AOD) has been widely used to estimate the near-surface PM_2.5 (fine particulate matter with particle size less than 2.5 μm). However, the total-column AOD obtained by passive remote sensing instruments can neither distinguish the contribution of AOD in various altitude layers nor obtain the vertical PM_2.5 concentration. In this study, we compared several AOD-PM_2.5 models including Extra Trees (ET), Random Forest (RF), Deep Neural Network (DNN), and Gradient Boosting Regression Tree (GBRT), and analyzed the corresponding results using AOD of different altitudes and auxiliary data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The results indicate that the ET model performs best in terms of the model effectiveness and feature interpretation on the training dataset. We conclude that the feature importance of the bottom layer AOD is higher than that of the upper and total column AOD. The results showed that regional differences existed in the optimal height of the AOD-PM_2.5 correlation in study area. The results of cross-validation indicate that ET manages the most appealing overall performance with an R² (RMSE) of 0.85 (17.77 μg/m³). Regarding the 729 sites involved in this study, 73% had R² > 0.7, and the region or season with higher AOD feature importance achieves better model performance. The results of the AOD-PM_2.5 model in each layer were corrected using the AOD weight, to obtained the PM_2.5 vertical concentrations from 2015 to 2019. The results highlight that the high PM_2.5 concentration area is primarily near the ground and decreases with height. Additionally, the PM_2.5 vertical concentration in Beijing-Tianjin-Hebei (-1.80 μg/m³, P < 0.001), Central China (-1.62 μg/m³, P < 0.001), and Pearl River Delta (-0.66 μg/m³, P < 0.001) show an apparent downward trend. We believe that the vertical distribution analysis of PM_2.5 can provide meaningful information for studying air pollution.

The rapid urbanization in China and the long‐range transport dust (LRTD) from arid and semi‐arid areas has resulted in an increase of PM₁₀ concentration. In this study, an interpretable deep learning model [deep forest (DF)] with FY‐4A top‐of‐the‐atmosphere reflectance (TOAR) data were used to obtain the hourly PM₁₀ in China. The optimal hourly average R² of 10‐fold cross validation can achieve 0.85 (13:00 Beijing time); The R² (RMSE, μg/m³) of the daily, monthly, and annual averages were 0.82 (24.16), 0.97 (6.53), and 0.99 (2.30), respectively. Using TOAR data, the DF model performed better than other machine learning models. The feature importance of the TOAR‐PM₁₀ model showed that TOAR and meteorological elements both contributed significantly to the model. In spring, the PM₁₀ in northern China was greater than that in southern China, which may be related to the LRTD. Excluding the dust weather periods, the areas with high PM₁₀ values in China were mainly in cities and their suburbs, where were correlated with human activities. During a dust weather process, LRTD increased PM₁₀ in northern China by 80.4%. During a mixture haze and dust weather process, the PM₁₀ increased by 130.2% in northern China, of which LRTD led to an increase of 73.7%. The sources (from the Taklimakan Desert in China) and transmission paths of these two LRTD processes were similar. The contribution of LRTD to PM₁₀ was related to dust intensity and meteorological conditions. The results showed that LRTD and local pollution to PM₁₀ was both important in haze periods.

The COVID-19 pandemic poses a serious global threat to human health. In China, the government immediately implemented lockdown measures to curb the spread of this virus. These measures severely affected transportation and industrial production across the country, resulting in a significant change in the concentration of air pollutants. In this study, the Euclidean distance method was used to select the most similar meteorological field during the COVID-19 lockdown period. Changes in the concentration of air pollutants in China were analyzed under similar meteorological background conditions. Results indicate that, compared with data from 2015–2019, air quality in China significantly improved; with the exception of ozone (O₃), the concentration of major air pollutants declined. Compared with baseline conditions, the reduction of air pollutants in China from 25 January to 22 February 2020 (Period 2) was the most significant. In particular, NO₂ decreased by 41.7% in the Yangtze River Delta. In Period 2, the reduction of air pollutants in areas other than Hubei gradually decreased, but the reduction of NO₂ in Wuhan reached 61.92%, and the reduction of air pollutants in various regions after February 23 was significantly reduced. By excluding the influence of meteorological factors and calculating the contribution of human activities to atmospheric pollutants by linear fitting, in Period 2 the effect of artificial controls on NO₂ in Wuhan attained 30.66%, and reached 48.17% from 23 February to 23 March (Period 3). Results from this investigation provides effective theoretical support for pollution prevention and control in China.

In this paper, the methods to detect dust based on passive and active measurements from satellites have been summarized. These include the visible and infrared (VIR) method, thermal infrared (TIR) method, microwave polarized index (MPI) method, active lidar-based method, and combined lidar and infrared measurement (CLIM) method. The VIR method can identify dust during daytime. Using measurements at wavelengths of 8.5, 11.0, and 12.0 µm, the TIR method can distinguish dust from other types of aerosols and cloud, and identify the occurrence of dust over bright surfaces and during night. Since neither the VIR nor the TIR method can penetrate ice clouds, they cannot detect dust beneath ice clouds. The MPI method, however, can identify about 85% of the dust beneath ice clouds. Meanwhile, the active lidar-based method, which uses the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data and five-dimensional probability distribution functions, can provide very high-resolution vertical profiles of dust aerosols. Nonetheless, as the signals from dense dust and thin clouds are similar in the CALIOP measurements, the lidar-based method may fail to distinguish between them, especially over dust source regions. To address this issue, the CLIM method was developed, which takes the advantages of both TIR measurements (to discriminate between ice cloud and dense dust layers) and lidar measurements (to detect thin dust and water cloud layers). The results obtained by using the new CLIM method show that the ratio of dust misclassification has been significantly reduced. Finally, a concept module for an integrated multi-satellites dust detection system was proposed to overcome some of the weaknesses inherent in the single-sensor dust detection.