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With the advancement of urbanization in China, effective control of pollutant emissions and air quality have become important goals in current environmental management. Nitrogen dioxide (NO2), as a precursor of tropospheric ozone and fine particulate matter, plays a significant role in atmospheric chemistry research and air pollution control. However, the uneven ground monitoring stations and low temporal resolution of polar-orbiting satellites set challenges for accurately assessing near-surface NO2 concentrations. To address this issue, a spatiotemporal refined NO2 retrieval model was established for China using the geostationary satellite Himawari-8. The spatiotemporal characteristics of NO2 were analyzed and its contribution factors were explored. Firstly, seven Himawari-8 channels sensitive to NO2 were selected by using the forward feature selection based on information entropy. Subsequently, a 2DCNN-LSTM network model was constructed, incorporating the selected channels and meteorological variables as retrieval factors to estimate hourly NO2 in China from March 2018 to February 2020 (with a resolution of 0.05°, per hour). The performance evaluation demonstrates that the full-channel 2DCNN-LSTM model has good fitting capability and robustness (R2 = 0.74, RMSE = 10.93), and further improvements were achieved after channel selection (R2 = 0.87, RMSE = 6.84). The 10-fold cross-validation results indicate that the R2 between retrieval and measured values was above 0.85, the MAE was within 5.60, and the RMSE iwas within 7.90. R2 varied between 0.85 and 0.90, showing better validation at mid-day (R2 = 0.89) and in spring and fall transition seasons (R2 = 0.88 and R2 = 0.90). To investigate the cooperative effect of meteorological factors and other air pollutants on NO2, statistical methods (beta coefficients) were used to test the factor interpretability. Meteorological factors as well as other pollutants were analyzed. From a statistical perspective, PM2.5, boundary layer height, and O3 were found to have the largest impacts on near-surface NO2 concentrations, with each standard deviation change in these factors leading to 0.28, 0.24, and 0.23 in standard deviations of near-surface NO2, respectively. The findings of this study contribute to a comprehensive understanding of the spatiotemporal distribution of NO2 and provide a scientific basis for formulating targeted air pollution policies. [ABSTRACT FROM AUTHOR]

Long‐range transport and vertical distribution of aerosols are important factors for assessing the uncertainty in aerosol radiative forcing. This paper reveals the vertical distribution and trends of aerosol optical properties in China using 15 years of Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) data. The Hybrid Single‐Particle Lagrangian integrated trajectory model was used to analyze the transport and trends of aerosols in the layer with the highest occurrence frequencies of dust, polluted dust, polluted continental and elevated smoke aerosols. The results indicated that (a) there were significant regional and seasonal differences in the vertical distribution of aerosols. The aerosol optical depth (AOD) trend in a given region depends on the changes in the aerosol type with the highest frequency and the layer corresponding to the largest AOD in the vertical profile. The frequency of polluted dust aerosols was the highest in Beijing‐Tianjin‐Hebei (BTH) and Central China. The considerable decrease in the 0–2 km AOD led to a significant trend of the column AOD. (b) The changes of AOD and main aerosol types in a region are also affected by the changes of aerosol sources and long‐range transport pathways. In the BTH, dust aerosols originated from the Mongolian Plateau, accounting for 57.88% of the total trajectories. The Pearl River Delta was dominated by elevated smoke aerosols, with trajectories mainly originating from the Myanmar and Vietnam, accounting for 27.38% and 29.59%, respectively. The trend of 15‐year backward trajectories of dust aerosols on the Tibetan Plateau indicated that the trajectory from India is increasing. Plain Language Summary: Aerosols are one of the main factors affecting global climate change, influencing global radiation through direct and indirect effects. Long‐range transport aerosols and their vertical distribution are important factors for assessing the uncertainty in aerosol radiative forcing. At present, there is limited research on the vertical distribution and transport pathways of different types of aerosols in multiple regions over China. Considering that aerosols in different regions of China exhibit large vertical distribution and transport differences, we revealed the vertical distribution and trends of aerosol optical properties in China using 15 years of Cloud‐Aerosol Lidar with Orthogonal Polarization data. The Hybrid Single‐Particle Lagrangian Integrated Trajectory model was used to analyze the transport and trends of different aerosols. We found significant regional and seasonal differences in the vertical distributions of aerosols. The changes in the aerosol optical depth and main aerosol types in a given region were also significantly affected by the changes in aerosol transport sources and transport pathways. This study provides comprehensive insight into the vertical distribution and transport characteristics of different types of atmospheric aerosols in the Chinese region over the last 15 years, which is very important to further evaluation of the impact of different aerosol types on the radiative effects and the environment. Key Points: The vertical distribution of aerosol optical properties exhibits notable seasonal and regional differences using 15 years of Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) dataThe trend of total aerosol optical depth (AOD) mainly depends on the joint changes of the highest frequency aerosol and the maximum AOD layer in a vertical profileThe changes of AOD and main aerosol types in a region are also affected by the changes of aerosol transport sources and transport pathways [ABSTRACT FROM AUTHOR]

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. Key Points: Hourly PM10 in China was estimated using interpretable deep forest and FY‐4A top‐of‐the‐atmosphere reflectance, and the daily and monthly mean R2 were 0.82 and 0.97Excluding dust weather periods, the areas with high PM10 values in China were mainly in cities and suburbs related to human activitiesThe contribution of long‐range transport dust and local pollution in China to PM10 was both important during haze periods [ABSTRACT FROM AUTHOR]

Satellite remote sensing aerosol optical depth (AOD) and meteorological elements were employed to invert PM2.5 (the fine particulate matter with a diameter below 2.5 µm) in order to control air pollution more effectively. This paper proposes a restricted gradient-descent linear hybrid machine learning model (RGD-LHMLM) by integrating a random forest (RF), a gradient boosting regression tree (GBRT), and a deep neural network (DNN) to estimate the concentration of PM2.5 in China in 2019. The research data included Himawari-8 AOD with high spatiotemporal resolution, ERA5 meteorological data, and geographic information. The results showed that, in the hybrid model developed by linear fitting, the DNN accounted for the largest proportion, and the weight coefficient was 0.62. The R2 values of RF, GBRT, and DNN were reported as 0.79, 0.81, and 0.8, respectively. Preferably, the generalization ability of the mixed model was better than that of each sub-model, and R2 (determination coefficient) reached 0.84, and RMSE (root mean square error) and MAE (mean absolute error) were reported as 12.92 and 8.01 µgm-3 , respectively. For the RGD-LHMLM, R2 was above 0.7 in more than 70 % of the sites and RMSE and MAE were below 20 and 15 µgm-3 , respectively, in more than 70 % of the sites due to the correlation coefficient having a seasonal difference between the meteorological factor and PM2.5. Furthermore, the hybrid model performed best in winter (mean R2 was 0.84) and worst in summer (mean R2 was 0.71). The spatiotemporal distribution characteristics of PM2.5 in China were then estimated and analyzed. According to the results, there was severe pollution in winter with an average concentration of PM2.5 being reported as 62.10 µgm-3. However, there was only slight pollution in summer with an average concentration of PM2.5 being reported as 47.39 µgm-3. The period from 10:00 to 15:00 LT (Beijing time, UTC + 8 every day is the best time for model inversion; at this time the pollution is also high. The findings also indicate that North China and East China are more polluted than other areas, and their average annual concentration of PM2.5 was reported as 82.68 µgm-3. Moreover, there was relatively low pollution in Inner Mongolia, Qinghai, and Tibet, for their average PM2.5 concentrations were reported below 40 µgm-3. [ABSTRACT FROM AUTHOR]

Due to urbanization and industrialization, PM 2.5 (particulate matter with a diameter less than 2.5 μm) pollution has become a serious environmental problem. The low spatial resolution and insufficient coverage of PM 2.5 observation stations affect research on pollution causes and human health risks. With the launch of FY-4A, new generation of Chinese geostationary weather satellites, it is possible to obtain PM 2.5 with high temporal and spatial resolution covering all China. In this study, FY-4A top-of-the-atmosphere reflectance data, meteorological factors, and geographic information were input into the deep forest (DF) model to obtain the hourly PM 2.5 in China. The samples based 10-fold cross validation of DF with an hourly R2 of 0.83–0.88, and the root mean square error is 8.81–14.7 μg/m3, while the R2 of the 10-fold cross validation result based on sites was 0.77. The monthly (R2 = 0.98) and seasonal (R2 = 0.99) estimated results showed high consistency with the observations. Feature importance showed that the contribution of estimated features to the model varies with regions and seasons. Estimation results indicated the substantial spatiotemporal differences in PM 2.5 , and pollution was the highest between 09:00–10:00 and then gradually decreased. Regions with highest pollution of PM 2.5 in China were mainly distributed in the Tarim Basin and Central China. The pollution assessment results in China indicated that: 1) In more than 80% of the winter days PM 2.5 was higher than the World Health Organization interim target 3 (37.5 μg/m3); 2) The bimodal distribution of PM 2.5 indicated that there are obvious differences in pollution between cities and suburbs; 3) In autumn and winter, the regions where population-weighted PM 2.5 was higher than IT-3 were mainly in Beijing-Tianjin-Hebei, Central China, Guanzhong Plain, Sichuan Basin, and Yangtze River Delta. Our results showed that FY-4A has advantages of high resolution and coverage and thus shows great potential for estimating pollutants. • FY-4 TOAR data were used to estimate PM 2.5 covering the whole of China. • Deep forest model performed well, with an R2 of 0.83–0.88 and RMSE of 8.81–14.7 μg/m3. • Under different conditions, the contribution of features to the model changes. • PM 2.5 concentration showed bimodal distribution characteristics. • PM 2.5 on more than 80% of days in winter was higher than the WHO interim target 3. [ABSTRACT FROM AUTHOR]

PM 2.5 (fine particulate matter with aerodynamics diameter <2.5 μm) is the most important component of air pollutants, and has a significant impact on the atmospheric environment and human health. Using satellite remote sensing aerosol optical depth (AOD) to explore the hourly ground PM 2.5 distribution is very helpful for PM 2.5 pollution control. In this study, Himawari-8 AOD, meteorological factors, geographic information, and a new deep forest model were used to construct an AOD-PM 2.5 estimation model in China. Hourly cross-validation results indicated that estimated PM 2.5 values were consistent with the site observation values, with an R2 range of 0.82–0.91 and root mean square error (RMSE) of 8.79–14.72 μg/m³, among which the model performance reached the optimum value between 13:00 and 15:00 Beijing time (R2 > 0.9). Analysis of the correlation coefficient between important features and PM 2.5 showed that the model performance was related to AOD and affected by meteorological factors, particularly the boundary layer height. Deep forest can detect diurnal variations in pollutant concentrations, which were higher in the morning, peaked at 10:00–11:00, and then began to decline. High-resolution PM 2.5 concentrations derived from the deep forest model revealed that some cities in China are seriously polluted, such as Xi 'an, Wuhan, and Chengdu. Our model can also capture the direction of PM 2.5 , which conforms to the wind field. The results indicated that due to the combined effect of wind and mountains, some areas in China experience PM 2.5 pollution accumulation during spring and winter. We need to be vigilant because these areas with high PM 2.5 concentrations typically occur near cities. [Display omitted] • The deep forest model effectively captured the hourly PM 2.5 , with R2 of 0.82–0.91. • Meteorological factors significantly affected the Deep Forest model performance. • Estimated PM 2.5 reflected the pollution condition in city-level areas well. • Seasonal distribution of heavy pollution was related to the wind field and terrain. [ABSTRACT FROM AUTHOR]

Rapid urbanization and industrialization in China had led to increased pollutants emission. PM 10 is one of the main components of air pollutants, which significantly impacts human health, environment, and regional or global climate. In this study, a new machine learning deep forest (DF) model was used to construct the aerosol optical depth (AOD) and near-ground PM 10 concentration (AOD-PM 10) model. The DF model combines the advantages of deep neural networks and tree models, which can provide model interpretability. Combined with the Himawari-8 AOD, meteorological, and auxiliary factors, the hourly PM 10 concentration in China (spatial resolution: 0.05 × 0.05°) was obtained. The results show that AOD has the highest contribution to the importance of features in the AOD-PM 10 model, accounting for approximately 13.5%, and the contributions of boundary layer height, temperature, and relative humidity to the importance of features were 11%, 8.6%, and 7%, respectively. A 10-fold cross-validation was used to evaluate the performance of the model. The hourly cross validation results from 09:00 to 16:00 (Beijing time) show that the R2 range was 0.82–0.88, and the root mean square error and absolute mean error were 18.55–23.12 μg/m³ and 11.54–16.82 μg/m³, respectively. The R2 values of daily, monthly, seasonal, and annual average PM 10 estimated by the model were 0.87, 0.91, 0.94, and 0.94, respectively. The areas with high PM 10 concentrations are mainly in northern China, especially in the North China Plain, and the peak value of daily average PM 10 can reach 91 μg/m³; the Intraday variation of PM 10 in southern China ranges from 67 μg/m³ to 72 μg/m³. A large-scale dust weather process was analyzed. Based on the AOD-PM 10 model, the contribution of long-range transport dust to PM 10 in China and Northern China were 25.6% and 38.1%, respectively. The PM 10 measured by the station and estimated by the DF model indicated good consistency. • An interpretable deep forest model was used to estimate the hourly PM 10 in China. • A 10-fold cross validation showed that the R2 of hourly PM 10 was 0.82–0.88. • The R2 values of monthly and seasonal average PM 10 were 0.91 and 0.94, respectively. • AOD contributed highest (∼13.5%) to the importance of features in the model. • The contribution of long-range transport dust to PM 10 in northern China was 38.1%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Carbon monoxide (CO) has notable effects on the atmospheric environment and human health. This study used Himawari-8 top-of-atmosphere radiation (TOAR) data, meteorological factors, and geographic information, combined with an interpretable deep forest (DF) model, to obtain high-spatiotemporal resolution near-surface CO concentrations (temporal resolution: 1 h; spatial resolution: 0.05°) in China from September 2015 to August 2021 and reveal the distribution and variation in city-level CO hotspots. Based on the feature importance method and the spectral absorption characteristics of pollutants, the optimal combination of Himawari-8 band for the TOAR-CO model was selected. The performance comparison of five machine learning models showed that the DF model was the most performant. The hourly average R2 value (using 10-fold cross-validation) of the TOAR-CO model ranged from 0.72 to 0.78 (10:00 to 15:00, Beijing time); the R2 values of daily, monthly, and annual averages were 0.79, 0.97 and 0.98, respectively. The feature importance of the model varied in the different seasons and regions, with TOAR and meteorological factors had significantly contributions to the model. During the study period, the CO concentration in most areas of China showed a decreasing trend, with an average decline rate of 7.2%·yr−1. Despite CO being a stable, long-lived gas, the TOAR-CO model still captured obvious intraday variation in CO, which reached a peak of 0.89 mg/m3 at 10:00 and dropped to 0.69 mg/m3 at 15:00. The spatiotemporal distribution of estimated CO was basically consistent with satellite and reanalysis data. The TOAR-CO model predictions reflected city-level CO pollution, and the distribution of CO hotspots was closely related to industrial structure and human activities. During the COVID-19 pandemic, CO concentration significantly dropped after the 4th week of control. The CO concentrations in the hotspot cities decreased more significantly, by over 50%. The research results provide some supports for the assessment of CO fine-scale pollution and related research. [Display omitted] • Using an interpretable deep forest model to obtain ground fine-scale CO in China. • The contribution of input features to the model existed spatiotemporal differences. • Despite the stable nature of CO, but the model still captured its diurnal variation of 22.5%. • CO city-level hotspots were mainly distributed in areas with frequent human activities. • During the COVID-19 control period, CO hotspots in China had a greater decline. [ABSTRACT FROM AUTHOR]

PM 2.5 (fine particulate matter with aerodynamics diameter <2.5 μm) is the most important component of air pollutants, and has a significant impact on the atmospheric environment and human health. Using satellite remote sensing aerosol optical depth (AOD) to explore the hourly ground PM 2.5 distribution is very helpful for PM 2.5 pollution control. In this study, Himawari-8 AOD, meteorological factors, geographic information, and a new deep forest model were used to construct an AOD-PM 2.5 estimation model in China. Hourly cross-validation results indicated that estimated PM 2.5 values were consistent with the site observation values, with an R 2 range of 0.82-0.91 and root mean square error (RMSE) of 8.79-14.72 μg/m³, among which the model performance reached the optimum value between 13:00 and 15:00 Beijing time (R 2  > 0.9). Analysis of the correlation coefficient between important features and PM 2.5 showed that the model performance was related to AOD and affected by meteorological factors, particularly the boundary layer height. Deep forest can detect diurnal variations in pollutant concentrations, which were higher in the morning, peaked at 10:00-11:00, and then began to decline. High-resolution PM 2.5 concentrations derived from the deep forest model revealed that some cities in China are seriously polluted, such as Xi 'an, Wuhan, and Chengdu. Our model can also capture the direction of PM 2.5 , which conforms to the wind field. The results indicated that due to the combined effect of wind and mountains, some areas in China experience PM 2.5 pollution accumulation during spring and winter. We need to be vigilant because these areas with high PM 2.5 concentrations typically occur near cities., (Copyright © 2022 Elsevier Ltd. All rights reserved.)