The planetary boundary layer height (PBLH) has a significant impact on the energy and material exchange in the atmosphere. The traditional gradient method (GM) determines the PBLH based on the gradient change of the aerosol profile. It is susceptible to the effect of complex atmospheric conditions, which leads to uncertainties in the estimation of PBLH. Here, a random forest (RF) algorithm that considers the vertical distribution of aerosols is proposed to find the PBLH under complex atmospheric conditions. The height of the three minimum local peaks of the range correction signal profile and 7 other variables, such as aerosol layer number (ALN), relative humidity, solar radiation, and other meteorological parameters, from January 2017 to December 2021 is used as RF model input. The radiosonde estimated PBLH (PBLHRS) is used as reference value. The sensitivity analysis indicates that the relative error of RF-estimated PBLH (PBLHRF) is smaller than that of GM-estimated PBLH (PBLHGM), and it decreases with an increase in aerosol optical depth. Moreover, RF achieves good performance under different atmospheric conditions. It can effectively overcome the effects of complex atmospheric conditions in PBLH estimation. Based on the correlation analysis, it is found that the estimation accuracy of the RF algorithm is greatly improved compared with the GM. The correlation coefficient between the PBLHRFand the PBLHRSreaches 0.8, which is much larger than that of the PBLHGM(0.47). Finally, long-term PBLHRFanalysis show that there are obvious diurnal and seasonal variations of PBLH. It increases and then decreases from early morning to late evening. It is highest in summer and lowest in winter. Overall, RF can effectively overcome the shortcomings of traditional GM and has high accuracy and robustness for various atmospheric conditions. The findings obtained here have great potential for lidar application in obtaining reliable PBLH estimations.

Vertical distributions of temperature and humidity are two essential factors for understanding the atmospheric structure, extreme weather events, and regional and global climate. The ground-based microwave radiometer (MWR), which acts as a passive sensor and operates continuously under all weather conditions, has an irreplaceable role in measuring the vertical information of the temperature and water content in the atmosphere. In this letter, we proposed a four-layer back-propagation neural network (BPNN) method to retrieve temperature and relative humidity (RH) profiles from the bright temperature measured by the MWR. In contrast to the traditional BPNN, this method has greater advantages in dealing with the problems of overfitting, gradient disappearance, and gradient explosion in vertical atmospheric retrieval. By adding dropout layers, it can also help to describe the nonlinear relationships for RH profiles. Results showed that the performance of the four-layer BPNN method was better than the quadratic regression (QR, provided by MWR manufacturer) method under both cloud and cloud-free conditions. Compared with measurements of radiosonde data, root-mean-square error of temperature and RH, BPNN achieves 1.88 K and 19.30% under cloud conditions and 2.03 K and 15.10% under cloud-free conditions, respectively, whereas the corresponding values by using the QR method were only 3.07 K and 24.28% under cloud conditions and 4.14 K and 18.96% under cloud-free conditions, respectively. Temperature and RH profiles retrieval with high precision have increased the efficiency of the MWR observations and provided a data foundation for further atmospheric climate research.

Particulate pollution threatens the ecological environment, air quality, and public health. Therefore, it has become an increasing concern for the public and governments in recent decades. In this study, a full coverage PM2.5(aerodynamic diameter of less than 2.5 microns) estimation strategy is proposed based on spatiotemporal machine learning approaches including the Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) and Random Forest (RF). The RF estimates PM2.5by considering the features of a single pixel, while the introduction of the CNN-LSTM (size of 7 × 7 × 4) assists in exploiting the spatiotemporal correlation of surrounding pixel features. Compared with linear models and empirical spatiotemporal weight methods, our CNN-LSTM+RF avoids the uncertainty and complexity owing to actual measurements of the surrounding sites. In addition, full coverage is achieved using both satellite data and reanalysis data. Results showed that, the Root Mean Squared Error (RMSE) and coefficient of determination (R2) of the CNN-LSTM+RF were 12.790 μg/m3and 0.910, respectively, in sample-based Cross-Validation (CV). From the perspective of the season, the best performance of the CNN-LSTM+RF was found in autumn (R2of 0.915) and the lowest was in summer (R2of 0.848). In the meantime, for the different regions of China, the CNN-LSTM+RF also showed stable performance. The proposed method can generate high-precision continuous PM2.5distribution maps that provide beneficial support for improving environmental and public health, and provide a reference for using deeper networks.

The vertical distribution of fine particles with a diameter$ < 2.5~\mu \text{m}$(PM2.5) plays an important role in understanding the transport of air pollution and in making decisions regarding the prevention and control of regional air pollution. However, the studies of the vertical distribution of PM2.5were limited by the lack of monitoring data obtained with vertical sampling strategies. The lidar system can obtain the aerosol profile, which provides the possibility to measure PM2.5profile. Here, the vertical distributions of PM2.5concentrations were investigated on the basis of lidar data from January 2014 to October 2015. Linear regression, improved linear regression, and random forest (RF) models were used to retrieve the PM2.5concentration profile from lidar data. The models were built based on the relationship among extinction coefficient (EC), temperature ($T$), relative humidity (RH), and surface PM2.5mass concentration. Comparison of the estimated and observed PM2.5showed that the RF model exhibited the best inversion effect. The correlation coefficient reached 0.75, and the root mean absolute error (RMAE) and root mean square error (RMSE) were 3.94 and 21.1$\mu \text{g}/\text{m}^{3}$, respectively. Error analysis indicated that the estimated PM2.5retrieved using the linear and improved linear models (ILMs) was smaller than the observed PM2.5when EC was less than 0.7 km−1, whereas PM2.5was evidently overestimated during winter pollution days. The reason might be that the effects of$T$and RH were inaccurately considered. Finally, the seasonal variation of the PM2.5profiles was investigated. Results indicated that the mass concentration of PM2.5was relatively large within 0.5–1.5 km, with a maximum of 60$\mu \text{g}/\text{m}^{3}$. The findings obtained here provide guidance for PM2.5vertical observation and regional pollutant transport.

Satellite observation is an effective way of obtaining global aerosol information. The study focuses on developing a new scheme to apply the traditional dark target (DT) method to the advanced Medium Resolution Spectral Imager (MERSI II), which is a part of the Chinese Fengyun-3-D satellite. Compared with the Moderate Resolution Imaging Spectroradiometer (MODIS), MERSI II shows higher ratios between red (0.65$\mu \text{m}$) and near-infrared ($2.13~\mu \text{m}$) bands in surface reflectance estimation and the green band ($0.55~\mu \text{m}$) that is more sensitive to cloud screening. Aerosol optical depth (AOD) is retrieved from earlier MERSI II observations by following the adapted DT method over land in Asia in 2018. Overall, AOD from MERSI II has a good performance compared with ground-based measurements with an expected error (EE%) of 66.38% and$R^{2}$of 0.834, which is close to the MODIS EE% of 70.59% and$R^{2}$of 0.829. Both sensors slightly overestimate the AOD over heavy aerosol loading regions, but MERSI-II has larger retrieval area covering a wider swath than MODIS in heavy hazy areas. On a spatial scale, the MERSI II effectively reflects the AOD distribution pattern but tends to overestimate and underestimate AOD at low and high latitudes, respectively, when compared with MODIS. The MERSI II sensor shows good aerosol detection potential, and the DT algorithm can be applied. MERSI II will provide important observation data on climate change and atmospheric pollution for the investigations in the future.