Abstract: According to the technical solution of the present invention, a vertical mass concentration curve of fine aerosol component distribution can be obtained based on remote sensing test cooperation of a ground Lidar and a solar photometer, specifically, an internal mixing model and a normalized volume distribution model are constructed according to the light absorptivity and the water solubility of aerosol components, and separates out profiles of inorganic salt components, black carbon components, water-soluble organic matter, water-insoluble organic matter, fine aerosol water content components and the like to obtain corresponding vertical distribution. A new thought and development direction can be provided for remote sensing inversion of Lidar and the like, and meanwhile, development of aerosol component spectrums is promoted.

Abstract: A chimney detection method based on AI technology includes collecting a remote sensing chimney image dataset, divide it into a training set and a validation set, and enhance the data; inputting the dataset into the main network for feature extraction, and transmit it to the neck network to extract feature information; transmitting the feature information obtained by the neck network to the feature pyramid, performing up and down sampling for feature fusion, strengthening features through an explicit visual center and global attention mechanism, and obtaining the corresponding enhanced feature map; and inputting the enhanced feature map into the head network respectively, and obtain the detection result of the remote sensing chimney image.

Abstract: A system for online-coupled atmospheric chemical transport modeling and data assimilation is built using online coupling and secondary parallelization. In the prototype code of a nested air quality forecasting model system, a parallel data assimilation framework routine is introduced. The system includes observation modules, model modules, and assimilation modules. The observation module is responsible for flexible access and preprocessing of various component-type observation data. The model module handles the model integration of initial fields, involving calculations of physical and chemical processes. The assimilation module performs analysis assimilation of model state variables. This system meets the requirements for coordinated assimilation of multiple chemical component variables, simultaneous introduction and flexible combination of various types of observation data, and effective handling of non-linear and non-Gaussian distribution issues in chemical assimilation.

Abstract: A method and a system for reconstructing aerosol chemical components based on CNN-BiLSTM-BO, including collecting multi-source environmental observation data through observation equipment, preprocessing and extracting key characteristic variables. The pre-treated multi-source environmental observation data are input into the CNN-BILSTM model for feature analysis, and the CNN-BiLSTM hyperparameters are adjusted by Bayesian optimization algorithm to generate a reconstructed model of aerosol chemical components. After verifying the performance and stability of the reconstructed model, the predicted results of the chemical components of the aerosol are output.

Abstract: According to the technical solution of the present invention, a vertical mass concentration curve of fine aerosol component distribution can be obtained based on remote sensing test cooperation of a ground Lidar and a solar photometer, specifically, an internal mixing model and a normalized volume distribution model are constructed according to the light absorptivity and the water solubility of aerosol components, and separates out profiles of inorganic salt components, black carbon components, water-soluble organic matter, water-insoluble organic matter, fine aerosol water content components and the like to obtain corresponding vertical distribution. A new thought and development direction can be provided for remote sensing inversion of Lidar and the like, and meanwhile, development of aerosol component spectrums is promoted.

Abstract: A method for inverting aerosol components using a LiDAR ratio and a depolarization ratio, includes: S1. identifying sand dust, a spherical aerosol and a mixture of the sand dust and the spherical aerosol based on a depolarization ratio; S2. calculating a proportion of the sand dust in the mixture of the sand dust and the spherical aerosol; and S3. identifying soot and a water-soluble aerosol in the spherical aerosol based on a LiDAR ratio. In the present disclosure, only a wavelength with a polarization channel is needed, to identify the aerosol components, achieving high accuracy with low detection costs.

Abstract: The present disclosure discloses a method for inverting aerosol components using a LiDAR ratio and a depolarization ratio, including: S1. identifying sand dust, a spherical aerosol and a mixture of the sand dust and the spherical aerosol based on a depolarization ratio; S2. calculating a proportion of the sand dust in the mixture of the sand dust and the spherical aerosol; and S3. identifying soot and a water-soluble aerosol in the spherical aerosol based on a LiDAR ratio. In the present disclosure, only a wavelength with a polarization channel is needed, to identify the aerosol components, achieving high accuracy with low detection costs.