Kaolinite clay mineral with a layered silicate structure is an abundant resource in China. Due to its advantages of excellent stability, high specific surface area and environmental friendliness, kaolinite is widely used in environment decontamination. By using kaolinite as a carrier, the photocatalytic technology in pure photocatalysts of poor activities, narrow spectral responses, and limited electron transport can be overcome, and the nano-Ag disinfectant’s limitation of the growth and aggregation of nanoparticles is released. Moreover, pure kaolinite used as an adsorbent shows poor surface hydroxyl activity and low cation exchange, leading to the poor adsorption selectivity and easy desorption of heavy metals. Current modification methods including heat treatment, acid modification, metal modification, inorganic salt modification, and organic modification are carried out to obtain better adsorption performance. This review systematically summarizes the application of kaolinite-based nanomaterials in environmental decontamination, such as photocatalytic pollutant degradation and disinfection, nano silver (Ag) disinfection, and heavy metal adsorption. In addition, applications on gas phase pollutant, such as carbon dioxide (CO₂), capture and the removal of volatile organic compounds (VOCs) are also discussed. This study is the first comprehensive summary of the application of kaolinite in the environmental field. The review also illustrates the efficiency and mechanisms of coupling naturally/modified kaolinite with nanomaterials, and the limitation of the current use of kaolinite.

Fine particulate matter (PM_2.5) is a major pollutant in Guanzhong Urban Agglomeration (GUA) during the winter, and GUA is one of China’s regions with the highest concentrations of PM_2.5. Daily surface PM_2.5 maps with a spatial resolution of 1 km × 1 km can aid in the control of PM_2.5 pollution. Thus, the Random Forest and eXtreme Gradient Boosting (RF-XGBoost) model was proposed to fill the missing aerosol optical depth (AOD) at the station scale before accurately estimating ground-level PM_2.5 using the recently released MODIS AOD product derived from Multi-Angle Implementation of Atmospheric Correction (MAIAC), high density meteorological and topographic conditions, land-use, population density, and air pollutions. The RF-XGBoost model was evaluated using an out-of-sample test, revealing excellent performance with a coefficient of determination (R²) of 0.93, root-mean-square error (RMSE) of 12.49 μg/m³, and mean absolution error (MAE) of 8.42 μg/m³. The result derived from the RF-XGBoost model indicates that the GUA had the most severe pollution in the winter of 2018 and 2019, owing to the burning of coal for heating and unfavorable meteorological circumstances. Over 90% of the GUA had an annual average PM_2.5 concentrations decrease of 3 to 7 μg/m³ in 2019 compared to the previous year. Nevertheless, the air pollution situation remained grim in the winter of 2019, with more than 65% of the study area meeting the mean PM_2.5 values higher than 35 μg/m³ and the maximum reaching 95.57 μg/m³. This research would be valuable for policymakers, environmentalists, and epidemiologists, especially in urban areas.

Eutrophication is a significant factor that damages the water ecosystem’s species balance. The total phosphorus (TP) concentration is a vital water quality indicator in assessing surface water eutrophication. This paper predicts the spatial distribution of TP concentration using remote sensing, measured data, and the partial least squares regression (PLSR) method. Based on the correlation analysis, the models were built and tested using the TP concentration and Sentinel-2 Multispectral Instrument (MSI) and Landsat-8 Operational Land Imager (OLI) image spectra. The results demonstrated that the best technique based on band combinations of the Sentinel-2 and Landsat-8 images achieved good precision. The coefficient of determination (R²), root mean square error of prediction (RMSEP), and residual prediction deviation (RPD) were 0.771, 0.023 mg/L, and 2.086 for Sentinel-2 images and 0.630, 0.032 mg/L, and 1.644 for Landsat-8 images, respectively. The TP concentration maps were interpolated using the inverse distance weighting method, and the inversion results obtained from the images were in good agreement. The western and northwestern regions of Taihu Lake, where significant cyanobacterial blooms occurred, had TP concentrations greater than 0.20 mg/L; nevertheless, the central and eastern regions had amounts ranging from 0.05 to 0.20 mg/L. In order to prove the extensibility of the model, the optimal algorithm was applied to the Sentinel-2 and Landsat-8 images in 2017. The optimal algorithm based on Landsat-8 images has a better verification effect (RMSEP = 0.027 mg/L, and R = 0.879 for one Landsat-8 image), and the optimal algorithm based on Sentinel-2 images has moderate verification effect (RMSEP = 0.054 mg/L and 0.045 mg/L, and R = 0.771 and 0.787 for two Sentinel-2 images). The interpolation and inversion maps are in good agreement, indicating that the model is suitable for the Landsat-8 and Sentinel-2 images, which can be complementary for higher temporal resolutions. Monitoring water quality using multiple remote sensing images can provide the scientific basis for water quality dynamic monitoring and prevention in China.

As an indicator of grain safety, grain temperature data assumes great importance to the analysis of grain storage condition and decision-making of preventive measures such as ventilation and cooling. However, obtaining a thorough picture of grain temperature distribution via grain IoT with sensors deployed in the granary remains a challenge, given numerous data gaps across various areas due to an insufficient coverage of the sensor network that fails to encompass the entire granary. Interpolation of grain temperature data, in this regard, is able to fill in the ’unsensored’ areas that are vacant in the records of data. Yet, little literature is found in the frontier scholarship of grain temperature interpolation. To fill this noticeable niche, this study develops a novel data fusion interpolation model named CAMNN featuring an integration of Convolutional Neural Network (CNN), attention mechanism, and Multilayer Perceptron (MLP). CNN is used to capture local spatial features of the temperature data, the attention mechanism enables the location of key and sensitive temperature areas, and MLP is incorporated for deep feature fusion. Performances of the proposed model are evaluated in a bin granary located in Shaanxi, China, and further validated in a larger bin granary of different storage types situated in Ningxia, China. Comparative assessments are conducted with five machine learning and deep learning models. Results indicate that CAMNN outperforms the other models, with a MAE of 0.5251 and a MSE of 1.0881, demonstrating robust cross-context applicability across bin granaries varying in terms of sizes, storage types, and climatic zones.

With the access of large-scale new energy sources, fast frequency regulation resources of the power system are scarce. A variable parameter control method for charging pile load aggregator to participate in the fast frequency market is proposed in this paper. It is that charging pile load aggregator actively adjusts the active power according to the frequency of the local power system, and the response speed is within 0-2 seconds. In addition, the variable parameter control method proposed in this paper specifically controls the power of charging pile according to the speed and amplitude of frequency drop, which has strong pertinence and simple control scheme, and further improves the frequency stability of the power grid.