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Accurate inversion of PM2.5 concentration is crucial for haze management. Currently commonly used inversion methods cannot accurately invert the concentration in non-site areas, so this paper proposes a PM2.5 concentration inversion method based on an integrated learning model. The method utilises the Top Atmospheric Reflectance (TOAR), observation angle and meteorological element data as input features, and screens the important features by Random Forest, and constructs an integrated inversion model using XGBoost and LightGBM. The results show that the model built by TOAR improves R2 by 2.9% and reduces RMSE and MAE by 2.67 and 1.45, respectively, compared with the AOD-based model, and our model has an inversion accuracy of 0.95, which is better than other models. We used the model to estimate and analyse the historical PM2.5 concentration changes at Huaihe station in Tianjin, China, and the results were consistent with the trend of the actual PM2.5 concentration distribution, and it is clear that the proposed model has a high inversion accuracy.

The accurate estimation of near-ground ozone (O3) concentration is of great significance to human health and the ecological environment. In order to improve the accuracy of estimating ground-level O3 concentration, this study adopted a deep forest algorithm to construct a model for estimating near-ground O3 concentration. It is pointed out whether input data on particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations also affect the estimation accuracy. The model first uses the multi-granularity scanning technique to learn the features of the training set, and then it adopts the cascade forest structure to train the processed data, and at the same time, it adaptively adjusts the number of layers in order to achieve a better performance. Daily near-ground O3 concentrations in Shijiazhuang were estimated using satellite O3 column concentrations, ground-based PM2.5 and NO2 concentration data, meteorological element data, and elevation data. The deep forest model was compared with six models, namely, random forest, CatBoost, XGBoost, LightGBM, Decision Tree, and GBDT. The R-squared (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) of the proposed deep forest model were 0.9560, 13.2542, and 9.0250, respectively, which had significant advantages over other tree-based regression models. Meanwhile, the model performance was improved by adding NO2 and PM2.5 features to the model estimations, indicating the necessity of synergistic observations of NO2, PM2.5, and O3. Finally, the seasonal distribution of O3 concentrations in the Shijiazhuang area was plotted, with the highest O3 concentrations in the summer, the lowest in the winter, and the O3 concentration is in the middle of spring and autumn. [ABSTRACT FROM AUTHOR]

In the application of rotorcraft atmospheric environment detection, to reflect the distribution of atmospheric pollutants more realistically and completely, the sampling points must be spread throughout the entire three-dimensional space, and the cooperation of multiple unmanned aerial vehicles (multi-UAVs) can ensure real-time performance and increase operational efficiency. In view of the problem of coordinated detection by multi-UAVs, the region division and global coverage path planning of the stereo space to be detected are studied. A whale optimization algorithm based on the simulated annealing-whale optimization algorithm (SA-WOA) is proposed, which introduces adaptive weights with the Levy flight mechanism, improves the metropolis criterion, and introduces an adaptive tempering mechanism in the SA stage. Path smoothing is subsequently performed with the help of nonuniform rational B-spline (NURBS) curves. The comparison of algorithms using the eil76 dataset shows that the path length planned by the SA-WOA algorithm in this paper is 10.15% shorter than that of the WOA algorithm, 13.25% shorter than the SA planning result, and only 0.95% difference from the optimal path length in the dataset. From the perspective of planning time, its speed is similar to WOA, with a relative speed increase of 27.15% compared to SA, proving that the algorithm proposed in this paper has good planning performance. A hardware system platform is designed and built, and environmental gas measurement experiments were conducted. The experimental results indicate that the multi-UAV collaborative environment detection task planning method proposed in this paper has certain practical value in the field of atmospheric environment detection. [ABSTRACT FROM AUTHOR]

In view of problems of single monitoring information, poor flexibility and complicated wiring existed in present monitoring system for mine safety, a mobile monitoring system for mine safety based on ARM was designed. The system uses environment parameter acquisition module and USB camera to collect on-site environmental information and video information, adopts S3C6410 processor to pack the collected data and transmits the data packets to upper computer for display by WiFi, so as to control the movement of the monitoring equipment. The system can real-timely monitor areas with inconvenience into or potentially dangerous for person.