Though land use regression (LUR) models have been widely utilized to simulate air pollution distribution, unclear spatial scale effects of contributing characteristic variables usually make results study-specific. In this study, LUR models for PM_2.5 in Houston Metropolitan Area, US were developed under scales of 100 m, 300 m, 500 m, 800 m, and 1000–5000 m with intervals of 500 m by employing the idea of statistically optimized analysis. Results show that the annual average PM_2.5 concentration in Houston was significantly influenced by area ratios of open space urban and medium intensity urban at a 100 m scale, as well as of high intensity urban at a 500 m scale, whose correlation coefficients valued −0.64, 0.72, and 0.56, respectively. The fitting degree of LUR model at the optimized spatial scale (adj. R² = 0.78) is obviously better than those at any other unified spatial scales (adj. R² ranging from 0.19 to 0.65). Differences of PM_2.5 concentrations produced by LUR models with best-, moderate-, weakest fitting degree, as well as ordinary kriging were evident, while the LUR model achieved the best cross-validation accuracy at the optimized spatial scale. Results suggested that statistical based optimized spatial scales of characteristic variables might possibly ensure the performance of LUR models in mapping PM_2.5 distribution.

As an extension of the traditional Land Use Regression (LUR) modelling, the generalized additive model (GAM) was developed in recent years to explore the non-linear relationships between PM_2.5 concentrations and the factors impacting it. However, these studies did not consider the loss of information regarding predictor variables. To address this challenge, a generalized additive model combining principal component analysis (PCA–GAM) was proposed to estimate PM_2.5 concentrations in this study. The reliability of PCA–GAM for estimating PM_2.5 concentrations was tested in the Beijing-Tianjin-Hebei (BTH) region over a one-year period as a case study. The results showed that PCA–GAM outperforms traditional LUR modelling with relatively higher adjusted R² (0.94) and lower RMSE (4.08 µg/m³). The CV-adjusted R² (0.92) is high and close to the model-adjusted R², proving the robustness of the PCA–GAM model. The PCA–GAM model enhances PM_2.5 estimate accuracy by improving the usage of the effective predictor variables. Therefore, it can be concluded that PCA–GAM is a promising method for air pollution mapping and could be useful for decision makers taking a series of measures to combat air pollution.

The harsh scientific research environment of Antarctic stations demands a reliable energy supply; however, traditional methods not only pose a challenge in supply but also harm the environment. Antarctic energy supply has become a new choice for energy development in Antarctica due to its abundant wind energy resources. Using ERA5 10 m wind field reanalysis data, we compared and analyzed the correlation (r) and Root Mean Square Error (RMSE) between some observation stations and reanalysis data, with correlations above 0.67 and root mean square error below 2.3. This indicates that the accuracy of the ERA5 data is suitable for resource assessment at stations in Antarctica. We assessed the wind energy potential of the Great Wall, Zhongshan, Kunlun and Taishan Stations. The results show that the annual distribution and long-term trend of wind energy at Taishan Station are the best, followed by the Great Wall, Zhongshan and Kunlun Stations. Taishan Station has stable wind direction and abundant wind energy, the average wind power density is 800 W/m², with an annual growth trend of 2.02 W/m²·yr⁻¹. The effective wind speed occurrence and energy level occurrence are generally above 90% and the coefficient of variation is generally below 0.8. The dominant direction of wind energy is northeast and the wind direction is stable, which is conducive to the development and utilization of wind energy.

Employee education structure, job satisfaction and organizational performance have always been important issues in the study of organizational behavior. It is also applicable in the field of education. This paper is based on the survey data of middle school and primary school teachers and the academic level data of students in district H, Beijing. By using the empirical research method of mediating effect analysis, this paper examines the mediating role of job satisfaction in the effect of teacher qualifications structure on education quality. The research shows that the qualifications structure and job satisfaction of junior middle school teachers have a positive impact on the education quality. Moreover, job satisfaction has a mediating effect on the influence of teacher qualifications structure on education quality. This means that during the secondary compulsory education stage, schools need to appropriately enhance the educational structure of teachers. In addition, the purpose of improving education quality can be achieved by improving teachers' job satisfaction.

In order to promote the sharing of teaching and training resources for deepsea navigation and improve the level of information management, in view of the spatial and confidential characteristics of deep-sea nautical data resources, the deep-sea nautical resources intelligent query and management system was designed and applicated in this paper. The online sharing and informatization management system of deep-sea nautical data resources has been established. The system can meet the needs of users to retrieve deep-sea nautical data online based on the spatial attribute information. At the same time, the process of borrowing and approving military secret-related materials through the system is strict, meeting the needs of military information security management and control. The system improves the use efficiency and management support efficiency of deep-sea nautical data resources. It can provide a certain reference for the management of navigation data resources and the construction of a co-construction and sharing system for classified resources.