Xingyu Liu

This study utilized TROPOMI remote sensing data, MODIS remote sensing data, ground observation data, and other ancillary data to construct a high-resolution spatiotemporal distribution and evaluation of ground-level NO2 concentrations in the Beijing–Tianjin–Hebei (BTH) region using the Geographic Temporal Neural Network Weighted Regression (GTNNWR) model. Through this model, we obtained the daily distribution of ground-level nitrogen dioxide (NO2) concentrations in the Beijing–Tianjin–Hebei region at a resolution of 500 m for the period of 2019–2022. The research results exhibited higher accuracy and more detailed features compared to other models, enabling a more accurate reflection of the spatial distribution and temporal variations of ground-level NO2 concentrations in the region, while retaining more details and trends and excluding the influence of noisy data. Furthermore, we conducted an evaluation analysis considering important events such as public health incidents and the Winter Olympics. The results demonstrated that the GTNNWR model outperformed the Random Forest (RF), Convolutional Neural Network (CNN), and Geographic Neural Network Weighted Regression (GNNWR) models in performance metrics such as R2, RMSE, MAE, and MAPE, showcasing greater reliability when considering spatiotemporal heterogeneity and spatiotemporal non-stationarity. This study provides crucial data support and reference for atmospheric environmental management and pollution prevention and control in the Beijing–Tianjin–Hebei region.

The location and development of porphyry copper deposits is a key issue for the mining industry. In this study, the Gondwana metallogenic belt was chosen as the study area to compare multiple methods for extracting multi-source geological elements to maximize the accuracy of the datasets used for mining evaluation and to use them to assess porphyry copper mineability. By comparison, a support vector machine (SVM) with an overall classification accuracy of 97.6573% and a Kappa coefficient of 0.9806 was used to extract the lithological distribution of the study area. Spectral feature-enhanced principal component analysis (EPCA) was combined with ASTER images to extract alteration information, with significant improvements in spatial aggregation and overall area compared to other alteration extraction methods, while a hierarchical alteration interpolation method was proposed to overcome the limitations of relying solely on remote sensing images to obtain surface alteration information and qualitatively extend deep alteration information. In addition, by overlaying various geoscientific factors affecting copper mineralization and mining, a Pearson correlation analysis is carried out in conjunction with currently proven or mined copper occurrences, and a weight of evidence approach is used to classify the study area into four mineability classes, which is important for narrowing down potential target areas for mineral exploration and assessing their mining value while contributing to an in-depth understanding of the role of geological elements in mineralization and development.