Forecasting air quality plays a crucial role in preventing and controlling air pollution. It is particularly significant for improving preparedness for heavily polluted weather conditions and ensuring the health and safety of the population. In this study, a novel deep learning model for predicting air quality spatio-temporal variations is introduced. The model, named graph long short-term memory with multi-head attention (GLSTMMA), is designed to capture the temporal patterns and spatial relationships within multivariate time series data related to air quality. The GLSTMMA model utilizes a hybrid neural network architecture to effectively learn the complex dependencies and correlations present in the data. The extraction of spatial features related to air quality involves the utilization of a graph convolutional network (GCN) to collect air quality data based on the geographical distribution of monitoring sites. The resulting graph structure is imported into a long short-term memory (LSTM) network to establish a Graph LSTM unit, facilitating the extraction of temporal dependencies in air quality. Leveraging a Graph LSTM unit, an encoder-multiple-attention decoder framework is formulated to enable a more profound and efficient exploration of spatio-temporal correlation features within air quality time series data. The research utilizes the 2019–2021 multi-source air quality dataset of Qinghai Province for experimental assessment. The results indicate that the model effectively leverages the impact of multi-source data, resulting in optimal accuracy in predicting six air pollutants.

In the Qinghai-Tibet Plateau region, operational deficiencies and limited maintenance capacities often impair automatic air quality monitoring stations. This results in frequent data omissions, compromising the reliability of environmental assessment data. Therefore, an effective data imputation method is required to address the gaps in observational records. Utilizing a Sequence-to-Sequence framework, we introduce a model termed Bidirectional Recurrent Imputation for Time Series-Attention-based Long Short-Term Memory (BRITS-ALSTM). The encoder of BRITS-ALSTM applies BRITS to integrate single-station historical characteristics with multi-station correlation features. Concurrently, the decoder employs LSTM within an attention mechanism to capitalize on previously observed data, thereby generating hourly imputations for missing air quality data values. The model was trained using six types of air quality data from 16 stations across Qinghai Province. Through localized testing and parameter optimization, BRITS-ALSTM achieved a reduction in mean relative error (MRE) by 74.88% compared to the baseline mean-filling approach. Additionally, ablation studies demonstrated an improvement in the coefficient of determination R-squared (R²) from 0.67 to 0.76, outperforming the standalone BRITS. Consequently, BRITS-ALSTM enhances the accuracy of air quality data evaluations in the Tibetan Plateau and offers an efficacious strategy for data imputation in elevated terrains.

Unmanned Aerial Vehicle (UAV) path planning is critical for ensuring flight safety and enhancing mission execution efficiency. This problem is typically formulated as a complex, multi-constrained, and nonlinear optimization task, often addressed using meta-heuristic algorithms. The Crested Porcupine Optimizer (CPO) has become an excellent method to solve this problem; however, the standard CPO has limitations, such as the lack of adaptive parameter tuning to adapt to complex environments, slow convergence, and the tendency to fall into local optimal solutions. To address these issues, this paper proposes an algorithm named QCPO, which integrates CPO with Q-learning to improve UAV path optimization performance. Q-learning is employed to adaptively adjust the key parameters of the CPO, thereby overcoming the limitations of traditional fixed-parameter settings. Inspired by the porcupine’s defense mechanisms, a novel audiovisual coordination strategy is introduced to balance visual and auditory responses, accelerating convergence in the early optimization stages. A refined position update mechanism is designed to prevent excessive step sizes and boundary violations, enhancing the algorithm’s global search capability. A B-spline-based trajectory smoothing method is also incorporated to improve the feasibility and smoothness of the planned paths. In this paper, we compare QCPO with four outstanding heuristics, and QCPO achieves the lowest path cost in all three test scenarios, with path cost reductions of 30.23%, 26.41%, and 33.47%, respectively, compared to standard CPO. The experimental results confirm that QCPO offers an efficient and safe solution for UAV path planning.

The acquisition of plant phenotypic traits is essential for selecting superior varieties, improving crop yield, and supporting precision agriculture and agricultural decision-making. Therefore, it plays a significant role in modern agriculture and plant science research. Traditional manual measurements of phenotypic traits are labor-intensive and inefficient. In contrast, combining 3D reconstruction technologies with autonomous vehicles enables more intuitive and efficient trait acquisition. This study proposes a 3D semantic reconstruction system based on an improved ORB-SLAM3 framework, which is mounted on an unmanned vehicle to acquire phenotypic traits in tomato cultivation scenarios. The vehicle is also equipped with the A * algorithm for autonomous navigation. To enhance the semantic representation of the point cloud map, we integrate the BiSeNetV2 network into the ORB-SLAM3 system as a semantic segmentation module. Furthermore, a two-stage filtering strategy is employed to remove outliers and improve the map accuracy, and OctoMap is adopted to store the point cloud data, significantly reducing the memory consumption. A spherical fitting method is applied to estimate the number of tomato fruits. The experimental results demonstrate that BiSeNetV2 achieves a mean intersection over union (mIoU) of 95.37% and a frame rate of 61.98 FPS on the tomato dataset, enabling real-time segmentation. The use of OctoMap reduces the memory consumption by an average of 96.70%. The relative errors when predicting the plant height, canopy width, and volume are 3.86%, 14.34%, and 27.14%, respectively, while the errors concerning the fruit count and fruit volume are 14.36% and 14.25%. Localization experiments on a field dataset show that the proposed system achieves a mean absolute trajectory error (mATE) of 0.16 m and a root mean square error (RMSE) of 0.21 m, indicating high localization accuracy. Therefore, the proposed system can accurately acquire the phenotypic traits of tomato plants, providing data support for precision agriculture and agricultural decision-making.

Air pollution, particularly fine particulate matter (PM_2.5) and ozone (O₃) pollution, poses serious challenges to environmental quality and sustainable development. The Tibetan Plateau, often described as the “Third Pole,” functions as a key ecological shield for China and exerts wide-reaching influence on global climate systems, hydrological cycles, and cross-regional pollution transport. To better clarify the driving mechanisms of air pollution in this sensitive region, we propose an integrated MRG–HSW framework, which, for the first time, systematically couples statistical modeling and trajectory analysis by combining multivariate regression, residual-based screening, and HYSPLIT–WCWT trajectory analyses. Taking Qinghai Province as a case study, ERA5 and GDAS1 reanalysis products were coupled with in situ monitoring to identify the relative contributions of local emissions and long-range atmospheric transport. The results show that, in low-elevation zones, PM_2.5 levels are largely governed by local anthropogenic activities (R² = 0.631–0.803), whereas O₃ concentrations respond more strongly to meteorological variability (R² = 0.529–0.779). At higher elevations, however, local explanatory factors weaken, and long-range transport from the Hexi Corridor, Qaidam Basin, and even South Asia becomes the dominant influence for both pollutants. Additional sensitivity tests confirm that the framework performs robustly under diverse meteorological and seasonal conditions. Collectively, this work not only establishes a transferable methodology for source attribution in plateau environments but also underscores the pivotal role of the Tibetan Plateau in sustaining regional air quality and global environmental stability.

Maize plant height and stem diameter can effectively reflect plant growth status and lodging resistance. Therefore, accurate measurement of these parameters enables precise monitoring of plant growth and provides data to support agricultural decision-making. LiDAR enables the acquisition of three-dimensional (3D) crop point clouds, providing a direct source of data for phenotypic parameter extraction. However, existing 3D point cloud acquisition systems for crop phenotyping are often costly and offer limited operational flexibility under field conditions. This study proposes a workflow for extracting key phenotypic parameters from individual field-grown maize plants by integrating 3D reconstruction with point cloud instance segmentation. First, field-grown maize plants are reconstructed in 3D using a LiDAR–SLAM system mounted on an unmanned ground vehicle (UGV). Because accurate segmentation of individual maize plants from field LiDAR point clouds is challenging, a density-adaptive strategy is incorporated into an instance segmentation network based on central-axis offset aggregation. Specifically, each point is projected toward the central axis of its corresponding plant, and local point density is used to guide individual-plant instance segmentation. Plant height and stem diameter are subsequently extracted automatically from the segmented point cloud of each plant and evaluated against manual measurements. Experimental results show that the density-adaptive instance segmentation method achieves an mAP50-95 of 77.84%, representing an improvement of 8.54%. The mean absolute errors (MAEs) are 7.52 cm for plant height and 1.02 mm for stem diameter. These results demonstrate that, under the tested grain-filling-stage field plots, the proposed method enables effective individual-plant segmentation and extraction of key phenotypic parameters. Given that validation was limited to two plots acquired on a single date, claims of broad field robustness should be interpreted within this experimental scope.