Spatial Interpolation Research Articles

Spatial interpolators for Delineating management zones to mitigate Mucuna pruriens in sugarcane plantations in the Eastern Amazon

Unmanned Aerial Vehicles (UAVs) have emerged as essential tools in precision agriculture, employing aerial photogrammetry concepts to aid producers in various decision-making processes. This study evauates different spatial interpolators to define management zones in sugarcane fields, aiming to control potential infestations by Mucuna pruriens. We collected images using the EbeeSQ UAV, equipped with a multispectral sensor, and calculated vegetation indices, including NDVI, SAVI, NDRE, and GNDVI. Analysis revealed that GNDVI yielded the most favorable results, with a mean value of 0.304 and a coefficient of variation of 11.747 %. Using regular and random sampling grids, we applied Ordinary Kriging (OK) and Support Vector Machine (SVM) interpolators to assess spatial variability across 13 survey zones. The results indicated a Degree of Spatial Dependence averaging 57.197 % and a Moran Index of 0.609, confirming moderate spatial dependence. Cross-validation showed that OK with random sampling outperformed other methods, achieving a Root Mean Square Error (RMSE) of 0.064 and a coefficient of determination (r2) averaging 0.347. Furthermore, the relationship between the Fuzzy Performance Index (averaging 0.069) and Normalized Classification Entropy (averaging 0.077) enabled the creation of management zone maps. These maps effectively identify distinct classes within the study areas, enhancing decision-making for producers in managing velvet bean weed during critical developmental phases.

Adaptive Spline Surface Fitting With Arbitrary Topological Control Mesh.

Reconstructing a spline surface from a given arbitrary topological triangle mesh is a fundamental and challenging problem in computer-aided design and engineering. This article introduces a novel surface fitting method utilizing G-NURBS capable of handling control meshes with arbitrary topologies. This method employs adaptive control point adjustment, guided by the geometric attributes of the input model, ensuring precise representation of sharp features such as edges and corners. Two primary strategies are employed: A parameter correspondence approach designed for sharp features and a control mesh iterative refinement technique that incorporates geometrical feature information. The proposed method has been tested and evaluated on various CAD models to demonstrate its effectiveness. This method can achieve higher fitting accuracy while faithfully preserving the geometrical features with fewer control points.

Foamed calcium sulfoaluminate cement with controlled porous network for daily or seasonal heat storage in ettringite

Calcium sulfoaluminate (CSA) cement-based materials have high ettringite content enables heat storage, enhancing the thermal inertia of buildings. This study aims to optimize the porous network of foamed CSA material in terms of porosity, permeability and mechanical strength so that they can be effective in building applications as supporting structures with heat storage capacity. To achieve these goals, a foaming process method using hydrogen peroxide (H2O2) and surfactant was used to control the porous network. The H2O2 and surfactant contents varied from 0.5% to 1.2% and 0.01% to 0.05%, respectively, and generated a wide range of material densities from 589 to 1184 kg/m3 - and hence a wide range of properties in terms of porosity (43 - 70%), permeability (2.6 10-13 - 7.1 10-12 m2) and compressive strength (0.8 -9.4 MPa). Then, an abacus correlating material properties, found by surface fitting, linking porosity, gas permeability and compressive strength, allowed the material to be optimized in accordance with the needs of each building application. The CSA paste consisting of 1% H2O2 and 0.03% surfactant, with 66% porosity, 4.1 10-12 m2 permeability, and 1.8 MPa compressive strength, was the optimal mix-design for solar storage box applications (for short-term storage). The CSA paste consisting of 0.5% H2O2 and 0.01% surfactant, with 43% porosity, 5.6 10-13 m2 permeability, 9.4 MPa compressive strength, appeared to be optimal for self-supporting wall storing solar heat (long-term storage).

Fretting dynamic contact analysis of planetary gear-bearing interference fit surface considering non-uniform interference

Abstract The planetary gear-bearing interference fit surface is prone to fretting slip under alternating torque, which can lead to fretting wear and fatigue failure. However, the current studies often ignore the influence of non-uniform interference on the stress and strain of the fit surface. In this paper, the force analysis of the planetary gear-bearing interference fit surface is carried out based on thick cylinders theory and planetary gear dynamics, and the fretting slip mechanism is explained in detail. Based on the assumption of plane stress, the equation for interference distribution in the circumferential direction of the fit surface is derived. On this basis, a three-dimensional fretting contact model considering non-uniform interference is established and solved by the conjugate gradient method (CGM). The results show that under the action of alternating torque, the fit surface interference is non-uniformly distributed in the circumferential direction, which shows a trend of increasing first and then decreasing. The fretting slip distance decreases first and then increases in the circumferential direction, but the fretting slip curve considering non-uniform interference shows a slip distance of nil in the range where the interference value exceeds the initial value. The model in this paper is more in line with the actual situation, and can provide more accurate theoretical calculation for the planetary gear-bearing interference fit design.

The global daily High Spatial–Temporal Coverage Merged tropospheric NO2 dataset (HSTCM-NO2) from 2007 to 2022 based on OMI and GOME-2

Abstract. Remote sensing based on satellites can provide long-term, consistent, and global coverage of NO2 (an important atmospheric air pollutant) as well as other trace gases. However, satellites often miss data due to factors including but not limited to clouds, surface features, and aerosols. Moreover, as one of the longest continuous observational platforms of NO2, the Ozone Monitoring Instrument (OMI) has suffered from missing data over certain rows since 2007, significantly reducing its spatial coverage. This work uses the OMI-based tropospheric NO2 (OMNO2) product as well as a NO2 product from the Global Ozone Monitoring Experiment-2 (GOME-2) in combination with machine learning (eXtreme Gradient Boosting – XGBoost) and spatial interpolation (data-interpolating empirical orthogonal function – DINEOF) methods to produce the 16-year global daily High Spatial–Temporal Coverage Merged tropospheric NO2 dataset (HSTCM-NO2; https://doi.org/10.5281/zenodo.10968462; Qin et al., 2024), which increases the average global spatial coverage of NO2 from 39.5 % to 99.1 %. The HSTCM-NO2 dataset is validated using upward-looking observations of NO2 (multi-axis differential optical absorption spectroscopy – MAX-DOAS), other satellites (the Tropospheric Monitoring Instrument – TROPOMI), and reanalysis products. The comparisons show that HSTCM-NO2 maintains a good correlation with the magnitudes of other observational datasets, except for under heavily polluted conditions (> 6 × 1015 molec.cm-2). This work also introduces a new validation technique to validate coherent spatial and temporal signals (empirical orthogonal function – EOF) and confirms that HSTCM-NO2 is not only consistent with the original OMNO2 data but in some parts of the world also effectively fills in missing gaps and yields a superior result when analyzing long-range atmospheric transport of NO2. The few differences are also reported to be related to areas in which the original OMNO2 signal was very low, which has been shown elsewhere but not from this perspective, further confirming that applying a minimum cutoff to retrieved NO2 data is essential. The reconstructed data product can effectively extend the utilization value of the original OMNO2 data, and the data quality of HSTCM-NO2 can meet the needs of scientific research.

An explainable spatial interpolation method considering spatial stratified heterogeneity

Spatial interpolation is essential for handling sparsity and missing spatial data. Current machine learning-based spatial interpolation methods are subject to the statistical constraints of spatial stratified heterogeneity (SSH), normally involving separate modeling of each stratum and simple weighted averaging to integrate intra-stratum and inter-strata features. However, these models overlook the different contributions of inter-strata features to different locations within a stratum (heterogeneous inter-strata associations, HIA) and the explanation of spatial effects on the interpolation process, leading to suboptimal and unreliable interpolation outcomes. This article proposes a novel explainable spatial interpolation method considering SSH (X-SSHM). Spatial and environmental features are utilized to describe intra-stratum and inter-strata information, which are fed into random forest-based learners to achieve high-level semantic feature mapping. Geographically weighted regression is employed to integrate intra-stratum and inter-strata features to achieve a unified expression of SSH and HIA, obtaining the final interpolation result. Geographically weighted Shapley (GSHAP) is proposed to decompose the marginal contributions of intra-stratum and inter-strata features. Model performance is evaluated on simulated and soil organic matter datasets. X-SSHM outperformed five baselines regarding interpolation accuracy. Moreover, statistical methods validated X-SSHM’s ability to elucidate the mechanisms by which SSH, spatial autocorrelation and HIA affect the model interpolation process.

Model-Based Assessment of Energy Efficiency in Industrial Pump Systems: A Case Study Approach

Outdated, oversized variable speed pump drives (VSDPs) in industry lead to sub-optimal energy efficiency and considerable energy losses. This paper proposes methods to develop 2D efficiency maps for motors, converters, and pumps using polynomial surface fitting, which enables efficiency evaluation in a wide operating range. The method was applied to an oversized VSDP in an industrial chilled water supply system, comparing the original system with five alternative VSDP combinations with high-efficiency motors and pumps. The five VSDP variants demonstrated average energy savings of around 30%, with the synchronous reluctance motor (SRM) configurations outperforming the induction motor (IM) configurations by up to 7 percentage points, particularly at low loads. The high-efficiency SRM-based 252-IE5 variant delivered the best overall energy performance, highlighting the benefits of optimised system sizing and motor selection for energy savings. The proposed method can be used in both industrial and residential applications and offers great advantages in process systems that require variable flow and pressure of water or other fluids during operation, such as HVAC, water supply and wastewater treatment, district heating, etc. The development of a VSDP drive with efficient energy optimisation is an interdisciplinary problem of mechanical and electrical engineering, and without the interaction of engineers from both fields the result will not be optimal. We try to present our method so that it can be a reliable tool for mechanical, electrical, and other engineers or researchers to assist them in finding possible energy savings, performing energy audits, and selecting the most suitable components when modernising existing or developing new systems.

Application of Machine Learning Techniques in Calibration and Data Reduction of Multi-Hole Probes

Abstract This work presents procedures to implement machine learning methods in the existing algorithms for multi-hole probe calibrations and data reduction. It is shown here, that utilizing artificial neural networks (ANNs) can reduce the amount of calibration data that needs to be acquired in order to obtain a specific calibration uncertainty, by more than 50% while simultaneously reducing data reduction times significantly. ANNs were used instead of the surface fitting methods, where first, the directional calibration coefficients related to the flow angles are calculated based on the pressure measurements, and then the flow angles are used as a set of the input parameters for the following ANNs to define Mach number and static and total pressure iteratively. In a second approach, novel calibration coefficients were used to directly relate the pressure measurements from five-hole probe to the quantities of interest thus, eliminating the need for iterative algorithms used in the conventional surface fitting methods. The advantageous features of this method are an average increase of less than 1% in the calibration uncertainty for flow angles and significant reduction of the data reduction times (few seconds). In addition, we confirmed the methodology to avoid over-fitting and under-fitting.

Assessing the Spatial Distribution, Condition, and Intensity of Soil Acidity for Informed Management Decisions in the Semen Aari District of Ari Zone, Southwestern Ethiopia

Soil acidity poses a significant challenge to agricultural productivity in Ethiopia's highlands, particularly affecting the Semen Ari district in the Ari zone. The common practice of applying agricultural lime to mitigate soil acidity is hampered by a lack of detailed information on the extent, severity, and spatial distribution of acidic soils. This study aims to determine how soil acidity varies spatially by identifying and mapping the specific geographic patterns of soil acidity levels in the Semen Ari district. Seventy-one composite soil samples from the 0–20 cm layer were geo-referenced and analyzed. Using statistical analysis and ArcGIS software for spatial interpolation through ordinary kriging, soil pH ranged from 3.29 to 5.68, classifying 99% of the soils as strongly acidic. The root mean squared error (RMSE) of the interpolation was 0.30. Soil pH showed a significant negative correlation with exchangeable acidity but a non-significant negative correlation with organic carbon and total nitrogen. The results highlight the need for targeted soil management strategies, such as appropriate lime application rates and the cultivation of acid-tolerant crops, to enhance crop yields. Further research is recommended to include comprehensive soil property datasets to better understand the factors influencing soil pH variability, thus supporting more precise management of acidic soils in the region. The generated high-resolution soil acidity map serves as a valuable tool for agricultural planning and decision-making.

Backpropagation-based inference for spatial interpolation to estimate the blastability index in an open pit mine

The blastability index (BI) is a measure that indicates the resistance of rock to fragmentation when blasting. With novel technologies, miners are now able to collect and calculate BI at different depths while drilling. In this research, we propose an approach to estimate the BI at multiple depths for new areas using only spatial locations and observed BI measurements of previously drilled holes. Spatial interpolation techniques are investigated. This study introduces a novel treatment for Gaussian Processes (GPs) and Inverse Distance Weighting (IDW). Variography is leveraged to ensure an appropriate fit between the data and the spatial component. The parameters controlling anisotropy are constrained to intervals chosen to reflect the observed anisotropy. Gradient descent with back-propagation is used for optimization. The proposed approach improves the performance of GP and IDW at predicting BI. The similarities between the IDW variant proposed and a single-layer neural network are discussed.

Analysis of the degree of correlation of spatial distribution of electricity theft and exogenous variables: case study of Florianopolis, Brazil

Abstract This article presents a geospatial study case on electricity theft. The main objective is to identify the degree of correlation between exogenous variables and areas with a high density of irregular cases. Firstly, the geospatial study is carried out to asses the null hypothesis and check whether the data pattern presents clustering, for this the ANN method is applied, which ruled out the null hypothesis for the data set. Once the clustering pattern is confirmed, the spatial weight matrix is created to study spatial autocorrelation by applying Global Moran’s I and Local Moran’s I. Moran scatterplot is used to evaluate the degree of fitness, identify outliers, and local pockets of stationarity. The Local Moran index is used to determine the location of the clusters and the relationship between the points. In the data pre-processing step, spatial interpolation is implemented to the exogenous variables as a tool to better association of consumer units points and socioeconomic variables, the method utilized is IDW interpolation. The R-squared value of the spatial lag model after model tuning by feature selection was 87 % indicating that the model fit the observed data well.

Processing of Idiopathic Pulmonary Fibrosis images based on spatial interpolation using DFT, FFT, DCT and LPF

The work focuses on the study of existing issues with some medical images obtained from patients with Idiopathic Pulmonary Fibrosis (IPF) who have survived this COVID-19 pandemic. This study analyzes potential causes of incorrect medical diagnoses with this disease. In this regard, we employ numerical algorithms such as DFT (Discrete Fourier Transform), FFT (Fast Fourier Transform), DCT (Discrete Cosine Transform), and LPF (Low-pass filter). The main objective of this work is to demonstrate that with these numerical algorithms based on Continuous and Discrete Fourier Theory, it is possible to filter existing noise in IPF radiography images. Furthermore, it is possible to compress, enhance, and improve their resolution so that better decisions can be made in a medical protocol. In this way, it contributes to the understanding of the true images that would be used for an optimal medical diagnosis.

Análise de cluster e regionalização hidrológica para Estados brasileiros

ABSTRACT Streamflow data from gauging stations are essential for effective water resources management. However, some regions in Brazil lack the necessary data. Hydrological regionalization is an alternative technique for obtaining data such regions. However, not all regions in Brazil have defined hydrological regionalization models, including the state of Goiás and the Brazilian Federal District. The objective of this study was to develop a hydrological regionalization methodology based on the separation of hydrologically homogeneous regions and multiple linear regression, using a Geographic Information System (GIS) program. Historical series data were used to calculate reference flows with 90 or 95% duration over time in the watercourse (Q90 and Q95) and the mean flow (Ǭ). Rain gauge station data were used to calculate the mean annual rainfall in each watershed through spatial interpolation by ordinary kriging. Subsequently, the physiographic characteristics of each watershed were calculated. The hydrologically homogeneous regions were delimited based on these data using cluster analysis, which identified seven hydrologically homogeneous regions in Goiás, two of them belonging to the Federal District. Multiple regression allowed the development of seven regionalization models. Models for regions 1, 3, 4, 5, and 7 showed better performance.

Clustering-Based Spatial Interpolation of Parametric Postprocessing Models

Abstract Since the start of the operational use of ensemble prediction systems, ensemble-based probabilistic forecasting has become the most advanced approach in weather prediction. However, despite the persistent development of the last three decades, ensemble forecasts still often suffer from the lack of calibration and might exhibit systematic bias, which calls for some form of statistical postprocessing. Nowadays, one can choose from a large variety of postprocessing approaches, where parametric methods provide full predictive distributions of the investigated weather quantity. Parameter estimation in these models is based on training data consisting of past forecast-observation pairs; thus, postprocessed forecasts are usually available only at those locations where training data are accessible. We propose a general clustering-based interpolation technique of extending calibrated predictive distributions from observation stations to any location in the ensemble domain where there are ensemble forecasts at hand. Focusing on the ensemble model output statistics (EMOS) postprocessing technique, in a case study based on 10-m wind speed ensemble forecasts of the European Centre for Medium-Range Weather Forecasts, we demonstrate the predictive performance of various versions of the suggested method and show its superiority over the regionally estimated and interpolated EMOS models and the raw ensemble forecasts as well.

APPLICATION OF SUPPORT VECTOR MACHINE AND ORDINARY KRIGING (SVM-OK) TO SPATIAL INTERPOLATION OF POROSITY ATTRIBUTE OF AGBABU BITUMEN DEPOSIT

The lack of geostatistical modelling of volumetric characteristics of Nigeria's long-discovered heavy oil and bitumen deposits in Agbabu was accountable for the discrepancies in the hydrocarbon-in-place calculations. The absence of spatial variability evaluation in such efforts is responsible for the inconsistency and divergence that define the impacts of the existing attempts to estimate the porosity attribute. Due to its optimum features and simplicity of estimate, Kriging is reported as the most generally used approach to evaluate the connection between variables (inputs and output). However, Kriging reveals that the relationship between variables requires a linear form, which is frequently not the case. Thus, the necessity for a procedure with no fixed shape is built based on information acquired directly from the data. Consequently, support vector regression, a form of the support vector machine, is employed as a choice in contrast to kriging. This research is a comparative application of support vector machine and kriging (SVM-OK) to the spatial interpolation of the porosity property of the Agbabu bitumen field. The porosity values from existing data are trained, cross-validated, and verified with the help of the support vector machine, and they are interpolated by ordinary kriging. The geographical patterns of the predictions have been checked visually using graphs based on these three diagnostics: variance of the error terms and correlation of the error terms. The findings of this study illustrate the performance of the combination of the Support vector machine and conventional kriging in the spatial interpolation of volumetric characteristics in the Agbabu bitumen deposit. The approach exhibited great predictive performance. SVM-kriging will be employed in reservoir characterisation with the least mean absolute error (MAE) and mean squared error (MSE).

Research on multibeam line laying scheme based on geometric analysis and optimization modeling

The multibeam full-coverage bathymetry technique has greatly improved the accuracy and efficiency of marine mapping and has been widely used in many fields. In order to measure the seafloor topography in the sea area with known topographic features, this paper designs the optimal survey line arrangement scheme. It is finally concluded that the fitting surface function of the sea depth is first fitted using the least squares method, after which the gradient of the sea depth data at each point is calculated, and the hierarchical clustering method is used to determine the optimal clusters for clustering. The surface formed by the set of points with similar gradients is approximated as a flat slope with a fixed slope angle, and based on the optimization model constructed, survey lines are laid along the isobath direction for each approximated slope after clustering.

Spatial Interpolation of Seasonal Precipitations Using Rain Gauge Data and Convection‐Permitting Regional Climate Model Simulations in a Complex Topographical Region

ABSTRACTIn mountainous areas, accurately estimating the long‐term climatology of seasonal precipitations is challenging due to the lack of high‐altitude rain gauges and the complexity of the topography. This study addresses these challenges by interpolating seasonal precipitation data from 3189 rain gauges across France over the 1982–2018 period, using geographical coordinates, and altitude. In this study, an additional predictor is provided from simulations of a Convection‐Permitting Regional Climate Model (CP‐RCM). The simulations are averaged to obtain seasonal precipitation climatology, which helps capture the relationship between topography and long‐term seasonal precipitation. Geostatistical and machine learning models are evaluated within a cross‐validation framework to determine the most appropriate approach to generate seasonal precipitation reference fields. Results indicate that the best model uses a machine learning approach to interpolate the ratio between long‐term seasonal precipitation from observations and CP‐RCM simulations. This method successfully reproduces both the mean and variance of observed data, and slightly outperforms the best geostatistical model. Moreover, incorporating the CP‐RCM outputs as an explanatory variable significantly improves interpolation accuracy and altitude extrapolation, especially when the rain gauge density is low. These results imply that the commonly used altitude‐precipitation relationship may be insufficient to derive seasonal precipitation fields. The CP‐RCM simulations, increasingly available worldwide, present an opportunity for improving precipitation interpolation, especially in sparse and complex topographical regions.

Evaluation of the dimensional stability of 3D-printed dental casts

ObjectiveThe objective was to investigate the dimensional stability of different types of 3D printed dental models, and to measure the dimensional changes over time. MethodsFour dental casts with different constructions were printed. The four types of models were as follows: hollow casts with 2.5 mm wall thickness (2,5mm.H), hollow casts with 2 mm wall thickness (2mm.H), hollow casts with 2 mm wall thickness with stabilization bars (2mm.B) and hollow casts with 2 mm wall thickness with gypsum base (2mm.G). The casts were digitized with a laboratory scanner (3Shape E3 Red E Scanner) to obtain the reference Standard Tessellation Language (STL) files. All models were stored at room temperature and scanned again after 1 day and after 1, 2 and 10 weeks. This data was compared to the reference STL file and was analysed by comparing the deformation using surface fitting software (Geomagic Control X, 3D Systems). The results were statistically evaluated using paired Student's t-tests, with the significance level set at p < 0.05. ResultsThere were significant differences in dimensional stability after 10 weeks between the four different dental casts. According to our results, the 2mm.B casts showed the least deformation which was followed by the 2mm.H casts. However, both the 2,5mm.H and the 2mm.G casts showed significant deformation compared to the 2mm.B casts. ConclusionsWithin the limitations of this study – using only one printer and one type of resin – we found that the deformation of all investigated casts remained within the clinically acceptable range. However, there were significant differences between the various construction types printed with the Bego Varseo Printer and Bego Varseo Wax Model Gray material. Clinical significanceIt is crucial to determine how long 3D printed models can maintain their accuracy to prevent potential adverse effects, especially given the extended storage periods required for the time-consuming procedures in prosthodontic and orthodontic treatments.

Development of Polymorphic Index Model for Assessing Subtropical Secondary Natural Oak Forest Site Quality Under Complex Site and Climate Variables

Site and climate conditions are the key determinants controlling dominant height growth and forest productivity, both independently and interactively. Secondary natural oak forests are a typical forest type in China, especially in Hunan Province, but little is known about the site index of this forest under the complex site and climate variables in the subtropics. Based on survey data of dominant trees and site variables from 101 plots in Hunan oak natural secondary forests and climate data obtained using spatial interpolation, we used the random forest method, correlation analysis, and the analysis of variance to determine the main site and climate factors affecting oak forest dominant height and proposed a modeling method of an oak natural secondary forest site index based on the random effect of site–climate interaction type. Of the site variables, elevation affected stand dominant height the most, followed by slope direction and position. Winter precipitation and summer mean maximum temperature had the greatest impact on stand dominant height. To develop the modeling method, we created 10 popular base models but found low performance (R2 ranged from 0.1731 to 0.2030). The optimal base model was Mitscherlich form M3 (R2 = 0.1940) based on parameter significance tests. Since site and climate factors affect the site index curve, the dominant site and climate factors were combined into site types and climate types, respectively, and a nonlinear mixed-effects approach was used to simulate different site types, climate types, site–climate interaction types, and their combinations as random effects. Site–climate interaction type as a random factor enhanced model (M3.4) performance and prediction accuracy (R2 from 0.1940 to 0.8220) compared to the optimum base model. After clustering the 62 site–climate interaction types into three, five, and eight groups using hierarchical clustering, a mixed-effects model with the random effects of eight groups improved model performance (R2 = 0.8265) and applicability. The modeling method developed in this study could be used to assess a regional secondary natural oak forest site index under complex site and climate variables to evaluate the forest productivity.

Seasonal Variations of PM2.5 Pollution in the Chengdu–Chongqing Urban Agglomeration, China

During the development of the Chengdu–Chongqing Urban Agglomeration (CCUA) in China, PM2.5 pollution severely threatened public health, presenting a significant environmental challenge. This study employs a novel spatial interpolation method known as High Accuracy Surface Modeling (HASM), along with the geographical detector method, local and regional contributions calculation model, and the Hybrid Single–Particle Lagrangian Integrated Trajectory model to analyze the seasonal spatial distribution of PM2.5 concentrations and their anthropogenic driving factors from 2014 to 2023. The transport pathway and potential sources of seasonal PM2.5 concentrations were also examined. The results showed the following: (1) HASM was identified as the most suitable interpolation method for monitoring PM2.5 concentrations in the CCUA; (2) The PM2.5 concentrations exhibited a decreasing trend across all seasons, with the highest values in winter and the lowest in summer. Spatially, the concentrations showed a pattern of being higher in the southwest and lower in the southeast; (3) Industrial soot (dust) emissions (ISEs) and industry structure (IS) were the most important anthropogenic driving factors influencing PM2.5 pollution; (4) The border area between the eastern part of the Tibet Autonomous Region and western Sichuan province in China significantly contribute to PM2.5 pollution in the CCUA, especially during winter.