Distribution Area Research Articles

Deconstructing the Properties of Solar Super Active Region 13664 in the Context of the Historic Geomagnetic Storm of 2024 May 10–11

Abstract Active regions (ARs) are sites of strong magnetic fields on the solar surface whose size can be several times that of Earth. They spawn dynamic activity, sometimes resulting in severe space weather. Some ARs characterized by extraordinary magnetic properties and exhibiting extreme activity are termed as super active regions (SARs). Recently, solar AR 13664 produced 23 X-class flares and unleashed multiple coronal mass ejections, which triggered a severe geomagnetic storm during 2024 May 10–11—the strongest storm on record since 2003. Here, we put AR 13664 in historical context over the cumulative period of 1874 May–2024 June. We find that AR 13664 stands at the 99.95th percentile in the distribution of area over 1874 May–2024 June and at the 99.10th percentile in terms of flux content among all ARs over the period 1996 April–2024 June. Our analysis indicates that five of its magnetic properties rank at the 100.00th percentile among all ARs observed during 2010 May–2024 June by the Solar Dynamic Observatory. A total of 16 magnetic properties of AR 13664 are ranked higher than the 99.00th percentile when compared to other ARs recorded in Space-weather Helioseismic Magnetic Imager Active Region Patch data series, which includes all well-established flare relevant parameters. Furthermore, we demonstrate that AR 13664 reached its most dynamic flare productive state following a rapid rate of rise of its flare-relevant parameters and that the X-class flares were more frequent near their peak values. Our analyses establish AR 13644 to be an SAR and provide a paradigm for investigating their flare-relevant physical characteristics.

Halophilic Fungi—Features and Potential Applications

Extremophiles are of significant scientific interest due to their unique adaptation to harsh environmental conditions and their potential for diverse biotechnological applications. Among these extremophiles, filamentous fungi adapted to high-salt environments represent a new and valuable source of enzymes, biomolecules, and biomaterials. While most studies on halophiles have focused on bacteria, reports on filamentous fungi remain limited. This review compiles information about salt-adapted fungi and details their distribution, adaptation mechanisms, and potential applications in various societal areas. Understanding the adaptive mechanisms of halophilic fungi not only sheds light on the biology of extremophilic fungi but also leads to promising biotechnological applications, including the development of salt-tolerant enzymes and strategies for bioremediation of saline habitats. To fully realize this potential, a comprehensive understanding of their ecology, diversity and physiology is crucial. In addition, understanding their survival mechanisms in saline environments is important for the development of astrobiology. The significant potential of applications of halophilic fungi is highlighted.

Dynamics of urban green spaces in a megacity under the green economy framework and their influencing factors: a case study of Chongqing urban area

IntroductionIn the context of escalating public health crises in megacities, promoting green and healthy urban spatial development is crucial. It not only contributes to economic growth and environmental sustainability but also significantly impacts the public health of urban residents.MethodsThis study utilized land use data from 2000 to 2021 in Chongqing, China, to investigate the characteristics and patterns of change in urban green space distribution. Spatial analysis methods were employed, and an econometric model was constructed to analyze the mechanisms of these changes, considering factors such as economic drivers, governmental regulation, and social dynamics.ResultsThe results reveal that urban green spaces in the Chongqing metropolitan area are primarily concentrated in the inner suburbs with limited distribution in central urban areas, exhibiting a distinct spatial gradient. The overall size of urban green spaces has been decreasing, particularly in the inner suburbs. The primary patterns of this reduction include edge erosion in the inner suburbs, inward contraction in central urban areas, and minor perforation in new towns.DiscussionThe analysis indicates that economic drivers, such as industrial development and economic growth, are the dominant factors contributing to the reduction of urban green spaces. The impact of social dynamics, such as public demand, appears to be relatively insignificant. Conversely, government planning policies and public investments play a crucial role in the protection and development of urban green spaces. These findings emphasize the need for effective urban planning strategies that prioritize the conservation and expansion of green spaces to enhance public health and environmental sustainability in megacities.

Understanding the spread of agriculture in the Western Mediterranean (6th-3rd millennia BC) with Machine Learning tools

The first Neolithic farmers arrived in the Western Mediterranean area from the East. They established settlements in coastal areas and over time migrated to new environments, adapting to changing ecological and climatic conditions. While farming practices and settlements in the Western Mediterranean differ greatly from those known in the Eastern Mediterranean and central Europe, the extent to which these differences are connected to the local environment and climate is unclear. Here, we tackle this question by compiling data and proxies at a superregional and multi-scale level, including archaeobotanical information, radiocarbon dates and paleoclimatic models, then applying a machine learning approach to investigate the impact of ecological and climatic constraints on the first Neolithic humans and crops. This approach facilitates calculating the pace of spread of farming in the Western Mediterranean area, modelling and estimating the potential areas suitable for settlement location, and discriminating distinct types of crop cultivation under changing climatic conditions that characterized the period 5900 – 2300 cal. BC. The results of this study shed light onto the past climate variability and its influence on human distribution in the Western Mediterranean area, but also discriminate sensitive parameters for successful agricultural practices.

Is the skull of the black-headed gull dimorphic? Analysis of shape variation

The black-headed gull (Chroicocephalus ridibundus) is a small bird belonging to the Laridae family. Their distribution and breeding broad area primarily cover the northern part of the Eurasian continent. Gulls are considered monomorphic birds, and distinguishing the sexes based on external morphological features is extremely difficult. In this study, a 3D shape analysis of the skull of black-headed gulls from central-northern Poland was performed to reveal the variation in shape between individuals and whether male skulls differ from females. For these purposes, 82 skulls were used (32 females and 50 males). 3D surface models of the skull were extracted using computed tomography. Twenty-one landmarks were designed on each skull. The principal components method was used to assess the variability of skull shapes. The first two principal components accounted for 48.28% of the cumulative shape variation. The most significant shape variation relative to PC1 was observed in the braincase. Positive PC2 values reflected the neurocranium expanding more rapidly dorso-ventrally with a wider orbit, and the nasofrontal hinge moved dorsally. According to the Procrustes ANOVA, the centroid size was statistically different between the sexes (p < 0.0001). Male skulls were longer than females. Neurocranium was wider in male gulls. The nasofrontal hinge was more caudal in females. The results of this study are consistent with the observations of other authors applying linear morphometry.

Research on Optimal Crop Planting Strategy Based on Improved Quantum Genetic Algorithm

In order to effectively cope with the complex agricultural environment and market uncertainty, this study proposed a crop planting optimization strategy based on the improved quantum genetic algorithm (QGA). Single-objective and multi-objective optimization models were proposed, focusing on profit maximization and risk balance under market fluctuations. The single-objective model used QGA to evaluate the optimal planting scheme under surplus and discount sales scenarios, and analyzed the crop area distribution and total income. For multi-objective optimization, the enhanced MO-QGA model combined with orthogonal experimental design generated a scheme that maximized expected income while minimizing the worst outcome. In addition, this study combined Pearson correlation and hierarchical clustering to quantify the substitutability and complementarity of crops, and optimized the planting strategy through crop combination analysis. The experimental results show that the enhanced QGA effectively balances profits and risks, and the effect is significantly better than traditional methods, which is conducive to improving agricultural decision-making efficiency

Two new species of Boaedon from Ethiopia and Somalia, with a review of the species of East Africa

We conducted a molecular study based on 112 mitochondrial 16S rRNA sequences, which revealed 20 monophyletic species-level groups in Africa, five of these in East and Northeast Africa. Based on genetic data, we describe two new species, B. broadleyi sp. nov. from Ethiopia and B. subniger sp. nov. from Somalia, and we elevate B. fuliginosus arabicus to full species level. We detected further morphologically divergent specimens that may represent a distinct taxon but refrain from a formal description due to insufficient data. The morphological variation of the recently described B. montanus from Central Africa is shown and discussed over its whole distribution area. Moreover, we provide a new country record for Sudan and South Sudan, namely of B. paralineatus. Finally, we provide an updated distribution map for all Boaedon species occurring in Northeast and East Africa.

A Study on the Residual Oil Distribution in Tight Reservoirs Based on a 3D Pore Structure Model

A tight reservoir is characterized by low porosity and permeability as well as a complex pore structure, resulting in low oil recovery efficiency. Understanding the micro-scale distribution of residual oil is of great significance for improving oil production and water flooding recovery rates. In this study, a 3D pore structure model of tight sandstone was established using CT scanning to characterize the residual oil distribution after water flooding. The effects of displacement methods and wettability on residual oil distribution at the micro-scale were then studied and discussed. Moreover, increasing the displacement rate has little effect on the distribution area and dominant seepage channels. Microscopic residual oil is classified into five discontinuous phases according to the oil–water–pore–throat contact relationship. The microscopic residual oil exhibits characteristics of being dispersed overall but locally concentrated. Under water-wet conditions, the injected water tends to strip the oil phase along the pore walls. Under oil-wet conditions, the pore walls have an improved adsorption capacity for the oil phase, resulting in a large amount of porous and membranous residual oil retained in the pores, which leads to a decrease in the overall recovery rate.

Texture mapping of warp knitted shoe upper based on ARAP parameterization method

Abstract In order to realize the design of the warp knitted upper based on the three-dimensional shoe model, this study investigates the texture mapping technique based on angle-preserving and area-regularizing as-rigid-as-possible (ARAP) parameterization. The ARAP algorithm, based on both partial and global considerations, is utilized to parameterize the 3D mesh. Moreover, detailed methods for calculating 3D vertex adjustments and selecting seam lines during the unfolding process are provided. Additionally, discrete harmonic and parametric optimization unfolding algorithms are employed to enhance texture mapping quality. To reduce mesh distortion, the results of discrete harmonic parameterization are used as inputs for ARAP parameterization, making the mapping effect more natural and reducing the number of required optimizations. Experimental results demonstrate that the proposed method maintains a balanced distribution of angles and areas while minimizing conformal distortions, thereby achieving superior texture mapping.

A Promising Strategy for Searching High Energy Density Materials Based on 2,4,5‐Trinitro‐Imidazole Functional Backbone

AbstractA series of high energy density compounds are designed by altering the triazole, tetrazine, oxadiazole rings and energetic groups (such as −CN, −N3, −NH2, −NHNH2, −NO2, −NHNO2, −C(NO2)3, −CH(NO2)2) into 2,4,5‐trinitro‐imidazole skeleton. Their structures are optimized by density functional theory (DFT) methods at B3LYP/6‐311G (d,p) method. Based on the optimized structures, the impact of different rings and energetic groups on their energy gaps, heats of formation, detonation performance and sensitivities are investigated. The results show that compound F4 possesses the highest values of heats of formation due to their high nitrogen content. However, compound F2 possesses the highest values of detonation pressure and detonation velocity which indicates that the detonation performance are determined by values of density rather than those of heats of formation. Taking both detonation performance and impact sensitivities into consideration, compounds B4, C4, F4, I4, J4 and J8 are screened as high energy density compounds due to their excellent detonation performance and acceptable sensitivities compared to those of RDX. Finally, the distribution of frontier molecular orbital, the electrostatic potential area distribution, thermodynamic properties and weak interactions of the screened compounds are fully investigated.

Diagnosis of Protected Agriculture in Imbabura—Ecuador, Period 2016–2023

Protected agriculture in Ecuador began in the 1990s and has expanded due to its comparative advantages over open field production. However, there are no statistics on this sector, which limits decision-making. The aim of this research was to provide a baseline of greenhouse agriculture in Imbabura. Sentinel-2 satellite imagery was used to estimate the spatial distribution of plastic-covered surface area in 2016 and 2023. To minimize biases in estimation, manual verification was also conducted. Based on population data, a structured survey was administered to a probabilistic sample of 234 greenhouses. The results highlight the presence of 1958 greenhouses that cover 527 hectares, with an average of 0.26 hectares. The greenhouses were characterized in terms of their design, construction materials and equipment. The main crop under plastic is tomato, with 76.9%, of which the management characteristics and the productive and economic results obtained in 2023 were identified. The findings could inform the formulation of public policies or specific interventions to strengthen protected agriculture in the region; however, support mechanisms are needed to fully exploit its potential. Among these, producer organization could be a viable strategy to address food security challenges in the context of climate change.

Improving Forest Canopy Height Mapping in Wuyishan National Park Through Calibration of ZiYuan-3 Stereo Imagery Using Limited Unmanned Aerial Vehicle LiDAR Data

Forest canopy height (FCH) is a critical parameter for forest management and ecosystem modeling, but there is a lack of accurate FCH distribution in large areas. To address this issue, this study selected Wuyishan National Park in China as a case study to explore the calibration method for mapping FCH in a complex subtropical mountainous region based on ZiYuan-3 (ZY3) stereo imagery and limited Unmanned Aerial Vehicle (UAV) LiDAR data. Pearson’s correlation analysis, Categorical Boosting (CatBoost) feature importance analysis, and causal effect analysis were used to examine major factors causing extraction errors of digital surface model (DSM) data from ZY3 stereo imagery. Different machine learning algorithms were compared and used to calibrate the DSM and FCH results. The results indicate that the DSM extraction accuracy based on ZY3 stereo imagery is primarily influenced by slope aspect, elevation, and vegetation characteristics. These influences were particularly notable in areas with a complex topography and dense vegetation coverage. A Bayesian-optimized CatBoost model with directly calibrating the original FCH (the difference between the DSM from ZY3 and high-precision digital elevation model (DEM) data) demonstrated the best prediction performance. This model produced the FCH map at a 4 m spatial resolution, the root mean square error (RMSE) was reduced from 6.47 m based on initial stereo imagery to 3.99 m after calibration, and the relative RMSE (rRMSE) was reduced from 36.52% to 22.53%. The study demonstrates the feasibility of using ZY3 imagery for regional forest canopy height mapping and confirms the superior performance of using the CatBoost algorithm in enhancing FCH calibration accuracy. These findings provide valuable insights into the multidimensional impacts of key environmental factors on FCH extraction, supporting precise forest monitoring and carbon stock assessment in complex terrains in subtropical regions.

Surrogate-assisted global and distributed local collaborative optimization algorithm for expensive constrained optimization problems

This paper presents a surrogate-assisted global and distributed local collaborative optimization (SGDLCO) algorithm for expensive constrained optimization problems where two surrogate optimization phases are executed collaboratively at each generation. As the complexity of optimization problems and the cost of solutions increase in practical applications, how to efficiently solve expensive constrained optimization problems with limited computational resources has become an important area of research. Traditional optimization algorithms often struggle to balance the efficiency of global and local searches, especially when dealing with high-dimensional and complex constraint conditions. For global surrogate-assisted collaborative evolution phase, the global candidate set is generated through classification collaborative mutation operations to alleviate the pre-screening pressure of the surrogate model. For local surrogate-assisted phase, a distributed central region local exploration is designed to achieve intensively search for promising distributed local areas which are located by affinity propagation clustering and mathematical modeling. More importantly, a three-layer adaptive selection strategy where the feasibility, diversity and convergence are balanced effectively is designed to identify promising solutions in global and local candidate sets. Therefore, the SGDLCO efficiently balances global and local search during the whole optimization process. Experimental studies on five classical test suites demonstrate that the SGDLCO provides excellent performance in solving expensive constrained optimization problems.

Genome‐wide SNP Detection in the Horse Mackerel (Trachurus trachurus, T. mediterraneus, T. picturatus) of Turkish Waters Using Illumina and Nanopore Sequencing

The horse mackerel, distributed in the Pacific, Atlantic, Indian Ocean, and Mediterranean waters, is economically valuable due to its high nutritional content and fishing capacity. Global market demand for this species has been increasing over the years. This rising demand causes fishing pressure, endangering the species' sustainability. Especially the horse mackerel species (Trachurus trachurus, Trachurus mediterraneus and Trachurus picturatus), which are found as mixed stocks in Turkish waters and under fishing pressure, should be considered as different stocks and evaluated with distinct management strategies. However, due to the difficulty of distinguishing these three Trachurus species based on morphological characteristics, conflicting reports have emerged, particularly regarding their distribution areas, and species identification is not always precise. In our study, we sequenced the genomes of these species using next-generation sequencing (Illumina) and third-generation sequencing technologies (Oxford Nanopore), and identified SNP (single nucleotide polymorphism) differences to distinguish the species. We identified a total of 2311386 SNPs in the Trachurus trachurus genome, of which 1313612 were transitions and 997774 were transversions. 5099766 SNPs in the Trachurus picturatus genome, of which 2927066 transitions and 2172700 transversions. Similarly, 5007863 SNPs in the Trachurus mediterraneus genome, of which 2940560 transitions and 2067303 transversions. These SNP datasets provide a robust genetic marker toolkit for accurately distinguishing Trachurus species and evaluating genetic diversity within and between populations. The findings of this study contribute essential genetic resources for defining the stock structures of these species, informing sustainable fisheries management strategies, and guiding conservation initiatives. By addressing the challenges of species identification and genetic diversity assessment, this research lays the groundwork for mitigating the impact of overfishing and ensuring the sustainability of horse mackerel populations in Turkish and global waters.

Survey of the Current Distribution Areas of Cassida Species Detected on Centaurea behen L. in Hakkari Province in Turkey with Maxent Model

Survey of the Current Distribution Areas of Cassida Species Detected on Centaurea behen L. in Hakkari Province in Turkey with Maxent Model

The Use of Glycan Ensemble Structures and Nonpolar Surface Area Distributions for Correlating with Liquid Chromatography Retention Times.

The separation and structural identification of glycans are of great bioanalytical importance. To obtain a good understanding of the structural flexibility of glycans, replica exchange molecular dynamics (REMD) simulations were used based on AMBER force field calculations to create ensembles of glycan structures. Nonpolar surface area (NPSA) calculations based on continuum solvation (CS) models (Dhakal, R., et al. Int. J. Mass Spectrom. 2021, 461, 116495) were used to quantitatively characterize the polarity of the glycans. Retention times determined by tandem liquid chromatography-mass spectrometry (LC-MS) were correlated with CS-NPSA results obtained from analysis of the investigated glycan ensembles. Three classes of glycans with increasingly complex structures were investigated: linear glycans, fucosylated and sialylated biantennary glycans, and sialylated triantennary glycans. The linear and biantennary structures displayed bimodal distributions in their energies and CS-NPSA values, suggesting two sets of structures, while the more complex triantennary glycans displayed only a single distribution. The peak values of the CS-NPSA distributions (histogram structures) were selected as representatives to be correlated with the experimental retention times. For comparison, the most stable ensemble structures and those obtained from straightforward geometry optimizations were considered, as well. Overall, the histogram structures were found to correlate well with the retention times. In the case of the linear glycans, the CS-NPSA values for all three structural choices correlated very well with the retention times. For the biantennary glycans, the histogram data missed the retention-time ordering in one case but predicted the correct ordering for the triantennary case. Principal component analysis was performed to characterize the main glycan modes of the molecular dynamics.

Spatiotemporal distribution of global peatland area during the Holocene

Peatlands are a key component of terrestrial ecosystems, and their development has an important impact on global carbon cycle and climate change. However, the long-term evolution of global peatlands remains uncertain, particularly their spatial distribution. We compiled 4700 basal peatland data during Holocene, and 669 pollen data of Sphagnum with basal and end ages, to allow a more robust reconstruction of the spatial distribution of peatlands. Using buffer analysis (BA) and inverse distance weighted (IDW) interpolation of peat data, we reconstructed spatiotemporal changes in global peatland area at a spatial resolution of 0.5° × 0.5° for every 1,000 years period during Holocene. The results show that peatland area have expanded substantially in North America, Europe, and Western Siberia during early-Holocene, and increased rapidly from 2.18(0.32) Mkm2 to 4.03(3.08) Mkm2 during 12-6 ka BP, then slowly to 4.15(4.23) Mkm2 after 6 ka BP according to BA (IDW) methods. The database will be useful for analyzing the global/regional terrestrial carbon cycle and climate change during Holocene, especially for modeling peatland methane emissions.

Effects of Topography on Vegetation Coverage Changes in Rural Areas Under Multi-scale Scenarios

Revealing the characteristics of vegetation coverage change and the effects of topography on vegetation coverage change in rural areas since the reform and opening up has direct implications for further understanding of human-land coupling processes and resource and environmental changes and provides a reference for ecological environment protection in rural revitalization. Using the Taihua Town in Yixing City, Jiangsu Province, as a rural case study, we revealed the basic features of rural vegetation coverage change from 1986 to 2020. Furthermore, using 0.25 km2（scenario 1）, 0.50 km2（scenario 2）, and 1.00 km2 （scenario 3） as scale scenarios in the study area, both the single and comprehensive topographic effects on vegetation coverage change were explored. The study produced several important results： ① Vegetation coverage in the study area generally maintained an upward trend from 1986 to 2020, during which the spatial pattern of vegetation coverage distribution was generally high in the southeast and southwest and low in the north （northwest）. During this period, the vegetation coverage in the study area increased from 74.15% to 83.44%, and the spatial pattern of vegetation coverage distribution generally remained similar. ② Under the multi-scale scenarios, the vegetation coverage distribution in the study area generally maintained a similar spatial pattern from 1986 to 2020, excepting a few local differences. Under the same-scale scenario, Moran's index of vegetation coverage in the study area was higher in 2020 than in 1986. Taking scenario 1 as an example, Moran's index of vegetation coverage in the study area increased from 0.603 to 0.652 between 1986 and 2020. ③ Under the multi-scale scenarios, from 1986 to 2020, vegetation coverage in the study area generally maintained an upward trend with increase of elevation, slope, and topographic relief. As an example of the vegetation coverage change with elevation, in scenario 1 in 2020, the vegetation coverage in the study area increased from 64.50% in the area with elevations of 0-50 m to 98.77% in the area with elevations above 350 m. ④ Under the multi-scale scenarios, the comprehensive topographic effects on mean vegetation coverage change in the study area from 1986 to 2020 showed that elevation, slope, and topographic relief all had positive effects, and their effects were greater in scenario 3 than in scenario 2 and then in scenario 1 for the same topographic factor. According to the comprehensive topographic effects on mean vegetation coverage change in the study area from 1986 to 2020, the equation simulation coefficients of elevation under scenarios 1, 2, and 3 were 0.271, 0.281, and 0.283, respectively. The respective slope values were 0.283, 0.289, and 0.294, and the topographic relief values were 0.283, 0.292, and 0.295. This suggests that the scale variation has impacts on the comprehensive topographic effects. The results of this study are valuable for understanding the changes in the rural ecological environment under multi-scale scenarios since the reform and opening up.

Influencing Mechanism of Precipitation Pollution Intensity in Qinhuai River Basin Based on Random Forest

Analyzing the distribution characteristics of precipitation pollution intensity in the basin and identifying the main factors affecting the precipitation pollution intensity are the important basis for realizing the accurate management of diffused pollution. Based on the surface water quality data from four typical sections of the main stream of Qinhuai River Basin and rainfall data collected from 2021 to 2022, the distribution characteristics of precipitation pollution intensity in the basin were analyzed, and representative natural and social factors were selected to construct models of the precipitation pollution intensity of ammonia nitrogen （NH4+-N）, permanganate index, and total phosphorus （TP） based on random forest algorithm. Additionally, the main driving factors of precipitation pollution intensity were identified, and the influencing mechanism was analyzed. The results showed that rainfall was the main driving factor for the water quality exceeding the standard in the Qinhuai River Basin, and the proportion of rainfall events within 3 days of when the water quality of the sections in the study area exceeded the standard was between 61.4% and 97.4%. The precipitation pollution intensity of NH4+-N and TP showed a gradual increase from upstream to downstream. NH4+-N was the primary pollutant in the midstream Qiqiaoweng section and the downstream Sanchahekou section, with the precipitation pollution intensity ranging from 0.12 to 2.98 and from 0.31 to 3.84, respectively. The precipitation pollution intensity of permanganate index in the upstream Yangqiao section was the largest, ranging from 0.41 to 1.35, and the precipitation pollution intensity of the Jiangjundadao section, which is located on the channel of diverted water, was at a lower level compared with that of the other three sections. Water diversion was an important influencing factor for the reduction of precipitation pollution intensity of NH4+-N and permanganate index （P&lt;0.01）； the proportion of built-up area was significantly positively correlated with the precipitation pollution intensity of NH4+-N and TP； the proportion of cultivated area was significantly positively correlated with the precipitation pollution intensity of permanganate index, and the proportion of forest land area was significantly negatively correlated with the precipitation pollution intensity of these three indicators. Under the conditions of light rain, moderate rain, and heavy rain, the rainfall was significantly positively correlated with the precipitation pollution intensity of each indicator, and the precipitation pollution intensity remained high under the condition of torrential rain. The post-rain pollution of NH4+-N and TP mainly originated from the middle and lower reaches of the study area, which was significantly affected by regional characteristics such as the percentage of built-up area and water diversion （P&lt;0.01）, and the post-rain pollution of permanganate index mainly originated from the upstream area with a wide distribution of cultivated land area and was significantly affected by flow （P&lt;0.01）.

Spatiotemporal Distribution and Potential Source Areas of PM2.5 Pollution in Xianyang City, Guanzhong Basin

This study employed PM2.5 concentration data for Xianyang City spanning the years 2014 to 2021, in conjunction with the global data assimilation system （GDAS）. Various analytical techniques, including backward trajectory clustering analysis, potential source contribution function （PSCF）, concentration weighted trajectory analysis （CWT）, and relevant statistical methods, were employed to investigate the temporal and spatial variations in PM2.5 pollution. Furthermore, this research aimed to elucidate the source characteristics and potential areas contributing to PM2.5 pollution within Xianyang City. The results revealed a fluctuation in PM2.5 pollution concentration in Xianyang City from 2014 to 2021, with an initial increase followed by a subsequent decrease. The peak average annual concentration, reaching 81.25 μg·m-3, was recorded in 2016. Seasonal variations indicated higher PM2.5 concentrations in autumn and winter, contrasting with lower levels in spring and summer. Winter exhibited the highest PM2.5 concentration at 116 μg·m-3, while the lowest was recorded in summer at 31.58 μg·m-3. Spatially, the annual mean distribution of PM2.5 in Xianyang City demonstrated heightened pollution in the southern and central regions, juxtaposed with lower pollution in the northern areas. Cluster analysis highlighted that Xianyang City experienced substantial influence from northwest airflow during spring, autumn, and winter, while short-distance transport dominated during the summer months. PSCF and CWT analyses indicated that the high-value potential source contribution areas were most extensive during winter, followed by spring and autumn. Conversely, the high-value area in summer was the smallest. The potential source areas were concentrated within Xianyang City and extended northwest to southeast, encompassing regions in western Inner Mongolia, central and eastern Gansu Province, central and southern Ningxia, and central Henan. In analyzing periods of heavy pollution, the study demonstrated that PM2.5 pollution in Xianyang City was primarily induced by westerly airflow. High PM2.5 concentrations were influenced by the surrounding areas of Xianyang City and the source zone, akin to regions with high potential source values during the winter. As a consequence, mitigating PM2.5 pollution in Xianyang City necessitates stringent control measures for both local and regional pollution sources. Additionally, regional collaborative efforts should be emphasized to prevent external pollution sources from exacerbating persistent pollution episodes in the region.