Spatial-temporal Heterogeneity Research Articles

Comprehensive Sustainability Indicator for Land Resource-Carrying Capacity in a Farming-Pastoral Region

The Northeast Farming-pastoral ecotone (NFPE) in China is crucial for farming and pastoralism. However, sustainable development of land resources faces several challenges from human activities and environmental degradation. Thus, assessing land resource-carrying capacity (LRCC) is crucial for effective land-use planning and management. This study proposes a comprehensive indicator system to evaluate LRCC in the NFPE. We integrate remote sensing, model simulation, and statistical data, employing Geographic Information System analysis and statistical techniques to assess spatiotemporal evolution characteristics of LRCC. Findings indicate increasing LRCC with spatial-temporal heterogeneity. The northern region exhibits relatively high and growing LRCC, while some eastern regions have low LRCC but a high growth trend. The southern region displays both low LRCC and growth. Balancing regional development and ecological protection requires considering spatial heterogeneity of LRCC in land-use planning and management decisions, facilitating differentiated land-use strategies for sustainable development.

A review of neuroimaging-based data-driven approach for Alzheimer's disease heterogeneity analysis.

Alzheimer's disease (AD) is a complex form of dementia and due to its high phenotypic variability, its diagnosis and monitoring can be quite challenging. Biomarkers play a crucial role in AD diagnosis and monitoring, but interpreting these biomarkers can be problematic due totheir spatial and temporal heterogeneity. Therefore, researchers are increasingly turning to imaging-based biomarkers that employ data-driven computational approaches to examine the heterogeneity of AD. In this comprehensive review article, we aim to provide health professionals with a comprehensive view of past applications of data-driven computational approaches in studying AD heterogeneity and planning future research directions. We first define and offer basic insights into different categories of heterogeneity analysis, including spatial heterogeneity, temporal heterogeneity, and spatial-temporal heterogeneity. Then, we scrutinize 22 articles relating to spatial heterogeneity, 14 articles relating to temporal heterogeneity, and five articles relating to spatial-temporal heterogeneity, highlighting the strengths and limitations of these strategies. Furthermore, we discuss the importance of understanding spatial heterogeneity in AD subtypes and their clinical manifestations, biomarkers for abnormal orderings and AD stages, the recent advancements in spatial-temporal heterogeneity analysis for AD, and the emerging role of omics data integration in advancing personalized diagnosis and treatment for AD patients. By emphasizing the significance of understanding AD heterogeneity, we hope to stimulate further research in this field to facilitate the development of personalized interventions for AD patients.

Spatial-temporal heterogeneity of global ports resilience under Pandemic: a case study of COVID-19

ABSTRACT As a major global public health emergency, the COVID-19 epidemic has had a significant impact on the development of the global port operation. Based on the data from 1 January 20191 January 2019 to 31 December 202231 December 2022, this study utilizes the global spatial autocorrelation and local spatial autocorrelation to measure and evaluate the resistance and resilience of global ports. From the outcomes, we find that the overall level of resistance and resilience for the global ports is relatively low, further have not been effectively restored and adjusted, and the resilience of ports in the world is generally heterogeneous. In addition, the impact of the epidemic on global ports is temporary, where the resilience of ports has been significantly improved after the end of 2021, and most ports have recovered from the impact of the epidemic and have a positive trend in 2022. Meanwhile, the ports have a significant positive spatial correlation in response to the effect of the COVID-19 epidemic.

Epidemiological characteristics and spatio-temporal distribution of pulmonary tuberculosis cases reported in students from Guizhou Province, 2011-2020

Objective: To analyze the trend of epidemiological characteristics and spatiotemporal distribution of pulmonary tuberculosis (PTB) among smear-positive or other types of students in Guizhou Province from 2011 to 2020, and to provide a reference for improving prevention and control measures. Methods: Data were collected from the Chinese Information System's Notifiable Disease and Tuberculosis Management Information System for disease control and prevention, the Joinpoint 4.9.1.0 software was used to analyze the trend of registration rate; the ArcGIS 10.6 software was used to construct a ring map and to perform spatial autocorrelation analysis; the SaTScan 9.7 software was used for spatial-temporal scan statistics. Results: A total of 32 682 student PTB cases were reported in Guizhou Province from 2011 to 2020, including 5 949 (18.20%) smear-positive cases. Most cases occurred from high school students of 16 to 18 years old (43.99%, 14 376/32 682); the annual average registered rate was 36.22/100 000, the highest in 2018 (52.90/100 000), and the registration rate showed an increasing trend. Meanwhile, a similar trend of registration rate was observed among smear-positive or other types of students. The spatialtemporal heterogeneity was found that the "high-high" clustering patterns of smear-positive or other types were aggregated in Bijie City. Six spatialtemporal clusters with statistically significant (all P<0.001) were detected among smear-positive or other cases, respectively. Conclusions: Upward trend with spatial- temporal clusters of PTB cases reported in students from Guizhou Province from 2011 to 2020. Surveillance should be strengthened for high school students, and regular screening should be conducted in high-risk areas to control the source of infection and reduce the risk of transmission.

A pathway to sustainable development: Digitization and green productivity

Green productivity plays an important role in sustainable development, social energy conservation, and emission reduction targets. To investigate the impact of digital development on green total factor productivity, this research employs panel data of China's Yangtze River Delta from 2002 to 2020. First, this paper presents the super SBM method with unexpected output to comprehensively and systematically calculate the green total factor productivity (GTFP) index. Second, we use the partial linear function coefficient (PLFC) model, which can distinguish spatial-temporal heterogeneity, analyze how digital development improves GTFP, and evaluate the effect of human capital (HC) and industrial structure upgrading (IS) in this relationship. In addition, we conduct a comparative study between this delta region and other regions in China. The results of this paper are as follows. (1) Digitization has a significant and positive influence on GTFP for all samples. (2) HC and IS affect the role of digital development in promoting GTFP of cities in the Yangtze River Delta, but their impact is not significant in the three northeastern provinces. (3) The stimulation effect shows significant temporal and spatial heterogeneity under the influence of HC and IS. Provincial capitals and economically developed cities have the most prominent stimulating influence. Lastly, based on the non-parametric relevance between digital development and GTFP, this paper gives constructive and practical policy suggestions to improve GTFP and promote green economic development.

Traffic demand prediction based on spatial-temporal guided multi graph Sandwich-Transformer

The ability of spatial-temporal traffic demand prediction is crucial for urban computing, traffic management and future autonomous driving. In this paper, a novel Spatial-Temporal Guided Multi-graph Sandwich-Transformer (STGMT) is suggested to address the ubiquitous spatial-temporal heterogeneity in traffic demand forecasting. Compared to the original Transformer, we employ Time to Vector (Time2Vec) and Node to Vector (Node2Vec) in the embedding layer to obtain universal representations for temporal nodes and spatial nodes, respectively, which are then combined to form Spatial-Temporal Embedding (STE) blocks. The STE guides the attention mechanism, maintaining a unique parameter space for spatial-temporal nodes and enabling the learning of node-specific patterns. In STGMT, we develop Multi-head Temporal Attention (MTA) and Multi-head Temporal Interactive Attention (MTIA) for extracting temporal features, while Multi-head Spatial Attention (MSA) is employed for extracting spatial features. Furthermore, MSA incorporates both the accessibility graph determined by road topology and the similarity graph determined by specific traffic events to characterize the pairwise relationships among spatial nodes. Various attentions and feed-forward layers are rearranged and combined to form the Sandwich-Transformer. Extensive experiments are conducted on public datasets of node-level tasks of two different types (highway and urban) and indicate that the STGMT outperforms state-of-the-art models. The proposed STGMT effectively addresses the ubiquitous spatial-temporal heterogeneity challenge in traffic demand forecasting, thereby enhancing the accuracy of traffic demand prediction and offering valuable guidance for traffic planning and operations. Our code and data are open source at https://github.com/YanJieWen/STGMT-Tensorflow-implementation.

Empirical evidence that mean soil contaminant concentration is not a conservative indicator of external exposure to wildlife

Adequate information on external exposure is often a limiting factor in ecological risk assessments because of a lack of exposure data for free-ranging animals as they interact with a heterogeneous distribution of contaminants. Instead, most wildlife exposure data are estimated from model simulations. However, empirical exposure data are rarely available to verify model results, or to test fundamental assumptions inherent in exposure assessments. We used novel, GPS-coupled radiation dosimeters on reindeer (Rangifer tarandus) to quantify their external exposure for five months in a Chernobyl fallout affected mountainous area of Norway. The exposure data were then used to test a fundamental assumption in screening-level risk assessments: Mean soil contaminant concentrations can be used to conservatively estimate chronic external exposures to representative animals. External exposure and GPS coordinates were obtained hourly and compared to model simulations of exposure based on airborne surveys of 137Cs soil contamination. Our objectives were to determine 1) if modelled dose estimates based on mean 137Cs soil concentrations were conservative, and 2) if modelled dose estimates could be improved by adjusting for spatial–temporal heterogeneity of contaminants using 137Cs concentrations within reindeers’ GPS-defined home ranges and core areas. We found that external exposure modelled from mean soil contaminant concentrations underestimated exposure by 70% compared to empirical measurements from GPS-dosimeter collars worn on individual animals. Using area-weighted mean contaminant levels within the animals’ home range improved the model estimates, but still underestimated the field dosimeter data by 53%. The best estimate of external exposure was obtained when high-use areas within their home range, along with spatial heterogeneity of the contaminant, was accounted for by using area-weighted mean soil contaminant levels within GPS-derived core areas. Accounting for temporal use of core areas within animals’ home range provided a 24% improvement. A comparison of our analyses to similar work performed on Chernobyl wolves found the two studies concur that risk assessors should be cautious in relying on the paradigm of using averaged soil contaminant levels to provide conservative estimates of external exposure to representative individuals. Results of modelled external exposure based on mean soil contaminant concentrations could misguide assessors and cause inappropriate management decisions.

Spatial-Temporal Heterogeneity in Large Three-Dimensional Nanofibrillar Cellulose Hydrogel for Human Pluripotent Stem Cell Culture

One approach to cell expansion is to use large hydrogel for growing a large number of cells. Nanofibrillar cellulose (NFC) hydrogel has been used for human induced pluripotent stem cell (hiPSCs) expansion. However, little is known about the status of hiPSCs at the single cell level inside large NFC hydrogel during culture. To understand the effect of NFC hydrogel property on temporal-spatial heterogeneity, hiPSCs were cultured in 0.8 wt% NFC hydrogel with different thicknesses with the top surface exposed to the culture medium. The prepared hydrogel exhibits less restriction in mass transfer due to the presence of macropores and micropores interconnecting the macropores. More than 85% of cells at different depths survive after 5 days of culture inside 3.5 mm thick hydrogel. Biological compositions at different zones inside the NFC gel were examined over time at a single-cell level. A dramatic concentration gradient of growth factors estimated in the simulation along 3.5 mm NFC hydrogel could be a reason for the spatial-temporal heterogeneity in protein secondary structure and protein glycosylation and pluripotency loss at the bottom zone. pH change caused by the lactic acid accumulation over time leads to changes in cellulose charge and growth factor potential, probably another reason for the heterogeneity in biochemical compositions. This study may help to develop optimal conditions for producing high-quality hiPSCs in large nanofibrillar cellulose hydrogel at scale.

CSFPre: Expressway key sections based on CEEMDAN-STSGCN-FCM during the holidays for traffic flow prediction.

The implementation of the toll free during holidays makes a large number of traffic jams on the expressway. Real-time and accurate holiday traffic flow forecasts can assist the traffic management department to guide the diversion and reduce the expressway's congestion. However, most of the current prediction methods focus on predicting traffic flow on ordinary working days or weekends. There are fewer studies for festivals and holidays traffic flow prediction, it is challenging to predict holiday traffic flow accurately because of its sudden and irregular characteristics. Therefore, we put forward a data-driven expressway traffic flow prediction model based on holidays. Firstly, Electronic Toll Collection (ETC) gantry data and toll data are preprocessed to realize data integrity and accuracy. Secondly, after Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) processing, the preprocessed traffic flow is sorted into trend terms and random terms, and the spatial-temporal correlation and heterogeneity of each component are captured simultaneously using the Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN) model. Finally, the fluctuating traffic flow of holidays is predicted using Fluctuation Coefficient Method (FCM). Through experiments of real ETC gantry data and toll data in Fujian Province, this method is superior to all baseline methods and has achieved good results. It can provide reference for future public travel choices and further road network operation.

European bee diversity: Taxonomic and phylogenetic patterns

AbstractAimWild bees still face striking shortfalls in knowledge of biodiversity in key regions of the world. This includes Europe, where despite a long tradition of data gathering, the continental scale distribution patterns of wild bees have not been systematically analysed to date. This study aims to characterise large‐scale biodiversity patterns to: (i) understand spatial–temporal heterogeneity in large‐scale databases, (ii) locate genuine diversity hotspots and their relationship with biogeographical patterns or habitats of interests and (iii) identify understudied species and areas to further design conservation actions for most at risk species in key regions.LocationEurope.TaxonBees.MethodsWe present a continental and standardised study of bee taxonomic and phylogenetic diversity patterns in Europe, using a large compilation of occurrence records of nearly three million validated occurrence records for 1515 wild bee species.ResultsSouthern and eastern Europe suffer from the largest gaps in data availability while northern and western regions benefit from better historical coverage. Our models show that higher wild bee diversity in Europe is hosted in xeric, warm areas, as highlighted by a clear latitudinal gradient. However, phylogenetic diversity is predicted to be more homogenous across Europe than taxonomic diversity, suggesting that policies and strategies targeted to protect species richness may differ from those targeting greater phylogenetic diversity.Main conclusionsThis study represents a significant advance in the characterisation of wild bee distribution patterns across Europe and is an important stepping stone towards the design of more targeted survey efforts and conservation actions of this key group of pollinators. This, in turn, will provide the data necessary to improve the spatiotemporal coverage in a context of ongoing and future Europe‐wide monitoring schemes, to ultimately develop cost‐effective, coordinated and evidence‐based conservation actions and tailored habitat management actions that can be implemented on a smaller scale.

Spatial–temporal variation and temperature effect of urbanization in Guangdong Province from 1951 to 2018

In this paper, we focus on Guangdong province, which is a frontier area of Reform and Opening up, and analyze the variation of urban and rural temperature to detect the urbanization warming effect from 1951 to 2018, based on the daily temperature data of 85 weather stations. We classified the metrological stations into urban and rural stations according to the urbanization population size, population density and urban built-up area data. The combined methodology of mathematical statistics, trend analysis, urban/rural station comparison and spatial analysis was adopted to reveal the urbanization warming and its spatial–temporal heterogeneity. The results show that the warming process in Guangdong Province is very complex and has high temporal and spatial heterogeneity. From 1951 to 2018, the average temperature in the coldest month in Guangdong Province increased with the fastest (0.27 °C/10a) rate, and the annual average temperature increased slightly faster than the average temperature in the warmest month (0.15 °C/10a vs. 0.12 °C/10a). The comparative study on the temperature of urban and rural stations shows that the temperature increase rate of urban stations is faster than that of rural stations (0.30 °C/10a vs. 0.23 °C/10a), especially the average temperature in the coldest month, which is most significantly driven by urbanization, and the contribution rate of urbanization is about 24.09% in general. Affected by the background climate and the stages of urbanization, the warming effect of urbanization fluctuates obviously in different periods. Stronger urbanization effect can be witnessed during 1980–2000, when the urbanization developed into mid-level with highest rate. The spatial differentiation of urbanization warming effect is prominent. The temperature of urban stations in Eastern Guangdong increases most rapidly. The urbanization contribution rates to the coldest month average, annual average and the warmest month average temperature reach 33.86%, 25.70% and 17.64%, respectively, which are around 1.69, 1.50 and 1.19 times that of the Pearl River Delta. The average temperature of the warmest month in Western Guangdong shows a weak cooling effect. Multi-faceted, multi-level regulation of sustainable urbanization is the key to mitigating the warming caused by urbanization.

Spatio-Temporal Differentiation and Driving Factors of Carbon Storage in Cultivated Land-Use Transition

Revealing the response of carbon storage to cultivated land-use transition (CLUT) and identifying its driving factors are of great significance for maintaining ecosystem stability and promoting regional carbon peak and carbon neutrality goals. Given the knowledge gap regarding the driving factors of carbon storage in CLUT, this study takes the Songhua River Basin in the black soil region of China as the case study area. The study aimed to reveal the spatial–temporal heterogeneity of carbon storage in CLUT based on the grid element method and carbon storage density. In addition, the driving factors were demonstrated using the geodetector model. The results show that the cultivated land area gradually decreased from 1990 to 2020, and the transition between cultivated and construction land was the most significant. The carbon storage in cultivated land-use transitions showed a substantial decreasing trend. The conversion of cultivated land to construction land resulted in the loss of 130,443,200 tons of carbon reserves. Moreover, the transformation from unused land to cultivated land led the highest increase in carbon storage, which increased by 29,334,600 tons. The gravity center of carbon storage was stable, moving 28.77 km to the northeast between 1990 and 2020. Conversely, the spatial structure of carbon storage showed a transformation trend from multicore fragmentation to mononuclear agglomeration, with obvious regional accumulation, a weakened degree of fragmentation, and uniform distribution. Carbon reserves increased by 388,600 tons from 1990 to 2000, and carbon reserves lost 60,121,700 tons from 2010 to 2020, nearly 155.700 times. The mean annual rainfall was the main carbon storage factor. The interaction between mean annual rainfall and land-use intensity had strong explanatory power, and the spatial heterogeneity of carbon storage resulted from multiple factors.

Spatiotemporal heterogeneity between agricultural carbon emission efficiency and food security in Henan, China.

It is significant to investigate the coupling and coordination between agricultural carbon emission efficiency (ACEE) and food security and to achieve peak carbon dioxide emissions and carbon neutrality in agriculture as early as possible while ensuring national food security. The Super-SBM (slack-based model) and the comprehensive index method were used to measure the ACEE and food security level in Henan province from 2010 to 2020. The coupling coordination degree (CCD) and the relative state of ACEE and food security were analyzed using the coupling coordination degree model (CCDM) and the relative development degree model (RDDM). In addition, the interaction between ACEE and food security and the spatial-temporal heterogeneity were analyzed by combining with the geographically and temporally weighted regression (GTWR) model. The results showed that: Firstly, the overall level of ACEE was high, and the spatial heterogeneity of ACEE was significant. The spatial pattern of food security is relatively stable, with high levels in the south and low levels in the north. Secondly, The CCD between ACEE and food security in Henan province generally shows a decreasing trend. In the spatial dimension, the CCD between ACEE and food security in Henan province exhibits a spatial divergence characteristic of low in the center and high in the north and south, with significant regional variations. Finally, there is spatial and temporal heterogeneity between ACEE and food security. The regression coefficients differ significantly among different cities, the regression coefficients do not show a consistent positive or negative correlation, and the regression coefficients are distributed both positively and negatively. This study serves as a guide for achieving the goal of double carbon in agriculture and ensuring food security.

Spatial–Temporal Heterogeneity of Urbanization and Ecosystem Services in the Yellow River Basin

Taking 736 counties in the Yellow River Basin of China as the research area, the comprehensive urbanization development level and ecosystem service capacity from 2000 to 2020 were measured. Combined with spatial autocorrelation, the spatial pattern evolution characteristics of the two systems in the Yellow River Basin were revealed. The spatio–temporal geographically weighted regression (GTWR) model was used to analyze the spatio–temporal heterogeneity of the impact of various elements of the system on urbanization and ecosystem service capacity. The results showed that (1) the urbanization level and ecosystem service capacity of the Yellow River Basin were on the rise but the urbanization level and ecosystem service capacity were low, while the spatial and temporal heterogeneity was significant. (2) The two systems are positively correlated in space, and the agglomeration characteristics are significant. The evolution trend of urbanization from an L–L agglomeration area to an H–H agglomeration area is occurring gradually. The spatial change in the ecosystem service agglomeration area is small, and the stability is strong. (3) The impact of ecosystem services on comprehensive urbanization is enhanced by time, and the spatial ‘center–periphery’ diffusion characteristics are significant. (4) The influence of urbanization on the comprehensive ecosystem service capacity is enhanced and shows the law of east–west differentiation in space. There are obvious transition zones in the spatial heterogeneity interval of the interaction between the two systems.

Spatial-temporal evolution of influencing mechanism of urban flooding in the Guangdong Hong Kong Macao greater bay area, China

Extreme weather has been more frequent in recent years. Urban agglomerations, as areas with a high density of human activities, have been plagued by storm flooding. Historically, the main focus of attention on flood control in urban agglomerations has gradually shifted from underground pipe networks to the impervious surface, reflecting profound changes in the influencing mechanism of urban flooding. Exploring the evolution of the mechanisms influencing urban flooding in the Guangdong Hong Kong Macao Greater Bay Area (GBA) urban agglomeration is of great reference significance for formulating flood prevention and control measures and promoting high-quality development of the GBA city cluster. In this paper, we fully use the collected information on urban flooding events from 1980 to 2018 in the GBA city cluster. Correlation analysis and geographically weighted regression (GWR) are used to analyze the influence of impervious surface percentage (ISP), impervious surface aggregation index (AI), impervious surface mean shape index (Shape_MN), vegetation cover (FVC), water surface ratio (WSR), relative elevation (RE) and slope on flooding in urban clusters and their evolution characteristics over time from a global perspective and spatial heterogeneity, respectively. The results show that: 1) ISP, AI, Shape_MN, and WSR are positively correlated with urban flooding, while FVC, RE, and Slope are negatively correlated with urban flooding. The correlations of each factor showed a general trend of gradual strengthening over time, and the increase rate slowed down after 2000, while the correlation of WSR showed a relatively noticeable decrease. 2) The GWR results show that each factor’s influence on urban flooding has pronounced spatial-temporal heterogeneity, and each factor shows different distribution characteristics. This study uses long time series of urban flooding point data to explore the spatial-temporal evolution of the influencing mechanism of urban flooding in the GBA urban agglomeration. We hope to provide a scientific basis for an in-depth understanding of the causes of urban flooding in the GBA, intending to provide auxiliary decision-making support for the formulation of waterlogging prevention and control measures.

Spatial-temporal Evolution and Heterogeneity of Carbon Emissions at County-level in China

Counties are the key spatial units in achieving the reduction and control of carbon emissions. It is of great significance to study and reveal the spatial-temporal evolution characteristics and influencing mechanism of carbon emissions for realizing the "carbon peak and carbon neutral" goal. In this study, the spatial-temporal evolution and heterogeneity of carbon emissions at the county level in China from 2000 to 2017 were analyzed by using mathematical statistics and panel data regression modeling, and the influencing mechanism was explored. The results showed that: ① from 2000 to 2017, the annual growth rate of carbon emissions was 7.12%, which experienced the three stages of "sharp rise, slow rise, and high fluctuation" and finally stabilized at approximately 90×108 t. At the county scale, there was a significant positive spatial autocorrelation. ② The general panel regression model showed that GDP, construction land area, population, per capita GDP, and per capita deposit balance of financial institutions were significantly correlated with carbon emissions, and the former three had the strongest promoting effect on carbon emissions. ③ The goodness of fit of the geographically and temporally weighted regression model was high, and the direction and intensity of the other impact factors changed greatly in spatial-temporal characteristics, except that GDP showed a stable promoting effect globally. The results showed that carbon emission levels and main influencing factors varied among counties in China. This study revealed the heterogeneity of carbon emissions at the county level, which is helpful to optimize the spatial-temporal implementation path of the "dual carbon" target.

Analysis of Multiyear Blowing Snow Occurrences in the French Alps

Abstract Wind-driven snow transport has important implications for spatial–temporal heterogeneity of snow distribution and snowpack evolution in mountainous areas, such as the French Alps. Due to the paucity of near-surface observations, our knowledge on the spatiotemporal variability of blowing snow occurrences is rather limited. Based on multiyear in situ observations, the spatial–temporal variability in the occurrence of blowing snow events in the French Alps was presented to investigate potential links with ambient meteorological conditions. Statistical analysis of the observations demonstrates that blowing snow events are frequently observed with substantial spatiotemporal variability. Most stations experienced snow transport one out of every five days throughout winter, and the corresponding cumulative hours with blowing snow occurrence accounted for 8% of the month in winter. Blowing snow events generally last 4–8 h in winter and early spring. The likelihood of blowing snow occurrences increases with wind speed but with divergent patterns across snow types. The frequency of blowing snow occurrences with concurrent snowfall is substantially higher than that without concurrent snowfall, although high spatiotemporal variability was found. The considerable variation in snow transport frequency can be explained by contrasting meteorological conditions, local climate, snowpack properties, and topography (elevation and aspect). The temperature-based empirical scheme failed to recognize individual occurrence of blowing snow events because of the significantly overestimated threshold wind speeds, highlighting the importance of validation using in situ observations. Our results contribute to the understanding of spatiotemporal occurrence of blowing snow events and facilitate the development of blowing snow models.

Leveraging machine learning and remote sensing to monitor long-term spatial-temporal wetland changes: Towards a national RAMSAR inventory in Pakistan

In Pakistan, wetlands are of primary focus as they withstand the effects of floods, recharge groundwater, and provide several services in the context of economic, cultural, and climate mitigation aspects. However, the lack of field data and huge monitoring costs hinder their sustainable management in Pakistan. In connection with this, the current study leverages Google Earth Engine (GEE), earth observation data, and machine learning-based Random Forest (RF) algorithm to evaluate spatial-temporal heterogeneities in wetlands in Pakistan between 1990 and 2020. Additionally, the first high-resolution long-term inventory of wetlands in Pakistan is presented to provide a baseline. Our results ascertain an increase in wetlands areas over the last 30 years. The swamps’ area increased from 1391.19 km2 in 1990 to 8510.43 km2 in 2020 (2.62% annual change rate). Similarly, the marshes area increased between 1990 and 2020 with a ∼1.04% annual change rate. Conversely, the water area decreased from 8371.97 km2 in 1990 to 7818.34 km2 in 2020. The increase in wetlands could be associated with good conservation and planting practices in Pakistan. While these results provide important insights to implement conservation practices in the context of wetland sustainability, the resultant data is essential to the national wetlands inventory database for future evaluations.

Spatial–Temporal Multivariate Correlation Analysis of Ecosystem Services and Ecological Risk in Areas of Overlapped Cropland and Coal Resources in the Eastern Plains, China

The overlapped areas of cropland and coal resources play a fundamental role in promoting economic and social progress. However, intensive mining operations in high water-level areas have brought significant spatial–temporal heterogeneity and ecological problems. From the dual dimensions of the ecosystem service value (ESV) and ecological risk (ER), it is of great significance to explore the influence characteristics of underground mining on the landscape, such as above-ground cultivated land, which is valuable to achieving regional governance and coordinated development. In this study, taking Peixian as the research area, a multiple-dimensional correlation framework was constructed based on the revised ESV and ER, integrating the grey relational degree, spatial–temporal heterogeneity, disequilibrium, and inconsistency index to explore the ESV and ER assessment and correlation characteristics from 2010 to 2020. The results show that (1) the ESV showed a high agglomerated distribution pattern in the east, with a net decrease of 13.61%. (2) The ER decreased by 78.18 and was concentrated in the western and southern regions, with overall contiguous and local agglomeration characteristics. This indicates that the ecological security of the region has improved. (3) The comprehensive grey correlation between the cultural service value and the ecological risk index was the highest. Furthermore, the spatial–temporal heterogeneity of the ESV and ER weakened, and the disequilibrium rose and then fell, indicating that the ecosystem gradually tended to be stable. The study is crucial for overlapped cropland and coal resource areas to maintain stability and sustainable development. The multivariate correlation framework provides practical value for ecosystem management and risk control.

Exploring the Effects of Industrial Land Transfer on Urban Air Quality Using a Geographically and Temporally Weighted Regression Model.

This paper explores the spatial-temporal heterogeneity of the impact of industrial land transfer on urban air quality using the air quality index (AQI) and primary land market transaction data of 284 cities from 2015 to 2019 in China. Based on a three-dimensional conceptual framework including scale, price and style effect of industrial land transfer, we find that: (1) The scale effect shows an obvious characteristic of spatial agglomeration, and the agglomerations transfer from central and northern China to the western and southeast coastal regions. (2) Industrial land transfer price has a greater impact on air quality than transfer scale no matter whether the effect is positive or negative, which may be because the expansion scale of construction land is restricted strictly by indicators. (3) The scale of industrial land transferred by agreement in the west and northeast will reduce the air quality. (4) The impact of industrial land price transferred by bidding, auction and listing on AQI is gradually decreasing, but that of land transferred by agreement is still high in the northwest and northeast regions. Finally, we put forward policy recommendations based on the spatial and temporal heterogeneity of these effects, which will help alleviate or avoid environmental problems caused by land resources mismatch and industrial development.