Spatial Effects Research Articles

Microbial communities change along the 300 km length of the Grand River for extreme high- and low-flow regimes.

The Grand River watershed is the largest catchment in southern Ontario. The river's northern and southern sections are influenced by agriculture, whereas central regions receive wastewater effluent and urban runoff. To characterize in-river microbial communities, as they relate to spatial and environmental factors, we conducted two same-day sampling events along the entire 300 km length of the river, representing contrasting flow seasons (high flow spring melt and low flow end of summer). Through high-throughput sequencing of 16S rRNA genes, we assessed the relationship between river microbiota and spatial and physicochemical variables. Flow season had a greater impact on communities than spatial or diel effects and profiles diverged with distance between sites under both flow conditions, but low-flow profiles exhibited higher beta diversity. High-flow profiles showed greater species richness and increased presence of soil and sediment taxa, which may relate to increased input from terrestrial sources. Total suspended solids, dissolved inorganic carbon, and distance from headwaters significantly explained microbial community variation during the low-flow event, whereas conductivity, sulfate, and nitrite were significant explanatory factors for spring melt. This study establishes a baseline for the Grand River's microbial community, serving as a foundation for modeling the microbiology of anthropogenically impacted freshwater systems affected by lotic processes.

Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models

Spatial transcriptomics (ST) assays represent a revolution in how the architecture of tissues is studied by allowing for the exploration of cells in their spatial context. A common element in the analysis is delineating tissue domains or “niches” followed by detecting differentially expressed genes to infer the biological identity of the tissue domains or cell types. However, many studies approach differential expression analysis by using statistical approaches often applied in the analysis of non-spatial scRNA data (e.g., two-sample t-tests, Wilcoxon’s rank sum test), hence neglecting the spatial dependency observed in ST data. In this study, we show that applying linear mixed models with spatial correlation structures using spatial random effects effectively accounts for the spatial autocorrelation and reduces inflation of type-I error rate observed in non-spatial based differential expression testing. We also show that spatial linear models with an exponential correlation structure provide a better fit to the ST data as compared to non-spatial models, particularly for spatially resolved technologies that quantify expression at finer scales (i.e., single-cell resolution).

The impact of regional sports industry aggregation on residents’ health level in China

Health is the basis for human survival and development and is an important symbol to evaluate a country's economic growth and social progress. This article measures the degree of sports industry agglomeration in different regions of China and uses the Moran index to verify the existence of global autocorrelation in sports agglomeration. Next, the spatial Durbin model was used to verify the spatial spillover effect of sports industry agglomeration on the health level of residents, and the following conclusions were obtained.Firstly, there is spatial autocorrelation and heterogeneity in the clustering level of China's sports industry. The spatial distribution is extremely uneven, and different regions have formed relatively stable spatial patterns. Secondly, the degree of aggregation of the sports industry can reduce the number of per capita visits and have a positive spatial spillover effect on the health of residents. Not only can it promote the health level of residents in the province, but it also has spatial spillover effects on surrounding areas.Finally, based on the research results, the following conclusions are proposed in this article. Policy recommendations include increasing investment in sports talent cultivation, accelerating the construction of sports center cities, and increasing residents' attention to sports to improve residents' health.

Boron Cluster Renders Organic Radicals Water-Stable for Photothermal Anti-Infections.

Water-stable organic radicals are promising photothermal conversion candidates for photothermal therapy (PTT). However, organic radicals are usually unstable in biological environments, which greatly hinders their wide application. Here, we have developed a chaotropic effect-based and photoinduced water-stable supramolecular radical (MB12-2) for efficient antibacterial PTT. The supramolecular radical precursor MB12-1 was constructed by the chaotropic effect between closo-dodecaborate cluster (B12H122-) and N,N'-dimethylated dipyridinium thiazolo [5,4-d] thiazole (MPT2+). Subsequently, with triethanolamine (TEOA) serving as an electron donor, MB12-1 could transform to its radical form MB12-2 through photoinduced electron transfer (PET) under 435-nm laser irradiation. The N2 adsorption-desorption analysis confirmed that MB12-2 was tightly packed through the introduction of B12H122-, which effectively enhanced its stability via a spatial site-blocked effect. Moreover, the half-life of MB12-2 in water was calculated through ultraviolet-visible light (UV-vis) absorption spectra results for periods as long as 20 days. In addition, in the skin infection model, MB12-2, as a wound dressing, showed remarkable photothermal antibacterial activity (>97%) under 660-nm laser irradiation and promoted wound healing. This study presents a simple method for designing long-term water-stable supramolecular radicals, offering a novel avenue for noncontact treatments for bacterial infections.

Research on the Accounting and Spatial Effects of Emergy Ecological Footprint and Industrial Green GDP--The Case of Yangtze River Economic Belt

The booming development of industrial enterprises at the cost of ecological resource consumption has inevitably harmed the ecological environment, especially since the phenomenon of water resource pollution is more serious. Exploring the relationship between the ecological environment and the industrial economy is crucial to the green and sustainable development of the region. This study uses the Spatial Durbin Model (SDM) to investigate the impact of emergy ecological footprint on the industrial green of gross domestic product (GDP) under the perspective of water resources, based on the panel data of 11 provinces in Chinas Yangtze River Economic Belt (YREB) from 2012 to 2021. The results show that first, the overall industrial green GDP of the YREB has increased year by year, and the effect of water resource protection is outstanding. Second, the emergy ecological footprint has a significant positive effect on industrial green GDP, and the spillover effect is also significant. Third, heterogeneity analysis shows that the spatial relationship between emergy ecological footprint and industrial green GDP is different in different regions, and the local effect and neighborhood effect of the high industrial green economic development areas are consistent with those of the YREB, while the relationship between the low industrial green economic development areas is not obvious. This paper is of great significance for the green development of industrial enterprises in the YREB and guides the formulation of sustainable development policies.

A Gaussian Function Model of Mesoscale Eddy Temperature Anomalies and Research of Spatial Distribution Characteristics

Mesoscale eddies are ubiquitous oceanic phenomena and play an important role in ocean circulation, ocean dynamics, and the transport of material energy. Temperature anomalies are a crucial parameter that reflects the state of mesoscale eddies. This study proposes a Gaussian function model to fit the vertical temperature anomaly (TA) profile to facilitate the analysis of variations, and the principle of the model is based on the fact that each TA profile tends to fluctuate around one or more peaks. The model is extracted and validated using Argo profiles within cyclonic and anticyclonic eddies in the Northwest Pacific Ocean spanning over the period from 2002 to 2021. The validation demonstrates that the model can accurately recover the vertical TA profiles with a limited number of parameters. This makes it suitable for analysing the spatial distribution patterns that require a large sample count. The analysis indicates that eddies with different TA profiles have a spatial aggregation effect in geographic distribution. Eddies with lower extreme temperature anomalies, at depths of 200–300 m, are mainly distributed along two bands on the north side of the Kuroshio Extension (KE) and the North Equatorial Current. Eddies with extreme TAs at the deepest depth (500–600 m) are distributed along the KE.

Carbon emission reduction enabled by informatization construction: an analysis of spatial effects based on China's experience.

The "dual-carbon" objective presents a huge challenge for China and the world, with profound implications for the advancement of China's eco-friendly economy. Additionally, informatization development has a significant impact on the level of carbon emissions in both local and neighbouring regions. Therefore, we employ panel data from 30 provinces in China spanning the years 2012 to 2021, and use the Kernel density estimate and Moran's index to explore informatization level and carbon emissions space agglomeration characteristics. We elucidate the nonlinear relationship and heterogeneity between informatization improvement and carbon emissions based on the spatial Durbin model. The primary findings are as follows. Firstly, we discover a distinct spatial clustering phenomenon which the informatization level is high in coastal areas and low in inland areas, whereas carbon emissions are low in the south and high in the north. Secondly, the effect of the informatization level on carbon emissions is shown as a U-shaped and non-linear correlation, signifying inhibitory and subsequently promoting phases. Thirdly, we reveal the negative influence on carbon emissions caused by spatial lag terms of the informatization level, and find that a higher local informatization level will have an inhibitory effect on carbon emissions in neighbouring areas. Finally, there is a spatial heterogeneity in the impact of the informatization level on carbon emissions, which presents the U-shaped relation between informatization level and carbon emissions varies across the North-South subregion and the three major economic subregion of China.

Seasonal changes in ecosystem health and their spatial relationship with landscape structure in china's Loess plateau

Environmental managers have sought to optimize landscape structure to maintain regional ecosystem health (EH). However, we still lack a full understanding of the effects of changing landscape structure on EH, especially in terms of shorter time scales (e.g., monthly or seasonal scales) and spatial heterogeneity. To address this gap, this study used the vigor-organization-resilience-services (VORS) model to assess the annual and seasonal variation of EH in the Loess Plateau (LP) during 2000–2015, and then applied the geographically weighted regression (GWR) model to investigate the spatial effects of different aspects (i.e., landscape composition and configuration) of the landscape structure change on EH over time. Our results showed that the EH level in the LP from 2000 to 2015 displayed temporal non-stationarity at annual and seasonal scales, and a spatial pattern of decreasing from southeast to northwest. The effects of landscape structure on EH had obvious spatial and temporal heterogeneity, depending on the local landscape composition and configuration. In general, landscape configuration exerted a greater impact on EH than landscape composition apart from Shannon’s diversity index and percentage of forestland area, but the intensity and direction of the impact displayed higher temporally instability and therefore should receive more attention. Furthermore, regulation of the dominant patch size, landscape connectivity and forest spatial spacing is the optimal choice for regional ecosystem management. The results suggest that EH responses to changes in landscape structure in the LP vary by time and place, and that the integrating temporal variation and spatial characteristics can help decision-makers make precise spatial arrangements and temporal adjustments to keep the negative ecological impacts within reasonable limits.

Differential Analysis of Carbon Emissions between Growing and Shrinking Cities: A Case of Three Northeastern Provinces in China

Carbon emission issues are becoming increasingly severe, and the carbon emissions in shrinking cities, primarily characterized by population loss, are often overlooked and insufficiently studied. This paper focuses on the carbon emissions from county-level administrative units in China’s three northeastern provinces from 2001 to 2017. The study scientifically identified shrinking cities and measured the differences in carbon emission characteristics between growing and shrinking cities using the Theil index. Ultimately, the paper constructs a panel spatial econometric model to analyze the factors influencing them and explore their spatial effects. (1) The total carbon emissions in the Three Northeastern Provinces exhibited an inverted U-shaped trend, increasing from 734.21 million tons in 2001 to 1731.73 million tons in 2017, with the Mann–Kendall trend test showing a significant increase; spatially, this manifests as a significant positive spatial autocorrelation. (2) The region has 138 shrinking cities, accounting for over 50%; regarding carbon emission characteristics, the Theil index has consistently remained above 0.18, indicating significant differences between the carbon emissions of growing and shrinking cities. (3) The panel spatial econometric model results show that the influencing factors of carbon emissions in shrinking cities have unique directions, intensities, and spatial effects. In shrinking cities, aside from localized GDP effects and per-capita GDP acting as a suppressant, the population size has a pronounced inhibitory effect on local and surrounding carbon emissions. The analysis reveals significant differences in the carbon emission patterns and mechanisms between growing and shrinking cities; based on these results, the paper proposes differentiated carbon control strategies.

Can green finance reduce carbon emission? A theoretical analysis and empirical evidence from China.

As an important way for China to achieve its dual-carbon goal, green finance has become the foundation for promoting high-quality economic development in China. In order to clarify the mechanism of green finance on carbon emissions, this paper puts green finance into the economic model and deduces the relationship between green finance and carbon emission reduction. This paper is based on the panel data of 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2008 to 2019, using the individual fixed effect model, dynamical model, mediator model, and SDM model to study the impact of green finance on carbon emissions and its impact path of upgrading of the industrial structure and the development of science and technology based on the measurement of the green finance development index of each province by the entropy method. The findings show that the development of green finance can reduce carbon emission significantly, which can be sustained until at least the third phase and generates spatial spillover effects; regional heterogeneity analysis finds that the development of green finance shows geographical discrepancies: compared with the eastern and western regions, the development of green finance in central region can reduce carbon emissions more significantly; not only can the development of green finance directly reduce carbon emission, but also through the upgrading of industrial structure and technological innovation. The research not only provides a new perspective and supplementary empirical evidence for understanding the carbon emission reduction effect of green finance, but also offers some useful references for green finance to contribute to carbon emission reduction.

Analysis of Spatial Spillover Effects of Digital Inclusive Finance on Urban-Rural Income Gap

This article mainly analyzes the changes in urban-rural disparities under digital inclusive finance, which can be divided into three levels of research objectives, gradually and layer by layer. Firstly, based on the theoretical foundation of accelerating the development of rural financial markets and reducing economic disparities between rural and urban areas through digital inclusive finance, a panel threshold model is constructed to quantitatively analyze the threshold effect of digital inclusive finance on the urban-rural income gap, and to study how to reduce the threshold effect and narrow the digital divide between rural and urban areas; Secondly, from the perspective of spatial spillover, this study examines the positive or negative spatial spillover effects of digital inclusive finance on regional urban-rural income disparities, in order to analyze the direction of the impact of digital inclusive finance on urban-rural income disparities. Using the Spatial Panel Lag Model (SAR) and Spatial Durbin Model (SDM) to empirically test their inherent correlation and impact; Thirdly, there are differences in policies and resource allocation among provinces and cities, as well as differences in urban-rural income disparities in different regions. Based on this, a quantitative analysis model is constructed to analyze the path and methods of digital inclusive finance promoting urban-rural income disparities.

Correction: Limited access to improved drinking water, unimproved drinking water, and toilet facilities among households in Ethiopia: Spatial and mixed effect analysis

Correction: Limited access to improved drinking water, unimproved drinking water, and toilet facilities among households in Ethiopia: Spatial and mixed effect analysis

Thermal shock triggers microexplosion combustion in graded fuel and oxidizer encapsulation microspheres with improved combustion efficiency

The combustion agglomeration of aluminum (Al) and the long heat and mass transfer distance between Al and oxidizer cause serious two-phase flow loss and slag accumulation in the engine of solid propellants. Here, we present an effective strategy by in-situ encapsulating graded nano-aluminum (nAl) and micron-aluminum (μAl) in ammonium perchlorate (AP) simultaneously by using hydroxymethyl cellulose (HMC) as adhesive and constructing a compact coupling fuel oxidizer complex high-energy microsphere (μAl/nAl@AP/HMC) to enhance the energy release efficiency and combustion efficiency. Compared with physical mixture (PM), the ignition delay time and combustion time of μAl/nAl@AP/HMC microspheres are reduced by 69.58 % and 69.87 %, owing to the decomposition process control of AP through the adsorption and spatial constraint effect on the interface between metal fuels and oxidizer. Furthermore, the nAl embedded in the AP surface forms a hot spot around the μAl to promote the combustion reaction, thereby shortening the cavitation threshold and tensile limit time of μAl and accelerating the occurrence of microexplosion. Microexplosion effect in μAl/nAl@AP/HMC microsphere significantly weakens combustion agglomeration and improves combustion efficiency and energy release efficiency by sharp shrinkage of combustion condensation products, which addresses the challenges on energy output and combustion efficiency in high-density solid propellants.

Spatio-temporal evolution and driving factors of green innovation efficiency in the Chinese urban tourism industry based on spatial Markov chain

Green innovation in the tourism industry is a sustainable development concept for resource conservation and environmental optimization. The effective measurement of green innovation efficiency in the tourism industry and an accurate understanding of its spatial relationship was significantly important for promoting its sustainable development. Using the SBM-undesirable model, kernel density estimation, and a spatial Markov chain, we explored the spatio-temporal evolution characteristics and influencing mechanisms of urban tourism green innovation efficiency (TGIE) in China between 2000 and 2020. We found that (1) the temporal and spatial changes of TGIE were generally at a lower than medium level and fluctuated throughout country, with a transition in the east, collapse in the middle, and stagnation in the northeast. (2) The dynamic evolution of TGIE always exhibited polarization, but regional coordination was gradually enhanced with strong stability, although it was difficult to achieve leap-forward development. The cities with spatial upward transfer were concentrated mainly in the central and western region and while there were few cities with a downward adjustment, there were obvious asymmetrical spatial spillover effects. (3) The driving factors of TGIE were the overall economic level, industrial structure, government regulation, and education level. These factors had a significant positive relationship with TGIE, while the degree of opening up to the outside world has no significant effect, but the degree of influence, mechanism, and conditions of each factor were strongly regional.

Comparison of linear and nonlinear modeling approaches to develop an abundance index based on voyage and market data for a data-limited fishery

IntroductionWorldwide coastal fish resources face severe threats from fisheries overexploitation. However, the evaluation of abundance trends in most coastal fisheries is constrained by limited data. This study took blackmouth croaker (Atrobucca nibe), a stock depleted by coastal trawl fishery in southwestern Taiwan, as an example to showcase the development of a relative abundance index from data-limited fishery (only landing data were available).MethodsThis study employed unique data sourcing from voyage data recorders (VDRs) to estimate fishing effort (in combination with landing data to estimate the catch per unit effort, CPUE) that demonstrated the potential application in global data-limited fisheries and assessed alternative approaches for predictors of fishery-targeting practices to condition effort for producing more accurate metrics of relative abundance. The nominal CPUE was standardized using three statistical models: generalized linear model, generalized additive model (GAM), and vector-autoregressive spatiotemporal models (VASTs) with two treatments of each of the four effects: environmental (sea temperature, salinity, density of mixing layer, seafloor temperature, and chlorophyll), vessel, spatial, and targeting effects. A total of 15 models were designed and compared for these effects, and their explanatory power (EP) was evaluated using cross-validation R2 and other metrics.Results and discussionResults indicated that the targeting effect exerted the most significant influence on standardization and was suggested to be addressed through the principal component analysis (PCA) approach. Both vessel and spatial effects demonstrated considerable influence, whereas the environmental effect exhibited a limited impact, possibly due to the small fishing area in this study. Regarding models’ EP, given the nonlinear nature of the PCA algorithm and environmental data, the study highlighted the superiority of the GAM over linear-based models. However, incorporating nonlinear features in VAST (M15) makes it the most effective model in terms of predictive power in this study. Concerning the stock status, despite variations in relative CPUE trends among major models, a general declining trend since 2015 signals the potential decline of the blackmouth stock and urges fishery managers to consider further design of management measures.

Spatial isolation effect improves the acidity and redox capacity of ZSM-5 encapsulating Pt catalyst to achieve efficient toluene oxidation

This paper introduces a novel approach of encapsulating Pt nanoparticles within the pores of a molecular sieve. By comparing the dispersion, reducibility and surface acid sites, the effect of the combination of molecular sieves and Pt particles on the catalytic performance of toluene was studied. The findings demonstrate that Pt nanoparticles were encapsulated within the molecular sieve framework of ZSM-5 molecular sieves using an in-situ synthesis method, exhibiting remarkable catalytic activity towards toluene oxidation (T90=172 ℃). Mainly due to the spatial confinement of the ZSM-5 molecular sieve framework reduces the size of Pt nanoparticles, enhances their dispersion of the Pt@ZSM-5 catalysts. In addition, the space separation effect of ZSM-5 on Pt metal enhances their interaction and promotes the valence state conversion of Pt species. It leads to a large number of acid sites and oxygen vacancies in the catalyst, which improves the adsorption and migration of toluene, thereby improving the catalytic activity. This study will provide valuable insights into the preparation of new nanocatalysts and the catalytic treatment of toluene.

Do Factor Misallocations Affect Food Security? Evidence from China

The reasonable allocation of grain production factors is of vital importance to food security and agricultural development. To assess the impact of agricultural factor misallocation on food security, this paper, based on the panel data from China spanning from 2005 to 2019, conducted a comprehensive evaluation of agricultural factor misallocation and food security coefficients across 31 provinces in China, using a spatial Durbin model to examine the effects of factor misallocations on food security. The findings are as follows: (1) Production factor misallocation has significant negative impacts on food security, among them, capital misallocation and labor misallocation inhibiting food security in the local and neighboring areas, and land misallocation has a significant negative impact on food security in local areas, while its spatial spillover effect is no longer significant. (2) Mechanism analysis shows that capital misallocation and labor misallocation hinder the development of transportation infrastructure and the transfer of rural labor, thereby reducing food security. Land misallocation has accelerated the construction of transportation infrastructure, promoted the migration of rural labor, and helped ensure food security. (3) The regional heterogeneity test reveals that capital misallocation and labor misallocation hinder food security in major grain-producing areas and both sides of the Hu Huanyong Line (Hu Line). Meanwhile, land misallocation hinders food security development in various grain-producing areas, as well as the southeast of the Hu Line. Based on the above conclusions, this paper proposes suggestions to improve the efficiency of land and labor resource allocation, accelerate the construction of transportation infrastructure, and encourage the transfer speed of surplus agricultural labor.

The coupling coordination between health service supply and regional economy in China: spatio-temporal evolution and convergence

BackgroundThe coordination of health service supply and regional economy is an integral path to promote China’s prosperity.MethodsBased on the coupling mechanism of health service supply and regional economy, we sampled the data from 30 provinces in China from 2009 to 2021 in this study and constructed the evaluation index system. Additionally, we calculated the coupling coordination degree (HED) of the two through the coupling coordination degree model. We further used the kernel density estimation, Moran’s I index, and spatial β convergence model to assess the dynamic evolution trends, spatial aggregation effect, and spatial convergence characteristics of coupling coordination.Conclusion(1) HED in China showed a rising trend during the study period but with large regional differences, forming a gradient distribution pattern of “high in the east and low in the west.” (2) The results of Kernel density estimation show that HED has formed a gradient differentiation phenomenon within each region in China. (3) HED has modeled spatial clustering characteristics during the study period, with high-value clusters mainly appearing in the eastern region and low-value clusters appearing in the northwestern region. (4) There are absolute β-convergence and conditional β-convergence trends in HED in China and the three major regions during the study period, but there is an obvious regional heterogeneity in the control factors. The research provides a reference for accurately implementing policies according to different levels of health service supply and economic development, in addition to narrowing the regional differences of the coupling coordination between the regional economy and health service supply.

The Role of Digital Inclusive Finance in Weakening Real Estate Market Speculation

A multitude of studies have extensively examined strategies for achieving sustainable development in the real estate market. As the pivotal component of land economy, the real estate market plays a crucial role in ensuring its sound operation. However, it is currently undergoing significant adjustments and grappling with rampant speculative activities, resulting in an alarming bubble. By scrutinizing the speculative motivations of different entities, we present a novel perspective on mitigating speculation. Our analysis reveals that digital inclusive finance effectively curbs residents’ and enterprises’ speculative behavior, as evidenced by diminished prevention motivation and investment substitution motivation. Utilizing data from 280 cities, this study measures real estate market speculation by establishing a model that the volatility of the housing market turnover, as a proportion of GDP, deviates from the actual housing demand transactions based on economic fundamentals. Furthermore, it investigates the relationship between digital inclusive finance and real estate market speculation, along with its spatial effects. The findings indicate that digital inclusive finance significantly curbs real estate market speculation and has a negative spatial spillover effect. This research provides a novel model and perspective for exploring real estate market speculation while positively impacting sustainable development within the real estate market.

Engineering Symmetry Breaking in Twisted MoS2-MoSe2 Heterostructures for Optimal Thermoelectric Performance.

Engineering symmetry breaking in thermoelectric materials holds promise for achieving an optimal thermoelectric efficiency. van der Waals (vdW) layered transition metal dichalcogenides (TMDCs) provide critical opportunities for manipulating the intrinsic symmetry through in-plane symmetry breaking interlayer twists and out-of-plane symmetry breaking heterostructures. Herein, the symmetry-dependent thermoelectric properties of MoS2 and MoSe2 obtained via first-principles calculations are reported, yielding an advanced ZT of 2.96 at 700 K. The underlying mechanisms reveal that the in-plane symmetry breaking results in a lowest thermal conductivity of 1.96 W·m-1·K-1. Additionally, the electric properties can be significantly modulated through band flattening and bandgap alteration, stemming directly from the modified interlayer electronic coupling strength owing to spatial repulsion effects. In addition, out-of-plane symmetry breaking induces band splitting, leading to a decrease in the degeneracy and complex band structures. Consequently, the power factor experiences a notable enhancement from ∼1.32 to 1.71 × 10-2 W·m-1·K-2, which is attributed to the intricate spatial configuration of charge densities and the resulting intensified intralayer electronic coupling. Upon simultaneous implementation of in-plane and out-of-plane symmetry breaking, the TMDCs exhibit an indirect bandgap to direct bandgap transition compared to the pristine structure. This work demonstrates an avenue for optimizing thermoelectric performance of TMDCs through the implementation of symmetry breaking.