Spatial Perspective Research Articles

St John’s Parish in Cape Town and a history of the lived spatial justice acts: 1956–2020

St John’s is an Anglican parish in the city of Cape Town. Its six member churches are in the southern suburbs of the city. In its history as a parish, it has gained land space in the southern suburbs of Cape Town. In recent years, numerous congregants and involved leaders began to think on how to use that land space in a way that brings justice in this area of the city. This article seeks to look at the parish’s historical spatial actions, in order to see what insights toward spatial justice may arise. This article provides detail information regarding the history of the Anglican Church and St John’s Parish. It also explores in detail the spatial knowledge and perspectives of some leaders and members of the parish. Finally, it explores what these interviews suggest for a church in search of spatial justice.Contribution: This article aligns with the scope of the journal as it created a conversation around church history, spatial history, and spatial justice within the South African context.

Tissue niches and immunopathology through the lens of spatial tissue profiling techniques.

Spatial organization plays a fundamental role in biology, influencing the function of biological structures at various levels. The immune system, in particular, relies on the orchestrated interactions of immune cells with their microenvironment to mount protective or pathogenic immune responses. The COVID-19 pandemic has underscored the significance of studying immunity within target organs to understand disease progression and severity. To achieve this, multiplex histology and spatial transcriptomics have proven indispensable in providing a spatial context to protein and gene expression patterns. By combining these techniques, researchers gain a more comprehensive understanding of the complex interactions at the cellular and molecular level in distinct tissue niches, key functional units modulating health and disease. In this review, we discuss recent advances in spatial tissue profiling techniques, highlighting their advantages over traditional histopathology studies. The insights gained from these approaches have the potential to revolutionize the diagnosis and treatment of various diseases including cancer, autoimmune disorders, and infectious diseases. However, we also acknowledge their challenges and limitations. Despite these, spatial tissue profiling offers promising opportunities to improve our understanding of how tissue niches direct regional immunity, and their relevance in tissue immunopathology, as a basis for novel therapeutic strategies and personalized medicine.

Oil price distortion and its impact on green economic efficiency in China’s transportation: A spatial effect perspective

China's oil prices have been supervision for a long time, which has led to oil price distortion (OPD) to some extent. To figure out whether OPD will impede the sustainability of energy, economy, and environment (measured by green economic efficiency (GEE)), this research first estimates OPD from 2003 to 2019. Then, using a non-radial direction distance function under nonconvex technology assumptions, this research calculates the provincial GEE by incorporating capital, labor, energy, and environment efficiencies into a unified framework. Furthermore, the OPD-GEE nexus and their influencing channels are analyzed using the spatial Durbin model. The results indicate that there are serious OPD and the average GEE in the transportation is relatively low. Local OPD correction can improve local GEE but OPD correction in spatially related regions will cause local GEE loss. The heterogeneity analysis implies that the GEE promotion effect resulting from local OPD correction is greater in oil-rich regions, new energy vehicle pilot regions, and regions with water transport. Furthermore, the mechanism analysis implies that OPD correction can improve local GEE through optimizing energy structure, mitigating resource mismatch, and promoting green technological progress; meanwhile, OPD correction in spatially adjacent regions can cause local GEE losses by exacerbating their resource mismatch. This study offers a new understanding of the impact of OPD on GEE and contributes to the promotion of sustainable development through market-oriented price reforms.

Distribution characteristics and influencing factors of household consumption carbon emissions in China from a spatial perspective

Household consumption carbon emissions (HCCEs) have become the main growth point of China's carbon emissions in the future. It is important to investigate the factors affecting the demand-side carbon emissions in order to find the accurate entry point of emission reduction and achieve carbon peaking and carbon neutrality goals. Different from previous studies, this study analyzed the spatial and temporal evolution characteristics of provincial HCCEs in China from a spatial perspective by using the Theil index and spatial auto-correlation and explored the key influencing factors and spatial spillover effects of HCCEs in different regions by using an econometric model. The results of the study showed that: (1) Per capita HCCEs increased by 11.90% annually, and the eastern region > northeastern region > western region > central region. (2) There were regional differences in per capita HCCEs, but the decrease was significant at 40.32%. (3) The spatial agglomeration effect of per capita HCCEs was significant, and the hot spots were mainly concentrated in the eastern coastal areas. (4) From the national level, every 1% increase in residents' consumption power would increase HCCEs by 2.489%. Which was the main factor for the growth of HCCEs, while the increase in fixed asset investment would restrain HCCEs. At the regional level, the change in population size significantly increased the HCCEs in the eastern and central regions. While for the western region, a 1% increase in population would reduce the HCCEs by 0.542%. For the eastern and central regions, the degree of aging and the consumption structure of residents could suppress regional HCCEs. However, the consumption structure of residents drove the growth of HCCEs in the western region. For the Northeast region, residents' consumption capacity and cooling degree days were the main factors for the growth of residents' consumption, while fixed asset investment could inhibit the growth of HCCEs.

The spatial turn in Catalan cultural studies

In recent years several projects have emerged that address Catalan culture from a spatial point of view. This article aims to review the theoretical framework of what is known as the spatial turn in order to evaluate its main contributions, and some potential future orientations, in Catalan cultural studies. The paper is divided into three parts. The first part provides an overview of the main approaches inside spatial studies. In the second part, a selection of studies on Catalan culture and literature is reviewed from the perspective of their connections with spatial studies. Finally, the third part presents three case studies focused on examples from Catalan poetry, film, and science fiction and interprets them from three different spatial perspectives: the first based on the phenomenological perspective, the second on the methodology of emotional and sensory geography and the concept of cognitive mapping , and the third on the spatial figurations of postmodern empire. This final section aims to broaden the applications of spatial analysis, extending it to the interpretation of an open, diverse selection of representations of contemporary Catalan culture.

Perspective Taking Reflects Beliefs About Partner Sophistication: Modern Computer Partners Versus Basic Computer and Human Partners.

We investigate partner effects on spatial perspective taking behavior in listeners, comparing behavior with a human versus a computer partner (Experiments 1 and 2), and with computer partners of different perceived capabilities (Experiment 3). Participants responded to spoken instructions from their partner which could be interpreted egocentrically (from their own perspective) or othercentrically (from their partner's perspective). In contrast to earlier work, we found that participants were more egocentric with a computer than a human partner. Participants were also more egocentric with a computer partner that appeared more modern and capable, compared to one that appeared outdated and limited in ability. Our results show that perspective taking behavior is sensitive to information about one's partner; in particular, listeners consider their partner's potential ability to collaborate, adjusting their egocentric tendencies accordingly. Moreover, we highlight what appears to be a shift in listeners' expectations regarding computers' collaborative capabilities, leading to greater willingness to push the burden of perspective taking onto a computer partner.

Influence of urbanization on schistosomiasis infection risk in Anhui Province based on sixteen year's longitudinal surveillance data: a spatio-temporal modelling study

BackgroundUrbanization greatly affects the natural and social environment of human existence and may have a multifactoral impact on parasitic diseases. Schistosomiasis, a common parasitic disease transmitted by the snail Oncomelania hupensis, is mainly found in areas with population aggregations along rivers and lakes where snails live. Previous studies have suggested that factors related to urbanization may influence the infection risk of schistosomiasis, but this association remains unclear. This study aimed to analyse the effect of urbanization on schistosomiasis infection risk from a spatial and temporal perspective in the endemic areas along the Yangtze River Basin in China.MethodsCounty-level schistosomiasis surveillance data and natural environmental factor data covering the whole Anhui Province were collected. The urbanization level was characterized based on night-time light data from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and the National Polar-Orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The geographically and temporally weighted regression model (GTWR) was used to quantify the influence of urbanization on schistosomiasis infection risk with the other potential risk factors controlled. The regression coefficient of urbanization was tested for significance (α = 0.05), and the influence of urbanization on schistosomiasis infection risk was analysed over time and across space based on significant regression coefficients. Variables studied included climate, soil, vegetation, hydrology and topography.ResultsThe mean regression coefficient for urbanization (0.167) is second only to the leached soil area (0.300), which shows that the urbanization is the most important influence factors for schistosomiasis infection risk besides leached soil area. The other important variables are distance to the nearest water source (0.165), mean minimum temperature (0.130), broadleaf forest area (0.105), amount of precipitation (0.073), surface temperature (0.066), soil bulk density (0.037) and grassland area (0.031). The influence of urbanization on schistosomiasis infection risk showed a decreasing trend year by year. During the study period, the significant coefficient of urbanization level increased from − 0.205 to − 0.131.ConclusionsThe influence of urbanization on schistosomiasis infection has spatio-temporal heterogeneous. The urbanization does reduce the risk of schistosomiasis infection to some extend, but the strength of this influence decreases with increasing urbanization. Additionally, the effect of urbanization on schistosomiasis infection risk was greater than previous reported natural environmental factors. This study provides scientific basis for understanding the influence of urbanization on schistosomiasis, and also provides the feasible research methods for other similar studies to answer the issue about the impact of urbanization on disease risk.

A Head/Tail Breaks-Based Approach to Characterizing Space-Time Risks of COVID-19 Epidemic in China’s Cities

The novel coronavirus pneumonia (COVID-19) pandemic has caused enormous impacts around the world. Characterizing the risk dynamics for urgent epidemics such as COVID-19 is of great benefit to epidemic control and emergency management. This article presents a novel approach to characterizing the space-time risks of the COVID-19 epidemic. We analyzed the heavy-tailed distribution and spatial hierarchy of confirmed COVID-19 cases in 367 cities from 20 January to 12 April 2020, and population density data for 2019, and modelled two parameters, COVID-19 confirmed cases and population density, to measure the risk value of each city and assess the epidemic from the perspective of spatial and temporal changes. The evolution pattern of high-risk areas was assessed from a spatial and temporal perspective. The number of high-risk cities decreased from 57 in week 1 to 6 in week 12. The results show that the risk measurement model based on the head/tail breaks approach can describe the spatial and temporal evolution characteristics of the risk of COVID-19, and can better predict the risk trend of future epidemics in each city and identify the risk of future epidemics even during low incidence periods. Compared with the traditional risk assessment method model, it pays more attention to the differences in the spatial level of each city and provides a new perspective for the assessment of the risk level of epidemic transmission. It has generality and flexibility and provides a certain reference for the prevention of infectious diseases as well as a theoretical basis for government implementation strategies.

Human or climate? Differentiating the anthropogenic and climatic drivers of lake storage changes on spatial perspective via remote sensing data

Lakes are an essential part of the terrestrial water system in which storage changes are controlled by water balance and human impact. Although there are some attempts to define storage changes on a global scale, examination of spatial relations is poorly quantified. In this study, therefore, lake storage changes have been investigated using remote-sensing-derived data around the globe. Hence, 372 artificial/natural lakes were obtained, covering between 1992 and 2019. Watersheds belong to river was extracted via HydroSHED data. Based on watershed, dominant climate types were determined via Köppen-Geiger classification. Similarly, the areal average CRU TS v.4.05 monthly gridded precipitation time series and human footprint data based on watersheds were obtained to understand the drivers of lake storage changes. The nonparametric Mann-Kendall and Sen's slope trend analyses were applied to the lake storage change and precipiation values in order to determine long-term increases and decreases. A bivariate map was constructed between storage changes trend vs precipitation trend and human footprint to reveal the drivers of lake storage changes in terms of spatial aspects. The trend analysis and bivariate map results show that North America, the East African Highlands, and the Tibet plateau are important increasing hotspots, where precipitation is a significant driver for storage oscillations, except for the Tibet plateau. Besides, the Brazilian Highlands, Pacific Mountain System, and Intermontane of conterminous USA are other decreasing hotspots in which human footprint and decreasing precipitation collectively affect these changes. Furthermore, results clearly show that anthropogenic influence is low in the northern and mountainous areas, and storage changes have a linear relationship with precipitation. In contrast, intense human climate interaction influences lake changes in plains areas and arid/temperate climates.

Disasters Worth Remembering: Stories of Destruction and Reconstruction as Told in the Texas Official Historical Markers

ABSTRACT This study examines patterns of disaster commemoration in Texas as witnessed in 1,590 markers erected by the state between 1936 and 2019. In the first half of the analysis, we highlight spatiotemporal trends of commemoration, which focus on a few well-known historic disasters that occurred between 1875 and 1916. From a spatial perspective, markers concentrate in the coastal cities of Galveston, Houston, and Corpus Christi, but no discernible temporal patterns emerge concerning the timing of commemoration—that is, when a marker is erected. After looking at the when and where of commemoration, in the second part of the analysis we look at how each disaster term is described in the text of the markers. Here, the narrative is one of initial destruction quickly followed by recovery and the reestablishment of communities. We also note how the selective nature of commemoration extends to disaster.

How FinTech affects total factor energy efficiency? Evidence from Chinese cities

The advancement of Financial Technology (FinTech) is crucial for government entities, the National Grid, and various energy corporations to facilitate the transition towards sustainable and green production methods. This study investigates the relationship between FinTech and Total Factor Energy Efficiency (TFEE) using data from a selected sample of 254 city groups in China. We examine how the development of FinTech impacts TFEE from both non-spatial and spatial perspectives. The results from the non-spatial panel model indicate that FinTech development has a significant positive impact on TFEE. Comparative studies were conducted using fixed effects (FE), feasible generalized least squares (FGLS) models, and system generalized method of moments (GMM) models, and the main findings remained consistent, confirming the robustness of our conclusions. Spatial autocorrelation results reveal a significant positive spatial spillover effect on TFEE. Both the spatial Durbin model and the dynamic spatial Durbin model demonstrate that FinTech also has a significant positive impact on TFEE, and this effect increases over time. These conclusions remain robust even after considering various spatial weight matrices and alternative methods for calculating TFEE. Additionally, we discovered that the digital economy plays a vital role in strengthening the relationship between FinTech and TFEE. Heterogeneity analysis indicates that, compared to cities without resource-based economies, FinTech development in growing resource-based cities has a more substantial impact on TFEE.

Spatial-temporal offshore wind speed characteristics prediction based on an improved purely 2D CNN approach in a large-scale perspective using reanalysis dataset

Accurate prediction of spatial–temporal wind speed with a large-scale perspective is vital for the wind farm dynamic regulation, the floating wind turbine deployment and its early warning. Machine-learning approaches have been applied for wind prediction to alleviate the computational challenges of traditional numerical models. Yet, most previous studies emphasized the forecasting accuracy employing location-specific dataset, such methodologies are site-specific and ignore the importance of spatial–temporal fidelity required by the 2D regional wind farm blueprint. Furtherly, the Recurrent Neural Networks based approach they employed exhibit low efficiency in terms of model convergence on the aspect of practical engineering purposes. This study firstly proposed an improved Unet-based machine learning method using residual learning to predict the spatial–temporal wind field in the North Atlantic Ocean with great potential wind-energy in a large spatial-scale perspective. Several state-of-the-art machine learning methods were established for a better performance comparison. A public reanalysis dataset with very high resolution (0.25° x 0.25°) was employed to verify the proposed method with multi-steps ahead predictions, the model performance was furtherly compared with the rest approaches under different criteria, consisting of Pearson correlation coefficient (PCC), Mean absolutely error (MAE), Root mean square error (RMSE) and symmetric mean absolute percentage error (SMAPE), all exhibiting considerable improvements. The overall forecasting RMSE for the proposed method dropped to 0.088 m/s and 0.087 m/s for wind component U and V correspondingly. Furtherly, comprehensive discussions were provided with both temporal and spatial perspective, revealing that the proposed model can offer an accurate 2D wind speed prediction along timeline (the PCC equals to 0.99). The proposed data-driven method presents promising potential to accurately preserve the spatial-resolved patterns which are variant in both spatial and temporal dimensions, and show superiority in terms of computational efficiency in terms of achieving large-scale wind pattern predictions.

A multi-graph spatial-temporal attention network for air-quality prediction

Air pollution poses a grave threat to human health and everyday life. Accurate air-quality prediction plays a crucial role in effectively preventing and controlling air pollution. A multi-graph spatial-temporal attention network is proposed to predict the air quality in a given area by analyzing the interconnections between stations and the individual characteristics of each station. Firstly, this paper constructs multi-scale spatial-temporal graphs from spatial and temporal perspectives to effectively capture the interconnections between stations, thereby comprehensively understanding the spatial-temporal relationships among them. Secondly, incorporating the nonlinear temporal correlation of station data, this paper proposes a temporal multi-graph attention-fusion module to integrate information from both neighboring stations and the station itself. Finally, predictions are made using a spatial-temporal graph network. The experiments in this paper were based on air-quality data from Beijing and Tianjin, and the following experimental results were obtained. (1) The proposed model outperforms the baselines significantly in both single-step and multi-step prediction tasks. (2) The ablation study confirms that the graph constructed in this paper contributes to improving the performance of the model. (3) Comparing the performance of attention components, the proposed attention mechanism exhibits better performance than other attention mechanisms.

Urban polycentrism and total-factor energy efficiency: An analysis based on the night light data

Improving energy efficiency is crucial for environmental protection and resource conservation. Considerable urban system distribution is vital to realizing high energy efficiency. However, studies on energy efficiency from this spatial perspective remain limited. Using the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) and visible infrared imaging radiometer suite (VIIRS) nighttime light data, we measured urban polycentrism (UPC) and employed improved stochastic frontier analysis (SFA) for total-factor energy efficiency (TEE) measurement. Analyzing panel data from 332 prefecture-level cities across 27 provinces of China, we used the spatial economic and threshold models to calculate UPC's impact on TEE and its threshold conditions. The results revealed UPC's effect on TEE and the control variables (economic development, urbanization rate, and industrial structure). The pattern suggests that only when economic development, urbanization rate, and industrial structure reach a certain (range) level in some zones, does the effect change from one condition (inhibition/promotion) to another (promotion/inhibition). Under certain conditions, a win-win situation between UPC and TEE is achieved. Finally, relevant policy recommendations for energy efficiency improvements based on urbanization are discussed. To the best of our knowledge, this is the first study to systematically investigate the impact of UPC on TEE at the city level.

Delineating and Characterizing the Metropolitan Fringe Area of Shanghai—A Spatial Morphology Perspective

The metropolitan fringe area is of great significance to a city’s future growth. However, relatively little attention has been paid to delineating and characterizing the metropolitan fringe area from a spatial morphology perspective, which contributes to the planning and design of metropolitan fringe areas. Therefore, the aim of this study is to develop a morphology–based method to delineate the metropolitan fringe area and investigate the characteristics of its spatial morphology. Drawing upon a large–scale dataset on the spatial morphology of Shanghai, this study finds that the metropolitan fringe area is generally circular in shape and is dominated by residential, industrial, agricultural and forestry land. The metropolitan fringe area accounts for 24.65% of the total area of Shanghai and is mainly located between its outer ring and suburban ring areas. The distributions of spatial characteristics of the metropolitan fringe area suggest that the area has a relatively lower level in terms of building height, building density, and development intensity. Furthermore, the metropolitan fringe area of Shanghai contains five key spatial elements, including residential and industrial clusters, shadow spaces accompanying clusters, corridor lines, green wedges, and surfaces. The interaction of the five spatial elements lays the foundation for the prototype of the spatial structure of the metropolitan fringe area of Shanghai, which is of great significance to understanding the heterogeneity within the metropolitan fringe area in terms of the distribution of spatial morphological characteristics. Such heterogeneity also needs to be considered in the planning and design of the metropolitan fringe area.

The Influence of New-Type Urbanization and Environmental Pollution on Public Health: A Spatial Durbin Model Study

The rapid pace of urbanization in recent years, accompanied by the tension between urbanization and environmental pollution as well as public health, has become increasingly prominent, potentially constraining the normal pace of urbanization development, environmental sustainability and public health enhancement. This paper aims to clarify the relationship between new urbanization, environmental pollution and public health from both local and spatial perspectives, using a spatial Durbin model and a mediating effects model based on panel data from 275 prefecture-level cities in China from 2011 to 2020 and to test the existence of regional heterogeneity with a sub-sample of three major regions: eastern, central and western China. After incorporating environmental pollution as a variable across the entire scope of China, it was found that the new-type urbanization was related to public health in a ‘positive U-shaped’ form in terms of both local and spatial spillover effects (β2 = 14.5620, β3 = −17.8938, p < 0.05; θ2 = 19.2527, θ3 = −29.0973, p < 0.1) and environmental pollution exerts a negative impact on public health (β1 = 6.3704, θ1 = 2.5731, p < 0.05). A “reverse U-shaped” local effect was observed between new-type urbanization and environmental pollution (β2 = 0.6281, β3 = −0.5315, p < 0.05). Environmental pollution plays a partially mediating role in the impact mechanism of new-type urbanization on public health. There was regional heterogeneity in the relationship between new-type urbanization, environmental pollution and public health. The empirical results for the western regions and the whole of China were generally consistent, but the differences were significant between the eastern and central regions. In the eastern region, new-type urbanization and public health were related in an ‘inverted U-shaped’ form from the perspective of local and spatial spillover effect, and in the central region, there was an ‘inverted U-shaped’ form from the perspective of local effect. There was a threshold effect relationship between new-type urbanization and environmental pollution and between new-type urbanization and public health. At the same time, there was regional heterogeneity in the relationships between the three. Therefore, this paper argues that governments should formulate scientific urban planning and sustainable development policies that take into account the actual situation of each region and aim to promote sustainable urbanization, environmental quality and public health as a whole.

Spatially Resolved Co-Imaging of Polyhalogenated Xenobiotics and Endogenous Metabolites Reveals Xenobiotic-Induced Metabolic Alterations.

A large group of polyhalogenated compounds has been added to the list of persistent organic pollutants in a global convention endorsed by over 100 nations. Once entering the biotas, these pollutants are transported to focal sites of toxicological action and affected endogenous metabolites, which exhibited distinct tissue or organ distribution patterns. However, no study is available to achieve simultaneous mapping of the spatial distributions of xenobiotics and endogenous metabolites for clarifying the molecular mechanism of toxicities. Herein, we present a sensitive mass spectrometry imaging method─tetraphenyl phosphonium chloride-enhanced ionization coupled with air flow-assisted ionization-Orbitrap mass spectrometry─which simultaneously determined the spatial distributions of polyhalogenated xenobiotics and endogenous metabolites. The spatially resolved toxicokinetics and toxicodynamics of typical polyhalogenated compounds (chlorinated paraffins (CPs) and hexabromocyclododecane (HBCD)) were assessed in zebrafish. Co-imaging of polyhalogenated compounds and metabolites visualized the major accumulation organs and maternal transfer of HBCD and CPs, and it clarified the reproductive toxicity of HBCD. CPs were accumulated in the liver, heart, and brain and decreased the concentrations of polyamine/inosine-related metabolites and lipid molecules in these organs. HBCD accumulated in the ovary and was effectively transferred to eggs, and it also disrupted normal follicular development and impaired the production of mature eggs from the ovary by inhibiting expressions of the luteinizing hormone/choriogonadotropin receptor gene. The toxic effects of metabolic disruptions were validated by organ-specific histopathological examinations. These results highlight the necessity to assess the distributions and bioeffects of pollutants in a spatial perspective.

Indoor public spaces and the mobility of religious knowledge in late medieval Deventer and Amiens

Abstract This article will address the transfer of religious knowledge in two north-western European cities from a spatial perspective. Our starting point will be the thesis that immobile knowledge in closed places of knowledge (lieux de savoir) does not exist: (religious) knowledge only becomes functional in the dynamic encounter with users and it is disseminated through social networks. This approach, which involves the movement from closed spaces to processes and practices, also entails a questioning of outdoor and indoor spaces; of private and public spaces. The article will take its start from several case-studies of indoor public spaces, the transmission of religious knowledge and social networks, based on documentation from Deventer in the northern Low Countries and Amiens on the border of the southern Low Countries and France.

A Spatial and Adversarial Representation Learning Approach for Land Use Classification with POIs

Points-of-interests (POIs) have been proven to be indicative for sensing urban land use in numerous studies. However, recent progress mainly relies on spatial co-occurrence patterns among POI categories, which falls short in utilizing the rich semantic information embodied in POI hierarchical categories and in sensing the spatial distribution patterns of POIs at an individual zonal scale. In this context, we present a spatial and adversarial representation learning approach (SARL) for predicting land use of urban zones with POIs. SARL deeply mines the information from POIs from both spatial and categorical perspectives. Specifically, we first utilize a convolutional neural network to sense the spatial distribution patterns of POIs in each urban zone. We then leverage an autoencoder and an adversarial learning strategy to mine the POI categorical information in all hierarchical levels, which emphasizes the prominent and definitive POIs while preserves the overall POI hierarchical structures in each zone. Finally, we fuse these information from the two perspectives via a Wide & Deep network and carry out land use prediction with the fused embeddings. We conduct comprehensive experiments to validate the effectiveness of SARL in four European cities with real-world data. The results demonstrate that SARL substantially outperforms several competitive baselines.

The role of temporal scales in extracting dominant meteorological drivers of major airborne pollutants

Abstract. The influence of individual meteorological factors on different airborne pollutants has been widely studied. However, few studies have considered the effect of temporal scales on the extracted pollutant–meteorology association. Based on convergent cross mapping (CCM), we compared the influence of major meteorological factors on PM2.5, PM10 and O3 concentrations in 2020 at the 3 and 24 h scales respectively. In terms of the extracted dominant meteorological factor, the consistence between the analysis at the 3 and 24 h scales was relatively low, suggesting a large difference even from a qualitative perspective. In terms of the mean p value, the effect of temporal scale on PM (PM2.5 and PM10)–meteorology association was consistent, yet largely different from the temporal-scale effect on O3. Temperature was the most important meteorological factor for PM2.5, PM10 and O3 across China at both the 3 and 24 h scales. For PM2.5 and PM10, the extracted PM–temperature association at the 24 h scale was stronger than that at the 3 h scale. Meanwhile, for summer O3, due to strong reactions between precursors, the extracted O3–temperature association at the 3 h scale was much stronger. Due to the discrete distribution, the extracted association between all pollutants and precipitation was much weaker at the 3 h scale. Similarly, the extracted PM–wind association was notably weaker at the 3 h scale. Due to precursor transport, summertime O3–wind association was stronger at the 3 h scale. For atmospheric pressure, the pollutant–pressure association was weaker at the 3 h scale except for summer, when interactions between atmospheric pressure and other meteorological factors were strong. From the spatial perspective, pollutant–meteorology associations at 3 and 24 h were more consistent in the heavily polluted regions, while extracted dominant meteorological factors for pollutants demonstrated more difference at 3 and 24 h in the less polluted regions. This research suggests that temporal scales should be carefully considered when extracting natural and anthropogenic drivers for airborne pollution.