Statistical modeling of spatially stratified heterogeneous data

Spatial statistics is an important methodology for geospatial data analysis. It has evolved to handle spatially autocorrelated data and spatially (locally) heterogeneous data, which aim to capture the first and second laws of geography, respectively. Examples of spatially stratified heterogeneity (SSH) include climatic zones and land-use types. Methods for such data are relatively underdeveloped compared to the first two properties. The presence of SSH is evidence that nature is lawful and structured rather than purely random. This induces another “layer” of causality underlying variations observed in geographical data. In this article, we go beyond traditional cluster-based approaches and propose a unified approach for SSH in which we provide an equation for SSH, display how SSH is a source of bias in spatial sampling and confounding in spatial modeling, detect nonlinear stochastic causality inherited in SSH distribution, quantify general interaction identified by overlaying two SSH distributions, perform spatial prediction based on SSH, develop a new measure for spatial goodness of fit, and enhance global modeling by integrating them with an SSH q statistic. The research advances statistical theory and methods for dealing with SSH data, thereby offering a new toolbox for spatial data analysis

Influence of Urban Scale and Urban Expansion on the Urban Heat Island Effect in Metropolitan Areas: Case Study of Beijing–Tianjin–Hebei Urban Agglomeration

Global large-scale urbanization has a deep impact on climate change and has brought great challenges to sustainable development, especially in urban agglomerations. At present, there is still a lack of research on the quantitative assessment of the relationship between urban scale and urban expansion and the degree of the urban heat island (UHI) effect, as well as a discussion on mitigation and adaptation of the UHI effect from the perspective of planning. This paper analyzes the regional urbanization process, average surface temperature variation characteristics, surface urban heat island (SUHI), which reflects the intensity of UHI, and the relationship between urban expansion, urban scale, and the UHI in the Beijing–Tianjin–Hebei (BTH) urban agglomeration using multi-source analysis of data from 2000, 2005, 2010, and 2015. The results show that the UHI effect in the study area was significant. The average surface temperature of central areas was the highest, and decreased from central areas to suburbs in the order of central areas > expanding areas > rural residential areas. From the perspective of spatial distribution, in Beijing, the southern part of the study area, the junction of Tianjin, Langfang, and Cangzhou are areas with intense SUHI. The scale and pace of expansion of urban land in Beijing were more than in other cities, the influencing range of SUHI in Beijing increased obviously, and the SUHI of central areas was most intense. The results indicate that due to the larger urban scale of the BTH urban agglomeration, it will face a greater UHI effect. The UHI effect was also more significant in areas of dense distribution in cities within the urban agglomeration. Based on results and existing research, planning suggestions are proposed for central areas with regard to expanding urban areas and suburbs to alleviate the urban heat island effect and improve the resilience of cities to climate change

Rising vulnerability of compound risk inequality to ageing and extreme heatwave exposure in global cities

Abstract Continued warming trends lead to an increasing risk of exposure to extreme heatwaves, which threaten the health of urban residents, especially the ageing population. Here, we project the spatiotemporal trend of future exposure risk across 9188 global urban settlements between 2020 and 2100 under the shared socioeconomic pathway (SSP) 2-4.5 and SSP5-8.5 scenarios. Results show that urban heatwave exposure risk increases by 619% and 1740% for SSP2-4.5 and SSP5-8.5, respectively, and by 1642% to 5529% for the elderly. Notably, 69% of the elderly exposure risk comes from middle-income countries, where the increasing trend on the regional average is 1.2 times higher than that of high-income countries. There is an increasing trend towards greater concentration on large cities, especially in low- and lower-middle-income countries. In high-income countries, climate effects contribute 39% to 58% of increasing exposure for elderly individuals, whereas ageing effects play more prominent role in lower-income countries. This emphasizes the disproportionately higher heat-related burden for elderly individuals and inequitable trends in lower income countries. Understanding the vulnerable and priority regions in future heatwave exposure will inform adaptation strategies to support urban climate-resilient development

Effects of Tillage and N Applications on the Cassava Rhizosphere Fungal Communities

Cassava (Manihot esculenta Crantz) is mainly cultivated in marginal land in the south of China where seasonal drought stress occurs frequently and the soil becomes more compact year by year. The study aimed to explore the effect of Fenlong tillage (FLT) combined with nitrogen applications on cassava rhizosphere soil particle composition and fungal community diversity. Conventional tillage (CT) was set as the control. The results indicated that the contents of clay and silt of the cassava rhizosphere soil were influenced by the tillage method, nitrogen (N), and their interaction. There was no difference in the richness and diversity of rhizosphere soil fungal communities among all treatments in 2019, while the richness of FLT was lower than that of CT in 2020. FLT caused a stronger influence on the community structure of rhizosphere fungi than N applications in the first year. The differences in the community structure of all treatments were reduced by continuous cropping of cassava in the second year. The top 10 dominant rhizosphere fungi at the class level of cassava found in 2019 and 2020 were Sordariomycetes, Dothideomycetes, Eurotiomycetes, Agaricomycetes, Intramacronucleata, norank_p__Mucoromycota, unclassified_p__Ascomycota, unclassified_k__Fungi, Pezizomycetes, and Glomeromycetes, which had an important relationship with soil pH, activity of urease, available nitrogen, available phosphorus, organic matter, and clay. These results indicated that FLT created a better soil environment for cassava growth than CT, thus promoting the formation of more stable rhizosphere fungal community structures

Statistical Modeling of Spatially Stratified Heterogeneous Data

Spatial statistics is an important methodology for geospatial data analysis. It has evolved to handle spatially autocorrelated data and spatially (locally) heterogeneous data, which aim to capture the first and second laws of geography, respectively. Examples of spatially stratified heterogeneity (SSH) include climatic zones and land-use types. Methods for such data are relatively underdeveloped compared to the first two properties. The presence of SSH is evidence that nature is lawful and structured rather than purely random. This induces another “layer” of causality underlying variations observed in geographical data. In this article, we go beyond traditional cluster-based approaches and propose a unified approach for SSH in which we provide an equation for SSH, display how SSH is a source of bias in spatial sampling and confounding in spatial modeling, detect nonlinear stochastic causality inherited in SSH distribution, quantify general interaction identified by overlaying two SSH distributions, perform spatial prediction based on SSH, develop a new measure for spatial goodness of fit, and enhance global modeling by integrating them with an SSH q statistic. The research advances statistical theory and methods for dealing with SSH data, thereby offering a new toolbox for spatial data analysis

Enhancement of Fenton processes at initial circumneutral pH for the degradation of norfloxacin with Fe@Fe₂O₃ core-shell nanomaterials

<p>The degradation of norfloxacin by Fenton reagent with core-shell Fe@Fe₂O₃ nanomaterials was studied under neutral conditions in a closed batch system. Norfloxacin was significantly degraded (90%) in the Fenton system with Fe@Fe₂O₃ in 30 min at the initial pH 7.0, but slightly degraded in Fenton system without Fe@Fe₂O₃ under the same experimental conditions. The intermediate products were investigated by gas chromatography-mass spectrometry, and the possible Fenton oxidation pathway of norfloxacin in the presence of Fe@Fe₂O₃ nanowires was proposed. Electron spin resonance spectroscopy was used to identify and characterize the free radicals generated, and the mechanism for norfloxacin degradation was also revealed. Finally, the reusability and the stability of Fe@Fe₂O₃ nanomaterials were studied using x-ray diffraction and scanning electron microscope, which indicated that Fe@Fe₂O₃ is a stable catalyst and can be used repetitively in environmental pollution control.</p

Development of Dual-Aptamers for Constructing Sandwich-Type Pancreatic Polypeptide Assay

Pancreatic polypeptide (PP) is a specific biomarker of nonfunctional pancreatic neuroendocrine tumors (NF-pNETs). Clinical significance of PP inspires researchers to make great efforts in developing sensitive and specific sensors. However, there is no existing biosensor for detecting PP that combines facility and functionality. Addressing this challenge, a pair of aptamers which could be used to develop a sandwich assay for PP is reported. First, several high affinity aptamers are screened through graphene oxide-based SELEX, and appropriate dual-aptamers which could bind to different epitopes of PP are identified through fluorescence assays. Then the feasibility of the dual-aptamers for constructing the sandwich assay is validated via dynamic light scattering. This sandwich assay shows considerable sensitivity and specificity. The above results imply that the dual-aptamers have the potential toward developing novel sensors for PP in clinical samples