Spatiotemporal Analysis Research Articles

Spatial-temporal correlation effects and persistent synergistic control benefits of fine particulate matter and carbon emissions in China.

The synergistic reduction of air pollutants and carbon dioxide (CO2) emissions is a key component in achieving China's strategy of pollution and carbon reduction. This study quantitatively evaluates the spatiotemporal linkages between PM2.5 and CO2 emissions, as well as the benefits of sustained synergistic control, across over 360 Chinese cities from 2005 to 2020. We employed spatiotemporal analysis, coupled coordinateness modeling, the Hurst index, and generalized linear mixed modeling (GLMM). The results indicate that PM2.5 concentrations decreased at an annual rate of 1.5μg/m³/year from 2005 to 2020, while CO2 emissions increased at an annual growth rate of 2.3×10⁵ t/year (p<0.05). Spatiotemporal association analysis revealed that the coupling degree of PM2.5 and CO2 emissions increased from 0.64 to 0.81, while the coordination degree decreased from 0.40 to 0.32, exhibiting significant latitudinal zonation and spatial clustering. In terms of synergistic control effectiveness, over 95% of the cities experienced "pollution reduction and carbon enhancement" during the study period, with no cities achieving simultaneous reduction of both PM2.5 and CO2 emissions. Further analysis of the persistence of synergistic control showed that as coupling and coordination levels increased, the city-specific synergistic control efficiency index (CI) decreased. Notably, 86.3% of cities showed persistent trends in their future CIs. The GLMM regression results suggest that the effective integration of scientific and technological innovation, infrastructure development, and economic growth will be pivotal in advancing the successful implementation of China's pollution and carbon reduction strategy.

Spatiotemporal Analysis of Drought and Its Driving Factors in the Yellow River Basin Based on a Standardized Precipitation Evapotranspiration Index

Spatiotemporal Analysis of Drought and Its Driving Factors in the Yellow River Basin Based on a Standardized Precipitation Evapotranspiration Index

Spatio-temporal analysis of surface water quality on urban tanks in Coimbatore

Spatio-temporal analysis of surface water quality on urban tanks in Coimbatore

Long-Term Spatiotemporal Analysis of Crop Water Supply–Demand Relationship in Response to Climate Change and Vegetation Greening in Sanjiang Plain, China

Long-Term Spatiotemporal Analysis of Crop Water Supply–Demand Relationship in Response to Climate Change and Vegetation Greening in Sanjiang Plain, China

Spatiotemporal stability analysis of soil-borne disease dynamics in tomato plants

Spatiotemporal stability analysis of soil-borne disease dynamics in tomato plants

Spatial and Temporal Analysis of Earthquake Events in Kahramanmaraş on 6 February 2023

Earthquakes are one of the most destructive natural disasters that occur as a result of the energy resulting from the movements of the earth's crust spreading and shaking the earth's surface. Since it is not possible to predict when and where earthquakes will occur, it has become possible to make various inferences and take necessary precautions by analyzing previous earthquakes. Spatial and temporal analysis of natural disasters such as earthquakes with geographic information systems provides useful information to local and global decision-makers in measures to be taken and mitigation studies. The objective of this study is to demonstrate that the general orientation of stress accumulation on fault lines can be monitored by weighted average centre points with the use of Geographic Information Systems (GIS). Spatio-temporal analyses of two earthquakes with a magnitude of 7.7 Mw and 7.6 Mw in Kahramanmaraş and aftershocks were performed. Ten-week spatial distributions of the aftershocks were examined with the weighted average center method and it was determined that the earthquake intensity and average centers shifted towards the north. When the spatial distribution of the earthquake centers is examined with the help of standard deviation ellipses weekly, it has been determined that they show orientation along the Eastern Anatolian Fault System.

Spatio-Temporal Analysis of NO2 and CO Production with Sentinel 5P Tropomy Time Series Images: The Case of Torbalı and Kemalpaşa Districts of Izmir Province in Türkiye

Air pollution is an environmental problem that seriously impacts human health and ecosystems and is associated with anthropogenic sources such as industry, traffic, and fossil fuel use. Nitrogen dioxide (NO₂) and carbon monoxide (CO) are the main air pollutants that cause respiratory diseases and contribute to climate change. This study combines Sentinel-5P TROPOMI satellite data with local air quality monitoring data provided by the National Air Quality Monitoring Network (UHKIA) of the Turkish Ministry of Environment, Urbanization, and Climate Change to perform a spatial and temporal analysis of NO₂ and CO emissions in the Kemalpaşa and Torbalı districts of Izmir, Türkiye. For the 17 months between January 2022 and July 2023, analysis using the Google Earth Engine (GEE) platform revealed that emission concentrations increase in winter months due to industrial activities and domestic heating and decrease in summer months due to reduced traffic density and meteorological conditions. It was observed that CO emissions spread over a wider area, while NO₂ emissions are concentrated in industrial and traffic-oriented regions. Accuracy analyses between satellite and UHKIA data showed a general agreement, but small deviations were recorded due to atmospheric effects and resolution differences. These analyses emphasized the importance of the integrated use of satellite and ground data in air pollution monitoring. The results provide a valuable scientific basis for local governments and policymakers for air quality management and strategic decision-making, especially in the Kemalpaşa and Torbalı districts.

Developing Orthogonal Fluorescent RNAs for Photoactive Dual‐Color Imaging of RNAs in Live Cells

AbstractFluorogenic RNA aptamers have revolutionized the visualization of RNAs within complex cellular processes. A representative category of them employs the derivatives of green fluorescent protein chromophore, 4‐hydroxybenzlidene imidazolinone (HBI), as chromophores. However, the structural homogeneity of their chromophoric backbones causes severe cross‐reactivity with other homologous chromophores. This limitation impairs their multiplexing capabilities, which are essential for the simultaneous visualization of multiple RNA species in live cells. Herein, we rationally designed a series of red‐shifted chromophores and employed SELEX‐independent engineering to develop a novel fluorogenic RNA aptamer, mSquash. mSquash displays specific and intense fluorescence upon binding with our red‐shifted chromophore DFHBFPD (Ex/Em=501/624 nm). The mSquash/DFHBFPD allows orthogonal imaging of selected RNA targets alongside the established Broccoli/DFHBI‐1T (Ex/Em=472/501 nm), facilitating multiplexed live cell imaging of various targets. Moreover, we expanded the application of fluorescent RNA to photoactive imaging by constructing two genetically encoded photoactivatable fluorescent RNAs for the first time. This innovative approach allows photoactivatable control of fluorescent RNAs via specific light wavelengths (365 nm and 450 nm), enabling spatiotemporal dual‐color imaging of RNAs in live cells. Our findings represent a significant advancement in fluorescent RNA‐based orthogonal imaging and spatiotemporal analysis of RNAs.

Spatio-temporal analysis of formaldehyde and its association with atmospheric and environmental variables over the Southeast Asian region using satellite data.

This study investigates the spatio-temporal distribution of formaldehyde (HCHO) over the mainland Southeast Asian region (including Northeast India) from 2019 to 2022 using TROPOMI satellite data. HCHO is a key atmospheric trace gas which is influenced by both natural processes and anthropogenic activities. We analyze HCHO levels in relation to atmospheric species including carbon monoxide (CO), nitrogen dioxide (NO2), and environmental factors such as land surface temperature (LST), precipitation (PPT), fire radiative power (FRP), and enhanced vegetation index (EVI). Peak levels of HCHO are particularly observed in March and April, which coincide with the dry and warm seasons and reflect seasonal variability arising from both fluctuating emission sources and regional climate patterns. Correlation analyses reveal significant associations between HCHO and CO (r = 0.727), followed by HCHO and NO2 (r = 0.577) and HCHO and LST (r = 0.539). Conversely, a negative correlation with PPT (r = - 0.233) is observed as HCHO decreases with increased precipitation due to washout. The negative correlation with EVI (r = - 0.319) is unexpected since biogenic emissions are significant contributors to HCHO. This outcome likely results from the confounding effect of precipitation. A robust multiple regression model incorporating these variables is developed which is able to explain 61.8% of the variance in HCHO. It enhances predictive capabilities facilitating the estimation of HCHO distribution and supporting air quality management efforts in the region. This research contributes to understanding the complex interactions of HCHO with atmospheric chemistry and climate variability in Southeast Asia. Insights gained from this study are crucial for informing environmental policies aimed at reducing air pollution and protecting public health in rapidly developing regions.

The origin and transmission of HIV-1 CRF80_0107 among two major first-tier cities in China

BackgroundCRF01_AE and CRF07_BC are the two most prevalent HIV-1 genotypes in China, and the co-circulation of these two genotypes has led to the continuous generation of CRF_0107 viruses in recent years. However, little is known about the origin and spread of CRF_0107 viruses thus far. This study focused on HIV-1 CRF80_0107, which we previously identified among the MSM population in Beijing and Hebei Province, to explore the demographic distribution, transmission links, and temporal-spatial evolutionary features of the HIV-1 CRF80_0107 strain in China.MethodsWith the partial pol region fragment of the HIV-1 CRF80_0107 subtype standard sequence as a reference, BLAST was used to search for highly similar sequences in the Los Alamos HIV Sequence Database, followed by preliminary subtype identification via COMET. Further phylogenetic and recombination breakpoint analyses were conducted to verify the subtypes and recombination patterns. We also performed a distance-based molecular network analysis to identify potential relationships among different HIV-positive individuals. In addition, spatiotemporal evolutionary dynamics analysis of the candidate CRF80_0107 sequences was performed via a Bayesian approach.ResultsA total of 36 partial pol gene sequences of HIV-1 CRF80_0107 were identified from 2009 to 2018 from 5 provinces in China. Phylogenetic and spatial-temporal dynamics analyses indicated that CRF80_0107 likely originated in Beijing around 2009 and spread to Guangdong Province around 2012. Population dynamics analysis revealed that CRF80_0107 experienced a significant increase in population size from 2009 to 2011 and then stabilized. The study also found that the number of cases in Guangdong Province was second only to that in Beijing and formed 2 relatively independent transmission clusters in the MSM population in Shenzhen, Guangdong Province.ConclusionsThe HIV-1 CRF80_0107 strain has spread to cities beyond its origin, particularly the MSM population in Shenzhen city, Guangdong Province, which is an area with a high incidence of HIV. This highlights the importance of continuous monitoring for the emergence and dynamic changes of novel HIV-1 recombinant viruses and the necessity of implementing effective preventive measures targeting specific populations in particular regions.

DOP133 Spatiotemporal analysis of Crohn’s disease reveals PECAM2 signaling at the basis of inflammation-to-fibrosis transition

Abstract Background Crohn’s disease is a chronic inflammatory disease of the bowel often complicated by fibrotic strictures(1,2,3). Medical treatment is lacking, and surgery is commonly required(3). The mechanisms underlying the progression from chronic inflammation to fibrosis are not yet defined. We aim to unravel Crohn’s disease pathogenesis using cutting-edge computational and bioinformatics tools (Figure 1). Methods Spatial transcriptomics was performed on 13 surgical specimens of inflamed and fibrotic Crohn’s disease and healthy controls. Spatial transcriptomics results were integrated with single-cell data to track the cellular and molecular evolution from healthy intestine to fibrosis by employing CellChat(4) and pseudotime analysis. Computational data were confirmed by immunostaining of tissues from an independent cohort of Crohn’s patients. Results We demonstrated that intestinal cytoarchitecture was rearranged while chronic inflammation progressed. Crohn’s disease-associated fibrosis evolved within the mesenchymal compartment, driven by PECAM2 signaling through PECAM1-CD38 interaction. Notably, CD38 positivity was found in the mesenchymal compartment in an independent cohort of Crohn’s disease patients. In parallel, ApoA signaling, particularly APOA1-ABCA interaction, emerged as relevant for maintaining epithelial and stromal homeostasis, while its downregulation was associated with fibrosis development (Figure 2). Conclusion Our results provide insights into CD38-driven fibrosis and suggest that PECAM2 signaling blockade could reduce the development of strictures in patients with Crohn’s disease, potentially offering a new treatment target.

Monitoring phycocyanin in global inland waters by remote sensing: Progress and future developments.

Cyanobacterial blooms are increasingly becoming major threats to global inland aquatic ecosystems. Phycocyanin (PC), a pigment unique to cyanobacteria, can provide important reference for the study of cyanobacterial blooms warning. New satellite technology and cloud computing platforms have greatly improved research on PC, with the average number of studies examining it having increased from 5 per year before 2018 to 17 per year thereafter. Many empirical, semi-empirical, semi-analytical, quasi-analytical algorithm (QAA) and machine learning (ML) algorithms have been developed based on unique absorption characteristics of PC at approximately 620 nm. However, most models have been developed for individual lakes or clusters of them in specific regions, and their applicability at greater spatial scales requires evaluation. A review of optical mechanisms, principles and advantages and disadvantages of different model types, performance advantages and disadvantages of mainstream sensors in PC remote sensing inversion, and an evaluation of global lacustrine PC datasets is needed. We examine 230 articles from the Web of Science citation database between 1900 and 2024, summarize 57 of them that deal with construction of PC inversion models, and compile a list of 6526 PC sampling sites worldwide. This review proposed the key to achieving global lacustrine PC remote sensing inversion and spatiotemporal evolution analysis is to fully use existing multi-source remote sensing big data platforms, and a deep combination of ML and optical mechanisms, to classify the object lakes in advance based on lake optical characteristics, eutrophication level, water depth, climate type, altitude, population density within the watershed. Additionally, integrating data from multi-source satellite sensors, ground-based observations, and unmanned aerial vehicles, will enable future development of global lacustrine PC remote estimation, and contribute to achieving United Nations Sustainable Development Goals inland water goals.

Epidemiological and cluster characteristics of dengue fever in Yunnan Province, Southwestern China, 2013–2023

BackgroundYunnan Province, located in the southwestern part of China and neighboring endemic dengue regions of Southeast Asia, has experienced annual autochthonous outbreaks of dengue fever from 2013 to 2023. This study examines the epidemiological and spatiotemporal clustering characteristics of dengue within the province.MethodsDescriptive epidemiological methods were used to analyse outbreak characteristics. Excel 2019 and ArcGIS 10.3 software were used to establish a case database and for mapping. Spatial autocorrelation and spatio-temporal clustering analysis were employed to study the features of spatial clustering.ResultsFrom 2013 to 2023, 30,666 dengue cases were reported in Yunnan Province, of which 22,806 (74.37%) were indigenous and 7,860 (25.63%) were imported. Imported cases from Myanmar, Laos, and Vietnam were reported each year, with indigenous outbreaks occurring from June to December. The majority of cases occurred among young adults, mainly farmers, service workers, homemakers, and the unemployed. There was a significant temporal and spatial clustering of dengue incidence, with intense local clustering in the southwestern border areas of the province from July to November, particularly in Xishuangbanna, Dehong, Lincang, Pu’er, and Honghe prefectures.ConclusionAutochthonous outbreaks of dengue fever in Yunnan Province occurred predominantly in the border areas adjacent to Myanmar, Laos, and Vietnam. The largest outbreak in the province’s history occurred in 2023, related to the increase in imported cases following the reopening of borders after the COVID-19 pandemic. Dengue fever is considered primarily an imported disease in Yunnan, emphasizing the need for enhanced control of cross-border transmission and mosquito vector management.

Assessing water quality restoration measures in Lake Pampulha (Brazil) through remote sensing imagery.

Urban reservoirs are frequently exposed to impacts from high population density, polluting activities, and the absence of environmental control measures and monitoring. In this study, we investigated the use of satellite imagery to assess restoration measures and support decision-making in a hypereutrophic urban reservoir. Since 2016, Lake Pampulha (Brazil) has undergone restoration measures, including the application of Phoslock®, to mitigate its poor water quality conditions. Satellite images from Landsat-8 (L8) and Sentinel-2 (S2) and historical monitoring data were used to estimate total suspended matter (TSM), chlorophyll-a (Chla), and Secchi disk depth (SDD). We explored both established models from existing literature and novel alternatives, the latter employing machine learning approaches. Model performance was assessed through the coefficient of determination (R ) during calibration and using the root mean square error (RMSE) and its normalization by the observed mean (nRMSE) during validation. Random forest regressor presented superior performance for the three water quality parameters and both satellites. The performance metrics for Landsat-8 were TSM (n=44) R of 0.62; Chla (n=48) R of 0.74; and SDD (n=23) R of 0.67. For Sentinel-2, TSM (n=53) R of 0.72; Chla (n=54) R of 0.64; SDD (n=33) R of 0.89. The selected models were then applied to cloud-free images from 2013 to 2022 to provide modeled TSM, Chla, and SDD values. These were used for spatiotemporal analysis, encompassing cluster analysis, sample comparisons, and trend analysis. Three distinct regions within Lake Pampulha were identified, with the upstream region displaying poorer conditions for TSM, Chla, and SDD. Restoration measures have contributed positively to improving water quality in Lake Pampulha; however, rainy periods pose a challenge due to water quality deterioration associated with urban runoff. Remote sensing has proven to be a robust tool for assisting managers in assessing recovery measures in urban lakes.

Spatiotemporal analysis of future drought vulnerability in the Hindu Kush Region: A case study of the Karnali River Basin

Spatiotemporal analysis of future drought vulnerability in the Hindu Kush Region: A case study of the Karnali River Basin

Spatio-temporal clustering of drowning mortality in Iran, from 2005 to 2022

BackgroundDrowning is a serious and neglected public health threat, and prevention of drowning has a multisectoral nature and requires multidimensional research. Therefore, this study aimed to evaluate the spatio-temporal variation...

Spatial-Structural Mechanisms of Racialized Disparities in Overdose Mortality: A Spatiotemporal Analysis.

Efforts to understand and respond to the opioid crisis have focused on overdose fatalities. Overdose mortality rates (ratios of overdoses resulting in death) are rarely examined though they are important indicators of harm reduction effectiveness. Factors that vary across urban communities likely determine which community members are receiving the resources needed to reduce fatal overdose risk. Identifying communities with higher risk for overdose mortality and understanding influential factors is critical for guiding responses and saving lives. Using incident reports and mortality data from 2018 to 2021, we defined overdose mortality ratios across Milwaukee at the census tract level. To identify neighborhoods displaying higher mortality than predicted, we used Integrated nested Laplace approximation to define standardized mortality ratios (SMRs) for each tract. Geospatial and spatiotemporal analyses were used to identify emerging hotspots for high mortality risk. Overall, mortality was highest in Hispanic and lowest in White communities. Communities with unfavorable SMRs were predominantly Black or Hispanic, younger, less employed, poorer, less educated, and had higher incarceration rates and worse mental and physical health. Communities identified as hotspots for overdoses were predominantly non-White, poorer, and less employed and educated with worse mental and physical health, higher incarceration rates, and less housing stability. The findings demonstrate that overdose mortality rates vary across urban communities and are influenced by racial disparities. A framework that enables identification of challenged communities and guides community responses is needed.

Neonatal mortality in 2001-2017 in France: A cause-specific and spatiotemporal analysis.

In France, the infant mortality rate had a long period of decline, but it stopped decreasing after 2010 and then rose. Neonatal mortality is a large part of infant mortality. The aim of this study was thus to describe its main changes, by cause of death and gestational age, and the main changes in socio-spatial distribution, from 2001 to 2017. For this purpose, we investigated data on neonatal deaths reported in France from 2001 to 2017. Crude, cause-specific and gestational age-specific neonatal mortality rates were computed and an ecological analysis, according to several contextual factors at commune level, was performed using quasi-Poisson regressions. The average neonatal mortality rate was 2.42 per 1000 live births in France during the study period, showing an increase from 2011 onwards. This increase was mostly related to perinatal conditions and more births at very low gestational age. Gestational age-specific neonatal mortality rates did not increase during the period. The analysis of socio-spatial factors showed increased mortality rates in large cities, deprived areas and cities with higher percentages of migrants. This study suggests that a shift in the distribution of gestational age at birth toward low gestational ages may have contributed to the rise in neonatal mortality in France. Furthermore, there is notable spatial heterogeneity in neonatal mortality. Nevertheless, this observation poorly explains the specificity of the high level and recent upsurge in infant mortality in France, in contrast to its European counterparts.

Enhanced spatial analysis assessing the association between PFAS-contaminated water and cancer incidence: rationale, study design, and methods

BackgroundCancer is a complex set of diseases, and many have decades-long lag times between possible exposure and diagnosis. Environmental exposures, such as per- and poly-fluoroalkyl substances (PFAS) and area-level risk factors (e.g., socioeconomic variables), vary for people over time and space. Evidence suggests PFAS exposure is associated with several cancers; however, studies to date have various limitations. Few studies have used rigorous spatiotemporal approaches, and, to our knowledge, none have assessed cumulative exposures given residential histories or incorporated chemical mixture modeling. Thus, spatiotemporal analysis using advanced statistical approaches, accounting for spatially structured and unstructured heterogeneity in risk, can be a highly informative strategy for addressing the potential health effects of PFAS exposure.MethodsUsing population-based incident cancer cases and cancer-free controls in a 12-county area of southeastern Pennsylvania, we will apply Bayesian spatiotemporal analysis methods using historically reconstructed PFAS-contaminated water exposure given residential histories, and other potential cancer determinants over time. Bayesian group index models enable assessment of various mixtures of highly correlated PFAS chemical exposures incorporating mobility/residential history, and contextual factors to determine the association of PFAS-related exposures and cancer incidence.DiscussionThe purpose of this paper is to describe the Enhanced PFAS Spatial Analysis study rationale, study design, and methods.

Spatiotemporal Impact Analysis of Land Surface Temperature Variations: A Case Study of the Northeastern Part of Baghdad Province, Iraq (2000-2024)

Spatiotemporal Impact Analysis of Land Surface Temperature Variations: A Case Study of the Northeastern Part of Baghdad Province, Iraq (2000-2024)