Spatiotemporal Analysis Research Articles

Spatiotemporal Analysis, Predictive Modeling, and Driving Mechanism Investigation of Carbon Storage Dynamics in Changde City Under the Framework of LUCC

In the context of the worldwide attention on climate change, examining how land use relates to the carbon sink functions of regions is essential. This research innovatively utilizes the 2000–2020 land use data of Changde City, integrating the PLUS and InVEST models to analyze spatiotemporal changes and predict scenarios. It also combines the parameter geodetector and multiscale geographically weighted regression model to dissect driving factor distributions and mechanisms, capture interactions and multiscale impacts, uncover underlying laws, pioneer new paths for similar studies, and support regional ecological sustainability. The results show that from 2000–2020, forest and arable land areas declined while construction land expanded, leading to a yij1,172,200-ton carbon storage reduction in Changde City. Carbon storage decreased under natural development and arable land protection scenarios but increased in the ecological scenario. The main drivers of carbon storage in Changde City are the DEM, slope, and annual average temperature, with their interactions enhancing spatial heterogeneity. Human activities, especially in mountains and urbanizing regions, negatively impact carbon storage. This study aids in optimizing land resource allocation, improving land use efficiency, and promoting coordinated and sustainable development in Changde City’s ecological, economic, and social systems.

MiR-11903a modulates CLIPB9-mediated pathogen defense and longevity in Aedes aegypti.

Arthropod melanization is a crucial defense mechanism mediated by a complex cascade of CLIP domain serine proteases (CLIPs). In this study, it was confirmed that microRNA-11903a (miR-11903a) targets Aedes-CLIPB9 (AeCLIPB9) by bioinformatics prediction and dual-luciferase reporter assays. Following intrathoracic injection of miR-11903a agomir and antagomir, Real-time quantitative polymerase chain reaction confirmed that AeCLIPB9 is negatively regulated by miR-11903a. Spatiotemporal expression analysis revealed that miR-11903a is most abundant in 4th instar larvae, followed by pupae and adults, and highly expressed in the wings, head, and midgut of female adults. Following pathogen infection, AeCLIPB9 and miR-11903a exhibited opposite expression trends, indicating their potential roles in mosquito innate immunity. To further investigate the relationship between AeCLIPB9 and miR-11903a, double-strand CLIPB9 was synthesized and RNA interference was performed. Seven-d survival assays revealed that both AeCLIPB9 and miR-11903a were crucial immune factors in fighting pathogens. Finally, longevity assays demonstrated that miR-11903a influenced mosquito lifespan.

Multidimensional Spatiotemporal Clustering – An Application to Environmental Sustainability Scores in Europe

ABSTRACTThe assessment of corporate sustainability performance is extremely relevant in facilitating the transition to a green and low‐carbon intensity economy. However, companies located in different areas may be subject to different sustainability and environmental risks and policies. Henceforth, the main objective of this paper is to investigate the spatial and temporal pattern of the sustainability evaluations of European firms. We leverage a large dataset containing information about companies' sustainability performances, measured by MSCI ESG ratings, and geographical coordinates of firms in Western Europe between 2013 and 2023. By means of a modified version of the Chavent et al. (2018) hierarchical algorithm, we conduct a spatial clustering analysis, combining sustainability and spatial information, and a spatiotemporal clustering analysis, which combines the time dynamics of multiple sustainability features and spatial dissimilarities, to detect groups of firms with homogeneous sustainability performance. We are able to build cross‐national and cross‐industry clusters with remarkable differences in terms of sustainability scores. Among other results, in the spatio‐temporal analysis, we observe a high degree of geographical overlap among clusters, indicating that the temporal dynamics in sustainability assessment are relevant within a multidimensional approach. Our findings help to capture the diversity of ESG ratings across Western Europe and may assist practitioners and policymakers in evaluating companies facing different sustainability‐linked risks in different areas.

Water quality assessment of river Ganga, India using water quality index and multivariate statistical techniques.

The River Ganga is a vital lifeline for millions, and several authors reported that its quality has been deteriorating. Several initiatives have been taken by the government, but the situation is not up to the mark. Hence, water quality assessments are essential for sustainable river water management and restoring its ecological balance. This work employed spatiotemporal analysis of 20 hydrochemical variables, water quality indices (WQI), and multivariate statistics to assess the water quality of River Ganga. Water samples (n = 220) were collected at 20 locations (divided into four zones, namely upper (UZ), middle (MZ), lower (LZ), and estuarine (EZ)) based on the geographical nature and anthropogenic pressure. The study found that most of the water variables (specific conductivity (SC), pH, BOD, total alkalinity (TA), total hardness (TH), Ca-H, Mg-H, chlorinity (Cl-), salinity, nitrate-N, silicate and total dissolved solids (TDS)) were lowest reported at UZ while the highest at EZ. TH, salinity, Mg-H, Ca-H, TDS, Cl-, and SC in EZ, were above the drinking water limits and these variables markedly affect the river's water quality attributes, possibly increased by tidal influences. WQI indicated that the UZ was clean (26-50) in all seasons, MZ and LZ were good to poor (50-75) in all seasons, and EZ was unsuitable for drinking (> 100) in all seasons. Seasons were grouped into three clusters: less polluted (monsoon); slightly polluted (post-monsoon); and polluted (winter and pre-monsoon). The principal component analysis formed five clusters based on eigenvalue > 1: PC1 having TH, salinity, Mg-H, Ca-H, TDS, Cl-, and SC mainly influenced by tidal factor; PC2 having temperature, transparency, and DO was influenced by metrological source; PC3 (pH, TA, velocity) and PC4 (BOD, silicate) thought to be both natural as well as manmade; and PC5 was influenced by agricultural runoff (total phosphorus and NO3-N)and sewage water (TN) discharge. The study emphasized the significance of multivariate statistical techniques in discerning the variability patterns of parameters, as well as in formulating management strategies to enhance river water quality by pinpointing the most impactful parameters contributing to water quality degradation.

Forest Disturbance and Restoration in China's North-South Transition Zone: A Case from the Funiu Mountains

Accurate monitoring and assessment of forest disturbance and recovery dynamics are essential for sustainable forest management, particularly in ecological transition zones. This study analyzed forest disturbance and recovery patterns in China’s Funiu Mountains from 1991 to 2020 by integrating the LandTrendr algorithm with space-time cube analysis. Using Landsat time series data and the Geodetector method, we examined both the spatiotemporal characteristics and driving factors of forest change across three periods. The results showed that (1) between 1991 and 2020, the study area experienced 131.19 km2 of forest disturbance and 495.88 km2 of recovery, with both processes most active during the 1990s; (2) spatiotemporal analysis revealed that both disturbance and recovery patterns were predominantly characterized by cold spots, suggesting relatively stable forest conditions despite localized changes; (3) human activities were the primary drivers of forest disturbance in the early period, while forest recovery was consistently influenced by the combined effects of topographic conditions and precipitation. Additionally, forest fires emerged as an important factor affecting both disturbance and recovery patterns after 2010. These findings enhance our understanding of forest dynamics in transition zones and provide empirical support for regional forest management strategies. The results also highlight the importance of considering both spatial and temporal dimensions when monitoring long-term forest changes.

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.

Modeling the impact of pandemic on the urban thermal environment over megacities in China: Spatiotemporal analysis from the perspective of heat anomaly variations

Modeling the impact of pandemic on the urban thermal environment over megacities in China: Spatiotemporal analysis from the perspective of heat anomaly variations

Machine-learning based spatiotemporal performance analysis of degraded tropical peatland groundwater level numerical model

Machine-learning based spatiotemporal performance analysis of degraded tropical peatland groundwater level numerical model

Exposure-crossover observations of air pollution after large-scale fireworks in two Korean megacities, Seoul and Busan: Empirical evidence toward sustainable festivals.

Firework burning can significantly contribute to emissions of ambient air pollutants such as particulate matters (PM), which might pose serious public health concerns. Nevertheless, environmental research and public health attention to this matter are limited in many countries, particularly in Korea where firework festivals remain popular in megacities. This study aimed to examine temporal and spatial patterns of ambient air pollution during large-scale firework festivals in two megacities of Korea, focusing on each event held in Seoul (the second highest population in the world, as a metropolitan area) and Busan (the second highest population in Korea) in 2023. We used self-matched exposure-crossover design to compare ambient air pollution trends on exposure-event days (firework festival dates) with those on reference days (one week before and after festival dates) to evaluate a sole contribution of firework display. We analyzed data from air quality monitoring stations and visualized spatiotemporal changes in concentrations of air pollutants (i.e., PM2.5, PM10, and SO2) during the festival period. Analysis of the Seoul festival revealed significant increases in PM2.5 and PM10 concentrations following fireworks, with peaks reaching 320 and 371μg/m3. Similar patterns were observed after the Busan festival, with peak concentrations of 241 and 253μg/m3 for PM2.5 and PM10. These concentrations were 7.4-12.2 times higher than those observed on reference days. Spatiotemporal analysis demonstrated that PM2.5 and PM10 emitted from fireworks dispersed in the direction of wind. In contrast to high increases in PM2.5 and PM10, SO2 levels showed light increases after both festivals, with a peak concentration of 4.9ppb in Seoul and 5.7ppb in Busan. Considering the estimated attendance of about a million at each festival and the high-density population area around two firework locations, the potential health risk posed by firework-related air pollution is a significant public health concern.

Enhancing satellite-based emergency mapping: Identifying wildfires through geo-social media analysis

ABSTRACT When a disaster emerges, timely acquisition of information is crucial for a rapid situation assessment. Although automation in the standard satellite-based emergency mapping workflow has been advanced, delays still occur at crucial steps. In order to speed up the provision of satellite-based crisis products to emergency managers, this paper proposes a geo-social media-based approach that detects disaster events based on the spatio-temporal analysis of georeferenced, disaster-related Tweets. The proposed methodology is validated on the basis of two use cases: wildfires in Chile and British Columbia. The results show the general ability of Twitter to forecast events several days in advance, at least for the Chile use case. However, there are large spatial differences, as there is a correlation between population density and the reliability of Twitter data. Consequently, only few meaningful alerts could be generated for British Columbia, an area with very low population numbers.

A spatio-temporal analysis of water investment allocation in Colombia and its impacts on drinking water quality

A spatio-temporal analysis of water investment allocation in Colombia and its impacts on drinking water quality

Caspase‐mediated cleavage of a scaffold protein, MPRIP, yields a truncated form that is involved in repetitive bleb formation

Membrane blebbing is a hallmark of apoptotic cell death. However, the molecular mechanism that regulates this event has not been fully elucidated. To understand this underlying mechanism, we developed visualization systems suitable for spatiotemporal analysis. By monitoring the plasma membrane labeled with a fluorescent protein and reconstructing the image data as three‐dimensional (3D) volumes based on the rendering technique, we observed that dying cells exhibit cycles of bleb formation at the same region of the cell surface. In addition, a Förster Resonance Energy Transfer (FRET)‐based biosensor incorporating a regulatory myosin light chain (RMLC) displayed phosphorylation at the base of the retracting bleb, and dephosphorylation before re‐expansion, implying the involvement of not only a kinase but also a phosphatase in the regulation of RMLC. To extend these observations, we focused on a scaffold protein, myosin phosphatase Rho interacting protein (MPRIP), which interacts with RhoA and myosin phosphatase targeting subunit 1 (MYPT1), involved in activation of Rho‐associated coiled‐coil kinase‐I (ROCK‐I) or protein phosphatase 1 (PP1), respectively. We found that MPRIP is cleaved both in dying cells and in an in vitro cleavage assay in a caspase‐dependent manner. A cleaved C‐terminal peptide fragment maintains the interaction with MYPT1. Cytological analysis showed that this fragment forms a complex with MYPT1 and myosin after translocating to the cytoplasm. These results suggest that this complex formation promotes the dephosphorylation of RMLC. Collectively, our study indicates that repetitive bleb formation, which is unique to apoptosis, is regulated by both phosphorylation and dephosphorylation of RMLC through MPRIP in a coordinated manner.

Spatiotemporal Correlation Analysis for the Incidence of Esophageal and Gastric Cancer From 2010 to 2019: Ecological Study.

Esophageal and gastric cancer were among the top 10 most common cancers worldwide. In addition, sex-specific differences were observed in the incidence. Due to their anatomic proximity, the 2 cancers have both different but also shared risk factors and epidemiological features. Exploring the potential correlated incidence pattern of them, holds significant importance in providing clues in the etiology and preventive strategies. This study aims to explore the spatiotemporal correlation between the incidence patterns of esophageal and gastric cancer in 204 countries and territories from 2010 to 2019 so that prevention and control strategies can be more effective. The data of esophageal and gastric cancer were sourced from the Global Burden of Disease (GBD). Spatial autocorrelation analysis using Moran I in ArcGIS 10.8 (Esri) was performed to determine spatial clustering of each cancer incidence. We classified different risk areas based on the risk ratio (RR) of the 2 cancers in various countries to the global, and the correlation between their RR was evaluated using Pearson correlation coefficient. Temporal trends were quantified by calculating the average annual percent change (AAPC), and the correlation between the temporal trends of both cancers was evaluated using Pearson correlation coefficients. In 2019, among 204 countries and territories, the age-standardized incidence rates (ASIR) of esophageal cancer ranged from 0.91 (95% CI 0.65-1.58) to 24.53 (95% CI 18.74-32.51), and the ASIR of gastric cancer ranged from 3.28 (95% CI 2.67-3.91) to 43.70 (95% CI 34.29-55.10). Malawi was identified as the highest risk for esophageal cancer (male RR=3.27; female RR=5.19) and low risk for gastric cancer (male RR=0.21; female RR=0.23) in both sexes. Spatial autocorrelation analysis revealed significant spatial clustering of the incidence for both cancers (Moran I>0.20 and P<.001). A positive correlation between the risk of esophageal and gastric cancer was observed in males (r=0.25, P<.001). The ASIR of both cancers showed a decreasing trend globally. The ASIR for esophageal and gastric cancer showed an AAPC of -1.43 (95% CI -1.58 to -1.27) and -1.76 (95% CI -2.08 to -1.43) in males, and -1.93 (95% CI -2.11 to -1.75) and -1.79 (95% CI -2.13 to -1.46) in females. In addition, a positive correlation between the temporal trends in ASIR for both cancers was observed at the global level across sexes (male r=0.98; female r=0.98). Our study shows that there was a significant spatial clustering of the incidence for esophageal and gastric cancer and a positive correlation between the risk of both cancers across countries was observed in males. In addition, a codescending incidence trend between both cancers was observed at the global level.

Spatiotemporal Analysis of Air Pollution by NO2 in Urban and Rural Areas of Brazil Using Google Earth Engine and R

O sensoriamento remoto avançou notavelmente em termos de informações que podem ser extraídas dos produtos gerados. O uso combinado de diferentes sistemas de sensores se mostra eficaz na detecção de mudanças ambientais e na redução de incertezas sobre onde ocorrem emissões significativas de poluentes atmosféricos. Este estudo teve como objetivo analisar e comparar a variação espaço-temporal da poluição atmosférica por NO2 em áreas urbanas e rurais dos estados de São Paulo, Rio de Janeiro e Minas Gerais, Brasil, de setembro de 2023 a setembro de 2024. Dados de NO2 troposférico foram coletados usando o sensor TROPOMI no satélite Sentinel-5P. Os resultados indicaram que as áreas urbanas exibiram os maiores níveis médios de poluição atmosférica de NO2. Na região metropolitana de São Paulo, a concentração média de NO2 atmosférico foi de 1,90E-04 mol.m−2, seguida pela região metropolitana do Rio de Janeiro com 1,10E-04 mol.m−2 e pela região metropolitana de Belo Horizonte com 0,83E-04 mol.m−2. Em áreas rurais — especificamente regiões agrícolas e de pastagens — a maior média diária de poluição atmosférica de NO2 foi registrada em São Paulo com 1,50E-04 mol.m−2, seguida pelo Rio de Janeiro com 0,94E-04 mol.m−2 e Belo Horizonte com 0,66E-04 mol.m−2. De junho a agosto de 2024, foram observados níveis elevados de poluição do ar por NO2. Este estudo conclui que as regiões metropolitanas têm níveis mais altos de poluição por NO2 em comparação com as áreas rurais em todos os três estados do Brasil. A queima de combustíveis fósseis em indústrias, emissões de veículos automotores e resíduos urbanos são provavelmente fatores que contribuem para os níveis mais altos de poluição em áreas metropolitanas em comparação com regiões rurais.

P-1454. Characterizing the Prevalence of Recurrent Antimicrobial Resistant Infections in an Urban County

Abstract Background Individuals treated for antimicrobial resistant organism (AMRO) infections are exposed to broader antibiotics, which can contribute to an increased likelihood of future resistant infections. Given that community transmission and AMRO recurrence remains underexplored, we conducted spatiotemporal analyses of recurrent AMRO infections in an urban county. Figure 1 Map of spatiotemporal hotspots and recurrent infection counts per 1,000 population (CP1K) for five patterns of antimicrobial resistance at a census block group level. Methods We mapped the spatial prevalence of recurrent AMRO infections for census block groups using electronic health records from two major health systems in Tarrant County (Texas) from 2010-2019. We delineated recurrence across five different patterns: AmpC beta-lactamase producing bacteria (AmpC), Carbapenem-Resistant Enterobacterales (CRE), Extended spectrum beta lactamase (ESBL), Methicillin-Resistant Staphylococcus aureus (MRSA), and Vancomycin-resistant Enterococcus (VRE). We compared recurrent AMRO rates against space-time permutation model identified hotspots. We used spatial lag regressions to explore the relationship between recurrent AMRO rates and Area Deprivation Index (ADI), a measure of neighborhood inequity. Results Of 105,291 patients, 19,161 (18.2%) had at least one AMRO infection. There were 10,611 (10.1%) patients with multiple AMRO infections: AmpC = 202, CRE = 136, ESBL = 741, MRSA = 1255, and VRE = 205. We observed that AmpC, ESBL, and MRSA recurrence had the greatest co-location with spatiotemporal hotspots (Figure 1). In spatial regressions, increasing neighborhood ADI rank (increasing deprivation) was significantly associated with an increase in block group-rates of recurrent AmpC (β = 0.02), ESBL (β = 0.03), MRSA (β = 0.04) and VRE (β = 0.01) (p-value &amp;lt; 0.05). Conclusion We geographically and temporally identified patterns of recurrent AMROs in a large urban county and demonstrated a significant relationship between inequity and AMRO recurrence. This work can enhance our understanding of local infection dynamics and inform antibiotic stewardship. Disclosures John J. Hanna, MD, Pieces Technologies: Advisor/Consultant

Spatiotemporal analysis of the impact of green finance on carbon dioxide emissions based on panel data of cities in China

ABSTRACT Green finance represents a resource-saving and environmentally friendly economic approach. It plays a critical role in achieving economic growth and environmentally sustainable development. To investigate its tangible effects on China since the implementation of green finance, we utilized the nighttime lighting (NTL) data to evaluate urban CO2 emission of Chinese cities from 2010 to 2020. Then, this study utilized the geographically and temporally weighted regression (GTWR) model to examine the correlations between green finance and carbon intensity in different cities across four dimensions including green credit, green insurance, green investment, and government support. The findings revealed significant regional disparities in the influence of green finance development on carbon intensity. Overall, both green credit and green insurance emerge as pivotal factors in mitigating carbon intensity. Notably, green credit exhibits the strongest mitigation of carbon intensity in the eastern region, while green insurance demonstrates the highest impact in the western region. However, government support has a negative effect, while the impact of green investment impact is insignificant. These findings underscore the importance of implementing region-specific strategies to bolster the efficacy of green finance in mitigating carbon emissions.

P-1298. Spatiotemporal Dynamics and Risk Factors of Scrub Typhus in 2013–2019 South Korea: a Bayesian Hurdle Model

Abstract Background Scrub typhus (ST), also known as tsutsugamushi disease, is a common febrile vector-borne illness in South Korea, with rodents being the main host. Previous studies on vector-borne diseases have often lacked integrated spatiotemporal analyses covering disease dynamics, vectors, and environmental shifts. Therefore, we aimed to comprehensively explore high-risk areas and spatiotemporal trends of ST, incorporating vectors and environmental information.Figure 1.Flowchart of study subject selection. Scrub typhus was diagnosed with A75.3 in ICD-10-CM Code. Epiweek, epidemiological week. NHIS, National Health Insurance Service in South Korea. Methods ST cases were extracted from the 2013–2019 Korea National Health Insurance Service data at 250 municipal levels. Spatial and temporal clusters were assessed using Getis-Ord Gi* and Hot and cold spot trend analyses. Bayesian hurdle models with spatiotemporal interaction terms were employed to identify associations between ST incidence and diverse regional factors, encompassing rodent suitability, as well as human and environmental factors. Stratification by gender and age group (0–19, 20–39, 40–59, 60–79, and ≥80 years) was performed.Figure 2.Scrub typhus incidence per 100,000 population by year. Results Between 2013 and 2019, 95,601 ST patients were reported (male-to-female incidence ratio=0.71:1). ST incidence had positive spatial autocorrelation across South Korea (I=0.600, p=0.01), with spatial expansion from southwestern to northeastern regions. Municipalities with higher rodent suitability (coefficient=0.230, 95% credible interval=0.126–0.333), forest area (coefficient=0.254, 95% credible interval=0.117–0.394), and worse financial independence (coefficient=-0.262, 95% credible interval=-0.326 to -0.198) had higher likelihoods of increased ST incidence, even after adjusting for spatiotemporal autocorrelation. However, significant risk factors varied by age group.Figure 3.Results of hot and cold spot trends by visualizing a space-time cube of scrub typhus incidence per 100,000 population.Colors represent the patterns of hot and cold spot trends, classified as “up trend,” “down trend," or “no significant trend” with 90, 95, and 99% confidence levels. Conclusion We elucidated the intricate spatiotemporal dynamics of scrub typhus in South Korea, accounting for factors such as rodent suitability, forest coverage, and economic independence, even after adjusting for spatiotemporal autocorrelation. Our findings lay the groundwork for evidence-based strategies and policies to tackle scrub typhus, integrating a holistic One Health perspective and emphasizing rodent distribution as a key determinant of disease risk.Table.Results of multivariable Bayesian spatiotemporal models on scrub typhus incidence by gender and age group, as expressed via coefficient (95% credible interval)WAIC, Watanabe-Akaike information criterion. Disclosures All Authors: No reported disclosures

A comprehensive review of remote sensing techniques for monitoring Ulva prolifera green tides

In recent years, Ulva prolifera green tide, as a large-scale marine ecological phenomenon, has occurred frequently in coastal areas such as the Yellow Sea and the East China Sea, significantly affecting marine ecosystems and fishery resources. With the continuous advancement of remote sensing technologies, these technologies have become indispensable tools for monitoring Ulva prolifera green tides. This review provides a comprehensive overview of the advances in remote sensing band indices for detecting green tides, including spatiotemporal distribution analysis, area and biomass estimation, drift trajectory modeling, and investigations of their driving mechanisms. Additionally, it identifies the limitations and unresolved challenges in current approaches, such as constraints on data resolution, algorithmic biases, and environmental variability. The potential for integrating multi-source remote sensing data with marine environmental parameters and deep learning techniques is discussed, emphasizing their roles in improving the accuracy and reliability of monitoring and predicting Ulva prolifera green tides. This review aims to guide future research efforts and technological innovations in this field.

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

As a natural disaster, drought can endanger global ecology, socio-economic systems, and sustainable development. To address sudden droughts in the future, assess drought disasters, and propose mitigation measures, in-depth research on the spatiotemporal variations in and driving factors of meteorological drought is essential. To study drought in the Yellow River Basin, we calculated the multi-scale Standardized Precipitation Evapotranspiration Index (SPEI), derived from monthly meteorological data recorded at weather stations from 1968 to 2019. We examined the features of drought and its driving factors using the trend-free pre-whitening Mann–Kendall (TFPW-MK) test and Sen’s slope estimator, as well as a drought frequency analysis, center of gravity migration model, standard deviation ellipse model, and geographic detector. Our analysis shows that (1) from 1968 to 2019, the Yellow River Basin exhibited a shift from aridity to increased moisture on an annual basis, with the smallest SPEI of −1.47 in 2002 indicating a moderate drought; SPEI3 showed a growing tendency in all seasons, particularly in winter (0.00388/year), followed by spring (0.00214/year), summer (0.00232/year), and fall (0.00196/year). The SPEI3 exhibited higher fluctuations in frequency compared to the annual-scale SPEI12; (2) in terms of spatial variability, there was no significant change in drought conditions at any scale, with the probability of a drought event being greater in the eastern and northwestern portions of the watershed. The epicenter of the drought exhibited a tendency to migrate southwestward; (3) among the seven driving factors, land use and night lighting were the dominant factors affecting drought conditions, with driving force values of 0.75 and 0.63, respectively.

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

Effective monitoring and predicting surface water quality are vital for sustainable water resource control. Traditional in-situ techniques are regularly constrained by their time-consuming nature and restrained spatial coverage. This study seeks to develop a predictive version that combines physio-chemical water quality parameters with remote sensing indices derived from the Sentinel-2A dataset to enhance accuracy and spatial attainment. The research focuses on four urban tanks in Coimbatore namely Krishnampathy, Selvampathy, Kumaraswamy and Ukkadam Periyakulam. The physio-chemical parameters for assessing the water quality which include pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), Dissolved oxygen (DO), Calcium (Ca), Magnesium (Mg), Total hardness, Chloride (Cl-), Carbonate (Co3-) and Bicarbonate (HCo3-) have been measured, additionally for the detection of surface water extent using remote sensing indices namely Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index (MNDWI), Water Ratio Index, Normalized Difference Chlorophyll Index (NDCI) indices which were extracted from sentinel-2A datasets. The parameters such as EC, TDS, Ca2+, Cl- and Total hardness show a high coefficient of determination (R2). Correlation and regression techniques have been employed to integrate these datasets, resulting in the development of a robust predictive model. By combining these two information sources, the model is constructed using stepwise regression analysis. The model's accuracy turned into established towards ground-truth records, showing large improvements whilst far away Remote sensing indices had been covered.