Search Results (286)

This study assesses subsidence-induced risk to urban infrastructure in three major Italian cities—Rome, Bologna, and Florence—by integrating satellite-based persistent scatterer interferometric synthetic aperture radar (PSInSAR) ground displacement data with urban vulnerability metrics into a novel risk assessment workflow, incorporating land use and population data from the Copernicus Land Monitoring Service (CLMS)—Urban Atlas. This analysis exploits ERS-1/2, ENVISAT, and COSMO-SkyMed PSInSAR datasets from the Italian Extraordinary Plan of Environmental Remote Sensing, plus Sentinel-1 datasets from CLMS—European Ground Motion Service (EGMS), and spans a 30-year period, thus capturing both historical and recent subsidence trends. Angular distortion is introduced as a critical parameter for assessing potential structural damage due to differential settlement, which helps to quantify subsidence-induced hazards more precisely. The results reveal variable subsidence hazard patterns across the three cities, with specific areas exhibiting significant differential ground deformation that poses risks to key infrastructure. A total of 36.15, 11.44, and 0.43 km² of land at high to very high risk are identified in Rome, Bologna, and Florence, respectively. By integrating geospatial and vulnerability data at the building-block level, this study offers a more comprehensive understanding of subsidence-induced risk, potentially contributing to improved management and mitigation strategies in urban areas. This study contributes to the limited literature on embedding PSInSAR data into urban risk assessment workflows and provides a replicable framework for future applications in other urban areas. Full article

The North Java coastal area, known as the Pantura region, is experiencing significant land subsidence, with certain areas sinking up to 10 cm per year. Pekalongan is among the most affected, with subsidence rates between 10 and 19 cm annually, mainly due to groundwater extraction, sediment compaction, and coastal erosion. Other coastal cities, like Semarang and Demak, show rates averaging 4 to 10 cm per year. This rapid subsidence is due to favorable geological conditions and ongoing urban development. This study investigates land subsidence in Pekalongan using the PS-InSAR method and dynamic visualization of time-series land cover data. PS-InSAR was applied to 45 scenes from ALOS-2 PALSAR-2 to monitor subsidence from 2014 to 2022. The results were validated with in situ subsidence benchmarks. Urban development dynamics were analyzed through land cover and land use change (LULC) and population density over the same period, using the GLC_FCS30D dataset in the GEE to detect non-natural LULC. The PS-InSAR results indicated that over 60.9% of investigation points experienced subsidence, up to 100 cm between 2014 and 2022. Ground validation showed an 83% agreement with PS-InSAR results. A statistical analysis of LULC from 2014 to 2022 did not show significant built-up area development, but the extension of salt marshes and water bodies indicated subsidence expansion. The population density reached 6873 people per square km by 2022, causing extensive groundwater use for domestic and industrial purposes, further aggravating the subsidence. Full article

The Fucheng-1 (FC-1) satellite has successfully transitioned from its initial operational phase and is now undergoing a detailed performance assessment for time-series deformation monitoring. This study evaluates the surface deformation monitoring capabilities of the newly launched FC-1 satellite using the interferometric synthetic aperture radar (InSAR) technique, particularly in urban applications. By analyzing the observation data from 20 FC-1 scenes and 20 Sentinel-1 scenes, deformation velocity maps of a university in Mianyang city were obtained using persistent scatterer interferometry (PSI) and distributed scatterer interferometry (DSI) techniques. The results show that thanks to the high resolution of 3 × 3 m of the FC-1 satellite, significantly more PS points and DS points were detected than those detected by Sentinel-1, by 13.4 times and 17.9 times, respectively. The distribution of the major deformation areas detected by both satellites in the velocity maps is generally consistent. FC-1 performs better than Sentinel-1 in monitoring densely structured and vegetation-covered areas. Its deformation monitoring capability at the millimeter level was further validated through comparison with leveling measurements, with average errors and root mean square errors of 1.761 mm and 2.172 mm, respectively. Its high-resolution and high-precision interferometry capabilities make it particularly promising in the commercial remote sensing market. Full article

This study presents an innovative approach utilizing artificial intelligence (AI) for the prediction and classification of water quality parameters based on physico-chemical measurements. The primary objective was to enhance the accuracy, speed, and accessibility of water quality monitoring. Data collected from various water samples in Algeria were analyzed to determine key parameters such as conductivity, turbidity, pH, and total dissolved solids (TDS). These measurements were integrated into deep neural networks (DNNs) to predict indices such as the sodium adsorption ratio (SAR), magnesium hazard (MH), sodium percentage (SP), Kelley’s ratio (KR), potential salinity (PS), exchangeable sodium percentage (ESP), as well as Water Quality Index (WQI) and Irrigation Water Quality Index (IWQI). The DNNs model, optimized through the selection of various activation functions and hidden layers, demonstrated high precision, with a correlation coefficient (R) of 0.9994 and a low root mean square error (RMSE) of 0.0020. This AI-driven methodology significantly reduces the reliance on traditional laboratory analyses, offering real-time water quality assessments that are adaptable to local conditions and environmentally sustainable. This approach provides a practical solution for water resource managers, particularly in resource-limited regions, to efficiently monitor water quality and make informed decisions for public health and agricultural applications. Full article

In recent years, land subsidence has become a crucial factor affecting urban safety and sustainable development, especially in Wan’an Town. To accurately monitor and analyze the land subsidence in Wan’an Town, this study uses the PS-InSAR technique combined with an improved DEM for detailed research on land subsidence in Wan’an Town. PS-InSAR, or Permanent Scatterer Interferometric SAR, is suitable for high-precision monitoring of surface deformation. The natural neighbor interpolation method optimizes DEM data, improving its spatial resolution and accuracy. In this study, multiple periods of SAR imagery data of Wan’an Town were collected and preprocessed through radiometric calibration, phase unwrapping, and other steps. Using the PS-InSAR technique, the phase information of permanent scatterers (PS points) on the surface was extracted to establish a deformation model and preliminarily analyze the land subsidence in Wan’an Town. Concurrently, the DEM data were optimized using the natural neighbor interpolation method to enhance its accuracy. Finally, the optimized DEM data were combined with the surface deformation information extracted through the PS-InSAR technique for a detailed analysis of the land subsidence in Wan’an Town. The research results indicate that the DEM data optimized by the natural neighbor interpolation method have higher accuracy and spatial resolution, providing a more accurate reflection of the topographical features of Wan’an Town. The research found that the optimized DEM provided a more accurate reflection of Wan’an Town’s topographical features. By combining PS-InSAR data, subsidence information from 2016 to 2024 was calculated. The study area showed varying degrees of subsidence, with rates ranging from 6 mm/year to 10 mm/year. Four characteristic deformation areas were analyzed for causes and influencing factors. The findings contribute to understanding urban land subsidence, guiding urban planning, and providing data support for geological disaster warning and prevention. Full article

Advanced statistics can enable the detailed characterization of ground deformation time series, which is a fundamental step for thoroughly understanding the phenomena of land subsidence and their main drivers. This study presents a novel methodological approach based on pre-existing open-access statistical tools to exploit satellite differential interferometric synthetic aperture radar (DInSAR) data to investigate land subsidence processes, using European Ground Motion Service (EGMS) Sentinel-1 DInSAR 2018−2022 datasets. The workflow involves the implementation of Persistent Scatterers (PS) time series classification through the PS-Time tool, deformation signal decomposition via independent component analysis (ICA), and drivers’ investigation through spatio-temporal correlation with geospatial and monitoring data. Subsidence time series at the three demonstration sites of Bologna, Ravenna and Carpi (Po Plain, Italy) were classified into linear and nonlinear (quadratic, discontinuous, uncorrelated) categories, and the mixed deformation signal of each PS was decomposed into independent components, allowing the identification of new spatial clusters with linear, accelerating/decelerating, and seasonal trends. The relationship between the different independent components and DInSAR-derived displacement velocity, acceleration, and seasonality was also analyzed via regression analysis. Correlation with geological and groundwater monitoring data supported the investigation of the relationship between the observed deformation and subsidence drivers, such as aquifer resource exploitation, local geological setting, and gas extraction/reinjection. Full article

The exploitation of coal resources has caused problems such as ground deformation, affecting the ecological environment. Spatiotemporal varying characteristics between land subsidence and ecological environmental quality (EEQ) are an important research hotspot. Using the SBAS-InSAR method, 64 Sentinel-1 images were utilized to monitor land subsidence in the Shenfu mining area, one of China’s largest coal source regions. And the remote sensing ecological index (RSEI) was used to monitor and evaluate EEQ of the Shenfu mining area. Global and local spatial autocorrelation methods were used to assess the spatial aggregation degree and change patterns over time. Spatial Econometric Models were employed to explore the impacts of land subsidence on EEQ. The results showed the following: (1) The average RSEI values in the Shenfu mining area were 0.531, 0.488, and 0.523 in 2016, 2018, and 2020, respectively; there was a slight downward trend in EEQ. The permanent scatter (PS) point deformation rate ranged from −353.40 mm/year to +246.24 mm/year, with average deformation rates of 0.1642, 0.2181, and 0.2490 mm/year, respectively. (2) There was a significant correlation and spatial agglomeration effect between land surface subsidence and EEQ. Low–high, high–low, and low–low clusters were the main types of relationships, indicating that land subsidence primarily has a negative spatial impact on the ecological environment. (3) The relationship between land subsidence and EEQ varied spatially in the Shenfu mining area at 500 × 500 grid units. This research can provide scientific guidance for disaster prevention and sustainable development in mining areas by considering long-term differences in ecological environmental quality and its correlation with land subsidence. Full article

Groundwater resource is crucial for the development of agriculture and urban communities in valley basins of arid and semiarid regions. This research investigated the groundwater chemistry of a typical urbanized valley basin on the Tibetan Plateau to understand the hydrochemical status, quality, and controlling mechanisms of groundwater in arid urbanized valley basins. The results show groundwater is predominantly fresh and slightly alkaline across the basin, with approximately 54.17% of HCO₃-Ca type. About 12.5% and 33.33% of sampled groundwaters are with the hydrochemical facies of Cl-Mg·Ca type and Cl-Na type, respectively. Groundwater is found with the maximum TDS, NO₃⁻, NO₂⁻, and F⁻ content of 3066 mg/L, 69.33 mg/L, 0.04 mg/L, and 3.12 mg/L, respectively. Groundwater quality is suitable for domestic usage at all sampling sites based on EWQI assessment but should avoid direct drinking at some sporadic sites in the urban area. The exceeding nitrogen and fluoride contaminants would pose potential health hazards to local residents, but high risks only existed for infants. Both minors and adults are at medium risk of these exceedingly toxic contaminants. Groundwater quality of predominant sites in the basin is suitable for long-term irrigation according to the single indicator of EC, SAR, %Na, RSC, KR, PI, and PS and integrated irrigation quality assessment of USSL, Wilcox, and Doneen diagram assessment. But sodium hazard, alkalinity hazard, and permeability problem should be a concern in the middle-lower stream areas. Groundwater chemistry in the basin is predominantly governed by water-rock interaction (silicate dissolution) across the basin in natural and sporadically by evaporation. Human activities have posed disturbances to groundwater chemistry and inputted nitrogen, fluoride, and salinity into groundwater. The elevated nitrogen contaminants in groundwater are from both agricultural activities and municipal sewage. While the elevated fluoride and salinity in groundwater are only associated with municipal sewage. It is imperative to address the potential anthropogenic contaminants to safeguard groundwater resources from the adverse external impacts of human settlements within these urbanized valley basins. Full article

Due to the limitations inherent in SAR satellite imaging modes, utilizing time-series InSAR technology to process single-orbit satellite image data typically only yields one-dimensional deformation information along the LOS direction. This constraint impedes a comprehensive representation of the true surface deformation of landslides. Consequently, in this paper, after the SBAS-InSAR and PS-InSAR processing of the 30-view ascending and 30-view descending orbit images of the Sentinel-1A satellite, based on the imaging geometric relationship of the SAR satellite, we propose a novel computational method of fusing ascending and descending orbital LOS-direction time-series deformation to extract the landslide’s downslope direction deformation of landslides. By applying this method to Baige landslide monitoring and integrating it with an improved tangential angle warning criterion, we classified the landslide’s trailing edge into a high-speed, a uniform-speed, and a low-speed deformation region, with deformation magnitudes of 7~8 cm, 5~7 cm, and 3~4 cm, respectively. A comparative analysis with measured data for landslide deformation monitoring revealed that the average root mean square error between the fused landslide’s downslope direction deformation and the measured data was a mere 3.62 mm. This represents a reduction of 56.9% and 57.5% in the average root mean square error compared to the single ascending and descending orbit LOS-direction time-series deformations, respectively, indicating higher monitoring accuracy. Finally, based on the analysis of landslide deformation and its inducing factors derived from the calculated time-series deformation results, it was determined that the precipitation, lithology of the strata, and ongoing geological activity are significant contributors to the sliding of the Baige land-slide. This method offers more comprehensive and accurate surface deformation information for dynamic landslide monitoring, aiding relevant departments in landslide surveillance and management, and providing technical recommendations for the fusion of multi-orbital satellite LOS-direction deformations to accurately reconstruct the true surface deformation of landslides. Full article

Synthetic aperture radar tomography (TomoSAR) has gained significant attention for three-dimensional (3D) imaging in urban environments. A notable limitation of traditional TomoSAR approaches is their primary focus on persistent scatterers (PSs), disregarding targets with temporal decorrelated characteristics. Temporal variations in coherence, especially in urban areas due to the dense population of buildings and artificial structures, can lead to a reduction in detectable PSs and suboptimal 3D reconstruction performance. The concept of partially coherent scatterers (PCSs) has been proven effective by capturing the partial temporal coherence of targets across the entire time baseline. In this study, an novel approach based on an iterative sub-network generation method is introduced to leverage PCSs for enhanced 3D reconstruction in dynamic environments. We propose a coherence constraint iterative variance analysis approach to determine the optimal temporal baseline range that accurately reflects the interferometric coherence of PCSs. Utilizing the selected PCSs, a 3D imaging technique that incorporates the iterative generation of sub-networks into the SAR tomography process is developed. By employing the PS reference network as a foundation, we accurately invert PCSs through the iterative generation of local star-shaped networks, ensuring a comprehensive coverage of PCSs in study areas. The effectiveness of this method for the height estimation of PCSs is validated using the TerraSAR-X dataset. Compared with traditional PS-based TomoSAR, the proposed approach demonstrates that PCS-based elevation results complement those from PSs, significantly improving 3D reconstruction in evolving urban settings. Full article

Landslides pose significant threats to life and property, particularly in mountainous regions. To address this, this study develops a landslide susceptibility model integrating Earth Observation (EO) data, historical data, and Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) ground movement results. The model categorizes areas into four susceptibility classes (from Class 1 to Class 4) using a multi-class classification approach. Results indicate that the Xtreme Gradient Boosting (XGB) model effectively predicts landslide susceptibility with area under the curve (AUC) values ranging from 0.93 to 0.97, with high accuracy of 0.89 and a balanced performance across different susceptibility classes. The integration of MT-InSAR data enhances the model’s ability to capture dynamic ground movement and improves landslide mapping. The landslide susceptibility map generated by the XGB model indicates high susceptibility along the Pacific coast. The optimal model was validated against 272 historical landslide occurrences, with predictions distributed as follows: 68 occurrences (25%) in Class 1, 142 occurrences (52%) in Class 2, 58 occurrences (21.5%) in Class 3, and 4 occurrences (1.5%) in Class 4. This study highlights the importance of considering temporal changes in environmental conditions such as precipitation, distance to streams, and changes in vegetation for accurate landslide susceptibility assessment. Full article

Correlates of Protection (CoP) are biomarkers above a defined threshold that can replace clinical outcomes as primary endpoints, predicting vaccine effectiveness to support the approval of new vaccines or follow up studies. In the context of COVID-19 vaccination, CoPs can help address challenges such as demonstrating vaccine effectiveness in special populations, against emerging SARS-CoV-2 variants or determining the durability of vaccine-elicited immunity. While anti-spike IgG titres and viral neutralising capacity have been characterised as CoPs for COVID-19 vaccination, the contribution of other components of the humoral immune response to immediate and long-term protective immunity is less well characterised. This review examines the evidence supporting the use of CoPs in COVID-19 clinical vaccine trials, and how they can be used to define a protective threshold of immunity. It also highlights alternative humoral immune biomarkers, including Fc effector function, mucosal immunity, and the generation of long-lived plasma and memory B cells and discuss how these can be applied to clinical studies and the tools available to study them. Full article

Objective: SARS-CoV-2 remains the third most common cause of death in North America. We studied the effects of methotrexate and tumor necrosis factor inhibitor (TNFi) on neutralization responses after COVID-19 vaccination in immune-mediated inflammatory disease (IMID). Methods: Prospective data and sera of adults with inflammatory bowel disease (IBD), rheumatoid arthritis (RA), spondyloarthritis (SpA), psoriatic arthritis (PsA), and systemic lupus (SLE) were collected at six academic centers in Alberta, Manitoba, Ontario, and Quebec between 2022 and 2023. Sera from two time points were evaluated for each subject. Neutralization studies were divided between five laboratories, and each lab’s results were analyzed separately using multivariate generalized logit models (ordinal outcomes: absent, low, medium, and high neutralization). Odds ratios (ORs) for the effects of methotrexate and TNFi were adjusted for demographics, IMID, other biologics and immunosuppressives, prednisone, COVID-19 vaccinations (number/type), and infections in the 6 months prior to sampling. The adjusted ORs for methotrexate and TNFi were then pooled in random-effects meta-analyses (separately for the ancestral strains and the Omicron BA1 and BA5 strains). Results: Of 479 individuals (958 samples), 292 (61%) were IBD, 141 (29.4%) were RA, and the remainder were PsA, SpA, and SLE. The mean age was 57 (62.2% female). For both the individual labs and the meta-analyses, the adjusted ORs suggested independent negative effects of TNFi and methotrexate on neutralization. The meta-analysis adjusted ORs for TNFi were 0.56 (95% confidence interval (CI) 0.39, 0.81) for the ancestral strain and 0.56 (95% CI 0.39, 0.81) for BA5. The meta-analysis adjusted OR for methotrexate was 0.39 (95% CI 0.19, 0.76) for BA1. Conclusions: SARS-CoV-2 neutralization in vaccinated IMID was diminished independently by TNFi and methotrexate. As SARS-CoV-2 circulation continues, ongoing vigilance regarding optimized vaccination is required. Full article

We were tasked by Canada’s COVID-19 Immunity Task Force to describe severe adverse events (SAEs) associated with emergency department (ED) visits and/or hospitalizations in individuals with immune-mediated inflammatory diseases (IMIDs). At eight Canadian centres, data were collected from adults with rheumatoid arthritis (RA), axial spondyloarthritis (AxS), systemic lupus (SLE), psoriatic arthritis (PsA), and inflammatory bowel disease (IBD). We administered questionnaires, analyzing SAEs experienced within 31 days following SARS-CoV-2 vaccination. About two-thirds (63%) of 1556 participants were female; the mean age was 52.5 years. The BNT162b2 (Pfizer) vaccine was the most common, with mRNA-1273 (Moderna) being second. A total of 49% of participants had IBD, 27.4% had RA, 14.3% had PsA, 5.3% had SpA, and 4% had SLE. Twelve (0.77% of 1556 participants) SAEs leading to an ED visit or hospitalization were self-reported, occurring in 11 participants. SAEs included six (0.39% of 1556 participants) ED visits (including one due to Bell’s Palsy 31 days after first vaccination) and six (0.39% of 1556 participants) hospitalizations (including one due to Guillain-Barré syndrome 15 days after the first vaccination). Two SAEs included pericarditis, one involved SLE (considered a serious disease flare), and one involved RA. Thus, in the 31 days after SARS-CoV-2 vaccination in our IMID sample, very few serious adverse events occurred. As SARS-CoV2 continues to be a common cause of death, our findings may help optimize vaccination acceptance. Full article

MoMo30 is an antiviral protein isolated from aqueous extracts of Momordica balsamina L. (Senegalese bitter melon). Previously, we demonstrated MoMo30’s antiviral activity against HIV-1. Here, we explore whether MoMo30 has antiviral activity against the COVID-19 virus, SARS-CoV-2. MLV particles pseudotyped with the SARS-CoV-2 Spike glycoprotein and a Luciferase reporter gene (SARS2-PsV) were developed from a three-way co-transfection of HEK293-T17 cells. MoMo30’s inhibition of SARS2-PsV infection was measured using a luciferase assay and its cytotoxicity using an XTT assay. Additionally, MoMo30’s interactions with the variants and domains of Spike were determined by ELISA. We show that MoMo30 inhibits SARS2-PsV infection. We also report evidence of the direct interaction of MoMo30 and SARS-CoV-2 Spike from WH-1, Alpha, Delta, and Omicron variants. Furthermore, MoMo30 interacts with both the S1 and S2 domains of Spike but not the receptor binding domain (RBD), suggesting that MoMo30 inhibits SARS-CoV-2 infection by inhibiting fusion of the virus and the host cell via interactions with Spike. Full article