Search Results (3,927)

In the realm of Intelligent Transportation Systems (ITS), vehicular communication technologies such as Dedicated Short-Range Communications (DSRC), Cellular Vehicle-to-Everything (C-V2X), and LoRa 2.4 GHz play crucial roles in enhancing road safety, reducing traffic congestion, and improving transport efficiency. This article explores the integration of these communication protocols within smart intersections, emphasizing their capabilities and synergies. DSRC, based on IEEE 802.11p, provides reliable short-range communication with data rates up to 27 Mbps and latencies below 50 ms, ideal for real-time safety applications. C-V2X leverages LTE and 5G networks, offering broader coverage up to 10 km and supporting data rates up to 100 Mbps, with latencies as low as 20 ms in direct communication mode (PC5). LoRa 2.4 GHz, known for its long-range (up to 15 km in rural areas, 1–2 km in urban settings) and low-power characteristics, offers data rates between 0.3 and 37.5 kbps, suitable for non-critical data exchange and infrastructure monitoring. The study evaluates the performance and interoperability of these technologies in urban environments, focusing on data latency, transmission reliability, and scalability. Experimental results from simulated and real-world scenarios show that DSRC maintains reliable communication within 1 km with minimal interference. C-V2X demonstrates superior scalability and coverage, maintaining robust communication over several kilometers in high-density urban settings. LoRa 2.4 GHz exhibits excellent penetration through urban obstacles, maintaining connectivity and efficient data transmission with packet error rates below 10%. Full article

Although digital twins have been established in manufacturing for a long time, they are only more recently making their way into the urban environment and present a relatively new concept for the construction industry. The concept of a digital twin—a model of the physical environment that has a real-time two-way link between the physical and the digital, with the virtual model changing over time to reflect changes in the real world—lends itself well to the continually changing environment of a construction project. Predictive capabilities built into a twin also have great potential for construction planning—including in supply chain management and waste disposal as well as in the construction process itself. Underpinning this opportunity is location data, which model where something is happening and when and can be used to solve a wide range of problems. In particular, location (the power of where) can integrate diverse data sources and types into a single system, overcoming interoperability challenges that are known to be a barrier to twin implementation. This paper demonstrates the power of location-enabled digital twins in the context of a highway construction project, documenting and addressing data engineering tasks and functionality development to explore the potential of digital twins in the context of two case studies—health and safety and construction monitoring. We develop two demonstrators using data from an existing construction project (building on data and requirements from industry partner Skanska) to build twins that make use of the powers of 4D data presentation offered by the Unreal Gaming Engine and CesiumJS mapping, while software development expertise is sometimes available to construction firms, we specifically explore to what extent the no-code approach available within Unreal can be deployed in this context. Our findings provide evidence to construction companies as to the benefits of digital twins, as well as an understanding of the data engineering and technical skills required to achieve these benefits. The overall results demonstrate the potential for digital twins to unlock and democratise construction data, taking them beyond the niche use of experts and into the boardroom. Full article

Change detection (CD) in high-resolution remote sensing imagery remains challenging due to the complex nature of objects and varying spectral characteristics across different times and locations. Convolutional neural networks (CNNs) have shown promising performance in CD tasks by extracting meaningful semantic features. However, traditional 2D-CNNs may struggle to accurately integrate deep features from multi-temporal images, limiting their ability to improve CD accuracy. This study proposes a Multi-level Feature Cross-Fusion (MFCF) network with 3D-CNNs for remote sensing image change detection. The network aims to effectively extract and fuse deep features from multi-temporal images to identify surface changes. To bridge the semantic gap between high-level and low-level features, a MFCF module is introduced. A channel attention mechanism (CAM) is also integrated to enhance model performance, interpretability, and generalization capabilities. The proposed methodology is validated on the LEVIR construction dataset (LEVIR-CD). The experimental results demonstrate superior performance compared to the current state-of-the-art in evaluation metrics including recall, F1 score, and IOU. The MFCF network, which combines 3D-CNNs and a CAM, effectively utilizes multi-temporal information and deep feature fusion, resulting in precise and reliable change detection in remote sensing imagery. This study significantly contributes to the advancement of change detection methods, facilitating more efficient management and decision making across various domains such as urban planning, natural resource management, and environmental monitoring. Full article

Groundwater is an essential water resource and plays a crucial role, especially in areas with limited surface water availability. However, the exacerbation of groundwater droughts, fueled by phenomena such as climate change, urbanization, and industrialization, highlights the necessity for predictive tools to aid in sustainable groundwater management. While artificial neural networks (ANN) have been increasingly used for groundwater level prediction, most studies have focused solely on point-scale predictions from groundwater observation wells, which can be resource-intensive and time-consuming. In this study, we propose a multi-scale groundwater-based drought prediction model that can predict both zonal average values and the values at well locations for the standardized groundwater level index (SGI). Specifically, we develop a zone-scale SGI prediction model through long short-term memory (LSTM) and propose a model that can accurately predict point-scale SGI through a simple downscaling process. Our model was developed and tested for Jeju Island, a volcanic island in South Korea where groundwater serves as the primary water source. Specifically, we partitioned Jeju Island into 16 sub-watersheds, termed zones, and constructed an individual model for each zone. Forecasting the standardized groundwater level index (SGI) for each zone was based on input datasets including the daily temperature, precipitation, snowfall, vapor pressure deficit (VPD), wind speed, and preceding SGI values. Additionally, we downscaled the predicted values of each zone to the specific SGI values at groundwater monitoring wells within the zone. This was achieved by applying the spatial deviation of each well relative to the zonal mean over the preceding 4 days to the predicted zone-scale SGI value. Our findings indicate high accuracy of the model in SGI predictions across both scales, with the Nash–Sutcliffe efficiency coefficient (NSE) exceeding 0.9 and the root mean square error (RMSE) remaining less than 0.3 for both the representative zone and observation well. By leveraging the proposed model, stakeholders and policymakers can efficiently generate and utilize both zone-scale and point-scale groundwater-based drought predictions, contributing to effective groundwater management practices. Full article

Based on outdoor air pollution and meteorological daily time series observational and in-situ monitoring data, this study investigated the impacts of environmental factors under different urban climates on COVID-19 transmission in four hotspot European metropolises (Berlin, London, Madrid, and Paris) from March 2020 to March 2022. Through applied statistical methods and cross-correlation tests involving multiple datasets pertaining to the main air pollutants (inhalable particulate matter PM2.5 and PM10, nitrogen dioxide (NO₂), and ozone (O₃)) and climate parameters (air temperature at 2 m height, relative humidity, wind speed intensity and direction, planetary boundary layer height, and surface solar irradiance), a direct positive impact of aerosol loading (PM2.5, PM10, and aerosol optical depth (AOD)) on COVID-19 spreading and severity was revealed. Despite some urban differences existing between the selected cities, particularly for the spring–summer periods, we have observed negative correlations between daily new COVID-19 cases and deaths and daily average ground-level ozone concentration, air temperature at 2 m height, planetary boundary layer height, and surface solar irradiance. Air relative humidity and urban population density have a direct impact on COVID-19 diffusion in large metropolitan areas, and the findings of this study highlight the crucial role of air pollution, in synergy with climate variability, in viral pathogens dispersion in COVID-19 transmission in large urban areas. This information can be used by decision-makers to develop targeted interventions during epidemic periods to reduce the potential risks associated with air pollution exposure and to promote the sustainable development of urban economies. Full article

The viaduct is an important infrastructure for urban sustainable development, but it will inevitably pass through a coal mining subsidence area in coal resource-based cities, which poses a threat to the construction and operation of the viaduct. However, there is a lack of research on long time-series monitoring and assessing the safety of elevated bridges above subsidence areas, both domestically and internationally. In this study, a resource-based city viaduct in Shandong, China, was selected as the research object, utilizing SBAS-InSAR technology for deformation monitoring during bridge construction and post-opening phases. The viaduct based on the goaf was analyzed by the key settlement subsection. Before completing construction (March 2019 to December 2020), research revealed that the cumulative maximum deformation in the bridge area was 44mm and the maximum uplift was 22 mm, with overall stability in the underlying subsidence area. After completion (January 2021 to July 2023), the cumulative maximum deformation value in the elevated bridge area was 10mm and the maximum uplift was 6 mm, indicating minimal fluctuations over three years, maintaining overall stability. This stable condition ensures the safety of construction and operation of regional elevated bridges. These findings not only support the safe operation of bridges in underlying subsidence areas but also provide a new approach to sustainable areas globally, especially in coal resource-based urban areas. Full article

The emission of carbon dioxide (CO₂) is considered one of the main factors responsible for one of the greatest challenges faced by the world today: climate change. On the other hand, with the increase in energy demand due to the increase in population and industrialization, the emission of CO₂ has increased rapidly in the past few decades. However, the world’s leaders, including the United Nations, are now taking serious action on how to minimize the emission of CO₂ into the atmosphere. Towards this end, accurate modeling and monitoring of historical CO₂ can help in the development of rational policies. This study aims to analyze the carbon emitted by the Group Twenty (G20) countries for the period 1971–2021. The datasets include CO₂ emissions, nonrenewable energy (NREN), renewable energy (REN), Gross Domestic Product (GDP), and Urbanization (URB). Various regression-based models, including multiple linear regression models, quantile regression models, and panel data models with different variants, were used to quantify the influence of independent variables on the response variable. In this study, CO₂ is a response variable, and the other variables are covariates. The ultimate objective was to choose the best model among the competing models. It is noted that the USA, Canada, and Australia produced the highest amount of CO₂ consistently for the entire duration; however, in the last decade (2011–2021) it has decreased to 12.63–17.95 metric tons per capita as compared to the duration of 1971–1980 (14.33–22.16 metric tons per capita). In contrast, CO₂ emissions have increased in Saudi Arabia and China recently. For modeling purposes, the duration of the data has been divided into two independent, equal parts: 1971–1995 and 1996–2021. The panel fixed effect model (PFEM) and panel mixed effect model (PMEM) outperformed the other competing models using model selection and model prediction criteria. Different models provide different insights into the relationship between CO₂ emissions and independent variables. In the later duration, all models show that REN has negative impacts on CO₂ emissions, except the quantile regression model with tau = 0.25. In contrast, NREN has strong positive impacts on CO₂ emissions. URB has significantly negative impacts on CO₂ emissions globally. The findings of this study hold the potential to provide valuable information to policymakers on carbon emissions and monitoring globally. In addition, results can help in addressing some of the sustainable development goals of the United Nation Development Programme. Full article

The need to find a trade-off between protecting water-related ecosystems and increasing safe water-use for human society is recognized in the 2030 Agenda of the European Union. We assess the ecological status of a riverine system in order to mitigate human impacts, considering its importance for supplying drinking water to more than 4 million users in Rome. We used an integrated approach, analyzing animal and plant communities at riverbanks and the riverbed. A macrobenthos analysis revealed a well-structured community with a good ecology for all sampling stations. The highest value was found immediately upstream and downstream of the springs collection system, while the lowest richness value was where the river collects urban wastewater. A floristic inventory showed Hemicryptophytes composing almost 45% of all species, and prevalence of Euroasiatic (35%) and Orophilous (34%) chorotypes. A positive correlation between riverbed vegetation and the quality of the benthic community was revealed, while tree height seems to have a negative trend. Our data suggest a river stretch affected by resurgence and water abstraction did not highlight irreversible alterations to the landscape. Indeed, the composition of vegetation and correlated animal communities mirrored a clinal gradient expected for an Apennine river system. Our study has the potential to improve the approach used to monitor the impacts of humans on freshwater ecosystems, aiming at preserving the integrity of the water-related landscape. Full article

Childhood asthma is a major health issue in Australia, and traffic emissions play a causative role. Two urban planning policies that impact children’s exposure to traffic emissions are considered in terms of the potential health risks to children in a Melbourne suburb with high truck volumes and hospital attendances for childhood asthma. Firstly, the health impact assessment component of the state planning approval of a major road project, and secondly, local government placement of childcare centres and schools in relation to freight routes. Three sources of air quality monitoring data were examined: (i) a Victorian EPA reference site; (ii) a site with planning approval for development into a childcare centre; and (iii) five sites within the boundary of the West Gate Tunnel Project, an AUD 10 billion road and tunnel project. The Australian Urban Research Infrastructure Network data was utilised to assess distances of childcare centres and schools from major truck routes. A range of cconcentration–response functions for childhood asthma (0–18 years) from international systematic meta-analyses and a smaller Australian cross-sectional study were applied to comparative elevations in fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) concentrations between the EPA reference monitor (used for project risk assessment) and local roadside data. It was found that comparative elevations in NO₂ concentrations were associated with the following risk increases: developing asthma 13%, active asthma 12%, and lifetime asthma 9%. Overall, 41% of childcare centres (n = 51) and 36% of schools (n = 22) were ≤150 m to a high-density truck route. Truck emissions likely make a substantial contribution to childhood asthma outcomes in the project area. This study exemplifies how current practices may not be commensurate with guiding policy objectives of harm minimisation and equitable protection. Full article

Fungi have always posed an unquestionable threat to heritage collections worldwide. Now, in a future of climate change, biological risk factors may have to be considered even more than before. Models and simulations to assess possible impacts a changing outdoor climate will have on indoor environments and, in turn, on biodeterioration are still underdeveloped and require a more substantial data basis. This study aimed at filling some of these knowledge gaps through a broad-based approach combining microclimatic and microbiological monitoring in four historic libraries in Austria with an uncontrolled indoor climate: Altenburg Abbey, Melk Abbey, Klosterneuburg Monastery and the Capuchin Monastery in Vienna. Data were generated from thermohygrometric sensors, cultivation-dependent air- and surface sampling and further surface dust sampling for cultivation-independent analyses. Results gave insights on the status quo of microbiological loads in the libraries and outdoor–indoor relationships. Influences of the geographic location and room-use on corresponding indoor fungal profiles were identified. Lower fungal diversities were found at the most rural site with the strongest climatic fluctuations and extreme values than in the most urban, sheltered library with a very stable climate. Further, the humidity-stabilizing potential of large collections of hygroscopic materials, such as books, was also examined. Implications for a sustainable approach to prevent future biodeterioration are discussed, supporting the long-term preservation of these valuable historic collections. Full article

Chronic kidney disease (CKD) poses a significant global health challenge with increasing prevalence and associated morbidity. Point-of-care testing (POCT) provides an opportunity to improve CKD management and outcomes through early detection and targeted interventions, particularly in underserved communities. This review evaluates the roles of POCT in CKD, focusing on utility (through screening programs, monitoring of kidney function, and assessing participants on renally excreted medications), accuracy, and acceptability. Screening programs employing POCT have demonstrated promising outcomes, with improved rates of CKD diagnosis in groups with disparate health outcomes, offering a vital avenue for early intervention in high-risk populations. These have been conducted in rural and urban community or pharmacy settings, highlighting convenience and accessibility as important facilitators for participants. In addition, POCT holds significant promise in the monitoring of CKD, particularly in groups requiring frequent testing, such as kidney transplant recipients and patients on renin-angiotensin-aldosterone inhibitors. The consideration of the variable analytical performance of different devices remains crucial in assessing the utility of a POCT intervention for CKD. While the convenience and improved accessibility of home self-testing versus healthcare professional management is important, it must be balanced with acceptable levels of accuracy and precision to maintain patient and clinical confidence. Despite challenges including variability in accuracy and the user-friendliness of devices, patient feedback has generally remained positive, with studies reporting increased patient satisfaction and engagement. However, challenges regarding wider uptake are limited by healthcare professional confidence (in test reliability), the potential for increased workload, and early prohibitive costs. In conclusion, POCT represents a growing and valuable tool in enhancing CKD care, particularly in resource-limited settings, but careful consideration of device selection and implementation strategies is essential to achieve desired outcomes. Full article

The article addresses anthropogenic and geological conditions related to the development of soil subsidence in the western zone of Recife (Brazil). Over the past 50 years, human activity has intensified in areas previously affected by soft soils (clay, silt, and sandstone) resulting in subsidence due to additional loads (landfills and constructions). The duration of the settlement process can be significantly influenced by the specific characteristics of the soil composition and geological conditions of the location. This work presents, for the first time, accurate InSAR time series maps that describe the spatial pattern and temporal evolution of the settlement, as well as the correlation with the geological profile, and validation with Global Navigation Satellite System (GNSS) data. Persistent Scatterer Interferometry (PS-InSAR) was employed in the analysis of Single Look Complex (SLC) images generated by 100 ascending COSMO-SkyMed (CSK) and 65 PAZ (32 ascending, and 33 descending) from the X-band, along with 135 descending Sentinel-1 (S1) acquisitions from the C-band. These data were acquired over the period from 2011 to 2023. The occurrence of subsidence was identified in several locations within the western region, with the most significant displacement rates observed in the northern, central, and southern areas. In the northern region, the displacement rates were estimated to be approximately −20 mm/year, with the Várzea and Caxangá neighborhoods exhibiting the highest rates. In the central region, the displacement rates were estimated to be approximately −15 mm/year, with the Engenho do Meio, Cordeiro, Torrões, and San Martin neighborhoods exhibiting the highest rates. Finally, in the southern region, the displacement rates were estimated to be up to −25 mm/year, with the Caçote, Ibura, and Ipsep neighborhoods exhibiting the highest rates. Additionally, east–west movements were observed, with velocities reaching up to −7 mm/year toward the west. These movements are related to the lowering of the land. The study highlights that anthropogenic effects in the western zone of Recife contribute to the region’s vulnerability to soil subsidence. Full article

Extensive hydrologic and water quality modeling within a watershed benefits from long-term flow and nutrient data sets for appropriate model calibration and validation. However, due to a lack of local water quality data, simpler water quality modeling techniques are generally adopted. In this study, the monitoring sites were established at two different locations to collect hydraulic data for the hydraulic calibration and validation of the model. In addition, water quality samples were collected at eight monitoring sites and analyzed in the lab for various parameters for calibration. This includes total suspended solids (TSS), soluble phosphorus, five-day biochemical oxygen demand (BOD₅), and dissolved oxygen (DO). The Personal Computer Storm Water Management Model (PCSWMM) 7.6 software was used to simulate all the pollutant loads using event mean concentrations (EMCs). The performance of the model for streamflow calibration at the two USGS gauging stations was satisfactory, with Nash–Sutcliffe Efficiency (NSE) values ranging from 0.51 to 0.54 and coefficients of determination (R²) ranging from 0.71 to 0.72. The model was also validated with the help of historical flow data with NSE values ranging from 0.5 to 0.79, and R² values ranging from 0.6 to 0.95. The hydraulic calibration also showed acceptable results with reasonable NSE and R² values. The water quality data recorded at the monitoring stations were then compared with the simulated water quality modeling results. The model reasonably simulated the water quality, which was evaluated through visual inspection using a scatter plot. Our analysis showed that the upstream tributaries, particularly from agricultural areas, were contributing more pollutants than the downstream tributaries. Overall, this study demonstrates that the PCSWMM, which was typically used for modeling urban watersheds, could also be used for modeling larger mixed land use watersheds with reasonable accuracy. Full article

Sub-Saharan Africa (SSA) is undergoing rapid urbanization, yet research comparing urban expansion and agricultural land loss in peri-urban areas is scarce. This study utilizes multi-temporal Landsat imagery to examine the impact of urban growth on agricultural land and fragile ecosystems in Kampala (a mega city) and Mbarara (a regional urban center) in Uganda. We distinguish between random and systematic land-use and land-cover (LULC) transitions in the landscape. The results reveal substantial urban expansion. Kampala’s urban area surged from 7.14% in 1989 to 55.10% in 2015, while Mbarara increased from 6.37% in 2002 to 30.95% in 2016. Correspondingly, agricultural land decreased, from 48.02% to 16.69% in Kampala, and from 39.92% to 32.08% in Mbarara. Notably, a significant proportion of urban growth in both cities encroached upon agricultural land (66.7% in Kampala and 57.8% in Mbarara). The transition from agricultural to built-up areas accounted for 14.72% to 28.45% of the landscapes. Additionally, unsustainable practices led to the conversion of wetlands and forests to agricultural land, with approximately 13% of wetlands and 23% of Savannah and forests being converted between 2001 and 2015. These findings underscore the necessity of monitoring LULC changes for sustainable urban growth management, emphasizing the importance of preserving agricultural land and ecosystems to ensure present and future food security. This research contributes to the understanding of urbanization’s impact on peri-urban agricultural land and ecosystems in SSA, providing insights that are crucial for informed urban planning and policy formulation aimed at sustainable development in the region. Full article

(1) Background: Haemorrhagic strokes (HS), including intracerebral (ICH) and subarachnoid haemorrhages (SAH), account for approximately 10–15% of strokes worldwide but are associated with worse functional outcomes and higher rates of mortality, and financial burden than ischemic stroke. There is evidence that confirmed poor air quality may increase the incidence of haemorrhagic strokes. The aim of our study was to evaluate the association between individual ambient air pollutants and the risk of haemorrhagic stroke in an urban environment without high levels of air pollution. (2) Methods: A time-series cross-sectional study design was used. A daily air pollution concentration (Agency of Regional Air Quality Monitoring in the Gdansk Metropolitan Area) and incidence of haemorrhagic strokes (National Health Fund) were obtained and covered the time period from 1 January 2014 to 31 December 2018. A generalised additive model with Poisson regression was used to estimate the associations between 24-h mean concentrations of SO₂, NO, NO₂, NOx, CO, PM10, PM2.5, and O₃ and a daily number of haemorrhagic strokes. (3) Results: The single-day lag model results showed that NO₂, NO and NO_x exposure was associated with increased risk of ICH (88% events) with RR of 1.059 (95% CI: 1.015–1.105 for lag0), 1.033 (95% CI: 1.007–1.060 for lag0) and 1.031 (95% CI: 1.005–1.056 for lag0), but not for SAH (12% events). Exposure to CO was related to a substantial and statistically significant increase in incidence for 1.031 (95% CI: 1.002–1.061 for lag0) but not for SAH. Higher SO₂, PM₁₀, PM_2.5, and O₃ exposures were not significantly related to both ISC and SAH. (4) Conclusions: In this time-series cross-sectional study, we found strong evidence that supports the hypothesis that transient elevations in ambient NO₂, NO and CO are associated with a higher relative risk of intracerebral but not subarachnoid haemorrhage. Full article