Search Results (4,708)

The quality of the air exerts considerable effects on human health, and meteorological factors affect air quality. The relationships between meteorological factors and air quality parameters are complex dependency correlations. This article is based on the air quality monitoring data and meteorological monitoring data obtained from a monitoring station in Binjiang District, Hangzhou City, China, spanning from 01:00 on 14 April 2021 to 23:00 on 31 December 2021. The Informer model was used to explore the air quality parameters’ response to meteorological factors. By analyzing 12 different kinds of 2-Minute Average Wind Speed (2-MAWSP), 10-Minute Average Wind Speed (10-MAWSP), and Maximum Wind Speed (MXSPD); 16 different kinds of Hourly Precipitation (HP) and Air Temperature (AT); 11 different kinds of Relative Humidity (RH); and 8 different kinds of Station Pressure (STP), the following results were obtained: (1) The influence of wind speed on various air quality parameters is multifaceted and lacks a standardized form, potentially influenced by factors like wind direction and geographical location. One clear effect of wind speed is evident in the levels of particulate matter 10 (with an aerodynamic diameter smaller than 10 μm, PM₁₀), as the values of this parameter first decrease and then increase with increasing wind speed. (2) HP has an evident reducing effect on most air quality parameters, including particulate matter (including PM_2.5 and PM₁₀), ozone (O₃), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂), as well as nitrogen oxides (NO_x). (3) The increase in AT has a clear reducing effect on the concentration of NO₂, while the trend for the concentrations of PM₁₀ and NO_x is one of initial decrease followed by a gradual rise. (4) RH only reduces the concentrations of SO₂ and PM₁₀. (5) With the rise in STP, the concentrations of most air quality parameters generally rise as well, except for the decrease in NO_x concentration. This can give some indications and assistance to meteorological and environmental departments for improving air quality. This model can be used for a performance analysis and the forecasting of multi-parameter non-correlated systems. Full article

Establishing the utilization of lignocellulosic biomass in integrated biorefineries can reduce environmental impacts and dependency on imported raw materials by substituting fossil-based products. Whereas energetic biomass utilization is common, chemical utilization is still poorly established, primarily due to the lack of feedstock availability. Hence, literature-based estimation and geographical mapping of biomass potentials are key to implementing successful production networks for biobased chemicals. Using the example of Germany, a geographical information system (GIS) analysis was conducted to allocate residual biomass potentials spatially. Based on the obtained GIS data model, a facility location optimization model was developed. The results of a location-allocation analysis for innovative biorefineries, which are integrated with biogas plants, showed an optimal location network for maximizing the amount of residue biomass covered. In a promising model scenario, each biorefinery has a maximum catchment radius of 23 km and a minimum input of 94,500 tonnes of dry matter per year (t DM/a) (31.5 kt DM/a × 3), allowing only existing biogas locations as locations for biorefineries. The results show that a mix of lignocellulosic residual biomass in certain areas can sustainably satisfy the demand for running 69 decentralized, integrated and multi-feed small-to-mid-scale biorefineries in Germany. Full article

Located in the south of the Iberian Peninsula, the Doñana salt marshes occupy around half of Doñana National Park and are currently considered among the most important wetlands worldwide due to the importance of their ecosystem. In this research work, using a novel patented procedure, the effects of climate change on the study area between 2009 and 2020 were evaluated. For this reason, DEMs were downloaded from the 30-meter Shuttle Radar Topography Mission (SRTM). Furthermore, to check the depth of the flooded area, 792 satellite images (L5 TM, L7 ETM+, and L8 OLI) with a resolution of 30 m were analyzed. The results show how the combined use of geomatic techniques, such as radar, optical, and geographic information system (GIS) data, along with regression models and iterative processes, plays a key role in the prediction and analysis of the flooded area volume in the Doñana salt marshes. Another significant contribution of this work is the development of a new remote sensing index. In conclusion, given that the study area depends on its aquifers’ status, it would be advisable to implement policies aimed at eradicating illegal aquifer extraction, as well as recovery plans to avoid the complete clogging of this salt marsh. Full article

This study harnesses Geographic Information Systems (GISs) and the Analytical Hierarchy Process (AHP) to identify optimal areas for olive cultivation in Jordan amidst the challenges of climate change. Recognizing the olive tree’s economic, environmental and cultural significance in the Mediterranean, this research focuses on the impact of altered climatic conditions, such as rising temperatures, shifting precipitation patterns and increased weather extremes, over the period from 1994 to 2021. By integrating climatic and topographical data, including factors such as altitude, slope, aspect and temperature variations, a multi-criteria analysis pinpoints regions suitable for sustainable olive agriculture. The methodology combines GIS-based spatial analysis with AHP to assess the suitability of various regions for olive farming, taking into account the complex interplay between climate and topography. This approach allows for the evaluation of potential agricultural zones under both current and projected climate scenarios, highlighting northern and western Jordan as areas with the highest suitability due to favorable precipitation and temperature conditions. The results of this study are vital for strategic agricultural planning, offering insights that can help enhance the resilience of olive production against climate variability. By identifying regions with optimal growing conditions and suggesting adaptive strategies, this research aims to support policymakers, agricultural developers and farmers in making informed decisions that ensure the sustainable future of olive farming in Jordan. Full article

Forest fires in central China pose significant threats to ecosystem health, public safety, and economic stability. This study employs advanced Geographic Information System (GIS) technology and Convolutional Neural Network (CNN) models to comprehensively analyze the factors driving the occurrence of these fire events. A predictive model for forest fire occurrences has been developed, complemented by targeted zoning management strategies. The key findings are as follows: (i) Spatial analysis reveals substantial clustering and spatial autocorrelation of fire points, indicating high-density areas of forest fire occurrence, primarily in Hunan and Jiangxi provinces, as well as the northeastern region. This underscores the need for tailored fire prevention and management approaches. (ii) The forest fire prediction model for the central region demonstrates exceptional accuracy, reliability, and predictive power. It achieves outstanding performance metrics in both training and validation sets, with an accuracy of 86.00%, precision of 88.00%, recall of 87.00%, F1 score of 87.50%, and an AUC value of 90.50%. (iii) Throughout the year, the occurrence of forest fires in central China varies by location and season. Low-occurrence periods are observed in summer and winter, particularly in Hunan and Hubei provinces, due to moderate weather conditions, agricultural practices, and reduced outdoor activities. However, spring and autumn also present localized risks due to uneven rainfall and dry climates. This study provides valuable insights into the dynamics of forest fire occurrences in central China, offering a solid framework for proactive fire management and policy formulation to effectively mitigate the impacts of these events. Full article

This paper proposes a novel approach to map-based navigation for unmanned aircraft. The proposed approach employs pattern matching of ground objects, not feature-to-feature or image-to-image matching, between an aerial image and a map database. Deep learning-based object detection converts the ground objects into labeled points, and the objects’ configuration is used to find the corresponding location in the map database. Using the deep learning technique as a tool for extracting high-level features reduces the image-based localization problem to a pattern-matching problem. The pattern-matching algorithm proposed in this paper does not require altitude information or a camera model to estimate the horizontal geographical coordinates of the vehicle. Moreover, it requires significantly less storage because the map database is represented as a set of tuples, each consisting of a label, latitude, and longitude. Probabilistic data fusion with the inertial measurements by the Kalman filter is incorporated to deliver a comprehensive navigational solution. Flight experiments demonstrate the effectiveness of the proposed system in real-world environments. The map-based navigation system successfully provides the position estimates with RMSEs within 3.5 m at heights over 90 m without the aid of the GNSS. Full article

The global air transportation system continues to be greatly impacted by operational changes induced by the COVID-19 pandemic. As air traffic management (ATM) focuses on balancing system capacity with demand, many facets of ATM and system operations more broadly were subjected to dramatic changes that deviate from pre-pandemic procedures. Since the start of the COVID-19 pandemic when air travel became one of the first transport modes to be impacted by lockdown procedures and travel restrictions, a geographically diverse cohort of researchers began investigating the impacts of the COVID-19 pandemic on air navigation service providers, airline and airport operations, on-time performance, as well as airline network structure, connectivity, crew scheduling, and service impacts due to pilot and crew shortages. In this study, we provide a comprehensive review of this aforementioned body of research literature published during one of the most tumultuous times in the history of aviation, specifically as it relates to air traffic management and air traffic control. We first organize the reviewed literature into three broad categories: strategic air traffic management and response, air traffic control and airport operational changes, and air traffic system resilience. Then, we highlight the main takeaways from each category. We emphasize specific findings that describe how various aspects of the air transportation systems could be improved in the domestic and global airline industry post-COVID. Lastly, we identify specific changes in operational procedures due to the COVID-19 pandemic and suggest future industry trends as informed by the literature. We anticipate this review article to be of interest to a broad swath of aviation industry and intercity transportation audiences. Full article

This study examines climate change, anthropogenic impacts, and their relationship with the sustainable development of the Brestnitsa karst geosystem. It is representative of the karst in Bulgaria, which is developed on a quarter of its territory. The geosystem approach was used to reveal the interrelationships in a typical karst territory. The data were collected from more than 10 years of integrated monitoring of the geosystem, including continuous instrumental monitoring in the show cave Saeva Dupka, which is part of the geosystem. Various data from other sources were also used, such as satellite observations (a digital terrain model, land cover, and satellite images and products), ground data, and climate reanalyses. A spatial analysis of the karst geosystem in the context of climate change and sustainable development was conducted using a complex of remote sensing methods, geographic information systems, and statistical methods. The main results include the state and trends in the climate of the area, changes and trends in the speleoclimate and gas composition of the air in the Saeva Dupka cave, and changes in land use in the territory of the geosystem. Conclusions about the connections between climate change, current karstogenesis, and the carbon cycle in the geosystem, as well as the response of the geosystem to the consequences of the combined impact of climate change and human activities, have been made. All these impacts on the karst geosystem have been assessed in order to make adequate management decisions to guarantee its sustainable development in the future. Full article

Rural electrification is a crucial step for the socio-economic development of isolated communities. Decentralized power generation, typically more favorable for renewable energies, requires an accurate analysis of the different electrification options, whose convenience depends on multiple factors. The application of Geographical Information Systems (GISs) to energy planning allows the assessment at a local level, considering the variability and demand distribution of spatial resources. This work introduces IntiGIS-local, a GIS-based model implemented in the ArcGIS environment, designed to calculate the levelized energy cost (LEC) for different electrification options. The model allows the comparison between three power generation alternatives: solar system, diesel generator set and solar–diesel hybrid system. Configurations are adjustable through input variables, with a special focus on the confrontation between individual systems and microgrids. The objective is to provide an adequate groundwork for developing a decision-making tool to assess diverse rural electrification options in future studies. The model IntiGIS-local is tested in the case study of the Guasasa community (Cuba). Full article

This comprehensive study explores the influence of aerosols and clouds on solar radiation in the urban environments of nine of Greece’s largest cities over the decade from 2014 to 2023. Utilizing a combination of Earth Observation data, radiative transfer models, and geographic information systems, the research undertook digital surface modeling and photovoltaic simulations. The study meticulously calculated the optimal rooftop areas for photovoltaic installation in these cities, contributing significantly to their energy adequacy and achieving a balance between daily electricity production and demand. Moreover, the research provides an in-depth analysis of energy and economic losses, while also highlighting the environmental benefits. These include a reduction in pollutant emissions and a decrease in the carbon footprint, aligning with the global shift towards local energy security and the transformation of urban areas into green, smart cities. The innovative methodology of this study, which leverages open access data, sets a strong foundation for future research in this field. It opens up possibilities for similar studies and has the potential to contribute to the creation of an updated, comprehensive solar potential map for continental Greece. This could be instrumental in climate change mitigation and adaptation strategies, thereby promoting sustainable urban development and environmental preservation. Full article

In the rapid development of urban construction, underground pipelines play a crucial role. However, the current underground pipelines have poor association with relevant management departments, and there are deficiencies in data completeness, accuracy, and information content. Managing and sharing information resources is relatively difficult, transforming the constructed 3D underground pipeline geographic information systems into an ‘Information silo’. This results in redundant construction and resource wastage of underground utilities. The complex distribution characteristics of underground utilities make rapid batch modeling and post-model maintenance challenging. Therefore, researching a 3D spatial data fusion model for urban underground utilities becomes particularly important. Given the above problem, this paper proposes a spatial data fusion model for underground pipeline scene modeling. It elaborates on the geometric, semantic, and temporal characteristics of underground pipelines, encapsulating these features. With underground pipeline objects as the core and pipeline characteristics as the foundation, a spatial data fusion model integrating multiple characteristics of underground pipelines has been constructed. Through software development, the data model designed in this paper facilitates rapid construction of underground pipeline scenes. This further enhances the consistency and integrity of underground pipeline data, enabling shared resources and comprehensive supervision of facility operations on a daily basis. Full article

The behavior of pedestrians in a non-constrained environment is difficult to predict. In wearable robotics, this poses a challenge, since devices like lower-limb exoskeletons and active orthoses need to support different walking activities, including level walking and climbing stairs. While a fixed movement trajectory can be easily supported, switches between these activities are difficult to predict. Moreover, the demand for these devices is expected to rise in the years ahead. In this work, we propose a cloud software system for use in wearable robotics, based on geographical mapping techniques and Human Activity Recognition (HAR). The system aims to give context to the surrounding pedestrians by providing hindsight information. The system was partially implemented and tested. The results indicate a viable concept with great extensibility prospects. Full article

The core promise of land use and zoning reforms is to metamorphose the car-dominated urban spatial structure—which is the legacy of use-based, modernist land use and transportation planning of the past century—into human-centered forms of urbanism characterized by walkable, accessible, transit-friendly, ecologically sustainable, equitable and resilient urban fabrics. This empirical study aims to measure the effectiveness of a reformed city planning framework, known as the form-based code (FBC), in terms of optimizing journey-to-work trips. To this end, the study integrates geographic information systems (GIS) and spatial analysis techniques with linear programming, including a variant of the transportation problem, to evaluate aggregated and disaggregated commuting efficiency metrics. Utilizing the zonal data (ZDATA) for the Orlando metropolitan region, the proposed models account for the commuting terrains associated with three major workforce cohorts, segmented along key industry sectors, within the context of three urban growth scenarios. The findings suggest that the FBC system holds the potential to enhance commuting patterns through various place-based strategies, including juxtaposing, densifying, and diversifying employment and residential activities at the local level. At the regional level, however, the resultant urban form falls short of an ideal jobs–housing arrangement across major industry sectors. Full article

The integration of urban agriculture into cityscapes necessitates a comprehensive understanding of multiple engineering and environmental factors, including urban fabric, building configurations, and dynamic energy and material flows. In contrast to rural settings, urban areas introduce complexities such as hygrothermal fluctuations, variable sunlight exposure and shadow patterns, diverse plant dimensions and shapes, and material interception. To address these challenges, this study presents an open-source Digital Twin model based on the use of a geographical information system (GIS) for near-real-time solar radiation mapping. This methodology aims to optimize crop productivity, enhance resilience, and promote environmental sustainability within urban areas and enables the near-time mapping of the salient features of different portions of the city using available open data. The work is structured into two main parts: (i) definition of the GIS-based Digital Twin model for mapping microclimatic variables (in particular solar radiation) to support sustainable urban agriculture design and (ii) application of the model to the city of Milan to verify its replicability and effectiveness. The key findings are connected to the possibility to integrate open data (solar radiation) with measurements in situ (illuminance and data referred to the specific crops, with related conversion coefficient) to develop a set of maps helpful for urban farmers but also for designers dealing with the synergy between buildings and urban farms. Initially tested on a neighborhood of Milan (Italy), the model will be applied in the Singapore context to verify analogies and differences. This correlation facilitates a more practical and straightforward examination of the relationships between solar irradiation and illuminance values of natural sunlight (involving both incident and diffuse light). The consistency of measurements allows for the precise documentation of these fluctuations, thereby enhancing the understanding of the influence of solar radiation on perceived luminance levels, particularly in urban environments characterized by diverse contextual factors such as vegetation, nearby structures, and geographical positioning. Full article

Semi-Interlocking Masonry (SIM) represents an innovative building system developed at the Centre for Infrastructure Performance and Reliability at the University of Newcastle, Australia. This system employs a unique approach to interlocking mortar-less engineered masonry panels constructed from SIM units. These units are designed to offer substantial energy dissipation capacity, primarily attributed to the friction occurring on the sliding bed joints between the units within the panel during seismic events. The primary aim of this study is to evaluate the viability of incorporating semi-interlocking masonry as infill panels in the construction of multi-story buildings across diverse geographical locations with varying seismic conditions in Australia. To assess the feasibility of SIM panels in different conditions (according to Australian Standard AS1170) in Australia, a comprehensive analysis using Strand7 R3.1.4 is conducted on a three-story structure incorporating traditional masonry infill panels and SIM panels. Displacement and base shear capacities are compared in response to seismic events across various scenarios in different locations within Australia. The insights gained from this comparative analysis contribute valuable information regarding the viability of implementing SIM as a contemporary construction material. Full article