Rapid Urban Development Research Articles

Analysis of the Availability and Utilization of Green Open Space (GOS) in Kahayan Hilir Subdistrict, Pulang Pisau Regency Using the DPSIR Approach

This study aims to analyze the availability and utilization of Green Open Space (GOS) in Kahayan Hilir Subdistrict, Pulang Pisau Regency, utilizing the DPSIR (Driving Forces, Pressures, States, Impacts, Responses) approach and the Indonesian Green Blue Index (IHBI) model. This research addresses the growing demand for adequate GOS due to rapid urbanization and economic development. The study identifies the driving forces behind GOS changes, pressures arising from land conversion and population growth, the current state of GOS, and the public's perception of GOS utilization. Data collection involved interviews with stakeholders, systematic field observations, and a comprehensive analysis of regional planning documents. The results indicate that the availability of GOS in Kahayan Hilir is still below government standards, resulting in environmental issues such as declining air quality, biodiversity loss, and increased urban heat island effects. Public perception indicates an awareness of the importance of GOS for health and recreation, although accessibility and facilities remain limited. This study recommends increasing both the quantity and quality of GOS through stricter policies, community participation, and public-private partnerships to support environmental quality and the well-being of residents in Kahayan Hilir Subdistrict.

Spatiotemporal Changes and Driving Mechanisms of Ecosystem Service Supply–Demand Contradictions Under Urbanization

Clarifying the driving mechanisms of ecosystem service (ES) supply and demand under urbanization is of significant importance for urban ecological planning and management. However, how the balance of ES supply and demand and its driving mechanisms vary with the degree of urbanization has been little studied. In this study, we analyzed the spatiotemporal changes and the correlations between ES supply and demand and the degree of urbanization in the Zhengzhou Metropolitan Area (ZZMA) from 2000 to 2020 and further explored the driving mechanisms behind these changes. The results showed that, (1) between 2000 and 2020, the ZZMA experienced a deficit in comprehensive ES supply and demand, and regions with rapid urbanization development were more likely to trigger imbalances in ES supply and demand; (2) the spatial mismatch between low–high ES supply and demand was primarily distributed in the built-up areas of various cities, while the high–low spatial mismatch was mostly found in forest and grassland areas; (3) the comprehensive urbanization level of the ZZMA was spatially negatively correlated with the ratio of ES supply and demand. Regions with lower ES balance were more susceptible to disturbances caused by urbanization; (4) population density was the key factor influencing the supply and demand of carbon sequestration, oxygen release, water conservation, and food provision services, while the proportions of forest land and construction areas had the greatest influence on the supply and demand of air purification and leisure services. It is important to ensure the ecological status of the northwestern, southwestern, and central mountainous and forested areas; maintain the agricultural status of the main grain-producing areas in the eastern plains; strengthen ecological restoration and green infrastructure in built-up areas; and formulate differentiated management policies to promote the sustainable supply of ES and safeguard the ecological security of the region.

Multi-class Object Detection in Urban Scenes Based on Deep Learning

Abstract. The rapid development of urbanization presents challenges and requirements for multi-class object detection in urban scenes. Accurately identifying buildings, vehicles, and trees in urban scenes can optimize urban planning, traffic management, monitoring environmental conditions, and promote the development of smart cities. Traditional target detection methods perform poorly in complex urban environments, while deep learning technology achieves accurate target recognition and positioning by automatically extracting high-level semantic features. In this study, we chose to use the YOLOv5s algorithm for multi-class target detection in urban scenes. YOLOv5s is a lightweight deep learning model with small storage space and efficient detection speed. In this paper, the Potsdam area data published by ISPRS is used to make the label data of buildings, vehicles and trees. The YOLOv5s algorithm is used to iteratively train the model. The results show that the mAP value detected by the YOLOv5s model can reach 82.83%. The experimental results show that the algorithm shows higher accuracy than SSD and Faster R-CNN in tree detection. Although it has a slight decline in building and vehicle detection, considering the factors such as detection accuracy, speed, and model size, the YOLOv5s algorithm has a better recognition and detection effect for the detection of multi-class targets in urban scenes.

The Research on Factors Influencing Air Pollution

The rapid development of urbanization contributes to that the people’ life is more and more beautiful and their quality of life is better and better. There are a lot of complex and diversity influencing of air pollutants. This paper finds a lot of articles and reports of government and focus on four air pollutants: carbon monoxide, ozone, nitrogen oxides, and particulate matter, including ultra-fine particles. Air pollution can cause three major environmental problems, including greenhouse effect, acid rain, and ozone hole, which can cause significant damage to the lungs and respiratory tract of the human body, such as chronic lung disease, neurological damage, and heart disease. The purpose of the article is to find some factors that affect air pollution. Using the method of multiple linear regression turns out that there is a significant linear regression relationship between human activities, exhaust emissions from vehicles, ships, airplanes, industrial pollutant, meteorological conditions, landform and PM2.5, while the speed and direction of wind fail the important test.

Safety evaluation for the dismantling of long-span spatial lattice structures based on deep learning and graph traversal

The demolition issue of long-span spatial lattice structures has become increasingly prominent with the rapid urban development and building renewal. Compared with traditional demolition techniques, building deconstruction offers superior economic and social benefits. Dismantling in blocks is one of the primary ways to realize the deconstruction of long-span spatial lattice structures, yet the safety is rarely studied. To fill this gap, a safety evaluation framework was established based on deep learning and graph traversal algorithm in this study. Hazardous configuration of the remaining structure is the basis of dismantling safety evaluation. To collect the hazardous configurations, an identification method in conjunction with deep neural network (DNN) and depth-first search (DFS) algorithm was proposed. The dismantling safety is measured by the improved structural well-formedness whose allowable range is determined by statistical analysis of hazardous configurations. The applicability of the framework was illustrated by the safety evaluation for the dismantling of single-layer reticulated domes. Results indicated that the dismantling safety is significantly affected by the configuration quality of the remaining structure, and the DNN-based prediction model is effective to classify the configuration state. Additionally, a large number of hazardous configurations were obtained through the identification method, and the allowable value of the safety evaluation index was determined to be 0.7. The configuration quality mainly depends on the geometry and boundary conditions. This novel framework provides scientific and effective methodological guidance for engineers, promoting the further application of building deconstruction.

Identity of Colonial Architecture Preservation in Thakhek City Center

This paper examines the conservation of colonial architecture in the center of Thakhek, which is a unique and historically significant feature of the city. Researching the historical context, architectural features and cultural values ​​of the buildings in the colonial era in Laos and efforts to conserve and promote them to become urban heritage. The research uses mixed methods, combining documentary research, observation from the field through surveys, measurements and interviews with local stakeholders. The findings reveal the challenges faced in preserving the colonial architecture at Thakhek, including limited resources. This research also highlights the potential benefits of preserving colonial architecture, such as promoting cultural tourism, promoting community pride and contributing to urban regeneration. The study concluded with recommendations to strengthen the preservation of colonial architecture in the center of Thakhek, emphasizing the need for a comprehensive preservation plan, increasing public awareness and cooperation between local stakeholders. This research contributes to the broader discussion on heritage conservation in Southeast Asia, offering insights into the challenges and opportunities of preserving colonial architecture in old urban areas in a context of rapid urban development.

Variability of bio-optical properties of Sundarbans mangrove estuarine ecosystem using elemental analysis, Sentinel 3 OLCI imageries and neural network models

Coastal water bodies face significant contamination risks stemming from various factors, such as growing populations, rapid urban development, dam construction, heightened industrial operations (notably in ports), and increasing tourism activities. The release of untreated sewage from municipalities, industrial discharges, and various recreational and commercial activities along the coast significantly deteriorates water quality and presents a serious risk to marine ecosystems, food chains, and human health. Hence, continual monitoring of coastal water quality is essential for the ecological sustainability of marine and aquatic ecosystems. This study examines the spatiotemporal variability of optical characteristics within the Sundarbans estuary system, a transboundary ecosystem shared by India and Bangladesh. The study employs Sentinel-3 OLCI satellite data alongside in-situ measurements of critical water quality parameters, including chlorophyll-a (Chl-a), total suspended matter (TSM), and chromophoric dissolved organic matter (CDOM), collected from 100 sampling sites across the Sundarbans estuarine system during the pre-monsoon, monsoon, and post-monsoon periods of 2021 and 2022.This study employs the sophisticated C2RCC model, a machine learning-based neural network-driven approach recognized for its robustness and precision. It examines the various bio-optical properties in tropical estuarine environments. The C2RCC model addresses this challenge directly. This advanced processor, powered by neural network technology, effectively interprets the complex optical signals found in Case-2 waters, offering essential insights into their quality. The findings indicate the highest TSM levels during the pre-monsoon seasons of both years. Additionally, the estimated Chl-a concentrations indicated a rise during the monsoon season (0.03–4 mg m3), whereas the intensified pre-monsoon CDOM levels showed consistency across both years. The strong positive correlation observed between in-situ and satellite data, with an average R2 value ranging from 0.90 to 0.99, underscores the reliability of the high-resolution remote sensing approach. The study illustrates the efficacy of OLCI data in observing near-shore and coastal ecosystems, clearly exceeding conventional methods for evaluating Chl-a, TSM, and CDOM. This study underscores the potential of high spatial and spectral resolution of Sentinel-3 Ocean color data for effective monitoring and management of the complex estuarine ecosystem of the Sundarbans. The findings of this study provide important insights to inform the development of sustainable coastal water resource management strategies. Furthermore, the findings will be crucial in achieving Sustainable Development Goal 14, which focuses on life below water, particularly in the conservation and management of marine and coastal biodiversity.

Fire Recognition Method Based on PSO-BP Neural Network and ResNet50

The rapid development of modern society and continuous urbanization have resulted in a proliferation of functional buildings, which offer significant convenience to individuals, but pose significant fire hazards as well. How to detect the fire at the early stage is always the focus of research. This paper proposes a multi-information source fusion fire recognition method based on particle swarm optimization (PSO)-backpropagation (BP) neural networks and ResNet50. The PSO algorithm is applied to optimize the initial parameters of a BP neural network model, while data from three sensors — temperature, humidity and smoke — are integrated, through iterative training of the system, accurate recognition of sensor data can be achieved. Additionally, a method is proposed for the recognition of infrared fire images using ResNet50 and transfer learning. By improving the ResNet50 network model and migrating the ResNet50 pre-trained network weight, infrared fire image recognition accuracy is further enhanced. Then the sensor information recognition results and image information recognition results are input into the fuzzy system for fusion reasoning again, and the final decision is output according to the set fuzzy rules. Experimental findings demonstrate that the multi-information source fusion approach utilizing the PSO-BP neural network and ResNet50 significantly enhances the accuracy and response time of fire recognition, and achieves a remarkable recognition effect.

Land use change from rice paddies to upland fields aggravates soil microbial metabolic limitation and reduces soil quality index and ecosystem multifunctionality

AbstractWith rapid urbanization and economic development, the area of paddy fields has seriously declined in rice‐producing region. However, the impacts of land use change (LUC) on soil enzymatic stoichiometry, microbial metabolic limitations and ecosystem multifunctionality (EMF) have not been well‐documented. Hence, four adjacent paired soil samples were collected in rice‐producing areas. Soil microbial carbon (C), phosphorus (P) and nitrogen metabolic limitations were assessed via vector model. The impacts of LUC on soil structure, microbial activity, quality and multifunctionality were evaluated via soil structure stability index (SSI), soil biological activity (SBA), soil quality index (SQI) and EMF, respectively. Result showed that LUC from rice paddies to upland fields markedly decreased soil structure stability by 19.6%. Soil microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), microbial biomass phosphorus (MBP) and nutrient‐acquiring enzymes activities in the paddy soils were significantly higher than that in upland soil. Compared with paddy soil, microbial relative C and P limitations in upland soil were significantly aggravated by 10.9% and 4.2%, respectively. Additionally, variation partitioning and redundancy analyses revealed that soil pH, available P and P‐acquiring enzymes were the crucial edaphic factors affecting microbial metabolic limitations. Comprehensive assessment showed that LUC from rice paddies to upland fields significantly reduced SBA, SQI and EMF by 5.2%, 17.4% and 17.2%, accordingly. Overall, LUC from rice paddies to upland fields significantly destroyed soil structure, aggravated microbial relative C and P limitations and reduced soil quality. Therefore, artificial managements should be strengthened to counteract the adverse effects of LUC on soil quality and EMF. This study deepened our understanding of sustainable development and efficient management of field under LUC condition.

The Ecological Architecture Approach Analysis at Kusuma Agrotourism Resort & Convention Hotel

Resort areas are popular recreational destinations for urban populations, but rapid urban development often leads to significant environmental degradation in these areas. This degradation is evidenced by the decline in water, air, and soil quality, the extinction of local flora and fauna, and the destruction of natural ecosystems. As a response, the application of ecological architecture has become essential in designing and constructing city facilities, including resorts, to mitigate environmental damage. This study aims to examine the implementation of various principles of ecological architecture in resort areas and identify sustainable materials suitable for such applications. A qualitative descriptive method is employed, combining literature reviews and field observations to analyze the integration of ecological architectural principles in the resort area. The findings reveal that key principles such as the use of local materials, energy-efficient systems, and environmental management strategies have been applied. However, the study also highlights areas where improvements can be made to better preserve the ecosystem and minimize the negative impacts of architectural design.

Recent Advances and Implications for Aviation Emission Inventory Compilation Methods

With the rapid development of industrialization and urbanization in China, civil aviation plays an increasingly important role in the transportation industry. However, pollutants and greenhouse gas (GHG) emissions from civil aviation are becoming an increasingly concerning environmental problem. In order to mitigate the resulting environmental pollution, such as air quality deterioration, regional and global climate warming, and declining human health, more and more efforts have been devoted to reducing both pollutants and GHG emissions. Among these efforts, emissions inventories from civil aviation provide a basis for quantifying pollutants and GHG emissions, establishing evaluation standards of environmental impact, and formulating management policies for both air quality improvement and climate change mitigation. In this paper, we reviewed both compilation approaches and data collection methods for civil aviation emissions inventories, introduced several typical calculation methods for aviation emissions inventories, and analyzed specific cases of actual application based on typical methods of inventory compilation. We also described in detail the activity level and emission index calculation methods of several pollutants and greenhouse gases. Furthermore, based on the above research methods, four typical application cases were investigated, including a specific airport, the landing and takeoff (LTO) cycle of a nation, the entire period with the LTO cycle and the climb–cruise–descent (CCD) phase of a country, and global emissions inventories from civil aviation. The results suggest that, in addition to quantifying the emissions of both pollutants and GHG produced by civil aviation, the selection of inventory compilation methods is likely to be important for improving aviation emission inventory accuracy and for further reducing the environmental, economic, and health impacts resulting from aviation emissions. Moreover, this paper can also provide a reference and theoretical basis for the development of aviation emission inventory compilation methods in the future.

Discussion On Safety Management Of Urban Gas Non-Resident Users

with the rapid development of urbanization, the number of non-residential users of gas consumption continues to increase, and the corresponding security wind direction and problems are increasing. By combing the gas safety management process of non-residential users in large-scale urban gas enterprises, embedding the whole life cycle management process, improving the quality of gas safety management of non-residential users, eliminating some potential safety hazards and reducing the accident development rate, at the same time for other enterprises in the same industry to provide reference.

Hypertension risk pathways in urban built environment: the case of Yuhui District, Bengbu City, China.

The rapid development of urbanization has brought about changes in residents' living environment and behavior, leading to health challenges such as hypertension. An improvement in the built-up environment in the community could contribute to the construction of a healthy city, promote the active life of the residents, and prevent and relieve hypertension. However, there is little research on the relationship between the built environment of the community and hypertension. This cross-sectional study aims to evaluate the relationship between communities' built environment, health behavior, and hypertension grade of residents in Yuhui District of Bengbu City. This study is based on data from the 2022 Health Survey of Residents in 21 communities. To investigate the impact of the community's built environment on residents' hypertension and the underlying mechanisms, regression and structural equation modeling were employed. The results show that the built environment of urban communities has a significant impact on the residents' hypertension. The presence of high densities of supermarkets, convenience stores, parks and plazas, but low densities of clinics and hospitals, has been identified as a significant risk factor for the development of high blood pressure among the residents. Nevertheless, the adoption of healthy behaviors, including regular walking, physical activity, and a diet rich in fruit and vegetables, can play an important role in reducing the risk of hypertension. The findings of this study show that enhancements to the built environment in urban neighborhoods could contribute to a reduction in the prevalence of hypertension among residents. Furthermore, the implementation of efficacious health interventions in urban settings would facilitate the alteration of residents' health behaviors and enhance their overall health status.

Managing Flow: Drainage and Flood Control in Eighteenth-Century London

The rapid expansion and urban development of eighteenth-century London altered existing drainage patterns and increased flood risk. The early modern sewer commissions, set up to deal with the status quo, struggled to cope with these changes. As the city expanded, a network of public and private drains and sewers was built up haphazardly over time, resulting in an exceedingly complex system that was difficult to manage and almost impossible to know. While most previous histories of London’s sanitation and drainage have been centered on the larger-scale sanitation schemes of the nineteenth century, this study sheds light on the water infrastructure and administration that preceded these interventions. It shows that eighteenth-century London’s management of water was an iterative process of reactive decision-making rather than a grand plan.

Development sustainable concrete with high-volume wastes tile ceramic: Role of silica nanoparticles amalgamation

As the global concrete industry shifts towards sustainable practices, there is an increasing focus on mitigating the environmental impacts of ordinary Portland cement (OPC)-based concrete, which is notorious for its significant greenhouse gas emissions, high energy consumption, and substantial demand for natural resources. This urgency is compounded by the growing production of waste tile ceramic materials due to rapid urban development, leading to enhanced research into their recyclability within concrete for improved durability and sustainability. This study explores the development of high-strength concrete utilizing waste tile ceramic aggregates (WTCAs) and waste tile ceramic powder (WTCP) as replacements for natural aggregate and OPC, respectively. To further enhance the concrete's mechanical properties and microstructure, silica nanoparticles derived from waste bottle glass (WBGNPs) were integrated in varying proportions ranging from 2 % to 10 % of the binder content. Optimal results were achieved with a full replacement of natural aggregate by WTCAs, 60 % substitution of OPC by WTCP, and the inclusion of 4 % WBGNPs, which collectively exhibited superior compressive, splitting tensile, and flexural strengths. Microstructural analyses revealed that the addition of WBGNPs improved the hydration process, increased gel density, and reduced porosity. From the obtained numerical results, the coefficient of determination value of 0.92 further confirms the model's predictive strength, demonstrating that the Random Forest algorithm can reliably estimate the compressive strength. The findings indicate that the concrete formulated with WTCP and WBGNPs not only meets diverse construction demands but also significantly contributes to environmental sustainability by reducing impacts on global warming and minimizing landfill usage. This study advocates for the strategic incorporation of WTCP and WBGNPs in concrete applications to promote environmentally sustainable construction practices. It is highly recommended that WTCP be utilized in sustainable binders as a replacement for OPC and natural aggregates to enhance it strength and durable properties, reduce the environmental issues, costs, and the depletion of natural resources.

An innovative approach for detecting buildings and construction anomalies in Zenata City

<p>Rapid urban development in Morocco has led to increased construction activities and significant environmental concerns. Recently Zenata city has undergone significant urban development, marking a crucial step in its trajectory toward a modern smart city. As a part of this growth, our research incorporates an innovative method within the You Only Look Once version 8 (YOLOv8) model, representing a significant advance over conventional methods. The YOLO algorithm has been updated with new features and improvements that infuse our work with a dash of innovation. YOLOv8 integration improves construction and irregular construction detection accuracy beyond what is possible with traditional applications. We trained our algorithm using orthophoto captured by DJI MATRICE 300 RTK drone split into georeferenced tiles and annotated using LabelImg software. Through this process, we were able to create a solid 742 image dataset for training, testing, and validation purposes related to construction. Utilizing drone imagery and the YOLOv8 object detection algorithm, buildings and construction irregularities are detected with high accuracy after 300 training epochs on Kaggle's GPU P100. Insights for early detection and effective building site management are provided by this all-encompassing strategy, which supports Zenata City's sustainable urban growth.</p><div> </div>

Nonlinear impacts of landscape and climatological interactions on urban thermal environment during a hot and rainy summer

Investigating the nonlinear impacts of urban landscape and climatic parameters on urban temperatures, a critical issue within urban climatology. Chengdu, characterized by its hot, rainy summers and rapid urban development, serves as an ideal model to illustrate these dynamics. Our investigation utilizes advanced analytical methods such as Random Forests (RF), SHapley additive explanation (SHAP), and Partial Dependence Plots (PDP) to analyze how landscape and climatic factors influence air temperature (AT) and land surface temperature (LST). Significant findings reveal profound thermal heterogeneity across Chengdu’s urban fabric, underscored by spatially distinct phenomena where some regions exhibit strong contrasts in temperature impacts due to varying climatic and landscape factors. The study identifies relative humidity and rainfall as key drivers of temperature variations during the summer months, reflecting Chengdu’s specific climatic idiosyncrasies. These insights are critical, as they highlight how urban planning and green infrastructure can be strategically used to mitigate adverse thermal effects. The research not only enhances understanding of the complex interplays within urban microclimates but also offers new perspectives on urban heat management. It contributes to the scientific community by providing evidence-based strategies for urban planners to counter the urban heat island effect and enhance urban resilience against climate change. This comprehensive analysis underscores the importance of incorporating multiple variables into urban climate models, lays the groundwork for more refined urban environmental policies and practices.

Piezocatalytic techniques and materials for degradation of organic pollutants from aqueous solution

With the rapid development of industry, agriculture, and urbanization, various organic pollutants have accumulated in natural water, posing a potential threat to both the ecological environment and human beings, and removing organic pollutants from water is an urgent priority. Piezoelectric techniques, with the advantages of green, simple operation, and high efficiency, are highly sought after in the degradation of environmental organic pollutants. Moreover, combining piezoelectric techniques with advanced oxidation processes (AOPs), photocatalysis, or electrocatalysis can further effectively promote the efficient degradation of target pollutants. Therefore, a perspective is presented on the recent progress of piezoelectric techniques for the degradation of various organic pollutants from aqueous solutions. The classification of various piezoelectric materials, as well as modification strategies for improving piezocatalysis, are first systematically summarized. Furthermore, the latest research on piezocatalysis and its combination with other technologies, such as AOPs, photocatalysis, and electrocatalysis, in the degradation of environmental pollutants is discussed. The potential mechanisms of piezocatalysis are also analyzed in depth. Finally, the urgent challenges and future opportunities for piezoelectric techniques in the degradation of organic pollutants are provided.

Problems of Studying the Urbanization Process for Students of the Educational Program "Geography" in Kazakhstan

The study of the urbanization process by students of the educational program "Geography" in Kazakhstan is topical in the context of rapid urban development and evolutions in the geographical space of the country. Various key problems were revealed in the process of the studies such as the need for in-depth research of the theoretical foundations of urbanization, limited access to current data and no work experience on modern methods of data processing and analysis. An online survey was conducted on the basis of the Google Forms and the survey questions were worked out to have the further study of urbanization processes and update knowledge in this field. The study applied a data analysis method using the SPSS Base software package. This method provided the opportunities to structure and process the survey results among 331 teachers and master’s students. The approach ensured an accurate analysis and interpretation of the data which enabled to identify key trends and consistent patterns in respondents’ answers. The results point to both positive and negative conclusions based on the data. The conclusions were made on having evaluated the results with the help of SWOT analysis.

Improving the sustainability of WiFi-enabled indoor localization systems through meta-heuristic based instance selection approach

Due to rapid urban development, many indoor and semi-indoor structures, such as underground malls, metro stations, and bus-stops, have emerged. Traditional GNSS outdoor navigation fails in these locations. To meet urban citizens’ needs, we require sustainable ubiquitous localization solutions, leveraging existing WiFi infrastructure. In such applications, processing data at the edge within constrained environments is imperative. Many prior studies neglect the practical challenge of implementing machine learning-based edge device localization, as they assume reliance on cloud servers. Selecting well-distributed data instances that preserve location class boundaries is essential for effective training in such conditions. In this work, we propose an instance selection approach that is modeled by applying a modified Binary Particle Swarm Optimization (BPSO) technique. The k-differentiating neighbor-based measure is incorporated to determine instance hardness while exploring the solution space through BPSO. The work is implemented on three publicly available benchmark datasets that are collected from two university campuses and one shopping mall. The proposed work is found to have achieved an instance reduction of 35% with only 1%–2% decrease in the accuracy measurement and an appreciable error deviation metric of 2.78 m.