Dense Urban Environments Research Articles

A Study on the Cooling Impact of the River Yamuna and its Surrounding LULC: A Case Study of New Delhi, India

Abstract. The unplanned growth of urban cities has adversely affected the thermal conditions. The Land Use and Land Cover (LULC) present in urban areas significantly impacts the thermal conditions of the surrounding environment. This study investigated the cooling effect of river in dense urban environments. It is conducted on the Yamuna river that flows through New Delhi, the capital city of India. The River Cooling Intensity (RCI) and River Cooling Range (RCR) assessment has been done for the summer season of May 2023 with the help of Landsat 8 images. The RCI ranged from 3.58°C to 1.16°C while the RCR varied from 1320 m to 360 m. It is observed that the river cooling effect is higher in the built-up class while the vegetation class weakens the effect of river cooling intensity and range. Zonal mean temperature varies from 32.02°C to 34.82°C according to the LULC distribution. This study provides valuable insights into the relationship between river cooling and temperature regulation according to the LULC classes. It also establishes the presence of an urban cool island inside the city region due to the river, as the mean temperature of the river surrounding (33.83°C) was lower than that of the city (35.73°C).

Key Factors in the Design of Urban Underground Metro Lines

Designing sustainable underground metro lines in dense urban environments is a highly challenging task that requires the collaboration of numerous stakeholders and consultants to make crucial decisions influenced by several factors. While it is impossible to address every issue influencing the decision-making process, identifying key factors and their interdependencies is essential for optimal design. This study focuses on six critical aspects of the reference design of metro systems: (1) track alignment, (2) tunneling strategy, (3) station typology, (4) operations and maintenance, (5) procurement strategy, and (6) environmental aspects. Amongst these aspects, we identify track alignment as the primary driving factor that influences the other factors. We analyze the decision between shallow and deep alignments as an engineering choice that necessitates balancing conflicting factors and constraints. Our contribution lies in mapping these factors and their dependencies, thus offering policymakers, project managers, and designers a framework to navigate the design process. Our discussion also provides guidance to public agencies in tendering for design teams more efficiently. Drawing from lessons learned by experienced design managers, this study aims to fill the gap in the literature by offering a generalist perspective on metro design.

Urban environmental acupuncture as a tool to support nature-based solution implementation: does it deliver what it promises?

Urban environmental acupuncture (UEA) is a novel approach designed to engage with a variety of stakeholders to successfully implement nature-based solutions in dense urban areas. The Interreg Central Europe project SALUTE4CE piloted this concept through small-scale, low-budget interventions in four cities: Chorzow (Poland), Erfurt (Germany), Alessandria (Italy), and Liptovski Mikulas (Slovakia). The pilot projects were designed to be quickly implementable, utilizing abandoned or undesirable sites, with the potential to catalyze both physical and social transformation. Interviews with local actors in these cities after project implementation provided valuable feedback, which was used to develop principles of urban environmental acupuncture. While the limited number of interviews means these findings offer only initial insights, they are largely supported by existing research. Nevertheless, this perspective provides important lessons that can inform future transformation projects and help evaluate the success of the UEA approach. The key contributions of this work are the introduction of urban environmental acupuncture concept, deriving principles of UEA based on feedback from local stakeholders and offering insights in future design and evaluation of similar nature-based solution initiatives in dense urban environments. This perspective helps to establish the potential of UEA as a tool for engaging diverse stakeholders and catalyzing sustainable urban transformation (specifically expansion of nature-based solutions) through low-cost interventions.

Estimating daily road traffic pollution in Hong Kong using CFD modelling: Validation and application

Road traffic is a major source of urban air pollution, exposing residents to harmful levels of pollutants, particularly in densely populated urban centers. In this study, Computational Fluid Dynamics (CFD) modelling is applied to investigate the impact of road traffic emissions on air pollution within a dense urban environment. The model, applied in Mong Kok, Hong Kong, incorporates high-resolution traffic activity, meteorological, and background concentration data. It demonstrates strong agreement with roadside measurements, deviating by 18.8 % for NOx, 15.5 % for CO, and 0.7 % for PM10, with potential underestimation linked to unmodeled NOx-O3 photochemistry during midday hours (12:00–17:00). Results reveal that local road traffic significantly increases background NOx levels (41 % average increase), highlighting the importance of dispersion modelling for accurate pollution assessments. Traffic-induced increases in CO and PM10 background levels are less pronounced. Two applications demonstrate the model's utility. Firstly, analysis of daily pollution distribution reveals that approximately 10 % of the studied area experiences moderate to very high pollution levels during peak hours. Secondly, an investigation into pollution sources within a leisure park identifies heavy-duty and light-duty vehicles as the primary NOx polluters. This modelling approach provides a valuable decision-making tool for policymakers and local authorities. It enables the assessment of urban design changes, strategic placement of sensitive structures, and future traffic fleet composition choices to mitigate pollution exposure. As air quality regulations tighten, this study offers a robust method for guiding impactful mitigation strategies.

STRUCTURAL DESIGN AND ANALYSIS OF A 20-STORY MIXED-USE HIGH-RISE RESIDENTIAL AND COMMERCIAL BUILDING IN DHAKA: SEISMIC AND WIND LOAD CONSIDERATIONS

This study, conducted in the Department of Civil Engineering at Stamford University Bangladesh, focuses on the structural design and analysis of a 20-story mixed-use high-rise building in Dhaka, accommodating both residential and commercial functions. The objective of the study is to gain insights into the design and analysis of an intermediate moment-resisting frame that addresses the specific challenges posed by seismic and wind loads in a dense urban environment. A commercial building, defined as a structure primarily dedicated to business activities, requires specialized design considerations that extend beyond residential construction, including the need for larger beams, increased floor space, and accommodation for various commercial functions. In high-density urban areas, like Dhaka, the design of multi-story commercial buildings, such as shopping malls and office complexes, presents additional challenges related to space constraints, vertical shopping preferences, and the high cost of land. The study emphasizes the importance of creating a structural design that balances rentable space, aesthetics, cost, safety, and comfort, while addressing the lateral forces caused by wind and seismic activity. This analysis aims to assist engineers and architects in meeting the growing demand for multifunctional high-rise structures, ensuring both safety and functionality in the face of complex environmental and structural factors.

Navigating Planning Crises: Karachi’s Response to COVID-19

The COVID-19 pandemic has invariably highlighted the critical role of spatial planning in managing public health emergencies, particularly with the need for social distancing. Historically, effective spatial planning has been essential for governments in navigating crises such as floods and earthquakes, employing strategies like designating containment zones, repurposing infrastructure, and constructing temporary facilities. The research focuses on Karachi’s response to the pandemic. It examines how the city’s spatial planning strategies were adapted to meet the challenges of its dense urban environment and socio-economic diversity. As Pakistan’s largest city and economic hub, Karachi confronted significant challenges in balancing public health measures with financial activities. The study investigates how state institutions implemented awareness campaigns and emergency spatial planning, including the creation of quarantine centers, isolation zones, inoculation centers, and modifications to public spaces. Similar to other mega-cities, Karachi is vulnerable to natural disasters and emergencies, as it balances a range of issues that require effective spatial and urban planning. The city’s response to COVID-19 reflects broader trends in emergency planning, highlighting its approach to managing crises through strategic interventions. The study aims to uncover the complex relationship between spatial planning and crisis management by illustrating response timelines, temporary structures taxonomy, and infographics. It offers insights into how cities can better prepare for and respond to future crises. It emphasizes the importance of restructuring governance and adaptive and context-specific planning in urban health management.

Machine Learning-Aided Cooperative Localization under A Dense Urban Environment: Demonstrates Universal Feasibility

Machine Learning-Aided Cooperative Localization under A Dense Urban Environment: Demonstrates Universal Feasibility

Optimizing Coziness and Productivity: An All-Encompassing Ergonomic Investigation on Diners and Restaurant Operators in Dense Urban Living Environments

This research aims to determine and optimize the welfare of visitors and food stall owners in dense urban environments, especially in the context of dense residential areas in big cities. As ergonomics research, its main focus is to assess the physical and psychological conditions of visitors and food stall owners operating in dense urban environments. This research method involves a detailed analysis of ergonomic aspects, including room layout, furniture design, lighting and acoustics, taking into account the unique demands of dense housing. In addition, in-depth interviews with food stall owners and observations of visitor behavior were conducted to understand the non-physical aspects that influence their well-being. The research location is in the Paseban area, Central Jakarta. The results of this research provide an in-depth understanding of how environmental conditions can influence comfort and productivity in food stalls, both from the perspective of visitors and owners. The practical implications of these findings can serve as a guide for architects, designers and policy makers in improving the design of public spaces in dense urban environments, with the ultimate goal of improving the welfare of local communities. This study makes a significant contribution to our understanding of how ergonomics can be applied effectively to improve the quality of life amidst the challenges of dense housing in large cities.

Assessing electromagnetic field exposure levels in multi-active reconfigurable intelligent surface assisted 5G network

As 5G mobile networks continue to proliferate in dense urban environments, it becomes increasingly important to understand and mitigate excessive electromagnetic field (EMF) exposure. This study investigates how the downlink EMF exposure levels of 5G millimeter wave (mm-wave) mobile networks are influenced by the integration of multi-active reconfigurable intelligent surfaces (RISs), using a ray-tracing approach. Our research employs a comprehensive two-step methodology: Firstly, we introduce a new RIS-assisted 5G mm-wave network planning technique. This technique leverages a machine learning (ML) approach for the classification of multi-RIS clusters. The primary goal is to optimize coverage while minimizing the number of required RIS deployments. This is achieved by strategically placing RISs based on the ML classification, ultimately aiming to enhance network efficiency. Secondly, we conducted a thorough comparative analysis, evaluating the impact of both passive and active RISs on EMF exposure level throughout a dense urban environment. Passive RIS and active RIS differ in their adaptability to changing network conditions. The result shows that the influence of multi-active RISs on EMF exposure is significant (about 7.5 times higher) compared to passive RISs.

Assessment of Risk Factors in Condominium Buildings Construction in Metro Manila using Analytic Hierarchy Process (AHP)

The rapid urbanization of Metro Manila led to a surge in condominium construction projects, which, despite offering efficient housing solutions, faced significant challenges due to dense urban environment, susceptibility to natural disasters & the health and safety issues related to accidents, losses, injuries, damages, and/or delays arising during the execution of a project which may adversely affect its success. This study explored the efficacy of the Analytic Hierarchy Process (AHP) in assessing and prioritizing risks specific to Metro Manila's condominium construction projects, addressing a critical gap in current risk assessment methodologies. The study engaged mixed methods, integrating Meta-Analysis, Experts Consultation, and AHP, to identify and prioritize Risk Factors (RFs) in condominium construction projects. A systematic review through meta-analysis revealed nine critical RFs, including Technical/Design, Financial/Economic, Environmental, Regulatory/Legal, Socio-Political, Logistics/Supply Chain, Management/Supervision, Quality, and Health and Safety Risks. Inputs from experts from a diverse group of qualified professionals had been pooled to validate these findings. Through AHP analysis, risks specific to Metro Manila's condominium construction projects were revealed, with Health and Safety Risk (19.52%), Quality Risk (15.97%) and Management/Supervision Risk (15.77%) as the major concerns. An internal consistency of 0.71% attested to the reliability testing of the results. Given the complexity of condominium construction projects, this study responds to an important demand: a more systemic understanding of risk factors. Moreover, this study is relevant to further enhance and improve sustainability in condominium projects as well as performance of the construction industry.

A Novel Stacking Ensemble Learning Approach for Predicting PM2.5 Levels in Dense Urban Environments Using Meteorological Variables: A Case Study in Macau

Air pollution, particularly particulate matter such as PM2.5 and PM10, has become a focal point of global concern due to its significant impact on air quality and human health. Macau, as one of the most densely populated cities in the world, faces severe air quality challenges. We leveraged daily pollution data from 2015 to 2023 and hourly meteorological pollution monitoring data from 2020 to 2022 in Macau to conduct an in-depth analysis of the temporal trends of and seasonal variations in PM2.5 and PM10, as well as their relationships with meteorological factors. The findings reveal that PM10 concentrations peak during dawn and early morning, whereas PM2.5 distributions are comparatively uniform. PM concentrations significantly increase in winter and decrease in summer, with relative humidity, temperature, and sea-level atmospheric pressure identified as key meteorological determinants. To enhance prediction accuracy, a Stacking-based ensemble learning model was developed, employing LSTM and XGBoost as base learners and LightGBM as the meta-learner for predicting PM2.5 concentrations. This model outperforms traditional methods such as LSTM, CNN, RF, and XGB across multiple performance metrics.

UAV Path Planning Based on Random Obstacle Training and Linear Soft Update of DRL in Dense Urban Environment

The three-dimensional (3D) path planning problem of an Unmanned Aerial Vehicle (UAV) considering the effect of environmental wind in a dense city is investigated in this paper. The mission of the UAV is to fly from its initial position to its destination while ensuring safe flight. The dense obstacle avoidance and the energy consumption in 3D space need to be considered during the mission, which are often ignored in common studies. To solve these problems, an improved Deep Reinforcement Learning (DRL) path planning algorithm based on Double Deep Q-Network (DDQN) is proposed in this paper. Among the algorithms, the random obstacle training method is first proposed to make the algorithm consider various flight scenarios more globally and comprehensively and improve the algorithm’s robustness and adaptability. Then, the linear soft update strategy is employed to realize the smooth neural network parameter update, which enhances the stability and convergence of the training. In addition, the wind disturbances are integrated into the energy consumption model and reward function, which can effectively describe the wind disturbances during the UAV mission to achieve the minimum drag flight. To prevent the neural network from interfering with training failures, the meritocracy mechanism is proposed to enhance the algorithm’s stability. The effectiveness and applicability of the proposed method are verified through simulation analysis and comparative studies. The UAV based on this algorithm has good autonomy and adaptability, which provides a new way to solve the UAV path planning problem in dense urban scenes.

Progressive supervised pedestrian detection algorithm for green edge–cloud computing

In this paper, we present a novel supervised pedestrian detection algorithm tailored for Green Edge–Cloud Computing (GECC), aimed at addressing the challenges associated with pre-trained models in real-world settings. These models often exhibit performance degradation due to the divergence between training datasets and complex real-world scenarios. To bridge this gap, our proposed algorithm employs a progressive occlusion-aware iterative training strategy that significantly enhances the representation of occluded pedestrians, a prevailing issue in dense urban environments. Additionally, we introduce a K-stratified hard sampling strategy, which accelerates the training process and substantially lowers the energy consumption required for model retraining. We also explore a refined non-maximal value suppression algorithm that employs neighboring centroids separation, improving the precision of pedestrian detection within crowded scenes. Through rigorous experiments on established pedestrian detection datasets, we demonstrate that our algorithm not only elevates pedestrian detection accuracy in high-density situations but also markedly reduces the model training duration.

City-scale analysis of PV potential and visibility in heritage environment using GIS and LiDAR

Increasing the share of renewables, especially utilizing solar power in our energy mix is a highly prioritized aim of the EU. Budapest, the largest city and the capital of Hungary, contains almost 1 million flats in more than 190 thousand buildings, organized in various characteristic urban fabric types. Although on-site renewable energy production is severely limited in dense urban environments, the roofs offer a considerable potential to generate energy on-site.The present paper introduces a method to estimate the PV potential of rooftops on a large scale applying a GIS approach. Using LiDAR data, a Digital Surface Model (DSM) was created. The DSM was utilized to evaluate roof slope, orientation, and global irradiation. Solar potentials, available roof area, photovoltaic (PV) system capacities and estimated annual electricity production were derived from the GIS model. The method was used to develop an open access website called the Budapest Solar Map, which provides decision-makers with solar PV data. However, the nominal PV potential is limited by layers of heritage protection regulations that safeguard the sight of our case study area, Budapest, which is a World Heritage Site. Visibility is also surveyed with GIS and geometrical modeling from designated high points and from the street level, respectively.

Uncertainty analysis of autonomous delivery robot operations for last-mile logistics in European cities

Although autonomous delivery robots (ADRs) are widely anticipated to significantly enhance the efficiency of last-mile logistics operations in dense urban environments in the coming years, their impact on logistics service providers’ supply chains has yet to be accurately assessed on a large scale. The primary objective of this article is to quantify the uncertainty in the cost of ADR operations as a function of the stochastic behavior of given input variables. First, ADR operations are modeled using the continuous approximation methodology. The mathematical formulations proposed in this article relate certain ADR and service area input parameters to provide an estimate of the carrier’s last-mile operating costs. An uncertainty analysis based on the Monte Carlo approach is then performed. The numerical results indicate that the implementation of a two-echelon delivery scheme with heavy-duty vehicles cooperating with ADRs through an urban logistics micro-hub would, on average, reduce last-mile operating costs by more than 10% in small and medium-sized European cities, given adequate operational conditions.

Evaluation of a single-layer urban energy balance model using measured energy fluxes by scaled outdoor experiments in humid subtropical climate

Urban energy balance models can simulate the localized climate and fluxes in urban areas. They are often evaluated using real urban site observations. However, uncertainties in anthropogenic emission, geometry and materials can hinder interpretation. These uncertainties can be eliminated by scaled outdoor models. We assess the performance of a single-layer urban canopy model (SLUCM) offline in predicting surface energy balance (SEB) fluxes within a street canyon-like urban configuration using field measurements from a scaled outdoor setup in Guangzhou, China. We utilize observations of SEB fluxes and surface temperature from a homogenous scaled urban site characterized by street canyons. The scaled observations in June–December 2020 in a dense urban environment with a height-to-width ratio (H/W) of 2 are employed to assess seasonal variations in model performance. Additionally, the December data from two distinct urban scenarios (H/W = 2(2020) and 0.5(2019)) are utilized to compare model performance in deep and shallow street canyon settings. Results show that QS(R2 = ∼0.47, RMSE = ∼46.0) is the most challenging to predict, followed by QH (R2 = ∼0.92, RMSE = ∼65.5), Q*(R2 = ∼0.99, RMSE = ∼39.5), L↑(R2 = ∼0.95, RMSE = ∼33.7), and K↑(R2 = ∼0.99, RMSE = ∼6.2). SLUCM faces difficulties simulating L↑ particularly during nighttime and cloudy conditions. It also tends to underestimate daytime net radiation (Q*), especially during hot, humid months. Moreover, H/W ratio slightly affects SLUCM's performance in simulating SEB fluxes under dry cool conditions denser urban settings (H/W = 2) show better model performance in K↑, QH, and QS but worse performance in L↑ and Q*.

Residential Rooftop Urban Agriculture: Architectural Design Recommendations

It is evident that, due to population growth, future urbanization and urban growth are inevitable. It is estimated that the food supply demand of future urban centers will grow, which will place an additional burden on the agriculture sector to produce more food. It is projected that securing the food supply chain for future urban centers will be a challenge. Urban agriculture can be regarded as a remedy for possible future challenges that the global food system will face. It might be able to reduce the future burden on the agriculture sector. This research proposes that urban rooftop agriculture, as a subset of urban agriculture, can produce local fresh food in dense urban environments. The principal aim of this research is to suggest a series of design recommendations for architects interested in designing residential buildings capable of rooftop food production. This research attempts to highlight the specific design recommendations and the principal limitations regarding designing residential rooftop farms. To extract the data for developing the proposed design recommendations and limitations, a review of the literature within the fields of urban agriculture, building-integrated agriculture, and horticulture was conducted. Based on the literature review results, this research suggests that the following three types of farming methods can be developed on residential rooftops: (1) open-air rooftop food production, (2) “low-tech” rooftop greenhouses, and (3) “high-tech” rooftop greenhouses. In addition, factors that can be considered principal limitations are suggested. In sum, this research proposes that current and future residential buildings can be designed so that their rooftops are utilized as farms. In this way, such buildings can contribute to delivering local fresh food to current and future metropolitan dwellers.

From District to City Scale: The Potential of Water-Sensitive Urban Design (WSUD)

The summer of 2022 was one of the hottest and driest summers that Germany experienced in the 21st century. Water levels in rivers sank dramatically with many dams and reservoirs running dry; as a result, fields could not be irrigated sufficiently, and even power generation and supply were affected. The impact of abnormally high temperatures for extended periods (heatwaves) is not restricted to nature and the economy but is also a considerable public health burden. Experts worldwide agree that these extreme weather events are being driven by climate change and will increase in intensity and frequency in the future. The adverse impact of these extreme weather events multiplies among dense urban environments, e.g., through heat islands. This calls for cities to take action to heat-proof and water-secure their urban developments. Water-Sensitive Urban Design (WSUD) is one such approach to mitigate the aforementioned challenges by leveraging the urban water ecosystem with special attention to the subject of water reclamation, retention, treatment and distribution. This paper introduces and builds upon a prototype of WSUD that centers around an artificial lake as an integrated water resource management system (IWRMS) fed by treated grey water and storm water obtained from two housing blocks flanking the water reservoir. Based on the specifications of this prototype, indicators of site suitability are derived and applied to identify potential locations for replicable projects in the city of Darmstadt. The results confirm the impact WSUD can have: a total of 22 sites with 2527 apartments are found suitable for prototype implementation in Darmstadt. Savings in town water consumption from these 22 sites would add up to 147 million liters. Further benefits include the provision of 24 million liters of irrigation water, storm water retention, adiabatic cooling during heatwave, increased biodiversity and the improvement in livability of the sites and the city.

Field Trial of Coexistence and Simultaneous Switching of Real-Time Fiber Sensing and Coherent 400 GbE in a Dense Urban Environment

Field Trial of Coexistence and Simultaneous Switching of Real-Time Fiber Sensing and Coherent 400 GbE in a Dense Urban Environment

Novel approach to roof rainwater harvesting and aquifer recharge in an urban environment: Dry and wet infiltration wells comparison

In urban environments there is a severe reduction of infiltration and groundwater recharge due to the existence of large impervious areas. During rain events, large volumes of water that could have recharged groundwater and surface water bodies are diverted into the municipal drainage system and lost from the freshwater storage. Moreover, extreme rain events impose high peak flows and large runoff volumes, which increase the risk of urban floods. Recent studies have suggested the use of rainwater harvesting for groundwater recharge, as a plausible solution for these challenges in dense urban environments. While the benefits of this approach are well understood, research on its practical, engineering, and hydrological aspects is relatively limited. The objective of the present study was to examine the use of infiltration wells for groundwater recharge with harvested rainwater collected from building rooftops under Mediterranean climate conditions. Two types of wells with similar hydraulic and technical properties were examined: a well that reaches the groundwater (wet well); and a well that discharges the harvested water into the unsaturated zone (dry well). Infiltration capacities of the wells were compared in controlled experiments conducted during summer months, and in operational recharge of harvested rainwater, during winter. Both dry and wet wells were found to be suitable for purposes of groundwater recharge with rooftop-harvested rainwater. Infiltration capacity of the wet well was about seven times greater than the infiltration capacity of the dry well. While the infiltration capacity of the wet well was constant throughout the entire length of the study (∼10 m3/h/m), the dry well infiltration capacity improved during winter (from 0.5 m3/h/m to 1.5 m3/h/m), a result of development of the dry well with time. Considering Tel-Aviv, Israel, as a case study for a dense modern city in a Mediterranean climate, it is demonstrated herein that the use of infiltration wells may reduce urban drainage by ∼40 %.