Urban Activities Research Articles

How do external environmental stimuli and internal psychological cultural identity affect tourists’ behavioral intentions in public cultural spaces?

The role of cities and regions is becoming increasingly crucial in the process of global localization. As vital carriers of urban activities, public spaces are receiving widespread attention; however, the substantial impact of multidimensional experiences of public cultural spaces on urban tourism and the role of cultural identity within these spaces have yet to be extensively studied. This study establishes a framework based on scenescape and stimulus-organism-response theories to explore how external stimuli influence tourists’ behavioral intentions. We developed and conducted a questionnaire survey targeting individuals who have visited the “Most Beautiful Public Cultural Spaces” in Nanjing, China. A total of 287 valid samples were collected for analysis. Four types of external environmental stimuli—physical, activity, mechanism, and virtual spaces—were found to have direct and indirect effects on tourists’ behavioral intentions with cultural identity playing an intermediary role. These new insights provide theoretical enlightenment and practical recommendations for enhancing tourism experiences in public cultural spaces.

Soundscapes in Urban Green Spaces of a Megacity Across an Urban–Rural Gradient: A Case Study of Shanghai

Urban soundscapes are valuable indicators of urban biodiversity and ecosystem health. This study investigated the spatial and temporal characteristics of soundscapes in megacity green spaces and their relationship with the urban environment. An acoustic index was formulated and calculated using long-term data from Shanghai’s urban green spaces. The results show a significant increase in the acoustic index during spring and summer, peaking during these periods, while a decrease was observed from December to February, dominated by abiotic sounds. Diurnal variations indicate a rapid increase in the acoustic index at sunrise, reflecting heightened avian activity during the dawn chorus. Human interference in the urban center limited acoustic activity, whereas the outer suburbs, with their more natural environments, supported greater acoustic richness.

Chemoresistive Gas Sensors Based on Electrospun 1D Nanostructures: Synergizing Morphology and Performance Optimization

Gas sensors are essential for safety and quality of life, with broad applications in industry, healthcare, and environmental monitoring. As urbanization and industrial activities intensify, the need for advanced air quality monitoring becomes critical, driving the demand for more sensitive, selective, and reliable sensors. Recent advances in nanotechnology, particularly 1D nanostructures like nanofibers and nanowires, have garnered significant interest due to their high surface area and improved charge transfer properties. Electrospinning stands out as a promising technique for fabricating these nanomaterials, enabling precise control over their morphology and leading to sensors with exceptional attributes, including high sensitivity, rapid response, and excellent stability in harsh conditions. This review examines the current research on chemoresistive gas sensors based on 1D nanostructures produced by electrospinning. It focuses on how the morphology and composition of these nanomaterials influence key sensor characteristics—sensitivity, selectivity, and stability. The review highlights recent advancements in sensors incorporating metal oxides, carbon nanomaterials, and conducting polymers, along with their modifications to enhance performance. It also explores the use of fiber-based composite materials for detecting oxidizing, reducing, and volatile organic compounds. These composites leverage the properties of various materials to achieve high sensitivity and selectivity, allowing for the detection of a wide range of gases in diverse conditions. The review further addresses challenges in scaling up production and suggests future research directions to overcome technological limitations and improve sensor performance for both industrial and domestic air quality monitoring applications.

Assessing the Impacts of Nature-Based Solutions on Ecosystem Services: A Water-Energy-Food-Ecosystems Nexus Approach in the Nima River Sub-Basin (Colombia)

Forest ecosystem services are critical for maintaining ecological balance and supporting human well-being from different perspectives. However, rapid land use changes driven by agricultural expansion, urbanization, and industrial activities have significantly altered forest ecosystems, degrading the services they provide. We here conduct an ecosystem service assessment through biophysical and economic estimates for a multipurpose Andean water sub-basin in western Colombia. We compare a business as usual (BAU) with a forest nature-based solution (NbS) scenario focused on forest landscape restoration. The research employed participatory methods for the NbS selection and economic valuation techniques to evaluate water flow regulation, water provisioning, water purification, and food provisioning services. Results show that the NbS scenario yielded a net positive economic impact across most evaluated ecosystem services, with notable trade-offs. Specifically, the NbS scenario increased water retention by 2.9% compared to BAU. Water flow regulation demonstrated the most substantial economic benefit, increasing by EUR 11.39 million/year in the NbS scenario. On the other hand, the food provisioning service presented a reduction of EUR 3.2 million/year in the NbS scenario. These findings highlight the potential of forest-based NbS to address the Water–Energy–Food–Ecosystem (WEFE) nexus challenges. The study’s outcomes provide valuable insights for policymakers and practitioners, supporting the development of Payment for Ecosystem Services schemes and integrating ecosystem service valuation into land use planning and decision-making processes.

Assessing distributional justice around Cultural Ecosystem Services (CES) provided by urban green areas: The case of Bologna

Cultural Ecosystem Services (CES) and related flows of benefits – heavily influencing societal and individual health and wellbeing - are usually ascribed non-consumptive values. However, despite the increasing recognition of their contribution to citizens’ quality of life, the intangible nature of CES makes it difficult to quantify them and hard to be integrated in decision making and planning processes. Nevertheless, the assessment of the societal relevance of CES would largely help to improve people wellbeing and quality of life. CES depend not only on the characteristics and features of urban green areas, but also on preferences and needs of the users that interact with them and that contribute to co-produce ES related benefits and values. These diverse needs, coupled with the uneven distribution of urban green area and diverse ways of managing them in the city, could affect the way ES are produced and further exacerbate existing inequalities and disparities. This contribution investigates the case study of the city of Bologna, introducing a spatial approach based on a new and pluralistic notion of urban green area that consider sport facilities and Green Stewards activities, to assess the related CES co-production paths and distributional dimension of justice in the city. Results show a good accessibility to urban green area throughout the city of Bologna (around .70 %). Nevertheless, CES co-production paths are limited in those areas of the city with higher population vulnerability index, due to lower urban green area facilities and green stewards activities, thus highlighting areas with higher distributional and procedural injustice path.

Evaluation of Urbanization Influences on Beach Sediment Contamination with Heavy Metals Along the Littoral Zone of Alexandria City, Egypt

The western coastal area of Alexandria City, Egypt, faces significant environmental challenges due to heavy metal (HM) contamination in beach sediments, driven by intensive urbanization, tourism, commercial harbors, and industrial activities. Therefore, this study focuses on geochemically assessing HM levels in sediment samples from 28 sites and employed various descriptive and multivariate statistical approaches, pollution indices, and sediment quality guidelines (SQGs) to identify pollution hotspots, define contamination grades, and assess the quality of beach sediments. The average concentrations of Cd, Co, Cr, Cu, Pb, and Zn were 25.7, 30, 2.2, 7.5, 2.9, and 8.0 times, respectively, compared to the geochemical background (carbonate sedimentary rocks). et al.-Hanuvil Beach, relatively elevated contents of these metals were recorded. In addition, samples showed Zn levels higher than the corresponding probable effect concentration (PEC) and effect range medium (ERM) values, suggesting adverse impacts on biota. The principal component (PC) analysis revealed the anthropogenic origin of Cd, Co, and Pb in PC1, the mixed origin of Cr, Cu, Mn, and Zn in PC2, and the natural source of Fe in PC3. According to the applied single pollution indices, HM contamination grades were ranked in ascending order of Mn < Fe < Cr < Cd < Cu < Pb < Zn < Co. In conclusion, the HMs (Cu, Zn, Cd, Co, Pb, and Cr) in the study area are anthropogenic, attributed mainly to untreated discharge of municipal and industrial wastewater and solid wastes, atmospheric deposition of air pollutants, and tourism activities.

Energy factors affecting environmental pollution for sustainable development goals: The case of India

This study investigates the nexus between electricity consumption, fossil fuel dependency, renewable energy adoption, population growth, trade activities, economic growth, and environmental pollution in India. The primary objective is to understand how these factors interrelate and influence each other, focusing on their implications for sustainable development. The study used data from the World Bank from 2000 to 2023; the methodology adopted includes vector autoregression modeling, Granger causality tests, cointegration analysis, impulse response functions, and variance decomposition. These econometric techniques were selected due to their ability to capture dynamic relationships, determine causality, and identify long-term equilibrium among the variables. The findings reveal that economic growth significantly increases electricity consumption and fossil fuel usage, leading to higher carbon dioxide emissions. On the other hand, renewable energy adoption reduces environmental pollution. The study also highlights the complex interplay between population growth, urbanization, and trade activities in shaping India's energy demand and environmental outcomes. The implications of these findings are critical for India's sustainable development. The results suggest that while economic growth is essential, it must be balanced with sustainable energy practices to mitigate environmental pollution. The findings emphasize the need for policy interventions that promote renewable energy, enhance energy efficiency, and enforce environmental regulations. Recommendations include accelerating renewable energy adoption, implementing stringent energy efficiency standards, and developing integrated policies that simultaneously address the economic, energy, and environmental dimensions. These actions will help India achieve a sustainable balance between economic growth and environmental protection, ensuring a healthier future for its population.

Prediction of Commercial Street Location Based on Point of Interest (POI) Big Data and Machine Learning

Identifying optimal locations for sustainable commercial street development is crucial for driving economic growth and enhancing social vitality in cities. This study proposes a data-driven approach to predict potential sites for commercial streets in Foshan City, China, utilizing Points of Interest (POI) big data and machine learning techniques. Decision tree algorithms are employed to quantitatively assess and predict optimal locations at a fine-grained spatial resolution, dividing the study area into 9808 grid cells. The analysis identifies 2157 grid cells as potential sites for commercial street development, highlighting the significant influence of Medical Care, Shopping, and Recreation and Entertainment POIs on site selection. The study underscores the importance of considering population base, human activity patterns, and cultural elements in sustainable urban development. The main contributions include providing a novel decision-support method for data-driven and sustainable commercial street site selection and offering insights into the complex interplay between urban land use, human activities, and commercial development. The findings have important implications for urban planning and policy-making, showcasing the potential of data-driven approaches in guiding sustainable urban development and fostering vibrant commercial areas.

Engaging the everyday: the concept and practice of ‘everyday heritage’

ABSTRACT In this paper, we explore what it means to link heritage to the everyday, how this has been framed in the field of heritage studies to date and how engagement with the everyday might generate future opportunities for heritage practice. We highlight distinctive aspects of everyday heritage, including its post-representative nature, the importance of urban activism, digital and historical forms of cultural participation, as well as its aesthetic re-framing of the ordinary. We conclude that while the term is often used to describe a non-hegemonic, unofficial form of heritage, everyday heritage tends to merge the authorised and the unauthorised. We finish by distilling our findings into a working manifesto for everyday heritage as a provocation for future discussion and debate on this topic.

Mitochondrial genome of the Indian spot-billed duck and its phylogenetic and conservation implications.

The Indian spot-billed duck, Anas poecilorhyncha is a large dabbling and non-migratory breeding bird. The identification and phylogenetic relationship of A. poecilorhyncha remain uncertain due to the presence of overlapping meristic characters and hybridization with closely related species. Molecular data aids when there are challenges in morphological identification. However, genetic characterization of A. poecilorhyncha has been paid less attention. Apart from their functional and physiological role, mitochondrial genome can also be used for various purposes, including species identification, phylogenetic analysis, understanding the domestication history of species etc. Therefore, the present study aimed to sequence the mitochondrial genome of A. poecilorhyncha and its closely related domestic species A. platyrhynchos (mallard duck) to understand their mitochondrial genome structure and phylogenetic relationships. The length of mitochondrial genome of A. poecilorhyncha and A. platyrhynchos was 16,608 and 16,604bp respectively. Mitochondrial genome contained 37 genes and a non-coding control region. Overall, the characteristics of mitochondrial genome of both species were found to be conserved. The phylogenetic tree exhibited seven major clades (A to G) with a high bootstrap support. Notably, the Indian A. poecilorhyncha population formed a distinct clade (C) whereas the A. poecilorhyncha that were probably sampled from China grouped along with A. zonorhyncha (clade B). Besides, one of the A. poecilorhyncha probably sampled from China was placed in the clade A, which predominantly consisted of A. platyrhynchos. It suggests that Indian A. poecilorhyncha population is genetically different from Chinese A. poecilorhyncha population. Further, it sheds light on the importance of conducting a comprehensive phylogenetic study on these species. The newly sequenced mitochondrial genome of A. poecilorhyncha and A. platyrhynchos would be useful not only to have a better understanding of the phylogeny and evolution of Anas species but also to help in the conservation of A. poecilorhyncha which is under constant threat from rapid urbanization, interspecific hybridization and other human activities.

Data-driven effects of human activities and environmental factors on inland aquatic dissolved organic matter in China: Insights from machine learning

Aquatic DOM, a major carbon pool in inland waters, plays a key role in the global carbon cycle and is influenced by human activities and environmental factors. While watershed surveys have identified some drivers of aquatic DOM changes, national-scale knowledge remains limited. Spectral monitoring data from 721 aquatic DOMs in inland China were collected to predict and quantify the spectral properties of aquatic DOMs and the coupled effects of human activities and environmental factors by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), random forest (RF) and structural equation modeling (SEM). Our results show that the dataset is relatively evenly distributed in terms of flow regimes, and the data are adequately representative of various types of water in China. Although the performance of CEEMDAN is affected by the skewed distribution of data sampling points (453 and 449 for eastern regions and eutrophic waters, respectively), CEEMDAN effectively processes the spatial sequences of aquatic DOM spectra, which significantly improves the reliability of the machine learning model (by 13%–68%) and reduces the error (by 63%–92%). CEEMDAN-RF and SEM results indicate that longitude and latitude are the most important environmental factors affecting the spectral properties of aquatic DOM through temperature, light and land cover differences, reducing the biogenic properties of aquatic DOM. Unlike observations in small watersheds and estuaries, environmental factors of season, precipitation and salinity have weak effects on aquatic DOM. Furthermore, the biogenic character of aquatic DOM is enhanced by urban human activities, represented by urbanization, population, and impervious land fraction, which have long-lasting and strong impacts on aquatic DOM by driving water eutrophication and enhancing phytoplankton and microbial activity. Our study informs the prediction and quantification of coupled environmental and anthropogenic impacts on aquatic DOM at large scales.

POI VizNet: New QGIS Tool to construct Visibility Networks in Cities

Abstract. Network science and graph theory offer attractive models to describe urban phenomena and examine cases of urban planning and design. However, none of the existing network analysis tools consider simultaneously human visual perception and locations of urban activities. In this paper we introduce POI VizNet, a new QGIS plug-in that constructs various 2-dimentianal undirected graphs of unobstructed lines of sight. The plug-in integrates an increasing amount of available GIS-based data of Point Of Interest (POI) and visibility into one readily accessible analytical framework. Graphs are created between two types of decision points during urban travel – street intersections and POIs, origins, and destinations of travel, by connecting these potential observer’s decision locations in an open space between buildings. In addition, POI VizNet provides advanced options to build graphs using a predefined viewing distance and perceptual perspective. Visibility graphs are constructed and visualised as new layers in QGIS and delivered as network files suitable for further exploration, analysis, and visualisation in various network software packages.

Nighttime Street View Imagery: A new perspective for sensing urban lighting landscape

Urban lighting reflects nocturnal activities and it is traditionally observed using Nighttime Lights (NTL) satellite imagery. Few studies systematically measure the nightscape from a human perspective. This study brings a new paradigm — urban lighting sensing via Nighttime Street View Imagery (SVI). The paradigm draws on the accomplishments of (daytime) SVI and gives attention to its ignored nighttime counterpart. We put forward this idea by manually collecting 2,831 nighttime SVIs across various urban functional areas in Singapore. We investigated their values by developing a use case for clustering nighttime lighting patterns. To mitigate the scarcity of nighttime SVI, deep learning regression models were trained to predict nighttime brightness based on corresponding daytime SVIs obtained from widely available sources. The results were compared with brightness data derived from satellite imagery, to affirm the novelty and uniqueness of nighttime SVI. As a result, there are 7 lighting patterns within the collected nighttime SVI, distinct in lighted spot features and total brightness. The identified patterns effectively characterize different urban function scenarios. The best trained brightness prediction model performs well in revealing the city-scale lighting landscape. The SVI-predicted brightness shows a distribution similar to the brightness from satellite imagery and complements it in urban areas with complex vertical lighting structures. This study demonstrates the potential of nighttime SVI as a valuable data source for mapping urban lighting and activities, offering advantages over satellite data. The proposed paradigm contributes significantly to cross-modal information mining in urban studies and has potential applications in scenarios such as light pollution mitigation and crime prevention.

The Role of Machine Learning in Enhancing Particulate Matter Estimation: A Systematic Literature Review

As urbanization and industrial activities accelerate globally, air quality has become a pressing concern, particularly due to the harmful effects of particulate matter (PM), notably PM2.5 and PM10. This review paper presents a comprehensive systematic assessment of machine learning (ML) techniques for estimating PM concentrations, drawing on studies published from 2018 to 2024. Traditional statistical methods often fail to account for the complex dynamics of air pollution, leading to inaccurate predictions, especially during peak pollution events. In contrast, ML approaches have emerged as powerful tools that leverage large datasets to capture nonlinear, intricate relationships among various environmental, meteorological, and anthropogenic factors. This review synthesizes findings from 32 studies, demonstrating that ML techniques, particularly ensemble learning models, significantly enhance estimation accuracy. However, challenges remain, including data quality, the need for diverse and balanced datasets, issues related to feature selection, and spatial discontinuity. This paper identifies critical research gaps and proposes future directions to improve model robustness and applicability. By advancing the understanding of ML applications in air quality monitoring, this review seeks to contribute to developing effective strategies for mitigating air pollution and protecting public health.

Spatial Negative Influence of Cairo Mega Movement corridors on adjacent Local Areas Land Uses and Movement

The research is focused on discussing the importance of transportation in metropolitan areas and analyzing the positive and negative spatial impacts of the major movement corridors in Greater Cairo on the surrounding local areas. It aims to highlight the regional benefits of these corridors while also addressing the spatial challenges faced by the local areas. The study emphasizes the significance of movement circulation for pedestrians and vehicles in the urban functionality of local movement, serving as a connection between land uses and daily activities. While the mega movement corridors have significantly improved access between Greater Cairo's main districts, they have also introduced challenges at the local level. The research examines the impact of the Ain Shams corridor as an example of a new movement corridor introduced by the government in the last 5 years, in two major areas, and additionally, identifies the resulting challenges in land uses and movement circulation in these areas. The findings underscore the importance of segregating movement circulation between the regional and local levels to enhance accessibility for pedestrians and vehicles in local areas. Furthermore, the study reinforces the importance of comprehensive measures to enhance the efficiency of the corridor. Implementing solutions such as dedicated service roads, improved parking infrastructure, and optimized traffic management strategies can play a pivotal role in mitigating congestion and addressing the specific needs of local traffic. By addressing these challenges, the corridor can achieve improved functionality, accommodate the diverse traffic demands, and enhance the overall transportation experience for commuters and urban activities.

A Risk Assessment Model of Gas Pipeline Leakage Based on a Fuzzy Hybrid Analytic Hierarchy Process

Given the rising urban demand for gas, it has emerged as a primary energy source for urban activities and daily life. However, China’s urban gas pipeline network has witnessed a surge in accidents, leading to significant losses and disasters. Therefore, it is particularly necessary to study the disaster risk assessment model caused by urban gas pipeline leakage. There are some problems in the previous evaluation methods, such as less consideration of the influence relationships between disaster factors. To redress this issue, a novel fuzzy hybrid analytic hierarchy process evaluation methodology is proposed. First, a hybrid hierarchical risk assessment model is developed by combining the analytic hierarchy process and the network analytic hierarchy process. Membership matrices and impact matrices are utilized to calculate comprehensive factor weights. This approach enhances the understanding of relationships between risk factors within the hierarchical structure model. Subsequently, employing a fuzzy evaluation method, the risk level matrix is derived by using multiplication and bounded operators to ascertain the risk level state. This solves the problem of the fuzzy boundaries when measuring the index factors of the gas pipeline network. Finally, experimental analysis is carried out on the gas pipeline network in the central area of a city and validates the model’s accuracy in practical applications.

Evaluation of Urban Public Building Renovation Potential Based on Combination Weight Cloud Model—Case Study in China

Currently, urban renovation activities in China are booming. And promoting the renovation of public buildings is a key feature of urban renovation due to its large scale, high cost, and significant impact to the natural and social environment. To reduce the ambiguity and uncertainty in evaluating the potential for the renovation of existing public buildings, a renovation potential evaluation model integrating a game theory-based combination weighting method and cloud model theory is proposed. This paper constructs a comprehensive evaluation index system based on relevant standards and the literature. Game theory is used to optimize the weights obtained by AHP and entropy weight methods to obtain a combined weight. MATLAB programming is used to calculate the comprehensive cloud parameters of the evaluation index for the potential renovation of existing public buildings and therefore generate cloud Graphs. Through a case study in Nanjing, China, it was demonstrated that the combination weight cloud model can objectively reflect the relationship between the fuzziness and randomness of evaluation indicators for public building renovation potential. The visual expression of cloud Graphs can intuitively reflect the magnitude of renovation and renovation potential and the degree of uncertainty in evaluation results. The research result provides useful references for the sustainable utilization of building resources in the era of building.

Use of data science in the development of concrete based on construction and demolition waste: a brazilian view

The growth in urbanization and construction activity has led to a growing increase in construction and demolition waste and the extraction of natural resources, energy, and CO2. The projection is that these figures will expand even more in the coming years, making it worrying for a sustainable balance in construction, especially in the manufacture of concrete, which is one of the most consumed materials globally. In this context, recycled concrete has emerged as a sustainable alternative to traditional concrete. Concepts such as urban mining combined with data mining and machine learning techniques are emerging as alternatives that serve as a basis for producing recycled concrete with the required qualities. Therefore, this study aims to map the existing research in the areas of materials and technology, focusing on the recycling and reuse of construction and demolition waste (CDW) and the manufacture of recycled concrete. Surveying this research will provide guidance for future research and encourage more sustainable standards in the construction sector.

Multimodal Hong Kong: Perceiving pedestrian footbridge spaces in a high-density area through soundscape and smellscape perspectives

In prior research on Hong Kong's acoustic environment, noise has been the primary focus, highlighting its diverse sources and its detrimental impact on physical and mental health. However, there is still limited exploration of multi-sensory perspectives in Hong Kong. Mong Kok is one of the world's most densely populated areas, with a density of 130,000 people per square kilometre. Amid the bustling urban life, interactions and activities of residents, tourists, and vendors contribute to the complex soundscape. The area offers a diverse array of markets, street food, and intense aromas, creating a dynamic smellscape. This study focuses on the Mong Kok East pedestrian footbridge (connecting Mong Kok East Station to Nathan Road, approximately 400 metres), selecting six locations on and beneath the footbridge. During the on-site sensory walk experiment (n=30), we recorded ambient sounds and reproduced the soundscape in the laboratory to compare experiences stemming from various sensory modes. The sensory walk includes evaluating soundscape, smellscape, and an overall assessment. Participant feedback helps highlight what they are most concerned about, and the usage patterns and preferences of the six locations reflect the desired soundscape and smellscape, guiding recommendations for sensory design in high-density spaces.

Characterization and source apportionment of ambient VOC concentrations: Assessing ozone formation potential in the Barnett shale oil and gas region

Atmospheric concentration and distribution of volatile organic compounds (VOC) are influenced by localized emissions and the fate and transport of secondary products formed through photochemical reactions. This study identifies the sources of VOC and its contribution to ozone formation in the Barnett Shale region, specifically in Denton, Texas, during the ozone seasons of 2015 through 2022. A total of 31 critical ozone precursor VOC compounds were observed at this site with an average total VOC concentration of 146 ppb-C, with n-alkanes from extensive oil and gas activities in the area contributing to 96.64% of the total VOC. Source apportionment using dispersion normalized positive matrix factorization (DN-PMF) identified eight VOC sources, with natural gas emissions (72%) being the dominant source followed by a combined source of natural gas combustion and aviation emissions (8.3%). Ozone formation potential (OFP) by VOC species was calculated using the propylene-equivalent concentration (propy-equiv) method. OFP calculated for all the apportioned sources highlighted that natural gas sources contributed significantly to the ozone formation in this region besides isoprene from biogenic sources and reactive alkenes from urban and industrial activities. This study underscores the critical role of slow-reacting n-alkanes along with other highly reactive VOC from urban and industrial sources influencing ambient air quality and it identifies strategies for mitigating ground-level ozone in urban areas affected by oil and gas extraction and production.