Search Results (28)

Open habitats in forests perform several important functions. In addition to enriching biodiversity, they have an impact on the diversification of the forest landscape, and through complex processes and trophic relationships, they provide greater sustainability in forest communities. It turns out that they are also important not only for the functioning of nature, but also due to their regenerative properties, through which people can improve their well-being and recover their mental balance. The purpose of this study was to determine whether and to what extent mid-forest open dunes and wetlands are able to provide people with feelings of relaxation and mental renewal. The study was conducted in June 2024 in one of the forest complexes located in the suburban zone of Warsaw. The experiment was conducted with the participation of 52 young adult volunteers. The experiment used a pre–posttest method. The subjects’ mood and well-being were determined using four psychological questionnaires: the Positive and Negative Affect Schedule (PANAS), Restorative Outcome Scale (ROS), Subjective Vitality Scale (SVS), and Profile of Mood States (POMSs). The key findings from the research conducted are as follows: compared to the control sample (before entering the forest), exposure to all three variants of greenery had a restorative effect on the experimental participants; the water environment had the most favorable effect compared to the control sample (before entering the forest); and exposure to this environment provided the greatest increase in positive feelings and the greatest decrease in negative feelings. Research has shown that areas important for preserving forest biodiversity are also important for humans because of their high restorative properties. Preserving non-forest enclaves in forests and promoting their regenerative values for humans can be one nature-based solution to improve people’s health and well-being, especially for people living in urban areas. Full article

A comfortable outdoor environment, like its indoor counterpart, can significantly enhance the quality of life and improve the physical and mental health of elderly populations. Urban spatial morphology is one of the key factors influencing outdoor environmental performance. To explore the interactions between urban spatial morphology and the outdoor environment for the elderly, this study utilized parametric tools to establish a performance-driven workflow based on a “morphology generation–performance evaluation–morphology optimization” framework. Using survey data from 340 elderly neighborhoods in Beijing, a parametric urban morphology generation model was constructed. The following three optimization objectives were set: maximizing the winter pedestrian Universal Thermal Climate Index (UTCI), minimizing the summer pedestrian UTCI, and maximizing sunlight hours. Multi-objective optimization was conducted using a genetic algorithm, generating a “morphology–performance” dataset. Subsequently, the XGBoost (eXtreme Gradient Boosting) and SHAP (Shapley Additive Explanations) explainable machine learning algorithms were applied to uncover the nonlinear relationships among variables. The results indicate that optimizing spatial morphology significantly enhances environmental performance. For the summer elderly UTCI, the contributing morphological indicators include the Shape Coefficient (SC), Standard Deviation of Building Area (SA), and Deviation of Building Volume (SV), while the inhibitory indicators include the average building height (AH), Average Building Volume (AV), Mean Building Area (MA), and floor–area ratio (FAR). For the winter elderly UTCI, the contributing indicators include the AH, Volume–Area Ratio (VAR), and FAR, while the inhibitory indicators include the SC and porosity (PO). The morphological indicators contributing to sunlight hours are not clearly identified in the model, but the inhibitory indicators for sunlight hours include the AH, MA, and FAR. This study identifies the morphological indicators influencing environmental performance and provides early-stage design strategies for age-friendly neighborhood layouts, reducing the cost of later-stage environmental performance optimization. Full article

The dual challenges of global aging and intensifying urban heat demand innovative, evidence-based strategies to foster thermally and psychologically comfortable environments for vulnerable populations, particularly the elderly. Despite the documented benefits of urban greenery, the species-specific impacts of urban trees on thermal comfort and well-being remain underexplored. This study investigates how distinct tree species—Camphora officinarum (camphor), Platanus acerifolia (London plane), and Ginkgo biloba (ginkgo)—regulate urban microclimates and support elderly well-being during hot summer days. Conducted at five sites in Shanghai, including a control site and four vegetated plots, this study engaged 210 elderly participants. Microclimatic variables were measured using the physiological equivalent temperature (PET) alongside air temperature, humidity, and wind speed. Physiological responses, assessed through heart rate variability (HRV), and psychological outcomes, evaluated via validated self-report scales, were analyzed. The results revealed that dense-canopy trees significantly reduced PET, enhanced thermal comfort, and improved ROS and SVS scores, while lower LF/HF ratios indicated reduced physiological stress. Correlation analyses underscored the pivotal role of canopy density (SVF) in fostering psychological and physiological well-being. Camphor and London plane trees consistently provided the greatest benefits, emphasizing the importance of species selection in urban greening strategies. These findings underscore the critical role of species selection in urban forestry to mitigate heat stress and foster age-friendly resilience. Practical implications emphasize integrating dense-canopy species into urban landscapes to enhance microclimate regulation and public health. Full article

Currently, there is a marked increase in the importance of the environmental and cultural ecosystem services of the forest. Among the social benefits, protection of human health and improvement of human well-being come to the fore. Forests, both reserve and commercial, benefit human health. However, people often have negative attitudes toward forest management activities, especially near urban areas, and prefer forest management that results in a forest that is close to natural. Second-growth forest and clear-cutting are the most visible manifestations of ongoing forest management in forests. The purpose of this study was to compare the benefits derived from exposure to second-growth forest, clear-cutting and mature forest stands. Fifty-five students of the Warsaw University of Life Sciences-SGGW (WULS-SGGW) participated in the study. The study was conducted in October 2023 in a forest within the city of Otwock, within the range of the Warsaw metropolitan area. Participants were exposed for 15 min to selected plots: second- growth forest, clear-cutting area and mature forest stand. Before the experiment (PRE-Test control trial) and after each of the exposures four psychological questionnaires were administered: the Positive and Negative Affect Schedule (PANAS), the Restorative Outcome Scale (ROS), the Subjective Vitality Scale (SVS) and Profile of Mood States (POMS). The results indicate that not all analyzed features of the forest landscape have a regenerative effect on people. Exposure to a commercial forest, only in the case of a mature stand, yielded the most benefits in terms of mood improvement. In contrast, exposure to clear-cutting area resulted in a decrease in positive feelings and an increase in negative feelings, leading to a complete disturbance of mood, an increase in tension, depression and a decrease in feelings of vigor. Clear-cutting area has significantly worse restorative properties compared to mature forest stands and second-growth forest. This observation is relevant to forest management within city limits and in the suburban zone of large cities. Not only is clear-cutting area not accepted by people visiting forests for recreational purposes, but it leads to a detraction from the health benefits provided by forests. Full article

The widespread availability of Land Surface Temperature (LST) data from various sources presents a contemporary challenge for urban climate studies: how to efficiently compare these data with the results of traditional methods of temperature monitoring, which typically assume measurements at 2 m under sheltered conditions. In this line, the current study is based primarily on data extracted from a network of 31 points of hourly temperature monitoring at the 2 m level (Tair2m), in use between 2019 and 2021, in the city of Suceava in north-eastern Romania. These data allowed a detailed mapping for each hourly time step through multiple regression, adjusted by IDW, which was identified as the best interpolation method of Tair2m. These data were analyzed in parallel with LST data derived from Landsat imagery available in the analyzed period for 35 summer days with no or low cloud cover. The mapping results of both the Tair2m and LST data describe the main characteristics of the Suceava urban agglomeration (SvMA) heat island, which presents polynuclear features with intensities—as expressed by the temperature difference between the cores of the heat island and the surrounding rural areas—spanning during the summer noontime between 3.0 °C based on Tair2m and 7.1 °C on LST, respectively. The values of the Tair2m–LST differences were 0.68 °C on average, ranging from 5.33 to −19.17 °C, directly proportional to the imperviousness ratio (IMD) values, reaching the highest values in the local climate zones (LCZs) with a high built-up ratio (up to −19.17 °C) and the lowest (0.5 ÷ −0.5 °C) for those with bare soils, with isolated bushes and trees, with few or no buildings. The study results could serve as a tool to downscale the LST data to the level of Tair2m, which is useful for interpretation of the data derived from these commonly used tools in urban climate monitoring. Full article

Human contact with the natural environment effectively counteracts negative symptoms of stress and has many positive psychological effects. For this reason, forests within city limits are increasingly seen as part of public health promotion. Being in an urban forest can take many forms, but there is still little known about whether being in a forest alone, without engaging one’s senses, is as effective for human renewal as an experience that involves greater, more conscious activation of the senses of sight, hearing, smell. A study measuring the effect of the forest (spring aspect) on human mental relaxation during passive exposure was conducted on a sample of 19 Polish young adults. The experiment consisted of two series. In the first, participants read an academic textbook in a tree stand for 20 min, while in the next session—conducted at the same location—they contemplated the landscape. Before the experiment and after each of the two series of tests, four psychological questionnaires were administered: the Profile of Mood State (POMS), Positive and Negative Affect Schedule (PANAS), the Restorative Outcome Scale (ROS) and the Subjective Vitality Scale (SVS). Analyses showed that being in a forest environment positively influenced the subjects’ mental relaxation, while the level of benefit from the activation of the senses, especially vision (looking at the forest) was significantly higher compared to the passive activity of reading a text. The research indicates that forest bathing is the best form of relaxation that can be obtained through contact with the forest. Full article

The province of Gauteng in South Africa has a rich history of gold mining, which has driven economic growth and urbanization. Gold mining has also created over 270 gold mine tailings (GMT), now surrounded by human settlements due to a housing shortage. These GMT pose a health risk as they harbor elevated uranium, which over time undergoes radioactive decay to produce radon, a known lung carcinogen. This study aimed to investigate the potential correlation between the proximity to gold mine tailings (GMT) and indoor radon concentrations in Gauteng’s residential dwellings. Volume activity of radon (VAR) inside 330 residential dwellings was measured in residential dwellings located proximally (<2 km) and distally (>2 km) to gold mine tailings using AlphaE radon monitors during winter. An interviewer-administered questionnaire was utilized to obtain data on factors that may influence indoor radon activities. Descriptive statistics and bivariate logistic regression analyzed the influence of proximity to gold mine tailings and dwelling characteristics on VAR. Furthermore, VAR was compared to the World Health Organization (WHO) radon reference level of 100 Bq/m³. Residential dwellings near gold mine tailings had significantly higher average indoor radon concentrations (103.30 Bq/m³) compared to the control group (65.19 Bq/m³). Residential dwellings proximal to gold mine tailings were three times more likely to have VAR beyond the WHO reference level of 100 Bq/m³. Furthermore, they had estimated annual effective doses of 2.60 mSv/y compared to 1.64 mSv/y for the control group. This study highlighted a concerning association between proximity to gold mine tailings and elevated indoor radon levels. Public health interventions prioritizing residential dwellings near gold mine tailings are crucial. Educational campaigns and financial assistance for radon mitigation systems in high-risk dwellings are recommended. Residents near gold mine tailings are encouraged to ensure continuous natural ventilation through frequent opening of windows and doors. Full article

An L3 smart vehicle (L3 SV) could behave differently from human-driven vehicles due to its intelligent configuration and decision-making logic, and may consequently exert influences on traffic flow. In order to clarify the L3 SV’s traffic impacts and accelerate L3 SV implementation, this paper conducted an evaluation on the traffic efficiency and energy consumption influences of L3 SVs based on a microscopic traffic simulation. Taking Beijing as a case, the relevant traffic economic benefits were calculated with the help of the previously proposed traffic economic benefits model. Before modeling L3 SVs, a two-dimensional general modeling architecture for SVs was proposed. According to the architecture, the driving behavior model, as well as behavior selection models of L3 SVs, was eventually determined, based on which the intelligent driving model of L3 SVs was established. Urban expressways were selected as the simulation road type, and scenario analysis was conducted on various proportions of L3 SVs and L3 connected and autonomous vehicles (L3 CAVs), as well as the input traffic flow rates. It was found that L3 SVs can significantly reduce the travel time and energy consumption and enlarge the actual road capacity. The improvement will become particularly prominent under saturated or supersaturated traffic flow and increasing the proportion of L3 SVs and L3 CAVs can also amplify the effect of traffic optimization. The related economic benefit is considerable, which is CNY 3.104 billion a year based on Beijing’s travel and traffic conditions. Full article

Research simultaneously examining building energy consumption and outdoor thermal comfort within urban environments remains limited. Few studies have delved into the sensitivity of design parameters based on building energy consumption and outdoor thermal comfort. The purpose of this study is to investigate the correlations between urban morphological design parameters and performance indicators, focusing on building energy consumption and outdoor thermal comfort (UTCI), across different urban block layouts in hot-humid regions, like Guangzhou. By establishing six fundamental morphological models—three individual unit layouts and three group layouts—the research explores both control and descriptive parameters through extensive simulation studies. Scatter plot visualizations provide insights into the impacts of various design parameters on energy consumption and UTCI, facilitating a comprehensive analysis of trends and quantitative relationships. Additionally, the study conducts sensitivity analyses on design parameters under different layout conditions to highlight their influences on target performance indicators. The findings reveal common trends, such as the significant impacts of plan dimensions and the Floor Area Ratio (FAR) on energy efficiency and outdoor comfort, as well as differential patterns, such as the varying sensitivities of the Shape Factor (S/V) and the Sky View Factor (SVF), across individual and collective layouts. Ultimately, this study offers a nuanced understanding of urban block morphology’s role in creating sustainable, comfortable, and energy-efficient urban environments, providing valuable guidelines for urban form design in hot-humid climates. Full article

Emergency vehicles (EMVs) play an important role in saving human lives and mitigating property losses in urban traffic systems. Due to traffic congestion and improper priority control strategies along the rescue route, EMVs may not be able to arrive at rescue spots on time, which also increases traffic risk and has a negative impact on social vehicles (SVs). The greater the negative impact on SVs, such as increased delay times and queue length, the more profound the negative impacts on urban environmental sustainability. Proper rescue route selection and priority control strategies are essential for addressing this problem. Consequently, this paper systematically reviews the studies on EMV routing and priority control. First, a general bibliometric analysis is conducted using VOSviewer. This study also classifies the existing studies into three parts: EMV travel time prediction (EMV-TTP), EMV routing optimization (EMV-RO), and EMV traffic priority control (EMV-TPC). Finally, this study provides future research suggestions on five aspects: 1. uncovering authentic demand characteristics through EMV data mining, 2. incorporating the distinct characteristics of EMV in EMV-RO models, 3. implementing active EMV-TPC strategies, 4. concentrating more on the negative impacts on SVs, and 5. embracing the emerging technologies in the future urban traffic environment. Full article

We present data from six years (January 2016–December 2021) of monitoring the isotope composition of precipitation at the Sv. Urban station in Eastern Slovenia. The 68 precipitation samples were collected as a monthly composite. The complete dataset (193 data pints) includes information on the stable isotope composition of hydrogen (δ²H) and oxygen (δ¹⁸O) and tritium activity concentration (A), obtained using isotope ratio mass spectrometry (IRMS) and liquid scintillation counting (LSC) following electrolytic enrichment (EE), respectively. The isotope data, together with meteorological data, are reported. Calculations of the deuterium excess (d-excess), monthly, seasonal, and annual unweighted and precipitation-weighted means and local meteoric water lines (LMWLs) were conducted. The mean values for δ²H, δ¹⁸O, d-excess, and A, weighted by precipitation, were −59.9‰, −8.81‰, 10.6‰, and 7.7 TU. The disparities between unweighted and precipitation-weighted δ²H, δ¹⁸O, d-excess, A, and LMWLs underscore the significance of non-uniformly distributed precipitation. Annual variations in slope and intercept of the LMWLs emphasize the importance of longer data records (48+ months) to capture consistent trends, while combining data over longer periods may distort accuracy due to distinct isotope differences between individual years related to the variability of climate conditions typical for Slovenia. Full article

Determining the optimal planning scale for urban life circles and analyzing the associated built environment factors are crucial for comprehending and regulating residential differentiation. This study aims to bridge the current research void concerning the nonlinear hierarchical relationships between the built environment and residential differentiation under the multiscale effect. Specifically, six indicators were derived from urban crowdsourcing data: diversity of built environment function (DBEF1), density of built environment function (DBEF2), blue–green environment (BGE), traffic accessibility (TA), population vitality (PV), and shopping vitality (SV). Then, a gradient boosting decision tree (GBDT) was applied to derive the analysis of these indicators. Finally, the interpretability of machine learning was leveraged to quantify the relative importance and nonlinear relationships between built environment indicators and housing prices. The results indicate a hierarchical structure and inflection point effect of the built environment on residential premiums. Notably, the impact trend of the built environment on housing prices within a 15 min life circle remains stable. The effect of crowd behavior, as depicted by PV and SV, on housing prices emerges as the most significant factor. Furthermore, this study also categorizes housing into common and high-end residences, thereby unveiling that distinct residential neighborhoods exhibit varying degrees of dependence on the built environment. The built environment exerts a scale effect on the formation of residential differentiation, with housing prices exhibiting increased sensitivity to the built environment at a smaller life circle scale. Conversely, the effect of the built environment on housing prices is amplified at a larger life circle scale. Under the dual influence of the scale and hierarchical effect, this framework can dynamically adapt to the uncertainty of changes in life circle planning policies and residential markets. This provides strong theoretical support for exploring the optimal life circle scale, alleviating residential differentiation, and promoting group fairness. Full article

In recent years, a growing body of research has investigated the factors influencing land surface temperature (LST) in different cities, employing diverse methodologies. Our study aims to be one of the few to examine the socio-environmental variables (SV) of LST with a holistic approach, especially in primate cities in developing countries, which are particularly vulnerable to the impacts of climate change. In this context, the study preliminarily identifies the SV of LST while investigating the most vulnerable areas related to extreme LST at the neighborhood level. The combined 11 variables are analyzed using spatial modeling methods (GWR and MGWR). The MGWR model outperforms the GWR model with an adjusted R² of 0.96. The results showed that: (1) the 65+ population is negatively associated with LST in 95% of neighborhoods; the socioeconomic index–LST relationship is negative in 65% of neighborhoods. (2) In 90% of the neighborhoods where the relationship between LST and the built environment ratio is positive, the socioeconomic level decreases while household size increases in 98% of the neighborhoods. (3) In 62% of the neighborhoods where the relationship between the 65+ population and LST is negative, the relationship between the socioeconomic level and LST is negative. This study aids decision-makers and planners in managing urban resources to reduce extreme LST exposure region by region and recommending multiscale policies to control determinant influences on LST. Full article

Twenty-first century rural development (RD) demands a new paradigm of sustainability capable of addressing the difficulties and leveraging on the possibilities, such as climate change, demographic shift, international competitiveness, and rapid technological progress. Amidst these challenges, it is necessary to have a guiding framework from a long-term perspective that aids the integration of current RD policies while allowing space for location and community-specific innovations for implementing sustainable and resilient development strategies. India has witnessed several schemes and programmes for RD with exclusive objectives, varied focus areas, and separate domains, resulting in compartmentalisation in policy frameworks and disjointed implementation. Such initiatives were also often ideated from an urban perspective when it came to peri-urban rural areas or offered a generalist rural perspective (when referring to other rural regions, including those nested in ecological zones, thereby disregarding their local relevance). Accordingly, this study proposes a synchronised SMART village framework to tailor existing RD approaches for sustainable transformations aligned with the sustainable development goals and with a possibility of scaling its applicability in the local context. We initially conducted a bibliometric analysis to gain a comprehensive understanding of the emerging transformative approaches to RD, such as smart village (SV). Though in its nascent stage, the SV initiatives in India primarily envision information and communication technology enabled transformations in rural areas, often forcing villages to establish the relevance of such interventions. The study recognises key challenges to RD in India by using the problem tree analysis and further defines a SMART village framework that can be catalytic in transforming rural areas towards a sustainable and resilient state. Full article

Recently, the local climate zone (LCZ) system has been presented to establish the connection between urban landscape and local thermal environment. However, LCZ entities are very difficult to be identified by pixel-based classifiers or object-oriented image analysis, as they are often a complicated combination of multiple ground objects (e.g., buildings, roads, grassland, etc.). Scene classifiers, especially deep learning methods can exploit the structure or contextual information of image scenes and then improve the performance of LCZ classification. However, the square and uniform-sized image patches often bring about extra challenges, as they cannot exactly match LCZ entities of diverse sizes and shapes in most cases. In this study, a sequential virtual scene method is presented to identify LCZ entities of diverse shapes and sizes, which consists of a small “core patch” for scanning diverse entities and sequential virtual scenes for providing abundant context. Specifically, the Bidirectional Long Short-Term Memory (Bi-LSTM) were used to learn the spatial relationship among virtual scenes, respectively. Importantly, a “self-attention” mechanism is designed to weigh the contribution of every virtual scene for alleviating the influences of mixed patches, according to the similarity between its hidden state and the final hidden state. Experiments prove SVS achieves better accuracies than random forest and ResNet and has the outstanding capacity of identifying irregular LCZ entities. It is a promising way to carry out LCZ mapping in cities of different types due to its flexibility and adaptability. Full article