Search Results (28)

Urbanization markedly influences the microclimatic conditions in semi-arid regions by elevating land surface temperatures (LST) and contributing to ecological degradation. This study examined the spatial and temporal evolution of LST and urban heat island (UHI) effects in Benguerir, Morocco, over a 30-year period (1994–2024), employing high-resolution satellite imagery and in situ sensor data. Urban expansion was quantified using thermal bands from Landsat imagery, the Normalized Difference Built-up Index (NDBI), and the Built-up Index (BU), whereas thermal comfort was evaluated through the Universal Thermal Climate Index (UTCI) and Predicted Mean Vote (PMV) using air temperature and humidity data collected via spatial sensor and the Sniffer Bike mobile sensor network. These urban transformations have intensified the UHI effect, resulting in a 29.34 °C increase in mean LST to 41.82 °C in 2024 across built-up areas. Statistical modeling revealed strong linear relationships between LST and urban indices, with R² values ranging from 0.93 to 0.96, and correlation coefficients around 0.98 (all p-values < 0.001), indicating a reliable model fit. Furthermore, the analysis of thermal comfort trends underscores urbanization’s impact on human well-being. In 1994, 34.2% of the population experienced slight warmth and 65.8% experienced hot conditions. By 2024, conditions had shifted dramatically, with 76.7% experiencing hot conditions and 16.2% exposed to very hot conditions, leaving only 7.1% in the slight warmth category. These findings highlight the urgent need for adaptive urban planning strategies. The implementation of urban greening initiatives, the use of reflective materials, and the integration of data-driven planning approaches are essential to mitigate thermal stress and enhance urban resilience. Leveraging climate modeling and spatial analytics can support the identification of high-risk zones and inform targeted interventions to effectively address the escalating UHI phenomenon. Full article

Understanding the impacts of climate change on species’ geographic distributions is fundamental for biodiversity conservation and resource management. As a key plant group for ecological restoration and windbreak and sand fixation in arid and semi-arid ares in China’s Three Northern Regions (Northeast, North, and Northwest China), Caragana spp. exhibit distribution patterns whose regulatory mechanisms by environmental factors remain unclear, with a long-term lack of climatic explanations influencing their spatial distribution. This study integrated 2373 occurrence records of 44 Caragana species in China’s Three Northern Regions with four major environmental variable categories. Using the Biomod2 ensemble model, current and future climate scenario-based suitable habitats for Caragana spp. were predicted. This study innovatively combined quantitative analyses with Kira’s thermal indexes (warmth index, coldness index) and Wenduo Xu’s humidity index (HI) to elucidate species-specific relationships between distribution patterns and hydrothermal climatic constraints. The main results showed that (1) compared to other environmental factors, climate is the key factor affecting the distribution of Caragana spp. (2) The current distribution centroid of Caragana spp. is located in Alxa Left Banner, Inner Mongolia. In future scenarios, the majority of centroids will shift toward lower latitudes. (3) The suitable habitats for Caragana spp. will expand overall under future climate scenarios. High-stress scenarios exhibit greater spatial changes than low-stress scenarios. (4) Hydrothermal requirements varied significantly among species in China’s Three Northern Regions, and 44 Caragana species can be classified into five distinct types based on warmth index (WI) and humidity index (HI). The research findings will provide critical practical guidance for ecological initiatives such as the Three-North Shelterbelt Program and the restoration and management of degraded ecosystems in arid and semi-arid regions under global climate change. Full article

Humans mostly perceive tactile sensations in daily life as a combination of warmth, vibration, and pressure. To understand the complex tactile perception and cognitive processes, in this study, we aimed to develop a multimodal stimulator and investigate changes in neuronal activity. An actuator that can display warmth (W), vibration (V), and pressure (P) on the distal region of the index finger has been developed. Preliminary experiments were conducted with nine subjects. Electroencephalograms were measured for six tactile stimuli—three single stimuli (W, V, and P) and three combination stimuli (W + V, V + P, and W + V + P)—and event-related desynchronization/synchronization (ERD/S) analysis were performed. The actuator can present all kinds of stimuli in the same location and control stimulation parameters quantitatively. For all experiments, there was an ERD in the α and β bands about 0.5 s after stimulation followed by ERS was observed in the C3 area. The change in the peak-to-peak value was the largest for warmth and the smallest for pressure. In contrast, in the duration of the ERD, W was the shortest and P was the longest. As stimulus presented simultaneously, the ERD became longer in both the alpha and beta bands. In the beta band, the peak of ERD became larger. The developed system was confirmed to be capable of providing valid tactile stimulation, inducing appropriate neuronal activation, and enabling multimodal tactile research. Full article

This study investigates the relationship between Green View Index (GVI) and street thermal environment in Hangzhou’s main urban area during summer, quantifying urban greenery’s impact on diurnal/nocturnal thermal conditions to inform urban heat island mitigation strategies. Multi-source data (3D morphological metrics, LCZ classifications, mobile measurements) were integrated with deep learning-derived street-level GVI through image analysis. A random forest-multiple regression hybrid model evaluated spatiotemporal variations and GVI impacts across time, street orientation, and urban-rural gradients. Key findings include: (1) Urban street Ta prediction model: Daytime model: R² = 0.54, RMSE = 0.33 °C; Nighttime model: R² = 0.71, RMSE = 0.42 °C. (2) GVI shows significant inverse association with temperature, A 0.1 unit increase in GVI reduced temperatures by 0.124°C during the day and 0.020 °C at night. (3) Orientation effects: North–south streets exhibit strongest cooling (1.85 °C daytime reduction), followed by east–west; northeast–southwest layouts show negligible impact; (4) Canyon geometry: Low-aspect canyons (H/W < 1) enhance cooling efficiency, while high-aspect canyons (H/W > 2) retain nocturnal heat despite daytime cooling; (5) Urban-rural gradient: Cooling peaks in urban-fringe zones (10–15 km daytime, 15–20 km nighttime), contrasting with persistent nocturnal warmth in urban cores (0–5 km); (6) LCZ variability: Daytime cooling intensity peaks in LCZ3, nighttime in LCZ6. These findings offer scientific evidence and empirical support for urban thermal environment optimization strategies in urban planning and landscape design. We recommend dynamic coupling of street orientation, three-dimensional morphological characteristics, and vegetation configuration parameters to formulate differentiated thermal environment design guidelines, enabling precise alignment between mitigation measures and spatial context-specific features. Full article

By analyzing the last 50–60 years of climate changes in Arctic and Subarctic Yakutia, we have identified three distinct periods of climate development. The cold (1965–1987), pre-warming (1988–2004), and modern warming (2005–2023) periods are clearly identifiable. Yakutia’s Arctic and Subarctic regions have experienced mean annual air temperature increases of 2.5 °C and 2.2 °C, respectively, compared to the cold period. The thawing index rose by an average of 171–214 °C-days, while the freezing index dropped by an average of 564–702 °C-days. During the pre-warming period, all three characteristics show a minor increase in warmth. Global warming intensified between 2005 and 2023, resulting in elevated permafrost temperatures and a deeper active layer. Monitoring data from the Tiksi site show that warming has been increasing at different depths since the mid-2000s. As a result, the permafrost temperature increased by 1.7 °C at a depth of 10 m and by 1.1 °C at a depth of 30 m. Soil temperature measurements at meteorological stations and observations at CALM sites both confirm the warming of the permafrost. A permafrost–climatic zoning study was conducted in Arctic and Subarctic Yakutia. Analysis identified seven regions characterized by similar responses to modern global warming. These study results form the foundation for future research on global warming’s effects on permafrost and on how northern Yakutia’s environment and economy adapt to the changing climate. Full article

The increasing frequency of extreme climate events may significantly alter the species composition, structure, and functionality of ecosystems, thereby diminishing their stability and resilience. This study draws on temperature and precipitation data from 53 meteorological stations across Mongolia, covering the period from 1983 to 2016, along with MODIS normalized difference vegetation index (NDVI) data from 2001 to 2016. The climate anomaly method and the curvature method of cumulative NDVI logistic curves were employed to identify years of extreme climate events and to extract the start of the growing season (SOS) in Mongolia. Furthermore, the study assessed the impact of extreme climate events on the SOS across different vegetation types and evaluated the sensitivity of the SOS to extreme climate indices. The study results show that, compared to the multi-year average green-up period from 2001 to 2016, extreme climate events significantly impact the SOS. Extreme dryness advanced the SOS by 6.9 days, extreme wetness by 2.5 days, and extreme warmth by 13.2 days, while extreme cold delayed the SOS by 1.2 days. During extreme drought events, the sensitivity of SOS to TN90p (warm nights) was the highest; in extremely wet years, the sensitivity of SOS to TX10p (cool days) was the strongest; in extreme warm events, SOS was most sensitive to TX90p (warm days); and during extreme cold events, SOS was most sensitive to TNx (maximum night temperature). Overall, the SOS was most sensitive to extreme temperature indices during extreme climate events, with a predominantly negative sensitivity. The response and sensitivity of SOS to extreme climate events varied across different vegetation types. This is crucial for understanding the dynamic changes of ecosystems and assessing potential ecological risks. Full article

The desert steppe in Ningxia is the largest natural steppe type in the region, characterized by a fragile ecological environment and low carrying capacity. Gravel–sand mulching, a local agricultural practice, involves using a sand and gravel covering layer to maintain soil warmth and moisture. However, this method has led to ecological problems, such as habitat loss and biodiversity loss. Tenebrionidae, one of the most widely distributed beetle families, is commonly used to indicate habitat degradation and is a significant steppe pest in desert steppe areas. This study, conducted in the gravel–sand mulching areas of Shapotou District, Zhongwei City, Ningxia, classified the habitat loss from low to high in these areas into five grades (I-V) and examined the effects of habitat loss on Tenebrionidae. We collected 6565 Tenebrionidae, with Blaps femoralis, Anatolica nureti, and Pterocoma vittata being the dominant species. The findings reveal that habitat loss grade I had the highest abundance, diversity index, and evenness index of Tenebrionidae, significantly higher than those of grades II–IV. Habitat loss had a significant negative effect on Tenebrionidae abundance, a significant positive effect on the richness index, no significant effect on the vegetation diversity index, a significant positive effect on soil available potassium (APP), and a significant negative effect on soil total phosphorus (TP). Redundancy analysis indicated a positive correlation between Tenebrionidae abundance and the vegetation diversity index; a negative correlation between Tenebrionidae richness, the diversity index, and vegetation indices; a positive correlation between Tenebrionidae abundance and soil TP; and a negative correlation between the Tenebrionidae diversity index and soil TP and soil APP. These findings will contribute to biodiversity conservation and ecological restoration and provide a theoretical basis for steppe management, sustainable agricultural development, and pest monitoring in desert steppe environments. Full article

Rapid urbanization across hot arid environments transforms local microclimates, yet linkages between density, layout, and outdoor thermal comfort remain inadequately characterized. This study investigates these correlations in the desert city of Biskra, Algeria. We conducted extensive in-situ measurement campaigns during peak summer and winter periods across six neighborhoods spanning low to high-rise density fabrics. We captured air temperature, humidity, and wind velocity to enable thermal comfort assessment via the Effective Temperature (ET) and Equivalent Temperature (TEK) stress indices. Index sensitivity to density metrics from 23–86% was explored through One-Variable-At-A-Time (OVAT) analysis. Results show marked summer comfort deterioration with rising density due to constrained ventilation. However, higher densities demonstrate superior winter warmth through wind blocking despite reduced sunlight exposure. Sharp nonlinear differences emerge between seasons. Precision density thresholds prove difficult to define conclusively given typological diversities. However, indicative ranges tied to hazards are identified, providing guidelines for informed climatic adaptation. This research delivers vital evidence to advance bioclimatic strategies for enhancing habitability amidst urban expansion across delicate hot deserts. Full article

The Climate Risk and Vulnerability Assessment (CRVA) is a systematic process used to identify gaps in regional climate adaptation strategies. The CRVA method assesses regional vulnerability, adaptation capacity, exposure, and sensitivity to climate change to support improved adaptation policies. This CRVA study assesses Georgia’s climate exposure, geographic sensitivity, and socio-economic sensitivity by focusing on the impacts of climate change on regional hydrology. The projected change in climate extreme indices, defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), is assessed against the 1961–1990 baseline under future Representative Concentration Pathway (RCP) scenarios. These indices encompass various climate factors such as the maximum daily temperature, warmth duration, total precipitation, heavy and extreme precipitation, maximum 5-day precipitation, and consecutive drought duration. This evaluation helps us understand the potential climate exposure impacts on Georgia. The climate-induced geographic sensitivity is examined based on water stress, drought risk, and changes in soil productivity using the Normalized Difference Vegetation Index (NDVI). The climate-induced socio-economic sensitivity is determined using the Gross Domestic Product per capita (GDP), Human Development Index, Education Index, and population density. The highest vulnerability to climate change was found in the Kakheti and Kvemo Kartli regions, with the vulnerability index values ranging from 6 to 15, followed by Mtskheta-Mtianeti, Samtskhe–Javakheti, and Shida Kartli with vulnerability index values ranging from 2 to 8. The location of these regions upstream of the Alazani-Iori, Khrami-Debeda, and Mktvari river basins indicates that the country’s water resources are vulnerable to climate change impacts in the future under the RCP 4.5 and 8.5 scenarios. Full article

To effectively combat climate change and put plans in place to enhance the health and quality of forests, research on the connections between long-term climate change and tree ring width is essential. Here, Pinus densiflora Siebold & Zucc. and Robinia pseudoacacia L. in the Mount Tai area were studied. Specifically, their tree ring width characteristics were investigated according to the principles of dendrochronology, based on the analysis of multiyear climate data (1972–2022). The results showed that (1) the variation in tree ring width of Pinus densiflora Siebold & Zucc. generally decreased. Its basal area increment sequence presented a “growth-decline” change process. The trend for Robinia pseudoacacia L. was stability at first and then a reduction. Its basal area increment sequence presented a “growth-stabilization” change process. (2) The standard chronologies of both tree species contained more environmental information than the residual chronologies, rendering the former more appropriate for analysis. (3) The tree ring width of Pinus densiflora Siebold & Zucc. was positively correlated with the mean maximum temperature in February, March, and July, the mean temperature in May, and the mean precipitation in December of the previous year and June of the current year. The tree ring width of Robinia pseudoacacia L. was positively correlated with the mean and maximum temperatures in October of the previous year, the mean maximum temperature in May of the current year, and the mean temperature in July of the current year. This variable was positively correlated with the mean precipitation in November of the previous year and July of the current year but negatively correlated with the mean minimum temperature in November of the previous year and the mean temperature in December of the current year. (4) During 1987–1988, 1994–1995, 2005–2006, and 2018–2019, the tree ring width was restricted by rising temperatures and low precipitation, with variations in tree ring width observed in various hydrological periods. (5) The moisture index, followed by the warmth index, precipitation and temperature, was the primary climate factor determining the tree ring width in both tree species. Full article

Global warming is precipitating an amplification of severe meteorological occurrences such as prolonged dry spells and episodes of elevated temperatures. These phenomena are instigating substantial elevations in environmental warmth, with metropolitan regions bearing the brunt of these impacts. Currently, extreme heat is already impacting 30% of the global populace, and forecasts suggest that this figure will escalate to 74% in the forthcoming years. One of the objectives outlined in the United Nations 2030 agenda, specifically within Sustainable Development Goal 11 (SDG11), is the attainment of sustainable urban development. To achieve this, it is imperative to scrutinize and delve into urban environmental conditions in order to understand their dynamics comprehensively. This understanding serves as the foundation for implementing mitigation and resilience strategies against climate change, ultimately enhancing the well-being of city residents. In this context, the field of remote sensing and geographic information systems has made substantial advancements. Notably, the UrbClim model, developed by the European Space Agency, facilitates the assessment of environmental conditions within numerous European urban centers. This research, utilizing data from UrbClim, examines the evolution of the heat stress index (Hi) during extreme heat conditions in Barcelona during the summer of 2017. Leveraging Landsat 8 satellite imagery, we derived the following variables: the normalized difference vegetation index and the normalized building difference index. Our findings reveal that during extreme heat conditions, the Hi index experiences an escalation, with areas characterized by a higher population density and industrial zones displaying lower resistance in contrast to regions with a lower population density and rural areas, which exhibit greater resilience to Hi. This disparity can be attributed to higher vegetation coverage and reduced building density in the latter areas. In this way, Hi increases more quickly and intensely and decreases more slowly when using high temperatures compared to average temperatures. This is of utmost importance for the future planning of new urban developments. Full article

The quality of parent–child relationships plays a significant role in the development of child anxiety, especially regarding aspects of parental control, intrusive behavior, and a lack of warmth. Nevertheless, the underlying mechanisms of these parenting behaviors that are associated with the risk of child anxiety have yet to be revealed. The present study aims to examine the contribution of a cognitive aspect of parenting, i.e., openness to different ways of thinking, to the development of child anxiety through its impact on parenting style. A sample of 300 Israeli parents (72% women) over the age of 18 (M = 38.8, SD = 6.2), with at least one child over the age of 6 (M = 13.3, SD = 5.5 of oldest child), was recruited through social media platforms. Participants provided demographic information and filled out self-reported questionnaires dealing with child anxiety (using the Child Behavior Checklist), openness to different ways of thinking (using the Interpersonal Reactivity Index), and parenting style (using the Parental Behavior Inventory). The analysis confirmed the mediation role of hostile/coercive parenting style in the association between parental openness to different ways of thinking and child anxiety. However, the association between supportive/engaged parenting and child anxiety was non-significant. Apparently, openness to different ways of thinking allows for parents to consolidate parenting that does not resort to coercive and hostile behaviors, control, obedience, and severe strictness. As a result, the child develops self-regulation and coping mechanisms that reduce the risk for developing anxiety. Full article

This study aimed to use the Alexa vocal assistant as an administerer of psychometric tests, assessing the efficiency and validity of this measurement. A total of 300 participants were administered the Interpersonal Reactivity Index (IRI). After a week, the administration was repeated, but the participants were randomly divided into groups of 100 participants each. In the first, the test was administered by means of a paper version; in the second, the questionnaire was read to the participants in person, and the operator contemporaneously recorded the answers declared by the participants; in the third group, the questionnaire was directly administered by the Alexa voice device, after specific reprogramming. The third group was also administered, as a post-session survey, the Engagement and Perceptions of the Bot Scale (EPVS), a short version of the Communication Styles Inventory (CSI), the Marlowe–Crowne Social Desirability Scale (MCSDS), and an additional six items to measure degrees of concentration, ease, and perceived pressure at the beginning and at the end of the administration. The results confirmed that the IRI did keep measurement invariance within the three conditions. The administration through vocal assistant showed an empathic activation effect significantly superior to the conditions of pencil–paper and operator-in-presence. The results indicated an engagement and positive evaluation of the interactive experience, with reported perceptions of closeness, warmth, competence, and human-likeness associated with higher values of empathetic activation and lower values of personal discomfort. Full article

During the few last years, climate change, including global warming, which is attributed to human activities, and its long-term adverse effects on the planet’s functions have been identified as the most challenging discussion topics and have provoked significant concern and effort to find possible solutions. Since the warmth arising from the Earth’s landscapes affects the world’s weather and climate patterns, we decided to study the changes in Land Surface Temperature (LST) patterns in different seasons through nonlinear methods. Here, we particularly wanted to estimate the noninteger dimension and fractal structure of the Land Surface Temperature. For this study, the LST data were obtained during the daytime by a Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite. Depending on the time of the year data were collected, temperatures changed in different ranges. Since equatorial regions remain warm, and Antarctica and Greenland remain cold, and also because altitude affects temperature, we selected Riley County in the US state of Kansas, which does not belong to any of these location types, and we observed the seasonal changes in temperature in this county. According to our fractal analysis, the fractal dimension may provide a complexity index to characterize different LST datasets. The multifractal analysis confirmed that the LST data may define a self-organizing system that produces fractal patterns in the structure of data. Thus, the LST data may not only have a wide range of fractal dimensions, but also they are fractal. The results of the present study show that the Land Surface Temperature (LST) belongs to the class of fractal processes with a noninteger dimension. Moreover, self-organized behavior governing the structure of LST data may provide an underlying principle that might be a general outcome of human activities and may shape the Earth’s surface temperature. We explicitly acknowledge the important role of fractal geometry when analyzing and tracing settlement patterns and urbanization dynamics at various scales toward purposeful planning in the development of human settlement patterns. Full article

Species distribution modeling (SDM) has been widely used to predict the distribution of invasive plant species based on bioclimatic variables. However, the specific selection of these variables may affect the performance of SDM. This investigation elucidates a new bioclimate variable dataset (i.e., CMCC-BioClimInd) for its use in SDM. The predictive performance of SDM that includes WorldClim and CMCC-BioClimInd was evaluated by AUC and omission rate and the explanatory power of both datasets was assessed by the jackknife method. Furthermore, the ODMAP protocol was used to record CMCC-BioClimInd to ensure reproducibility. The results indicated that CMCC-BioClimInd effectively simulates invasive plant species’ distribution. Based on the contribution rate of CMCC-BioClimInd to the distribution of invasive plant species, it was inferred that the modified and simplified continentality and Kira warmth index from CMCC-BioClimInd had a strong explanatory power. Under the 35 bioclimatic variables of CMCC-BioClimInd, alien invasive plant species are mainly distributed in equatorial, tropical, and subtropical regions. We tested a new bioclimate variable dataset to simulate the distribution of invasive plant species worldwide. This method has great potential to improve the efficiency of species distribution modeling, thereby providing a new perspective for risk assessment and management of global invasive plant species. Full article