Several Algerian cities are increasingly experiencing notable traffic congestion due to the unequal distribution of urban functions. This research specifically analyses and assesses the urban functions planning’s impacts on transportation in Béjaïa city, as well as the consequences of the traffic generated on the environment and citizens’ life quality. For such objectives, the study analyzed the distribution of trip-generating activities, the adequacy of their planning with the urban transport network, and the inhabitants’ perceptions regarding the environment and life quality. To this end, the study employed a mixed-methods approach for data collection and analysis. This methodology includes qualitative field observations and quantitative data collected through questionnaire surveys. The results indicated a significant correlation between the urban functions’ planning and the generated traffic flows. The zoning observed in Béjaïa generates a disconnection between origin and destination, leading to longer distances traveled and a deteriorated environment. Indeed, around 80% of the respondents expressed dissatisfaction with the location of Béjaïa’s urban functions and life quality. Thus, it is recommended to revise zoning regulations, reevaluate industrial zones, improve the mobility plan sustainably, and promote community participation in the urban planning process. These research findings serve as a reference for researchers and decision-makers to enhance future urban planning in Béjaïa and other cities around the world.

Bandung City has the highest land conversion rate in Indonesia and was named a city with a moderate environmental quality index status in 2022. This status has been exacerbated by the diminishing green spaces in the city due to rapid urbanization. Conducting ecological assessments has become increasingly important, one approach being the utilization of remote sensing data. Remote sensing data, specifically Landsat 8 OLI/TIRS, processed to derive the RSEI (Remote Sensing Ecological Index) based on the PCA value of PC1, requires further development. Several limitations of the RSEI in assessing ecological quality, such as the subjectivity of remote sensing data, the use of equal interval methods for index classification, and the inability to validate the results, are the focus of development in this study. Based on these weaknesses, the RSEIT offers advancements in integrating actual data to support RSEI, determining index thresholds, and enabling model validation. The findings of this study demonstrate that: (1) ecological issues such as floods, waste accumulation, and landslides are the most prevalent problems in the study area; (2) compared to RSEI, which relies solely on remote sensing data, RSEIT is a model that can be validated with actual data. During the dry and rainy seasons, it achieves threshold values of 0.474 and 0.566, respectively, demonstrating a model performance accuracy exceeding 70%. The average validation results show an overall accuracy of 83.34%, a sensitivity of 78.55%, and a specificity of 87.50% across both seasons; and (3) urban centers, characterized by extensive surface hardening, minimal vegetation, and numerous ecological issues, predominantly fall under the poor RSEIT category, especially during the dry season. In contrast, suburban areas with higher proportions of green space and fewer ecological problems are largely classified under the good RSEIT category, particularly during the rainy season. This study can be further enhanced by refining the threshold aspects and strengthening actual data collection through the involvement of various stakeholders with expertise in ecology.

Sinkholes have frequently occurred over the past 20 years in the Khlong I Pan sub-watershed (KIPs) in Surat Thani and Krabi Province, Thailand. It was found that the earth collapsed more than 34 times. The objective of this research is to evaluate the sinkhole susceptibility using Logistic Regression (LR) analysis at the sub-watershed scale. This methodology used 14 variables affecting sinkhole occurrence to analyze the area, and create a sinkhole susceptibility map using LR. The results found that the variables that affect sinkhole formation include Well Density (WD), geology, Land Use (LU), Total Hardness (TH), Total Dissolved Solids (TDS), slope, Chlorine (Cl), distance to stream, elevation, Topographic Wetness Index (TWI), distance to village, soil, distance to active fault, and distance to well, respectively. All such variables are expressed by the exp β value coefficient. When prepared as a Karst sinkholes (KS) susceptibility map, it was found that a very high sinkhole susceptibility level covers an area of up to 399.86 km2 (19.16% of the total area). They appear mainly in the eastern region of the KIPs, especially at the confluence of the Khlong I Pan stream and the Khlong Trom stream. The other area is the central mountain range and the western mountain range, where geological structures with a casque topography are found. The results of this research suggest using the KS Susceptibility Map as a guideline for planning and monitoring potential future sinkholes.

Formulating hypotheses about the drivers of land use and cover change (LULCC) involves identifying patterns within the dynamics of the territory. Conventional basin-level analyses often mask localized patterns driven by social issues such as governance and community dynamics. This study examines the variations in LULCC patterns over 35 years (1985– 2019) by employing hierarchical intensity analysis of change across different spatial extents of the Grande and Chico River basins in the Colombian Andes. To better capture the influence of governance dynamics, the basin was delineated into two subzones with distinct governance characteristics: Zone A, where community-led conservation efforts and protected areas coexist, and Zone B, characterized by limited community participation and less active governance. Results reveal that the intensity of change accelerated significantly after 2010. During this period, forest and paramo ecosystems in the entire basin showed stationary losses, while pasture and non-vegetated areas exhibited systematic gains. Notably, Zone A demonstrated systematic pasture expansion. In contrast, pasture change in Zone B remained statistically dormant. Transition analysis indicated that cropland was the primary source of pasture gains. Qualitative insights from 3 semi-structured interviews corroborated that governance structures, local institutions, and the growing economic appeal of dairy farming are key drivers of LULCC, particularly in Zone A. These findings emphasize the need to integrate multi-scale quantitative assessments with local governance contexts to inform more effective land-use planning and conservation policy.

Reproductive characteristics are important tools for sustainable forestry and to transmit present gene diversity to future generations by forestry practices. Knowledge and estimation of fertility variation and its linkage parameters, such as population size and gene diversity in seed crops calculated by reproductive traits, are used widely because of their many advantages. Forestry practices use estimates of these parameters for various purposes, including natural regeneration, establishment, and management of seed sources.. In this study, cone and seed production and their effect on fertility variation were examined in two natural populations (P1 & P2) of Taurus cedar (Cedrus libani A. Rich.) sampled from the southern part of Türkiye. Numbers of mature cones, which were two years old and filled with seeds, were counted from fifty trees selected phenotypically from each population in 2023. The averages of cone and seed number were 90 and 33, and 5321 and 3115 per tree in the populations P1 and P2, respectively. Among individuals within a population, and between populations, there were large differences in cone and seed production. The percentages of filled seeds were 94% in P1 and 83% in P2. There were significant differences (p<0.05) between populations in terms of the production and percentage of filled seeds, according to results of analysis of variance. Estimated fertility variations (Ψ<2) were in good accordance with the target (Ψ<3). The effective number of parents ranged from 30.1 (60% of number of individuals) to 41.4 (83%). Besides, data sets can be used to fill the FLR-Library.

Urban planning is a complex process that addresses present conditions while shaping future development. However, it often relies on subjective assessments by planners and managers. This study explores the spatial network of Da Nang City through Multiple Centrality Assessment (MCA) and Space Syntax Analysis to provide an objective basis for urban planning. Key indicators, including Connectivity (Space Syntax), are calculated to assess movement, accessibility, flow, and social interaction within the urban network. Additionally, Closeness, Betweenness, Straightness, and Angular Centrality (MCA) are measured, highlighting the significance of streets and intersections in shaping urban dynamics. The findings are evaluated against Da Nang’s urban planning framework to assess its effectiveness and propose solutions for optimizing the master plan. The study identifies strengths and areas for improvement in the city’s layout, resulting in a proposed urban structure organized around five functional cores to enhance connectivity, efficiency, and sustainable growth. This research offers data-driven insights to assist urban planners in refining Da Nang’s spatial framework, contributing to the city’s longterm resilience and sustainable development.

Fine particulate matter (PM2.5), classified as airborne, adversely affects human health and the environment. This study examined the concentration and variability of PM2.5 and its correlation with meteorological variables in Brazil. The annual average highest concentration of PM2.5 (kg-m-3) 5.65×10-9 was found in the western part of the country. A low concentration of PM 2.5 (kg-m-3), 0.21×10-9 was reported in North, East, and South Brazil. Mann-Kendall and Sen’s slope statistics were applied to find the trend and magnitude in the time series. Mann-Kendall (MAK)-Tau shows a positive significant trend (1 to 0.41) detected in the south, midwest, and southeastern Brazil. The Mann-Kendall (MAK)-Tau trend test was applied. The Sen’s Slope rate ranged from 6.98 to 4.54 in the midwest, south, and southeast regions of Brazil, respectively. In 24 years, an overall negative PM2.5 trend of -3.17 and -5.18 is shown in the north and northeast, respectively. This study evaluated PM2.5 correlation with prevailing meteorological variables using various statistical techniques computed in R-Studio. Cross-wavelet Transform (CWT) analysis was used to examine the time and magnitude of PM2.5 with prevailing meteorological variables. The CWT analysis is statistically significant. The application of CWT analysis has revealed high leading and lagging in-phase and anti-phase correlations with prevailing meteorological variables, e.g., relative humidity, precipitation, temperature, and wind speed variables that have influenced the temporal concentration of PM2.5.

Reservoirs are facing increasing hydrological pressure, making continuous and accurate monitoring of these resources essential for sustainable management. In this study, we utilized a method involving Google Earth Engine (GEE), a platform with strong data processing capabilities for big data, to analyze and interpret satellite images. The Otsu method was applied to automatically determine the threshold value for extracting the water surface of the Song Hinh reservoir using Landsat 5, 8, and 9 satellite imagery, and to assess changes in the reservoir’s surface area. The research results indicated that the water surface area of the Song Hinh reservoir initially increased 4.4 times (1999-2000) and then remained relatively stable (2000-2024). However, during the 2000-2015 period, the water surface area experienced minor expansions and contractions, while during the 2015-2024 period, the surface area expanded insignificantly, with less contraction than in the previous period. Additionally, the analysis results of water surface area changes were used to support the development of Earth Engine Apps, also known as WebGIS, as a tool for monitoring surface water changes in the Song Hinh reservoir. In summary, the results obtained in this study are highly useful as a foundation for developing effective monitoring measures and sustainable resource management for the Song Hinh reservoir area.

The considerable influence of extensive land use change on the increasing levels of carbon emissions has significant implications for the occurrence of a multitude of disasters. The objective of this research is to develop a predictive model of future carbon stocks based on land use type. The data set includes land use maps from 2014, 2018, and 2022, obtained through visual interpretation of Pleiades data and associated driving variables, including socio-economic, locational, physical, land, and spatial planning factors. To predict land use in relation to future carbon stock values, the Multilayer Perceptron Neural Network Markov Chain (MLPNN-MC) algorithm was employed. Research related to this modeling is capable of producing an accuracy rate of 98%. The results of the prediction demonstrate that by 2034, there will be a reduction in the area of land used with high to low carbon stock, with a decrease of 153.2 ha, which equates to a reduction in carbon stock of 9,050 tonnes C/ha. To reduce carbon emissions, it is essential to implement policies that regulate land use change, optimize forest management, and conserve mangrove ecosystems. The monitoring and prediction of future carbon stocks plays a pivotal role in climate change mitigation, enabling more targeted and measurable actions to be taken.

The incorporation of nature-based solutions into urban planning and development policies has become a pressing issue in many large cities worldwide, aiming to improve the urban environment and the well-being of city-dwellers. However, the establishment and management of urban green infrastructure can be expensive and may lead to spatial injustice or be ecologically inefficient due to the planning decisions. This study focuses on the Spatial Justice-Ecological Efficiency Nexus of urban green infrastructure in large Caucasian cities of Russia, where urbanization rates are rapidly increasing. The hypothesis of this study is that green infrastructure in Russian cities predominantly has an ecological aspect, meaning that it provides a large volume of ecosystem services that are still unavailable to a significant portion of the urban population. To explore this topic, we aim to assess the balance between the social and ecological aspects of green infrastructure in six case study cities, including Makhachkala, Grozny, Nalchik, Maykop, Vladikavkaz, and Stavropol. The assessment framework includes 12 indicators, divided into two categories: spatial justice (6 indicators) and ecological efficiency (6 indicators). The spatial justice indicators assess the availability, accessibility, and distribution of green infrastructure, while the ecological efficiency indicators evaluate the performance of regulating and supporting ecosystem services. The results revealed that despite the common prevalence of ecological side in large Russian cities, the spatial justice side in the southern cities generally dominates over the ecological side, with most cities having an unbalanced nexus. Green infrastructure in the studied cities has a low ecological input, with a mean total score of around 300 points (out of 600), with most cities lacking protected areas and green areas beyond the edge effect. Meanwhile, the social side of the nexus is more developed, with an average score of 400. The study highlights the need for a more integrated approach to urban green infrastructure planning, considering both justice and ecological aspects to ensure a more just and sustainable urban environment. Overall, in this research we introduce a multidimensional approach to understanding the functions and qualities of green infrastructure that will allow for a more comprehensive assessment and planning of the rapidly growing southern cities. This study contributes to the understanding of the complex relationships between urban green infrastructure spatial justice and ecological efficiency, providing valuable insights for urban planners, policymakers, and stakeholders seeking to create more sustainable and equitable urban environment.

When considering the possible use of climate model data, it is necessary to choose which model is most appropriate to use. There are many methods for evaluating and selecting climate models in the literature, but there is no established consensus on which method is the most robust for determining model skill. In this article, we tested seven widely used methods for evaluating climate models in the Arctic using CMIP6 surface air temperature data: a single statistical metric method (root mean square error, spatial trends), a single skill score method (Taylor skill score, probability density function), a combination of several statistical metric methods (Taylor diagram, interannual variability skill score, comprehensive rating metric, etc.), and a multiple statistical criteria method (percentile-based approach). To evaluate their consistency, each method was applied to two periods: 1951-1980 and 1981-2010. For each method, the models were ranked and classified into three quality groups (very good, satisfactory, unsatisfactory). The comparison of methods was performed by comparing the differences in the average values of the normalized statistical measures, the differences in the model ranks, and the definition of the model quality groups. For each method, an optimal set of models corresponding to the top 25% was selected. One of the main objectives of the study was to compare the ability of the methods to identify the best model for the selected ensemble, regardless of the time period (i.e., without sensitivity to natural variability). The results suggest a preference for methods using root mean square error and a percentile-based approach.

In southern Italy, especially in Calabria, rivers are dry for most of the time, but intense rainfalls may turn them into roaring monsters, causing devastating floods. These rivers, locally known as “fiumaras”, are poorly studied though they play an important role in landscape shaping and pose serious threats to the local infrastructures and urban settlements. Basic catchment and channel geomorphic data of several rivers were collected from the literature and in the field. A comparison is made with river catchments of similar size in more humid environments to demonstrate that local physiography, watershed geomorphology, and channel characteristics may exacerbate the risk of flooding. The sedimentology of the study rivers is investigated to verify if fiumaras have specific bedform associations or stratigraphic arrangements that can be used to interpret ancient sandstones and conglomerates as deposited in an active tectonic setting under the Mediterranean climate. Four representative rivers were selected for investigation on the alluvium architecture, and field campaigns were carried out to collect bed material samples and to identify the occurrence of coarse and fine-grained bedforms. The fiumaras have a braided morphology, but the longitudinal bars do not have a fine tail and result from dissection processes rather than large bedform deposition and downstream migration. The braid bar characteristics, the poor internal organization of the tabular beds, and the occurrence of the largest boulders on top of the bars indicate the prevalence of high deposition rates from hyperconcentrated flows.

The current study presents the results of air quality research in the small mining and touristic city of Apatity (Kola Peninsula, Russian Federation, 67o34’03’’N, 33o23’36’’E) during the two winter expeditions in 2022 and 2024. A PurpleAir PA-II portable device was used for ground-based aerosol observations. Two measurement campaigns allowed to conduct route measurements in various synoptic conditions, including both frosty windless weather, characterized by temperature inversion (2022), and contrasting conditions of “warm” winter unusual in the Arctic and Kola Peninsula (January 2024). The obtained results demonstrate that, depending on the synoptic situation in the city, there can be both traditional accumulation of concentrations of PM 2.5 particles (up to 300 µg/m3) dangerous for the health of inhabitants (in some areas exceeding the 20 min maximum allowable concentration of 160 µg/m3 almost twice), and significant improvement of air quality due to precipitation and air mixing under warm winter conditions (on average, about 17 µg/m3). The latter circumstance can noticeably improve the region’s tourism potential in a warmer climate.

The paper consists of a review of the public health consequences of the COVID-19 pandemic. The study focuses on the assessment of the impact of the COVID-19 pandemic on the incidence of the leading disease categories, such as Diseases of the Circulatory System (DCS), Malignant Neoplasms (MN) and External Causes of Morbidity and Mortality (EC) in the Russian Federation. Time series of standardized incidence for each category were examined for the period 2007–2019 (preCOVID-19), and 2020–2023 (COVID-19 and post-COVID-19). The post-COVID trends were compared to those hypothetically expected with no COVID impact. For the majority of the RF regions, upward trends of DCS and MN incidence were detected both in pre-COVID and post-COVID years. In the first year of the pandemic, a decline in morbidity was observed for all categories. The EC incidence trend was decreasing in pre-COVID years, but it increased in the post-COVID period. The median incidence rates of MN in the post-COVID period were lower than expected in most of the country, while those of DCS demonstrated heterogeneous distribution with no clear spatial patterns. A decline in the incidence of all nosoforms in 2020 may not have been related to the actual decrease of morbidity, but rather to the significant reduction of healthcare and diagnostics accessibility, which led to a reduction in the detection of diseases new cases.

This study demonstrates the effectiveness of a multi-objective validation approach for a distributed hydrological model in a high mountain river basin. Focusing on the Baksan River Basin in the Central Caucasus, where snow and glacier melt play a crucial role in runoff formation, we applied the ECOMAG model, which has proven its reliability in high-altitude hydrology. To enhance the validation accuracy, we integrated diverse data sources, including observed river discharge, MODISderived snow cover, stable isotope hydrograph separation, glacier mass balance observations, and glacial runoff simulations from the A-Melt model. The results confirm the high performance of the model across multiple hydrological components. The simulated and observed discharge values showed strong agreement, with the Nash-Sutcliffe efficiency exceeding 0.8 for both the calibration and validation periods. The model successfully captured seasonal snow cover variations, achieving an R² of 0.85 when compared with the MODIS data. Isotopic hydrograph separation further validated the accuracy of the simulated meltwater and rainfall contributions to runoff. Although glacier ablation simulations showed some deviations, particularly for the Djankuat Glacier, these findings highlight opportunities for refining glacial process representation. Overall, this study confirms the robustness and applicability of multi-objective validation for hydrological modeling in complex mountainous regions. The integration of multiple observational datasets significantly enhances the reliability of modeling results, providing valuable insights into water resource management, climate impact assessments, and sustainable development in glacier-fed river basins.