Search Results (137)

Kazakhstan, located in Central Asia, is experiencing faster warming than the global trend, making it an important region regarding the study of how climate change is affecting climatic zones. This research aims to identify projected shifts in Köppen–Geiger climate zones under high-emission Shared Socioeconomic Pathway (SSP) 5-8.5 climate scenarios. The Köppen–Geiger climate classification system is a practical tool that effectively captures climate types based on just two variables: temperature and precipitation. Monthly temperature and precipitation data from Climatic Research Unit (CRU,) ERA5-Land, and Coupled Model Intercomparison Project Phase 6 (CMIP6) ensembles from 1951 to 2100 were used to generate climatic zone maps. CMIP6 models were evaluated against meteorological station data and ERA5-Land, with bias metrics used to identify the best-performing models for temperature and precipitation in Kazakhstan. Based on these results, two inter-model datasets were developed and used to generate Köppen–Geiger climate maps for high-emission scenarios for the 2061–2100 time period. This research resulted in two key outcomes. First, to facilitate this analysis, a Google Earth Engine (GEE) application was developed as an open accessible tool for dynamic visualization of Köppen–Geiger climate maps. Second, projected maps based on CMIP6 SSP5-8.5 scenario projections indicate that southern Kazakhstan may shift to BSh (Hot Semi-Arid) and Csa (Mediterranean) climates, and the southwest region of the country is projected to shift to a BWh (Hot Desert) climate. These projected Köppen–Geiger climate maps contributed to climate adaptation efforts by identifying regions at risk of desertification and aridification. This study provides a comprehensive analysis of climate zone transformations in Kazakhstan and offers a practical scalable geovisualization tool for monitoring climate change impacts. This allows users easy access to climate-related information and insights into data processing procedures. Full article

To enhance the climate adaptability and diagnostic precision of university sustainability frameworks, this study presents a critical advancement to the PICSOU (Performance Indicators for Core Sustainability Objectives of Universities) framework’s Indoor Environmental Quality (IEQ) module. The research employs a comparative approach across two distinct climate zones: the campus of Chengdu Jincheng College in a humid subtropical climate (CDJCC; Köppen Cwa) with natural ventilation, and the campus of Tallinn University of Technology in a temperate climate (TalTech; Köppen Dfb) with mechanical ventilation. A key innovation at CDJCC was the deployment of a novel, integrated sensor that combines a Frequency-Modulated Continuous Wave (FMCW) radar module for real-time occupancy detection with standard IEQ sensor suite (CO₂, PM_2.5, temperature, humidity), enabling unprecedented analysis of occupant-IEQ dynamics. At TalTech, comprehensive IEQ monitoring was conducted using standard sensors. Results demonstrated that mechanical ventilation (TalTech) effectively decouples indoor conditions from external fluctuations. In contrast, natural ventilation (CDJCC) exhibits strong seasonal coupling, reflected by a Seasonal Ventilation Efficacy Coefficient (${\lambda }_{\mathit{season}}$), indicating that seasonal differences in effective ventilation are present but vary by indoor space type under occupied conditions. Consistent with this stronger indoor–outdoor linkage, PM_2.5 infiltration was also pronounced in naturally ventilated spaces, as evidenced by a high infiltration factor ($I/O$ ratio) that remained consistently elevated. This work conclusively validates a conditional, climate-resilient workflow for PICSOU’s IEQ category, integrating these empirical coefficients to transform its IEQ assessment into a dynamic and actionable tool for optimizing campus sustainability strategies globally. Full article

This systematic review assesses indoor air quality (IAQ) in tropical residences (Köppen Af/Am/Aw), explicitly linking IAQ to ventilation from in situ monitoring and, when relevant, occupant surveys (surveys synthesized qualitatively). This focus is warranted by the scarcity of tropical, housing-specific evidence. Searches were performed exclusively in Google Scholar (25 August 2024–5 August 2025; English/Spanish) under PRISMA, with documented queries/filters; eligible studies reported residential settings, tropical climate, and IAQ–ventilation linkage. Results show a regulatory mosaic with few binding residential limits and heterogeneous protocols that hinder comparison. Robust patterns include cooking-related particle peaks, penetration of traffic dust, humidity-driven VOC/formaldehyde emissions, and mold growth under deficient hygrothermal control. CO₂ is a useful operational indicator of ventilation yet insufficient for risk assessment without PM and VOC monitoring. Evidence supports source control, cross-ventilation and/or on-demand extraction/outdoor-air supply, humidity management, and filtration/purification to avoid particle ingress during ventilation. Reporting of sensor performance (calibration, drift, RH/T effects) is inconsistent, and targeted evaluations of TVOC/formaldehyde and window screens (mesh) are scarce. We conclude that tropical residential IAQ management requires multi-parameter, continuous monitoring, standardized reporting, and trials integrating ventilation, dehumidification, and filtration under real occupancy, alongside adaptive regulation and passive tropical design augmented by light mechanical support and informed occupant behavior. Full article

Scale mites of the genus Geckobia (Pterygosomatidae) are highly specialized permanent parasites of geckos, but their diversity and ecology in arid environments remain poorly understood. We examined 1135 museum specimens of Ptyodactylus geckos collected from 1965 to 1991 across Israel and the West Bank’s Mediterranean–desert climate gradient to investigate environmental effects on Geckobia mite distributions and population structure. We analyzed prevalence, intensity, population structure, and seasonal patterns across three climate zones using standard parasitological methods and Köppen–Geiger climate classification. We describe two new species, Geckobia inermis sp. nov. and G. parva sp. nov., from Ptyodactylus puiseuxi and provide the first descriptions of previously unknown life stages: the male and nymphchrysalis of G. squameum and the imagochrysalis and larva of G. bochkovi. We report P. oudrii as a new host for G. synthesys and address taxonomic confusion regarding northern Israeli host populations following recent phylogenetic revisions of Ptyodactylus. Only 37 hosts were infected (3.26% prevalence), with a significant female bias in G. squameum populations. Most mites (94.6%) concentrated in the tympanum, where we documented a “double skin plug”, closing the ear opening and creating favorable microenvironments for mite survival. The results demonstrate climate as the primary factor structuring mite distributions: environmental filtering showed systematic prevalence decline from Mediterranean zones (4.3%) to desert-edge areas (1.1%), representing a 3.9-fold gradient that exceeded host species effects by 5.2-fold. Populations exhibited phenological plasticity, with Mediterranean mites peaking in winter versus spring activity in semi-arid zones. These findings reveal how climate constrains ectoparasite persistence in arid systems, with implications for understanding parasite responses to environmental change. Full article

This study assessed the impact of 39 active and passive energy efficiency measures on the energy demand of a prototype dwelling, modeled through parametric simulations in DesignBuilder across nine climatic zones in Chile, classified according to the Köppen system. Each measure was evaluated individually (single-measure scenarios); three variation levels were evaluated to quantify their relative influence on energy demand. Results indicate that passive strategies are more effective in cold and humid climates, where increasing wall insulation thickness reduced energy demand by up to 45%, and improving airtightness achieved a 43% reduction. In contrast, in tundra climates or areas with high thermal variability, some measures, such as green façades or overhangs, increased energy demand by up to 49% due to the loss of useful solar gains. In desert climates, characterized by high diurnal temperature variation, thermal mass played a more significant role: high-inertia walls without additional insulation outperformed lightweight EPS-based solutions. The findings suggest that measure selection must be climate-adapted, prioritizing high-impact passive strategies and avoiding one-size-fits-all solutions. This work provides quantitative evidence to inform residential thermal design and support climate-sensitive energy efficiency policies. This study delivers a single-measure comparative atlas; future research should integrate multi-measure optimization together with comfort/cost metrics. Full article

The escalating global demand for electricity, coupled with environmental concerns and economic considerations, has driven the exploration of alternative energy sources, creating competition for land with other sectors. A comprehensive analysis of a 10 MW floating photovoltaic (FPV) system deployed across different Köppen climate zones along with techno-economic analysis involves evaluating technical efficiency and economic viability. Technical parameters are assessed using PVsyst simulation and HOMER Pro. While, economic analysis considers return on investment, net present value, internal rate of return, and payback period. Results indicate that temperate and dry zones exhibit significant electricity generation potential from an FPV. The study outlines the payback period with the lowest being 5.7 years, emphasizing the system’s environmental benefits by reducing water loss in the form of evaporation. The system is further integrated with hydrogen generation while estimating the number of cars that can be refueled at each location, with the highest amount of hydrogen production being 292,817 kg/year, refueling more than 100 cars per day. This leads to an LCOH of GBP 2.84/kg for 20 years. Additionally, the comparison across different Koppen climate zones suggests that, even with the high soiling losses, dry climate has substantial potential; producing up to 18,829,587 kWh/year of electricity and 292,817 kg/year of hydrogen. However, factors such as high inflation can reduce the return on investment to as low as 13.8%. The integration of FPV with hydropower plants is suggested for enhanced power generation, reaffirming its potential to contribute to a sustainable energy future while addressing the UN’s SDG7, SDG9, SDG13, and SDG15. Full article

Physical and chemical processes related to global and regional climate changes are important factors in shaping the Earth’s surface. These processes form various erosion and deposition landforms on the Earth’s surface. These landforms reflect the traces of past and present climate conditions. This study shows that geomorphometric parameters can effectively distinguish between geomorphometrically and climatically distinct geomorphic provinces. In this context, supervised machine learning models were developed using geomorphometric parameters and the Köppen-Geiger climate classes observed in Türkiye. These models, Random Forest, Support Vector Machines, and K-Nearest Neighbor algorithms, were developed using a training data set. Classification analysis was performed using these models and a test dataset that was independent of the training dataset. According to the classification results, the overall accuracy values for the Random Forest, Support Vector Machines, and K-Nearest Neighbor models were calculated as 99.27%, 99.70%, and 99.30%, respectively. The corresponding kappa values were 0.99, 0.99, and 0.99, respectively. This study shows that among the geomorphometric parameters used in the analyses, maximum altitude, elevation, and valley depth were determined as important parameters in distinguishing geomorphic provinces. Full article

The urbanisation process of cities disrupts the natural energy balance and surface radiation, making cities relatively warm. While vegetation has been widely recognised as a key factor in mitigating urban heat, its effectiveness is shaped by interactions with urban morphology, surface cover types, and the background climate. This paper presents a bibliometric analysis of studies examining the role of vegetation in mitigating urban heat, with a particular focus on its interactions within the urban environment across four major Köppen–Geiger climate groups: tropical, arid, temperate, and cold. A total of 130 publications were reviewed, categorised, and analysed according to geographic distribution, study period, and methodological approaches. This review identifies underexplored areas, synthesises key findings, and summarises the most significant results. Vegetation and water bodies emerged as primary contributors to heat mitigation, along with building configuration, wind speed, and shading. Temperate climates were the most frequently studied. Remote sensing was the predominant methodological approach, followed by fixed in situ observations. Meso-scale studies, examining entire cities and their surroundings, dominated in terms of spatial scale. This review offers methodological recommendations for analysing urban vegetation within the context of urban climate research. As climate change intensifies, it is increasingly important to design and implement adaptation strategies that incorporate but are not limited to vegetation. Such strategies are essential to supporting sustainable and resilient urban development in diverse climatic contexts. Full article

Avian influenza (AI), particularly the H5 subtypes, poses a significant and persistent threat globally. While the influence of environmental factors on AI seasonality is recognized, a comprehensive understanding of the hierarchical and interactive effects of multi-scale drivers in a vast and ecologically diverse country like China remains limited. We developed an interpretable machine learning framework (XGBoost with SHAP) to analyze the spatiotemporal risk of 1800 H5 AI outbreaks in mainland China from 2000 to 2023. We integrated multi-source data, including dynamic poultry density, Köppen climate classifications, Important Bird and Biodiversity Areas (IBAs), and daily meteorological variables, to identify key drivers and quantify their nonlinear and synergistic effects. The model demonstrated high predictive accuracy (5-fold cross-validation $R^2$ = 0.776). Our analysis revealed that macro-scale ecological contexts, particularly poultry density and specific Köppen climate zones (e.g., Cwa), and strong seasonality were the most dominant drivers of AI risk. We identified significant nonlinear relationships, such as a strong inverse relationship with temperature, and a critical synergistic interaction where high temperatures substantially amplified risk in areas with high poultry density. The final predictive map identified high-risk hotspots primarily concentrated in eastern and southern China. Our findings indicate that H5 AI risk is governed by a hierarchical interplay of multi-scale environmental drivers. This interpretable modeling approach provides a valuable tool for developing targeted surveillance and early warning systems to mitigate the threat of avian influenza. Full article

This study highlights the spatial, seasonal, and climatic variations in bird species richness across Türkiye, a country with rich avian richness situated at the intersection of major migratory routes. Bird species richness was calculated for each province. Differences between regions, Köppen–Geiger climate classes, and seasons were analyzed using the Kruskal–Wallis method. Non-parametric analysis of longitudinal data in factorial experiments was also employed to determine seasonal differences within regions and climate classes. The results revealed significant spatial variations in species richness, particularly between temperate and cold climate regions. While seasonal differences were generally less pronounced, they were critical for both migratory and resident bird species. Wetlands, coastal areas, and transitional habitats were identified as biodiversity hotspots for both resident and migratory birds. This study underscores the need to integrate regional, climatic, and seasonal variations into ecosystem-based management plans. Protecting critical habitats, enhancing connectivity through ecological corridors, and adopting adaptive conservation strategies are essential for sustaining Türkiye’s rich avian diversity. These results provide valuable insights for conservation planning and emphasize the importance of addressing spatial and seasonal dynamics to ensure long-term biodiversity preservation. Full article

Precise photovoltaic (PV) performance modeling is essential for optimizing system design, operational monitoring, and reliable power forecasting—yet spectral correction is often overlooked, despite its significant impact on energy yield uncertainty. This study employs the FARMS-NIT model to assess the impact of spectral irradiance on eight PV technologies across 79 European sites, grouped by Köppen–Geiger climate classification. Unlike previous studies limited to clear-sky or single-site analysis, this work integrates satellite-derived spectral data for both all-sky and clear-sky scenarios, enabling hourly, tilt-optimized simulations that reflect real-world operating conditions. Spectral analyses reveal European climates exhibit blue-shifted spectra versus AM1.5 reference, only 2–5% resembling standard conditions. Thin-film technologies demonstrate superior spectral gains under all-sky conditions, though the underlying drivers vary significantly across climatic regions—a distinction that becomes particularly evident in the clear-sky analysis. Crystalline silicon exhibits minimal spectral sensitivity (<1.6% variations), with PERC/PERT providing highest stability. CZTSSe shows latitude-dependent performance with ≤0.7% variation: small gains at high latitudes and losses at low latitudes. Atmospheric parameters were analyzed in detail, revealing that air mass (AM), clearness index (K_t), precipitable water (W), and aerosol optical depth (AOD) play key roles in shaping spectral effects, with different parameters dominating in distinct climate groups. Full article

The Brazilian savanna, South America’s second-largest biome, is vital to Brazil’s economy but has suffered from environmental degradation due to unregulated agricultural and urban expansion. This study assesses climate change in the biome from 1961 to 2021 using the Köppen climate classification, drought indices, historical trend analyses, and the climatological water balance. Fourteen municipalities across the biome were analyzed. According to the Köppen classification, most municipalities were identified as Aw (tropical with dry winters) and Am (tropical monsoon), with Dourados, MS, and Sapezal, MT, alternating between Am and Aw. The standardized precipitation index (SPI) revealed changes in rainfall distribution. The Mann–Kendall test detected rising air temperatures in 13 of the 14 municipalities, with Sen’s slope ranging from 0.0156 to 0.0605 °C per year. Rainfall decreased in seven municipalities, with decreases from −4.54 to −12.77 mm per year. The climatological water balance supported the observed decrease in precipitation. The results indicated a clear warming trend and declining rainfall in most of the Brazilian savanna, highlighting potential challenges for water availability in the face of ongoing climate change. Full article

Understanding spatial variations in land surface temperature (LST) is critical for analyzing urban climate dynamics, especially within the framework of two-dimensional (2D) turbulence theory. This study assesses the spatial sampling behavior of urban thermal fields across eight metropolitan areas, encompassing diverse morphologies, surface materials, and Köppen–Geiger climate zones. We analyzed thermal infrared (TIR) imagery from two remote sensing platforms—MODIS (1 km) and Landsat (30 m)—to evaluate resolution-dependent turbulence indicators such as spectral slopes and breakpoints. Power spectral analysis revealed systematic divergences across spatial scales. Landsat exhibited more negative breakpoint values, indicating a greater ability to capture fine-scale thermal heterogeneity tied to vegetation, buildings, and surface cover. MODIS, in contrast, emphasized broader thermal gradients, suitable for regional-scale assessments. Seasonal differences reinforced the turbulence framework: summer spectra displayed steeper, more variable slopes, reflecting increased thermal activity and surface–atmosphere decoupling. Despite occasional agreement between sensors, spectral metrics remain inherently resolution-dependent. MODIS is better suited for macro-scale thermal structures, while Landsat provides detailed insights into intra-urban processes. Our findings confirm that 2D turbulence indicators are not fully scale-invariant and vary with sensor resolution, season, and urban form. This multi-sensor comparison offers a framework for interpreting LST data in support of climate adaptation, urban design, and remote sensing integration. Full article

Ecuador faces a significant housing deficit, prompting government policies aimed at improving access to social housing for vulnerable families. Despite its relatively small geographic size, the country exhibits substantial climatic diversity, encompassing ten distinct Köppen–Geiger climate zones. These range from tropical rainforests to high-altitude Andean regions, each requiring specific housing strategies. However, social housing units are typically designed using a standardized model that disregards regional climatic variations, leading to suboptimal thermal performance and energy inefficiencies. This study evaluates the thermal comfort performance of standardized free-running social housing across six distinct cantons, using the ASHRAE 55-2020 adaptive comfort model. Dynamic simulations were conducted for both current climatic conditions and future scenarios for 2050 and 2100, employing tools such as Meteonorm 8.1 (for weather data), EnergyPlus 9.4.0, and DesignBuilder 7.0 (for thermal modeling). The findings reveal significant differences in indoor comfort levels among identical housing units due to localized climate conditions. Notably, high-altitude regions showed improved thermal performance under future scenarios, whereas coastal lowland areas experienced increased discomfort. These results underscore the urgent need for climate-responsive, adaptive housing designs tailored to local climatic realities across all regions of Ecuador. Full article

This study investigated the synergistic effects of vegetation configurations and microclimate factors on seasonal thermal comfort in a semi-enclosed university courtyard in Wuhan, located in China’s Hot Summer and Cold Winter climate zone (Köppen: Cfa, humid subtropical). By adopting a field measurement–simulation–validation framework, spatial parameters and annual microclimate data were collected using laser distance meters and multifunctional environmental sensors. A validated ENVI-met model (grid resolution: 2 m × 2 m × 2 m, verified by field measurements for microclimate parameters) simulated 15 vegetation scenarios with varying planting patterns, evergreen–deciduous ratios (0–100%), and ground covers. The Physiological Equivalent Temperature (PET) index quantified thermal comfort improvements relative to the baseline. The optimal grid-based mixed planting configuration (40% evergreen trees + 60% deciduous trees) significantly improved winter thermal comfort by raising the PET from 9.24 °C to 15.42 °C (66.98% increase) through windbreak effects while maintaining summer thermal stability with only a 1.94% PET increase (34.60 °C to 35.27 °C) via enhanced transpiration and airflow regulation. This study provides actionable guidelines for climate-responsive courtyard design, emphasizing adaptive vegetation ratios and spatial geometry alignment. Full article