Air Temperature Variations Research Articles

Microplastic dynamics and risk projections in West African coastal areas: Developing a vulnerability index, adverse ecological pathways, and mitigation framework using remote-sensed oceanographic profiles

Microplastic pollution presents a serious risk to marine ecosystems worldwide, with West Africa being especially susceptible. This study sought to identify the key factors driving microplastic dynamics in the region. Using NASA's Giovanni system, we analyzed environmental data from 2019 to 2024. Results showed uniform offshore air temperatures due to turbulence (25.22–45.62 K) with significant variations nearshore. Salinity levels remained largely stable (4 PSU) but slightly decreased in southern Nigeria. Surface wind speeds rose from 4.206–5.026 m/s in Nigeria to over 5.848 m/s off Mauritania, while eastward stress hotspots were prominent in Nigeria and from Sierra Leone to Senegal. Photosynthetically available radiation (PAR) beam values peaked off Mauritania and dipped from Nigeria to Sierra Leone, with the inverse pattern observed for diffuse PAR. Hotspots of high absorption, particulate backscattering, elevated aerosol optical depth, and remote sensing reflectance all pointed to substantial particulate matter concentrations. The Microplastic Vulnerability Index (MVI) identifies the coastal stretch from Nigeria to Guinea-Bissau as highly vulnerable to microplastic accumulation due to conditions that favor buildup. In contrast, moderate vulnerability was observed from Guinea-Bissau to Senegal and in Mauritania, where conditions were less extreme, such as higher offshore temperatures that could promote widespread microplastic suspension and cooler nearshore temperatures that favor sedimentation. Increased turbulence and temperatures in coastal areas of Senegal and Mauritania may enhance microplastic transport and impact marine life. In Nigeria, stable coastal conditions—characterized by consistent temperatures, low turbulence, and uniform salinity—may lead to increased persistence and accumulation of microplastics in sensitive habitats like mangroves and coral reefs. These findings highlight the need for region-specific management strategies to address microplastic pollution and effectively protect marine ecosystems.

Accelerated warming of High Mountain Asia predicted at multiple years ahead

High-Mountain Asia (HMA) is an important source of freshwater since it holds the largest reservoir of frozen water outside the polar regions. HMA feeds ten great rivers, ultimately supporting more than 2 billion people. However, the threat of accelerated glacier melt, which is a consequence of unprecedented global warming since the early 1950s, threatens water resources in the surrounding countries. Accurate predictions of the near-term temperature change and synergistic mass loss of glaciers are essential but challenging to implement because of the impacts of internal climate variability. Here, based on large ensembles of state-of-the-art decadal climate prediction experiments, we provide evidence that the internally generated surface air temperature variations in HMA can be predicted multiple years in advance, and the model initialization has robust added value to the decadal prediction skill. Real-time decadal forecasts suggest that the HMA will experience accelerated warming in 2025–2032, where the surface warming will increase by 0.98 °C (0.67 to 1.33 °C; 5 % to 95 % range) relative to the reference period 1991–2020, which is equivalent to 1.75 times the observed warming during 2016–2023. The decadal predictability originates from extratropical Pacific decadal variability modes, which modulate the convective heating in the tropical Pacific and influence HMA via the eastward-propagating atmospheric Kelvin waves. Accelerated warming in the coming decade will likely increase the shrinkage of the glacier volume over the HMA by 1.4 %. This change poses unprecedented challenges, including potential water scarcity, ecosystem disruption, and increased risk of natural disasters, to HMA and surrounding regions.

Predictability of the early summer surface air temperature over Western South Asia

Variability of the Surface Air Temperature (SAT) over the Western South Asia (WSA) region leads to frequent heatwaves during the early summer (May-June) season. The present study uses the European Centre for Medium-Range Weather Forecast’s fifth-generation seasonal prediction system, SEAS5, from 1981 to 2022 based on April initial conditions (1-month lead) to assess the SAT predictability during early summer season. The goal is to evaluate the SEAS5’s ability to predict the El Niño-Southern Oscillation (ENSO) related interannual variability and predictability of the SAT over WSA, which is mediated through upper-level (200-hPa) geopotential height anomalies. This teleconnection leads to anomalously warm surface conditions over the region during the negative ENSO phase, as observed in the reanalysis and SEAS5. We evaluate SEAS5 prediction skill against two observations and three reanalyses datasets. The SEAS5 SAT prediction skill is higher with high spatial resolution observations and reanalysis datasets compared to the ones with low-resolution. Overall, SEAS5 shows reasonable skill in predicting SAT and its variability over the WSA region. Moreover, the predictability of SAT during La Niña is comparable to El Niño years over the WSA region.

Influence of air temperature and interrelationship with greenhouse gases (CO2 and CH4) over Iraq using AIRS data

Global and regional observations of air temperature (AT) and specific atmospheric greenhouse gases (GHGs) such as methane (CH4) and carbon dioxide (CO2) are required for a variety of applications, including constraining global or regional estimates of their significant impacts on the climate system. The present study employs Atmospheric Infrared Sounder (AIRS) -Level3 monthly products for AT, CH4, and CO2, at two standard pressure levels (925 and 500 hPa) over Iraq during 2010–2016. Both CO2 and CH4 shows significant seasonal variation, with maximum (minimum) CO2 observed in June (October), while CH4 recorded three maximum peaks during April, August, and November, and a minimum in February. CH4 shows a negative correlation during winter (DJF), spring (MAM), summer (JJA), and autumn (SON) with correlation coefficients (R) −0.627, −0.734, −0.491, and −0.688, respectively. The P-value is below 0.05 (4.14 × 10−15, 2.13 × 10−22, 1.1 × 10−8, and 5.2 × 10−19) for the four seasons, indicating a negative linear relationship. CO2 shows a low negative correlation in DJF and SON, and a low positive correlation in MAM and JJA seasons, with R values equal to −0.315, −0.221, 0.059, and 0.079, for DJF, SON, MAM and JJA seasons, respectively. The P-value was greater than 0.05 (0.061, 0.728, 0.647, and 0.195) for the four seasons, respectively, indicating a nonlinear relationship with AT. The monthly averaged time-series for CH4 and CO2 shows an evident increase, with an annual average increase of 1.81% (4.75) ppbv/year and 3.31% (1.84) ppm/year, respectively. Analysis reveals that the major sink and sources for CH4 are the presence of hydroxyl (OH) radicals and vegetation, whereas the major sources for CO2 are anthropogenic emissions, burning fossil fuels, and land-use change. The satellite observations of AIRS can efficiently show the spatiotemporal variations of air temperature versus CH4 and CO2 for the study area.

Impact of refrigerant undercharge faults on building indoor conditions and HVAC system operation in residential Buildings: A simulation study

This study investigates the impact of refrigerant undercharge on indoor temperature and HVAC system performance in residential buildings. Simulation models for typical residential buildings in Orlando, FL and Indianapolis, IN were developed using the ResStock database. A refrigerant undercharge fault model was then applied to the simulations with varying levels of fault intensity. The paper offers an extensive analysis, revealing that variations in supply air temperature, equipment runtime, and cooling energy consumption due to the level of refrigerant undercharge faults are notably significant on a summer representative day. Similarly, on a winter representative day, changes in supply air temperature and runtime are significant as well as changes in supplemental heat energy consumption. We find that occupants may remain oblivious to these faults during the cooling season, particularly when the HVAC system is oversized; in that case, supply air temperature data could help detect a fault. Another challenge is that during the heating season, when the supplemental heater operates, it is difficult to identify a refrigerant undercharge fault using only indoor and supply air temperature data. This study finds that supply air temperature, equipment runtime, and supplemental heater energy consumption data can help in detecting refrigerant undercharge faults.

On urban microclimate spatial-temporal dynamics: Evidence from the integration of fixed and wearable sensing and mapping techniques

Urban Heat Island (UHI) is acknowledged to generate harmful consequences on human health, and it is one of the main anthropogenic challenges to face in modern cities. Due to the urban dynamic complexity, a full microclimate decoding is required to design tailored mitigation strategies for reducing heat-related vulnerability. This study proposes a new method to assess intra-urban microclimate variability by combining for the first time two dedicated monitoring systems consisting of fixed and mobile techniques. Data from three fixed weather stations were used to analyze long-term trends, while mobile devices (a vehicle and a wearable) were used in short-term monitoring campaigns conducted in summer and winter to assess and geo-locate microclimate spatial variations. Additionally, data from mobile devices were used as input for Kriging interpolation in the urban area of Florence (Italy) as case study. Mobile monitoring sessions provided high-resolution spatial data, enabling the detection of hyperlocal variations in air temperature. The maximum air temperature amplitudes were verified with the wearable system: 3.3 °C in summer midday and 4.3 °C in winter morning. Physiological Equivalent Temperature (PET) demonstrated to be similar when comparing green areas and their adjacent built-up zone, showing up the microclimate mitigation contribution of greenery in its surrounding. Results also showed that mixing the two data acquisition and varied analysis techniques succeeded in investigating the UHI and the site-specific role of potential mitigation actions. Moreover, mobile dataset was reliable for elaborating maps by interpolating the monitored parameters. Interpolation results demonstrated the possibility of optimizing mobile monitoring campaigns by focusing on targeted streets and times of day since interpolation errors increased by 10% only with properly reduced and simplified input samples. This allowed an enhanced detection of the site-specific granularity, which is important for urban planning and policymaking, adaptation, and risk mitigation actions to overcome the UHI and anthropogenic climate change effects.

Thermal resilience of buildings: The role of partition walls and slabs in the optimization of the building external envelope

Nowadays it is advisable for buildings to be thermally resilient, so as to passively resist to the daily external air temperature fluctuations, reducing the interventions of air conditioning systems to a minimum. The decrement factor and time lag are parameters notoriously used to evaluate the responses of the building envelope to periodic oscillations in external air temperature. However, in their traditional definition, these parameters do not consider the effects of internal elements, such as partition walls and slabs, which instead influence the passive thermal behaviour of the building. For this reason, a choice of the external wall stratigraphy, made exclusively on the basis of the traditional decrement factor and time lag, seems to be simplistic. In this paper, problems concerning the passive behaviour of buildings, in case of sinusoidal outdoor air temperature variations, are investigated. Within a lumped-capacitance scheme and using the heat transfer matrix method, the problem of the determination of a wall stratigraphy minimizing the decrement factor for given values of the overall thermal resistance and capacity, usable for the realization of both external and internal walls of a given room, is solved. After developing an analysis on the influence of the internal walls and slabs on the room thermal balance, the resistance-capacity distribution, within the external wall, minimizing the decrement factor is determined, for assigned thermal resistances and capacities of internal elements and the external wall. Results indicate that there is no resistance-capacity distribution that is absolutely better than the others, but it is dependent on the thermo-physical characteristics of the internal elements. The optimized external wall stratigraphy can be composed of an even number of resistive and capacitive layers, alternating with each other, or of an odd number of layers (with one more resistive layer than the capacitive ones). Even the number of layers that makes optimized the passive behaviour of the external wall varies with the characteristics of the internal elements, the results show that already with a number of layers varying from 2 to 5 high performances can be obtained.

On the influence of solar insolation and increase of outdoor temperature on energy savings obtained in heating system with forecast control

Throughout the world there is an ongoing emphasis on the implementation of modern technologies and services to reduce the carbon footprint and increase the energy efficiency of existing and new buildings. Of particular interest are energy-efficient measures that can be easily implemented in existing building stock and at a low cost, such as a forecast controller for heating systems. However, there is a lack of detailed measured data for assessing the influence of varying outdoor conditions on energy savings from a heating system controlled by a forecast controller. The work aimed to assess the influence of variations in solar insolation and outdoor air temperature on the supply temperature, power demand, and heat consumption within thirteen buildings equipped with a forecast control system. The application employs simple correlations, utilizing an equivalent outdoor temperature derived from an online weather prediction application (API) leveraging the data from nearby meteorological stations. The algorithm utilises the weather predictions for changes in outdoor conditions, such as wind speed, temperature, and solar insolation, to optimise the conditions of the heat supplied to buildings.The field research was conducted for one year. It was found that the use of the forecast controller in four public buildings and nine residential buildings reduced heat consumption by an average of 9.0% and 11.8%, respectively, for the case when the variations of the outdoor temperature or the solar insolation were taken into account independently. Higher energy savings (16.5%) were achieved for the case when the outdoor temperature increase as well as solar insolation were taken into account at the same time in the process of forecast control of the heating system.

Integrated effects of pavement simulation models and scale differences on the thermal environment of tropical cities: physical and numerical modeling experiments

Simulation methods attempt to explain what happens in full-scale environments. However, as simplification procedures, they also have their limitations and opportunities. One of the applications is to use the output data of a physical model to calibrate numerical simulation, or even to use outputs of numerical simulations to analyze urban scale studies. But it is uncertain the error in the interaction between these models. This study aims to analyze the impact of scale analysis and pavements simulation model modification on ambient and surface temperature of asphalt pavement in a physical model of a tropical city street canyons. Therefore, a scaled outdoor experiment was conducted, and a numerical simulation model, using ENVI-met software, was used to investigate the spatiotemporal variation of air and pavement surface temperature, in urban (1:1) and reduced (1:15) scales. For studies on the surface temperature of pavements, within the temperature range of 12 ºC to 37 ºC, it is recommended to calibrate physical models using as input, data derived from numerical simulation models, yielding a mean absolute percentage error (MAPE) of 4.9%. For estimating data in real-world urban scale, within the air temperature range of 15 ºC to 37 ºC, it is proposed to use output data from simulated models in ENVI-met, that presented a mean absolute error (MAE) of ± 0.59 or physical models (MAE = ± 0.66). These results would be useful for the development of urban surface temperatures parametrizations.

Comparing the efficacy of different climate indices for prediction of labor loss, body temperatures, and thermal perception in a wide variety of warm and hot climates.

The purpose of this study was to investigate which climate/heat indices perform best in predicting heat-induced loss of physical work capacity (PWCloss). Integrating data from earlier studies, data from 982 exposures (75 conditions) exercising at a fixed cardiovascular load of 130 beats·min-1, in varying temperatures (15-50°C), humidities (20-80%), solar radiation (0-800 W·m-2), wind (0.2-3.5 m·s-1), and two clothing levels, were used to model the predictive power of ambient temperature, universal thermal climate index (UTCI), wet bulb globe temperature (WBGT), modified physiologically equivalent temperature (mPET), heat index, apparent temperature (AT), and wet bulb temperature (Twb) for the calculation of PWCloss, skin temperature (Tskin) and core-to-skin temperature gradient, and thermal perception (thermal sensation vote, TSV) in the heat. R2, RMSE, and Akaike information criterion were used indicating model performance. Indices not including wind/radiation in their calculation (Ta, heat index, AT, and Twb) struggled to provide consistent predictions across variables. For PWCloss and TSV, UTCI and WBGT had the highest predictive power. For Tskin, and core-to-skin temperature gradient, the physiological models UTCI and mPET worked best in seminude conditions, but clothed, AT, WBGT, and UTCI worked best. For all index predictions, Ta, vapor pressure, and Twb were shown to be the worst heat strain predictors. Although UTCI and WBGT had similar model performance using the full dataset, WBGT did not work appropriately in windy, hot-dry, conditions where WBGT predicted lower strain due to wind, whereas the empirical data, UTCI and mPET indicated that wind in fact increased the overall level of thermal strain. The findings of the current study highlight the advantages of using a physiological model-based index like UTCI when evaluating heat stress in dynamic thermal environments.NEW & NOTEWORTHY There is an urgent need to determine the optimal heat stress metric when forecasting the impact of heat stress on human performance, physiological stress, and perception. We analyzed a wealth of laboratory data, simulating heart rate (HR)-paced work with wide variations in air temperature, humidity, wind speed, solar radiation, and clothing. We conclude that the universal thermal climate index (UTCI) [followed by wet-bulb globe temperature (WBGT)] is the optimal heat index to reliably predict reductions in performance, and elevations in physiological and perceptual stress.

Spatio‐temporal analysis of snow depth and snow water equivalent in a mountainous catchment: Insights from in‐situ observations and statistical modelling

AbstractThis research, conducted in the mountainous catchment near Abant Lake in the Western Black Sea region of Türkiye, aimed to investigate the spatiotemporal variations of snow depth (SD) and snow water equivalent (SWE) throughout the snow season from December 2019 to March 2020, encompassing both accumulation and melting periods. In total, 14 snow surveys were conducted, covering 58 permanent snow measurement points (PSMP) marked with snow poles. The classification and regression tree (CART) method was employed to statistically analyse their relationships with eight variables: snow period, forest canopy, aspect, slope, elevation, slope position, plan and profile curvature. The root mean square error (RMSE) for SD and SWE was determined to be 0.15 m and 46 mm, respectively. The study findings revealed that mean SD and SWE values were higher in forest gaps compared with under‐forest and open areas. Although the snow cover disappeared earliest in under‐forest areas, the melting rate was observed to be 43% and 17% slower compared with forest gaps and open areas, respectively. Wind redistribution resulted in minimum snow accumulation on western aspects, upper slope positions and ridges, while maximum accumulation was observed on southern aspects, valleys and lower slope positions. Higher elevations (>1580 meters) experienced faster snow melting rates, leading to earlier disappearance of snow cover. PSMPs located on slopes with lower degrees (<15°) exhibited lesser accumulation and earlier snow disappearance. The CART model identified the snow period as the most significant factor in predicting SD and SWE, based on variations in snowfall and air temperature. Other significant variables included forest canopy, aspect and elevation. The study suggests that the CART method is well‐suited for modelling complex snow dynamics, providing valuable insights into spatiotemporal variations in SD and SWE in mountainous regions.

Time-dependent variations of groundwater radon: Insights from a twelve-year study in the Baikal region, East Siberia, Russia

Time-dependent variations of 222Rn concentration (Q) in groundwater have been monitored for twelve years (2012–2023) at eight sites of groundwater discharge within the Baikal region in East Siberia, Russia. The concentrations of radioactive gas at different sites vary from 30% to 60% of average values (Qav). The sampled waters are of three groups with Qav ≈ 15 Bq/l (I), ≈30 Bq/l (II1), and ≈50 Bq/l (II2). Cluster analysis shows closest linkage between the two subgroups of group II due to similarity in the discharge mechanisms. Fourier analysis of periodic 222Rn behavior reveals major cycles of 365, 180, 126, and 30 days correlated with variations of air temperature and pressure, as well as with the patterns of groundwater discharge. In addition, radon anomalies are related with seismicity. Earthquakes are reflected in the radon field as three distinct anomaly types, occurring either subsequent to or prior to the seismic event. The anomalies responding to earthquake nucleation can be considered as precursors and used in earthquake prediction. The revealed trends make basis for a model designed to predict Q variations in groundwater of the area to 80% average efficiency. The external and internal factors that affect the concentration of radioactive gas in groundwater are linked in a hierarchic system and are classified according to the degree and type of their influence.

Accounting for climate changes when constructing the Rossander graph

The temperature of the atmospheric air affects the design and indicators of energy efficiency of all elements of heat supply systems. An extremely important tool to develop heat sources that allows us to assess changes in consumer heat loads and model algorithms for heat source operation, taking into account the variability of outdoor air temperature in the geographical conditions of the design objects, is a graph of the duration of heat loads during the heating period (Rossander’s graph). It is impossible to construct the Rossander’s graph without data on the duration of the outdoor temperature standing during the heating period. In this paper, the problem of the lack of temperature data necessary for the construction of the Rossander’s graph in the current regulatory documents is revealed. The analysis of previous studies that produced temperature data with the duration of temperature standing is carried out, and the need for their refinement is substantiated. The arrays of hourly outdoor air temperatures were processed according to two weather models, as well as the array of average daily temperatures obtained as a result of measurements from the weather station for the geographical location of the main building of KNUCA. As a result of the comparative analysis, a model closer to reality was identified and, on its basis, actual data on the duration of outdoor temperatures standing for the city of Kyiv with a temperature step of 1℃ were developed. The obtained temperature data were compared with those that were developed in previous studies, as well as with the temperature data that are contained in the cancelled standard. The curve of the long-term run of the moving average outdoor air temperatures for the winter periods of 1973-2023 and its polynomial trend are constructed and compared with the curve that characterizes the run of the moving average five-year temperatures for the winter periods of 1861-1970 and its polynomial trend.

Assessing the Volatility of Daily Maximum Temperature across Germany between 1990 and 2022

Climate change causes a global rise in mean air temperature and an increased frequency of temperature extremes. Recent studies link sharp temperature changes between consecutive days to increased mortality, reduced economic growth, and negative effects on ecosystems. This study assesses the variability of the daily maximum air temperature between two consecutive days (i.e., temperature volatility) across Germany from 1990 to 2022. Using observation-based raster data of maximum daily temperature at 0.1° × 0.1° spatial resolution, we assess temperature volatility regarding: (1) magnitude, (2) seasonality, (3) directionality (day-to-day warming vs. cooling), and (4) trends. Further, we analyse land cover changes during the period and examine their correlation to extreme temperature volatility (Tve). The results show that Tve mostly occurred during spring and summer. The magnitude of Tve increased with distance to the coast north-west to south during all seasons and was highest during spring and summer (>10 °C). Overall, Tve was particularly associated with day-to-day cooling (in spring, summer, autumn), while in winter, >60% of days were associated with warming in north and central Germany. Less than 12% of Germany showed significant trends in median Tve associated with warming over the period. Significant trends included increases (>0.26 °C/year) and decreases (−0.09 °C/year) in extreme day-to-day warming during autumn and winter in northeastern Germany. In spring, the majority (>60%) of southern areas showed significant positive trends (up to 0.16 °C/year) in Tve associated with day-to-day cooling. During winter, summer, and autumn, trends in extreme day-to-day cooling were insignificant in over 80% of Germany. Within all land cover types, Tve predominantly varied between 6 °C and 9 °C. Changes in land cover, especially transforming coniferous forests for agricultural purposes, were accompanied by increasing Tve up to 0.49 °C. Understanding rapid temperature changes is crucial for climate change mitigation strategies and limiting the impacts on human health and on the environment.

Anthropogenic Changes in Interannual-to-Decadal Climate Variability in CMIP6 Multiensemble Simulations

Abstract As well as having an impact on the background state of the climate, global warming due to human activities could affect its natural oscillations and internal variability. In this study, we use four initial-condition ensembles from the CMIP6 framework to investigate the potential evolution of internal climate variability under different warming pathways for the twenty-first century. Our results suggest significant changes in natural climate variability and point to two distinct regimes driving these changes. The first is a decrease in internal variability of surface air temperature at high latitudes and all frequencies, associated with a poleward shift and the gradual disappearance of sea ice edges, which we show to be an important component of internal variability. The second is an intensification of the interannual variability of surface air temperature and precipitation at low latitudes, which appears to be associated with El Niño–Southern Oscillation (ENSO). This second regime is particularly alarming because it may contribute to making the climate more unstable and less predictable, with a significant impact on human societies and ecosystems.

Modeling pan-Arctic seasonal and interannual landfast sea ice thickness and snow depth between 1979 and 2021

ABSTRACT Landfast sea ice (LFSI) is sensitive to local climate change, making it an important component of the cryosphere system. In this study, the LFSI around the pan-Arctic domain was simulated from 1979 to 2021 using a well-validated snow and ice thermodynamic model (HIGHTSI) under the framework of the Fast Ice Prediction System (FIPS), forced by the ERA5 reanalysis. The simulation results agree well with the in-situ observations in the Canadian Arctic, with a mean error of −0.06 ± 0.29 m for ice thickness and −0.04 ± 0.12 m for snow depth. A decrease of −2.8 ± 0.4 cm/10a in thickness and −16.2 ± 1.5 km3/a in volume for the Arctic LFSI was modeled during this period. There was significant spatial variability among the different domains, with the fastest decline found in the Vilkitsky Strait. The modeled snow depth shows large interannual and spatial variations, which was confirmed by other modeling results. The spatiotemporal variations in both air temperature and precipitation are the driving factors for the multi-decadal variations in LFSI thickness. The decreasing air temperature during the 2010s aligned with a slower thickness decrease and a slight volume increase for LFSI, which agreed with the pan-Arctic sea ice pattern.

Effect of storage conditions on lignocellulose biofuels properties

This article examines the effects of different storage conditions on selected physicochemical properties of three types of agro-biomass pellets: sunflower husks, wheat straw and hemp hurds, and wood pellets. The tests were carried out in a climatic chamber, which allows simulation of real storage conditions, i.e. conditions with high air humidity and variable (±) ambient air temperatures. The results showed higher degradability of agro-biomass pellets compared to woody biomass. The pellets degraded to a less extent at varying ± temperatures than at high humidity (90% RH). After complete moisture saturation, durability decreases for agro-pellets by an average of 9%, while after freezing and defreezing for sunflower husk pellets and woody pellets durability decreases by 2%, and for hemp hurd pellets by 11%. In contrast, strength-by-dropping index for agro-pellets decreased by 20% after being in the environment (30 °C and 90%RH) and 15% under varying temperature conditions. No change in the energy parameters of all pellets in the dry matter was noted. On the other hand, an increase in the moisture content of pellets when they are stored under different environmental conditions results in a decrease in calorific value.

An arctic breeding songbird overheats during intense activity even at low air temperatures

Birds maintain some of the highest body temperatures among endothermic animals. Often deemed a selective advantage for heat tolerance, high body temperatures also limits birds’ thermal safety margin before reaching lethal levels. Recent modelling suggests that sustained effort in Arctic birds might be restricted at mild air temperatures, which may require reductions in activity to avoid overheating, with expected negative impacts on reproductive performance. We measured within-individual changes in body temperature in calm birds and then in response to an experimental increase in activity in an outdoor captive population of Arctic, cold-specialised snow buntings (Plectrophenax nivalis), exposed to naturally varying air temperatures (− 15 to 36 °C). Calm buntings exhibited a modal body temperature range from 39.9 to 42.6 °C. However, we detected a significant increase in body temperature within minutes of shifting calm birds to active flight, with strong evidence for a positive effect of air temperature on body temperature (slope = 0.04 °C/ °C). Importantly, by an ambient temperature of 9 °C, flying buntings were already generating body temperatures ≥ 45 °C, approaching the upper thermal limits of organismal performance (45–47 °C). With known limited evaporative heat dissipation capacities in these birds, our results support the recent prediction that free-living buntings operating at maximal sustainable rates will increasingly need to rely on behavioural thermoregulatory strategies to regulate body temperature, to the detriment of nestling growth and survival.

Bayesian Data Fusion for Enhanced Monitoring of Bridge Displacements Using Satellite InSAR and Topographic Techniques

This paper investigates the effectiveness of satellite Interferometric Synthetic Aperture Radar (InSAR) technology to remotely monitor the structural health of road bridges and detect possible unexpected behaviours. The study is based on an Italian multi-span prestressed concrete bridge and a dataset of eight years of X-band SAR images collected by the COSMO-SkyMed satellite constellation. The method implemented for this study is as follows. First, SAR images are analysed using the Multi-Temporal InSAR technique to extract displacement time series of reflective targets on the bridge decks. Then, the extracted displacements are interpreted to validate their consistency with the bridge's expected responses to environmental loads, such as air temperature and river water flow variations, and detect possible unexpected behaviours. Eventually, the correlation with the air temperature is investigated to identify different bridge spans based only on monitoring data. The findings from this study advance the knowledge on using satellite InSAR-based monitoring for early detection of anomalous bridge behaviours on a large scale, providing valuable insights for the maintenance and safety of critical infrastructure.

Air Temperature Projections over the Mid and Southern of Iraq during the 21th Century Under Representative Concentration Pathways (RCPs), RCP 4.5 and RCP 8.5 Scenarios

Global climate conditions and hydrologic processes have changed significantly as a result of climate change, and as long as global warming persists, extreme events will probably more frequently happen. Climate change significantly affects Iraq through variations in air temperature, environment, agriculture and water supply. This study aimed to evaluate the observed period of temperature 19 80–2023 and the Projecting future trends of air temperature for mid and southern Iraq using BNU-ESM, CCSM4, HadGEM2-AO, and MIRIOC5 climate models from CMIP5 under the RCP4.5 and RCP8.5 scenarios during the periods 2016–2035, 2046–2065, and 2081–2100. The findings verified that, in each of the two RCPs scenarios temperature would increase with different levels. the results showed that the temperature anomaly increased to +15.6 °C, and +18.8 °C under RCR4.5 and RCP8.5 in the MIRIOC5 model, especially in the last decade of the twentieth century. The areas of Iraq most impacted by high temperatures are the south and southeast (Basra, Missan, Thi-Qar, and Muthanna) provinces. In the first period, On the other hand, the less effect areas for the temperature increase were (Anbar and northeast of Diyala) +2.4°C, and +4.4°C for HadGEM2-AO model under RCR4.5 and RCP8.5 respectively. Through all periods, CCSM4, and MIRIOC5 models were the most identical in results. The findings can aid in the evaluation of future drought risks in Iraq by policymakers. Developing suitable adaptation techniques to counter act climate change can also be beneficial.