Air Temperature Research Articles

The importance of local observation wells, reanalysis, and satellite data on gravity anomaly, climate, and land use to improve groundwater management in the Urucuia aquifer system

The Urucuia Aquifer System (UAS) is an important water source for multiple users and the perennity of the San Francisco River. However, it has been subject to intensive land use transition (LUT), which provoked concerns due to continuous groundwater depletion. Therefore, understanding this depletion and potential drivers is essential for improved management towards water security. In this study, satellite data of the GRACE-project and in-situ data of the RIMAS-project were used to quantify the groundwater depletion between 2003 and 2021. Two important drivers were assessed for their impact on the UAS: (i) climate variability and (ii) LUT. The impact of climate variability was evaluated by using the vertical water balance (VWB; rainfall - evapotranspiration) on basis of validated rainfall and air temperature data from reanalysis (1979–2020). LUT was retrieved from the MAPBIOMAS database (1985–2022). The UAS was subdivided into quadrants of 0.75 × 0.75° (6700 km2) to assess the spatial variation of groundwater depletion and its drivers. Groundwater depletion was characterized by trend analysis and contrasted with the temporal behavior of VWB and LUT across the UAS. The results show a continuous decrease of the groundwater table in the study period (−22 mm/y) with a steeper decline between 2012 and 2018 (−52 mm/y). A direct cause-effect-relation can be observed with the negative trend of the VWB, pushed by decreasing rainfall and increasing air temperature over time. On the other hand, groundwater depletion correlates with LUT and is more accentuated where native vegetation is replaced by irrigated crops. Moreover, the GRACE dataset could be used for regional studies, but in-situ monitoring is recommended for local management as validation (R2 = 56%) indicates an underestimation of groundwater depletion by satellite data. In conclusion, extending the in-situ monitoring network, limiting LUT, and irrigation are crucial to long-term sustainability of the UAS.

Prediction of urban ultrafine particle emission fluxes using generalized additive models

Ultrafine particles (UFP) are abundant in urban atmospheres. To assess the strength and temporal variation of urban UFP emission sources, information on the surface-atmosphere exchange, i.e. the turbulent vertical flux of particles, is vital. A three-year time series of UFP emission fluxes (FUFP) observed at an urban site in Berlin, Germany, using the eddy covariance technique was utilized to develop and evaluate generalized additive models (GAM) for FUFP. GAM allow to account for non-linear relationships between response and predictor variables. Two separate models for summer and winter were developed. The predictors that most strongly influenced modelled FUFP in the summer model were traffic activity, friction velocity, land use, air temperature and PM10 concentration, whereas the winter model additionally incorporated relative humidity. The GAM were evaluated by ten-fold cross-validation for the first two study years, and by predicting the third year based on the model trained with observational data of the first two years. The coefficients of determination of the two validation methods were R2 = 0.52 (uncertainty of -47 to 88% for FUFP) and R2 = 0.48 (-45 to 82% for FUFP) for the winter model, whereas the summer model yielded R2 = 0.48 and 0.44 (uncertainty of -51 to 102%). GAM were shown to successfully capture the non-linear relationships between predictor variables and FUFP for the three-year data set at this urban site.

Accurate short-term GHI forecasting using a novel temporal convolutional network model

Global energy demand is on the rise, driven by factors such as population growth and economic development.Utilizing renewable energy is crucial to meeting this energy demand in light of global warming and the depletion of fossil resources.One of the renewable energy sources that is extensively utilized in multiple countries worldwide is photovoltaic energy.While integrating photovoltaic (PV) energy into the grid offers substantial economic and environmental advantages, its intermittent nature presents challenges to maintaining grid stability at high penetration levels. Accurate solar energy estimations are essential for photovoltaic (PV) based energy facilities to enable early participation in energy markets and efficient resource planning. This study proposes and evaluates a Temporal Convolutional Network (TCN) model that can rapidly predict global horizontal irradiance (GHI) using univariate and multivariate approaches. To our knowledge, this is the first study to employ a TCN model for GHI forecasting. The univariate model solely considers GHI, while the multivariate time series models incorporate a combination of six variables: global horizontal irradiance, active power of a module plant, PV module temperature, wind speed, air temperature, and air pressure. The proposed model’s effectiveness is validated by comparing its performance to benchmark models, including MLP, RNN, GRU, and LSTM. The prediction models’ performance is evaluated using root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). The results demonstrate that the effectiveness of univariate TCN models is evident in their performance for 5, 10, 15, and 20 min ahead short-term GHI forecasting, its mean absolute error (MAE) values achieved are 22.935 W/m2 33.47 W/m2, 36.9994 W/m2, and 41.9946 W/m2, respectively. The multivariate model, incorporating additional variables, yielded higher MAE values : 37.928 W/m2, 55.88 W/m2, 66.61 W/m2, and 74.82 W/m2, respectively. These results suggest that the TCN model’s capability to capture both long-term and short-term dependencies within the data contributes to its accuracy for GHI forecasting compared to other models.

Advancing personal thermal comfort prediction: A data-driven framework integrating environmental and occupant dynamics using machine learning

Various individual thermal preferences do not always lead to optimal thermal comfort, despite the high energy consumption of Heating, Ventilation, and Air Conditioning (HVAC) systems. Thermal comfort models often struggle to accurately predict occupants’ thermal sensations due to their inability to adapt to diverse scenarios. This research employs machine learning techniques to develop personalized data-driven thermal comfort models using a global database of 17,814 samples from the ASHRAE Global Thermal Comfort Database II. Specifically, the study implements random forest and k-nearest neighbor algorithms to analyze the influence of environmental factors (such as air temperature, outdoor temperature, relative humidity, and air velocity) and personal factors (including clothing insulation, activity level, and individual differences) on thermal comfort perception. The study systematically compares the predictive performance of different output types (thermal sensation vs. preference vote), input parameters (6 vs. 12), and machine learning methods. The optimized random forest model with 12 inputs demonstrated an accuracy of over 70% in predicting thermal preference votes, which was significantly higher than the 34% accuracy of the PMV model. The results present new optimized data-driven models and offer insights into the relative influence of parameters such as climate, building cooling strategies, and occupant age and gender. This research aims to enable personalized thermal comfort predictions to improve satisfaction and energy efficiency in various buildings.

Distribution, relationship, and environmental driving factors of chlorophyll-a and algal cell density: A national view of China

Chlorophyll-a (Chl-a) and algal cell density (ACD) are vital for assessing algal proliferation and eutrophication in aquatic ecosystems. Although Chl-a is often used as a proxy for ACD, its accuracy requires validation, and studies on their linear correlation are scarce. Additionally, Chl-a and ACD are influenced by various factors, including nutritional, economic, biochemical, physicochemical, and meteorological factors. However, the relative importance of these factors on Chl-a and ACD remains insufficiently studied. This study analyzed data from 57 lakes and reservoirs across China from March 2021 to February 2023, investigating the distribution patterns of Chl-a and ACD across different regions and seasons. The study employed a linear regression model to explore the correlation between Chl-a and ACD in various regions and seasons throughout China. Furthermore, Mantel test and generalized linear model were used to evaluated the relative importance of nutritional factors (total nitrogen (TN), total phosphorus (TP), TN/TP ratio (TN/TP), and ammonia nitrogen (NH3-N)), economic factors (gross domestic product), meteorological factors (surface pressure, net solar radiation, air temperature, wind speed (WS), and rainfall (RF)), as well as biochemical and physicochemical factors (turbidity (TUR), pH value, water temperature (WT), permanganate index, and dissolved oxygen) on Chl-a and ACD. Results showed that the average concentrations of Chl-a and ACD in South China were highest, at 12μg/L and 19.5 × 106 cells/L, respectively. Seasonally, Chl-a peaked in spring and was lowest in winter, while ACD peaked in summer and was lowest in winter. Significant seasonal and regional variations in the Chl-a and ACD correlation were observed, with spring showing the strongest relationship. In Central China, Chl-a and ACD were significantly correlated throughout the four seasons, whereas correlations were less distinct in Eastern and Western regions. Therefore, Chl-a as a proxy for ACD requires caution. Nutrient factors (TN, TP, TN/TP, NH3-N) were identified as the primary drivers of Chl-a and ACD. Meteorological factors (WS, RF), along with biochemical and physicochemical factors (WT, TUR) also emerged as critical predictors of Chl-a and ACD, with spatial variations. This study validates Chl-a as a proxy for ACD, analyzes their spatiotemporal distribution, and assesses the influence of various factors on Chl-a and ACD, enhancing our understanding of algal dynamics in Chinese lakes and reservoirs.

Downscaled‐GRACE Data Reveal Anthropogenic and Climate‐Induced Water Storage Decline Across the Indus Basin

AbstractGRACE (Gravity Recovery and Climate Experiment) has been widely used to evaluate terrestrial water storage (TWS) and groundwater storage (GWS). However, the coarse‐resolution of GRACE data has limited the ability to identify local vulnerabilities in water storage changes associated with climatic and anthropogenic stressors. This study employs high‐resolution (1 km2) GRACE data generated through machine learning (ML) based statistical downscaling to illuminate TWS and GWS dynamics across twenty sub‐regions in the Indus Basin. Monthly TWS and GWS anomalies obtained from a geographically weighted random forest (RFgw) model maintained good consistency with original GRACE data at the 25 km2 grid scale. The downscaled data at 1 km2 resolution illustrate the spatial heterogeneity of TWS and GWS depletion within each sub‐region. Comparison with in‐situ GWS from 2,200 monitoring wells shows that downscaling of GRACE data significantly improves agreement with in‐situ data, evidenced by higher Kling‐Gupta Efficiency (0.50–0.85) and correlation coefficients (0.60–0.95). Hotspots with the highest TWS and GWS decline rate between 2002 and 2023 were Dehli Doab (−442, −585 mm/year), BIST Doab (−367, −556 mm/year), Rajasthan (−242, −381 mm/year), and BARI (−188, −333 mm/year). Based on a general additive model, 47%–83% of the TWS decline was associated with anthropogenic stressors mainly due to increasing trends of crop sown area, water consumption, and human settlements. The decline rate of TWS and GWS anomalies was lower (i.e., −25 to −75 mm/year) in upstream sub‐regions (e.g., Yogo, Gilgit, Khurmong, Kabul) where climatic factors (downward shortwave radiations, air temperature, and sea surface temperature) explained 72%–91% of TWS/GWS changes. The relative influences of climatic and anthropogenic stressors varied across sub‐regions, underscoring the complex interplay of natural‐human activities in the basin. These findings inform place‐based water resource management in the Indus Basin by advancing the understanding of local vulnerabilities.

Integrating Machine Learning for Improved Prediction of Temperature and Moisture in Pavement Granular Layers

Abstract Pavement temperature and moisture content within the base and subgrade layers affect the load-bearing capacity of the pavement and dominate the pavement performance in cold regions. Accurately predicting pavement temperature and moisture content can improve pavement design and management. Conventional approaches, including numerical and statistical models, have been implemented to predict pavement temperature and soil moisture content. However, they have weaknesses, such as being only suitable for warm regions or only for predicting pavement temperature within the asphalt layer. Furthermore, none of them can simultaneously predict the pavement temperature and moisture content. To address this issue, data collected from an instrumented test road in Alberta, Canada, were used to train a model to predict the daily average pavement temperature and moisture content at various depths through three parameters, namely depth, day of the year, and air temperature. The MATLAB toolbox, Neural Net Fitting, was used, and the performance of three built-in algorithms, Levenberg–Marquardt, Bayesian regularization, and Scaled conjugate gradient backpropagation, was compared. The model with Bayesian regularization showed the highest accuracy, with an R2 value of 0.99 and a root mean square error (RMSE) of 1.49°C for pavement temperature prediction, and an R2 value of 0.95 and an RMSE of 0.025 m3/m3 for moisture content prediction. The model developed in this research is the first to simultaneously estimate pavement temperature and moisture content, so its performance was separately compared with two existing models in the literature. The artificial neural network (ANN) model shows higher accuracy than the two existing models, so it was found that the ANN could be a robust method for pavement temperature and moisture content prediction at various depths.

Tick-Borne Relapsing Fever Infection from a Bunker, a Case Report on a "One Health" Approach.

Background: Tick-borne relapsing fever (TBRF) caused by Borrelia persica is an endemic disease in Israel and highly prevalent in military personnel. Prevention among the Israel Defense Force soldiers is based on increased awareness mainly in hyperendemic areas and selective postexposure prophylaxis with doxycycline. In this study, we report the presence of a suspected outbreak of TBRF in four soldiers who spent 30 h inside a deserted bunker. Materials and Methods: Clinical data on TBRF suspected cases were retrieved from clinical records, soft ticks were collected using carbon dioxide (CO2) traps and their DNA was extracted and analysed by PCR and nucleotide sequencing. Environmental conditions such as relative humidity, air temperature, wind speed, and type of soil, as well as presence or absence of animal traces inside the bunkers were documented. Results: TBRF-like clinical symptoms in the patients included: tick bite scars, fever (37.5-39.2°C), rash, tachycardia, hypotension, myalgia, cough, headache, cervical lymphadenopathy and nausea. Microscopic search for B. persica in blood smears was performed in three patients and was negative. Out of the 255 Ornithodoros tholozani ticks collected from the bunker, 198 were analyzed and 2 (1%) were infected with B. persica. To determine if tick infestation in military bunkers is a common phenomenon, we surveyed nine additional military bunkers located in four different geographical areas for the presence of soft ticks. Only one additional bunker was infested with two O. tholozani ticks, both negative for B. persica. Presence of earth that probably helped sustain a relatively big tick population was observed on the floor in the highly infested bunker. Environmental treatment with lambda-cyhalothrin at 9.7% was performed and showed efficacy with no ticks recovered in the infested bunker 124 days after intervention. Conclusion: This study shows that military bunkers may harbor soft ticks infected with B. persica and entrance into bunkers should be considered as a risk for acquiring this infection like entrance into natural caves and archeological ruins.

Cooling performance study of a new cooling system in subway tunnel based on field measurement and CFD simulation

Abstract Some cities’ subways were constructed early and have been in operation for a long time. A large amount of heat accumulates in the rocks around the subway tunnels, causing the phenomenon of heat accumulation. This situation leads to the inadequate cooling capability of train air-conditioning systems, which, may even cease to function under extreme conditions. Currently, few solutions are available to address this issue. Therefore, this study proposes a new cooling system in subway tunnel. Considering the dusty environment inside the tunnel, the terminal equipment mainly consists of natural convection copper tube finless heat exchangers and a self-flushing device without fans, which cool using piston wind. By comparing field measurements of two tunnels with and without the cooling system in similar locations, the results show that the air temperature in the tunnels is reduced after the cooling system is installed. The results indicate that the average temperature in the tunnels decreases from 30.93 °C to 19.80 °C, marking a reduction of 11.13 °C after the cooling system runs for 24 hours. The temperature change in the tunnel is a long-term process, and actual measurements require significant time consumption. In this study, the long-term effect is predicted using CFD simulation in tunnels. The accuracy and credibility of the CFD simulation have been confirmed through its reasonable agreement with experimental data, with the final temperature after 24 hours achieving a relative error of less than 0.26%. Through the simulation, the temperature at a depth of 10 cm inside the tunnel wall after 24 hours is determined to be 27.56 °C, indicating a reduction of 3.44 °C compared to the initial temperature of 31 °C. This study can provide a reference for other subway tunnel cooling systems and serves as a basis for CFD simulations to verify cooling effects.

The impact of weather Condition Changes on Vertical Distribution of Sulfides in Lake Maninjau Based on Observation Data

Sulfide is a crucial parameter in volcanic lakes, as its levels and fluctuations in the lake determine the origin of sulfide and the extent of its impact on the lake ecosystem. In stratified lakes, the sulfide produced tends to be retained beneath the oxic layer. The sulfides rise towards the surface as the oxic layer thins triggered by decreased water column thermal stratification. Meanwhile, the strength or weakness of thermal stratification is greatly influenced by weather conditions. Lake Maninjau is a volcanic lake with a relatively high sulfide content. Its vertical distribution in the water column is highly dependent on the stratification of the water column. When stratification disappears, sulfide rises to the surface (locally known as tubo belerang) and has a negative impact on surface biota. The objective of this study is to examine the distribution of sulfides in the water column of Lake Maninjau under two different weather conditions. We perform two surveys to measure physicochemical parameters and sulfide concentration on 26‒29 November 2022 and 25‒26 August 2023 considering the seasonal pattern. We found that air temperatures and sunshine duration combined with precipitation and wind speed drive the thermal stratification of the water column. The lower air temperature, shorter sunshine duration, higher precipitation, and stronger wind speed in the first survey (west monsoon) compared with the second survey (east monsoon) resulted in lower stratification and triggered the elevated sulfide to the surface. In the middle of the lake, the surface sulfide measured during the first survey was 4.16 µg/L. Meanwhile, in the second survey, it was only observed at 1.16 µg/L. The distribution of sulfides within the water column of Lake Maninjau is regulated by the stratification of the water column, a process directly impacted by weather conditions.

Revolutionizing indoor air quality monitoring through IoT innovations: a comprehensive systematic review and bibliometric analysis.

Indoor air quality (IAQ) in the built environment is significantly influenced by particulate matter, volatile organic compounds, and air temperature. Recently, the Internet of Things (IoT) has been integrated to improve IAQ and safeguard human health, comfort, and productivity. This review seeks to highlight the potential of IoT integration for monitoring IAQ. Additionally, the paper details progress by researchers in developing IoT/mobile applications for IAQ monitoring, and their transformative impact in smart building, healthcare, predictive maintenance, and real-time data analysis systems. It also outlines the persistent challenges (e.g., data privacy, security, and user acceptability), hampering effective IoT implementation for IAQ monitoring. Lastly, the global developments and research landscape on IoT for IAQ monitoring were examined through bibliometric analysis (BA) of 106 publications indexed in Web of Science from 2015 to 2022. BA revealed the most significant contributing countries are India and Portugal, while the top productive institutions and researchers are Instituto Politecnico da Guarda (10.37% of TP) and Marques Goncalo (15.09% of TP), respectively. Keyword analysis revealed four major research themes: IoT, pollution, monitoring, and health. Overall, this paper provides significant insights for identifying prospective collaborators, benchmark publications, strategic funding, and institutions for future IoT-IAQ researchers.

Evaluating impacts of climate and management on reservoir water quality using environmental fluid dynamics code

Climate change and human interference, notably nutrient input, affect the water quality. Nitrogen (N) and phosphorus (P) are pivotal in managing eutrophication. This study investigated the effects of water dynamics and chemical constituents on water quality in Hongfeng Lake, a typical weakly stratified reservoir suffering from algae blooms in Southwest China, using the Environmental Fluid Dynamics Code. Leveraging climate, hydrological, and water quality data, we constructed, calibrated, and validated the temperature-hydrodynamics-water quality-sediment model. Various scenarios were analyzed, including wind speed, air temperature, solar radiation, rainfall, water discharge, N and P external input, and internal release. The findings revealed that no rain and warming increased trophic state index (TSI) and chlorophyll-a (Chl-a) concentration, and no solar radiation initially elevated nitrate concentration, followed by an increase in ammonium concentration. Besides, no solar radiation and changes in rainfall significantly increased total phosphate concentration. The management scenarios of N and P reduction, halving tributary, and mainstream flow scenarios improved water quality and reduced eutrophication. The wind speed under the N and P reduced scenarios showed that a doubling in wind led to increased concentrations of the particulate organic matter, Chl-a, and dissolved oxygen, alongside decreased ammonium and nitrate, while TSI exhibited minimal change. However, 5- and 10-times wind speed scenarios amplified TSI in shallow water, potentially due to a substantial rise in internal nutrient release. The degradation trend observed in drinking water quality amid climate change (warming and flooding) raises concerns regarding health-related risks. These simulations provided the quantified influence of climate change and environmental management strategies on water quality in the weakly stratified reservoir, notably highlighting the looming threat of exacerbated eutrophication due to warming, necessitating more stringent N and P reduction measures compared to current practices.

Effects of massive desiccation of olive waste residues on air quality

New industries are proliferating in the recovery of agri-food wastes, such as those involved in the revaluation of alperujo, generated in the production of olive oil. Despite the potential environmental benefits, their activity is not exempt from new forms of emissions, aggravated by the massification of waste treatments.This work reports a six-month field campaign carried out in an alperujo desiccation plant which can serve as a proxy for these emerging industries in the Mediterranean countries. The study focused on air quality parameters, covering criteria pollutants, metals and microbiological load of particulate matter and the characterization of volatile organic compounds (VOCs).The results show a slight contribution of the factory to the NOx levels in the surroundings (3.0–12.5 μg/m3). Statistically significant effects were not observed for ozone, CO, SO2, or PM10. Concerning the levels of metals, concentrations were low and calculated health risk indexes indicated safe conditions in the area. The most abundant elements were Na (6.5 × 102 ng/m3), K (4.0 × 102 ng/m3), Al (2.7 × 102 ng/m3), Zn (2.1 × 102 ng/m3), Ca (2.16 × 102 ng/m3), Fe (3.6 × 101 ng/m3) and Mg (3.2 × 101 ng/m3).Bacterial counts, with a mean value of 15.9 CFU/m3, showed a seasonal shift, mainly explained by weather (air moisture and temperature) and PM2.5 concentration. The genomic analysis showed Cutibacterium as the dominant genus during the cold months while Bacillus predominated in the warm season.The VOCs with higher average concentrations were acetic acid (130 μg/m3), nonanoic acid (124 μg/m3), benzoic acid (29.7 μg/m3), octanoic acid (19.9 μg/m3) and nonanal (4.70 μg/m3), with the rest of compounds in concentrations below 4 μg/m3. Odorant pollutants with the greatest contribution to olfactory nuisance were aldehydes (from pentanaldehyde to decanaldehyde), acetic acid and phenol.Although the observable effects of the waste treatments were low, several parameters showed an influence on the environment which should be assessed to foresee and prevent long-term consequences.

Human-safe and economic operation of renewable hydrogen-based microgrids under plateau conditions

The cold and hypoxic environment threatens the health and quality of people's lives in plateau areas. Appropriate air pressure, oxygen, and temperature are crucial for human-safe aspects in plateau microgrids. Facing extreme scenarios in these areas, we propose a two-stage robust operation strategy for renewable hydrogen-based microgrids considering the reliable supply of hydrogen and oxygen. To cope with rapid weather changes in these areas, the distributionally robust optimization (DRO) model that incorporates first-moment information is employed to depict renewable energy uncertainty. This study focuses on two weakly connected microgrids with distinct characteristics, showcasing their potential for mutual support. In the first stage, the plateau microgrid fulfills demands for electric power, hydrogen, and oxygen, with the power support from the another microgrid. In the second stage, reserves such as hydrogen fuel cells, diesel generators, and hydropower are utilized to mitigate renewable energy uncertainty. The operation optimization problem based on DRO is solved using the alternating direction method of multipliers. Case studies show that the strategy can coordinate complementary operation to enhance reliability, guarantee human safety, and save costs by 39.29 % and 38.73 % for the two microgrids.

Feedback-linearization decoupling based coordinated control of air supply and thermal management for vehicular fuel cell system

The coordinated control of the coupled system of air supply and thermal management is one of the important ways to improve the efficiency of proton exchange membrane fuel cell (PEMFC) system. The novelty of this paper is to design a feedback-linearization decoupling based coordinated controller combining with active disturbance rejection control (ADRC) method. Aiming at strong coupling of air supply and thermal management system, we have proposed a three-dimensional particle swarm coordinated optimization algorithm to calculate the steady-state reference, then, a feedback-linearization decoupling controller is designed to achieve independent control of air flow, cathode pressure and temperature. Besides, aiming at time-varying, nonlinear interference, we have designed an ADRC based on the decoupled method to achieve better dynamic response and stability. The current step load test reveals that the overshoot and the average tracking error are much smaller under the air flow-pressure-temperature decoupling coordinated controller compared that under the proportional-integral-derivative (PID) controller and the air flow-pressure decoupling controller. The test results under different ambient temperatures indicate that the proposed coordinated strategy reduces the tracking error by over 10 %, the efficiency of fuel cell system is increased by 3.5%–4.5 %.

Changes in urban heat island intensity during heatwaves in China

With rising occurrence of heatwaves and ongoing urban expansion, urban residents are facing severer heat-related stress under the combined effects of urban heat island (UHI) and heatwaves. Controversial results, however, have been reported regarding whether the UHI is exacerbated during heatwaves. In this study, we used fused ground and satellite daily maximum air temperature data to evaluate the variation of UHI intensities under heatwaves across 225 major cities in mainland China during 2003–2020. Overall, urban areas showed an enhanced UHI intensity of up to 0.94 °C during heatwaves, nearly double compared to normal periods. The interaction between UHIs and heatwaves was sensitive to local background precipitation. Under the similar urbanization and vegetation greenness, the amplified warming in urban areas during heatwaves was more pronounced in wet climates. In megapolitan regions characterized by continuous urban development, the UHI intensified much stronger during heatwaves due to the heat accumulation within urbanized areas and the advection of heat from neighboring cities. Our results contribute to understanding the interactions between UHIs and heatwaves which may strongly increase heat risk in cities. Further work on the variations of this interaction under future warming and consequent impacts on human health and energy use is needed.

Assessing the impacts of El Niño events on the Ecuadorian territory: a computational analysis of climatologies, anomalies, and deprivation index.

Abstract This study conducts a comprehensive computational analysis to assess the climatological and socio-economic impacts of El Niño events on Ecuador, employing a detailed examination of climatologies, anomalies, and deprivation indices from 1980 to 2023. We identify significant climatic shifts and implications on regional vulnerabilities by utilizing the Essential Climate Variables dataset, alongside the Oceanic Niño Index (ONI) and the Niño 1+2 index for sea surface temperature anomalies. Additionally, the Global Gridded Relative Deprivation Index (GRDI) is integrated to evaluate socio-economic impacts, highlighting areas of heightened deprivation. Notably, regions such as Chimborazo, Cotopaxi, Azuay, Tungurahua, and Bolívar emerge as significantly vulnerable, with pronounced susceptibility to temperature changes and precipitation anomalies. Several statistical methods, including Pearson Correlation, Spearman Rank Correlation, and Kendall’s Tau are employed to establish robust causal relationships between El Niño indices and climatic anomalies. The application of Convergent Cross Mapping (CCM) provides deeper insights, particularly demonstrating causality from ONI to air temperature anomalies and from the Niño 1+2 index to precipitation anomalies. The findings of this study may suggest targeted strategies for local adaptation and resilience, contributing to a broader understanding of global climate variability.

Performance of tube heat pipe solar collector with phase change material for seawater desalination system

Abstract Unlimited seawater resources resulted for utilization of desalination systems using seawater treatment process emerges as a promising technology for addressing the continually escalating need for freshwater. At present, the most notable desalination processes that are reverse osmosis, membrane distillation, multistage desalination, multiple-effect distillation, and electrodialysis, require energy generated by fossil fuels to obtain fresh water. Among the noteworthy sources of renewable energy, solar energy stands out with its manifold applications. The use of solar energy has strong advantages, such as a low maintenance and operation costs. In this study, a novel solar desalination system is introduced, which integrates tube heat pipe solar collector (THP-SC) equipped with phase change material (PCM). The aim of this research is to evaluate the performance of THP-SC equipped with PCM and without PCM. The feedwater sample used is water. Parameters measured for 24 hours were temperature, solar radiation, ambient air temperature, relative humidity, and wind speed. The greatest recorded solar radiation at noon (11.52 a.m.) is 900 W/m2 while the maximum recorded ambient temperature at 12.45 p.m. is 35.2°C. The experimental study showed that from morning to afternoon (06.00-15.00), the temperature of the evaporator section of the heat pipe on the THP-SC without PCM (46.70-56.21°C) was higher than the temperature of the evaporator section of the heat pipe on the THP-SC with PCM (33.18-44.43°C). This is because solar radiation will heat the PCM first before heating the heat pipe. PCM can store heat energy and release it at night. This can be seen from the water temperature in THP-SC with PCM (28.39-36.03) which is higher than the water temperature in THP-SC without PCM (27.17-34.01) at night, but the temperature difference is not significant. This can be caused by the large amount of heat lost to the environment in THP-SC with PCM, it is best to coat the heat pipe tube with insulation and create a vacuum to reduce heat loss.

Torpor use in the wild by one of the world's largest bats.

Torpor is widespread among bats presumably because most species are small, and torpor greatly reduces their high mass-specific resting energy expenditure, especially in the cold. Torpor has not been recorded in any bat species larger than 50 g, yet in theory could be beneficial even in the world's largest bats (flying-foxes; Pteropus spp.) that are exposed to adverse environmental conditions causing energy bottlenecks. We used temperature telemetry to measure body temperature in wild-living adult male grey-headed flying-foxes (P. poliocephalus; 799 g) during winter in southern Australia. We found that all individuals used torpor while day-roosting, with minimum body temperature reaching 27°C. Torpor was recorded following a period of cool, wet and windy weather, and on a day with the coldest maximum air temperature, suggesting it is an adaptation to reduce energy expenditure during periods of increased thermoregulatory costs and depleted body energy stores. A capacity for torpor among flying-foxes has implications for understanding their distribution, behavioural ecology and life history. Furthermore, our discovery increases the body mass of bats known to use torpor by more than tenfold and extends the documented use of this energy-saving strategy under wild conditions to all bat superfamilies, with implications for the evolutionary maintenance of torpor among bats and other mammals.

Assessment of various microwave brightness temperature products and methods for surface melt detection over Greenland ice sheet

Microwave brightness temperature have widely been used for the detection of the ice sheet's surface melt conditions and understanding their spatio-temporal variability. In this study, we investigated the sensitivity of microwave brightness temperature products from three different sensors, namely, Advanced Microwave Scanning Radiometer-2 (AMSR2), Special Sensor Microwave Imager/Sounder (SSMIS) and Indian scatterometer satellite (SCATSAT-1) for surface melt detection over the Greenland ice sheet (GrIS). In-situ air temperature measurements from GC-Net AWSs were used for sensitivity and inter-comparison analysis. Our findings show that brightness temperature (Tb) from SSMIS better correlates (Pcoef = 0.81 for 19 GHz) with air temperature measurements in comparison to AMSR2 (Pcoef = 0.7 for 18 GHz) and SCATSAT-1 (Pcoef = 0.67). However, interestingly, AMSR2 and SCATSAT-1 uniquely discriminated the surface conditions during pre- and post-melt period, due to their heterogeneity in Tb values during the two period. Error analysis with respect to AWS melt days shows that SSMIS 19 GHz Tb (Tb/SSMIS/19H) products with TED method giving the most promising observations. A wide variability in Tb values is observed during the melt season across the various AWS sites depending upon the elevation, location and frequency. During our study period, using Tb/SSMIS/19H for TED method, we observed the highest melt extent area for the extreme melt event in year 2019 (∼1.12 million km2), followed by another melt event year 2021 when it went as high as ∼1.02 million km2.