Search Results (90)

Climate change is jeopardizing global food security, with at least 713 million people facing hunger. To face this challenge, legumes as common beans could offer a nature-based solution, sourcing nutrients and dietary fiber, especially for rural communities in Latin America and Africa. However, since common beans are generally heat and drought susceptible, it is imperative to speed up their molecular introgressive adaptive breeding so that they can be cultivated in regions affected by extreme weather. Therefore, this study aimed to couple an advanced panel of common bean (Phaseolus vulgaris L.) × tolerant Tepary bean (P. acutifolius A. Gray) interspecific lines with Bayesian regression algorithms to forecast adaptation to the humid and dry sub-regions at the Caribbean coast of Colombia, where the common bean typically exhibits maladaptation to extreme heat waves. A total of 87 advanced lines with hybrid ancestries were successfully bred, surpassing the interspecific incompatibilities. This hybrid panel was genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Three yield components (yield per plant, and number of seeds and pods) and two biomass variables (vegetative and seed biomass) were recorded for each genotype and inputted in several Bayesian regression models to identify the top genotypes with the best genetic breeding values across three localities on the Colombian coast. We comparatively analyzed several regression approaches, and the model with the best performance for all traits and localities was BayesC. Also, we compared the utilization of all markers and only those determined as associated by a priori genome-wide association studies (GWAS) models. Better prediction ability with the complete SNP set was indicative of missing heritability as part of GWAS reconstructions. Furthermore, optimal SNP sets per trait and locality were determined as per the top 500 most explicative markers according to their β regression effects. These 500 SNPs, on average, overlapped in 5.24% across localities, which reinforced the locality-dependent nature of polygenic adaptation. Finally, we retrieved the genomic estimated breeding values (GEBVs) and selected the top 10 genotypes for each trait and locality as part of a recommendation scheme targeting narrow adaption in the Caribbean. After validation in field conditions and for screening stability, candidate genotypes and SNPs may be used in further introgressive breeding cycles for adaptation. Full article

Global warming and the increasing frequency and intensity of heat waves are resulting in more frequent unfavourable weather conditions for outdoor activities, especially during the summer. The building environment can alter weather conditions, resulting in higher temperatures (urban heat island). Human towers are cultural activities that typically take place outdoors and were declared a UNESCO Intangible Cultural Heritage in 2010. The objectives of this study are (i) to analyse the weather conditions (i.e., temperature and relative humidity) during the human tower exhibitions, (ii) to determine discomfort during the exhibitions based on the heat index (HI) resulting from the combination of temperature and humidity, and (iii) to compare records from the square with those recorded in the nearest automatic meteorological station (AMS) belonging to the Catalan Meteorological Service network. To determine the weather conditions in the squares during the human tower exhibitions, a pair of sensors recorded temperature and relative humidity data in six exhibitions performed in the summer of 2024. The temperature exceeded 30 °C in five of the six human tower exhibitions analysed. In the cases of the Santa Anna exhibition (El Vendrell) and the Sant Fèlix exhibition (Vilafranca del Penedès), one of the sensors recorded temperatures above 30 °C throughout the entire duration of the exhibition. There was a predominance of HI values falling within the caution threshold in the two sensors of three exhibitions and within the extreme caution threshold in the two sensors of the other three exhibitions. The temperature is higher in urban squares than in the surrounding rural areas. The key factor is the urban heat island phenomenon, which poses health risks to both human tower builders and attendees. Adaptation measures are therefore necessary to guarantee the safety of the participants. Full article

Low-cost sensors (LCSs) emerge as a popular tool for urban micro-climate studies by offering dense observational coverage. This study evaluates the performance of PurpleAir (PA) sensors for ambient temperature monitoring—a key but underexplored aspect of their use. While widely used for particulate matter, PA sensors’ temperature data remain underutilized and lack thorough validation. For the first time, this research evaluates their accuracy by comparing PA temperature measurements with collocated high-precision temperature data loggers across a dense urban network in a humid subtropical U.S. county. Results show a moderate correlation with reference data (r = 0.86) but an average overestimation of 3.77 °C, indicating PA sensors are better suited for identifying temperature trends but not for precise applications like extreme heat events. We also developed and compared eight calibration methods to create a replicable model using readily available crowdsourced data. The best-performing model reduced RMSE and MAE by 51% and 47%, respectively, and achieved an R² of 0.89 compared to the uncalibrated scenario. Finally, the practical application of PA temperature data for identifying heat wave events was investigated, including an assessment of associated uncertainties. In sum, this work provides a crucial evaluation of PA’s temperature monitoring capabilities, offering a pathway for improved heat mapping, multi-hazard vulnerability assessments, and public health interventions in the development of climate-resilient cities. Full article

Extreme weather events and rising temperatures pose significant risks, not only in urban areas but also in metropolitan forests, that affect the well-being of the people who visit them. City forests are considered one of the best bets for mitigating high temperatures within civic areas. Such areas modulate microclimates in contemporary cities, offering environmental, social, and economic advantages. Therefore, comprehending the intricate relationships between municipal forests and the climatic changes of various destinations is crucial for attaining healthier and more sustainable city environments for people. In this research, the thermal comfort index (Modified Temperature–Humidity Index (MTHI)) has been analysed using Landsat images of six urban forests in London during July 2022, when the area first experienced record-breaking temperatures of over 40 °C. Our results show a significant growth in the MTHI that goes from 2.5 (slightly hot) under normal conditions to 3.4 (hot) during the heat wave period. This situation intensifies the environmental discomfort for visitors and highlights the necessity to enhance their adaptability to future temperature increases. In turn, it was found that the places most affected by heat waves are those that have grass cover or that have small associated buildings. Conversely, forested regions or those with lakes and/or ponds exhibit lower temperatures, which results in enhanced resilience. These findings are noteworthy in their concentration on one of the UK’s most severe heat waves and illustrate the efficacy of integrating spectral measurements with statistical analyses to formulate customized regional initiatives. Therefore, the results reported will allow the implementation of new planning and adaptation policies such as incorporating thermal comfort into planning processes, improving green and blue amenities, increasing tree densities that are resilient to rising temperatures, and increasing environmental comfort conditions in metropolitan forests. Finally, the applicability of this approach in similar urban contexts is highlighted. Full article

A total of 108 maximum Kolmogorov entropy (S_K) values, calculated by means of chaos theory, are obtained from 108 time series (TSs) (each consisting of 28,463 hourly data points). The total TSs are divided into 54 urban meteorological (temperature (T), relative humidity (RH) and wind speed magnitude (WS)) and 54 pollutants (PM₁₀, PM_2.5 and CO). The measurement locations (6) are located at different heights and the data recording was carried out in three periods, 2010–2013, 2017–2020 and 2019–2022, which determines a total of 3,074,004 data points. For each location, the sum of the maximum entropies of urban meteorology and the sum of maximum entropies of pollutants, S_{K, MV} and S_{K, P}, are calculated and plotted against h, generating six different curves for each of the three data-recording periods. The tangent of each figure is determined and multiplied by the average temperature value of each location according to the period, obtaining, in a first approximation, the magnitude of the entropic forces associated with urban meteorology (F_{K, MV}) and pollutants (F_{K, P}), respectively. It is verified that all the time series have a fractal dimension, and that the fractal dimension of the pollutants shows growth towards the most recent period. The entropic dynamics of pollutants is more dominant with respect to the dynamics of urban meteorology. It is found that this greater influence favors subdiffusion processes (α < 1), which is consistent with a geographic basin with lower atmospheric resilience. By applying a heavy-tailed probability density analysis, it is shown that atmospheric pollution states are more likely, generating an extreme environment that favors the growth of respiratory diseases and low relative humidity, makes heat islands more stable over time, and strengthens heat waves. Full article

Cyclone Mocha, classified as an Extremely Severe Cyclonic Storm (ESCS), followed an unusual northeastward trajectory while exhibiting a well-defined eyewall structure. It experienced rapid intensification (RI) before making landfall along the Myanmar coast. It caused heavy rainfall (~90 mm) and gusty winds (~115 knots) over the coastal regions of Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) countries, such as the coasts of Bangladesh and Myanmar. The factors responsible for the RI of the cyclone in lower latitudes, such as sea surface temperature (SST), tropical cyclone heat potential (TCHP), vertical wind shear (VWS), and mid-tropospheric moisture content, are studied using the National Ocean and Atmospheric Administration (NOAA) SST and National Center for Medium-Range Weather Forecasting (NCMRWF) Unified Model (NCUM) global analysis. The results show that SST and TCHP values of 30 °C and 100 (KJ cm⁻²) over the Bay of Bengal (BoB) favored cyclogenesis. However, a VWS (ms⁻¹) and relative humidity (RH; %) within the range of 10 ms⁻¹ and >70% also provided a conducive environment for the low-pressure system to transform into the ESCS category. The physical mechanism of RI and recurvature of the Mocha cyclone have been investigated using forecast products and compared with Cooperative Institute for Research in the Atmosphere (CIRA) and Indian Meteorological Department (IMD) satellite observations. The key results indicate that a dry air intrusion associated with a series of troughs and ridges at a 500 hPa level due to the western disturbance (WD) during that time was very active over the northern part of India and adjoining Pakistan, which brought north-westerlies at the 200 hPa level. The existence of troughs at 500 and 200 hPa levels are significantly associated with a Rossby wave pattern over the mid-latitude that creates the baroclinic zone and favorable for the recurvature and RI of Mocha cyclone clearly represented in the NCUM analysis. Moreover the Q-vector analysis and steering flow (SF) emphasize the vertical motion and recurvature of the Mocha cyclone so as to move in a northeast direction, and this has been reasonably well represented by the NCUM model analysis and the 24, 7-, and 120 h forecasts. Additionally, a quantitative assessment of the system indicates that the model forecasts of TC tracks have an error of 50, 70, and 100 km in 24, 72, and 120 h lead times. Thus, this case study underscores the capability of the NCUM model in representing the physical mechanisms behind the recurving and RI over the BoB. Full article

Sorption isotherms enable postharvest technologists to estimate the degree and rate of drying of agricultural produce. They are also useful in the design and operation of desiccant systems that are used to condition air. However, the published data on sorption isotherms contain several inconsistencies. For example, under the conditions considered in this work, it is shown that the widely cited Chung–Pfost isotherm predicts moisture contents of canola that are less than zero as the relative humidity tends to zero. Furthermore, it is shown that a long-established form of empirical expression appears to grossly overestimate the differential heat of wetting, hence the integral heat of wetting of canola. In this work, algebraic expressions are derived that enable the relationship between the forms of isotherm equations on the speed of drying to be calculated. Prima facie, it is anticipated the heat of adsorption will augment the speed of temperature waves through beds of drying canola. However, it is found that this may not be the case. Anomalies in published isotherms for agricultural produce reinforce the need for accurate psychometric data to be measured over a wide range of temperatures and relative humidities. Full article

Wetlands play a vital role in ecosystem sustainability by regulating atmospheric temperature and enhancing human comfort levels. This study aims to evaluate the temperature regulation function of the Nawabganj Wetland, Uttar Pradesh (India), a Ramsar site designated in January 2020, located in a semi-arid region vulnerable to increasing heat waves. The primary objective is to assess the wetland’s influence on microclimatic conditions and human thermal comfort across different seasons. Field surveys were conducted to collect temperature, humidity, wind speed, and vegetation data over three consecutive days in each season: 15–17 May 2019 (pre-monsoon), 12–14 August 2019 (monsoon), and 5–7 October 2019 (post-monsoon). The human comfort index was calculated using field data, while vegetation density and frequency were analyzed based on seasonal variations using the quadrant method. The results indicate that the wetland significantly contributes to local temperature reduction and improved comfort levels. Vegetation plays a crucial role in amplifying this cooling effect, particularly during summer when temperatures range from an average low of 23 °C to a high of 40 °C. In winter, temperatures vary between an average low of 6 °C and a high of 22 °C, with a consistently high humidity level of approximately 94%, further influencing microclimatic conditions. The extent of weed cover varied between 10% and 60% from December to May, reflecting seasonal fluctuations in water levels and wetland health. The study highlights the necessity of effective water and vegetation management, especially during summer, to sustain the wetland’s cooling capacity. Integrating wetland-based strategies into urban planning can enhance environmental sustainability, mitigate climate extremes, and improve human well-being in rapidly urbanizing regions. Full article

Ambient air pollution is the most important environmental factor impacting human health. Urban landscapes present unique air quality challenges, which are compounded by climate change adaptation challenges, as air pollutants can also be affected by the urban heat island effect, amplifying the deleterious effects on health. Nature-based solutions have shown potential for alleviating environmental stressors, including air pollution and heat wave abatement. However, such solutions must be designed in order to maximize mitigation and not inadvertently increase pollutant exposure. This study aims to demonstrate potential applications of nature-based solutions in urban environments for climate stressors and air pollution mitigation by analyzing two distinct scenarios with and without green infrastructure. Utilizing low-cost sensors, we examine the relationship between green infrastructure and a series of environmental parameters. While previous studies have investigated green infrastructure and air quality mitigation, our study employs low-cost sensors in tropical urban environments. Through this novel approach, we are able to obtain highly localized data that demonstrates this mitigating relationship. In this study, as a part of the NERC-FAPESP-funded GreenCities project, four low-cost sensors were validated through laboratory testing and then deployed in two locations in São Paulo, Brazil: one large, heavily forested park (CIENTEC) and one small park surrounded by densely built areas (FSP). At each site, one sensor was located in a vegetated area (Park sensor) and one near the roadside (Road sensor). The locations selected allow for a comparison of built versus green and blue areas. Lidar data were used to characterize the profile of each site based on surrounding vegetation and building area. Distance and class of the closest roadways were also measured for each sensor location. These profiles are analyzed against the data obtained through the low-cost sensors, considering both meteorological (temperature, humidity and pressure) and particulate matter (PM₁, PM_2.5 and PM₁₀) parameters. Particulate matter concentrations were lower for the sensors located within the forest site. At both sites, the road sensors showed higher concentrations during the daytime period. These results further reinforce the capabilities of green–blue–gray infrastructure (GBGI) tools to reduce exposure to air pollution and climate stressors, while also showing the importance of their design to ensure maximum benefits. The findings can inform decision-makers in designing more resilient cities, especially in low-and middle-income settings. Full article

The continuous understanding of extreme weather events in the Amazon is fundamental due to the importance of this biome for the regional and planetary climate system. Climate characterization and the identification of changes in the current climate can be key findings for adaptation and mitigation measures. This study examined climatology and trends in 20 climate extreme indices associated with air temperature and precipitation in the Brazilian Legal Amazon (BLA). Daily observed data, interpolated at grid points, were analyzed from 1961 to 2020. Statistical tests were employed to determine the trend’s significance and magnitude. The results indicate that prolonged heat, hot days, and annual temperature records have become increasingly frequent in practically all of BLA over the last decades. Warm days and nights are increasing at approximately +11 days/decade. Heat waves have gone from 10 to 20 consecutive days on average in the 1960s to around 30–40 days in recent years. Indices associated with the intensity and frequency of extreme precipitation show a reduction, especially in the rainiest portion of the BLA, the western sector. In the east/south region of BLA, where consecutive dry days reach 100 days/year, they continue to increase at a rate of +1.5 days/decade, a fact related to the delay at the beginning of the rainy season. These aspects deserve attention since they impact local circulation, reducing the convergence of humidity not only over the BLA but also in central-southern region of Brazil. Full article

Indoor and outdoor heat stress, which can appear during warm periods of the year, often has a negative impact on health and reduces productivity at work and study. Intense heat waves (HWs) are causing increasing rates of morbidity and mortality. This study aimed to analyze the coupling and delay of indoor and outdoor heat stress during HW events, using the example of ten workplaces (WPs) situated in different offices and buildings in the medium-sized city of Freiburg, Germany. The relationships between air temperature, humidity, and thermal stress intensity in the WPs were explored during HW periods. It was found that the level of thermal load in the investigated WPs was very different compared to that outdoors (during HWs and the entire summer). The mean physiologically equivalent temperature (PET) for the summer of 2022 inside the investigated offices was 2 °C higher than outside. All classes of thermo-physiological stress were observed outdoors at a meteorological station during the study period. While at eight of the ten workplaces, the most frequent physiological stress was slight heat stress (ranging between 62.4% and 97.4% of the time), the other two WPs were dominated by moderate heat stress (53.7% and 60.6% of the time). The daily amplitudes as well as diurnal courses of air temperature, humidity, and PET during the summer differed significantly at the ten different WPs. It is suggested to use vapor pressure instead of relative humidity to characterize and compare different HWs both outside and inside. It is proposed for future work research to analyze not only room and building characteristics but also the characteristics of the surroundings of the building for a better understanding of the key factors that influence human thermal comfort in different workplaces. A framework of the drivers affecting the coupling of outdoor and indoor heat stress is proposed. Full article

Surface properties in complex urban environments can significantly impact local-level temperature gradients and distribution on several scales. Studying temperature anomalies and identifying heat pockets in urban settings is challenging. Limited high-resolution datasets are available that do not translate into an accurate assessment of near-surface temperature. This study developed a model to predict land surface temperature (LST) at a high spatial–temporal resolution in urban areas using Landsat data and meteorological inputs from NLDAS. This study developed an urban microclimate (UC) model to predict air temperature at high spatial–temporal resolution for inner urban areas through a land surface and build-up scheme. The innovative aspect of the model is the inclusion of micro-features in land use characteristics, which incorporate surface types, urban vegetation, building density and heights, short wave radiation, and relative humidity. Statistical models, including the Generalized Additive Model (GAM) and spatial autoregression (SAR), were developed to predict land surface temperature (LST) based on surface characteristics and weather parameters. The model was applied to urban microclimates in densely populated regions, focusing on Manhattan and New York City. The results indicated that the SAR model performed better (R² = 0.85, RMSE = 0.736) in predicting micro-scale LST variations compared to the GAM (R² = 0.39, RMSE = 1.203) and validated the accuracy of the LST prediction model with R² ranging from 0.79 to 0.95. Full article

Heat waves are deteriorating the indoor thermal environment of non-air-conditioned buildings, bringing more intensive heat-humid exposures, which poses a great threat to human cognitive performance that is closely related to human safety and health. Previous studies mainly focused on the thermos-physiological aspect, trying to establish predicting models of cognitive performance, but the subjective aspect also needs investigating. In order to explore the relationship between cognitive performance and subjective responses of subjects to hot-humid exposure, a 150-min experiment was conducted in four hot-humid experiments, during which five kinds of cognitive tasks were administered to simulate the sustained mental workload. ‘National Aeronautics and Space Administration-Task Load Index’ (NASA-TLX) and ‘Positive Affect and Negative Affect Schedule scale’ (PANAS) were selected to acquire the perceived mental workload and mood before and after these tasks. Thereafter, changes in the perceived workload and mood with air temperature and exposure time were analyzed. The results of cognitive tasks (response time and accuracy) were recorded online automatically, with which the cognitive performance index (CPI) was calculated. The results showed that five items of NASA-TLX, namely mental demand, physical demand, temporal demand, effort, and frustration, were negatively related to air temperature (p < 0.05), and they were also observed to have quasi-inverted-U relationships with exposure time. Another item, the performance, was found to have a quasi-U relationship with exposure time. Furthermore, a quasi-inverted-U relationship was observed between the positive mood and exposure time, while a quasi-U relationship between the negative mood and exposure time was detected. Finally, a performance-mood relation was established based on the correlation analysis among the CPI, mood, and mental workload, which produced a linear relation with the R² of 0.71. This study provided references for the self-evaluation of cognitive performances in buildings without air-conditioners, which is important in the circumstance where heat waves appear more. Full article

Ignoring Urban Heat Island (UHI) effects may lead to an underestimation of the building cooling demand. This study investigates the impact of the UHI on the cooling demand in hot-humid cities, employing the Local Climate Zones (LCZs) classification framework combined with the Urban Weather Generator (UWG) model to simulate UHI effects and improve building performance simulations. The primary aim of this research is to quantify the influence of different LCZs within urban environments on variations in the cooling energy demand, particularly during heat waves, and to explore how these effects can be incorporated into building energy models. The findings reveal significant discrepancies in both the average and peak cooling demand when UHI effects are ignored, especially during nighttime. The most intense UHI effect was observed in LCZ 2.1, characterized by compact mid-rise and high-rise buildings, leading to a cooling demand increase of more than 20% compared to suburban data during the heat waves. Additionally, building envelope thermal performance was found to influence cooling demand variability, with improved thermal properties reducing energy consumption and stabilizing demand. This research contributes to the theoretical understanding of how urban microclimates affect building energy consumption by integrating LCZ classification with UHI simulation, offering a more accurate approach for building energy predictions. Practically, it highlights the importance of incorporating LCZs into building energy simulations and provides a framework that can be adapted to cities with different climatic conditions, urban forms, and development patterns. This methodology can be generalized to regions other than hot-humid areas, offering insights for improving energy efficiency, mitigating UHI effects, and guiding urban planning strategies to reduce the building energy demand in diverse environments. Full article

As the climate becomes more extreme and heat waves become more prevalent, the effects of climate change spill over into previously unnoticed areas. One such prominent result of global warming is the adverse effect of outdoor weather on pedestrians at night. To investigate this rather overlooked effect, we carried out a large-scale field study in 232 different locations in three different cities in Israel–Tel Aviv-Yafo (106 locations), Haifa (49 locations), and Beersheba (77 locations). The study, involving 30,216 observations on the feeling/s of safety (FoS) performed by 491 participants, started in August 2019 and lasted almost one year. As the study reveals, people feel safer, with all other factors being constant, when the temperature is moderate and humidity is high. According to the study findings, if temperature increases from 25 °C to 30 °C, illumination should be increased by ~20 lx to maintain the same level of FoS. However, if the temperature drops, less illumination can be supplied, which makes a case for smart illumination policies. As providing sufficient FoS is important for an active life outdoors, this study generates knowledge that can help support active and secure mobility in urban areas and beyond. As temperatures rise and humidity patterns change, our findings may have broad implications for urban areas worldwide, both in Israel and beyond. Full article