Maximum Air Temperature Research Articles

Assessing thermal and economic performance of solar dryers in sustainable strategies for bottle gourd and tomato preservation.

The traditional approach of open-sun drying is facing contemporary challenges arising from the widespread adoption of energy-intensive methods and the quality of drying. In response, solar dryers have emerged as a sustainable alternative, utilizing solar thermal energy to effectively dehydrate vegetables. This study investigates the performance of a single-basin, double-slope solar dryer utilizing natural convection for drying bottle gourds and tomatoes, presenting a sustainable alternative to traditional open-sun drying. The solar dryer exhibited superior moisture removal efficiency, achieving a 94.42% reduction in tomatoes and 83.87% in bottle gourds, compared to open-sun drying. Drying rates were significantly enhanced, with maximum air and plate temperatures reaching 54.42°C and 63.38°C, respectively, accelerating the dehydration process. Moisture diffusivity analysis revealed a marked improvement in drying behavior under solar drying, with values ranging from 3.12 × 10-11 to 4.31 × 10-11 m2/s for bottle gourds, and 4.65 × 10-11 to 2.31 × 10-11 m2/s for tomatoes. Energy efficiency assessments highlighted the solar dryer's advantage, with exergy efficiency peaking at 61.78% for bottle gourds and 68.5% for tomatoes. Furthermore, the activation energy required for drying was significantly lower in the solar dryer (29.14-46.41kJ/mol for bottle gourds and 27.16-55.42kJ/mol for tomatoes) compared to open-sun drying, enhancing energy conservation. Visual inspections confirmed the superior quality of the solar-dried vegetables, free from dust and impurities. An economic analysis underscored the system's viability, with payback periods of 2 years for bottle gourds and 1.6 years for tomatoes. Overall, this study demonstrates the efficacy of solar dryers in optimizing vegetable preservation while promoting energy efficiency, aligning with global sustainability goals by reducing post-harvest losses and supporting eco-friendly practices.

Study of red vine phenotypic plasticity across central-southern Italy sites: an integrated analysis of the transcriptome and weather indices through WGCNA

The grapevine (Vitis spp., family Vitaceae) is characterized by marked phenotypic plasticity. Its ability to withstand specific environmental conditions depends on the activation of highly coordinated responses resulting from interactions among genotypes (G) and environmental factors (E). In this study, the transcriptomes of commercially ripe berries of the Cabernet Sauvignon and Aglianico genotypes grown in open fields at three different sites in central-southern Italy (Campania, Molise and Sicily) were analyzed with RNA sequencing. These transcriptomic data were integrated with a comprehensive set of weather course indices through weighted gene co-expression network analysis (WGCNA). A total of 11,887 differentially expressed genes (DEGs) were retrieved, most of which were associated with the Aglianico genotype. The plants from the Sicilian site presented the greatest number of DEGs for both genotypes. Most of the weather course data (daily maximum air temperature, relative humidity, air pressure, dew point, and hours of sun radiation) were significantly correlated with the “lightcyan1” module, confirming WGCNA as a powerful method for identifying genes of high biological interest. Within this module, the gene encoding the ACA10 cation transporter was highly expressed in plants of both genotypes from Campania, where the lowest anthocyanin content was recorded. The transcriptome was also correlated with quality traits, such as total soluble solids and polyphenol content. This approach could lead to the identification of a transcriptomic profile that may specifically identify a genotype and its growing site and to the discovery of hub genes that might function as markers of wine quality.

Investigation of the Influence of Environmental Thermal Characteristics on Thermal Modes of Transparent Boxes

This paper presents the results of experiments to investigate the influence of thermal characteristics of the environment on the thermal modes of transparent boxes. To conduct experiments on the non-stationary thermal model of solar greenhouses developed by us, two identical transparent boxes with dimensions of 0.80 x 0.65 x 0.80 m were constructed. The transparent boxes have rectangular shapes. One transparent box has glass walls and the other with polyethylene walls. The influence of the thermal characteristics of the environment and the thermal conditions inside the transparent boxes with film and glass transparent walls are investigated. The experimental results show that at a maximum ambient air temperature of 42 °C on 27.06.2024 at 13:48 hours, the air temperature increases to 10% and 23% in transparent boxes with polyethylene and with glass walls, respectively, and at 05:10 hours, the humidity decreases to 8% and 11%, respectively. Thus, the influence of the thermal characteristics of the environment on the thermal conditions of transparent boxes with glass walls, at the maximum ambient temperature, is greater by 1.2 times than in transparent boxes with polyethylene walls, and humidity decreases by half.

Prognosis of reflective pavement cracking development with Bayesian updating and surrogate models

ABSTRACT The main objective of this study is to establish an integrated prognostic method for predicting the development of thermal-induced reflective cracking in asphalt overlay on concrete pavement. The integrated prognosis process for crack growth is developed on the modified Paris’ law and includes three steps: (1) calculation of J-integral; (2) Bayesian updating of crack growth parameters and (3) prediction of remaining life. Finite element modelling (FEM) was conducted to calculate J-integrals at different crack depths and cold weather conditions. A surrogate model was built based on the original Kriging method and it shows high accuracy in predicting J-integrals from maximum air temperature, temperature drop and crack depth. The Bayesian updating process starts from the laboratory-measured distribution of fracture model parameters (A and n) in Paris’ Law and converges to the true values to match the measured crack depths at different loading cycles from field inspection. On the other hand, the consideration of material damage in asphalt overlay avoids over-estimation of remaining loading cycles for reflective cracking to reach the pavement surface. Further study is recommended to accurately assess in-situ pavement modulus and detect crack depth over concrete joints for implementation of the proposed prognostics method.

Assessment of Soil Moisture through Climatic Variables Using Remote Sensing Data in Semi-Arid Sokoto State, Nigeria

Soil moisture availability is an indispensable requirement for not only the growth of crops but also other living organisms in any ecological settings This study determined soil moisture condition in Sokoto State using climate-based water index. Satellite-based estimations of mean monthly rainfall data was obtained from the USGS/FEWS database for each of the local government areas. Also, approximations of monthly values of minimum and maximum air temperatures (AIRS) were downloaded from the NASA website. The latter variables were utilised in the computation of potential evapotranspiration using Hargreaves equation. It was very clear from the results that water surplus occurred in the months of July, August and September across almost all the locations. In addition, places lying in the southern part of the State such as Kebbe, Yabo, Tambuwal and Shagari recorded higher positive values of moisture availability in contrast to their counter parts in the extreme north bordering Niger republic. Thus, this study revealed slight spatio-temporal variation in soil moisture condition across the entire study area decreasing from the southern locations towards the north. Hence, the research could help improve agricultural planning and suggest irrigation during dry periods. Consequently, the findings could offer useful insights for managing water resources and improving farming in dry areas.

Individual tree mortality: Risks of climate change in the eastern Brazilian Amazon region

The mortality of trees in humid tropical forests plays a fundamental role in understanding forest development, particularly after disturbances such as those caused by logging and extreme weather events. The aim of this study was to evaluate estimates of individual tree mortality following Reduced Impact Logging (RIL) in the Eastern Brazilian Amazon at biennial intervals from 2005 to 2012. RIL is based on operations planning, personnel training, and investments in forest management, and harvesting through RIL must: (a) minimize environmental damage, (b) diminish operation cost by increasing work efficiency, and (c) reduce operational waste. A mortality model was constructed based on the estimation of three distance-independent competition-indices (DII) and five models for predicting the probability of individual tree mortality. The Kolmogorov-Smirnov statistical test was used to determine the most representative model, from which a Neural Network Autoregressive (NNAR) model was constructed to forecast mortality after RIL. Mortality data was correlated with the El Niño–Southern Oscillation (ENSO) and climate (Rainfall, Maximum, Minimum, and Average air temperature). The tested models showed similar and accurate estimates with R2 exceeding 0.90, although underestimation and overestimation trends were observed. The NNAR satisfactorily represented species mortality over the simulated years. The period from 2012 to 2014 was characterized by a Neutral and Weak El Niño event, and exhibited the highest mortality value for a 25 cm DBH (diameter at breast height), the smallest DBH class measured in this study. In the correlation matrix analysis, maximum air temperature showed the highest positive correlation with trees mortality. Despite the challenges in estimating individual tree mortality in tropical forests after selective logging, accurate estimates were achieved using traditional regression techniques and NNAR. These results can support technical and silvicultural decisions regarding forest management in the Eastern Amazon region of Brazil.

Effect of High Nighttime Temperatures on Growth, Yield, and Quality of Two Wheat Cultivars During the Whole Growth Period.

It is a consensus that Earth's climate has been warming. The impact of global warming is asymmetric, that is, there is more substantial warming in the daily minimum surface air temperature and lower warming in the maximum surface air temperature. Previous studies have reported diurnal temperature differences greatly affecting winter wheat yield. However, only a few studies have investigated the impact of global warming on the growth and yield of winter wheat, yet the influence of night warming on quality has not been deeply evaluated. In this study, two wheat cultivars were used as materials: Jimai 44 (JM44) with strong gluten and Jimai 22 (JM22) with medium gluten, to explore the effects of high nighttime temperatures (HNTs) on the growth, yield, and quality of wheat. The results show that HNTs significantly shortened seedling emergence and anthesis periods in both cultivars compared with ambient temperatures (ATs). In addition, HNTs increased the respiration rate at anthesis and grain-filling stages, impeding wheat pollination and grain maturity. HNTs also accelerated leaf senescence and increased the number of sterile spikelets and plant height, but decreased the effective tiller number, the number of spikes per unit area, and grains per spike. As a result, the grain yield of JM22 and JM44 was decreased by 24.6% and 21.2%, respectively. Moreover, HNTs negatively influenced the flour quality of the two wheat cultivars. The current findings provide new insights into the effects of HNTs on the growth, development, yield, and quality of different wheat genotypes during the whole growth period.

Detection of changes in local-scale statistical distribution of daily temperature and precipitation in dryland region: a case study of Ningxia, China

Identifying alterations in the local-scale statistical distributions of daily temperature indices and precipitation is crucial for adapting to climate change and combating desertification in arid regions. This study focuses on Ningxia, a representative dryland area in China, where we investigated changes in the statistical distributions of daily temperature and precipitation across ten weather stations over two 30-year periods: 1991–2020 compared to 1961–1990. From 1961 to 2020, annual air temperature indices (mean, maximum, and minimum) exhibited a significant upward trend at all ten weather stations (P < 0.05). Specifically, when comparing the decades of 1961–1970 with those of 2001–2020, annual air temperature indices increased by 1.2–2.2 °C for annual air temperatures, by 0.91–1.97 °C for maximum air temperature, and by 1.26–3.28 °C for minimum air temperature; these corresponded to rates of increase ranging from 0.20–0.40 °C per decade for mean air temperatures, from 0.15–0.33 °C per decade for maximum air temperatures, and from 0.21–0.55 °C per decade for minimum air temperatures, respectively during this period. The observed differences in the statistical distributions of daily temperature indices between the two periods were statistically significant (P < 0.05), indicating an increase rate of approximately 1 °C during the period from 1991 to 2020 relative to that from 1961 to 1990 across various weather stations. In contrast, there were no significant increasing or decreasing trends in annual precipitation (P > 0.05). Similarly, the changes in statistical distributions of daily precipitation also did not reach a significance level (P > 0.05) when comparing data from both periods within these stations. Furthermore, the correlation coefficients between daily temperature indices and daily precipitation have decreased across these stations during the later period compared to earlier years. These findings suggest that while there has been a notable shift toward higher values in the statistical distribution of daily temperature indices—indicating rising temperatures—the corresponding distribution patterns for daily precipitation have remained relatively stable between these time frames. Consequently, this research confirmed an escalation in localized drought conditions within certain dryland areas despite broader regional trends toward humidification observed over recent decades; thus implying an increased risk of desertification as aridity intensifies.

Statistical-empirical pavement temperature prediction models based on data from road weather stations in Estonia

Asphalt pavement properties are greatly affected by seasonal temperature variation. Binder grade selection needs to be aligned with forecasted operational pavement temperatures to maximise pavement longevity. Different widely adopted models for determining binder Superpave Performance Grading have been shown to provide inaccurate results for the Nordic-Baltic region, leading to under- or overestimation of operational pavement temperatures and erroneous selection of a suitable binder grade. This study proposes empirically formulated pavement temperature prediction models for Estonia based on temperature data collected from six different road weather stations in Estonia from 2019 to 2023. Models were derived using measurements from daily maximum and minimum air temperatures combined with pavement temperature profiles with depth.

Geographical variation and genetic diversity of Parashorea chinensis germplasm resources.

Parashorea chinensis is a rare monodominant species in southwest China known for its production of high-quality timber, is facing decline due to its narrow distribution, human interference and habitat destruction. However, there are no reports on genetic diversity and geographical variation of phenotypic traits of P. chinensis. In this study, phenotypic characters and genetic diversity of 15 germplasms resources from five provenances in southwest China were investigated, and their relationships with geographical and environmental factors was discussed. Our results revealed a rich phenotypic diversity among the germplasms, with variation coefficients ranging from 3.63% to 45.49%. Among the studied germplasms, NP03 from Napo and ML02 from Mengla region exhibited superior phenotypic traits. Notably, NP03 also demonstrated the highest genetic diversity. Genetic differentiation analyses including genetic differentiation coefficient (0.6264) and gene flow (0.3736) illustrated that genetic variation was most prevalent among populations. Furthermore, redundancy analysis showed that temperature related factors (maximum air temperature, annual mean temperature and minimum air temperature) significantly affected phenotypic variation. Similarly, altitude, longitude, latitude, annual mean precipitation and the minimum air temperature significantly impacted the level of genetic diversity. The molecular variation of the natural population of P. chinensis followed a certain geographical pattern. Our finding indicated abundant phenotypic variation among P. chinensis germplasms. However, populations exhibited low levels of genetic diversity alongside high genetic differentiation, potentially contributing to the species' rarity. Based on our results, NP03 and ML02 germplasm could be used as the parents for breeding superior germplasm of P. chinensis. Overall, this study provides valuable insights into germplasm diversity and conservation, genetic improvement, and utilization of P. chinensis.

Dynamics of extreme climatic variables for viticulture in the main zones of ampelocenosis of the Krasnodar region

Modern climate changes affect all branches of agriculture. Everywhere there is an increase in air temperature, changes in precipitation, an increase in extreme weather events. Since the productive lifespan of a grape plant is 30-40 years, it is necessary to assess climatic changes in order to create a variety adapted to changes. The purpose of the research is to assess changes in extreme heat supply and relative humidity in the main viticultural areas of the Krasnodar region. The average values of extreme heat supply and relative humidity variables of two climatological periods of 1961–1990 and 1991–2020, their changes over time and the course of variable’s anomalies of 1991–2020 compared with the average values of 1961–1990 are calculated. An increase in the absolute maximum air temperature by 0.2–1.6 °C for the period 1991–2020 was noted compared to the previous period, with the exception of Novorossiysk (decrease by 1.4 °C); an increase in the average absolute maximum air temperature by 1.5–2.5 °C, an increase in the number of days with a maximum air temperature above +35 °C by 1.0–2.3 days; a decrease in the average relative humidity of April–October by 0.7–2.7 % and an increase in the number of days with a minimum relative humidity of less than 30 % over the summer by 0.8–5.4 days. The variability of these variables over time for the period 1991–2020 is consistent with the change in the average. An increase in the absolute maximum was established (by 0.65–0.9 °C/10 years), the number of days with a maximum air temperature above +35 °C (by 0.8–1.1 days/10 years), the number of days with a minimum relative humidity of less than 30% over the summer (by 1.2–7.2 days/10 years); decrease in the average relative humidity of April–October (by 0.5–6.5 %/10 years). These changes indicate an increase in climate extremes and the frequency of unfavorable conditions for grapes in the summer, which requires an adjustment of the assortment.

Assessment of Wind Energy Potential and Optimal Site Selection for Wind Energy Plant Installations in Igdir/Turkey

Wind energy is an eco-friendly, renewable, domestic, and infinite resource. These factors render the construction of wind turbines appealing to nations, prompting numerous governments to implement incentives to augment their installed capacity of wind turbines. Alongside augmenting the installed capacity of wind turbines, identifying suitable locations for their installation is crucial for optimizing turbine performance. This study aims to evaluate potential sites for wind power plant installation via a GIS, a mapping technique. The Analytic Hierarchy Process (AHP) was employed to assess the locations, including both quantitative and qualitative aspects that significantly impact the wind farm suitability map. Utilizing the GIS methodology, all datasets were examined through height and raster transformations of land surface temperature, plant density index, air pressure, humidity, wind speed, air temperature, land cover, solar radiation, aspect, slope, and topographical characteristics, resulting in the creation of a wind farm map. The correlation between the five-year meteorological data and environmental parameters (wind direction, daily wind speed, daily maximum and minimum air temperatures, daily relative humidity, daily average air temperature, solar radiation duration, daily cloud cover, air humidity, and air pressure) influencing the wind power plant in Iğdır province, including Iğdır Airport, Karakoyunlu, Aralık, and Tuzluca districts, was analyzed. If wind energy towers are installed at 1 km intervals across an area of roughly 858,180 hectares in Igdir province, an estimated 858,180 GWh of wind energy can be generated. The GIS-derived wind power plant map indicates that the installation sites for wind power plants are located in regions susceptible to wind erosion.

Drought Analysis in Ketapang District using the Keetch-Byram Drought Index Method

Ketapang Regency is one of the areas in West Kalimantan that is prone to drought. Drought can trigger forest and land fires. In this research, the Keetch-Byram Drought Index (KBDI) method was used to determine the level of drought in Ketapang Regency. The KBDI method relies on annual rainfall accumulation, daily rainfall, and maximum air temperature. The KBDI values obtained were correlated with the number of hotspots using Pearson correlation. This research was conducted throughout 2018-2022. Based on the monthly average KBDI value, the highest drought in Ketapang Regency occurred in August and September, while the lowest drought occurred in December and January. In terms of the annual average, the highest drought occurred in 2019. During the ENSO phenomenon in 2019, the El Niño phase experienced higher drought than the La Niña phase and normal years. In the El Niño phase, drought levels reach high to extreme categories. The correlation value between annual KBDI and the number of hotspots is 0.88, indicating a solid relationship. An increase in the KBDI value will be followed by an increasing number of hotspots.

Analysing the influence of temperature dependence of active resistances on transmitted power in electric power systems

Subject of research: in this paper, the static stability of electric power systems is assessed taking into account the temperature dependence of active resistances. Purpose of research: to show how the consideration of the temperature dependence of active resistances will affect the static stability reserves of electric power systems. Methods and objects of research: calculations of the regime with account of influence of thermal processes in elements of power system are made. The main parameter to take into account thermal processes is the ambient air temperature, so the days with maximum and minimum average daily air temperature were determined. All parameters except ambient air temperature and wind speed were selected in such a way as to provide the worst conditions for thermal exchange. The results of the calculations show that taking into account the thermal processes in the elements of the system significantly affects the transmitted power and the reserves of static aperiodic stability in the power system. Main results of research: practical calculations have shown that the account of thermal processes allows to take into account more accurately the limits of the transmitted power through the elements of the power system, and at the same time the reserve coefficients of static stability.

A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models

Climate change affects the water cycle, water resource management, and sustainable socio-economic development. In order to accurately predict climate change in Weifang City, China, this study utilizes multiple data-driven deep learning models. The climate data for 73 years include monthly average air temperature (MAAT), monthly average minimum air temperature (MAMINAT), monthly average maximum air temperature (MAMAXAT), and monthly total precipitation (MP). The different deep learning models include artificial neural network (ANN), recurrent NN (RNN), gate recurrent unit (GRU), long short-term memory neural network (LSTM), deep convolutional NN (CNN), hybrid CNN-GRU, hybrid CNN-LSTM, and hybrid CNN-LSTM-GRU. The CNN-LSTM-GRU for MAAT prediction is the best-performing model compared to other deep learning models with the highest correlation coefficient (R = 0.9879) and lowest root mean square error (RMSE = 1.5347) and mean absolute error (MAE = 1.1830). These results indicate that The hybrid CNN-LSTM-GRU method is a suitable climate prediction model. This deep learning method can also be used for surface water modeling. Climate prediction will help with flood control and water resource management.

Spatio‐temporal variability in seed production of tree species: implications for restoration in the Cerrado–Amazonia transition zone

The use of propagules collected in ecotonal regions for restoration purposes is challenging because of the mix of species from different vegetation types and reproductive phenological variability. We used a database of the Xingu Seed Network in Brazil, which contains data on 139 native tree species for 8 years (2011–2018) in the Cerrado–Amazonia transition zone, to answer three questions: (1) What is the spatio‐temporal variability in the seed dispersal of tree species, and how does it relate to the species' preferential habitat? (2) Is there a relationship between the seed dispersal period and climate variables? (3) Is the frequency of tree species with different dispersal syndromes equally distributed among biomes and seed‐size classes? Independent of their preferred ecosystem (Amazonia or Cerrado), some species showed substantial spatio asynchrony in fruit dispersal, extending the dispersal period at a regional scale. The seed dispersal period was strongly correlated (rs &gt; 0.6) with precipitation and minimum or maximum air temperature in 41% (n = 57) of the studied species. We showed that three‐quarters of the species (n = 105) dispersed seeds in the late dry and early rainy periods, with little variation among dispersal syndromes. Our findings will enhance the effectiveness of restoration initiatives by increasing the accuracy of predictions of the location and time for collecting seeds in the extensive Cerrado–Amazonia transition zone.

Bayesian spatial-temporal analysis and determinants of cardiovascular diseases in Tanzania mainland

BackgroundCardiovascular Diseases (CVDs) are health-threatening conditions that account for high mortality in the world. Approximately 23.6 million deaths due to CVD is expected in the year 2030 worldwide. The CVD burden is more severe in developing countries, including Tanzania.ObjectivesThis study analyzed the spatial-temporal trends and determinants of cardiovascular diseases in Tanzania from 2010 to 2019.MethodsIndividual data were extracted from Jakaya Kikwete Cardiac Institute (JKCI), Mbeya Zonal Referral Hospital (MZRH), Kilimanjaro Christian Medical Centre (KCMC) and Bugando hospitals and the geographical data from TMA. The model containing spatial and temporal components was analyzed using the Bayesian hierarchical method implemented using Integrated Nested Laplace Approximation (INLA).ResultsThe results found that the incidence of CVD increased from 2010 to 2014 and decreased from 2015 to 2019. The southern highlands, lake, central and coastal zones were more likely to have CVD problems than others. It was also revealed that people aged 60–64 years OR = 1.49, females OR = 1.51, smokers OR = 1.76, alcohol drinkers OR = 1.48, and overweight OR = 1.89 were more likely to have CVD problems. Additionally, a 1oC increase in the average annual air maximum temperature was related to a 14% risk of developing CVD problems. The study revealed that the model, which included spatial and temporal random effects, was the best-predicting model.ConclusionThe study shows a decreased CVD incidence rate from 2015 to 2019. The CVD incidences occurred more in Tanzania’s coastal and lake areas between 2010 and 2019. The demographic, lifestyle and geographical risk factors were significantly associated with the CVD.

Effect of Cool Roof Paint Application on Metal Roof Surface Temperature and Indoor Air Temperature in Tropical Climates

Abstract Increasingly hot environmental temperatures and less comfortable indoor temperatures are the impacts of inappropriate choices of building roof materials. An effort to mitigate this problem is by using a cool roof. This study was conducted through a field experiment by applying cool roof paint to the metal roof of a building in Malang, Indonesia. Field measurement results show that the cool roof paint application can reduce the maximum surface temperature above the metal roof by up to 6.6ºC and under the metal roof by up to 6.3ºC, as well as the maximum indoor air temperature by up to 0.8ºC at a height of 120 cm above the floor and up to 1.2º C at a height of 50 cm below the metal roof.

Increased exposure of rice to compound drought and hot extreme events during its growing seasons in China

Against the backdrop of global warming, compound drought and hot extreme events (CDHEEs) have intensified markedly, attracting attention from both the scientific community and society. However, a comprehensive understanding of the characteristics of the CDHEEs and their impacts on rice production during growing seasons remains limited. Thus, in this study, we use daily maximum and minimum air temperatures to derive a meteorological drought composite index for CDHEE identification. Additionally, we employ the Crop Calendar and the CROPGRIDS data to define the rice-growing seasons and regions. Detailed analyses reveal that from 1961 to 2023, the CDHEE frequency in both single-cropping rice regions (Rice1) and double-cropping rice regions (Rice2) increased at rates of 0.79 days (decade)−1 and 1.26 days (decade)−1, respectively, with a notable acceleration in the post-1990 s. The duration of the CDHEEs has lengthened, and prolonged events have become more frequent. The intensity of the CDHEEs has also enhanced, primarily driven by hot extreme events in Rice1 and the synergistic effects of droughts and hot extreme events in Rice2. Furthermore, the time span of CDHEE occurrence has expanded, starting earlier (rates of − 3.21 days (decade)−1 in Rice1 and − 4.37 days (decade)−1 in Rice2) and ending later (rates of 4.81 days (decade)−1 in Rice1 and 8.20 days (decade)−1 Rice2) during rice-growing seasons, thereby extending the exposure duration of rice. Since the 1990 s, rice exposure to the CDHEEs has also intensified, especially for the persistent CDHEEs. These findings indicate the upward trends in the frequency, intensity and duration of the CDHEEs, as well as the exposure of rice to the CDHEEs, during the rice-growing seasons in China. This research provides crucial insights into CDHEE impacts and underscores the necessity of implementing targeted adaptation and mitigation strategies to safeguard rice production amidst escalating climate challenges.

Effect analysis on the low-temperature preheating performance of a novel micro-combustor air preheater for the cold start of the Li-ion battery packs

In this work, a micro-combustor (MC) air preheater as the heat source is developed. This novel air preheater has a compact size and outputs hot air with a wide range of temperatures with high temperature uniformity, which can well meet the preheating needs of Li-ion batteries in cold environments. Numerical studies are carried out to investigate the effects of baffle height, number of fins Nfin, hot air inlet velocity, cold air inlet velocity and combustor inlet velocity on the performance of the air preheater. The heating power of the air preheater can be adjusted by changing combustor inlet velocity Vcombustor, the outlet temperature can be easily adjusted by changing cold air inlet velocity Vcold and hot air inlet velocity Vhot, and the addition of baffles makes the outlet temperature very uniform. The results show that the height of the baffle of 20 mm and 30 mm makes ΔTair not exceed 1.5 K. The air preheater performance is better with fin number 6, and the maximum air temperature of 309.8 K is obtained with fin number and Vhot of 6 and 0.3 m/s respectively. Tair can vary between 287.8 K and 309.8 K when Vcombustor=12 m/s.