Maximum Relative Humidity Research Articles

Impact of temperature and humidity on airway obstruction in a cohort of elderly women

Abstract Background Temperature and relative humidity (RH) impact human health. However, little is known about the combined association between temperature and RH on respiratory health in the elderly. In this study, we evaluated the interactive associations of temperature and RH on airway obstruction in elderly German women. Methods Using data from the second follow-up (2007-2010) of the German SALIA cohort study, we assessed the short-term (Lag0-8) combined association between maximum and minimum temperature (Tmax, Tmin) and RH on airway obstruction. Airway obstruction was defined as the ratio of first second of forced expiration (FEV1) / forced vital capacity (FVC) < 0.70. Multivariable logistic regression with a distributed lag non-linear model (DLNM) was constructed for the interaction between temperature and RH and adjusted for a single pollutant, location, socioeconomic status and smoking status. Results 735 elderly women with an average (±SD) age of 73.46 (±3.09) years were included. Around 16.73% of them had airway obstruction. For non-linear antagonistic Tmax and RH interaction models, we found a U-shaped relationship from Lag 0-8, with an immediate adverse association from Lag0-1 and a significant delayed adverse association from Lag7-8. For non-linear antagonistic Tmin and RH interaction models, the relationship was U-shaped and showed an immediate adverse association from Lag0-1, while there was also a significant delayed association from Lag6-8. Conclusions Climate change increases the likelihood of exposure to adverse temperatures and weather conditions. Vulnerable populations like the elderly are more susceptible to the adverse associations of climate change. In this epidemiological study, we found that exposure to Tmax/Tmin and RH had immediate and delayed adverse associations by increasing respiratory obstruction in elderly women. Increasing public awareness of the danger of interactions between non-optimum temperature and RH on respiratory health is crucial. Key messages • Elderly women’s respiratory health is impacted by temperature and humidity interactions, requiring awareness and proactive measures. • Climate change heightens risk for vulnerable groups, emphasising the need to address environmental impacts on health.

Phenological trends and associated climate drivers of a tree community in lowland dipterocarp forest, Western Ghats, India.

Understanding phenological responses of tropical forest plant communities is crucial for identifying climate-induced changes in ecosystem dynamics. Monitoring phenology across diverse species in natural habitats provides cost-effective insights for conserving both species and forests. We studied tree phenology in a lowland evergreen dipterocarp forest in the Western Ghats, India. About 719 tree individuals representing 95 species were monitored for their vegetative and reproductive phenology from April 2021 to September 2023. Circular statistics detected seasonality in phenological events and Generalized Linear Mixed Modelling (GLMM) identified influence of climate variables on the phenological responses of the tree community. We also assessed how the activity and intensity of phenophases vary over the study period. Our results showed that leaf flushing and flowering peaked during the dry season, with mass flowering observed in two dominant dipterocarps. Fruit production peaked before the monsoon. We also observed diversity in vegetative and reproductive phenodynamics across species groups (forest strata, sexual system, and seed size). Leaf flushing was positively correlated with maximum relative humidity and negatively correlated with maximum temperature and the number of rainy days. Flowering had negative correlations with maximum relative humidity, rainfall days, and maximum temperature but showed a positive correlation with minimum temperature. Fruiting was positively correlated with maximum temperature and negatively correlated with rainy days. This detailed phenological information provides critical knowledge on resource availability and insights into how climate and seasonal changes affect plant growth cycles thereby aiding reforestation and biodiversity conservation strategies in vulnerable forest areas.

Analysis of spatio-temporal variability of temperature, relative humidity and temperature humidity index in Naivasha Sub-county, Kenya

The aim of the study was to understand the environmental conditions impacting the reproductive performance of Sahiwal cattle, focusing on temperature, relative humidity, and relative humidity index (RHI). These factors significantly influence reproductive rates, highlighting the importance of spatio-temporal variability studies for informed herd management decisions. This study analyzed climate variability trends from 1998 to 2019 at the Kenya Agricultural and Livestock Research Organization (KALRO) Dairy Research Institute (DRI)-Naivasha, in Malewa Ward, Naivasha Sub-County, Kenya. Daily minimum and maximum temperature (T) and relative humidity (RH) data sourced from the Kenya Meteorological Department were utilized. The Temperature-Humidity Index (THI) was computed using the THI equation developed by Mader. Time series analyses, including the Mann-Kendall test and Sen's slope estimates, were employed to assess seasonal variability. Results indicated significant decreasing trends in temperature across all the four seasons studied (DJF, MAM, JJA, and SON), with rates of cooling at -0.0625, -0.0175, -0.0125, and -0.0342 °C per year, respectively. In contrast, relative humidity showed statistically significant increases in all seasons, with rates of +0.5977, +0.999, +1.4493, and +1.0499 for DJF, MAM, JJA, and SON, respectively. Seasonal THI exhibited significant decreases in DJF and JJA, potentially impacting livestock reproductive performance. These findings are crucial for policymakers to create adaptation and mitigation methods to address climate change effects on livestock and livelihoods.

Epidemiological Study of Downy Mildew Disease of Opium Poppy

The downy mildew caused by Peronosporaspp is a major disastrous disease of many plant species. The symptoms of downy mildew disease incidence on opium poppy (Papaver somniferum L.) were collected in three years from 2019-20 to 2021-22 for prediction of weather parameters on the progression of downy mildew disease. The downy mildew disease initiation was recorded in the 52nd SMW (10.0%DMI) when the maximum temperature was 19.430C. At 3rd SMW, the maximum temperature dropped 17.330C and DMI reached up to 20.00 percent depicting the progression and spread of downy mildew disease in opium poppy with the decrease of maximum temperature. The maximum disease incidence was recorded 92.53 percent in 10th SMW when maximum and minimum temperature were 29.030C and 13.530C. Regression analysis between dependent variable downy mildew disease incidence Vs. independent variables (viz., rainfall, maximum and minimum temperature and relative humidity) showed that all the weather parameters contributed more than 85 percent variation (R2 = 0.869, 0.957, 0.859) in the downy mildew incidence of opium poppy. One unit change of maximum temperature, minimum temperature (1.00c), maximum and minimum relative humidity (1.0%) might cause to change 0.128, 0.70, 0.117 0.130 units in downy mildew incidence, respectively.

Design of low-cost humidity sensor based on chipless RFID

It is of great significance to design a low-cost chipless radio frequency identification(RFID) sensor for environmental humidity monitoring. To this end, polyvinyl alcohol (PVA) film is used as a humidity sensitive material, and the overall size of the rectangular substrate is 18 mm × 18 mm ×0.5 mm. Through the humidity sensing principle and simulation analysis, the change of environmental humidity causes the change of the dielectric constant of the humidity sensitive material PVA, which in turn affects the resonant frequency offset of the entire sensor. The simulation results show that the relative humidity working range of the designed humidity sensor is 21.9% ∼ 52.5%, the corresponding sensor resonant frequency range is 2.76 ∼ 2.51GHz, the total offset reaches 250 MHz, and the average sensitivity at the maximum relative humidity is 23.08MHz/% . The designed humidity sensor has the advantages of miniaturization, simple structure and low cost, and can be used for humidity monitoring in various target environments. Keywords.low cost; chipless RFID humidity sensor; polyvinyl alcohol; humidity monitoring; humidity sensitive material.

Spatiotemporal Dynamics of Hot-Dry Winds in Northern China: A Multidimensional Analysis Using Gridded Datasets

Abstract Hot-dry winds (HDWs), characterized by simultaneous high temperature, low humidity, and strong wind speed, represent prevalent agrometeorological hazards in northern China. To date, our comprehension of HDWs has been constrained to regional scales due to the dependence upon in situ meteorological observations and the lack of a universal definition of HDWs. In this study, we leveraged a gridded dataset to elucidate the spatiotemporal dynamics of HDWs in northern China from 1961 to 2022. We also introduced a standardized HDW index (sHDWI) to investigate HDW severity. The results indicated more HDW occurrences in eastern Northwest China (NW) and western Inner Mongolia (IM). While there were declining trends in HDW frequency, extent, and severity on average in northern China from 1961 to 1993, these trends were reversed by an overall increase afterward. Intriguingly, regions spanning from NW to IM exhibited significant upward trends in HDW severity according to sHDWI. The spatial extents of light, moderate, and heavy HDWs consistently decreased in North China (NC), with notable expansions over IM, NW, and Northeast China (NE) from 2001 to 2022. Importantly, the increased frequency, expanded extent, and intensified severity of HDWs, particularly, the heavy ones, underscored the escalation of HDW events in recent decades over the arid IM and hyperarid NW, known for their fragile ecological environment. The findings from this research carry significant implications for agricultural production and water management in northern China influenced by the changing patterns of HDWs in the context of climate change. Significance Statement This study aims to enhance our understanding of the large-scale spatiotemporal dynamics of hot-dry winds (HDWs) in northern China using a 0.25° × 0.25° gridded dataset spanning the period from 1961 to 2022. The adoption of the gridded dataset enables a multidimensional exploration of HDWs, encompassing their frequency, extent, and severity which cannot be fully captured by in situ observations. Meanwhile, to quantify changes in HDW severity, we introduce a standardized HDW index that surpasses existing HDW definitions relying on absolute thresholds or relative percentiles of maximum air temperature, relative humidity, and wind speed. Our results reveal a consistent reversal in HDW frequency, extent, and severity around 1993 in northern China and highlight the intensification of heavy HDWs, particularly in the arid and hyperarid regions within the study area.

Genotype‐by‐environment interaction for wheat falling number performance due to late maturity α‐amylase

AbstractLate maturity α‐amylase (LMA) is known to reduce falling number (FN) in wheat (Triticum aestivum L.), similar to the effect of preharvest sprouting (PHS) and frost, which can result in grain parcels testing below trading thresholds. Hence, Grains Australia mandates that new Australian wheat cultivars must be at a low risk of LMA expression to receive a milling classification. The multi‐environment trial dataset contained 34 environments not affected by PHS or frost and was analyzed using a five‐factor analytic linear mixed model. Factor 1 accounted for 71.4% of the genetic variation in FN, factor 2 accounted for 8.8%, factor 3 accounted for 5.7%, factor 4 accounted for 3.5%, and factor 5 accounted for 3.1%. The interaction class (iClass) summary method was used to assist in the characterization of crossover genotype‐by‐environment interaction (G × E). Poorer FN performance was best observed in the “ppp” iClass, which indicated a minor, but significant, response to crossover G × E. The environment loadings for factor 1 were associated with mild ripening conditions, characterized by fewer days above 28°C, increased rainfall, and increased variation in daily maximum temperature and relative humidity. Factors 2 and 3 were associated with “cool shock” conditions, where the maximum temperature for 1 day was above 24°C followed by at least three consecutive days below 18°C during the grain fill period. This study provides further evidence of the crossover G × E present for FN associated with LMA, poor FN performance in genotypes that express higher levels of LMA, and the environmental conditions that contribute to LMA expression.

A study on meteorological characteristics and its consequences on human mortality in Rajkot City, Gujarat

Abstract Background: Climate change presents a crucial threat to human health. The health of human is affected by heat waves and extreme weather events. Hence, the present study was planned to explore weather conditions and their consequences on mortality of human being by accessing the meteorological and mortality data. Methodology: Meteorological data include maximum and minimum temperature, average wind speed, and total rainfall; relative humidity was obtained for January 1, 2017–December 31, 2021, from India Meteorological Department, Pune. Trend analysis of climatic variables was done. Human mortality data were obtained from January 1, 2017, to December 31, 2020, from Health Department, Rajkot Municipal Corporation, Rajkot. The impact of temperature and rainfall on mortality was observed. Results: Upward trend of maximum temperature, rainfall, and relative humidity while downward trend for minimum temperature and average wind speed was observed. Human mortality due to communicable and noncommunicable diseases was not affected by maximum temperature. The effect of rainfall on mortality due to vector-borne diseases was observed. Conclusion: The effect of rainfall on human mortality was observed. Upward and downward trend of climatic variables was not found to be statistically significant. Further study needs to be conducted by analyzing climate data over a longer duration and across a larger geographical area.

Weather Influence on Fall Armyworm Infestation in Maize: A Study in Perambalur District, Tamil Nadu, India

Maize, Zea mays L (Family: Poaceae), is a main cereal crop cultivated across diverse agro-climatic regions worldwide. The Fall Armyworm (FAW), Spodopterafrugiperda (J. E. Smith) (Lepidoptera: Noctuidae), migratory insect is notorious for its economic impact, inflicting severe damage on various crops including maize, rice, sorghum, millet, sugarcane, vegetables, and cotton, and has been documented to affect over 80 other plant species. A roving survey was carried out in four major maize growing blocks of Perambalur district viz., Perambalur, Veppanthattai, Alathur and Veppur during Kharif and Rabi season of 2023-2024. The experiment consisted of sixty standard weeks from 3rd July 2023 to 4th August 2024. The results indicated a positive correlation between the number of fall armyworm larvae/plant with Tmax, relative humidity (Evening), and the negative correlation was observed between the number of fall armyworm larvae/plant with Tmin, relative humidity (Morning), Sunshine and rainfall. The data concluded that the maximum temperature significantly influenced the emergence of the fall armyworm and its infestation on maize. The incidence of FAW started in 46th mean standard week MSW (13 larvae/plant) Alathur, (17.25 larvae/plant) Perambalur, (17.25 larvae/plant) Veppanthattai, (18 larvae/plant) Veppur and the population increased gradually and reached the peak in the 47th mean standard week MSW (26 larvae/plant) Alathur, (32.75 larvae/plant) Perambalur, (33 larvae/plant) Veppanthattai, (42 larvae/plant). Correlation analysis revealed that larval population showed a significant positive correlation with the maximum temperature and relative humidity. At the same time, minimum temperature, sunshine, and rainfall exhibited a negative association with larval incidence of S. frugiperda. Propose adaptation strategies for maize farmers in Perambalur to manage fall armyworm infestations in the context of changing weather patterns. This could include adjustments in planting dates, pest monitoring techniques, or integrated pest management approaches.

Seasonal Incidence of Insect Pests on Blackgram [Vigna mungo (L.) Hepper] in Malwa Region of Madhya Pradesh, India

The study investigated the prevalence of major insect pests and their natural enemies in Blackgram crops, focusing on leaf hoppers, whiteflies, Bihar hairy caterpillars, spotted pod borers, stem flies, coccinellids and spiders. Throughout the crop period, the population dynamics of these pests were monitored, revealing varying levels of infestation. The high population of 5.60 leafhoppers per plant and 3.50 larvae per plant of Bihar hairy caterpillar were recorded during 35th week of the crop cycle. The high population of 6.90 whiteflies per plant, 2.60 spotted pod borer larvae per plant and 60 per cent of pod fly infestation were observed during 36th, 37th and 38th week of the crop cycle, respectively. The highest populations of coccinellids 2.40 beetles per plant in 35th week and 2.00 spiders per plant in 36th week recorded duringthe crop cycle. The maximum temperature and relative humidity showed significant positive correlation and minimum temperature and rainfall showed negative non-significant correlation with the population of leaf hopper, Bihar hairy caterpillar, whitefly, stem fly, spotted pod borer and coccinellids. Relative humidity showed positive significant correlation and maximum temperature, minimum temperature and rainfall showed negative non-significant correlation with population of spider.

Association of meteorological variables with leaf spot and fruit rot disease incidence in eggplant and YOLOv8-based disease classification

Eggplant is one of the major vegetables consumed worldwide. Several fungal, bacterial, and viral diseases challenge the yield and quality of eggplant. The incidence of plant diseases is strongly influenced by weather factors such as temperature, humidity, rainfall, and wind speed. Mattu Gulla (MG) is a GI-tagged traditional variety of eggplant grown in Mattu village of the Udupi district in Karnataka state, India, with a cultural legacy of more than four centuries. In this study, we investigated the relationships between weather parameters and disease incidence in Mattu Gulla. Leaf spot (LS) and fruit rot (FR) are the major diseases affecting this plant variety. The influence of plant age and weather parameters on the modulation of the disease incidence (%) [DI (%)] of leaf spot and fruit rot was recorded and analyzed via correlation and regression. Prediction equations for disease incidence was derived via regression. A significant negative correlation was observed between the leaf spot DI (%) and minimum temperature (Min. temp), and a positive correlation was observed between the DI (%) and fruit rot. In the case of FR, the DI (%) is also significantly positively correlated with wind speed (WS), temperature, maximum relative humidity (RH I), rainfall (RF), and wind speed (WS). An RH I of 86–87 % was favorable for the incidence of fruit rot in the field. Regression analysis revealed a significant association between Min. temp and leaf spot DI (%), and in the case of fruit rot DI (%), the association was with Min. temp and WS. An android application, “Leaf Guard,” has been developed for AI-based disease detection in eggplant. During testing, the accuracy of the trained model reached 98.2 %.

Machine learning models for daily net radiation prediction across different climatic zones of China

Net radiation (Rn), a critical component in land surface energy cycling, is calculated as the difference between net shortwave radiation and longwave radiation at the Earth’s surface and holds significant importance in crop models for precision agriculture management. In this study, we examined the performance of four machine learning models, including extreme learning machine (ELM), hybrid artificial neural networks with genetic algorithm models (GANN), generalized regression neural networks (GRNN), and random forests (RF), in estimating daily Rn at four representative sites across different climatic zones of China. The input variables included common meteorological factors such as minimum and maximum temperature, relative humidity, sunshine duration, and shortwave solar radiation. Model performance was assessed and compared using statistical parameters such as the correlation coefficient (R2), root mean square errors (RMSE), mean absolute errors (MAE), and Nash–Sutcliffe coefficient (NS). The results indicated that all models slightly underestimated actual Rn, with linear regression slopes ranging from 0.810 to 0.870 across different zones. The estimated Rn was found to be comparable to observed values in terms of data distribution characteristics. Among the models, the ELM and GANN demonstrated higher consistency with observed values, exhibiting R2 values ranging from 0.838 to 0.963 and 0.836 to 0.963, respectively, across varying climatic zones. These values surpassed those of the RF (0.809–0.959) and GRNN (0.812–0.949) models. Additionally, the ELM and GANN models showed smaller simulation errors in terms of RMSE, MAE, and NS across the four climatic zones compared to the RF and GRNN models. Overall, the ELM and GANN models outperformed the RF and GRNN models. Notably, the ELM model's faster computational speed makes it a strong recommendation for Rn estimates across different climatic zones of China.

Wild-caught adult black flies (Diptera: Simuliidae) from various ecological landscapes in Malaysia

Most studies on black flies focus on the taxonomy and ecology of their aquatic stages. Despite posing a public health threat, the adults remain poorly studied in many countries, including Malaysia. The present study represents the first investigation of the distribution of wild-caught black flies from various ecological landscapes and climatic conditions in Malaysia. CO2-baited Malaise traps were set randomly at 41 sampling sites across Peninsular Malaysia from 2020 to 2023. In total, 532 black flies belonging to 14 species of four subgenera were captured. To ensure taxonomic rigor, specimens were identified to species morphologically and molecularly. The subgenus Gomphostilbia was the most abundant (71.43 %), followed by Simulium (14.28 %) and Davieselleum and Nevermannia each representing 7.14 % of the total captures. These species represented 14.74 % of the total species recorded from Malaysia. The most frequently collected species were Simulium roslihashimi (24.39 %), followed by S. aureohirtum, S. vanluni, and S. (Gomphostilbia) sp. 1 with 7.32 % each. The highest relative abundance was found for S. vanluni (86.09 %) and S. roslihashimi (7.14 %). Most species were found at elevation below 300 m (78.57 %); fewer were at elevation higher than 1,000 m (21.43 %). Two principal components accounted for 85.3 % of the total intersite variance. Simulium roslihashimi was found at almost every site, with a maximum relative humidity of 90 %. Simulium aureohirtum and S. vanluni were found at sites with relative humidity up to 73 %, but S. aureohirtum was found at higher temperatures (31 °C) compared with S. roslihashimi (28 °C) and S. vanluni (29 °C). The present study establishes the groundwork for further studies of wild adults in Malaysia and identifies the need to use more traps over the range of seasons and environmental conditions, particularly near breeding sites.

Influence of Temperature and Relative Humidity on Spore Germination and Early Blight Disease Development of Tomato Caused by Alternaria solani

Early blight of tomato caused by Alternaria solani is one of the most important and devastating disease of tomato which causing extensive yield losses to the crop. Environmental factors play an important role in the development of disease. To investigate the congenial conditions to develop the disease and germination of fungal spores an experiment was conducted in the year 2019 and 2020 at vegetable pathology laboratory of Dr Yashwant Singh Parmar University of Horticulture and Forestry Nauni Solan, Himachal Pradesh, India. Under investigations effect of temperature and relative humidity (RH) on conidial germination and early blight diseases development of tomato caused by Alternaria solani was studied under in vitro and in vivo conditions. Under in vitro conditions, maximum germination of conidia (75.54%) was recorded at 30°C after 24 hours and minimum (23.24%) was recorded at 15°C. length of germ tube (129.55 µm) was maximum at 25°C, whereas, minimum (67.09 µm) was observed at 15°C. In case of relative humidity maximum conidial germination (95.18%) and germ tube length of 238.95 µm was observed at 100% level. Under in vivo conditions disease severity was maximum (81.48%) at 30°C followed by 25°C (78.21%), whereas, minimum (18.88%) was recorded at 40°C. Similarly, the maximum (88.09%) disease severity was observed at 100% RH level while, it was minimum at 38% level of humidity. Overall, the relative humidity of more than 90% and temperature range of 25–30°C was found most congenial for the development of disease.

Experimental study on the performance of various porous water-absorbing fiber materials for indoor humidification

To increase the relative humidity in heating rooms in winter, a new humidification method is proposed. The capillary water absorption performance of porous fiber materials is utilized to compose a bottom water-absorbing component, and the spray device of the traditional direct evaporative cooling system is eliminated, which can be used for miniaturized heating humidification devices. To compare the heating and humidification performance of various fiber materials, an experimental system was designed and constructed with fiber materials arranged in parallel as humidification components. The experimental materials include nonwoven fabric (Sample A), polyester fiber (Sample B) and fiber cotton (Sample C). The experimentally obtained temperature drop is lower than the conventional evaporative cooling system, and the relative humidity enhancement is between 6 % and 15 %. Sample B had a maximum temperature drop of 6.83 °C, a maximum relative humidity increment of 14.07 %, a maximum humidification capacity of 272.4 g/h, and a maximum humidification capacity per unit area of material of 972.86 g/(m2·h). The maximum humidification capacity of Sample B was 13.8 % higher than that of Sample A and 18.5 % higher than that of Sample C. The cooling energy density of the three samples was only within 330 J/m3 under all working conditions. Applying sample B to the fan coil unit increased the relative humidity by 33.8 % with only 0.3 °C temperature drop, providing humidification while heating the indoor air. This paper provides a theoretical basis for a fiber material humidification, which to some extent can meet the heating and humidification integration in winter.

Effect of Covers Terraces on Humidity Inside Buildings: Case Study in the City of Rabat

Covers terraces are becoming increasingly widespread, given their advantages in terms of regulating the building's climate and the speed at which rainwater runs off. The first article in this research consisted in selecting plantations capable of withstanding the conditions of terraces, while the second article dealt with the effect of terrace coverings on the temperature inside buildings. Although sedums are the most widely used plant species for covering building terraces and have shown the best resilience and adaptability for green roofs, additional ecosystem services can be provided by native plants, suggesting future research to optimize plant composition and cover for sustainable green roofs. This research compares the effect of terrace coverings of different thicknesses on humidity inside buildings. This research compares the effect of terrace covers with different thicknesses (5 cm and 10 cm), on humidity inside buildings during the four seasons. The results show that the 10 cm thick terrace cover has a humidity-increases effect during the warm period (+ 3,01%). The 5 cm thick terrace cover has a humidity-reducing effect during the cold period (-0,2%). Also, the maximum relative humidity of the 10 cm thick cover decreased by 9.48%, and the maximum relative humidity of the 5 cm thick cover decreased by 13.25%.

Machine learning-based analysis and prediction of meteorological factors and urban heatstroke diseases.

Heatstroke is a serious clinical condition caused by exposure to high temperature and high humidity environment, which leads to a rapid increase of the core temperature of the body to more than 40°C, accompanied by skin burning, consciousness disorders and other organ system damage. This study aims to analyze the effect of meteorological factors on the incidence of heatstroke using machine learning, and to construct a heatstroke forecasting model to provide reference for heatstroke prevention. The data of heatstroke incidence and meteorological factors in a city in South China from May to September 2014-2019 were analyzed in this study. The lagged effect of meteorological factors on heatstroke incidence was analyzed based on the distributed lag non-linear model, and the prediction model was constructed by using regression decision tree, random forest, gradient boosting trees, linear SVRs, LSTMs, and ARIMA algorithm. The cumulative lagged effect found that heat index, dew-point temperature, daily maximum temperature and relative humidity had the greatest influence on heatstroke. When the heat index, dew-point temperature, and daily maximum temperature exceeded certain thresholds, the risk of heatstroke was significantly increased on the same day and within the following 5 days. The lagged effect of relative humidity on the occurrence of heatstroke was different with the change of relative humidity, and both excessively high and low environmental humidity levels exhibited a longer lagged effect on the occurrence of heatstroke. With regard to the prediction model, random forest model had the best performance of 5.28 on RMSE and dropped to 3.77 after being adjusted. The incidence of heatstroke in this city is significantly correlated with heat index, heatwave, dew-point temperature, air temperature and zhongfu, among which the heat index and dew-point temperature have a significant lagged effect on heatstroke incidence. Relevant departments need to closely monitor the data of the correlated factors, and adopt heat prevention measures before the temperature peaks, calling on citizens to reduce outdoor activities.

Distribution Characteristics and Prediction of Temperature and Relative Humidity in a South China Greenhouse

South China has a climate characteristic of high temperature and high humidity, and the temperature and relative humidity inside a Venlo greenhouse are higher than those in the atmosphere. This paper studied the effect of ventilation conditions on the spatial and temporal distribution of temperature and relative humidity in a Venlo greenhouse. Two ventilation conditions, with and without a fan-pad system, were studied. A GA + BP neural network was applied to predict the temperature and relative humidity in fan-pad ventilation in the greenhouse. The results show that the temperature in the Venlo greenhouse ranged from 15.8 °C to 48.5 °C, and the relative humidity ranged from 24.9% to 100% during the tomato-planting cycle. The percentage of days when the temperature exceeded 35 °C was 67.3%, and the percentage of days when the average relative humidity exceeded 70% was 83.7%. The maximum temperature differences between the three heights under NV (Natural Ventilation) and FPV (Fan-pad Ventilation) conditions were 3.4 °C and 4.5 °C, respectively. The maximum relative humidity differences between the three heights under NV and FPV conditions were 8.4% and 21.7%, respectively. The maximum temperature difference in the longitudinal section under the FPV conditions was 3.2 °C, while the relative humidity was 11.4%. The cooling efficiency of the fan-pad system ranged from 16.6% to 70.2%. The non-uniform coefficients of the temperature under the FPV conditions were higher than those under the NV conditions, while the nonuniform coefficients of the relative humidity were the highest during the day. The R2, MAE, MAPE and RMSE of the temperature-testing model were 0.91, 0.94, 0.11, and 1.33, respectively, while those of relative humidity model were 0.93, 2.83, 0.10, and 3.86, respectively. The results provide a reference for the design and management of Venlo greenhouses in South China.

Vulnerability assessment of aerosol and climate variability for rice and maize yield using EO datasets for sustainable agriculture over India.

Human-generated aerosol pollution gradually modifies the atmospheric chemical and physical attributes, resulting in significant changes in weather patterns and detrimental effects on agricultural yields. The current study assesses the loss in agricultural productivity due to weather and anthropogenic aerosol variations for rice and maize crops through the analysis of time series data of India spanning from 1998 to 2019. The average values of meteorological variables like maximum temperature (TMAX), minimum temperature (TMIN), rainfall, and relative humidity, as well as aerosol optical depth (AOD), have also shown an increasing tendency, while the average values of soil moisture and fraction of absorbed photosynthetically active radiation (FAPAR) have followed a decreasing trend over that period. This study's primary finding is that unusual variations in weather variables like maximum and minimum temperature, rainfall, relative humidity, soil moisture, and FAPAR resulted in a reduction in rice and maize yield of approximately (2.55%, 2.92%, 2.778%, 4.84%, 2.90%, and 2.82%) and (5.12%, 6.57%, 6.93%, 6.54%, 4.97%, and 5.84%), respectively. However, the increase in aerosol pollution is also responsible for the reduction of rice and maize yield by 7.9% and 8.8%, respectively. In summary, the study presents definitive proof of the detrimental effect of weather, FAPAR, and AOD variability on the yield of rice and maize in India during the study period. Meanwhile, a time series analysis of rice and maize yields revealed an increasing trend, with rates of 0.888 million tons/year and 0.561 million tons/year, respectively, due to the adoption of increasingly advanced agricultural techniques, the best fertilizer and irrigation, climate-resilient varieties, and other factors. Looking ahead, the ongoing challenge is to devise effective long-term strategies to combat air pollution caused by aerosols and to address its adverse effects on agricultural production and food security.

ANALYSIS OF THE CALCULATION OF REFERENCE EVAPOTRANSPIRATION ACCORDING TO THE DATA OF THE STATE METEOROLOGICAL STATION

Since direct measurement of reference evapotranspiration (ET0) is a complex, time-consuming and expensive process, the most common procedure is to estimate ET0 from climate data. The purpose of this study was to perform reference evapotranspiration calculations based on the data of the state meteorological station Askania-Nova and compare them with the actual ET0 data obtained using an automatic Internet meteorological station. The data for the study were taken from the state meteorological station Askania-Nova (township Askania-Nova, Kakhovsky district, Kherson region, 46.45°N 33.88°E) and the automatic Internet meteorological station iMetos IMT 300 from the company "Pessl Instruments", which is located at the meteorological site of the Askaniysk DSDS (Tavrychanka village, Kakhovsky district, Kherson region, 46.55°N, 33.83°E). Standard evapotranspiration was calculated using the Penman-Monteith method (FAO56-RM). To assess the accuracy of ET0 calculations, mean absolute percent error (MAPE), root mean square error (RMSE) and Standard Error of Estimate (SEE ) were determined. According to the results of the comparison of indicators from two meteorological stations, it was found that the smallest errors are inherent in the daily average and maximum temperature and relative air humidity (MAPE<10%), for the minimum temperature and relative air humidity, the MAPE errors are 18,1 and 13,7%, respectively. The MAPE error for water vapor pressure deficit and solar radiation is 20,2 and 26,3%, respectively. The largest MAPE error of 40,3% was established for wind speed measurements. The average MAPE error between the calculated ET0, based on the meteorological data of the Askania-Nova station, and the actual ET0 data obtained from the automatic Internet meteorological station iMetos is 16,8%, RMSE – 0,65 mm, SEE – 0,56 mm. Applying a coefficient of 0,92 when calculating ET0 reduces the errors of MAPE, RMSE, and SEE by 3,2%, 0,15 mm, and 0,05 mm, respectively, for all calculation periods. For the May-August period, the MAPE error was 10,7%, which brings the calculations close to high accuracy (MAPE <10%). Based on the results of the calculations, it was established that on average over the years of research, the actual ET0 was 68 mm less than the calculated one. The absolute errors of determination of ETc depended on the crop and the average over the years of research ranged from 33 mm (winter wheat) to 68 mm (early tomatoes). The application of the refined value of ET0 in calculations reduces the absolute errors in the determination of c over the years of research, this error did not exceed 6 mm (early tomato). Research results confirm the possibility of using meteorological indicators obtained from state meteorological stations to calculate ET0. To increase the accuracy of calculations, it is necessary to use a refinement coefficient.