Climatic Elements Research Articles

Performance of agricultural factors on yield of sweet corn (Zea mays L. Saccharata ) - A review

Sweet corn producers and industries require more reliable cultivars which could be accomplished by hybrid breeding. However, progressive phenological growth may be affected by different factors. In this paper, we analyze the key factors that determine the growth and yield of sweet corn. Environmental factors such as temperature and photoperiod were strong determinants of dates of flowering and harvest which are often crucial to yield in diverse climates and agricultural systems, besides the country's pedological conditions, especially soil fertility, affected phenological development. The effectiveness of fertilization in improving sweet corn growth performance was significantly influenced by the soil characteristics, the water supply, the genotype, and the agrotechnological factors. Therefore, genetic improvement of hybrids should be incorporated into the climate and soil elements to stabilize sweet corn yields in various agroecosystems. Decisions made in the sowing period are very significant, as up to 30% of the obtained yield may depend on making the proper choice. Deviation from the optimum date (either early or late sowing) may decrease yield. When deciding about the sowing date of maize, one needs to consider climate, soil quality, geographical location, temperature, weed infestation, sowing seed quality, and the ripening time of the hybrid to be produced.

Analysis of the Sand Dunes Mobility Based on Climate Elements in East Azerbaijan Province

Analysis of the Sand Dunes Mobility Based on Climate Elements in East Azerbaijan Province

Impact of Climate on Ecotourism in the Muthanna Desert

This research addresses the measurement and statistical analysis of thermal comfort in the desert of Al Muthanna, aiming to identify the most influential climatic elements on human comfort in the study area. Additionally, it seeks to determine the most suitable climatic criteria that align with the climate and reality of the study area. The research has led to the following key conclusions: 1. The months (June, July, August, September) recorded values of (H-) indicating extreme discomfort according to the Thermal Comfort Index (THI), whereas the months of December and February experienced values of (C-) denoting discomfort. Extreme discomfort (H) was recorded in October and April, while relative comfort was noted in November and March, with THI values between 17 and 18. 2. The months (June, July, August) were recorded as (H+) indicating very hot conditions, while the months (May, September, October) recorded a value of (H), and ideal comfort was observed in December and January. The values of (P) indicating ideal warmth were recorded during the months (February, March, April, November.

A Novel Approach to Dual Feature Selection of Atrial Fibrillation Based on HC-MFS.

This investigation sought to discern the risk factors for atrial fibrillation within Shanghai's Chongming District, analyzing data from 678 patients treated at a tertiary hospital in Chongming District, Shanghai, from 2020 to 2023, collecting information on season, C-reactive protein, hypertension, platelets, and other relevant indicators. The researchers introduced a novel dual feature-selection methodology, combining hierarchical clustering with Fisher scores (HC-MFS), to benchmark against four established methods. Through the training of five classification models on a designated dataset, the most effective model was chosen for method performance evaluation, with validation confirmed by test set scores. Impressively, the HC-MFS approach achieved the highest accuracy and the lowest root mean square error in the classification model, at 0.9118 and 0.2970, respectively. This provides a higher performance compared to existing methods, thanks to the combination and interaction of the two methods, which improves the quality of the feature subset. The research identified seasonal changes that were strongly associated with atrial fibrillation (pr = 0.31, FS = 0.11, and DCFS = 0.33, ranked first in terms of correlation); LDL cholesterol, total cholesterol, C-reactive protein, and platelet count, which are associated with inflammatory response and coronary heart disease, also indirectly contribute to atrial fibrillation and are risk factors for AF. Conclusively, this study advocates that machine-learning models can significantly aid clinicians in diagnosing individuals predisposed to atrial fibrillation, which shows a strong correlation with both pathological and climatic elements, especially seasonal variations, in the Chongming District.

Typical and extreme weather datasets for studying the resilience of buildings to climate change and heatwaves

We present unprecedented datasets of current and future projected weather files for building simulations in 15 major cities distributed across 10 climate zones worldwide. The datasets include ambient air temperature, relative humidity, atmospheric pressure, direct and diffuse solar irradiance, and wind speed at hourly resolution, which are essential climate elements needed to undertake building simulations. The datasets contain typical and extreme weather years in the EnergyPlus weather file (EPW) format and multiyear projections in comma-separated value (CSV) format for three periods: historical (2001–2020), future mid-term (2041–2060), and future long-term (2081–2100). The datasets were generated from projections of one regional climate model, which were bias-corrected using multiyear observational data for each city. The methodology used makes the datasets among the first to incorporate complex changes in the future climate for the frequency, duration, and magnitude of extreme temperatures. These datasets, created within the IEA EBC Annex 80 “Resilient Cooling for Buildings”, are ready to be used for different types of building adaptation and resilience studies to climate change and heatwaves.

The Synoptic Relationship Between Days of Thermal Extremes and Pressure Systems During the Year 2020 in Iraq (Baghdad as An Example)

Temperature is one of the most important elements of weather and climate that directly and indirectly affects other weather and climatic phenomena. The study aims to daily detect thermal extreme days during the year 2020 over the months of the year for the maximum temperature (hot extreme days) at the Baghdad Climatic Station as well. The pressure systems varied in their strength or shallowness in each case. The study concluded that the number of days (cases) of thermal extremes during one month, with one day being unique in recording the highest or lowest extreme values, according to the prevailing lows or highs in the atmosphere, as the maximum extremes in the average maximum temperature (extreme hot days) during the study year were evident on the twenty-eighth day of July at a rate of (51.8) °C, followed by the high temperature extremes on the thirtieth day of the month. August, around (47.3)°C, and the cause of the extremes on the two days mentioned was the strongest pressure system controlling Iraq’s climate during the summer months, represented by the Indian seasonal low, and its deep appearance at the level of 850 millibars, accompanied by tropical air in the middle of the troposphere, that is, at the level of 500 millibars, in the form of a dent or barrier. My pressure, which increases the severity of the daily thermal extremes for that month, citing in our study the daily weather maps on each thermally extreme day.

Differential responses of vegetation phenology to climatic elements during extreme events on the Chinese loess plateau

Extreme, dry events have major impacts on vegetation phenology worldwide. However, the differential responses of vegetation phenology to climatic elements during these extreme events remain unclear. We investigated the response of vegetation phenology to climatic factors during extreme events in arid and semi-arid regions of the Chinese Loess Plateau, using the climate water deficit method, to identify extremely dry and wet events. The results revealed that extremely wet events extended the vegetation growth periods in addition to global warming, whereas extremely dry events did not completely counteract this effect. During different extreme events, phenological changes in vegetation on the Loess Plateau were primarily influenced by the interactive effects of climatic factors. During extremely dry events, a 100 mm increase in precipitation advanced the start of the season by 3.0, 9.2, and 16.7 days in forest, shrubland, and grassland, respectively. During extremely wet events, a 1 °C rise in temperature delayed the end of the season by 1.6, 0.6, and 3.8 days in forest, shrubland, and grassland, respectively. These findings provide crucial guidance for improving predictions of plant phenology changes under extreme climatic events and unraveling biosphere–atmosphere feedback cycles.

Effects of climatic conditions of Al Seeb in Oman on the performance of solar photovoltaic panels

Human activities and climatic elements, including temperature, humidity, and wind speed, have an impact on natural dust deposition. Therefore, this study aims to investigate the effects of wind speed, relative humidity, and ambient temperature on the performance of soiled photovoltaic panels in Al Seeb, Oman. The study was conducted by exposing the solar PV panels to outdoor sunlight for a duration of two months. Parameters such as solar radiation, voltage, current, solar panel temperature, wind speed, relative humidity, and ambient temperature were collected in a short time interval. It was observed that the dust densities of 20.7 g/m2, 27 g/m2, and 41.3 g/m2 resulted in electrical power reductions of 18 %, 33 %, and 40 % for the panels uncleaned for one week, two weeks, and three weeks, respectively. The effect of daily dust resulted in an energy reduction of 14 %. Moreover, dust deposition decreases when the wind speed increases, resulting in a higher power output and vice versa. The higher the humidity, the stronger the dust's adhesion to the surface, resulting in more deposition and reduced power output. The maximum power output of 82.3 W was achieved at the wind speed of 10 m/s, 34.9 % relative humidity, and ambient temperature of 38.5 °C.

Analysis of the effects of climatic elements on the mobility of wind-blown sand in the west of Lut desert

Analysis of the effects of climatic elements on the mobility of wind-blown sand in the west of Lut desert

Evaluating the relationship of vegetation dynamics with rainfall and land surface temperature using geospatial techniques in South Wollo zone, Ethiopia

This study was conducted in the South Wollo zone to assess vegetation dynamics and their relationship with rainfall and land surface temperature. To that end, 16-day 250-meter resolution Moderate Imaging Spectro-radiometer (MODIS) Normalized difference vegetation Index (NDVI), 5-kilometer resolution MODIS Land Surface Temperature (LST), 5-kilometer CHIRPS data were used. A Mann-Kendall (MK) trend test was used to determine the trend of each dataset using seasonal and annual time series. In addition, the Pearson correlation coefficient was used to estimate the relationship between NDVI and climatic elements. The results of the study revealed that there was no significant change in the annual and seasonal rainfall and LST during the period 2000 to 2021, except LST in the autumn season (p = 0.01) and NDVI was significant only in the mean annual (p = 0.027) and summer seasons (p = 0.03). The correlation between NDVI and rainfall was positive (r = 0.84, 0.78, 0.72 & 0.79) and obtained for the summer, autumn, winter, and spring season respectively. Similarly, the correlation between NDVI and LST was negative (r= -0.79, -0.75, -0.72, and -0.78) in the summer, autumn, winter, and spring seasons respectively. The outcome of the study indicates precipitation is the most significant factor in the distribution of vegetation and LST

The Influence of Vegetation on Climate Elements in Northwestern China

Vegetation plays a crucial role in maintaining the balance between nature, water and soil resources. However, understanding its impact mechanisms in arid and semi-arid areas remains limited. This study aims to analyze the spatial–temporal characteristics of the vegetation leaf area index (LAI) and climate elements in typical regions of northwest China and the correlations between LAI and climate elements; it also aims to explore the influence of regional vegetation growth on climate change. The results reveal significant correlations between LAI and various climate elements. Specifically, within the same region, surface temperature, precipitation, vegetation transpiration, and total evaporation show positive correlations with the LAI, whereas surface albedo shows a negative correlation. Vegetation may affect climate through both heat and water exchange between the land and atmosphere. Increased vegetation leads to the enhanced absorption of solar radiation by the land surface, elevating surface temperature. Increased levels of vegetation also increase vegetation transpiration and total evaporation, increasing the water vapor content in the atmosphere and thus leading to increased surface precipitation. Therefore, vegetation distribution plays a role in climate change, and ecological restoration projects in the northwest region hold significant potential for addressing ecological challenges in its arid and semi-arid areas.

Evolutionary characteristics of carbon sources/sinks in Chinese terrestrial ecosystems regarding to temporal effects and geographical partitioning

Whereas the current complex diversity in Chinese terrestrial ecosystems and the significant differences in net ecosystem productivity (NEP) among various regions, as well as the temporal effect between vegetation and climate factors, the NEP in Chinese terrestrial ecosystems was estimated between 2001 and 2020, and characterized the spatiotemporal dynamics of NEP and its driving mechanisms, taking into account geographic subdivisions. The results demonstrated that the multiannual mean of NEP in Chinese terrestrial ecosystems ranged from 42.79 to 61.91 g C/m2a over the past two decades, indicating a volatile upward trend. Spatially, the NEP of Chinese terrestrial ecosystems indicated significant spatial differentiation, with carbon sinks encompassing 68.83 %, primarily focused in Northeast Region (DB), East China (HD), South China (HN), and Central China (HZ). Moving forward, Chinese terrestrial ecosystems will be predominantly restored, with 40.88 % of the area going from degradation to improvement, and 14.17 % of the area undergoing sustained improvement. There were geographic differences in the temporal effects between NEP and climate elements (precipitation and temperature). Precipitation was identified as the dominant factor affecting the variation of NEP in DB, HB, HZ, XB, and XN. While solar radiation was the major factor influencing the variation of NEP in HD (51.72 %) and HN (76.07 %). Compared with human activities, climate change emerged as the primary driver impacting the spatiotemporal evolution of NEP in Chinese terrestrial ecosystems, with 61.40 % of the area of NEP increasing due to climate change, while 58.42 % increased due to human activities. Additionally, changes in land use/land cover played a crucial role in altering NEP of terrestrial ecosystems in China.

The abundance of soil fauna in Gunung Halimun Salak National Park at altitude: 1100 – 1300 masl

As a bioindicator of soil quality and controller of soil dynamic (fertility), soil fauna is influenced by various environmental factors like climate and edaphic elements. This research aimed to examine the abundance of soil fauna in a lower montane forest ecosystem at different elevations and analysed how the environment affects their abudance. The study used purposive sampling in Gunung Halimun Salak National Park, specifically at altitude of 1,100 masl; 1,200 masl; 1,300 masl; and an open area (1,230 masl) as a control. LSD test revealed a significant difference on soil fauna abundance between the 1,100 masl elevation and the control group. However, there was no significant difference of soil fauna abundance between altitude of 1,200 masl and 1,300 masl. The highest abundance of soil fauna was found at 1,100 masl, with 156 individual/m2. Conversely, the lowest abundance of soil fauna was recorded in the open area at 1,230 masl, with 3 individual/m2. The Pearson correlation analysis showed that soil fauna had positive correlation with light intensity and air temperature, and negative correlation with air humidity. Furthermore, Soil fauna abundance had also positive correlation with edaphic parameters (CEC, C-org., litter weight, soil respiration, porosity, and soil temperature, except soil pH and bulk density.

Climate and disease: tackling coffee brown-eye spot with advanced forecasting models.

Climate influences the interaction between pathogens and their hosts significantly. This is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown-eye spot, can reduce yields drastically. This study focuses on forecasting coffee brown-eye spot using various models that incorporate agrometeorological data, allowing for predictions at least 1 week prior to the occurrence of disease. Data were gathered from eight locations across São Paulo and Minas Gerais, encompassing the South and Cerrado regions of Minas Gerais state. In the initial phase, various machine learning (ML) models and topologies were calibrated to forecast brown-eye spot, identifying one with potential for advanced decision-making. The top-performing models were then employed in the next stage to forecast and spatially project the severity of brown-eye spot across 2681 key Brazilian coffee-producing municipalities. Meteorological data were sourced from NASA's Prediction of Worldwide Energy Resources platform, and the Penman-Monteith method was used to estimate reference evapotranspiration, leading to a Thornthwaite and Mather water-balance calculation. Six ML models - K-nearest neighbors (KNN), artificial neural network multilayer perceptron (MLP), support vector machine (SVM), random forests (RF), extreme gradient boosting (XGBoost), and gradient boosting regression (GradBOOSTING) - were employed, considering disease latency to time define input variables. These models utilized climatic elements such as average air temperature, relative humidity, leaf wetness duration, rainfall, evapotranspiration, water deficit, and surplus. The XGBoost model proved most effective in high-yielding conditions, demonstrating high precision and accuracy. Conversely, the SVM model excelled in low-yielding scenarios. The incidence of brown-eye spot varied noticeably between high- and low-yield conditions, with significant regional differences observed. The accuracy of predicting brown-eye spot severity in coffee plantations depended on the biennial production cycle. High-yielding trees showed superior results with the XGBoost model (R2 = 0.77, root mean squared error, RMSE = 10.53), whereas the SVM model performed better under low-yielding conditions (precision 0.76, RMSE = 12.82). The study's application of agrometeorological variables and ML models successfully predicted the incidence of brown-eye spot in coffee plantations with a 7 day lead time, illustrating that they were valuable tools for managing this significant agricultural challenge. © 2024 Society of Chemical Industry.

Atmospheric Patterns in Porto Velho, Rondônia, Southwestern Amazon, in a Rhythmic Context between 2017 and 2018

This paper aims to analyze the weather conditions in Porto Velho (Rondonia, Brazil, Western Amazon) and the influence of air masses on the climatic elements between 2017 and 2018, using rhythmic analysis. Climatic data were obtained through the official weather station, tabulated and statistically organized, and processed in R Studio programming language. The monitoring of air masses occurred through the synoptic charts of the Navy Hydrography Center. The results were analyzed by dry–rainy transition season, rainy season, wet–dry transition season, and dry season. Thus, the results point out that the Tropical Continental mass (mTc) acted up to 62.9%, responsible for the low precipitation index in October 2017. Although the mass has characteristics of warm and unstable weather, it is even lower than the action of the mEc. In January 2018, there was an 85.5% prevalence of the Continental Equatorial Mass (mEc), added to the action of the South Atlantic Convergence Zone (ZCAS), which contributed to an accumulated rainfall of 443 mm/month. In April 2018, the mEC acted with 56.7%, reaching 35.5% in August. Another highlight was the performance of the Tropical Atlantic mass (mTa) (27.4%) and mTc (19.4%), both of which had a crucial role in the dry season, followed by the Polar Atlantic mass (mPa) (17.7%), that contributed to the phenomenon of “coldness” in the region. Therefore, the mEc is extremely important in the control of the relative humidity of the air and the precipitations, while the mTc is a dissipator of winds that, at times, inhibits the performance of the mEc.

Understsanding the Effects of UV Radiation on Climate Change: A Comprehensive Review

‌This review paper explores the intricate relationship between ultraviolet (UV) radiation and climate change, delving into its multifaceted effects on atmospheric processes, global temperature dynamics, biological entities, and the intricate feedback mechanisms that shape the Earth's climate. Beginning with an examination of UV radiation's interaction with the atmosphere, we uncover its role in ozone layer dynamics and its potential to influence atmospheric circulation patterns.The discussion extends to the impact of UV radiation on global temperature patterns, emphasizing its contribution to the Earth's energy balance and its interaction with greenhouse gases. Moving beyond climatic elements, we explore the biological impacts of UV radiation, encompassing its effects on human health, plant productivity, and aquatic ecosystems. We unveil the intricate feedback loops, revealing how changes induced by UV radiation can cascade through the atmosphere, ecosystems, and human societies.The technological and policy implications are scrutinized, highlighting innovations in monitoring and mitigation strategies alongside international policies crafted to address UV radiation's impact on climate change. Looking toward the future, the paper offers insights into potential advancements, challenges, and opportunities, emphasizing the need for ongoing research, global collaboration, and sustainable practices to navigate the evolving dynamics of UV radiation and climate change. In conclusion, this review consolidates a comprehensive understanding of UV radiation's pivotal role in shaping our climate, offering a foundation for informed decision-making and sustainable stewardship of our planet

Impact of Soil Temperature and Precipitation on Vegetation Cover Over Selected Stations in Iraq

Records of precipitation and soil temperature, two of the most significant climatic elements impacting vegetation cover, were gathered as annual and monthly data over a period of 20 years from the European Center for Long-term Numerical Weather Forecasts. The goal of this study was to ascertain the impact of plant cover on climatic variations. The study’s final findings revealed that Mosul station had the greatest value of vegetation at 0.24 and Basra station had the lowest value at 0.15. The Mosul station recorded the maximum amount of precipitation, 1400 mm in the northern and central parts during the winter, and the lowest amount, 8 mm, during the fall and summer months in the districts west of the Rutba station. Using the Pearson test, it was discovered that the relationship between vegetation cover and soil temperature is inverse, and this, in turn, plays a significant role in the lack of vegetation cover in the southern regions because of nature. The desert of the region, as well as the lack of precipitation and high temperatures, is responsible for the region’s lack of vegetation. The highest value reached by the soil temperature was in the year 2018 at the Basra station.

Decoding seasonal variability of air pollutants with climate factors: A geostatistical approach using multimodal regression models for informed climate change mitigation

In response to changes in climatic patterns, a profound comprehension of air pollutants (AP) variability is vital for enhancing climate models and facilitating informed decision-making in nations susceptible to climate change. Earlier research primarily depended on limited models, potentially neglecting intricate relationships and not fully encapsulating associations. This study, in contrast, probed the spatiotemporal variability of airborne particles (CO, CH4, SO2, and NO2) under varying climatic conditions within a climate-sensitive nation, utilizing multiple regression models. Spatial and seasonal AP data were acquired via the Google Earth Engine platform, which indicated elevated AP concentrations in primarily urban areas. Remarkably, the average airborne particle levels were lower in 2020 than in 2019, though they escalated during winter. The study employed linear regression, Pearson's correlation (PC), Spearman rank correlation models, and Geographically Weighted Regression (GWR) models to probe the relationship between pollutant variability and climatic elements such as rainfall, temperature, and humidity. Across all seasons, APs showed a negative correlation with rainfall while displaying positive correlations with temperature and humidity. The GWR and PC models produced the most reliable results from all the models employed, with the GWR model superseding the rest. Moreover, heightened aerosol levels were detected within a rainfall range of 600 mm/season, a temperature range of 25–30 °C, and humidity levels of 75 %–85 %. Overall, this study emphasizes the growing levels of APs in correlation with meteorological changes. By adopting a comprehensive approach and considering multiple factors, this research provides a more sophisticated understanding of the relationship between AP variability and climatic shifts.

A latent class analysis of campus sexual assault.

Campus sexual assault (CSA) is a pervasive problem among U.S. college students. Large-scale studies frequently find that between 20%-25% of women and 6%-7% of men are sexually victimized during their time at U.S. colleges and universities (Cantor et al., 2015, 2019; Fisher et al., 2000; Krebs et al., 2007; Krebs et al., 2010). The purpose of the present study is to examine individual responses to campus climate surveys in an effort to determine how students perceive their risk of being sexually assaulted and how they interact with campus policies. The present study addresses the research gap by utilizing latent class analysis with campus climate survey data in order to identify subgroups of students based on their responses to elements of campus climate. Climate in this context is measured by the perceptions of campus constituencies and the policies of institutions of higher education. Additionally, the present study seeks to determine if latent subgroups could be predicted by victimization and gender. Results provided evidence for the existence of latent subgroups of students on elements of campus climate, and supported the hypothesis that latent class membership could be predicted by victimization status and gender. Implications of the present study may help institutions of higher education identify students who would benefit from focused outreach efforts to improve safety based on class membership. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Long term (1901−2021) trends and prediction of climatic variability in selected agro‐ecological zones of Himachal Pradesh using coupled statistical and machine learning approaches

This study analyses the trends of changing climatic elements in the hydrological regime of the Indian Himalayan Region with specific focus on Agro‐ecological zone II & III of Himachal Pradesh for the period of 1901−2021. The upper, middle, and lower catchment areas of Sutlej River Basin were studied to reveal regional trends in climatic parameters. The Mann‐Kendall test and Sen Slope analysis were used to estimate annual and seasonal trends and their magnitude. The results showed a significant decreasing trend in the lower catchment area, with a break point year estimated to be 1953 for the entire basin. Two blocks of analysis, 1901−1953 and 1954−2021, showed significant variations. Annual rainfall data revealed a statistically significant decreasing trend at different stations. The lower catchment area received a significant increase in rainfall compared to higher altitude stations. In terms of seasonal variation, the pre‐monsoon season showed a significant decrease, while the entire basin recorded a significant increase in average monthly temperature. The study concludes by generating future time series predictions using an ANN model for the period of 2022−2050. Overall, the study's findings indicate a significant change in climatic variables with signs of increasing monthly temperatures and decreasing annual rainfall.