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Background: Respiratory viral infections (RVIs) exhibit seasonal patterns influenced by biological, ecological, and climatic factors. Weather variables such as temperature, humidity, and wind impact the transmission of droplet-borne viruses, potentially affecting disease severity. However, the role of climate in predicting complications in pediatric RVIs remains unclear, particularly in the context of climate-change-driven extreme weather events. Methods: This retrospective cohort study analyzed 1610 hospitalization records of children (0–18 years) with lower respiratory tract infections in Rome, Italy, between 2018 and 2023. Viral pathogens were identified using nasopharyngeal molecular testing, and weather data from the week preceding hospitalization were collected. Several machine learning models were tested, including logistic regression and random forest, comparing the baseline (demographic and clinical) models with those including climate variables. Results: Logistic regression showed a slight improvement in predicting severe RVIs with the inclusion of weather variables, with accuracy increasing from 0.785 to 0.793. Average temperature, dew point, and humidity emerged as significant contributors. Other algorithms did not demonstrate similar improvements. Conclusions: Climate variables can enhance logistic regression models' ability to predict RVI severity, but their inconsistent impact across algorithms highlights challenges in integrating environmental data into clinical predictions. Further research is needed to refine these models for use in reliable healthcare applications. [ABSTRACT FROM AUTHOR]

This research study aims to investigate the effect of climatic variables, specifically precipitation, temperature, and humidity, on changes in vegetation indices of orange orchards in Hassan Abad, Darab County, using satellite data. Consequently, observational data, including orange tree phenology data and meteorological data from the agricultural weather station, were collected over a period of more than 10 years (2006 to 2016). MODIS images from 2006 to 2016 were referenced based on territorial data and 1:25000 maps from the Iran National Cartographic Center. These images were used to calculate remote sensing vegetation indices, namely the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). The results demonstrated that the variables of maximum humidity, minimum temperature, and precipitation have a significant positive effect on the NDVI variable. Additionally, the variables of maximum temperature and minimum humidity have a significant negative effect on both the NDVI and EVI. To determine the significance of each independent variable in predicting the dependent variables, the artificial neural network method was employed. The findings showed that the climatic elements of precipitation, minimum temperature, maximum temperature, minimum humidity, and maximum humidity had the greatest effect on EVI, with values of 0.39, 0.3, 0.13, 0.1, and 0.06 respectively. Moreover, the effect of these variables on the NDVI index is equal to their coefficients, which are 0.2, 0.28, 0.22, 0.11, and 0.17 respectively. Finally, the ARMAX regression method was used to improve the explanatory power of the model. The results indicated that this method enhanced the explanatory power of the model and reduced the forecasting error. [ABSTRACT FROM AUTHOR]

The authors aimed to forecast the cumulative COVID-19 confirmed cases and deaths by the most appropriate model of the top five impacted countries and three South Asian countries incorporating the climate factors as covariates. Different statistical and data science methods are used in this study. The results of the model selection criteria depict that the ELM algorithm is adequate for France, Germany, and Spain, while the MLP algorithm tends to have a better forecast for India and Pakistan. In Sri Lanka, Italy, and the United States, the well-known ARIMAX model tends to be a good match for death predictions. The findings showed that the inclusion of the meteorological variables improves the accuracy of modeling both COVID-19 cases and deaths, for all the chosen countries' cumulative confirmed cases except Italy and Sri Lanka. However, in the case of modeling deaths, it is observed that the inclusion of meteorological variables was not able to enhance the forecasting accuracy of the model in each of the selected countries. Though no single model was found to be suitable for all countries, the authors identify the most appropriate models for each country for forecasting and make the sixty-day-ahead forecast for each country. [ABSTRACT FROM AUTHOR]

Climate warming is constantly causing hydro-meteorological perturbations, especially in high-altitude mountainous regions, which lead to the occurrences of landslides. The impact of climatic variables (i.e., precipitation and temperature) on the distribution of landslides in the eastern regions of the Himalayas is poorly understood. To address this, the current study analyzes the relationship between the spatial distribution of landslide characteristics and climatic variables from 2013 to 2021. Google Earth Engine (GEE) was employed to make landslide inventories using satellite data. The results show that 2163, 6927, and 9601 landslides were heterogeneously distributed across the study area in 2013, 2017, and 2021, respectively. The maximum annual temperature was positively correlated with the distribution of landslides, whereas precipitation was found to have a non-significant impact on the landslide distribution. Spatially, most of the landslides occurred in areas with maximum annual precipitation ranging from 800 to 1600 mm and maximum annual temperature above 15°C. However, in certain regions, earthquake disruptions marginally affected the occurrence of landslides. Landslides were highly distributed in areas with elevations ranging between 3000 and 5000 m above sea level, and many landslides occurred near the lower permafrost limit and close to glaciers. The latter indicates that temperature change-induced freeze-thaw action influences landslides in the region. Temperature changes have shown a positive correlation with the number of landslides within elevations, indicating that temperature affects their spatial distribution. Various climate projections suggest that the region will experience further warming, which will increase the likelihood of landslides in the future. Thus, it is crucial to enhance ground observation capabilities and climate datasets to effectively monitor and mitigate landslide risks. [ABSTRACT FROM AUTHOR]

Wildfires present a major global environmental issue, exacerbated by climate change. The Iranian Northern Zagros Forests, characterized by a Mediterranean climate, are particularly vulnerable to fires during hot, dry summers. This study investigates the impact of climate change on forest fires in these forests from 2006 to 2023. The analysis revealed significant year-to-year fluctuations, with notable fire occurrence in years 2007, 2010, 2021, and 2023. The largest burned area occurred in 2021, covering 2655.66 ha, while 2006 had the smallest burned area of 175.27 ha. Climate variables such as temperature, humidity, precipitation, wind speed, heat waves, and solar radiation were assessed for their effects on fire behavior. Strong correlations were found between higher average temperatures and larger burned areas, as well as between heat waves and increased fire frequency. Additionally, higher wind speeds were linked to larger burned areas, suggesting that increased wind speeds may enhance fire spread. Multiple linear regression models demonstrated high predictive accuracy, explaining 84% of the variance in burned areas and 69.6% in the variance in fire frequency. These findings document the growing wildfire risk in the Northern Zagros region due to climate change, highlighting the urgent need to integrate scientific research with policies to develop effective wildfire management strategies for sustainable forest management. [ABSTRACT FROM AUTHOR]

International air quality indexes (AQIs) are derived from air pollution and are essential global tools for mitigating diseases such as asthma, as they are used to reduce exposure to triggers. The aim of this article is to systematically review the global literature on the use of AQIs in asthma-related studies. To evaluate the importance of the variables considered, a citation frequency index (Q) was used. The results suggest that the most frequently reported air pollutants related to asthma are PM (Q3) > NO2 (Q3) > O3 (Q3) > CO (Q3) > NO (Q3) > SO2 (Q3). In addition, climate variables play a relevant role in asthma research. Temperature (Q4) emerged as the most relevant climate variable, followed by atmospheric pressure (Q3) > wind direction (Q3) > solar radiation (Q3) > precipitation (Q3) > wind speed (Q3). AQIs, specifically the U.S.EPA Air Quality Index and the Air Quality Health Index, are directly associated with air pollution and the prevalence, severity and exacerbation of asthma. The findings also suggest that climate change presents additional challenges in relation to asthma by influencing the environmental conditions that affect the disease. Finally, this study provides a comprehensive view of the relationships among air quality, air pollutants and asthma and highlights the need for further research in this field to develop public health policies and environmental regulations. [ABSTRACT FROM AUTHOR]

Global climate change and landform alteration are correlated with a high impact on rainfall, land surface temperature (LST), vegetation conditions, and soil moisture. This study examines rainfall, temperature, vegetation condition, moisture, and drought index to identify the decadal change from 2002 to 2021 using Landsat and global Standardized Precipitation Evapotranspiration Index-base (SPEIbase) data in Google Earth Engine (GEE). In between 20 years, 50.54 mm of mean rainfall decreased in the study area while 6.31 °C of LST increased in the entire region. The most affected states are Bihar, Jharkhand, and West Bengal. Similarly, in northeast India, Assam, Tripura, and Meghalaya have huge effects on temperature variation. The green space (0.158), and moisture (−0.082) fluctuated mainly in Mizoram, Arunachal Pradesh, Manipur, and Tripura states. The SPEI has increased (−0.232) in Bihar, Manipur, Tripura, Assam, and Meghalaya. The analysis observed that decreased rainfall and high-temperature variation may impact crop production, moisture loss, cyclonic affected in West Bengal coastal areas, floods in the Brahmaputra River basin and Bihar, droughts, and urban heat island-related effects. Some policies like reduction of forest fire (northeast), flood (Assam), urban planning (West Bengal, Bihar, and Assam), industrial pollution, and jhum cultivation decrease can improve the climate change in those states, India.

Forest dynamics provide important information on the ecological environment. The Three-North Shelter Forest Program (TNSFP) is one of the world’s largest reforestation/afforestation programs, however the actual changes in forest cover in the Three-North Regions (TNR) of China resulting from this program are highly uncertain. This study quantified changes in fractional forest cover (FFC) at 30 m using Landsat data from 1996 to 2020. Using the Google Earth Engine platform, more than 40,000 images from Landsat-5, Landsat 7 and Landsat-8 were integrated, and the annual surface reflectance was normalized based on the multi-band least squares regression and maximum normalized difference vegetation index composite method. An ensemble learning model trained using high-resolution Gao-Fen 2 satellite imagery was used to generate the FFC long time-series product. FFC showed an increasing trend with average rates of 0.022/10a in the last 25 years, and 0.03/10a after 2010 largely corresponding to the fourth and fifth phases of the TNSFP. There are significant regional differences in the relationship between FFC and air temperature ([Formula: see text] = 0.37) and precipitation ([Formula: see text] = 0.49). The increased air temperature in arid and less rainy areas inhibit the FFC increase, whereas the increase in precipitation had a promoting effect. FFC appeared more sensitive to changes in solar radiation and heat conditions in humid and rainy areas. The attribution analysis revealed that 34% of FFC changes were caused by climatic variables and 66% were caused by non-climatic factors. Among them, afforestation associated with the TNSFP significantly increased FFC, and forest fire is a key factor of forest change in the Greater Khingan Ranges and Lesser Khingan Ranges regions. Planting single tree species caused biological disasters in forests of Xinjiang and Inner Mongolia. Further analysis of the increased FFC using high-level satellite products demonstrated an improvement in environmental conditions with cooler land surface temperature and higher vegetation gross primary production over the TNR.

This study deals with the statistical analysis of climate variables (temperature, relative humidity, and rainfall) and the analysis of data and values of climate characteristics for the period of 2006- 2016 in Aden Governorate. The normal distribution of climate variable data was verified, and the values of basic descriptive statistics were determined. The differences between their means were identified, the relationship between climate variables and rainfall was studied, and the effect of both temperature and relative humidity on the rainfall was considered. The data for the climate variables under study was found to be normally distributed, and there were no statistically significant differences between the values of the maximum and minimum temperature variables. It was found that there were statistically significant differences between the values of the relative humidity variable and the rainfall variable, as well as a strong statistically significant direct relationship between the maximum and minimum temperatures, and a strong statistically significant inverse relationship between maximum temperatures and relative humidity, as well as a moderate statistically significant inverse relationship between minimum temperatures and relative humidity, while there was a weak direct relationship between temperatures (Maximum and minimum) and the rainfall. Furthermore, there was a weak inverse relationship between relative humidity and rainfall, and this is a reflection of the location of Aden Governorate on the southern coastal plains of Yemen, which is located within the hot tropical region. There was no statistically significant effect of climate variables (maximum and minimum temperatures, and relative humidity) on the rainfall in Aden Governorate, due to its location and the influence of other factors such as the direction of the winds blowing onto it, especially in the summer. [ABSTRACT FROM AUTHOR]

Information on the biology and ecology of the desert millipede Orthoporus ornatus (Girard, 1853) is scarce despite its key role in soil biogeochemistry and prevalence on Sonoran Desert roads. The variation in the population structure of the desert millipede was described, analyzed, and discussed in conjunction with the abundance and size of females and males over a period of several years. The millipedes were sampled at night by vehicles that travelled at 20-25 km/h on a two-lane road during the summers from 2013 to 2022. The abundance was recorded in each sampling, and the millipedes were collected for laboratory measurements. The individuals that were measured were later released at the capture sites. Regression analyses were performed between the abundance of millipedes and their biological characteristics, including their weight and body sizes, number of legs and body segments. Regression analyses were also used to examine correlations between abundance and climatic variables. The results indicate that the females had larger variables, such as body size. The greatest abundance and size were related to the rainy months, high average temperatures, and relative humidity, which were considered favorable for the activity of the species in the Sonoran Desert. Resumen Información sobre la biología y ecología del milpiés del desierto Orthoporus ornatus (Girard, 1853) es escasa a pesar de jugar un papel clave en la biogeoquímica del suelo y ser una especie común en los caminos del Desierto de Sonora. Discutimos, describimos y analizamos la variación en la estructura poblacional del milpiés del desierto a lo largo de varios años de estudio; comparamos la abundancia y el tamaño de hembras y machos. Durante los veranos de 2013 a 2022 realizamos muestreos nocturnos en vehículo circulando a 20-25 km/h en una carretera de dos carriles. En cada muestreo registramos la abundancia y recolectamos milpiés para medirlos en el laboratorio. Los individuos medidos fueron liberados posteriormente en los sitios de captura. Con estos datos, realizamos análisis de regresión entre la abundancia de milpiés y las características biológicas: peso y tamaño corporal, número de patas y segmentos corporales; también realizamos análisis de regresión entre la abundancia y las variables climáticas. Los resultados indican que las hembras mostraron mayores variables como mayor tamaño corporal. La mayor abundancia y tamaño estuvieron relacionados con los meses de lluvia, altas temperaturas promedio y humedad relativa, variables consideradas favorables para la actividad de la especie en el Desierto Sonorense. [ABSTRACT FROM AUTHOR]

This study aims to investigate the factors influencing seasonal and long-term (2003–2021) changes in the near surface ozone (850 hpa) concentrations over different climatic sub-regions of India. Detailed comparison of daily (2019–2021) near surface ozone values of ERA-5 and CAAQMS (Continuous Ambient Air Quality Monitoring Stations) ground-based measurements revealed that ERA-5 is temporally in phase with CAAQMS measurements falling indifferent climatic sub-regions of India. ERA-5 near surface ozone shows statistically significant long-term (2003–2021) positive trends [2–4 percent per decade (ppd)] over most of the climatic sub-regions, over Indo-Gangetic Planes (IGPs), Southern and Central India trends are particularly strong. Trends were also estimated for each season separately, which were largely positive (2–6 ppd) over Central and Southern India in the Autumn and Winter seasons. Extensive climatological analysis reveals that the reversal of winds in the Indian monsoonal system plays a vital role in such trend patterns across the Indian subcontinent. South-westerly winds from June through September presumably bring ozone deficit air of marine origin, thus causing a dilution effect while the North-easterly winds during late Autumn and early Winters plausibly bring ozone-rich air from the stratospheric-tropospheric efflux dominated Himalayan region. It allows near surface ozone enhancement over Central and Southern India. Seasonal Principal component analysis (PCA) revealed that precursor gases (CH4 and NO2) and climatic variables especially specific humidity (SH) are the primary drivers of near surface ozone variability in the Winter season, while in Spring, climatic variables like boundary layer height (BLH), temperature (T) and SH have a significant role. Principal component regression (PCR) reveals a long-term increase in near surface ozone levels mostly dominated by precursor concentration over IGPs and Southern sub-regions. Whereas, BLH, T and SH significantly explain near surface ozone trends over North-eastern and Coastal India. [ABSTRACT FROM AUTHOR]

Ectotherms exhibit varying geographic size patterns shaped by environmental and evolutionary factors. This variability is noticeable within taxonomic groups. For instance, certain testudinids follow Bergmann's rule (body size increases with latitude) and Rensch's rule (sexual size dimorphism correlates with body size), while others do not. Here we hypothesize that body size patterns can even vary within a monophyletic lineage. To address this, we evaluated the body size patterns of the spur-thighed tortoise Testudo graeca that globally follows Bergmann's and Rensch's rules. We specifically investigated the influence of climate variables, latitude and elevation within the subspecies T. g. whitei throughout its natural distribution in North Africa, and in a recently expanded range in SE Spain (20 kya old). We found that males were smaller than females in both regions. The tortoises from SE Spain were smaller than those from North Africa, which showcased the smallest sizes ever reported for the species. Latitude was the main variable to explain tortoise body size. In particular, body size decreased with latitude in both regions, which contrasts with Bergmann's rule expectations based on species-level findings. Finally, to further contradict species-level expectations, we did not find any statistical correlation between sexual size dimorphism and body size across the two studied regions. Such contradictory outcomes reveal complex geographic size patterns within T. graeca and raise conservation questions about demographic viability at smaller-sized sites. [ABSTRACT FROM AUTHOR]

Ticks are vectors of disease‐causing pathogens and are found on domestic and wild animals; thus, they are also vectors of significant human diseases. For this reason, pre‐emptive measures to prevent tick‐borne diseases are necessary in the form of exploring their major habitats, population increase period, and factors affecting their population growth, all of which indicate the purpose of this study. In the study, a variation of Haemaphysalis longicornis, a major vector of fever‐causing conditions, was statistically analyzed to identify the spatial and climatic factors affecting the time‐dependent variations of its population. The survey occurred in different habitats (grassland, mixed forest, deciduous forest, and coniferous forest) in South Korea. In addition, we fitted a phenology model by using a probability function to find the peak occurrence time annually. As a result, the numbers of adults and nymphs were found to be related to temperature and relative humidity and their population peaked at the end of May in all habitats except deciduous forests. This study is expected to provide information on habitat types, times, and climate patterns that require attention to help control H. longicornis populations. [ABSTRACT FROM AUTHOR]

Awassi is a fat-tailed sheep breed, and the best breed in Syria is famous. Awassi sheep are Syria's main source of red meat and milk production. In this study, we estimated the influence of various factors on sheep meat and milk production using time-series data from 1961 to 2020. This study employed the Cobb-Douglas production function to analyze the data. The results obtained indicate that Awassi meat production in Syria was positively and significantly influenced by carcass weight (p

Abstract Anthropogenic and climate-related phenomena are among the main factors responsible for variations in vegetation structure and composition worldwide. However, studies that integrate the effects of human activities and climate variability in fragile tropical ecosystems, including the semi-arid Tigray region, are lacking. The objective of this study was to examine the effects of climate variability and environmental policy changes on the spatial distribution and pattern of vegetation cover in the semi-arid Tigray region of Ethiopia over the past four decades. We used satellite-based vegetation index (normalized difference vegetation index) and monthly rainfall data to analyze the relationship between vegetation cover and climatic variability. Residual analysis was also used to further disentangle the effects of climatic variability and environmental policy on vegetation cover. The regression analysis (r2 = 0.19) showed an insignificant causal relationship between vegetation dynamics and precipitation over the 41-years study period. This study also highlighted negative impact of the global rise in temperature on vegetation cover due to water stress caused by evapotranspiration. On the other hand, the residual analysis results (r = − 0.55, z-stat = − 11.58, p

Dysosma versipellis is an ethnobotanical plant that has been classified as a Class II protected endangered plant in China due to habitat destruction in recent years. Studying the impact of climate change on the distribution of wild plant resources is of great significance for the sustainable utilization of D. versipellis resources. In this study, distribution information of 104 D. versipellis samples, 19 climate variables, and two periods under two future climate scenarios were collected. By combining the maximum entropy model (MaxEnt) and Geographic Information System (GIS) technology, the potential distribution of D. versipellis under present and future climates, as well as the important climate variables affecting its distribution, were predicted. The results showed that the Maxent model had good predictive performance (AUC > 0.9) with high accuracy and reliability. The key climate variables for D. versipellis included annual precipitation (1054.8~1820.9 mm), mean diurnal range (6.2~8.2°C), precipitation of the wettest quarter (486.2~1071.5 mm), mean temperature of the driest quarter (4.4~14.7; 15.1~16.1°C, and temperature seasonality (511.6~578.6; 683.7~828.5). The highly suitable areas for D. versipellis were mainly distributed in Guizhou, western and southern Hunan, western Hubei, northeastern and southeastern Chongqing, northeastern and southeastern Sichuan, northern and southwestern Guangxi, northeastern and southeastern Yunnan, northwestern and eastern Jiangxi, southern Zhejiang, northern Fujian, and southern Taiwan. Under future climate change, the suitable areas for D. versipellis are projected to gradually shift towards Henan, Anhui, and Jiangsu. [ABSTRACT FROM AUTHOR]

Changes in glacier area, glacial lakes, debris cover, and geomorphological features such as debris fans have a significant impact on glacial dynamics. Therefore, precise and timely observation and tracking of glacier surface changes is a necessity. The availability of high spatial resolution remote sensing images has made it viable to analyse the glacier surface changes at a local level. However, with an increase in spatial resolution, the spectral variability increases, giving rise to additional challenges (such as false changes and misregistration) in the change detection process. These challenges can preferably be dealt with using an object-based change detection (OBCD) approach rather than the conventional pixel-based change detection approach. Therefore, this study has proposed an OBCD methodology using high-spatial-resolution remote sensing images to detect changes in glacier features. Variability in glacier features has been further analysed by associating it with important climate variables, that is, air temperature and precipitation. As a case study, the changes in Gangotri Glacier (Uttarakhand Himalayas in India) features have been studied using high-spatial-resolution WorldView-2 and Linear Imaging Self-Scanning System (LISS)-4 images for a 3-year period 2011–2014. The spectral correspondences between glacier surface and non-glacier surface have been handled by considering brightness temperature and slope as ancillary data to improvise their distinction. A change detection accuracy of ~ 84% has been obtained using the OBCD approach. Results further show that the variations in glacier features are in congruence with the climatic observations. [ABSTRACT FROM AUTHOR]

Research summary: The research aimed to build a tool for measuring environmental awareness of the climate variables of kindergarten teachers. The researcher used the experimental curriculum. The research community and sample represent some of the 25 kindergarten teachers. The data collection tool was the questionnaire form for the environmental awareness of the climate variables of kindergarten teachers from (preparation of the researcher). The results resulted in : - There are no statistically significant differences between the average scores of the experimental group in the first and second measurements of the questionnaire. [ABSTRACT FROM AUTHOR]

Understanding the characteristics and correlations between evapotranspiration and climate variables plays a crucial role in determining the probable impact of critical factors on crop water requirements, water resource management, and future planning. This work aims to evaluate the temporal trends of evapotranspiration and its sensitivity to climate variables from 1980 to 2019 in Southeast, Vietnam. The improved Innovative Şen Trend Analysis method was used to identify trends, and the Sobol technique, based on variance-based analysis, allowed for a rapid calculation of sensitivity indices. By estimating the changes in evapotranspiration, the study confirmed different quantitative trends, including a significant increase of 72–135 mm in annual and 12–84 mm in seasonal evapotranspiration. Results also conducted a sensitivity analysis of the historical meteorological quantiles obtained for three climate stations to analyze the sensitivity indices. The sensitivity analysis showed that evapotranspiration is more sensitive to solar radiation, relative humidity, and minimum temperature. The study presents pragmatic approaches for considering the possible interactions between evapotranspiration and climate variables, which may serve as a baseline for sustainable water management in areas with similar climate conditions and adaptation to climate change. [ABSTRACT FROM AUTHOR]

This study investigated the long-run and short-run relationships between solid waste generation in Nigeria and two key climate variables: rainfall and temperature. Employing a Vector Error Correction Model (VECM) analysis on data from 1982 to 2022, then revealed counterintuitive findings. In the long run, lagged rainfall exhibits a negative association with solid waste (p < 0.05), potentially explained by increased waste decomposition in wetter conditions. Conversely, lagged temperature showed a positive association (p < 0.05), aligning with theories of increased consumption and economic activity in warmer periods. The shortrun analysis unveils a self-correcting mechanism in solid waste generation and a statistically significant negative impact of lagged temperature (p < 0.05), requiring further investigation. Based on these findings, the study proposed policy implications for waste management strategies and data collection, emphasizing the need for sustainable solutions in the context of climate change. [ABSTRACT FROM AUTHOR]

Dengue is one of the world's most rapidly spreading mosquito-borne viral diseases. As it is found mostly in urban and semi-urban areas, urbanization and associated human activities that affect the environment and larval habitats could become risk factors (e.g., lane width, conditions of street ditches) for the spread of dengue. However, there are currently no systematic studies of micro-scale urbanization-based risk factors for the spread of dengue epidemics. We describe the study area, two micro-scale environmental risk factors associated with urbanization, and meteorological data. Since the observations involve spatial and temporal correlations, we also use some statistical methods for the analysis of spatial and spatial-temporal data for the relationship between urbanization and dengue. In this study, we analyzed data from Kaohsiung, a densely populated city in southern Taiwan, and found a positive correlation between environmental risk factors associated with urbanization (ditches positive for mosquito larvae and closely packed streets termed "dengue lanes") and clustering effects in dengue cases. The statistical analysis also revealed that the occurrence of positive ditches was significantly associated with that of dengue lanes in the study area. The relationship between climate variables and positive ditches was also analyzed in this paper, indicating a relationship between dengue and both rainfall and temperature, with temperature having a greater effect. Overall, this work is immediately relevant and applicable for policymakers in government, who will need to reduce these favorable habitats for vector-born disease spreaders and implement regulations for new urban constructions to thus reduce dengue spread in future outbreaks. [ABSTRACT FROM AUTHOR]

The hydroelectric crisis is fomenting studies that seek the decentralization of electric energy generation in Brazil. Due to the climatic dependence on alternative sources, knowing their intermittent behavior is the best way to ensure supply. This article presents an analysis of energy complementarity based on the occurrences of climate variables with precipitation for hydroelectric power, irradiation for photovoltaic, and wind speed for wind power. We aimed to analyze and spatialize the availability of renewable energy sources in western Paraná through historical series (2011 to 2020), statistical analysis (monthly and seasonal), and the use of Geographic Information System (GIS). Daily data records were made available by the Paraná Meteorological Institute (SIMEPAR), referring to eight weather stations throughout the mesoregion. The results identified spatial complementarity for the mesoregion West Paraná. Cascavel city stood out for temporal complementarity, recording the highest values for precipitation in spring and wind speed in winter. In addition, irradiation data excelled in summer for the city of Santa Helena when compared to the other municipalities considered. [ABSTRACT FROM AUTHOR]

Various inventions are presented in the water industry, essential for the water supply, distribution, treatment, storage, and consumption optimization. It is necessary to confirm their novelty, innovative steps, and industrial applicability to protect water-related patents. However, the impacts of climate change are not considered in the granting of patents, and water-related inventions are registered or rejected regardless of these impacts. For example, an invention that causes greenhouse gas emissions may be patented because it is new. This research addresses this significant challenge using a descriptive-analytical approach and a library-field method. Based on the results, it is necessary to impose strictness on inventions that aggravate climate change (29% of the inventions investigated) and protect inventions that adapt to climate change impacts (71%). Furthermore, it is possible to use the tool of compulsory licensing to adapt to climate change and reduce its negative impacts. Moreover, the patent offices should evaluate climate change impacts by examining innovative steps and industrial applications. An invention that has negative impacts will be deprived of patent protection and considered one of the limitations and exceptions. Also, it is necessary to provide new interpretations of protection elements of the patent system. HIGHLIGHTS Providing a new interpretation of the triple conditions of patenting can prevent the granting of patents that exacerbate climate change.; Compulsory license capacities and exceptions can be used for the use of inventions that have a positive effect on adapting to climate change.; Considering climate change impacts on the water industry, it may be necessary to use the invention without the inventor's permission.;

Wildfires present a major global environmental issue, exacerbated by climate change. The Iranian Northern Zagros Forests, characterized by a Mediterranean climate, are particularly vulnerable to fires during hot, dry summers. This study investigates the impact of climate change on forest fires in these forests from 2006 to 2023. The analysis revealed significant year-to-year fluctuations, with notable fire occurrence in years 2007, 2010, 2021, and 2023. The largest burned area occurred in 2021, covering 2655.66 ha, while 2006 had the smallest burned area of 175.27 ha. Climate variables such as temperature, humidity, precipitation, wind speed, heat waves, and solar radiation were assessed for their effects on fire behavior. Strong correlations were found between higher average temperatures and larger burned areas, as well as between heat waves and increased fire frequency. Additionally, higher wind speeds were linked to larger burned areas, suggesting that increased wind speeds may enhance fire spread. Multiple linear regression models demonstrated high predictive accuracy, explaining 84% of the variance in burned areas and 69.6% in the variance in fire frequency. These findings document the growing wildfire risk in the Northern Zagros region due to climate change, highlighting the urgent need to integrate scientific research with policies to develop effective wildfire management strategies for sustainable forest management.

Despite the increased frequency of extreme climate events including their significance in Nepal's socio-economy, climate studies have seldom considered extremes, and even fewer have considered them in combination with temperature and precipitation. This study aimed at examining the trend of climate variables in Gandaki Province, Nepal. Daily temperature and precipitation data of five stations between 1990 and 2020 were analyzed. Modified Mann–Kendall and Sen's slope methods were used to detect trend and magnitude. The Mann–Whitney–Pettitt test was used to detect abrupt changes, and the Pearson correlation coefficient was used to find the correlation. The result showed an increasing trend and a significant abrupt change in the maximum temperature for all stations. A decreasing trend in the minimum temperature was observed in the Himalayas and the Hill region, whereas an increasing trend was seen in Siwalik and Terai regions. The Jomsom station, however, behaved differently by showing an increasing trend in precipitation and the number of rainy days. The majority of the temperature indices showed an increasing trend unlike precipitation indices, which showed a mixed result. The maximum five-day precipitation and consecutive dry days showed a significant positive correlation with altitude. The results indicate an increase in the frequency and intensity of extreme climate conditions in Gandaki Province. HIGHLIGHTS The paper analyzes the trend in the mean and the extreme value of temperature and precipitation.; High altitude region could experience an extreme climate condition with frequent heavy rainfall and drought periods, while Siwalik and Terai regions could experience frequent drought periods.; The results will help the relevant stakeholders to understand the change happening in the Gandaki Province.;

Forests affect regional climates, livelihoods and global cycles of water, carbon and nitrogen. Anthropogenic activities and climatic change affect forest health and national growth. Therefore, developing effective forest management plans requires understanding of the drivers of forest growth. The primary objective of this study was to understand the long-term effect of abiotic factors on the growth of forests in the region. This study used Moderate Resolution Imaging Spectroradiometer (MODIS) data for vegetation indices like NDVI and EVI and NASA's Land Assimilation datasets (wind speed, evapotranspiration, soil moisture and temperature) to understand their role on forest growth through statistical techniques such as Pearson's correlation and Multiple Linear Regression. The study examined the relationship between standard monthly vegetation indices and abiotic variables (i.e., moisture and temperature at different soil profiles up to 2 m depth, land surface temperature, evapotranspiration, relative humidity, wind velocity, and air temperature at 2 m height) in selected forests of the Eastern Himalayas for two decades (2001–2020, n = 240). Rainfall, temperature, and other associated factors significantly affected forest growth in the region. It was observed that rainfall alone affected forest growth in the region. However, its impact was maximum after two months of the rain events, reflecting a significant lag effect. Soil moisture at different depths affected vegetation growth in all forest types. Reduced soil moisture had a more significant effect on old-growth forests than younger forests. Multiple Linear Regression models developed with the abiotic factors explained higher variability in forest growth. In conclusion, this study reveals that rainfall, temperature, and their associated variables significantly affected forest growth in the study area. The study has significant implications for forest management in the region for formulating better strategies to mitigate climate change effects on forests in the region in future.

Abstract Studies in the long-term recruitment and mortality of mangrove seedlings can help to understand mangrove demography and its relationship with climatic variables, environmental restoration and advances in the ecology of this ecosystem. A seven-year population dynamics study of seedling recruitment and mortality in cohorts of Rhizophora mangle L. was carried out to identify expansion processes and patterns of survival in the understory of mangrove forests on the Atlantic coast of Brazil. The present study aimed to evaluate the relationship between recruitment and mortality R. mangle seedlings at the population level, salinity, and climatic variables (precipitation, temperature and humidity). On an annual scale, seedling recruitment was positively correlated with mean temperature. Seedling density was negatively correlated with the number of recruits and positively with the number of deaths. The number of recruits was associated with dead seedlings, temperature and precipitation considering a population scale, without grouping the data. The seedling density in the stands increased with the number of dead seedlings. Our findings described the relationship between climate variability (durability and magnitude of the dry/rainy season) and the long-term population dynamics of R. mangle seedlings in a poorly studied region and from what moment, on a monthly and annual time scale, did this relationship become significant and changes occur. The findings of this study provide information on the population dynamics of the species that will help in understanding mangrove demography. These results have important implications for projections about the recruitment and survival of the species thinking about to long-term climate change that will modify current weather patterns and mangrove conservation efforts.

The effect of climate factors (temperature, humidity, precipitation, and frost days) on the population changes, damage, and infestation area of the leopard moth, Zeuzera pyrina L., was studied during 2006–2018 in four parts of Iran including Saman, Arak, Najaf-abad, and Baft. For trend analysis, the Mann–Kendall test was run on time series data of both climate and pest population. According to the results, the annual mean (Kendall's statistics, T = 0.64 and 0.48), annual minimum (T = 0.60 and 0.42), and January mean (T = 0.64 and 0.61, respectively) temperatures showed increasing trends in Saman and Najaf-abad. Moreover, the annual mean minimum and January temperatures (T = 0.41 and 0.45, respectively) in Arak and the annual mean maximum temperature (T = 0.79) in Baft showed increasing trends. The number of frost days/year (Kendall's statistics, T = −0.63, −0.53, −0.32 and −0.37) and annual mean relative humidity (T = −0.43, −0.63, −0.64 and −0.42, respectively) showed decreasing trends in Saman, Arak, Baft, and Najaf-abad stations. Trend analysis indicated significant increases in the mean number of moths caught (T = 0.59, 0.76 and 0.90), the percentage of infested branches/tree (T = 0.66, 0.58, and 0.90), the number of active holes/tree (T = 0.79, 0.55, and 0.68) and the infested areas (T = 0.99, 0.73, and 0.98, respectively) in Saman, Arak and Najaf-abad stations. According to stepwise regression, the mean temperatures of January, autumn, and winter were the most effective variables for increasing Z. pyrina damage and population, while relative humidity and the number of frost days played the major role in reducing it. [ABSTRACT FROM AUTHOR]

Net primary productivity is an essential measure of plant biology and the net flow of carbon between the atmosphere and the terrestrial environment. This aids in comprehending how much carbon is fixed by terrestrial plants and the factors that affect it, thus requiring a thorough grasp of net primary productivity dynamics and how they interact with the climate in arid and humid regions. This study applied remote sensing techniques to evaluate the spatial distribution and climatic variables of the net primary productivity in the Niger River Basin, using the Carnegie Ames Stanford Approach model and correlation analysis. The study revealed that the net primary productivity fell from 338.18 gC/m2 in 2000 to 334.44 gC/m2 in 2020. The correlation result shows that while precipitation (R2=0.87) and actual evapotranspiration (R2=0.83) revealed a positive correlation, temperature (R2=0.328), solar radiation (R2=0.585), and potential evapotranspiration (R2=0.78) shows a negative correlation with the net primary productivity. The study shows that precipitation has a major influence on changes in the net primary production of the NRB. The results of the study may help to better understand how climate studies affect environmental ecology while recommending policymakers to safeguard the Niger River Basin from activities that can deteriorate the environment. [ABSTRACT FROM AUTHOR]

The overarching goal of the study was the development of a potentially dynamic rockfall susceptibility model by including climate predictors. The work is based on previously defined critical thresholds relating three climate indices — effective water inputs (EWI), wet-dry cycles (WD) and freeze–thaw cycles (FT) — and rockfall occurrence. The pilot area is located in the Aosta Valley region (Italian Western Alps). The susceptibility model settings were optimized through a stepwise procedure, carried out by means of generalized additive models (GAM). Predictors included topographic, climatic and additional snow-related variables. As climatic predictors, the mean annual threshold exceedance frequency was calculated for each index. All models were developed including an automatic penalization of statistically non-significant variables (i.e. shrinkage). The initial susceptibility model was set without considering potential inventory bias. Secondly, a "visibility mask" was produced to limit the modelling domain according to the rockfall event census procedures. Thirdly, GAMs functional relationships were analysed to verify the physical plausibility of predictors. Finally, to reduce concurvity, a principal component analysis (PCA) including climatic and snow-related predictors was carried out. Key findings were as follows: (i) ignoring inventory bias led to excellent model performance but to physically implausible outputs; (ii) the selection of non-rockfall points inside a "visibility mask" is effective in managing inventory bias influence on outputs; (iii) the inclusion of climate predictors resulted in an improvement of the physical interpretability of the associated models and susceptibility maps, being EWI, WD and the maximum cumulated snow melting the most important physically plausible climate predictors; (iv) the PCA strategy can efficiently reduce model concurvity. [ABSTRACT FROM AUTHOR]

The long-distance spreading and transport of airborne particulate matter (PM) of biogenic or chemical compounds, which are thought to be possible carriers of SARS-CoV-2 virions, can have a negative impact on the incidence and severity of COVID-19 viral disease. Considering the total Aerosol Optical Depth at 550 nm (AOD) as an atmospheric aerosol loading variable, inhalable fine PM with a diameter ≤2.5 µm (PM2.5) or coarse PM with a diameter ≤10 µm (PM10) during 26 February 2020–31 March 2022, and COVID-19's five waves in Romania, the current study investigates the impact of outdoor PM on the COVID-19 pandemic in Bucharest city. Through descriptive statistics analysis applied to average daily time series in situ and satellite data of PM2.5, PM10, and climate parameters, this study found decreased trends of PM2.5 and PM10 concentrations of 24.58% and 18.9%, respectively compared to the pre-pandemic period (2015–2019). Exposure to high levels of PM2.5 and PM10 particles was positively correlated with COVID-19 incidence and mortality. The derived average PM2.5/PM10 ratios during the entire pandemic period are relatively low (<0.44), indicating a dominance of coarse traffic-related particles' fraction. Significant reductions of the averaged AOD levels over Bucharest were recorded during the first and third waves of COVID-19 pandemic and their associated lockdowns (~28.2% and ~16.4%, respectively) compared to pre-pandemic period (2015–2019) average AOD levels. The findings of this research are important for decision-makers implementing COVID-19 safety controls and health measures during viral infections. [ABSTRACT FROM AUTHOR]

Abstract Background The time between the appearance of successive leaves, or phyllochron, characterizes the vegetative development of annual plants. Hypothesis testing models, which allow the comparison of phyllochrons between genetic groups and/or environmental conditions, are usually based on regression of thermal time on the number of leaves; most of the time a constant leaf appearance rate is assumed. However regression models ignore auto-correlation of the leaf number process and may lead to biased testing procedures. Moreover, the hypothesis of constant leaf appearance rate may be too restrictive. Methods We propose a stochastic process model in which emergence of new leaves is considered to result from successive time-to-events. This model provides a flexible and more accurate modeling as well as unbiased testing procedures. It was applied to an original maize dataset collected in the field over three years on plants originating from two divergent selection experiments for flowering time in two maize inbred lines. Results and conclusion We showed that the main differences in phyllochron were not observed between selection populations but rather between ancestral lines, years of experimentation and leaf ranks. Our results highlight a strong departure from the assumption of a constant leaf appearance rate over a season which could be related to climate variations, even if the impact of individual climate variables could not be clearly determined.

Spatial Autoregressive (SAR) models are used to model the relationship between variables within a specific region or location, considering the influence of neighboring variables, and have received considerable attention in recent years. However, when the impact of exogenous variables becomes notably pronounced, an alternative approach is warranted. Spatial Expansion, coupled with the Casetti model approach, serves as an extension of the SAR model, accommodating the influence of these exogenous variables. This modeling technique finds application in the realm of rainfall prediction, where exogenous factors, such as air temperature, humidity, solar irradiation, wind speed, and surface pressure, play pivotal roles. Consequently, this research aimed to combine the SAR and Spatial Expansion models through the Casetti model approach, leading to the creation of the Spatial Autoregressive Exogenous (SAR-X) model. The SAR-X was employed to forecast the rainfall patterns in the West Java region, utilizing data obtained from the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) dataset. The practical execution of this research capitalized on the computational capabilities of the RStudio software version 2022.12.0. Within the framework of this investigation, a comprehensive and integrated RStudio script, seamlessly incorporated into the RShiny web application, was developed so that it is easy to use. [ABSTRACT FROM AUTHOR]

This study aims to estimate and evaluate the characteristic behavior of sunshine duration for long-term records. Sunshine duration and other climate variables such as cloudiness, precipitation, relative humidity, etc., have been measured in meteorological stations for a long time all over the world. But in some cases, such as missing data or unavailable station, the estimation of sunshine duration play a crucial role. Statistical models can be used to predict the sunshine duration over climate variables. To evaluate the behavior of sunshine duration, several climate variables were analyzed for different time scales. The data used in this study were collected from a ground-based meteorological station. In the first, all data were arranged according to different time scales as monthly, seasonal, and annual average values. Prediction models were constructed for each time scale. This study used multiple linear regression (MLR) to build the models and the Pearson correlation analysis to determine the relations between the climate elements. The created models for estimating sunshine duration were validated as well. According to the results, MLR can be utilized and recommended for the prediction of the sunshine duration over climate variables. [ABSTRACT FROM AUTHOR]

Background. More than half of the global population is predicted to be living in areas susceptible to dengue transmission with the vast majority in Asia. Dengue fever is of public health concern, particularly in the southern region of Thailand due to favourable environmental factors for its spread. The risk of dengue infection at the population level varies in time and space among sub-populations thus, it is important to study the risk of infection considering spatio-temporal variation. Methods. This study presents a joint spatio-temporal epidemiological model in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation with the CARBayesST package of R software. For this purpose, monthly dengue records by district from 2002 to 2018 from the southern region of Thailand provided by the Ministry of Public Health of Thailand and eight environmental variables were used. Results. Results show that an increasing level of temperature, number of rainy days and sea level pressure are associated with a higher occurrence of dengue fever and consequently higher incidence risk, while an increasing level of wind speed seems to suggest a protective factor. Likewise, we found that the elevated risks of dengue in the immediate future are in the districts of Phipun, Phrom Kili, Lan Saka, Phra Phrom and Chaloem Phakiat. The resulting estimates provide insights into the effects of covariate risk factors, spatio-temporal trends and dengue-related health inequalities at the district level in southern Thailand. Conclusion. Possible implications are discussed considering some anthropogenic factors that could inhibit or enhance dengue occurrence. Risk maps indicated which districts are above and below baseline risk, allowing for the identification of local anomalies and high-risk boundaries. In the event of near future, the threat of elevated disease risk needs to be prevented and controlled considering the factors underlying the spread of mosquitoes in the Southeast Asian region. [ABSTRACT FROM AUTHOR]

The study applies diverse methods of chaos detection to meteorological variable data (air temperature, relative humidity, surface pressure, precipitation, and wind speed for Ha'il, Saudi Arabia) to understand the nonlinear dynamics and to classify their nature. Additionally, Random Forest Algorithm model is used to predict the precipitation and wind speed. The results obtained by classical and modern approaches are compared. All the variables are found to be chaotic based on correlation dimension, approximate entropy, and 0-1 test. The chaos decision tree algorithm diagnoses air temperature, relative humidity, and wind speed as chaotic, while precipitation and surface pressure are identified as stochastic. This shows that the classical methods are well-validated with the modern methods. Nevertheless, some of them contradict modern methods. The analysis for 32 years of data showed no precipitation for 92% of the time during the entire period based on the Random Forest algorithm.

An analysis of a suite of climatological indices was undertaken on the basis of long-term (1979–2019) climatological data from the Polish Polar Station in Hornsund, SW Spitsbergen. It was followed by an attempt to assess the scale of their impact on the local environment. The temperature and precipitation indices were based on percentiles of the variables calculated for a population of daily values from the climate normals for 1981–2010. A greater share of both cyclonic and anticyclonic circulations from the S and SW sectors, forcing the advection of warm air masses from the south, was decisive for the trends of change in comparison with the long-term mean. Both extreme precipitation and drought events depend on the 500 hPa geopotential height and precipitable water anomalies, determined by the baric field over the North Atlantic. Climate changes impact on the dynamics of local geoecosystems by causing faster glacier ablation and retreat, permafrost degradation, intensification of the hydrological cycle in glaciated and unglaciated catchments, and changes in the condition and growth of tundra vegetation.

Determining the actual evapotranspiration value and analyzing its temporal trend is essential for optimal water resources management in a basin. In the present paper, the actual evapotranspiration time series is simulated and its trend is analyzed according to the trend of climatic variables and land use in the Ajichi basin during the period of 2015-1987. The comprehensive SWAT model was set up, calibrated, and validated for the Ajichi basin. Also, the average of simulated actual evapotranspiration of crops (in wet years) was compared with similar values in the National Water Document. The results of the Mann-Kendall trend test showed that the annual rainfall in most meteorological stations had a decreasing trend and the rainfall trend in the ten stations decreased significantly. While the annual maximum temperature at all stations and the annual minimum temperature in most of them have significantly increased. Investigation of land use maps illustrated that the irrigated land area of the basin has increased by a 39% during the study period. According the study's results, the potential evapotranspiration of the basin has had a significant increasing trend with a rate of 2.54 mm per year. The results indicated that despite the increasing trend of potential evapotranspiration and irrigated land area, the actual evapotranspiration of the basin had a significant decreasing trend with a rate of 2.2 mm per year due to the decrease in rainfall.

Background: The time between the appearance of successive leaves, or phyllochron, characterizes the vegetative development of annual plants. Hypothesis testing models, which allow the comparison of phyllochrons between genetic groups and/or environmental conditions, are usually based on regression of thermal time on the number of leaves; most of the time a constant leaf appearance rate is assumed. However regression models ignore auto-correlation of the leaf number process and may lead to biased testing procedures. Moreover, the hypothesis of constant leaf appearance rate may be too restrictive. Methods: We propose a stochastic process model in which emergence of new leaves is considered to result from successive time-to-events. This model provides a flexible and more accurate modeling as well as unbiased testing procedures. It was applied to an original maize dataset collected in the field over three years on plants originating from two divergent selection experiments for flowering time in two maize inbred lines. Results and conclusion: We showed that the main differences in phyllochron were not observed between selection populations but rather between ancestral lines, years of experimentation and leaf ranks. Our results highlight a strong departure from the assumption of a constant leaf appearance rate over a season which could be related to climate variations, even if the impact of individual climate variables could not be clearly determined. [ABSTRACT FROM AUTHOR]

An analysis of a suite of climatological indices was undertaken on the basis of long-term (1979-2019) climatological data from the Polish Polar Station in Hornsund, SW Spitsbergen. It was followed by an attempt to assess the scale of their impact on the local environment. The temperature and precipitation indices were based on percentiles of the variables calculated for a population of daily values from the climate normals for 1981-2010. A greater share of both cyclonic and anticyclonic circulations from the S and SW sectors, forcing the advection of warm air masses from the south, was decisive for the trends of change in comparison with the long-term mean. Both extreme precipitation and drought events depend on the 500 hPa geopotential height and precipitable water anomalies, determined by the baric field over the North Atlantic. Climate changes impact on the dynamics of local geoecosystems by causing faster glacier ablation and retreat, permafrost degradation, intensification of the hydrological cycle in glaciated and unglaciated catchments, and changes in the condition and growth of tundra vegetation. [ABSTRACT FROM AUTHOR]