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 < 0.001) and quantity of slaughtered sheep (p < 0.001), with elasticity coefficients of 0.994 and 1.000, respectively. The model results show that raw milk yield and milk animals have a positive and significant influence on milk production at the 0.1 % significance level, with an elasticity coefficient of 0.998. However, climatic factors (precipitation and temperature) have a significant effect and negative influence on milk production at the 1 % and 5 % significance levels, respectively. This result sheds light on the policies and procedures the government should implement to develop sheep production in Syria. Therefore, it can be stated that policymakers should enhance the quality and productivity characterization of sheep through the implementation of breeding programs, improve grazing potential, and protect natural pastures. Furthermore, feed rations should be supported to help breeders overcome the effects of drought and high feed prices.

Tropical forests are global biodiversity hotspots and are crucial in the global carbon (C) cycle. Understanding the drivers of aboveground carbon stock (AGC) in a heterogeneous and biodiverse system can shed light on the processes underlying the relationship between biodiversity and carbon accumulation. Here, we investigate how biodiversity, environment, and landscape structure affect AGC. We examined such associations in 349 plots comprising over 95,346 km2 the Atlantic Forest of southern Brazil, encompassing three forest types: Dense Ombrophylous Forest (DF), Mixed Ombrophylous Forest (MF), and Seasonal Deciduous Forest (SF). Each plot was described by environmental variables, landscape metrics, and biodiversity (species richness and functional diversity). We used diversity, environmental, and landscape variables to build generalized linear mixed models and understand which can affect the forest AGC. We found that species richness is associated positively with AGC in all forest types, combined and separately. Seasonal temperature and isothermality affect AGC in all forest types; additionally, stocks are positively influenced by annual precipitation in SF and isothermality in MF. Among landscape metrics, total fragment edge negatively affects carbon stocks in MF. Our results show the importance of species diversity for carbon stocks in subtropical forests. The climate effect was also relevant, showing the importance of these factors, especially in a world where climate change tends to affect forest stock capacity negatively.

Mercury (Hg) is a toxic metal that presents a major risk to ecosystems, biota, human health, and remains a priority concern. In temperate and boreal lakes Hg and methylmercury (MMHg) are expected to vary as a function of atmospheric Hg deposition, lake water chemistry, catchment characteristics and climate variables. The aim of this study was to quantify Hg and MMHg in unperturbed oligotrophic lakes and to identify the factors controlling their distribution. We first hypothesized that lake Hg (and MMHg to lesser extent) spatial variations are linked to atmospheric deposition, catchment characteristics, and terrestrial exportation of dissolved organic carbon (DOC). We secondly examined if lake Hg concentrations have followed the decrease in atmospheric Hg emission observed between the mid-1990s to the end-2010s. We found that overall, atmospheric Hg has little impact on lake Hg and MMHg concentrations, which are both primarily influenced by DOC input originating from the forest catchment. The relationship between DOC and Hg differed between the spring and the fall, with a Hg-to-DOC ratio twice as high in spring. This seems related to snowmelt input of Hg (with a relatively reduced input of DOC) or the internal lake build-up of Hg during the ice-covered period. Of the 10 lakes intensively visited over a 20-year period, only 3 showed significant lake Hg decreases despite significant negative trends in atmospheric Hg concentrations, suggesting a lag between atmospheric and surface water temporal trends. Overall, terrestrial catchments retain around 80% of atmospheric Hg implying that large Hg pools have been built up in soils in the last decades. As such, the reduction of atmospheric Hg alone will not necessarily result in Hg decreases in lakes, since the Hg concentrations may be modulated by DOC export trends and catchment characteristics. This stresses the need to improve our understanding of the processes governing Hg transfers from catchments into lakes.

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.

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.

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.

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.

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.

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.
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 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.
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.

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.
CONCLUSIONS: 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.