Comparative Analysis of Meteorological versus In Situ Variables in Ship Thermal Simulations.

Thermal simulations have become increasingly popular in assessing energy efficiency and predicting thermal behaviors in various structures. Calibration of these simulations is essential for accurate predictions. A crucial aspect of this calibration involves investigating the influence of meteorological variables. This study aims to explore the impact of meteorological variables on thermal simulations, particularly focusing on ships. Using TRNSYS (TRaNsient System Simulation) software (v17), renowned for its capability to model complex energy systems within buildings, the significance of incorporating meteorological data into thermal simulations was analyzed. The investigation centered on a patrol vessel stationed in a port in Galicia, northwest Spain. To ensure accuracy, we not only utilized the vessel's dimensions but also conducted in situ temperature measurements onboard. Furthermore, a dedicated weather station was installed to capture real-time meteorological data. Data from multiple sources, including Meteonorm and MeteoGalicia, were collected for comparative analysis. By juxtaposing simulations based on meteorological variables against those relying solely on in situ measurements, we sought to discern the relative merits of each approach in enhancing the fidelity of thermal simulations.

Short-term impact of noise, other air pollutants and meteorological factors on emergency hospital mental health admissions in the Madrid region.

BACKGROUND: A number of environmental factors, such as air pollution, noise in urbanised settings and meteorological-type variables, may give rise to important effects on human health. In recent years, many studies have confirmed the relation between various mental disorders and these factors, with a possible impact on the increase in emergency hospital admissions due to these causes. The aim of this study was to analyse the impact of a range of environmental factors on daily emergency hospital admissions due to mental disorders in the Madrid Autonomous Region (MAR), across the period 2013-2018. METHODOLOGY: Longitudinal ecological time series study analysed by Generalised Linear Models with Poisson regression, with the dependent variable being daily Emergency Hospital Mental Health Admissions (EHMHA) in the MAR, and the independent variable being mean daily concentrations of chemical pollutants, noise levels and meteorological variables. RESULTS: EHMHA were related statistically significantly in the short term with diurnal noise levels. Relative risks (RRs) for total admissions due to mental disorders and self-inflicted injuries, in the case of diurnal noise was RR: 1.008 95%CI (1.003 1.013). Admissions attributable to diurnal noise account for 5.5% of total admissions across the study period. There was no association between hospital admissions and chemical air pollution. CONCLUSION: Noise is a variable that shows a statistically significant short-term association with EHMHA across all age groups in the MAR region. The results of this study may serve as a basis for drawing up public health guidelines and plans, which regard these variables as risk factors for mental disorders, especially in the case of noise, since this fundamentally depends on anthropogenic activities in highly urbanised areas with high levels of traffic density.

Analyzing the relationship between air temperature and respiratory morbidity in children and the elderly in Porto Alegre, Brazil, before and during the COVID-19 pandemic.

The aim of the study was to analyze the relationship between air temperature data against hospital admissions due to respiratory diseases of children (under five years of age) and the elderly (over 65) in subtropical Porto Alegre, Brazil, comparing outcomes for 3 sequential years, 2018-2020, pre- and post-COVID 19 pandemic. Meteorological and hospital admission (HA) data for Porto Alegre, marked by a Koeppen-Geiger's Cfa climate type with well-defined seasons, were used in the analyses. HA was obtained for respiratory diseases (J00-99, according to the International Classification of Diseases, ICD-10) from the Brazilian DATASUS (Unified Health System database). We performed correlation analysis between variables (HA versus air temperature and heat stress) in order to identify existing relationships and lag effects (between meteorological condition and morbidity). Relative risk (RR) was also obtained for the two age groups during the three years. Results showed that the pandemic year disrupted observed patterns of association between analyzed variables, with either very low or non-existent correlations.

Climate and human health: a review of publication trends in the International Journal of Biometeorology.

The climate-health nexus is well documented in the field of biometeorology. Since its inception, Biometeorology has in many ways become the umbrella under which much of this collaborative research has been conducted. Whilst a range of review papers have considered the development of biometeorological research and its coverage in this journal, and a few have reviewed the literature on specific diseases, none have focused on the sub-field of climate and health as a whole. Since its first issue in 1957, the International Journal of Biometeorology has published a total of 2183 papers that broadly consider human health and its relationship with climate. In this review, we identify a total of 180 (8.3%, n = 2183) of these papers that specifically focus on the intersection between meteorological variables and specific, named diagnosable diseases, and explore the publication trends thereof. The number of publications on climate and health in the journal increases considerably since 2011. The largest number of publications on the topic was in 2017 (18) followed by 2021 (17). Of the 180 studies conducted, respiratory diseases accounted for 37.2% of the publications, cardiovascular disease 17%, and cerebrovascular disease 11.1%. The literature on climate and health in the journal is dominated by studies from the global North, with a particular focus on Asia and Europe. Only 2.2% and 8.3% of these studies explore empirical evidence from the African continent and South America respectively. These findings highlight the importance of continued research on climate and human health, especially in low- and lower-middle-income countries, the populations of which are more vulnerable to climate-sensitive illnesses.

Sero-molecular epidemiology of Japanese encephalitis virus in swine population of western Uttar Pradesh, India: Unraveling the geographical expansion of the virus.

BACKGROUND & OBJECTIVES: Swine is a good sentinel for forecast of Japanese encephalitis virus (JEV) outbreaks in humans. The present study was envisaged with objectives to know the sero-conversion period of JEV and to assess the prevalence of JEV in swine population of western Uttar Pradesh state of India. METHODS: A total of 252 swine serum samples were screened using IgM ELISA over the period of one year to determine the sero-conversion rate and compared seasonally to check the transmission peak of virus. Further, 321 swine blood and serum samples were collected from all seven divisions of western Uttar Pradesh to determine prevalence of JEV using real time RT-PCR and ELISA. RESULTS: Seasonal sero-conversion rate was high during monsoon and post-monsoon (32%) followed by winter (22.91%) and summer (10.71%) seasons. The sero-conversion was observed in all months indicating viral activity throughout the year in the region. The low degree of correlation was found between meteorological variables (day temperature, rainfall) and sero-conversion rate. A total of 52 samples (16.19%) were found positive by real time RT-PCR while sero-positivity of 29.91% was observed using IgG and IgM ELISA(s). The overall prevalence of JEV was 39.25%. INTERPRETATION & CONCLUSION: The presence of JEV was recorded throughout the year with peak occurrence during monsoon and post-monsoon season indicating that virus has spread its realm to western region of the state. The information generated in the present study will aid in initiating timely vector control measures and human vaccination program to mitigate risk of JEV infection in the region.

Evaluation of MODIS combined DT and DB AOD retrievals and their association with meteorological variables over Qena, Egypt.

The purpose of this study is to validate the daily Terra-MODIS level 2 combined dark target (DT) and deep blue (DB) aerosol optical depth (AOD) retrievals with a spatial resolution of 10 km against the ground-based AERONET AOD data to be used in evaluating the air pollution and impact of meteorological variables over Qena, Egypt, in 2019. The regression analysis demonstrated an accepted agreement between the MODIS and AERONET AOD data with a correlation coefficient (R) of 0.7118 and 74.22% of the collocated points fall within the expected error (EE) limits. Quality flag filtering and spatial and temporal collocation were found to have a significant impact on the regression results. Quality flag filtering increased R by 0.2091 and % within EE by 17.97, spatial collocation increased R by 0.0143 and % within EE by 1.13, and temporal collocation increased R by 0.0089 and % within EE by 4.43. By validating the MODIS AOD data seasonally and analyzing the temporal distribution of the seasonal AOD data to show the retrieval accuracy variations between seasons, it was found that the MODIS AOD observations overestimated the AERONET AOD values in all seasons, and this may be because of underestimating the surface reflectance. Perhaps the main reason for the highest overestimation in summer and autumn is the transportation of aerosols from other regions, which changes the aerosol model in Qena, making accurate aerosol-type assumptions more difficult. Therefore, this study recommends necessary improvements regarding the aerosol model selection and the surface reflectance calculations. Temperature and relative humidity were found to have a strong negative relationship with a correlation of - 0.735, and both have a moderate association with AOD with a correlation of 0.451 and - 0.356, respectively. Because Qena is not a rainy city, precipitation was found to have no correlation with the other variables.

Air pollution and meteorological variables' effects on COVID-19 first and second waves in Spain.

The aim of this research is to study the influence of atmospheric pollutants and meteorological variables on the incidence rate of COVID-19 and the rate of hospital admissions due to COVID-19 during the first and second waves in nine Spanish provinces. Numerous studies analyze the effect of environmental and pollution variables separately, but few that include them in the same analysis together, and even fewer that compare their effects between the first and second waves of the virus. This study was conducted in nine of 52 Spanish provinces, using generalized linear models with Poisson link between levels of PM10, NO2 and O3 (independent variables) and maximum temperature and absolute humidity and the rates of incidence and hospital admissions of COVID-19 (dependent variables), establishing a series of significant lags. Using the estimators obtained from the significant multivariate models, the relative risks associated with these variables were calculated for increases of 10 µg/m3 for pollutants, 1 °C for temperature and 1 g/m3 for humidity. The results suggest that NO2 has a greater association than the other air pollution variables and the meteorological variables. There was a greater association with O3 in the first wave and with NO2 in the second. Pollutants showed a homogeneous distribution across the country. We conclude that, compared to other air pollutants and meteorological variables, NO2 is a protagonist that may modulate the incidence and severity of COVID-19, though preventive public health measures such as masking and hand washing are still very important. Supplementary Information: The online version contains supplementary material available at 10.1007/s13762-022-04190-z.

Global COVID-19 pandemic trends and their relationship with meteorological variables, air pollutants and socioeconomic aspects.

Meteorological variables, air pollutants, and socioeconomic factors are associated with COVID-19 transmission. However, it is unclear what impact their interactions have on COVID-19 transmission, whether their impact on COVID-19 transmission is linear or non-linear, and where the inflexion points are. This study examined 1) the spatial and temporal trends in COVID-19 monthly infection rate of new confirmed cases per 100,000 people (Rn) in 188 countries/regions worldwide from March to November 2020; 2) the linear correlation between meteorological variables (temperature (T), rainfall (R), wind speed (WS), relative humidity (RH), air pressure (AP)), air pollutants (nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3)) and socioeconomic aspects (population density (PD), gross domestic product per capita (GDP), domestic general government health expenditure per capita (GHE)) and Rn, and 3) the interaction and non-linear effects of the different variables on Rn, based on GeoDetector and Boosted regression tree. The results showed that the global Rn had was spatially clustered, and the average Rn increased From March to November 2020. Global Rn was negatively correlated with meteorological variables (T, R, WS, AP) and positively correlated with air pollutants (NO2, SO2, O3) and socioeconomic aspects (GDP, GHE). The interaction of SO2 and O3, SO2 and RH, and O3 and T strongly affected Rn. The variables effect on COVID-19 transmission was non-linear, with one or more inflexion points. The findings of this work can provide a basis for developing a global response to COVID-19 for global sustainable development.

An optimized approach for estimating benzene in ambient air within an air quality monitoring network.

Benzene is a carcinogenic air pollutant for which European legislation has set an annual limit and criteria for the number of fixed monitoring sites within air quality networks (AQMN). However, due to the limited number of fixed sites for benzene measurement, exposure data are lacking. Considering the relationship between benzene levels and other variables monitored within an AQMN, such as NO2, O3, temperature, solar radiation, and accumulated precipitation, this study proposes an approach for estimating benzene air concentrations from the related variables. Using the data of the aforementioned variables from 23 fixed stations during 2016-2017, the proposed approach was able to forecast benzene concentration for 2018 with high confidence, providing enriched data on benzene exposure and its trends. Moreover, the spatial distribution of the estimated versus the most representative benzene levels was quite similar. Finally, an artificial neural network identified the most representative fixed benzene monitoring sites within the AQMN.

METEOROLOGICAL VARIABLES ASSOCIATED WITH SUBARACHNOID HEMORRHAGE: A SINGLE CENTER STUDY.

Spontaneous subarachnoid hemorrhage (SAH) can occur unexpectedly and independently of the classic risk factors. Several different factors could affect intracranial aneurysm (IA) rupture, such as morphological and hemodynamic factors. The aim of this study was to establish the potential association of meteorological data such as temperature, atmospheric pressure, and humidity, and the onset of clinical symptoms preceding hospital admission of patients with acute SAH due to IA rupture. This retrospective study included 130 consecutive patients admitted for non-traumatic SAH with a determinable onset of SAH symptoms. The effects of meteorological parameters of atmospheric pressure, ambient temperature, and relative air humidity on the day of acute SAH onset and 24 hours prior to the onset of symptoms were recorded and analyzed in each patient. Spearman rank correlation analysis was used to assess the risks of incident SAH on the basis of daily meteorological data. Seasonal incidence of acute SAH showed the peak incidence in winter and a trough in summer, with monthly incidence peak in January and December. The circadian rhythm analysis showed the peak incidence of SAH in the forenoon, followed by the evening. Acute SAH incidence showed moderate positive association with daily atmospheric pressure (p<0.05), while no association was found with ambient temperature and relative air humidity. Our results suggested no significant association of changes in ambient temperature and relative humidity with the risk of SAH. Increases in atmospheric pressure were weakly associated with a higher SAH risk. Additional studies are needed to establish in detail both meteorological and morphological factors important to predict IA rupture and SAH.

Global patterns and drivers of rainfall partitioning by trees and shrubs.

Spatiotemporal redistribution of incident rainfall in vegetated ecosystems results from the partitioning by plants into intercepted, stemflow, and throughfall fractions. However, variation in patterns and drivers of rainfall partitioning across global biomes remains poorly understood, which limited the ability of climate models to improve the predictions of biome hydrological cycle under global climate change scenario. Here, we synthesized and analyzed the partitioning of incident rainfall into interception, stemflow, and throughfall by trees and shrubs at the global scale using 2430 observations from 236 independent publications. We found that (1) globally, median levels of relative interception, stemflow, and throughfall accounted for 21.8%, 3.2%, and 73.0% of total incident rainfall, respectively; (2) rainfall partitioning varied among different biomes, due to variation in plant composition, canopy structure, and macroclimate; (3) relative stemflow tended to be driven by plant traits, such as crown height:width ratio, basal area, and height, while relative interception and throughfall tended to be driven by plant traits as well as meteorological variables. Our global assessment of patterns and drivers of rainfall partitioning underpins the role of meteorological factors and plant traits in biome-specific ecohydrological cycles. We suggest to include these factors in climate models to improve the predictions of local hydrological cycles and associated biodiversity and function responses to changing climate conditions.

Association of air pollution and meteorological variables with COVID-19 incidence: Evidence from five megacities in India.

Although lockdown of the industrial and transport sector and stay at home advisories to counter the COVID-19 pandemic have shown that the air quality has improved during this time, very little is known about the role of ambient air pollutants and meteorology in facilitating its transmission. This paper presents the findings from a study that was conducted to evaluate whether air quality index (AQI), three primary pollutants (PM2.5, PM10 and CO), Ground level ozone (O3) and three meteorological variables (temperature, relative humidity, wind speed) have promoted the COVID-19 transmission in five megacities of India. The results show significant correlation of PM2.5, PM10, CO, O3 concentrations, AQI and meteorological parameters with the confirmed cases and deaths during the lockdown period. Among the meteorological variables considered, temperature strongly correlated with the COVID-19 cases and deaths during the lockdown (r=0.54;0.25) and unlock period (r=0.66;0.25). Among the pollutants, ozone, and among the meteorological variables, temperature, explained the highest variability, up to 34% and 30% respectively, for COVID-19 confirmed cases and deaths. AQI was not a significant parameter for explaining the variations in confirmed and death cases. WS and RH could explain 10-11% and 4-6% variations of COVID-19 cases. A GLM model could explain 74% and 35% variability for confirmed cases and deaths during the lockdown and 66% and 19% variability during the unlock period. The results suggest that meteorological parameters may have promoted the COVID-19 incidences, especially the confirmed cases. Our findings may encourage future studies to explore more about the role of ambient air pollutants and meteorology on transmission of COVID-19 and similar infectious diseases.

COVID-19 in Asia: Transmission factors, re-opening policies, and vaccination simulation.

This work aims to provide insights on the COVID-19 pandemic in three prime aspects. First, we attempted to understand the association between the COVID-19 transmission rate, environmental factors (air pollution, weather, mobility), and socio-political parameters (Government Stringency Index, GSI). Second, we evaluated the efficiency of various strategies, including radical opening, intermittent lockdown, phase lift, and contact tracing, to exit the COVID-19 pandemic and get back to pre-pandemic conditions using a stochastic individual-based epidemiology model. Third, we used a deep learning approach and simulated the vaccination rate and the time for reaching herd immunity. The analysis was done based on the collected data from eight countries in Asia, including Iran, Turkey, India, Saudi Arabia, United Arab Emirates, the Philippines, South Korea, and Russia (as a transcontinental country). Our findings in the first part highlighted a noninfluential impact from the weather-driven parameters and short-term exposure to pollutants on the transmission rate; however, long-term exposure could potentially increase the risk of COVID-19 mortality rates (based on 1998-2017 p.m.2.5 data). Mobility was highly correlated with the COVID-19 transmission and based on our causal analysis reducing mobility could curb the COVID-19 transmission rate with a 6-day lag time (on average). Secondly, among all the tested policies for exiting the COVID-19 pandemic, the contact tracing was the most efficient if executed correctly. With a 2-day delay in tracing the virus hosts, a 60% successful host tracing, and a 70% contact reduction with the hosts, a pandemic will end in a year without overburdening a healthcare system with 6000 hospital beds capacity per million. Lastly, our vaccine simulations showed that the target date for achieving herd immunity significantly varied among the countries and could be delayed to October-november 2022 in countries like India and Iran (based on 60% immunized population and assuming no intermediate factors affecting the vaccination rate).

COVID-19 pandemic in Wuhan: Ambient air quality and the relationships between criteria air pollutants and meteorological variables before, during, and after lockdown.

As a result of the lockdown (LD) control measures enacted to curtail the COVID-19 pandemic in Wuhan, almost all non-essential human activities were halted beginning on January 23, 2020 when the total lockdown was implemented. In this study, changes in the concentrations of the six criteria air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) in Wuhan were investigated before (January 1 to 23, 2020), during (January 24 to April 5, 2020), and after the COVID-19 lockdown (April 6 to June 20, 2020) periods. Also, the relationships between the air pollutants and meteorological variables during the three periods were investigated. The results showed that there was significant improvement in air quality during the lockdown. Compared to the pre-lockdown period, the concentrations of NO2, PM2.5, PM10, and CO decreased by 50.6, 41.2, 33.1, and 16.6%, respectively, while O3 increased by 149% during the lockdown. After the lockdown, the concentrations of PM2.5, CO and SO2 declined by an additional 19.6, 15.6, and 2.1%, respectively. However, NO2, O3, and PM10 increased by 55.5, 25.3, and 5.9%, respectively, compared to the lockdown period. Except for CO and SO2, WS had negative correlations with the other pollutants during the three periods. RH was inversely related with all pollutants. Positive correlations were observed between temperature and the pollutants during the lockdown. Easterly winds were associated with peak PM2.5 concentrations prior to the lockdown. The highest PM2.5 concentrations were associated with southwesterly wind during the lockdown, and northwesterly winds coincided with the peak PM2.5 concentrations after the lockdown. Although, COVID-19 pandemic had numerous negative effects on human health and the global economy, the reductions in air pollution and significant improvement in ambient air quality likely had substantial short-term health benefits. This study improves the understanding of the mechanisms that lead to air pollution under diverse meteorological conditions and suggest effective ways of reducing air pollution in Wuhan.

Effect of meteorological factors on COVID-19 cases in Bangladesh.

This work is intended to examine the effects of Bangladesh's subtropical climate on coronavirus diseases 2019 (COVID-19) transmission. Secondary data for daily meteorological variables and COVID-19 cases from March 8 to May 31, 2020, were collected from the Bangladesh Meteorological Department (BMD) and Institute of Epidemiology, Disease Control and Research (IEDCR). Distributed lag nonlinear models, Pearson's correlation coefficient and wavelet transform coherence were employed to appraise the relationship between meteorological factors and COVID-19 cases. Significant coherence between meteorological variables and COVID-19 at various time-frequency bands has been identified in this work. The results showed that the minimum (MinT) and mean temperature, wind speed (WS), relative humidity (RH) and absolute humidity (AH) had a significant positive correlation while contact transmission had no direct association with the number of COVID-19 confirmed cases. When the MinT was 18 °C, the relative risk (RR) was the highest as 1.04 (95%CI 1.01-1.06) at lag day 11. For the WS, the highest RR was 1.03 (95% CI 1.00-1.07) at lag day 0, when the WS was 21 km/h. When RH was 46%, the highest RR was 1.00 (95% CI 0.98-1.01) at lag day 14. When AH was 23 g/m3, the highest RR was 1.05 (95% CI 1.01-1.09) at lag day 14. We found a profound effect of meteorological factors on SARS-CoV-2 transmission. These results will assist policymakers to know the behavioral pattern of the SARS-CoV-2 virus against meteorological indicators and thus assist to devise an effective policy to fight against COVID-19 in Bangladesh.

What is the role of meteorological variables on involuntary admission in psychiatric ward? An Italian cross-sectional study.

Weather affects physical and mental health through several modalities which are not fully elucidated. The aim of the present study was to investigate the impact of meteorological variables and other indexes in a large sample of hospitalized patients, focusing on subjects who were involuntarily admitted. We hypothesized a direct relation between the amount of involuntary admissions and mean sunshine hours. Furthermore, we supposed that specific meteorological factors may significantly influence hospitalizations of patients affected by severe psychiatric conditions. All subjects were consecutively recruited from the Psychiatric Inpatient Unit of San Luigi Gonzaga Hospital, Orbassano (Turin, Italy) from September 2013 to August 2015. Socio-demographic and clinical characteristics were carefully collected. Meteorological data were derived by the Italian Meteorology's Climate Data Service of Physics Department of the University of Turin (Latitude: 45°03'07,15″ Nord, Longitude: 007°40'53,30″ Est, Altitude: 254 m above the sea level) (http://www.meteo.dfg.unito.it/). Our data indicate significant differences regarding temperature (minimum, maximum, and medium), solar radiation, humidex and windchill index, and hours of sunshine in psychiatric patients who were involuntarily hospitalized. After logistic regression analyses, only maximum and medium temperature, and humidex index remained significantly associated with involuntary admission in an emergency psychiatric ward. The limitations of this study include the cross-sectional study design and the single hospital for patients' recruitment. Furthermore, results and seasonal patterns obtained by patients requiring hospitalization might significantly differ from those who were not hospitalized. Exploring in a more detailed manner those environmental factors associated with involuntary admissions could lead to early intervention and prevention strategies for such distressing hospitalizations.

Willow phenological modelling at different altitudes in central Italy.

In order to estimate the impact of climate change on the phenological parameters and to compare them with the historical record, a decision support system (DSS) has been applied employing a Phenological Modelling Platform. Biological observations of two willow species (Salix acutifolia and smithiana Willd) in 3 gardens at different altitudes located in Central Italy were utilized to identify suitable phenological models related to four main vegetative phase timings (BBCH11, BBCH91, BBCH 94, BBCH95), and male full flowering (BBCH 65) clearly identifiable in these species. The present investigation identifies the best phenological models for the main phenophases allowing their practical application as real-time monitoring and plant development prediction tools. Sigmoid model revealed high performances in simulating spring vegetative phases, BBCH11 (First leaves unfolded), and BBCH91 (Shoot and foliage growth completed). Salix acutifolia Willd. development appeared to be more related to temperature amount interpreted by phenological models in comparison to Salix smithiana Willd. above all during spring (BBCH11 and 91), probably due to a different grade of phenotypic plasticity between the 2 considered species.

Influence of season, rainfall and air temperature on the reproductive efficiency in Romanov sheep in Croatia.

The aim of this study was to investigate the influence of season, rainfall and air temperature on the reproductive efficiency in the Romanov breed of sheep in continental part of Croatia during five consecutive years (2012-2016). During this period, 5379 matings resulted in 5046 successful conceptions, i.e. lambings at eight medium-scale Romanov breed sheep farms. The conception rate was 93.81%, fecundity was 195% and average preweaning mortality until 90 days of age was 12.41%. The seasonal distribution of lambings was 47.64% for ewes that delivered in winter (n = 2422), 23.37% in spring (n = 1179), 18.82% in summer (n = 950) and 9.81% in autumn (n = 495). Sexual activity was lowest during spring and early summer when air temperatures were above average (very and extremely warm), and sexual activity peaked from August to September, especially during extremely wet and very wet seasons. Litter size was greater during winter than in other seasons (1.70 vs. 1.54) and was significantly different as compared to each of selected years of the study period. There was a statistically significant difference in the number of pregnant ewes between mating seasons. Most female Romanov lambs born during winter and early spring mated in late summer or autumn and delivered at the age of 1 year or earlier. The seasonal distribution of matings and lambing was not uniform throughout the seasons over five consecutive years. Thus, it can be assumed that air temperature and rainfall during different seasons could affect the reproductive efficiency in Romanov breed of sheep in continental part of Croatia.

The effects of meteorological variables on ambulance attendance for cardiovascular diseases in Rasht, Iran.

Climate change has a devastating effect on human societies, including their economic, cultural and health conditions. Our objective was to investigate the association between meteorological variables and ambulance attendance in the event of cardiovascular diseases using time-series analyses. We used a time series analysis to investigate the relationship between meteorological variables and ambulance attendance in the event of cardiovascular diseases from 2010 to 2015. To examine the effect of high temperatures on ambulance attendance, we investigated the relative risk of the daily volume of high temperature attendance, the 99th temperature percentile compared to the 75th temperature percentile. Upon examining the effect of cold temperatures on ambulance attendance, or the relative risk of the daily volume of attendance with low temperatures, the 1st temperature percentile compared to the 25th temperature percentile. In 1826 days, from March 21, 2010 to March 19, 2015, there were 7051 emergency calls for cardiovascular diseases. Significant variations were identified in the monthly (P < 0.001) and seasonal (P < 0.001) distributions. The highest seasonal incidence occurred in the winter and lowest was observed in the summer. With regard to association between cold temperature and calls for ambulance attendance in the event of cardiovascular diseases according to lag days, our findings showed a significant increase in lag 7 ((RR, 1.026; 95% CI, 1.003 to 1.050), lag 8 (RR, 1.023; 95% CI, 1.005to 1.041) and lag 9 (RR, 1.019; 95% CI, 1.002 to 1.036) respectively. These results suggest that the demand for an ambulance for cardiovascular diseases was higher in the cold weather and that humidity can increase this demand in the warm seasons.

Modeling pollen time series using seasonal-trend decomposition procedure based on LOESS smoothing.

Analysis of airborne pollen concentrations provides valuable information on plant phenology and is thus a useful tool in agriculture-for predicting harvests in crops such as the olive and for deciding when to apply phytosanitary treatments-as well as in medicine and the environmental sciences. Variations in airborne pollen concentrations, moreover, are indicators of changing plant life cycles. By modeling pollen time series, we can not only identify the variables influencing pollen levels but also predict future pollen concentrations. In this study, airborne pollen time series were modeled using a seasonal-trend decomposition procedure based on LOcally wEighted Scatterplot Smoothing (LOESS) smoothing (STL). The data series-daily Poaceae pollen concentrations over the period 2006-2014-was broken up into seasonal and residual (stochastic) components. The seasonal component was compared with data on Poaceae flowering phenology obtained by field sampling. Residuals were fitted to a model generated from daily temperature and rainfall values, and daily pollen concentrations, using partial least squares regression (PLSR). This method was then applied to predict daily pollen concentrations for 2014 (independent validation data) using results for the seasonal component of the time series and estimates of the residual component for the period 2006-2013. Correlation between predicted and observed values was r = 0.79 (correlation coefficient) for the pre-peak period (i.e., the period prior to the peak pollen concentration) and r = 0.63 for the post-peak period. Separate analysis of each of the components of the pollen data series enables the sources of variability to be identified more accurately than by analysis of the original non-decomposed data series, and for this reason, this procedure has proved to be a suitable technique for analyzing the main environmental factors influencing airborne pollen concentrations.