Weather Temperature Research Articles

Spatial Analysis and Risk Assessment Model Research of Arthritis Based on Risk Factors: China, 2011, 2013 and 2015

Arthritis is a public health issue that is of global concern. Arthritis is one of the chronic diseases with a high incidence of middle-aged and older adults. The patients have paid a heavy price for this and caused a substantial economic burden on society. In this study, we used spatial autocorrelation, spatial cluster analysis, multiple logistic regression, and random forest models to analyze the spatial distribution and possible risk factors for arthritis in elderly Chinese and assess arthritis risk. Global spatial autocorrelation analysis and significance test results show that Moran's I of arthritis spatial autocorrelation in 2011, 2013, and 2015 are statistically significant, so there is significant spatial autocorrelation three years. The results of local spatial autocorrelation and spatial clustering analysis show that the aggregation areas of arthritis patients are mainly in the southwest, northwest, and central China. Multivariate logistic regression analysis showed that gender, age, education level, Body Mass Index (BMI), Center for Epidemiologic Studies Depression Scale score (CES-D), altitude, region, weather temperature, hypertension, lung, liver, heart, stroke, digestive, and kidney disease were all arthritis affects factors (P <; 0.05). Compared with the multi-factor Logistic regression model, the random forest model better assesses performance and higher fit. The fitting accuracy is 82.2% in the random forest model, which is better than the multi-factor Logistic regression model (66.6%). According to the assessment risk map generated by the random forest model, Northeast, Southwest, Northwest, South, and Central are high-risk areas for arthritis. These results provide benchmark data for the control and prevention of arthritis diseases.

Seasonal Variation in Serum Biochemical, Electrolyte and Hormonal Profile of Deoni Cattle

The present study was designed to understand the impact of season on biochemical, metabolic and hormonal parameters in Deoni cow. Study period comprised winter, spring and summer seasons. Ambient temperature and relative humidity were recorded from sensor based automatic weather station and temperature relative humidity index was calculated. Blood samples were collected two times in each season in selected animals. Biochemical parameters viz., glucose, total protein, albumin, triglycerides, cholesterol, creatinine, BUN, Sodium and potassium were estimated using semi-automated biochemistry analyzer. The hormonal parameters viz., tri-iodothyronine, thyroxin and cortisol were estimated by radioimmune assay technique. Glucose, protein, triglycerides, cholesterol and creatinine was significantly lower in summer season. Significantly higher BUN, Na+ and K+ were noticed in summer. T3 and T4 were significantly higher in winter and lowest in summer. The cortisol was significantly higher in summer followed by winter and the lowest was recorded during spring.

EVENT BASED FOG CHARACTERISTICS OF ÇARŞAMBA AIRPORT

In this study the characteristics of fog events were examined at Çarşamba International Airport in Turkey. Historical meteorological observations were used between the years of 2008 and 2017. Fog events were classified by a simple conceptual model on fog formation process. Fog types, visibility, duration, wind direction and speed, sea surface temperature were reviewed. Results show three main fog types; classified, radiation, advection and cloud base lowering. No fog events were observed between July and September, whereas the most foggy periods are between the beginning of January and the end of May. This corresponds with the advection fog period. Sea surface temperature and weather temperature during fog onset was found to highly correlate with each other. The return period of dense fog for two years was determined to be 8.27 hours. Light, calm and eastern directions of wind were detected during the most of the fog events. Fog onset and dissipation times were figured out in monthly scale and the effect of solar radiation is clearly seen with the onset and dissipation processes. Determined characteristics of fog in Çarşamba airport can be useful for future modeling studies.

The Role of Statistical Analysis in Ensuring the Competitiveness of Tourism (Case of Uzbekistan)

This article discusses the importance of statistical data in planning the activities of tourism organizations. Also, the general trends in inbound and domestic tourism are identified and evaluated. The seasonality of inbound tourism turned out to be highly dependent on weather temperature. The importance of hotel classification and its influence on marketing power of tour packages is discussed.

WEATHER FORECASTING IN THE PRACTICE OF REINDEER HERDERS FROM THE SUBPOLAR URALS

The present article considers a complex of various signs used for predicting significant weather phenomena in the practice of reindeer herders from the Subpolar Urals. The authors obtained empirical data in the course of ethnographic expeditions (2007, 2012 and 2018) by conducting semi-structured interviews with hereditary reindeer herders, managers and specialists from the reindeer herding company of Saranpaul village (Beryozovsky District, Khanty-Mansi Autonomous Area — Yugra). Systematisation along with the thematic classification of the field data revealed that the practical knowledge and skills of reindeer herders include prompt and long-term weather observation, experience in predicting and evaluating changes in hydrometeorological conditions, the ability to determine their impact on the food supply, as well as on the behaviour and health of deer. A complex of various signs helps to predict short-term weather changes or seasonal prospects: incoming of spring / summer / winter, rainy weather and summer temperature, snow depth in winter, bad weather, rain, snow, snowstorm, wind, cold, frost, heat and other meteorological changes. For making a short-term forecast, the following points are taken into account: wind direction in the mountains; presence and location of fog in the mountains; colour of sunset; twinkling of stars; halo around the moon; beauty of Northern Lights; quality of carried sounds (voiced and voiceless); fire draught and smoke movement; cloud form; bird cries; and animal behaviour (deer, dogs, insects). For the most part, these signs are rationally explained by changes in atmospheric pressure and humidity. As a result of long-term weather observations, reindeer herders have noted certain climate mitigation, as well as an increase in the number of anomalous weather phenomena, which are uncharacteristic of their seasons.

Behavior improvement of self-compacting concrete in hot weather

The main aim of this research is studying the effect of hot weather on the properties of self-compacting concrete and conventional concrete in both fresh and hardened state. Also, this research extends to improve the behavior of self-compacting concrete in hot weather. The main parameters were surrounding weather temperature (5°C, 20°C and 35°C), concrete materials temperatures’ (25°C, 50°C), curing temperatures (25°C and 50°C) and admixtures (using a retarder). Two stages were carried out to achieve the research aim. The behavior of self-compacting concrete compared to conventional concrete was evaluated in the first stage. Based on the first stage, attempts to enhance the concrete properties were evaluated in the second stage. Precautions on mixing and placing concrete in these climates are considered. Results are a drive in terms of; workability tests, compressive strength, splitting tensile strength, and flexural strength. Test results showed that self-compacting concrete behavior and strengths were better than conventional concrete. Slump test, J-ring and V-funnel test were used to evaluate the fresh properties of the self-compacting concrete. Drying shrinkage of self-compacting concrete in hot weather were also evaluated.

Stochastic PV model for power system planning applications

Planning photovoltaic (PV) power systems integration into the grid necessitates accurate modelling of renewable power generation. Global solar irradiance, weather temperature and PV power losses due to overheating specifically in hot regimes are major factors contributing to PV power generation uncertainty. This study targets demonstrating the effectiveness of deploying advanced five parameter probabilistic distribution ‘Wakeby’ for modelling PV uncertain power generation, measured as a function of such factors, in power system planning applications. The impact of different approaches for incorporating weather temperature on PV energy estimation is studied. Wakeby-Monte Carlo Simulation for PV power data training with an emphasis on MCS stopping criteria for such advanced distribution is presented. The model is tested and verified in 31-bus distribution system to demonstrate its effectiveness over other literature uncertainty modelling approaches when planning integration of PV systems' integration into the grid to minimise the grid losses cost. Real PV power measurements are utilised as benchmark verifying the accuracy and suitability of the presented uncertainty modelling approach. Simulation results demonstrate a small error of $4.7 in the expected annual cost of grid losses when deploying Wakeby model compared to the benchmark case and that error can vary significantly when deploying other PV models.

Effects of subjective feeling of environmental temperature on appetite and food intake in Chinese sociodemographic and ethnic groups

BackgroundA better understanding of the effect of environmental temperature variation on appetite and food intake may inform the public of adopting appropriate prophylactic eating behaviors. Seasonal weather temperature variation has been shown to have adverse effects on appetite and food intake in humans. The main purpose of this study was to examine the effect of environmental temperature variation on the appetite and eating patterns in Chinese adults.MethodsCross-sectional analysis of data obtained from 1297 Chinese adults, including two Chinese ethnic groups, Han (N = 897) and Hui (N = 400). Participants responded to a modified paper-and-pencil self-administered dietary habits and food intake survey. In addition to sociodemographic factors and ethnic group affiliation questions, the survey included a question regarding the effect of the subjective feeling of environmental temperature variation (SFETV) on appetite and food intake as an independent variable.t tests and chi-square analyses were performed to analyze the parametric and nonparametric variables respectively.ResultsNo significant difference was found in the effect of SFETV between the following dependent variables: male and female respondents (p < .998), married and single respondents (p < .281), full-time and part-time employed or retired participants (p < .187). Although it has not affected their SFETV responses, there was a statistically significant difference in the education level between the married and single respondents (p < .001). The single respondents were more likely to be college educated with a rate of (68.48%) compared with their married counterparts with a rate of 30.2%. There was a statistically significant difference between the eight cities and one rural town (p < .001). The appetite and food intake of the respondents from these cities were more likely to be affected by the SFETV. The appetite and food intake of the Han ethnic group were more likely to be affected by the SFETV with a rate of 28.09% juxtaposed to the Hui ethnic group with a rate of 12.64%.ConclusionsThe findings suggest that exposure to outdoor temperature variation (hot or cold) has a limited effect on the appetite and food intake of Chinese adults. However, the appetite and food intake of the permanent residents of the eight cities and one rural town were more likely to be affected by the exposure to outdoor temperature variation (hot or cold).

THE TREATMENT AND HANDLING SYSTEMS OF DE/ANTI-ICING CONTAMINANTS WHICH GENERATED AND DISCHARGED INTO SURFACE RUNOFF FROM AIRPORTS TERRITORIES

It is essential to evaluate the fact that our living environment day by day faces with the damage caused by contaminants that are appearing within the surface runoff from airports. Even though, airports are not always being assumed as industrialized components, works, that are being carried out in everyday life in airports, including simple tasks such cleaning or washing vehicles or aircraft, procedures such repair works, fundamentally everything, that takes place on the airport area grounds, still do discharge contaminants into the environment. This sort of activities that are being carried out in everyday life, in fact create severe natural surroundings issues, pollution is not always are being controlled. Therefore, regarding these issues and in order to be able to control them, it is extremely important to observe a degree of contaminants discharged within the environment and to maintain their sorting and adequate processing. An industry as airlines manage numbers of threats and restrictions regarding the area of airline fields, taking into consideration anti-icing and de-icing processes. Regulations of the treatment processes of surface wastewater that are polluted by the drainage approaches in cold weather temperatures are essential for many airports that operate within north climate. The paper summarizes information of certain airports located in foreign countries, techniques and appropriate methods that are used in order to slow down or decrease contaminants that are being discharged within the surface runoff and backwaters. Taking into account international practice this paper introduces proposed surface runoff treatment technology at Vilnius International Airport in Lithuania.

Impact of age on the seasonal prevalence of hypernatremia in the emergency department: a single-center study

BackgroundHypernatremia is one of the most commonly encountered electrolyte disorders in the emergency department (ED). Few studies have reported the seasonal fluctuations of the prevalence of hypernatremia with conflicting results. We investigated the seasonal prevalence of hypernatremia in an emergency department in Japan.MethodsA total of 12,598 patients presented to the ED between January 2015 and December 2017 were reviewed. The adult group aged between 18 and 64 years old consisted of 5427 patients and the elderly group aged over 65 years consisted of 7171 patients. Information collected included age, sex, serum sodium, and serum creatinine. Hypernatremia was defined as a serum sodium leve1 > 145 mEq/L, and moderate to severe hypernatremia was defined as a serum sodium level ≥ 150 mEq/L.ResultsThe prevalence of hypernatremia was significantly higher in the elderly group than in the adult group (2.6% vs. 0.7%; p < 0.001). Similarly, the prevalence of moderate to severe hypernatremia was significantly higher in the elderly group than in the adult group (1.0% vs. 0.1%; p < 0.001). The prevalence of hypernatremia and moderate to severe hypernatremia was significantly higher in the elderly group than in the adult group in all seasons. In the elderly group, the seasonal prevalence of moderate to severe hypernatremia was significantly higher during the winter. Also, there was a correlation between weather temperature and the prevalence of moderate to severe hypernatremia in the elderly group (r = − 0.34, p = 0.04).ConclusionsHypernatremia is prevalent in the elderly and the prevalence is highest during the winter. Special attention should be paid in the elderly patients to prevent hypernatremia especially in the winter.

Impact of environmental factors on heart failure decompensations.

AimsHeart failure (HF) is a clinical syndrome caused by a structural and/or functional cardiac abnormality, resulting in a reduced cardiac output and/or elevated intracardiac pressures at rest or during stress. This disease often causes decompensations, which may lead to hospital admissions, deteriorating patients' quality of life and causing an increment on the healthcare cost. Environmental exposure is an important but underappreciated risk factor contributing to the development and severity of cardiovascular diseases, such as HF.Methods and resultsWe used two different sets of data (January 2012 to August 2017): one related to the number of hospital admissions and the other one related to the environmental factors (weather and air quality). Admissions related data were grouped in weeks, and then two different studies were performed: (i) a univariate regression to determine whether the admissions may influence future hospitalizations prediction and (ii) a multivariate regression to determine the impact of environmental factors on admission rates. A total number of 8338 hospitalizations of 5343 different patients are available in this dataset, with a mean of 4.02 admissions per day. In European warm period (from June to October), there are significant less admissions than that in the cold period (from December to March), with a clear seasonality of admissions, because there is a similar pattern every year. Air temperature is the most significant environmental factor (r = −0.3794, P < 0.001) related to HF hospital admissions, showing an inversed correlation. Some other attributes, such as precipitation (r = 0.0795, P = 0.05), along with SO2 (precursor of acid rain) (r = 0.2692, P < 0.001) and NOX air (major air pollutant formed by combustion systems and motor vehicles) (r = 0.2196, P < 0.001) quality parameters, are also relevant. Humidity and PM10 parameters do not have significant correlations in this study (r = 0.0469 and r = −0.0485 respectively), neither relevant P‐values (P = 0.238 and P = 0.324, respectively).ConclusionsSeveral environmental factors, such as weather temperature and precipitation, and major air pollutants, such as SO2 and NOX air, have an impact on the HF‐related hospital admissions rate and, hence, on HF decompensations and patient's quality of life.

Stress-Strength Reliability Estimation for the Type I Extreme-Value Distribution Based on Records

In this paper, we consider the stress-strength reliability for record data when the distribution of random stress and strength have the type I extreme-value distribution. First, classical inference methods, namely uniformly minimum variance unbiased estimate (UMVUE) and maximum likelihood estimate (MLE), are used for . Second, Bayesian inference of are considered for gamma priors assumption. When the common parameter of stress and strength variables is known, the exact Bayes estimate and Bayesian credible interval of are obtained. Markov Chain Monte Carlo (MCMC) method are used to derive the Bayes estimate and highest probability density (HPD) credible interval of when the common parameter is unknown. Finally, Monte Carlo simulations are performed to compare the performance of the obtained estimates. A real data set about the weather temperature is analyzed to illustrate the performances of the derived estimators in the paper.

Application of the Least Square Support Vector Machine for point-to-point forecasting of the PV Power

&lt;p&gt;In today's industrial world, the growing capacity of renewable energy sources is a crucial factor for sustainable power generation. The application of solar photovoltaic (PV) energy sources, as a clean and safe renewable energy resource has found great attention among the consumers in the recent decades. Accurate forecasting of the generated PV power is an important task for scheduling the generators and planning the consumption patterns of customers to save electricity costs. To this end, it is necessary to develop a global model of the generated power based on the effective factors which are mainly the solar radiation intensity and the ambient weather temperature. As a result of the wide numerical range of these parameters and various weather conditions, a large training database must be used for developing the models, which results in high-computational complexity of the algorithms used for training the models. In this paper, a novel algorithm for point to point prediction of the generated power based on the least squares support vector machine (LS-SVM) has been proposed which can handle the large training database with a very fewer deal of computation and benefits from reasonable accuracy and generalization capability. &lt;/p&gt;

A Method Based on Temporal Component Decomposition for Estimating 1-km All-Weather Land Surface Temperature by Merging Satellite Thermal Infrared and Passive Microwave Observations

Land surface temperature (LST) is a key variable at the land–atmosphere boundary. For many research projects and applications an all-weather LST product at moderate spatial resolution (e.g., 1 km) would be highly useful, especially in frequently cloudy areas. Merging thermal infrared (TIR) and microwave (MW) observations is able to overcome shortcomings of single-source remote sensing to derive such an LST. However, in current merging methods, models adopted for downscaling MW LST fail to quantify the effect of temporal variation of LST. Thus, accuracy of the merged LST can be deteriorated and therefore remain a major impediment for these methods to be generalized over large areas. In this context, we propose a new practical method to merge TIR and MW observations from a perspective of decomposition of LST in temporal dimension. The physical basis of the method is decomposing LST into three temporal components: annual temperature cycle component, diurnal temperature cycle component prescribed by solar geometry, and weather temperature component driven by weather change. The method was applied to MODIS and AMSR-E/AMSR2 data to generate an 11-year record of 1-km all-weather LST over Northeast China: the resulting merged LST has an accuracy of 1.29–1.71 K when validated against in situ LST; besides, no obvious differences in accuracy of the merged LST were found between clear-sky and unclear-sky conditions. Furthermore, the proposed method outperforms the previous method in both accuracy and image quality, indicating its good capability to generate daily 1-km all-weather LST, which will benefit continuous monitoring of earth’s surface temperature.

COMPARISON OF TWO ASPHALT MIXTURES USING COMPLEX MODULUS TEST IN LIBYAN WEATHER

The complex modulus test is dependent on temperature and loading frequency. Thus, the results produced from this test will give a more accurate representation of traffic load effects on asphalt pavement. Laboratory experiments were conducted on two different asphalt mixtures for road research projects (Libya/Roads). All specimens had the same mixtures of aggregate gradation GB-20 incorporated with two different asphalt binders PG70-10 and B (60/70). To obtain the master curve, there were some errors at low temperatures (-25, -10 ºC) and high temperature (54 ºC), so these values were discarded. In addition, 2-complex modulus (CM) and phase angles (Phi) in the test were measured at temperatures of -25, -10, -5, 10, 25, 35, and 54ºC, as well as frequencies of 25, 10, 5, 1, 0.5 and 0.1 Hz. The results displayed the influence of the type of binder on the rheology of the mixtures and gradation on the intensity. Hence, using binder PG 70-10 in Libyan asphalt roads may reduce the binder content, increase the mixture workability, and decrease the thermal cracking. The intrinsic characteristics related to binder properties and weather temperature exhibited the most significant impact on the predicted dynamic modulus.&#x0D; Keywords: complex modulus, frequencies, temperatures, sinusoidal, phase angles

Study of a passive solar air heater using palm oil and paraffin as storage media

This study evaluated a passive solar air heater using different storage media. The heater, which is very useful in low temperature applications like agricultural produce drying and heating of buildings, is made up of a double-glaze solar thermal collector of a flat plate type coupled with thermal storage medium. The heater was replicated using paraffin and palm oil as storage medium for systems ‘a’ and ‘b’ respectively. The evaluation of the systems without load under 24-h periods were carried out experimentally in Nsukka, Enugu State, Nigeria, within the weather temperature and daily radiation of 23.6–32.4 °C and 11.6–21.1 MJ m−2 respectively. The peak cumulative efficiencies were 57.3% for system ‘b’ and 46% for system ‘a’. Similarly, the mean efficiencies were 38.4% and 41.0% for systems ‘a’ and ‘b’ respectively. The results prove that for low temperature applications the systems can successfully be implored. From the study, it can be concluded that the use of palm oil as storage medium for solar air heaters is a good substitute for paraffin which is a non renewable medium with its future uncertainties due to depleting global crude oil reserves.

Southern Martian winter weather associated with baroclinic topography forced Rossby waves: analysing by Global Mars Multiscale Model

Meteorological and physical results from the Global Mars Multiscale Model (GM3) are used for investigating the impact of zonally asymmetric topography on atmospheric temperature and wind structures of Martian southern winter weather associated with the forced topographical Rossby waves. Analysis of the numerical simulations reveals significant thermal and wind patterns at the southern mid and high latitudes during southern winter due to the impact of asymmetric topography. This study shows the presence of warm air advection at southern high latitudes mostly around the cryptic regions controls by a warm ridge and the cold air advection at mid-latitudes is around the Hellas and Argyre basins associated with two cold troughs. Also, there is large temperature advection fluctuation around $50^{\circ}\mbox{S}$ causing by topographical aspects of Hellas and Argyre basins rather than flat topography simulation. This is associated with the baroclinicity structure causing by topography in Martian mid-latitudes. This can act to moderate the temperature field in the Martian southern hemisphere weather during wintertime because of the wind pattern structures. Also, the winter time south zonal westerly jet stream analysis shows the development of disturbances structure (meandering) in the north side of the jet dependent of longitudinal structure of Martian southern topography with a wave-like pattern below the Hellas and Argyre basins. Also, the vertical structure of the zonal wind shows the appearance of a vertical wind shear around $78^{\circ}\mbox{S}$ with smoothly tilting to the higher altitudes associated with the baroclinic structure of the southern Martian winter weather associated with the baroclinic forced topography Rossby waves.

Techno-economic assessment and optimization of a hybrid renewable co-supply of electricity, heat and hydrogen system to enhance performance by recovering excess electricity for a large energy consumer

This study attempts to optimize and examine the techno-economic feasibility of off-grid hybrid renewable energy systems (HRES) to satisfy simultaneously electric, heat and hydrogen load of a large energy consumer. An innovative method is adopted so as to recover surplus electricity to generate heat and cut emissions. The system is comprised of solar panels, wind turbine, diesel generator, electrolyzer and boiler to supply 9910 kWh/day of electricity, 14 kg/day of hydrogen and thermal energy in five different major cities in Iran, namely Bandar Abbas, Shiraz, Tabriz, Tehran and Yazd. These figures are computed on average and the thermal load differs according to yearly weather temperature, which are 75, 22, 2000, 5200, 2200 and 1800 kWh/day respectively. To serve this purpose, HOMER Pro software was used and thermal load controller (TLC) (including electric boiler) was added to generate thermal energy by converting excess electricity of renewable energy production. Thus, a decrease in conventional fuel consumption, emissions and cost of energy (COE), as well as an increase in the renewable fraction of HRES without increasing project’s size would be achievable. To meet peak demand and avoid intermittent behavior of HRES, a 1000 kW generator was picked up. Results indicate that recovering extra electricity can enhance renewable fraction by up to 35% and bring down COE and exhausted CO2 by 7.1% and 10.6% respectively, which highlight the necessity of TLC in HRES, chiefly in off-grid systems.

Mobile road weather sensor calibration by sensor fusion and linear mixed models.

Mobile, vehicle-installed road weather sensors are becoming ubiquitous. While mobile sensors are often capable of making observations on a high frequency, their reliability and accuracy may vary. Large-scale road weather observation and forecasting are still mostly based on stationary road weather stations (RWS). Though expensive, sparsely located and making observations on a relatively low frequency, RWS’ reliability and accuracy are well-known and accommodated for in the road weather forecasting models. Statistical analysis revealed that road weather conditions indeed have a great effect on how the observations of mobile and stationary road weather temperature sensors differ from each other. Consequently, we calibrated the observations of mobile sensors with a linear mixed model. The mixed model was fitted fusing ca. 20 000 pairs of mobile and RWS observations of the same location at the same time, following a rendezvous model of sensor calibration. The calibration nearly halved the MSE between the observations of the mobile and the RWS sensor types. Computationally very light, the calibration can be embedded directly in the sensors.

Moisture Susceptibility and Fatigue Performance of Hydrated Lime–Modified Asphalt Concrete: Experiment and Design Application Case Study

Hydrated lime has been recognized as an effective additive used to improve asphalt concrete properties in pavement applications. However, further work is still needed to quantify the effect of hydrated lime on asphaltic concrete performance under varied weather, temperature and environmental conditions and in the application of different pavement courses. A research project has been conducted using hydrated lime to modify the asphalt concretes used for the applications of wearing (surface), levelling (binder) and base courses. A previous publication has reported the experimental study on the resistance to Marshall stability and the volumetric properties, the resilient modulus and permanent deformation at three different weather temperatures. This paper reports the second phase experimental study for material durability, which investigated the effect of hydrated lime content on moisture susceptibility when exposed to a freeze-thaw cycle, and the fatigue life. The experimental results show an improvement in the durability of the modified asphalt concrete mixtures. Optimum hydrated lime contents for different course applications are suggested based on the series experimental studies. Finally, the advantage of using the optimum mixtures for a pavement application is demonstrated.