Average Sunshine Research Articles

The effects of climate on the incidence of benign paroxysmal positional vertigo.

Benign paroxysmal positional vertigo (BPPV) is one of the most common vestibular disorders. An investigation into the factors related to BPPV could contribute to its prevention and appropriate management. We investigated the association between climatic factors and incidence of BPPV in this study. A total of 365 patients who were diagnosed with idiopathic BPPV in the emergency room of our hospital in 2015 were included. The number of patients diagnosed with BPPV per week was calculated (every week). Climatic factors, including daily average humidity, temperature, atmospheric pressure, cloud amount, sunshine amount, and daylight time, were documented daily. The weekly mean climatic value in each week was calculated. Simple correlation analysis and multivariate regression analyses were performed to identify climatic factors associated with the number of patients diagnosed with BPPV. Simple correlation analysis revealed a significant association between the humidity (r = 0.276, p = 0.048), temperature (r = 0.275, p = 0.049), and cloud amount (r = 0.293, p = 0.035) and the number of BPPV patients diagnosed per week. Multivariate regression analysis revealed that only the cloud amount was a statistically significant factor associated with the number of BPPV patients diagnosed every week. A significant positive association was discovered between the cloud amount and BPPV incidence. Cloud amount can therefore have an association with the incidence of BPPV.

Anatomical structure of Nitraria spp. leaves from different habitats in Southern Xinjiang, China

The leaf anatomy of the Nitraria roborowskii Kom., Nitraria sibirica Pall., and Nitraria tangutorum Bobr. populations from southern Xinjiang, China, were studied. Cross-sections of the leaves were made, and the structural characteristics were determined. Seven major traits of the leaf cross-sections were analyzed by cluster analysis. The leaf morphology and anatomical structure of Nitraria leaves from different populations significantly differed, but the coefficient of variation between different populations was not significant. Leaf palisade and spongy tissues were positively correlated with the mean temperature, average sunshine, and annual precipitation of the habitat. Populations were divided into three main categories: Class I (XBC-2 and XBC-3), which included plants that grew at higher altitudes and were exposed to longer than average sunshine time; Class II (XBC-1, BC-1, and DBC-1), which included plants that grew at lower latitudes and had longer than average sunshine time; and Class III (BC-2, BC-3, DBC-1, and DBC-2), which included plants that grew at intermediate latitudes. Habitat and climate were the main factors associated with phenotype differences among the populations of the three Nitraria species.

Regional suitability analysis of endangered medicinal plant Dactylorhiza hatagirea

The wild plant Dactylorhiza hatagirea commonly used in Tibetan medicine. In recent years, it is on the verge of extinction due to improper excavation and habitat deterioration. Based on the literature review, and the meteorological and ecological data of Qinghai Province and data derived from the field survey, this study took the altitude, humidity index, annual average wind speed, monthly average sunshine, precipitation and temperature in growing season, soil type and vegetation type as the zoning indexes, and applied ArcGIS software for spatial superposition analysis. The results showed that the D. hatagirea was primarily found in the alpine meadow in the east and south of Qinghai Province. The overall suitable area was about 30 700 km~2. Taking Qinghai Lake as the boundary for reference, the area in the south appeared to be more suitable, while the area in the east appeared to be secondary in suitability. Henan County, Zeku County, Jiuzhi County and Banma County were the main suitable areas covering the expanse of 12 454 km~2. Through the field verification, such zoning results appeared to be accurate, precise and illustrative. The spatial distribution of ecological suitability provides the decision-making basis for the resource investigation, environment protection and comprehensive resource management. It also provides the empirical case for developing the interdisciplinary research method in the suitability study of medicinal plants.

Research on planting area and industrial development strategy of Tibetan medicine

Appropriate producing areas can guarantee the quality of Tibetan medicine, but research on the suitable ecological factors and suitable producing areas of Tibetan medicinal plants is scarce. This paper used the Geographic Information System for Global Medicinal Plants(GMPGIS) to analyze the ecological suitability of Tibetan medicinal plants nationwide. For the first time, the range of ecological factors and main soil types of Tibetan medicinal plants were extracted, such as the average annual temperature-19.4-24.2 ℃, annual average precipitation 17-4 088 mm, annual average sunshine 124.2-171.6 W·m~(-2). The main soil types were black calcareous soil, thin layer soil, chestnut soil and so on. Based on 337 sampling points, the largest ecological similarity area of Tibetan medicine across the country was obtained through ecological similarity analysis. In addition to Tibet and Qinghai provinces and Tibetan Autonomous Prefectures in Sichuan, Gansu, and Yunnan provinces, Jiuquan city and Linxia county in Gansu province, Panzhihua and Ya'an in Sichuan province, and Xinjiang, Inner Mongolia, and Shanxi provinces also had larger suitable cultivation areas. In addition, by analyzing the current situation of Tibetan medicine industry, the research pointed out that there were some problems such as unreasonable development and utilization of resources, lack of standards and norms, weak basic research and imperfect industrial system, and made corresponding countermeasures for sustainable development of resources, formulation of standards and specifications, promotion of medicine through science and technology, expansion of domestic and foreign markets, etc. This study provided the basis for guiding the rational layout of production bases, introduction and breeding of plant Tibetan medicine nationwide, laying the foundation for the scientific and standardized production of high-quality Tibetan medicine, clarifying the development direction of Tibetan medicine industry, and providing ideas for the development strategy of Tibetan medicine and other national medicine industry.

Development of a land suitability model for citrus cultivation using GIS and multi-criteria assessment techniques in Antalya province of Turkey

Abstract Location selection for citrus orchards is an important topic in terms of sustainable agriculture policies, agricultural planning, and community. This study presents a model to evaluate land suitability for cultivation of citrus in Antalya Province of Turkey. The aim of this study is to provide a Geographical Information System-Multi Criteria Evaluation based spatial decision support system, taking into account the viewpoints of local citrus growers and experts. 13 exclusion criteria, 3 main evaluation criteria (13 sub-criteria), and 52 value ranges were determined for citrus land suitability analysis. Analytic Hierarchy Process was utilised to calculate the weights of evaluation criteria. Inverse Distance Weighting interpolation approach was used to estimate the values of meteorological parameters. Thematic maps were presented for each of the criteria and final suitability map was prepared using Weighted Overlay method in Geographic Information System environment. The final suitability thematic map showed that the suitable zones (1281.19 km2) for citrus cultivation were located in the coastal regions of Antalya province. The average temperature in the winter season was determined to be the most important sub-criteria for citrus cultivation, followed by elevation, the average temperature in the flowering period, land use capability, daily average sunshine time, annual average precipitation, proximity to irrigation ponds and dam lakes and others. Most of the cultivated citrus orchards often coincided with the appropriate sites presented as a result of the study, and most of these sites were generally classified as high and moderate suitable zones. This study will be an important and useful regional guideline in terms of agricultural land use planning, citrus orchards management and community.

Empirical models for estimation of global solar radiation using the monthly average daily sunshine hours data for Makurdi, Benue State

This paper proposes empirical correlation models for estimating global solar radiation using data of sunshine hours for the location of Makurdi in Benue State of Nigeria. The paper suggests extrapolation of the empirical models for other locations with similar climatic conditions. The proposed models are: Linear, Quadratic, Cubic, Exponential, Power and Logarithmic models. Each of the models is based on Angstrom-Prescott equations for estimating global solar radiation. Any of the models can ease the use of sophisticated equipments, which are expensive, delicate and sometimes develop faults during measurement. The results of the models show that the cubic model is the best with slightly higher coefficient of determination. The coefficient of determination of each of the models was found to be 0.952, 0.965, 0.967, 0.965, 0.948& 0.924 respectively, while the absolute correlation was found to be unity. Errors evaluated include MBE, RMSE and MPE with minimal values. The percentage diffuse and direct solar radiations, clearness index and the diffuse fraction were also estimated using the models. The results of the estimations done using the proposed models indicate that there is an estimated average annual global solar radiation of 6056MJm-2, monthly value of 505MJm-2 and daily insolation of 16.82MJm-2 sufficient enough for maximum solar radiation exploitation.
 Keywords: Solar Radiation, Empirical Models, Diffuse Radiation, Direct Radiation

Leaf phenotypic variation of endangered plant Tetracentron sinense Oliv. and influence of geographical and climatic factors

To analyze the degree and pattern of phenotypic variation in leaves of Tetracentron sinense Oliv from the perspective of genetic and environmental adaptation and thus contribute to effective evidence-based conservation and management strategies for germplasm resources, we measured 17 morphological and epidermal micromorphological leaf traits from 24 natural populations of T. sinense. Nested analysis of variance, multiple comparison, principal component analysis (PCA), cluster analysis, and correlation analysis were used to explore phenotypic leaf variation among and within populations and potential correlations with geographic and environmental factors. There were significant differences in 17 leaf phenotypic traits among and within populations. The mean phenotypic differentiation coefficient of the 17 traits was 56.34%, and the variation among populations (36.4%) was greater than that within populations (27.2%). The coefficient of variation (CV) of each trait ranged from 4.6 to 23.8%, and the mean was 11.8%. Phenotypic variation of leaves was related to environmental factors such as average annual sunshine hours, average July temperature, and average annual rainfall. The variation changed along gradients of longitude, latitude, and altitude. The PCA clustered the 24 natural populations into four groups. Our study suggests that phenotypic variation in T. sinense occurred primarily among populations, with moderate levels of phenotypic differentiation among populations and low levels of phenotypic variation within populations. The plant’s poor adaptability to the environment is likely an important contributor to its endangerment. Accordingly, conservation strategies are proposed to protect and manage the natural populations of T. sinense.

Daily reference evapotranspiration prediction of Tieguanyin tea plants based on mathematical morphology clustering and improved generalized regression neural network

Tieguanyin tea plant is the most important tea cultivar in Fujian Province, China. It has suffered great economic losses due to high temperature and dry weather in recently years. This study proposed a prediction model for daily reference evapotranspiration (ET0) of Tieguanyin based on the combination of mathematical morphology clustering (MMC) and generalized regression neural networks (GRNN). Average air temperature, sunshine hours and relative humidity were chosen as the input factors of GRNN after a correlation analysis of the microclimate factors of the tea garden. The MMC was adopted to cluster the historical meteorological data to find the similar class as the training dataset of GRNN. The fruit fly optimization algorithm (FOA) was used to optimize the smoothing factor of GRNN. The meteorological data from JAN. 2018 to OCT. 2019 measured in Dabaofeng tea garden, Anxi County, Fujian Province, China were used to train and test the proposed model, and the performances was assessed with RMSE, MAE and model validity coefficient. The prediction results of different seasons show that the proposed model is efficient with high accuracy and has good adaptability under complex meteorological conditions.

Comparative Assessment of Reference Evapotranspiration Estimation Using Conventional Method and Machine Learning Algorithms in Four Climatic Regions

Reference evapotranspiration (ETo) is considered as an essential component in hydrological and agro meteorological processes. Its accurate estimation becomes an imperative in the planning and management of irrigation practices. ETo estimation also plays a vital role in improving the irrigation efficiency, water reuse and irrigation scheduling. The conventional physical model of Penmen Montieth (PM56) developed by Food and agriculture organization (FAO) has been recommended worldwide for ETo estimation. This model was firstly used in this study to determine ETo by using required meteorological data and obtained results used as referenced values. Afterward, five data machine learning algorithms/data driven models, support vector machine (SVM), multilayer perceptron (MLP), group method of data handling (GMDH), general regression neural network (GRNN) and cascade correlation neural network (CCNN), were applied to estimate ETo values. The climatic data of maximum and minimum temperatures, wind speed, average relative humidity and sunshine hours of six stations from Pakistan was used to train and test data driven model. Data driven models were also applied on other climatic stations without training data which lie in China, New Zealand and USA to further validate and investigate their performance. Comparison results indicated that model efficiency (ME) and correlation coefficient (r) of SVM were obtained (ranges: ME = 95–99%; r = 0.96–1) maximum for all the selected stations. Alternatively, model errors (RMSE = 0.016, MSE = 0.0001 & MAE = 0.08) for SVM were found minimum in comparison to GMDH, MLP, CCNN and GRNN. In addition, all data driven models show enough divergence from hyper arid to high humid climate except SVM which shows almost identical results for all the climatic zones in comparison to standard FAO-PM56 method. Finally, it can be concluded that SVM could be considered as a reliable alternative method for ETo estimation among data driven models.

Investigation of Climatological Components on Runoff Modeling using SWAT

It was planned most hydraulic projects, for instance barriers, it was defined the overflow of the rivers. If the river absences any position to measure the yield, the hydraulic models can be utilized to estimate it. SWAT is widely-used computerized mockups. It was required to feed such influential climatological information as precipitation, temperature, wind speed, solar radiation and relative humidity, in addition to, watershed information with the curve number and roughness constant to compute the watershed runoff. Watershed contain few climate positions and it was dangerous that the registered data in a position was not characterized the entire watershed. Consequently, the amount of the runoff estimation fault wants to be defined. This research considers the sensitivity of the runoff estimation for rivers, Using the SWAT prototypical, based on differences in such climatological components as precipitation, solar radiation, wind, humidity and temperature. The obtained consequences specify that with a 30.46% decline in the average monthly precipitation, sunshine, relative humidity, wind and temperature, it was set ermined 64.73% decline, 115.14% rise, 45.99% reduce, 126.58% rise and 40.15% rise in modeled runoff, individually. The wind speed and the solar radiation are the most sensitive and temperature is the least sensitive parameters in the runoff estimation.

Prediction of Weights during Growth Stages of Onion Using Agricultural Data Analysis Method

In this study, we propose a new agricultural data analysis method that can predict the weight during the growth stages of the field onion using a functional regression model. We have used onion weight on growth stages as the response variable and six environmental factors such as average temperature, average ground temperature, rainfall, wind speed, sunshine, and humidity as the explanatory variables in the functional regression model. We then define a least minimum integral squared residual (LMISE) measure to obtain an estimate of the function regression coefficient. In addition, a principal component regression analysis was applied to derive the estimates that minimize the defined measures. Next, to evaluate the performance of the proposed model, data were collected, and the following results were identified through analyses of the collected data. First, through graphical and correlation analysis, the ground temperature, mean temperature, and humidity have a very significant effect on the onion weights, but environmental factors such as wind speed, sunshine, and rainfall have a small negative effect on onion weights. Second, through functional regression analysis, we can determine that the ground temperature, sunshine, and precipitation have a significant effect on onion growth and are essential in the goodness-of-fit test. On the other hand, wind speed, mean temperature, and humidity did not significantly affect onion growth. In conclusion, to promote onion growth, the appropriate ground temperature and amount of sunshine are essential, the rainfall and the humidity must be low, and the appropriate wind or mean temperature must be maintained.

Design and Construction of Indirect Solar Coffee Dryer

Drying is the process of removing moisture contents from solid. Solar drying refers to a technique that utilizes incident solar radiation to convert it into thermal energy required for drying purposes. This project presents the design, construction and performance of an indirect type solar dryer for coffee product. In the dryer the air inters into the solar collector from the atmosphere through air inlet hole. This air will be heated in the collector and then pass to the drying chamber through the hole. Then the air exhausts through the outlet hole at the top of the drying chamber. The system designed can handle a capacity of up to 50kg of wet coffee per m2at a depth of 100 mm. The average sunshine at Bale Robe was found to be 12 hours per day. The daily solar insolation at the site was found to be 5.86kW/m2 of surface per day. By utilizing the solar collector in question and assuming a collector efficiency of 20 %, the total solar energy received is 5.86 kW-hrs/m2/day or 46.88 kW-hours per day (assuming the sunshine hours per day to be 8 hours). This solar dryer has a collector efficiency of 39.1%, a pick-up efficiency of 49.3%, and a system efficiency of 32.2%. the collector area of the system is calculated to be 1.11m2 and the total length of 1000mm by 300mm. The drying chamber is essentially a cabinetry dryer and measures 1020mm × 800mm × 30mm. It accommodates a drying bin which acts as the holding compartment for the wet coffee to be dried. The base of the drying chamber is made of a block of wood material 50mm deep, since wood is a good thermal insulator. The wood must be well seasoned and pre-treated to ensure it is protected from the humid environment. The air outlet is fitted at the top of the drying chamber which serves as the exit for the moisture ridden air. It is important since it ensures that moisture does not condense at the top of the drying chamber and speeds up the rate of drying through creating the suction effect. The drying bin measures 800mm × 800mm × 20mm.

Contrasting seasonal patterns of telomere dynamics in response to environmental conditions in the ectothermic sand lizard, Lacerta agilis

Telomeres, the protective, terminal parts of the chromosomes erode during cell division and as a result of oxidative damage by reactive oxygen species (ROS). Ectotherms rely on the ambient temperature for maintaining temperature-dependent metabolic rate, regulated through behavioural thermoregulation. Their temperature-dependant metabolism, hence also the ROS production, is indirectly regulated through thermoregulation. Consequently, a potential causal chain affecting telomere length and attrition is: temperature (in particular, its deviation from a species-specific optimum) – metabolism - ROS production – anti-oxidation - telomere erosion. We measured telomere length in sand lizards (Lacerta agilis) using qPCR on blood samples from 1998–2006. Effects of climatological parameters (mean temperature and average sunshine hours) in the summer and winter preceding telomere sampling were used as predictors of telomere length in mixed model analysis. During the lizards’ active period (summer), there was a largely negative effect of mean temperature and sun on telomere length, whereas a combined measure of age and size (head length) was positively related to telomere length. During the inactive period of lizards (winter), the results were largely the opposite with a positive relationship between temperature and sunshine hours and telomere length. In all four cases, thermal and age effects on telomere length appeared to be non-linear in the two sexes and seasons, with complex response surface effects on telomere length from combined age and thermal effects.

Impact of temperature and sunshine duration on daily new cases and death due to COVID-19.

Context:Control of COVID-19 has now become a critical issue for public health. Many ecological factors are proven to influence the transmission and survival of the virus. However, the association between different climatic factors and spread and mortality due to COVID-19 is unknown.Aim:To determine the association of different climatic factors with the spread and mortality due to COVID-19 during January 2020 to May 2020.Methods and Material:The climatic indicators included in the study were duration of sunshine, average minimum temperature, and average maximum temperature, with cumulative confirmed cases, deceased, and recovered cases. The data was performed for 138 different countries of the world, from January 2020 to May 2020.Statistical analysis used:Spearman's correlation analysis was used to assess the correlation between temperature and the spread and mortality of COVID-19 cases. Both univariate and multivariate analysis was performed for cumulative and month-wise analysis, using SPSS software.Results:Average maximum temperature and sunshine duration were significantly associated with COVID-19 confirmed cases, deceased, and recovered. For every 1° increase in average temperature, the confirmed, deceased, and recovered cases decreased by 2047 (P = 0.03), 157 (P = 0.016), and 743 (P = 0.005) individuals. The association remained significant even after adjusting for environmental as well as non-environmental variables. Average sunshine duration was inversely correlated with an increase in daily new cases (r = - 2261) and deaths (r = - 0.2985).Conclusion:Higher average temperature and longer sunshine duration are strongly associated with COVID-19 cases and deaths in 138 countries.

Incidence of major pests of okra in relation to weather factors and cropping systems

Incidence of major pests (shoot and fruit borer, Earias vittella; leafhopper, Amrasca biguttula biguttula; whitefly, Bemisia tabaci and red spider mite, Tetranychus urticae) of okra (cv. AO-1) was studied in two cropping systems (organic and conventional) in relation to weather parameters at Navsari Agricultural University, Navsari Gujarat, India during 2017-18. The investigation indicated peak shoot and fruit borer population (3.88 and 3.27 larvae/plant) and its associated fruit damage (39.56 and 35.60 %) during 20th week after germination (WAG) (11 SMW) indicating significant positive association with temperature (minimum and average), relative humidity (evening and average), bright sunshine in both the farming systems whereas, positive association with wind velocity in conventional farming system was also reported. Peak leafhopper population (11.66 and 12.70/ leaf) was noticed at 6 WAG (15 SMW) exhibiting significant positive correlation with maximum temperature. Adult whitefly population remained highest (3.07 and 3.61/ leaf) during 11 WAG (20 SMW) exhibiting significantly positive correlation with average temperature and bright sunshine in both the farming systems. Red spider mite population peaked (23.69 and 27.52 /2 cm2 leaf area) during 13 WAG (22 SMW) and 11 WAG (20 SMW) at organic and conventional farms, respectively which in turn indicated significant positive association with minimum and average temperature, evening and average relative humidity and wind velocity.

Association Between Air Temperature and the Incidence of Acute Coronary Heart Disease in Northeast China.

AimThe aim of this study was to examine the association between air temperature and incidence of acute coronary heart disease in Northeast China.MethodsWe obtained coronary heart disease (CHD) daily hospitalization data from January 2017 to June 2019, and collected meteorological data including average daily air temperature, air pressure, relative humidity, wind velocity, sunshine duration and water vapor pressure, for the same period. Totally, This study included data from 6775 patients with CHD.ResultsAfter adjusting for confounding factors, low air temperature was inversely associated with CHD. Additionally, in the warm season (April–September), the number of daily hospital admissions for CHD was higher (≥24.2°C) if the average daily air temperature was low (≤15.4°C).ConclusionLow air temperature might be a risk factor for CHD among the elderly, especially in the warm season.

Meteorological factors associated with hand, foot and mouth disease in a Central Highlands province in Viet Nam: an ecological study.

BackgroundHand, foot and mouth disease (HFMD) is a public health problem in Viet Nam, and studies have reported seasonal fluctuation in the occurrence of HFMD. This study sought to describe the occurrence of HFMD and its associated meteorological factors in Dak Lak province, Viet Nam.MethodsMonthly data on HFMD cases were collected from all commune health stations in Dak Lak province from 2012 through 2013. An HFMD case was defined as a brief febrile illness accompanied by a typical skin rash with or without mouth ulcers. Average temperature, maximum temperature, minimum temperature, humidity, rainfall, evaporation, sunshine duration and wind speed were recorded monthly at five local meteorological stations throughout Dak Lak.Data were aggregated at the district level, and the association between these meteorological factors and HFMD cases were examined by Poisson regression.ResultsIn 2012 through 2013, there were 7128 HFMD patients in Dak Lak. The number of HFMD cases increased during the rainy season. An increased risk of HFMD was associated with higher average temperature (risk ratio and 95% confidence interval: 1.06; 1.03–1.08 per 1 °C increase), higher rainfall (1.19; 1.14–1.24 per 200 mm increase) and longer sunshine duration (1.14; 1.07–1.22 per 60 hours increase). The risk of HFMD was inversely associated with wind speed (0.77; 0.73–0.81 per 1 m/s increase).ConclusionThis study suggests that there is a significant association between HFMD occurrence and climate. Temperature, rainfall, wind speed and sunshine duration could be used as meteorological predictors of HFMD occurrence in Viet Nam’s Central Highlands region. Intensified surveillance for HFMD during the rainy season is recommended.

Epidemiological characteristics of bacillary dysentery from 2009 to 2016 and its incidence prediction model based on meteorological factors

BackgroundThis study aimed to analyse the epidemiological characteristics of bacillary dysentery (BD) caused by Shigella in Chongqing, China, and to establish incidence prediction models based on the correlation between meteorological factors and BD, thus providing a scientific basis for the prevention and control of BD.MethodsIn this study, descriptive methods were employed to investigate the epidemiological distribution of BD. The Boruta algorithm was used to estimate the correlation between meteorological factors and BD incidence. The genetic algorithm (GA) combined with support vector regression (SVR) was used to establish the prediction models for BD incidence.ResultsIn total, 68,855 cases of BD were included. The incidence declined from 36.312/100,000 to 23.613/100,000, with an obvious seasonal peak from May to October. Males were more predisposed to the infection than females (the ratio was 1.118:1). Children < 5 years old comprised the highest incidence (295.892/100,000) among all age categories, and pre-education children comprised the highest proportion (34,658 cases, 50.335%) among all occupational categories. Eight important meteorological factors, including the highest temperature, average temperature, average air pressure, precipitation and sunshine, were correlated with the monthly incidence of BD. The obtained mean absolute percent error (MAPE), mean squared error (MSE) and squared correlation coefficient (R2) of GA_SVR_MONTH values were 0.087, 0.101 and 0.922, respectively.ConclusionFrom 2009 to 2016, BD incidence in Chongqing was still high, especially in the main urban areas and among the male and pre-education children populations. Eight meteorological factors, including temperature, air pressure, precipitation and sunshine, were the most important correlative feature sets of BD incidence. Moreover, BD incidence prediction models based on meteorological factors had better prediction accuracies. The findings in this study could provide a panorama of BD in Chongqing and offer a useful approach for predicting the incidence of infectious disease. Furthermore, this information could be used to improve current interventions and public health planning.

Four Novel SNPs of MYO1A Gene Associated with Heat-Tolerance in Chinese Cattle.

Simple SummaryChinese indigenous cattle were clustered into two groups of southern and northern breeds. The populations distributed in the hot south are mainly dominated by Bos indicus, and the populations distributed in the cold north are mainly dominated by Bos taurus. Myosin-1a (MYO1A) gene is a candidate gene related to pigmentation, which may be closely related to the heat tolerance traits of cattle. Therefore, our study aimed to explore the relationship between MYO1A gene and heat tolerance in Chinese indigenous cattle. Based on the previous GWAS research related to bovine heat tolerance trait, this study aimed to explore the effect of myosin-1a (MYO1A) gene on bovine heat tolerance trait, and find the molecular markers related to the heat tolerance of Chinese cattle. In our study, four novel candidate SNPs highly conserved in B. indicus breeds but barely existed in B. taurus were identified in MYO1A gene according to Bovine Genome Variation Database and Selective Signatures (BGVD). PCR and DNA sequencing were used to genotype 1072 individuals including 34 Chinese indigenous cattle breeds as well as Angus and Indian zebu. Two synonymous mutations (rs208210464 and rs110123931), one missense mutation (rs209999142; Phe172Ser), and one intron mutation (rs135771836) were detected. The frequencies of mutant alleles of the four SNPs gradually increased from northern groups to southern groups of Chinese cattle, which was consistent with the distribution of various climatic conditions of China. Additionally, four SNPs were significantly associated with four climatic conditions including annual mean temperature (T), relative humidity (H), temperature-humidity index (THI), and average annual sunshine hours (100-cloudiness) (SR). Among these, rs209999142 and Hap 1/1 had better performance than others. Our results suggested that rs209999142 was associated with heat-tolerance trait and rs208210464, rs110123931, and rs135771836 showed high phenotypic effect on heat-tolerance trait because of the strong linkage with rs209999142. These SNPs could be used as candidates for marker-assisted selection (MAS) in cattle breeding.

Spatio-temporal evolution of ozone pollution and its influencing factors in the Beijing-Tianjin-Hebei Urban Agglomeration

Ozone has become a major atmospheric pollutant in China as the pattern of urban energy usage has changed and the number of motor vehicles has grown rapidly. The Beijing-Tianjin-Hebei Urban Agglomeration, also known as the Jing-Jin-Ji Urban Agglomeration (hereafter, JJJUA), with a precarious balance between protecting the ecological environment and sustaining economic development, is challenged by high levels of ozone pollution. Based on ozone observation data from 13 cities in the JJJUA from 2014 to 2017, the spatio-temporal trends in the evolution of ozone pollution and its associated influencing factors were analyzed using Moran’s I Index, hot-spot analysis, and Geodetector using ArcGIS and SPSS software. Five key results were obtained. 1) There was an increase in the annual average ozone concentration, for the period 2014–2017. Comparing the 13 prefecture-level cities, ozone pollution in Chengde and Hengshui decreased, while it worsened in the remaining 11 cities. 2) Ozone pollution was worse in spring and summer than in autumn and winter; the peak ozone pollution season was from May to September; the average ozone concentration on workdays was higher than that on non-workdays, showing a counter-weekend effect. 3) Annual average concentrations were high in the central and southern parts of the study region but low in the north. 4) Prominent positive spatial correlations were observed in ozone concentration, with the best correlations shown in summer and autumn; concentrations were high in Baoding and Xingtai but low in Beijing and Chengde. 5) Concentrations of PM10, NO2, CO, SO2, and PM2.5, as well as average wind speed, sunshine duration, evaporation, precipitation, and temperature, all had significant effects on ozone pollution, and interactions between these influencing factors increased it.