Surface ozone in global cities:A synthesis of basic features,exposure risk,and leading meteorological driving factors

Long-term exposure to high surface ozone(O_(3))concentrations,a complex oxidative atmospheric pollutant,can adversely impact human health.Based on O_(3)monitoring data from 261 cities worldwide in 2020,generalized additive model(GAM)and spatial data analysis(SDA)methods were applied in this study to quantitatively evaluate the spatiotemporal distribution of O_(3)concentration,exposure risk,and dominant meteorological factors.Results indicated that over 40%of the cities worldwide were exposed to harmful O_(3)concentration ranges(40-60μg/m^(3)),with most cities distributed in China and India.Moreover,significant seasonal variations in global O_(3)concentrations were observed,presenting as summer(45.6μg/m^(3))>spring(47.3μg/m^(3))>autumn(38.0μg/m^(3))>winter(33.6μg/m^(3)).Exposure analysis revealed that approximately 12.2%of the population in 261 cities were exposed to an environment with high O_(3)concentrations(80-160μg/m^(3)),with about 36.32 million people in major countries.Thus,the persistent increase in high O_(3)levels worldwide is a critical factor contributing to threats to human health.Furthermore,GAM results indicated temperature,relative humidity,and wind speed as primary determinants of O_(3)variability.The synergy of meteorological factors is critical for understanding O_(3)changes.Our findings are important for enforcing robust air quality policies and mitigating public risk.

Short-term effects of low-level PM_(2.5),PM_(10),O_(3),and tropical meteorological conditions on emergency department visits for respiratory diseases in Haikou,China

Objective:To assess the correlation between atmospheric pollutants,meteorological factors,and emergency department visits for respiratory diseases in Haikou City.Methods:Daily data on atmospheric pollutants,meteorological factors,and emergency department visits for respiratory diseases in Haikou City from 2018 to 2021 were collected.The Spearman rank correlation test was used to analyze the correlation,and a distributed lag non-linear model was employed to analyze the health effects and lag impacts of environmental factors.Subgroup analyses were conducted based on sex and age.Results:According to the criteria of International Classification of Diseases(ICD-10:J00-J99),a total of 221913 cases were included,accounting for 21.3%of the total emergency department visits in Haikou City.For every 1℃increase in temperature,the risk of emergency department visits increased by 1.029%(95%CI 1.016%-1.042%).Relative humidity greater than 80%reduced the risk of visits,while higher atmospheric pressure(>1010 hpa)also decreased the likelihood of daily emergency department visits.Higher concentrations of PM_(2.5)(30-50μg/m^(3)),PM10(>60μg/m^(3)),and O_(3)(75-125μg/m^(3))were associated with increased visits.Higher temperatures(>25℃)have a greater impact on females and children aged 0-14 years,while males are more sensitive to low atmospheric pressure.Individuals aged 65 and above exhibited increased sensitivity to O_(3)concentration,and the effects of PM2.5,PM10,and O_(3)are more pronounced in individuals over 14 years old.Conclusions:Short-term exposure to high temperatures,particulate matter pollutants(PM_(2.5)and PM_(10)),and ozone(O_(3))is associated with increased emergency department visits for respiratory diseases.

Forecast Method of Ozone Concentration in Yulin City Based on Meteorological Conditions

Based on the monitoring data of ozone(O 3)concentration,conventional meteorological data and reanalysis products in Yulin City from 2018 to 2019,the weather situation of O 3 pollution was classified through case analysis and mathematical statistics.At 500 hPa,the weather situation was divided into continental high pressure type,subtropical high type and mixed type.At 850 hPa,it was divided into southwest air flow type,east air flow type and south air flow type.The correlation between meteorological element and O 3 concentration were analyzed,and factors with good correlation such as temperature,air pressure and wind speed were selected to establish regression equations.The fitting effect was good,and O 3 concentration could be objectively predicted.

Meteorological factors, ambient air pollution, and daily hospital admissions for depressive disorder in Harbin: A time-series study

BACKGROUND The literature has discussed the relationship between environmental factors and depressive disorders;however,the results are inconsistent in different studies and regions,as are the interaction effects between environmental factors.We hypo-thesized that meteorological factors and ambient air pollution individually affect and interact to affect depressive disorder morbidity.AIM To investigate the effects of meteorological factors and air pollution on depressive disorders,including their lagged effects and interactions.METHODS The samples were obtained from a class 3 hospital in Harbin,China.Daily hos-pital admission data for depressive disorders from January 1,2015 to December 31,2022 were obtained.Meteorological and air pollution data were also collected during the same period.Generalized additive models with quasi-Poisson regre-ssion were used for time-series modeling to measure the non-linear and delayed effects of environmental factors.We further incorporated each pair of environ-mental factors into a bivariate response surface model to examine the interaction effects on hospital admissions for depressive disorders.RESULTS Data for 2922 d were included in the study,with no missing values.The total number of depressive admissions was 83905.Medium to high correlations existed between environmental factors.Air temperature(AT)and wind speed(WS)significantly affected the number of admissions for depression.An extremely low temperature(-29.0℃)at lag 0 caused a 53%[relative risk(RR)=1.53,95%confidence interval(CI):1.23-1.89]increase in daily hospital admissions relative to the median temperature.Extremely low WSs(0.4 m/s)at lag 7 increased the number of admissions by 58%(RR=1.58,95%CI:1.07-2.31).In contrast,atmospheric pressure and relative humidity had smaller effects.Among the six air pollutants considered in the time-series model,nitrogen dioxide(NO_(2))was the only pollutant that showed significant effects over non-cumulative,cumulative,immediate,and lagged conditions.The cumulative effect of NO_(2) at lag 7 was 0.47%(RR=1.0047,95%CI:1.0024-1.0071).Interaction effects were found between AT and the five air pollutants,atmospheric temperature and the four air pollutants,WS and sulfur dioxide.CONCLUSION Meteorological factors and the air pollutant NO_(2) affect daily hospital admissions for depressive disorders,and interactions exist between meteorological factors and ambient air pollution.

Comparisons of different statistical models for analyzing the effects of meteorological factors on COVID-19

Objective:This general non-systematic review aimed to gather information on reported statistical models examing the effects of meteorological factors on coronavirus disease 2019(COVID-19)and compare these models.Methods:PubMed,Web of Science,and Google Scholar were searched for studies on"meteorological factors and COVID-19"published between January 1,2020,and October 1,2022.Results:The most commonly used approaches for analyzing the association between meteorological factors and COVID-19 were the linear regression model(LRM),generalized linear model(GLM),generalized additive model(GAM),and distributed lag non-linear model(DLNM).In addition to these classical models commonly applied in environmental epidemiology,machine learning techniques are increasingly being used to select risk factors for the outcome of interest and establishing robust prediction models.Conclusion:Selecting an appropriate model is essential before conducting research.To ensure the reliability of analysis results,it is important to consider including non-meteorological factors(e.g.,government policies on physical distancing,vaccination,and hygiene practices)along with meteorological factors in the model.

Research on the Correlation between Meteorological Factors and Flue-cured Tobacco Chemical Components in Different Altitudes of Lincang Region

In this study, the relationship between major meteorological factors and tobacco chemical components in different altitudes of Yongde County, Lincang Region of Yunnan Province was studied using correlation analysis and path analysis methods.The results showed that there was a difference on meteorological factors in different altitudes, causing different impacts on chemical components of flue-cured tobacco; contents of nicotine and total nitrogen decreased with the rising of altitude, conversely, total sugar and reducing sugar contents increased with the rising trend of altitude, and the differences on the contents of potassium （K） and chlorine（CI）, and the ratio of K to CI in different altitudes were not significant, at the altitude of 1 250-1 450 m, each chemical component indicator was better. The correlation between main meteorological factors and conventional chemical components of tobacco leaves was as follows： contents of reducing sugar and total sugar increased with the increase of rainfall and relative humidity, and decreased with the increase of sunshine duration; nicotine content increased with the increase of accumulated temperature and sunshine duration; total nitrogen （N） content increased with the increase of daily mean temperature and accumulated temperature; sugar-alkali ratio increased with the increase of rainfall. Path analysis showed that the direct effect of sunshine duration on reducing sugar and total sugar of tobacco leaves was faint, but the indirect effect was obvious; meanwhile, the direct and indirect effects of it on nicotine and total N were the greatest.

Path Analysis on the Meteorological Factors Impacting Soil Respiration Rate of Wheat Field

[Objective]The experiment aimed to study the effects of meteorological factors under different weather conditions on soil respiration. [ Method] The path analysis was used to analyze meteorological factors which influenced soil respiration of wheat field under different weather condition and at jointing stage. [ Result] In sunny day, the correlations between ground temperature at 5 cm, solar radiation, air relative humidity, air temperature and soil respiration were all at significant level while solar radiation and ground temperature at 5 cm were the major factors which influenced soil respiration. In cloudy day, solar radiation was a major factor which influenced soil respiration.[ Conclusion] The soil respiration and surplus path coefficient in sunny day were all higher than these in cloudy day, which demonstrated that except influenced by ground temperature, air temperature, solar radiation and air relative humidity, the soil respiration was also influenced by other factors especially biological factor.

Effects of Changes in Meteorological Factors on the Growth and Development of Wheat and Countermeasures

To explore main meteorological factors influencing wheat production in Dengzhou City, changes of meteorological factors and their effects on the growth and development of wheat in Dengzhou City from 2001 to 2015 were analyzed, and countermeasures to increase wheat yield were put forward. The results show that constant decrease of of sunshine duration have limited the increase of wheat yield, and it is necessary to improve photosynthetic efficiency to increase wheat yield.

Correlation Analysis of Meteorological Factors with Yield and Yield Components of Rapeseed

[Objective] The aim was to research relationship of meteorological factors with rapeseed yield and yield components. [Method] A field test was carried out, with meteorological information collected from local Meteorological Administration. The data were then through statistical work and regression analysis was made on correlation of meteorological factors with yield and yield components supported by DPS. [Result] The number of pod per plant is closely related to meteorological fac- tors in seedling stage. Specifically, the number is of positive correlation with precipi- tation in seedling stage and of negative correlation with average temperature and sunshine hour. The number of grain per pod is of positive correlation with precipita- tion in seedling stage and negative correlation with temperature in seedling stage and sunshine hour in anthesis. Thousand-seed weight is of positive correlation with sunshine hour in seedling stage and negative correlation with precipitation in seedling stage. [Conclusion] Moisture of soils should be guaranteed by surface cov- erage in order to promote seedling growth, maintain ventilation in fields and reduce effects of rainfall on rapeseed flowering.

Ensemble learning prediction of soybean yields in China based on meteorological data

The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early warning.Although previous studies have used machine learning algorithms to predict soybean yield based on meteorological data,it is not clear how different models can be used to effectively separate soybean meteorological yield from soybean yield in various regions. In addition, comprehensively integrating the advantages of various machine learning algorithms to improve the prediction accuracy through ensemble learning algorithms has not been studied in depth. This study used and analyzed various daily meteorological data and soybean yield data from 173 county-level administrative regions and meteorological stations in two principal soybean planting areas in China(Northeast China and the Huang–Huai region), covering 34 years.Three effective machine learning algorithms(K-nearest neighbor, random forest, and support vector regression) were adopted as the base-models to establish a high-precision and highly-reliable soybean meteorological yield prediction model based on the stacking ensemble learning framework. The model's generalizability was further improved through 5-fold crossvalidation, and the model was optimized by principal component analysis and hyperparametric optimization. The accuracy of the model was evaluated by using the five-year sliding prediction and four regression indicators of the 173 counties, which showed that the stacking model has higher accuracy and stronger robustness. The 5-year sliding estimations of soybean yield based on the stacking model in 173 counties showed that the prediction effect can reflect the spatiotemporal distribution of soybean yield in detail, and the mean absolute percentage error(MAPE) was less than 5%. The stacking prediction model of soybean meteorological yield provides a new approach for accurately predicting soybean yield.

The Impacts of Mosquito Density and Meteorological Factors on Dengue Fever Epidemics in Guangzhou, China, 2006-2014: a Time-series Analysis

Objective To explore the associations between the monthly number of dengue fever（DF） cases and possible risk factors in Guangzhou, a subtropical city of China. Methods The monthly number of DF cases, Breteau Index （BI）, and meteorological measures during 2006-2014 recorded in Guangzhou, China, were assessed. A negative binomial regression model was used to evaluate the relationships between BI, meteorological factors, and the monthly number of DF cases. Results A total of 39,697 DF cases were detected in Guangzhou during the study period. DF incidence presented an obvious seasonal pattern, with most cases occurring from June to November. The current month＇s BI, average temperature （Tare）, previous month＇s minimum temperature （Train）, and Tare were positively associated with DF incidence. A threshold of 18.25℃ was found in the relationship between the current month＇s Tmin and DF incidence. Conclusion Mosquito density, Tove, and Tmin play a critical role in DF transmission in Guangzhou. These findings could be useful in the development of a DF early warning system and assist in effective control and prevention strategies in the DF epidemic.

Multivariate analysis between meteorological factor and fruit quality of Fuji apple at different locations in China

China has the largest apple planting area and total yield in the world, and the Fuji apple is the major cultivar, accounting for more than 70％ of apple planting acreage in China. Apple qualities are affected by meteorological conditions, soil types, nutrient content of soil, and management practices. Meteorological factors, such as light, temperature and moisture are key environmental conditions affecting apple quality that are difficult to regulate and control. This study was performed to determine the effect of meteorological factors on the qualities of Fuji apple and to provide evidence for a reasonable regional layout and planting of Fuji apple in China. Fruit samples of Fuji apple and meteorological data were investigated from 153 commercial Fuji apple orchards located in 51 counties of 11 regions in China from 2010 to 2011. Partial least-squares regression and linear programming were used to analyze the effect model and impact weight of meteorological factors on fruit quality, to determine the major meteorological factors influencing fruit quality attributes, and to establish a regression equation to optimize meteorological factors for high-quality Fuji apples. Results showed relationships between fruit quality attributes and meteorological factors among the various apple producing counties in China. The mean, minimum, and maximum temperatures from April to October had the highest positive effects on fruit qualities in model effect loadings and weights, followed by the mean annual temperature and the sunshine percentage, the temperature difference between day and night, and the total precipitation for the same period. In contrast, annual total precipitation and relative humidity from April to October had negative effects on fruit quality. The meteorological factors exhibited distinct effects on the different fruit quality attributes. Soluble solid content was affected from the high to the low row preface by annual total precipitation, the minimum temperature from April to October, the mean temperature from April to October, the temperature difference between day and night, and the mean annual temperature. The regression equation showed that the optimum meteorological factors on fruit quality were the mean annual temperature of 5.5-18°C and the annual total precipitation of 602-1121 mm for the whole year, and the mean temperature of 13.3-19.6°C, the minimum temperature of 7.8-18.5°C, the maximum temperature of 19.5°C, the temperature difference of 13.7°C between day and night, the total precipitation of 227 mm, the relative humidity of 57.5-84.0％, and the sunshine percentage of 36.5-70.0％ during the growing period （from April to October）.

Temporal variations of reference evapotranspiration and its sensitivity to meteorological factors in Heihe River Basin, China

On the basis of daily meteorological data from 15 meteorological stations in the Heihe River Basin （HRB） during the period from 1959 to 2012, long-term trends of reference evapotranspiration （ET0） and key meteorological factors that affect ET0 were analyzed using the Mann- Kendall test. The evaporation paradox was also investigated at 15 meteorological stations. In order to explore the contribution of key meteo- rological factors to the temporal variation of ET0, a sensitivity coefficient method was employed in this study. The results show that： （1） mean annual air temperature significantly increased at all 15 meteorological stations, while the mean annual ET0 decreased at most of sites; （2） the evaporation paradox did exist in the HRB, while the evaporation paradox was not continuous in space and time; and （3） relative humidity was the most sensitive meteorological factor with regard to the temporal variation of ET0 in the HRB, followed by wind speed, air temperature, and solar radiation. Air temperature and solar radiation contributed most to the temporal variation of ETo in the upper reaches; solar radiation and wind speed were the determining factors for the temporal variation of ET0 in the middle-lower reaches.

Particulate Matters Pollution Characteristic and the Correlation between PM (PM_2.5, PM₁₀) and Meteorological Factors during the Summer in Shijiazhuang

In recent years, the haze occurs frequently and air pollution is getting worse in Beijing-Tianjin-Hebei Region, China. The particulate matter pollution characteristic researches are playing a sig-nificant role especially in the districts where have higher concentration PM and air pollution. In this study, we collected daily particulate matter (PM10, PM2.5) mass concentration data from 7 air pollution monitoring stations in Shijiazhuang City, Hebei, China over a 3-month period from June to August to investigate particulate matter pollution characteristic and the relationship with me-teorological conditions. Statistical results show that PM10 is the major pollutant in Shijiazhuang City;the average daily concentrations of PM2.5 and PM10 are 94.45 μg/m3 and 219.15 μg/m3, respectively. The daily average of PM10 and PM2.5 level over the period exceeded the first grade of the daily average limit of the ambient air quality standards (GB3095-2012). And there is a significantly positive correlation between atmospheric pressure and particulate matter pollution, but there is a significantly negative correlation between atmospheric temperature and PM concentrations. Precipitation has a clear role mainly in the coarse particles;however, there has little effect on fine particulate matter. Relative humidity and wind speed have a poor correlation with atmospheric pollutant concentrations (not remarkably high).

Relationships between temperature-light meteorological factors and seedcotton biomass per boll at different boll positions

Cotton growth and development are determined and influenced by cultivars, meteorological conditions, and management practices. The objective of this study was to quantify the optimum of temperature-light meteorological factors for seedcotton biomass per boll with respect to boll positions. Field experiments were conducted using two cultivars of Kemian 1 and Sumian 15 with three planting dates of 25 April （mean daily temperature （MDT） was 28.0 and 25.4°C in 2010 and 2011, respectively）, 25 May （MDT was 22.5 and 21.2°C in 2010 and 2011, respectively）, and 10 Jun （MDT was 18.7 and 17.9°C in 2010 and 2011, respectively）, and under three shading levels （crop relative light rates （CRLR） were 100, 80, and 60％） during 2010 and 2011 cotton boll development period （from anthesis to boll open stages）. The main meteorological factors （temperature and light） affected seedcotton biomass per boll differently among different boll positions and cultivars. Mean daily radiation （MDR） affected seedcotton biomass per boll at all boll positions, except fruiting branch 2 （FB2） fruting node 1 （FN1）. However, its influence was less than temperature factors, especially growing degree-days （GDD）. Optimum mean daily maximum temperature （MDTmax） for seedcotton biomass per boll at FB11FN3 was 29.9-32.4°C, and the optimum MDR at aforementioned position was 15.8-17.5 MJ m-2. Definitely, these results can contribute to future cultural practices such as rational cultivars choice and distribution, simplifying field managements and mechanization to acquire more efficient and economical cotton management.

Effects of meteorological factors on different grades of winter wheat growth in the Huang-Huai-Hai Plain, China

The sown area of winter wheat in the Huang-Huai-Hai（HHH） Plain accounts for over 65% of the total sown area of winter wheat in China. Thus, it is important to monitor the winter wheat growth condition and reveal the main factors that influence its dynamics. This study assessed the winter wheat growth condition based on remote sensing data, and investigated the correlations between different grades of winter wheat growth and major meteorological factors corresponding. First, winter wheat growth condition from sowing until maturity stage during 2011–2012 were assessed based on moderate-resolution imaging spectroradiometer（MODIS） normalized difference vegetation index（NDVI） time-series dataset. Next, correlation analysis and geographical information system（GIS） spatial analysis methods were used to analyze the lag correlations between different grades of winter wheat growth in each phenophase and the meteorological factors that corresponded to the phenophases. The results showed that the winter wheat growth conditions varied over time and space in the study area. Irrespective of the grades of winter wheat growth, the correlation coefficients between the winter wheat growth condition and the cumulative precipitation were higher than zero lag（synchronous precipitation） and one lag（pre-phenophase precipitation） based on the average values of seven phenophases. This showed that the cumulative precipitation during the entire growing season had a greater effect on winter wheat growth than the synchronous precipitation and the pre-phenophase precipitation. The effects of temperature on winter wheat growth varied according to different grades of winter wheat growth based on the average values of seven phenophases. Winter wheat with a better-than-average growth condition had a stronger correlation with synchronous temperature, winter wheat with a normal growth condition had a stronger correlation with the cumulative temperature, and winter wheat with a worse-than-average growth condition had a stronger correlation with the pre-phenophase temperature. This study may facilitate a better understanding of the quantitative correlations between different grades of crop growth and meteorological factors, and the adjustment of field management measures to ensure a high crop yield.

Relationship between onset of peptic ulcer and meteorological factors

AIM： To discuss the relationship between onset of peptic ulcer （PU） and meteorological factors （MFs）. METHODS： A total of 24 252 patients were found with active PU in 104 121 samples of gastroscoic examination from 17 hospitals in Nanning from 1992 to 1997. The detectable rate of PU （DRPU） was calculated every month, every ten days and every five days. An analysis of DRPU and MFs was made in the same period of the year. A forecast model based on MFs of the previous month was established. The real and forecast values were tested and verified. RESULTS： During the 6 years, the DRPU from November to April was 24.4 -28.8%. The peak value （28.8%） was in January. The DRPU from May to October was 20.0 -22.6%, with its low peak （20.0%） in June. The DRPU decreased from winter and spring to summer and autumn （P 〈 0.005）. The correlated coefficient between DRPU and average temperature value was -0.8704, -0.6624, -0.5384 for one month, ten days, five days respectively （P 〈 0.01）. The correlated coefficient between DRPU and average highest temperature value was -0.8000, -0.6470,-0.5167 respectively （P 〈0.01）. The correlated coefficient between DRPU and average lowest temperature value was -0.8091, -0.6617, -0.5384 respectively （P 〈0.01）. The correlated coefficient between DRPU and average dew point temperature was -0.7812, -0.6246, -0.4936 respectively （P 〈0.01）. The correlated coefficient between DRPU and average air pressure value was 0.7320, 0.5777, 0.4579 respectively （P 〈0.01）. The average temperature, average highest and lowest temperature, average air pressure and average dew point temperature value of the previous month, ten days and five days could forecast the onset of PU, with its real and forecast values corresponding to 71.8%, 67.9% and 66.6% respectively. CONCLUSION： DRPU is closely related with the average temperature, average highest and lowest temperature, average air pressure and average dew point temperature of each month, every ten days and every five days for the same period. When MFs are changed, the human body produces a series of stress actions. A long-term and median-term based medical meteorological forecast of the onset of PU can be made more accurately according to this.

Analysis on the Relationship between Air Pollution Status and Meteorological Factors in Zhumadian City

Based on monitoring data of air quality and corresponding meteorological observation data in Zhumadian City during 2013-2015,temporalspatial distribution characteristics and influence factors of air pollution were analyzed. The results showed that >Grade II of pollution occupied a certain proportion in Zhumadian City throughout the year,and annual pollution probabilities at three stations were 11%,11% and 6%; serious pollution occurred for six times at three stations,and they all occurred in autumn and winter; pollution probabilities at three stations in autumn and winter were 18%,17% and 12%,and pollution situation in autumn and winter was more serious than that in spring and summer,and seasonal sequence of pollution frequency from low to high was spring,summer,autumn and winter. Seen from three monitoring stations,there was little difference between new and old industrial zones. Since Branch II of China Meheco Topfond Pharma Co.,Ltd. which represented largescale pollution source took pollution prevention measures,pollution was relatively light,but serious pollution was easy to appear. Seen from temporal change of pollutant concentration,monthly distribution characteristics of three kinds of air pollutants( SO_2,NO_2 and PM_(10)) showed typical one-peak-one valley pattern,and peak occurred during December-January,while valley was during July-August. Due to straw burning,monthly change curve of PM_(10) concentration had two peaks in June and October. Dekad change characteristics of three kinds of air pollutants also showed one-peak-one-valley pattern,and peak occurred from middle dekad of December to middle dekad of January,while valley was from early dekad of July to last dekad of August. The concentration correlation among three kinds of pollutants was the most significant at station 3.Additionally,concentration correlation was significant in autumn and winter,but was relatively weaker in spring and summer. The correlation between pollutant concentration and meteorological factors in the same period was the most significant in autumn,followed by winter. Negative correlation between horizontal visibility and pollutant concentration was extremely significant in winter. There was positive correlation between air pressure and pollutant concentration in summer and autumn,while the correlation was unobvious in winter and spring. There was negative correlation between air temperature and pollutant concentration,which was the most significant in autumn. Negative correlation between relative humidity and pollutant concentration was significant in summer and autumn. Total cloud cover showed significantly positive correlation with pollutant concentration in winter,but the correlation was unobvious in other seasons. Average wind velocity and NO_2 concentration maintained significantly negative correlation in whole year,and there was significantly negative correlation between average wind velocity and concentrations of other two kinds pollutants in winter,but the correlation was worse in other seasons.

Effects of Meteorological Factors on the Yield and Quality of Special Rice in Different Periods after Anthesis

In order to investigate the effect of meteorological factors on the yield and quality of special rice during the filling stage, an experiment was conducted with 10 special rice varieties which were planted in three different regions during spring 2017. The results showed that the quality traits and yields from different regions of the same variety were different, which reached up to a significant level in most varieties. Among the quality traits, the grain chalkiness rate and chalkiness degree were the most sensitive to different climatic factors, and changes were found among them in different regions, while minor variation was found between brown rice rate and white rice rate. The parameters that were severely affected by temperature were gel consistency, gelatinization temperature, brown rice rate and yield during the filling stage. The critically affected factors by heat were brown rice rate, protein content, essential amino acid and amylose content while brown rice rate, chalkiness rate and gelatinization temperature were substantially affected by water factors. Grain yield and quality were closely related to meteorological factors on different stages after heading. Our results revealed that yield and quality of special rice were significantly influenced by meteorological factors during the grain filling stage.

Analysis of Security of High-yield Cultivation of Spring Peanut in the Southwest of Henan Province Based on Meteorological Factors

The changes of meteorological factors such as average temperature, pre- cipitation and sunshine duration in the whole growth period and at various growth stages of spring peanut in Dengzhou City during 2001-2016 were analyzed, and their effects on the growth and development of spring peanut were discussed. The results show that from 2001 to 2016, average temperature tended to increase in the growth period of spring peanut in Dengzhou City, but the increasing trend was slow; precipitation showed a decreasing trend on the whole （it increased at the sowing and flowering stage）; sunshine duration reduced year by year （the decreasing trend was different at various growth stages）, but the decrease of sunshine duration was more obvious than that of precipitation. Based on the analysis of meteorological conditions and the growth rules of spring peanut, it is needed to scientifically choose varieties of spring peanut, plan suitable sowing patterns, apply and manage- fertilizer and water to reasonably realize sustainable high-yield of spring peanut based on its safe stable yield.