Geographically and Temporally Weighted Regression in Assessing Dengue Fever Spread Factors in Yunnan Border Regions

Objective This study employs the Geographically and Temporally Weighted Regression(GTWR)model to assess the impact of meteorological elements and imported cases on dengue fever outbreaks,emphasizing the spatial-temporal variability of these factors in border regions.Methods We conducted a descriptive analysis of dengue fever’s temporal-spatial distribution in Yunnan border areas.Utilizing annual data from 2013 to 2019,with each county in the Yunnan border serving as a spatial unit,we constructed a GTWR model to investigate the determinants of dengue fever and their spatio-temporal heterogeneity in this region.Results The GTWR model,proving more effective than Ordinary Least Squares(OLS)analysis,identified significant spatial and temporal heterogeneity in factors influencing dengue fever’s spread along the Yunnan border.Notably,the GTWR model revealed a substantial variation in the relationship between indigenous dengue fever incidence,meteorological variables,and imported cases across different counties.Conclusion In the Yunnan border areas,local dengue incidence is affected by temperature,humidity,precipitation,wind speed,and imported cases,with these factors’influence exhibiting notable spatial and temporal variation.

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.

The effects of cold region meteorology and specific environment on the number of hospital admissions for chronic kidney disease:An investigate with a distributed lag nonlinear model

Objective:To explore the effects of daily mean temperature(°C),average daily air pressure(hPa),humidity(%),wind speed(m/s),particulate matter(PM)2.5(μg/m3)and PM10(μg/m3)on the admission rate of chronic kidney disease(CKD)patients admitted to the Second Affiliated Hospital of Harbin Medical University in Harbin and to identify the indexes and lag days that impose the most critical influence.Methods:The R language Distributed Lag Nonlinear Model(DLNM),Excel,and SPSS were used to analyze the disease and meteorological data of Harbin from 01 January 2010 to 31 December 2019 according to the inclusion and exclusion criteria.Results:Meteorological factors and air pollution influence the number of hospitalizations of CKD to vary degrees in cold regions,and differ in persistence or delay.Non-optimal temperature increases the risk of admission of CKD,high temperature increases the risk of obstructive kidney disease,and low temperature increases the risk of other major types of chronic kidney disease.The greater the temperature difference is,the higher its contribution is to the risk.The non-optimal wind speed and non-optimal atmospheric pressure are associated with increased hospital admissions.PM2.5 concentrations above 40μg/m3 have a negative impact on the results.Conclusion:Cold region meteorology and specific environment do have an impact on the number of hospital admissions for chronic kidney disease,and we can apply DLMN to describe the analysis.

Impact of Environmental Factors on Respiratory Diseases in Fuxin City and Its Prediction

Based on the daily data of visits for respiratory diseases in two grade A hospitals as well as meteorological factors and air pollution in Fuxin City from December 1, 2020 to November 31, 2021, PCA and RBF neural network were used to study the effects of meteorological factors and air pollution on respiratory diseases and predict them. The results showed that the number of daily visits was the largest in winter(accounting for 62.5%), followed by spring(15.2%), and it was the smallest in autumn(only 6.9%). The correlation between the number of daily visits and meteorological factors was higher than that of air pollution factors, and the correlation with temperature and ozone was the highest. The response coefficient of daily visits to each factor increased first and then decreased within 9 d, and the peak was 4-5 d behind. RBF and PCA-RBF neural network models were established to predict the number of daily visits, and the accuracy was 86.3% and 95.2%, respectively.

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.

Variations in stem radii of Larix principis-rupprechtii to environmental factors at two slope locations in the Liupan Mountains,northwest China

Relationships between stem growth and climatic and edaphic factors,notably air temperatures and soil moisture for different slopes,are not completely understood.Stem radial variations were monitored at the bottom and top slope positions in a Larix principis-rupprechtii plantation during the 2017 and 2018 growing seasons.Total precipitation during the growing season in 2017 and 2018 was 566 mm and 728 mm,respectively.Stem contractions typically occurred after mid-morning followed by swelling in the late afternoon in both plots,reflecting the diurnal cycle of water uptake and loss.Trees at the two locations showed the same growth initiation(mid-May)because of the small differences in air and soil temperatures.There were no significant differences in cumulative stem radial growth between the bottom plot(1.57±0.34 mm)and the top plot(1.55±0.26 mm)in 2018.However,in 2017,the main growth period of the bottom plot ceased 17 days earlier than in the top plot,while cumulative seasonal growth of the bottom plot(1.08±0.25 mm)was significantly less than the top plot(1.54±0.43 mm).Maximum daily stem shrinkage was positively correlated with air and soil temperatures,solar radiation,vapor pressure deficits,and negatively correlated with volumetric soil moisture content.The maximum daily shrinkage reflected transpiration rates as affected by environmental factors.Daily radial stem increment was correlated with precipitation and volumetric soil moisture in both years,but with air temperatures only in 2017.The seasonal growth of L.principis-rupprechtii Mayr thus shows interannual dynamics,while precipitation constitutes a key driving factor.

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.

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 of meteorological factors and air pollutants with medical care utilization for gastroesophageal reflux disease in urban area

BACKGROUND Gastroesophageal reflux disease(GERD)is a highly prevalent disease of the upper gastrointestinal tract,and it is associated with environmental and lifestyle habits.Due to an increasing interest in the environment,several groups are studying the effects of meteorological factors and air pollutants(MFAPs)on disease development.AIM To identify MFAPs effect on GERD-related medical utilization.METHODS Data on GERD-related medical utilization from 2002 to 2017 were obtained from the National Health Insurance Service of Korea,while those on MFAPs were obtained from eight metropolitan areas and merged.In total,20071900 instances of GERD-related medical utilizations were identified,and 200000 MFAPs were randomly selected from the eight metropolitan areas.Data were analyzed using a multivariable generalized additive Poisson regression model to control for time trends,seasonality,and day of the week.RESULTS Five MFAPs were selected for the prediction model.GERD-related medical utilization increased with the levels of particulate matter with a diameter≤2.5μm(PM2.5)and carbon monoxide(CO).S-shaped and inverted U-shaped changes were observed in average temperature and air pollutants,respectively.The time lag of each variable was significant around nine days after exposure.CONCLUSION Using five MFAPs,the final model significantly predicted GERD-related medical utilization.In particular,PM2.5 and CO were identified as risk or aggravating factors for GERD.

Relationship between NO_2 Concentration and Meteorological Factors in the Urban Area of Wanzhou District

Firstly,the daily variations of NO2 concentration in the urban area of Wanzhou in 2012 were analyzed,and then the relationship between NO2 concentration and meteorological factors（ precipitation,atmospheric pressure,wind speed,temperature,relative humidity and sunshine hours） was discussed. Finally,the multiple linear regression equation was established to predict NO2 concentration. The results showed that NO2 concentration in the urban area of Wanzhou did not exceed 80 μg /m3in most days from January 1 to December 31 in 2012. Among the six meteorological factors,NO2 concentration correlated significantly with three meteorological factors,that is,NO2 concentration correlated negatively with atmospheric pressure and wind speed but positively with relative humidity. NO2 concentration in the urban area of Wanzhou could be predicted using the multiple linear regression model. According to the rose diagram of wind directions,the wind blowing from the NNW was dominant in the urban area of Wanzhou.

Evaluating the impact and significance of meteorological factors upon dust storm occurrence

In this article, the quantitative impact and significance of factors on dust storm occurrence have been analyzed in detail, based on spring daily data sets of 17 meteorological factors and dust storm records during the period of 1954-2005 for 60 gauge stations distributed over Gansu Province of China. Results show that daily mean and maximum wind speeds and evaporation have a positive effect on dust storm occurrence, i.e., their increase can result in an increase of dust storm occurrence. Inversely, daily mean and minimum relative humidity, lowest surface air pressure, vapor pressure and number of sunny hours have a negative effect on dust storm occurrence. However, daily mean and highest surface air pressure; mean, highest and lowest surface air temperature; and precipitation of 20：00-08：00, 08：00-20：00 and 20：00-20：00 have a positive effect on dust storm occurrence in some places but negative in other places. On average, daily maximum and mean wind speeds, direction of the maximum wind, number of sunny hours and evaporation have a significant effect on dust storm occurrence in Gansu Province, but precipitation of 20：00--08：00, 08：00-20：00 and 20：00-20：00, and mean surface air pressure and temperature all have a minor influence upon dust storm occurrence.

Coronavirus Disease 2019 (COVID-19) in Conakry, Republic of Guinea: Analysis and Relationship with Meteorological Factors

The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), dates back to December 29, 2019, in Wuhan, China. It quickly spreads like wildfire to all continents in the following months. In Guinea, the first case of COVID-19 and death were all reported respectively on March 12 and April 16, 2020. Since then, several studies have found a relationship between certain environmental conditions such as the meteorological factors to have the potential of contributing to the spread of the virus. Thus, this study aims at examining the extent to which observed meteorological factors might have contributed to the spread of the coronavirus disease 2019 (COVID-19) cases in Conakry, from March 1 to May 31, 2020. Meteorological factors such as temperature (Tmin, Tmean and Tmax) and relative humidity (RHmin, RHmean and RHmax) were analyzed together with the data on the COVID-19. The dynamic of the COVID-19 in Guinea was analyzed along with that of some west African countries. The analysis on the dynamic of the COVID-19 pandemic in West Africa indicated Guinea as one of the most affected countries by the pandemic after Nigeria and Ghana. The study found that in general an increase in the temperature is linked to a decline in the COVID-19 number of cases and deaths, while an increase in the humidity is positively correlated to the number of cases and deaths. Nevertheless, from this study it was also observed that low temperature, mild diurnal temperature and high humidity are likely to favor its transmission. The study therefore, recommends that habitations and hospital rooms should be kept in relatively low humidity and relatively higher temperature to minimize the spread of the (SARS-CoV-2).

Preliminary Study on the Temporal Distribution of PM_( 2.5 ) Pollution and Influence of Meteorological Factors in Bengbu City

Based on the data of six automatic air monitoring stations in Bengbu City,the pollution characteristics and temporal distribution of fine particulate matter PM 2.5 in the air in Bengbu City from 2015 to 2019 were studied,and the correlation between meteorological factors and PM 2.5 concentration was analyzed.The results showed that from 2015 to 2019,PM 2.5 pollution in Bengbu City was relatively heavy in winter and spring and relatively light in summer and autumn,and PM 2.5 concentration had two peaks during the day and night.Precipitation,relative humidity,wind direction and wind speed had certain effects on PM 2.5 concentration in Bengbu City.The research provides reference for the monitoring,early warning and prevention of PM 2.5 pollution in the city.

Temporal Variations in Sea of Clouds and Their Relation with Meteorological Factors in Yandang Mountains

Based on data of meteorological elements in the meteorological station in North Yandang Mountains during 1960- 2013,temporal variations in days of sea of clouds over Yandang Mountains in nearly 50 years and their relation with air temperature,precipitation,relative humidity and wind speed were analyzed. The results showed that annual average days of sea of clouds over Yandang Mountains were 164. 92 d,and the maximum and minimum were 215 and 58 d,so there was a big difference between various years. The days of sea of clouds were the most in spring,and average days of sea of clouds（ average days of sea of clouds with low cloud cover ≥80%） were 50. 89 d（ 32. 77 d）,while they were the least in autumn. There was an obvious positive correlation between the days of sea of clouds and relative humidity. Precipitation occurred the day before or on the day when sea of clouds with low cloud cover ≥80% formed. On the day when sea of clouds with low cloud cover ≥80% appeared,average relative humidity was ≥80%,and average wind speed was ≤4. 5 m/s.

Individual Cotton Boll Weight as Influenced by Different Production Eco-Regions and Meteorological Factors

In order to explore systematically the physiological mechanism of high yield cotton ( Gossypium hirsutum L.) in Xinjiang, and further improve yield, the yield components were compared between three ecological regions. Boll number per plant was lower in South and North Xinjiang, but the harvested plant population were nearly 1.5 times higher than that in Nangong, so total boll numbers per unit area were greater in South and North Xinjiang. Weight per boll in south and north of Xinjiang was 5.896.50 g and 5.43 6.12 g respectively, 24 to 51% heavier than that in Nangong. The diurnal temperature difference between day and night was relatively greater in Xinjiang than in Nangong, benefitting the accumulation of photosynthetic product in bolls. The temperature difference and total hours of sunshine in boll period are the main reasons for cottons higher boll weight and yield in Xinjiang than in Nangong.

Analysis of the Diurnal Pattern of Evaporative Fraction and Its Controlling Factors over Croplands in the Northern China

A key issue of applying remotely sensed data to estimate evapotranspiration （ET） for water management is extrapolating instantaneous latent heat flux （LE） at satellite over-passing time to daily ET total. At present, the most commonly used extrapolation methods have the same assumption that evaporative fraction （EF） can be treated as constant during daytime （so-called EF self-preservation）. However, large errors are reported by many documents over various ecosystems with the same approach, which indicates that further analysis of the diurnal pattern of EF is still necessary. The aim of this study is to examine the diurnal pattern of EF under fair weather conditions, then to analyze the dependencies of EF to meteorological and plant factors. Long-term flux observations at four sites over semi-arid and semi-humid climate regions in the northern China are used to analyze the EF diumal pattern. Results show that the EF self-preservation assumption no longer holds over growing seasons of crops. However, the ratio of reference ET to available energy is almost constant during the daytime, which implies the climate factors do not have much effect on the variability of EF. The analysis of diurnal pattern of air temperature, vapor pressure deficiency （VPD）, and relative humidity （RH） confirms the assumption that ET diurnal pattern is mainly influenced by stomatal regulation.

The variation characteristics and effect factors of surface ozone concentration in the Taklimakan Desert hinterland

Based on automatic continuous surface ozone concentration observation data from June 10, 2010 to March 20, 2012 in the Taklimakan Desert hinterland, combined with corresponding meteorological data, the temporal, seasonal and daily variation characteristics of surface ozone concentrations under different weather conditions were analyzed. At the same time, the main fac- tors affecting ozone variation are discussed. Results show that： （1） Daily variation of ozone concentration was characterized by one obvious peak, with gentle changes during the night and dramatic changes during the day. The lowest concentration was at 09：00 and the highest was at 18：00. Compared to urban areas, there was a slight time delay. （2） Ozone concentration variation had a weekend effect phenomenon. Weekly variation of ozone concentration decreased from Monday to Wednesday with the lowest in Wednesday, and increased after Thursday with the highest in Sunday. （3） The highest monthly average concentration was 89.6 I.tg/m3 in June 2010, and the lowest was 32.0 ~g/m3 in January 2012. Ozone concentration reduced month by month from June to December in 2010. （4） Ozone concentration in spring and summer was higher than in autumn and winter. The variation trend agreed with those in other large and medium-sized cities. （5） Under four different types of weather, daily ozone concentration var- ied most dramatically in sunny days, followed by slight variation in rain days, and varied gently in cloudy days. Ozone concentra- tion varied inconspicuously before a sandstorm appearance, and dropped rapidly at the onset of a sandstorm. （6） Daily variation of radiation was also characterized by a single peak, and the variation was significantly earlier than ozone concentration variation. Sun radiation intensity had a direct influence on the photochemical reaction speed, leading to variation of ozone concentration. （7） Daily average ozone concentration in dust weather was higher than in slight rain and clear days. The variation of near surface ozone concentration could also be affected by meteorological factors such as relative humidity, wind speed, wind direction and sunshine hours. Thus, numerous factors working together led to ozone pollution.

Study on Relationship between Summer Electricity and Meteorology in Changsha City

[ Objective] The research aimed to study relationship between summer electricity and meteorological factors. [ Method] Electrical load characteristics in Changsha during 2007 -2010 were analyzed. Correlation analysis between electrical load and meteorological factors （daily average temperature, the maximum temperature, the minimum temperature, rainfall, wind, relative humidity and atmospheric pressure） during July - September of 2007 -2010 was conducted. [ Result] Changes of the meteorological factors could directly affect electrical load, and temperature was the first influence factor. Prediction model of summer electrical load in Changsha was established by regression analysis method.[ Conclusion] The research could provide reference basis for prediction of the electrical load in Changsha.