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 betw...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.展开更多
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.Metho...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.展开更多
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 m...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.展开更多
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 envir...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.展开更多
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 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 (T</span><sub><span style="font-family:Verdana;">min</span></sub><span style="font-family:Verdana;">, T</span><sub><span style="font-family:Verdana;">mean</span></sub><span style="font-family:Verdana;"> and T</span><sub><span style="font-family:Verdana;">max</span></sub><span style="font-family:Verdana;">) and relative humidity (RH</span><sub><span style="font-family:Verdana;">min</span></sub><span style="font-family:Verdana;">, RH</span><sub><span style="font-family:Verdana;">mean</span></sub><span style="font-family:Verdana;"> and RH</span><sub><span style="font-family:Verdana;">max</span></sub><span style="font-family:Verdana;">) 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).展开更多
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 stud...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.展开更多
In order to investigate the effects of meteorological factors on rape overwintering ability,forage yield and quality of rape in the North China plain,Brassia campestris L.and Brassica napus L.were used in this study.T...In order to investigate the effects of meteorological factors on rape overwintering ability,forage yield and quality of rape in the North China plain,Brassia campestris L.and Brassica napus L.were used in this study.The results showed that compared with the B.napus L.varieties,the growth period of B.campestris L.was shortened by 10-15 d,the overwintering rate(WR)increased by 50.6%,and the density after winter(PD)increased by 41.5%.The fresh forage yield(FFY)and dry forage yield(DFY)of the B.campestris L.type significantly increased by 40.9%and 38.1%compared with the B.napus L.type.,respectively,while the forage quality of the B.napus L.type rape was significantly better than that of the B.campestris L.type.Compared with the B.campestris L.type,the crude protein(CP),fat,ash and total fatty acid(TFA)contents of the B.napus L.type of rape increased by 27.6%,42.9%,23.9%and 52.3%,respectively,and the milk productivity(HM),relative forage value(RFV)and relative forage quality(RFQ)increased by 14.0%,16.2%and 42.1%,respectively.The light and heat resources before wintering increased the WR and PD(P<0.05),and were positively correlated with FFY and DFY(P>0.05),and lower temperature during the wintering period led to lower WR(P<0.01).The light and heat resources during the overwintering period and after regreening were negatively correlated with FFY and DFY(P>0.05).The contents of CP,fat and TFA of rape had an extremely significant negative correlation with the temperature and sunshine hours before wintering,but an extremely significant positive correlation with the temperature during the wintering period and after regreening,as well as the sunshine hours and rainfall during the wintering period;and HM had an extremely significant positive correlation with the temperature,sunshine hours and rainfall during the wintering period,while RFV and RFQ were only extremely significantly positively correlated with the maximum temperature and rainfall.In summary,in the North China Plain,for autumn sowing rape,the B.campestris L.type can be selected to improve the wintering rate,and the B.napus L.type should be the main choice to improve the forage quality of rape.Therefore,the B.napus L.variety HYZ62 can be selected for autumn sowing in the North China Plain.展开更多
Middle-season rice is an important food crop in southern rice areas of China,especially in Yunnan,the main rice-producing region.However,due to the impact of low temperature at the seedling stage and high or low tempe...Middle-season rice is an important food crop in southern rice areas of China,especially in Yunnan,the main rice-producing region.However,due to the impact of low temperature at the seedling stage and high or low temperature at the booting and heading stage of middle-season rice,the yield is not stable.Based on the data of yield factors of different middle-season rice varieties planted in the same ecological site in Jingdong County from 2009 to 2016,average development period was calculated using the data of development period measured in field during 2009-2016,and the average of meteorological factors(daily average temperature,daily maximum temperature,daily minimum temperature,and sunshine hours)and total precipitation were calculated.The correlation between meteorological factors in different development periods of each year and corresponding per unit area yield was analyzed.The results show that temperature is the most important factor affecting rice yield.Sufficient light is beneficial to the increase in the number of grains per spike and thousand seed weight at the sowing-seedling emergence stage and milk maturity-maturity stage.Excessive precipitation will reduce the number of grains per spike at the booting-heading stage.Excessive precipitation decreases the number of filled grains per spike at the jointing-booting stage,and proper drainage helps increase the formation rate of ears.This study provides scientific reference for rice production and management in this county in future.展开更多
Biomass burning(BB)is a very important emission source that significantly adversely impacts regional air quality.BB produces a large number of primary organic aerosol(POA)and black carbon(BC).Besides,BB also provides ...Biomass burning(BB)is a very important emission source that significantly adversely impacts regional air quality.BB produces a large number of primary organic aerosol(POA)and black carbon(BC).Besides,BB also provides many precursors for secondary organic aerosol(SOA)generation.In this work,the ratio of levoglucosan(LG)to organic carbon(OC)and the fire hotspots map was used to identify the open biomass burning(OBB)events,which occurred in two representative episodes,October 13 to November 30,2020,and April1 to April 30,2021.The ratio of organic aerosol(OA)to reconstructed PM_(2.5)concentration(PM_(2.5)^(*))increased with the increase of LG/OC.When LG/OC ratio is higher than 0.03,the highest OA/PM_(2.5)^(*)ratio can reach 80%,which means the contribution of OBB to OA is crucial.According to the ratio of LG to K^(+),LG to mannosan(MN)and the regional characteristics of Longfengshan,it can be determined that the crop residuals are the main fuel.The occurrence of OBB coincides with farmers’preferred choices,i.e.,burning biomass in“bright weather”.The“bright weather”refers to the meteorological conditions with high temperature,low humidity,and without rain.Meteorological factors indirectly affect regional biomass combustion pollution by influencing farmers’active choices.展开更多
Litter microorganisms play a crucial role in the biological decomposition in forest ecosystems;however,the coupling effect of meteorological and substrate changes on it during the different stages of leaf decompositio...Litter microorganisms play a crucial role in the biological decomposition in forest ecosystems;however,the coupling effect of meteorological and substrate changes on it during the different stages of leaf decomposition in situ remains unclear.Hence,according to meteorological factors dynamics,a one-year field litter of Quercus wutaishanica in situ decomposition experiment was designed for four decay stages in a warm temperate forest.Microbial community composition was characterized using Illumina sequencing of fungal ITS and bacterial 16S genes.Bacterial(6.6)and fungal(3.6)Shannon indexes were the largest after 125 days’litter decomposition(October).The relative abundance of Acidobacteria after 342 days and Bacteroidetes after 125 days were 3 and 24 times higher than after 31 days,respectively.Some non-dominant species(bacteria:Firmicutes,Planctomycotes,and Verrucomicrobia;fungi:Chytridiomycota and Glomeromomycota)may be absent or present at different decomposition stages due to litter properties or meteorological factors.Chemoheterotrophy and aerobic-chemoheterotrophy were the dominant bacterial functional groups,and the dominant fungal functional groups were saprotrophs,pathotrophs,and symbiotrophs.Precipitation and relative humidity significantly affected bacteria.Temperature,sunlight intensity,and net radiation significantly affected fungi.Besides,among the relative contributions of changes in bacterial and fungal community structure,leaf litter properties alone explained the variation of 5.51%and 10.63%.Microbial diversity and decay stage directly affected the litter mass-loss rate,with meteorological factors(precipitation,relative humidity,air temperature,and sunlight intensity)being indirect.Our findings highlight the importance of microbial diversity for leaf litter decomposition and the influence of meteorological factors.展开更多
Background:Hand,foot,and mouth disease(HFMD)has become an emerging infectious disease in China in the last decade.There has been evidence that meteorological factors can influence the HFMD incidence,and understanding ...Background:Hand,foot,and mouth disease(HFMD)has become an emerging infectious disease in China in the last decade.There has been evidence that meteorological factors can influence the HFMD incidence,and understanding the mechanisms can help prevent and control HFMD.Methods:HFMD incidence data and meteorological data in Minhang District,Shanghai were obtained for the period between 2009 and 2015.Distributed lag non-linear models(DLNMs)were utilized to investigate the impact of meteorological factors on HFMD incidence after adjusting for potential confounders of long time trend,weekdays and holidays.Results:There was a non-linear relationship between temperature and HFMD incidence,the RR of 5th percentile compared to the median is 0.836(95%CI:0.671-1.042)and the RR of 95th percentile is 2.225(95%CI:1.774-2.792),and the effect of temperature varied across age groups.HFMD incidence increased with increasing average relative humidity(%)(RR=1.009,95%CI:1.005-1.015)and wind speed(m/s)(RR=1.197,95%CI:1.118-1.282),and with decreasing daily rainfall(mm)(RR=0.992,95%CI:0.987-0.997)and sunshine hours(h)(RR=0.966,95%CI:0.951-0.980).Conclusions:There were significant relationships between meteorological factors and childhood HFMD incidence in Minhang District,Shanghai.This information can help local health agencies develop strategies for the control and prevention of HFMD under specific climatic conditions.展开更多
Background: Many studies have compared the performance of time series models in predicting pulmonary tuberculosis(PTB),but few have considered the role of meteorological factors in their prediction models.This study a...Background: Many studies have compared the performance of time series models in predicting pulmonary tuberculosis(PTB),but few have considered the role of meteorological factors in their prediction models.This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting PTB.Methods:: We collected the monthly reported number of PTB cases and records of six meteorological factors in three cities of China from 2005 to 2018.Based on this data,we constructed three time series models,including an autoregressive integrated moving average(ARIMA)model,the ARIMA with exogenous variables(ARIMAX)model,and a recurrent neural network(RNN)model.The ARIMAX and RNN models incorporated meteorological factors,while the ARIMA model did not.The mean absolute percentage error(MAPE)and root mean square error(RMSE)were used to evaluate the performance of the models in predicting PTB cases in 2018.Results: Both the cross-correlation analysis and Spearman rank correlation test showed that PTB cases reported in the study areas were related to meteorological factors.The predictive performance of both the ARIMA and RNN models was improved after incorporating meteorological factors.The MAPEs of the ARIMA,ARIMAX,and RNN models were 12.54%,11.96%,and 12.36%in Xuzhou,15.57%,11.16%,and 14.09%in Nantong,and 9.70%,9.66%,and 12.50%in Wuxi,respectively.The RMSEs of the three models were 36.194,33.956,and 34.785 in Xuzhou,34.073,25.884,and 31.828 in Nantong,and 19.545,19.026,and 26.019 in Wuxi,respectively.Conclusions: Our study revealed a possible link between PTB and meteorological factors.Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB,and the ARIMAX model was superior to the ARIMA and RNN models in study settings.展开更多
Airborne pollen is indicative of vegetation and climatic conditions.This study investigates airborne pollen trapping in the Betula microphylla-dominated wetland of Ebinur Lake in Northwestern China from September 2012...Airborne pollen is indicative of vegetation and climatic conditions.This study investigates airborne pollen trapping in the Betula microphylla-dominated wetland of Ebinur Lake in Northwestern China from September 2012 to August 2015 using Pearson correlation analysis and the Hybrid Single-particle Lagrangian Integrated Trajectory model.Higher temperatures and moderate precipitation during the flowering period facilitated an increase in birch pollen with more exotic spruce pollen carried from the Tianshan Mountains by airflows,leading to the highest arbor pollen concentrations from September 2012 to August 2013.Peak pollen concentrations from September 2013 to August 2014 were possibly due to an increase in herbaceous pollen resulting from higher temperatures,lower precipitation and more exotic pollen from the desert of southwest Ebinur Lake and Central Asia in summer and autumn.Between September 2014 and August 2015,unfavorable climate conditions in summer and autumn decreased the pollen dispersal of xerophytes such as Artemisia and Chenopodiaceae,with little pollen transported from the Kazakh hilly area in late summer,resulting in the lowest pollen concentrations.Climatic parameters and air mass movements both greatly affected the atmospheric pollen concentration.The results provide information concerning the dispersion and distribution of birch pollen,paleoenvironmental reconstruction and wetland conservation.展开更多
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 base...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.展开更多
Objective:Mumps is a seasonal infectious disease,always occurring in winter and spring.In this study,we aim to analyze its epidemiological characteristics,transmissibility,and its correlation with meteorological varia...Objective:Mumps is a seasonal infectious disease,always occurring in winter and spring.In this study,we aim to analyze its epidemiological characteristics,transmissibility,and its correlation with meteorological variables.Method:A seasonal Susceptiblee Exposede Infectious/Asymptomatice Recovered model and a next-generation matrix method were applied to estimate the time-dependent reproduction number(Rt).Results:The seasonal double peak of annual incidence was mainly in May to July and November to December.There was high transmission at the median of Rt¼1.091(ranged:0 to 4.393).Rt was seasonally distributed mainly from February to April and from September to November.Correlations were found between temperature(Pearson correlation coefficient[r]ranged:from 0.101 to 0.115),average relative humidity(r¼0.070),average local pressure(r¼-0.066),and the number of new cases.In addition,average local pressure(r¼0.188),average wind speed(r¼0.111),air temperature(r ranged:-0.128 to-0.150),average relative humidity(r¼-0.203)and sunshine duration(r¼-0.075)were all correlated with Rt.Conclusion:A relatively high level of transmissibility has been found in Xiamen City,leading to a continuous epidemic of mumps.Meteorological factors,especially air temperature and relative humidity,may be more closely associated with mumps than other factors.展开更多
Background:The trend of military patients becoming infected with vivax malaria reemerged in the Republic of Korea(ROK)in 1993.The common explanation has been that infective Anopheles mosquitoes from the Democratic Peo...Background:The trend of military patients becoming infected with vivax malaria reemerged in the Republic of Korea(ROK)in 1993.The common explanation has been that infective Anopheles mosquitoes from the Democratic People’s Republic of Korea have invaded Republic of Korea’s demilitarized zone(DMZ).The aim of this study was to verify the relationship between meteorological factors and the number of malaria patients in the military in this region.Methods:The authors estimated the effects of meteorological factors on vivax malaria patients from the military based on the monthly number of malaria cases between 2006 and 2011.Temperature,precipitation,snow depth,wind velocity,relative humidity,duration of sunshine,and cloud cover were selected as the meteorological factors to be studied.A systematic pattern in the spatial distribution of malaria cases was assessed using the Moran’s Index.Granger causality tests and cross-correlation coefficients were used to evaluate the relationship between meteorological factors and malaria patients in the military.Results:Spatial analysis revealed significant clusters of malaria patients in the military in Republic of Korea in 2011(Moran’s I=0.136,p-value=0.026).In the six years investigated,the number of malaria patients in the military in Paju decreased,but the number of malaria patients in the military in Hwacheon and Chuncheon increased.Monthly average,maximum and minimum temperatures;wind velocity;and relative humidity were found to be predicting factors of malaria in patients in the military in Paju.In contrast,wind velocity alone was not able to predict malaria in Hwacheon and Chuncheon,however,precipitation and cloud cover were able to predict malaria in Hwacheon and Chuncheon.Conclusions:This study demonstrated that the number of malaria patients in the military is correlated with meteorological factors.The variation in occurrence of malaria cases was principally attributed to differences in meteorological factors by regions of Republic of Korea.展开更多
Based on the real-time data released by 17 atmospheric automatic stations in Chongqing,the pollution characteristics and variation trends of PM 2.5 and PM 10 and their correlations with meteorological factors from Jan...Based on the real-time data released by 17 atmospheric automatic stations in Chongqing,the pollution characteristics and variation trends of PM 2.5 and PM 10 and their correlations with meteorological factors from January 2014 to December 2018 were analyzed.The results show that the annual average mass concentration of PM 2.5 in Chongqing reduced from 65μg/m 3 in 2014 to 40μg/m 3 in 2018,and the annual average mass concentration of PM 10 decreased from 98μg/m 3 in 2014 to 64μg/m 3 in 2018.However,the annual average mass concentration of PM 2.5 in Chongqing from 2014 to 2018 and the annual average mass concentration of PM 10 from 2014 to 2016 exceeded the national level II standard,and the maximum exceeding standard rate was up to 0.86 and 0.40 times respectively.The monthly average mass concentration of PM 2.5 and PM 10 changed obviously,and the overall distribution was"U"-shaped.The ratio of PM 2.5 to PM 10 mass concentration ranged from 47.4%to 80.7%,with an average of 62.4%.The Pearson correlation coefficient between the mass concentration of PM 2.5 and PM 10 varied from 0.961 to 0.989,and there was a significant correlation at the confidence level of 0.01(bilateral).The mass concentration of PM 2.5 and PM 10 was extremely significantly correlated with average temperature,precipitation,and average air pressure,while there was no significant correlation between the mass concentrations of PM 2.5 and PM 10 and average relative humidity.The mass concentration of PM 2.5 and PM 10 was significantly correlated with sunshine duration.展开更多
Wheat scab(WS,Fusarium head blight),one of the most severe diseases of winter wheat in Yangtze-Huaihe river region,whose monitoring and timely forecasting at large scale would help to optimize pesticide spraying and a...Wheat scab(WS,Fusarium head blight),one of the most severe diseases of winter wheat in Yangtze-Huaihe river region,whose monitoring and timely forecasting at large scale would help to optimize pesticide spraying and achieve the purpose of reducing yield loss.In the present study,remote sensing monitoring on WS was conducted in 4 counties in Yangtze-Huaihe river region.Sensitive factors of WS were selected to establish the remote sensing estimation model of winter wheat scab index(WSI)based on interactions between spectral information and meteorological factors.The results showed that:1)Correlations between the daily average temperature(DAT)and daily average relative humidity(DAH)at different time scales and WSI were significant.2)There were positive linear correlations between winter wheat biomass,leaf area index(LAI),leaf chlorophyll content(LCC)and WSI.3)NDVI(normalized difference vegetation index),RVI(ratio vegetation index)and DVI(difference vegetation index)which had a good correlation with LAI,biomass and LCC,respectively,and could be used to replace them in modeling.4)The estimated values of the model were consistent with the measured values(RMSE=5.3%,estimation accuracy=90.46%).Estimation results showed that the model could efficiently estimate WS in Yangtze-Huaihe river region.展开更多
Dead fine fuel moisture content(DFFMC)is a key factor affecting the spread of forest fires,which plays an important role in evaluation of forest fire risk.In order to achieve high-precision real-time measurement of DF...Dead fine fuel moisture content(DFFMC)is a key factor affecting the spread of forest fires,which plays an important role in evaluation of forest fire risk.In order to achieve high-precision real-time measurement of DFFMC,this study established a long short-term memory(LSTM)network based on particle swarm optimization(PSO)algorithm as a measurement model.A multi-point surface monitoring scheme combining near-infrared measurement method and meteorological measurement method is proposed.The near-infrared spectral information of dead fine fuels and the meteorological factors in the region are processed by data fusion technology to construct a spectral-meteorological data set.The surface fine dead fuel of Mongolian oak(Quercus mongolica Fisch.ex Ledeb.),white birch(Betula platyphylla Suk.),larch(Larix gmelinii(Rupr.)Kuzen.),and Manchurian walnut(Juglans mandshurica Maxim.)in the maoershan experimental forest farm of the Northeast Forestry University were investigated.We used the PSO-LSTM model for moisture content to compare the near-infrared spectroscopy,meteorological,and spectral meteorological fusion methods.The results show that the mean absolute error of the DFFMC of the four stands by spectral meteorological fusion method were 1.1%for Mongolian oak,1.3%for white birch,1.4%for larch,and 1.8%for Manchurian walnut,and these values were lower than those of the near-infrared method and the meteorological method.The spectral meteorological fusion method provides a new way for high-precision measurement of moisture content of fine dead fuel.展开更多
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 netw...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.展开更多
基金This study was reviewed and approved by the Ethics Committee of The First Psychiatric Hospital of Harbin.
文摘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.
基金funded by the National Natural Science Foundation of China(8177120753)the China-Australia International Collaborative Grant(NHMRC APP1112767,NSFC 81561128020)Zheng Y L and Guo Z were supported by the Edith Cowan University Higher Degree by Research Scholarship(ECU-HDR ST10469322 and ST10468211).
文摘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.
基金financially supported by the National Nonprofit Institute Research Grant of Chinese Academy of Agricultural Sciences(IARRP-2015-8)the European Union seventh framework"MODEXTREME"(modelling vegetation response to extreme events)programme(613817)
文摘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.
基金Gachon University Gil Medical Center,No.FRD2018-17 and No.FRD2019-11.
文摘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.
文摘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 (T</span><sub><span style="font-family:Verdana;">min</span></sub><span style="font-family:Verdana;">, T</span><sub><span style="font-family:Verdana;">mean</span></sub><span style="font-family:Verdana;"> and T</span><sub><span style="font-family:Verdana;">max</span></sub><span style="font-family:Verdana;">) and relative humidity (RH</span><sub><span style="font-family:Verdana;">min</span></sub><span style="font-family:Verdana;">, RH</span><sub><span style="font-family:Verdana;">mean</span></sub><span style="font-family:Verdana;"> and RH</span><sub><span style="font-family:Verdana;">max</span></sub><span style="font-family:Verdana;">) 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).
文摘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.
基金National Key Research and Development Program of China(2017YFD0200808)Seed Science and Technology Major Special Program of Tianjin(18ZXZYNC00100)+1 种基金Scientific Research Program(Natural Science)of Tianjin Education Committee(2019KJ039)Graduate Research Innovation Program of Tianjin(2020YJSS128).
文摘In order to investigate the effects of meteorological factors on rape overwintering ability,forage yield and quality of rape in the North China plain,Brassia campestris L.and Brassica napus L.were used in this study.The results showed that compared with the B.napus L.varieties,the growth period of B.campestris L.was shortened by 10-15 d,the overwintering rate(WR)increased by 50.6%,and the density after winter(PD)increased by 41.5%.The fresh forage yield(FFY)and dry forage yield(DFY)of the B.campestris L.type significantly increased by 40.9%and 38.1%compared with the B.napus L.type.,respectively,while the forage quality of the B.napus L.type rape was significantly better than that of the B.campestris L.type.Compared with the B.campestris L.type,the crude protein(CP),fat,ash and total fatty acid(TFA)contents of the B.napus L.type of rape increased by 27.6%,42.9%,23.9%and 52.3%,respectively,and the milk productivity(HM),relative forage value(RFV)and relative forage quality(RFQ)increased by 14.0%,16.2%and 42.1%,respectively.The light and heat resources before wintering increased the WR and PD(P<0.05),and were positively correlated with FFY and DFY(P>0.05),and lower temperature during the wintering period led to lower WR(P<0.01).The light and heat resources during the overwintering period and after regreening were negatively correlated with FFY and DFY(P>0.05).The contents of CP,fat and TFA of rape had an extremely significant negative correlation with the temperature and sunshine hours before wintering,but an extremely significant positive correlation with the temperature during the wintering period and after regreening,as well as the sunshine hours and rainfall during the wintering period;and HM had an extremely significant positive correlation with the temperature,sunshine hours and rainfall during the wintering period,while RFV and RFQ were only extremely significantly positively correlated with the maximum temperature and rainfall.In summary,in the North China Plain,for autumn sowing rape,the B.campestris L.type can be selected to improve the wintering rate,and the B.napus L.type should be the main choice to improve the forage quality of rape.Therefore,the B.napus L.variety HYZ62 can be selected for autumn sowing in the North China Plain.
文摘Middle-season rice is an important food crop in southern rice areas of China,especially in Yunnan,the main rice-producing region.However,due to the impact of low temperature at the seedling stage and high or low temperature at the booting and heading stage of middle-season rice,the yield is not stable.Based on the data of yield factors of different middle-season rice varieties planted in the same ecological site in Jingdong County from 2009 to 2016,average development period was calculated using the data of development period measured in field during 2009-2016,and the average of meteorological factors(daily average temperature,daily maximum temperature,daily minimum temperature,and sunshine hours)and total precipitation were calculated.The correlation between meteorological factors in different development periods of each year and corresponding per unit area yield was analyzed.The results show that temperature is the most important factor affecting rice yield.Sufficient light is beneficial to the increase in the number of grains per spike and thousand seed weight at the sowing-seedling emergence stage and milk maturity-maturity stage.Excessive precipitation will reduce the number of grains per spike at the booting-heading stage.Excessive precipitation decreases the number of filled grains per spike at the jointing-booting stage,and proper drainage helps increase the formation rate of ears.This study provides scientific reference for rice production and management in this county in future.
基金supported by the Natural Science Foundation of Heilongjiang Province(No.LH2020D011)the S&T Development Fund of CAMS(No.2020KJ003)the Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin,China Institute of Water Resources and Hydropower Research(No.201913)。
文摘Biomass burning(BB)is a very important emission source that significantly adversely impacts regional air quality.BB produces a large number of primary organic aerosol(POA)and black carbon(BC).Besides,BB also provides many precursors for secondary organic aerosol(SOA)generation.In this work,the ratio of levoglucosan(LG)to organic carbon(OC)and the fire hotspots map was used to identify the open biomass burning(OBB)events,which occurred in two representative episodes,October 13 to November 30,2020,and April1 to April 30,2021.The ratio of organic aerosol(OA)to reconstructed PM_(2.5)concentration(PM_(2.5)^(*))increased with the increase of LG/OC.When LG/OC ratio is higher than 0.03,the highest OA/PM_(2.5)^(*)ratio can reach 80%,which means the contribution of OBB to OA is crucial.According to the ratio of LG to K^(+),LG to mannosan(MN)and the regional characteristics of Longfengshan,it can be determined that the crop residuals are the main fuel.The occurrence of OBB coincides with farmers’preferred choices,i.e.,burning biomass in“bright weather”.The“bright weather”refers to the meteorological conditions with high temperature,low humidity,and without rain.Meteorological factors indirectly affect regional biomass combustion pollution by influencing farmers’active choices.
基金This research was supported by the National Natural Science Foundation of China(Grant Nos.41877074 and 42077072).
文摘Litter microorganisms play a crucial role in the biological decomposition in forest ecosystems;however,the coupling effect of meteorological and substrate changes on it during the different stages of leaf decomposition in situ remains unclear.Hence,according to meteorological factors dynamics,a one-year field litter of Quercus wutaishanica in situ decomposition experiment was designed for four decay stages in a warm temperate forest.Microbial community composition was characterized using Illumina sequencing of fungal ITS and bacterial 16S genes.Bacterial(6.6)and fungal(3.6)Shannon indexes were the largest after 125 days’litter decomposition(October).The relative abundance of Acidobacteria after 342 days and Bacteroidetes after 125 days were 3 and 24 times higher than after 31 days,respectively.Some non-dominant species(bacteria:Firmicutes,Planctomycotes,and Verrucomicrobia;fungi:Chytridiomycota and Glomeromomycota)may be absent or present at different decomposition stages due to litter properties or meteorological factors.Chemoheterotrophy and aerobic-chemoheterotrophy were the dominant bacterial functional groups,and the dominant fungal functional groups were saprotrophs,pathotrophs,and symbiotrophs.Precipitation and relative humidity significantly affected bacteria.Temperature,sunlight intensity,and net radiation significantly affected fungi.Besides,among the relative contributions of changes in bacterial and fungal community structure,leaf litter properties alone explained the variation of 5.51%and 10.63%.Microbial diversity and decay stage directly affected the litter mass-loss rate,with meteorological factors(precipitation,relative humidity,air temperature,and sunlight intensity)being indirect.Our findings highlight the importance of microbial diversity for leaf litter decomposition and the influence of meteorological factors.
基金This research was supported by the National Natural Science Foundation of China(grant number 81673239)the National Science Fund for Distinguished Young Scholars(grant number 81325017)+1 种基金Chang Jiang Scholars Program(grant number T2014089)the Fourth Round of Three-Year Public Health Action Plan of Shanghai,China(grant numbers 15GWZK0202,15GWZK0101).
文摘Background:Hand,foot,and mouth disease(HFMD)has become an emerging infectious disease in China in the last decade.There has been evidence that meteorological factors can influence the HFMD incidence,and understanding the mechanisms can help prevent and control HFMD.Methods:HFMD incidence data and meteorological data in Minhang District,Shanghai were obtained for the period between 2009 and 2015.Distributed lag non-linear models(DLNMs)were utilized to investigate the impact of meteorological factors on HFMD incidence after adjusting for potential confounders of long time trend,weekdays and holidays.Results:There was a non-linear relationship between temperature and HFMD incidence,the RR of 5th percentile compared to the median is 0.836(95%CI:0.671-1.042)and the RR of 95th percentile is 2.225(95%CI:1.774-2.792),and the effect of temperature varied across age groups.HFMD incidence increased with increasing average relative humidity(%)(RR=1.009,95%CI:1.005-1.015)and wind speed(m/s)(RR=1.197,95%CI:1.118-1.282),and with decreasing daily rainfall(mm)(RR=0.992,95%CI:0.987-0.997)and sunshine hours(h)(RR=0.966,95%CI:0.951-0.980).Conclusions:There were significant relationships between meteorological factors and childhood HFMD incidence in Minhang District,Shanghai.This information can help local health agencies develop strategies for the control and prevention of HFMD under specific climatic conditions.
基金This study was funded by the National Natural Science Foundation of China(81973103)National Key R&D Program of China(2017YFC0907000)+2 种基金Qing Lan Project of Jiangsu Province(2019)Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)The funding agencies had no role in the study design,data collection,analysis,decision to publish,or preparation of the manuscript.
文摘Background: Many studies have compared the performance of time series models in predicting pulmonary tuberculosis(PTB),but few have considered the role of meteorological factors in their prediction models.This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting PTB.Methods:: We collected the monthly reported number of PTB cases and records of six meteorological factors in three cities of China from 2005 to 2018.Based on this data,we constructed three time series models,including an autoregressive integrated moving average(ARIMA)model,the ARIMA with exogenous variables(ARIMAX)model,and a recurrent neural network(RNN)model.The ARIMAX and RNN models incorporated meteorological factors,while the ARIMA model did not.The mean absolute percentage error(MAPE)and root mean square error(RMSE)were used to evaluate the performance of the models in predicting PTB cases in 2018.Results: Both the cross-correlation analysis and Spearman rank correlation test showed that PTB cases reported in the study areas were related to meteorological factors.The predictive performance of both the ARIMA and RNN models was improved after incorporating meteorological factors.The MAPEs of the ARIMA,ARIMAX,and RNN models were 12.54%,11.96%,and 12.36%in Xuzhou,15.57%,11.16%,and 14.09%in Nantong,and 9.70%,9.66%,and 12.50%in Wuxi,respectively.The RMSEs of the three models were 36.194,33.956,and 34.785 in Xuzhou,34.073,25.884,and 31.828 in Nantong,and 19.545,19.026,and 26.019 in Wuxi,respectively.Conclusions: Our study revealed a possible link between PTB and meteorological factors.Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB,and the ARIMAX model was superior to the ARIMA and RNN models in study settings.
基金supported by the National Natural Science Foundation of China(Grant Nos.41971121&41572331)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA19050103)the Basic Research Foundation of Institute of Hydrogeology and Environmental Geology,Chinese Academy of Geological Sciences(Grant No.SK202012).
文摘Airborne pollen is indicative of vegetation and climatic conditions.This study investigates airborne pollen trapping in the Betula microphylla-dominated wetland of Ebinur Lake in Northwestern China from September 2012 to August 2015 using Pearson correlation analysis and the Hybrid Single-particle Lagrangian Integrated Trajectory model.Higher temperatures and moderate precipitation during the flowering period facilitated an increase in birch pollen with more exotic spruce pollen carried from the Tianshan Mountains by airflows,leading to the highest arbor pollen concentrations from September 2012 to August 2013.Peak pollen concentrations from September 2013 to August 2014 were possibly due to an increase in herbaceous pollen resulting from higher temperatures,lower precipitation and more exotic pollen from the desert of southwest Ebinur Lake and Central Asia in summer and autumn.Between September 2014 and August 2015,unfavorable climate conditions in summer and autumn decreased the pollen dispersal of xerophytes such as Artemisia and Chenopodiaceae,with little pollen transported from the Kazakh hilly area in late summer,resulting in the lowest pollen concentrations.Climatic parameters and air mass movements both greatly affected the atmospheric pollen concentration.The results provide information concerning the dispersion and distribution of birch pollen,paleoenvironmental reconstruction and wetland conservation.
基金supported by the Science and Technology Innovation Project of Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2016-AII)。
文摘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.
基金supported by the Bill&Melinda Gates Foundation(Grant INV-005834 to T.C.).
文摘Objective:Mumps is a seasonal infectious disease,always occurring in winter and spring.In this study,we aim to analyze its epidemiological characteristics,transmissibility,and its correlation with meteorological variables.Method:A seasonal Susceptiblee Exposede Infectious/Asymptomatice Recovered model and a next-generation matrix method were applied to estimate the time-dependent reproduction number(Rt).Results:The seasonal double peak of annual incidence was mainly in May to July and November to December.There was high transmission at the median of Rt¼1.091(ranged:0 to 4.393).Rt was seasonally distributed mainly from February to April and from September to November.Correlations were found between temperature(Pearson correlation coefficient[r]ranged:from 0.101 to 0.115),average relative humidity(r¼0.070),average local pressure(r¼-0.066),and the number of new cases.In addition,average local pressure(r¼0.188),average wind speed(r¼0.111),air temperature(r ranged:-0.128 to-0.150),average relative humidity(r¼-0.203)and sunshine duration(r¼-0.075)were all correlated with Rt.Conclusion:A relatively high level of transmissibility has been found in Xiamen City,leading to a continuous epidemic of mumps.Meteorological factors,especially air temperature and relative humidity,may be more closely associated with mumps than other factors.
文摘Background:The trend of military patients becoming infected with vivax malaria reemerged in the Republic of Korea(ROK)in 1993.The common explanation has been that infective Anopheles mosquitoes from the Democratic People’s Republic of Korea have invaded Republic of Korea’s demilitarized zone(DMZ).The aim of this study was to verify the relationship between meteorological factors and the number of malaria patients in the military in this region.Methods:The authors estimated the effects of meteorological factors on vivax malaria patients from the military based on the monthly number of malaria cases between 2006 and 2011.Temperature,precipitation,snow depth,wind velocity,relative humidity,duration of sunshine,and cloud cover were selected as the meteorological factors to be studied.A systematic pattern in the spatial distribution of malaria cases was assessed using the Moran’s Index.Granger causality tests and cross-correlation coefficients were used to evaluate the relationship between meteorological factors and malaria patients in the military.Results:Spatial analysis revealed significant clusters of malaria patients in the military in Republic of Korea in 2011(Moran’s I=0.136,p-value=0.026).In the six years investigated,the number of malaria patients in the military in Paju decreased,but the number of malaria patients in the military in Hwacheon and Chuncheon increased.Monthly average,maximum and minimum temperatures;wind velocity;and relative humidity were found to be predicting factors of malaria in patients in the military in Paju.In contrast,wind velocity alone was not able to predict malaria in Hwacheon and Chuncheon,however,precipitation and cloud cover were able to predict malaria in Hwacheon and Chuncheon.Conclusions:This study demonstrated that the number of malaria patients in the military is correlated with meteorological factors.The variation in occurrence of malaria cases was principally attributed to differences in meteorological factors by regions of Republic of Korea.
文摘Based on the real-time data released by 17 atmospheric automatic stations in Chongqing,the pollution characteristics and variation trends of PM 2.5 and PM 10 and their correlations with meteorological factors from January 2014 to December 2018 were analyzed.The results show that the annual average mass concentration of PM 2.5 in Chongqing reduced from 65μg/m 3 in 2014 to 40μg/m 3 in 2018,and the annual average mass concentration of PM 10 decreased from 98μg/m 3 in 2014 to 64μg/m 3 in 2018.However,the annual average mass concentration of PM 2.5 in Chongqing from 2014 to 2018 and the annual average mass concentration of PM 10 from 2014 to 2016 exceeded the national level II standard,and the maximum exceeding standard rate was up to 0.86 and 0.40 times respectively.The monthly average mass concentration of PM 2.5 and PM 10 changed obviously,and the overall distribution was"U"-shaped.The ratio of PM 2.5 to PM 10 mass concentration ranged from 47.4%to 80.7%,with an average of 62.4%.The Pearson correlation coefficient between the mass concentration of PM 2.5 and PM 10 varied from 0.961 to 0.989,and there was a significant correlation at the confidence level of 0.01(bilateral).The mass concentration of PM 2.5 and PM 10 was extremely significantly correlated with average temperature,precipitation,and average air pressure,while there was no significant correlation between the mass concentrations of PM 2.5 and PM 10 and average relative humidity.The mass concentration of PM 2.5 and PM 10 was significantly correlated with sunshine duration.
基金supported by the National Natural Science Foundation of China(No.41571323)Key Research&Development Plan of Jiangsu Province(BE2016730)+1 种基金Open Research Fund of Key Laboratory of Digital Earth Science,Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences(No.2016LDE007)the Fund of Jiangsu Academy of Agriculture Sciences(6111647).
文摘Wheat scab(WS,Fusarium head blight),one of the most severe diseases of winter wheat in Yangtze-Huaihe river region,whose monitoring and timely forecasting at large scale would help to optimize pesticide spraying and achieve the purpose of reducing yield loss.In the present study,remote sensing monitoring on WS was conducted in 4 counties in Yangtze-Huaihe river region.Sensitive factors of WS were selected to establish the remote sensing estimation model of winter wheat scab index(WSI)based on interactions between spectral information and meteorological factors.The results showed that:1)Correlations between the daily average temperature(DAT)and daily average relative humidity(DAH)at different time scales and WSI were significant.2)There were positive linear correlations between winter wheat biomass,leaf area index(LAI),leaf chlorophyll content(LCC)and WSI.3)NDVI(normalized difference vegetation index),RVI(ratio vegetation index)and DVI(difference vegetation index)which had a good correlation with LAI,biomass and LCC,respectively,and could be used to replace them in modeling.4)The estimated values of the model were consistent with the measured values(RMSE=5.3%,estimation accuracy=90.46%).Estimation results showed that the model could efficiently estimate WS in Yangtze-Huaihe river region.
基金supported by the National Key R&D Program of China (Project No.2020YFC2200800,Task No.2020YFC2200803)the Key Projects of the Natural Science Foundation of Heilongjiang Province (Grant No.ZD2021E001)。
文摘Dead fine fuel moisture content(DFFMC)is a key factor affecting the spread of forest fires,which plays an important role in evaluation of forest fire risk.In order to achieve high-precision real-time measurement of DFFMC,this study established a long short-term memory(LSTM)network based on particle swarm optimization(PSO)algorithm as a measurement model.A multi-point surface monitoring scheme combining near-infrared measurement method and meteorological measurement method is proposed.The near-infrared spectral information of dead fine fuels and the meteorological factors in the region are processed by data fusion technology to construct a spectral-meteorological data set.The surface fine dead fuel of Mongolian oak(Quercus mongolica Fisch.ex Ledeb.),white birch(Betula platyphylla Suk.),larch(Larix gmelinii(Rupr.)Kuzen.),and Manchurian walnut(Juglans mandshurica Maxim.)in the maoershan experimental forest farm of the Northeast Forestry University were investigated.We used the PSO-LSTM model for moisture content to compare the near-infrared spectroscopy,meteorological,and spectral meteorological fusion methods.The results show that the mean absolute error of the DFFMC of the four stands by spectral meteorological fusion method were 1.1%for Mongolian oak,1.3%for white birch,1.4%for larch,and 1.8%for Manchurian walnut,and these values were lower than those of the near-infrared method and the meteorological method.The spectral meteorological fusion method provides a new way for high-precision measurement of moisture content of fine dead fuel.
基金Supported by the Scientific Research Project of Liaoning Meteorological Bureau (ZD202208, ZD202257)Science and Technology Research Project of Fuxin Meteorological Bureau (FX2022-11, FX2022-13)。
文摘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.
