Simple model for predicting hourly air temperatures inside Chinese solar greenhouses

For an efficient energy greenhouse,temperature is the most important climate parameter,which not only affects crop growth and health but also determines the management of energy consumption.So reliable monitoring of temperature is of great significance,and often hourly values are required.However,due to the low level of automation for Chinese solar greenhouse,the loss or poor quality of climate data often occurs.In order to accurately supplement the missing data,as well as for the generation of future temperature,a 24-hour indoor temperature prediction model was established.It uses a piecewise Bezier curve equation that takes the characteristic temperature as the control point which was determined by the outside weather recording.The 130 d of observed hourly temperature data were used to build and validate the model,and the results showed that the temperature model proposed was accurate and sufficient for the simulation of the trend curve of hourly temperature change inside a solar greenhouse.(EF=0.98,R2=0.89).After validation,this temperature model proposed can be useful for the quantitative analysis of crop growth and optimal management.

Magnitude and direction of temperature variability affect hospitalization for myocardial infarction and stroke:population-based evidence from Guangzhou,China

Relationships between nonoptimal temperatures and cardiovascular disease(CVD)mortality have been well documented.However,evidence of the association between temperature variability(TV)and CVD morbidity is limited.This study aimed to quantify the risk and burden of CVD-related hospitalization associated with the magnitude and direction of TV.Data on meteorology and population-based hospitalizations for myocardial infarction(MI)and stroke were collected in Guangzhou,China,from 2013 to 2017.Hourly temperature variability(HTV)was measured as the standard deviation of hourly temperature records over specific exposure days.The direction(upward or downward)of HTV was defined as the average daily mean temperature change relative to that of the previous day during the exposure period.Quasi-Poisson regression was applied to assess the impact of HTV after adjusting for the daily mean temperature,and the hospitalization fractions attributable to HTV were calculated.A 1℃-increase in HTV was significantly associated with a 2.24%and 1.72%increase in hospitalizations for MI and hemorrhagic stroke(HS)at lag 0–1 d,respectively,and a 1.55%increase in hospitalizations for ischemic stroke(IS)at lag 0–3 d.During the study period,5.99%,4.64%,and 4.53%of MI,HS,and IS hospitalizations,respectively,were attributable to HTV.The upward TV exerts acute effects on CVD hospital admissions,whereas the impact of downward TV generally lags.These findings highlight the importance of the magnitude and direction of temperature fluctuations,in addition to the mean level,in assessing the adverse health impacts of temperature variations.