scholarly journals Influence of Weather Conditions on the Onset of Spontaneous Pneumothorax in the Region of Sousse (Tunisia): Analysis of Time Series

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Sana Aissa ◽  
Maher Maoua ◽  
Salsabil Selmi ◽  
Wafa Benzarti ◽  
Imen Gargouri ◽  
...  
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Spontaneous Pneumothorax ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Mean Temperature ◽  
Higher Temperature ◽  
The Mean ◽  
Analysis Of Time Series

Introduction. Weather conditions were implicated in the onset of spontaneous pneumothorax (SP). Aim. Investigate the influence of weather conditions on the onset of SP. Methods. A total of 200 patients with SP in Sousse (Tunisia) were enrolled in the study between January 2010 and December 2014. An analysis of two time series (meteorological data and pneumothorax cases) was performed. Data on weather conditions were collected daily throughout the 5-year period. Results. A comparison of the mean temperature between days with and without SP showed significantly higher temperatures during the days with SP. A decrease of 1% in the relative humidity one day lag (D-1) was associated with an increase in the risk of SP by 1.6% (p=0,02). The occurrence of clusters was associated significantly with higher temperature averages on the same days. This same observation was made regarding the mean duration of sunshine two days before the cluster onset (p = 0.05). The occurrence of storms two days before clusters was also significantly associated with a risk multiplied by 1.96. Conclusion. There was a correlation between clusters of spontaneous pneumothorax and weather conditions in the region of Sousse-Tunisia.

Statistical Analysis of Weather Effects on PM2.5

2012 ◽  
Vol 610-613 ◽  
pp. 1033-1040
Author(s):  
Wei Dai ◽  
Jia Qi Gao ◽  
Bo Wang ◽  
Feng Ouyang
Keyword(s):  
Statistical Analysis ◽  
Wind Speed ◽  
Relative Humidity ◽  
Statistical Methods ◽  
Predictive Analytics ◽  
Hypothesis Test ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Statistical Distribution ◽  
Weather Effects

Effects of weather conditions including temperature, relative humidity, wind speed, wind and direction on PM2.5 were studied using statistical methods. PM2.5 samples were collected during the summer and the winter in a suburb of Shenzhen. Then, correlations, hypothesis test and statistical distribution of PM2.5 and meteorological data were analyzed with IBM SPSS predictive analytics software. Seasonal and daily variations of PM2.5 have been found and these mainly resulted from the weather effects.

Summer

2019 ◽  
pp. 228-242
Author(s):  
Stephen Burt ◽  
Tim Burt
Keyword(s):  
Time Series ◽  
Detailed Analysis ◽  
Total Precipitation ◽  
Mean Temperature ◽  
The Mean ◽  
Weather Record ◽  
Extremes Of Temperature

Chapter 22 provides a detailed analysis of the long weather record at the Radcliffe Observatory, Oxford for summer. Averages and extremes of temperature, precipitation and sunshine are presented, with coverage relevant to the month or season including the incidence of snowfall, thunderstorms, gales and the like, illustrated by contemporary accounts and photography. Each chapter ends with a complete time series of the mean temperature, total precipitation and total sunshine for the month or season from the entire record, updated to 2018.

Spring

2019 ◽  
pp. 222-227
Author(s):  
Stephen Burt ◽  
Tim Burt
Keyword(s):  
Time Series ◽  
Detailed Analysis ◽  
Total Precipitation ◽  
Mean Temperature ◽  
The Mean ◽  
Weather Record ◽  
Extremes Of Temperature

Chapter 21 provides a detailed analysis of the long weather record at the Radcliffe Observatory, Oxford for spring. Averages and extremes of temperature, precipitation and sunshine are presented, with coverage relevant to the month or season including the incidence of snowfall, thunderstorms, gales and the like, illustrated by contemporary accounts and photography. Each chapter ends with a complete time series of the mean temperature, total precipitation and total sunshine for the month or season from the entire record, updated to 2018.

October

2019 ◽  
pp. 165-171
Author(s):  
Stephen Burt ◽  
Tim Burt
Keyword(s):  
Time Series ◽  
Detailed Analysis ◽  
Total Precipitation ◽  
Mean Temperature ◽  
The Mean ◽  
Weather Record ◽  
Extremes Of Temperature

Chapter 16 provides a detailed analysis of the long weather record at the Radcliffe Observatory, Oxford for October. Averages and extremes of temperature, precipitation and sunshine are presented, with coverage relevant to the month or season including the incidence of snowfall, thunderstorms, gales and the like, illustrated by contemporary accounts and photography. Each chapter ends with a complete time series of the mean temperature, total precipitation and total sunshine for the month or season from the entire record, updated to 2018.

June

2019 ◽  
pp. 123-131
Author(s):  
Stephen Burt ◽  
Tim Burt
Keyword(s):  
Time Series ◽  
Detailed Analysis ◽  
Total Precipitation ◽  
Mean Temperature ◽  
The Mean ◽  
Weather Record ◽  
Extremes Of Temperature

Chapter 12 provides a detailed analysis of the long weather record at the Radcliffe Observatory, Oxford for June. Averages and extremes of temperature, precipitation and sunshine are presented, with coverage relevant to the month or season including the incidence of snowfall, thunderstorms, gales and the like, illustrated by contemporary accounts and photography. Each chapter ends with a complete time series of the mean temperature, total precipitation and total sunshine for the month or season from the entire record, updated to 2018.

scholarly journals Preventive Control of Powdery Mildew Disease of Mango

1999 ◽  
Vol 4 (1) ◽  
pp. 23
Author(s):  
K.P. Akhtar ◽  
I.A. Khan ◽  
M.R. Kazmi ◽  
R.I. Hassan ◽  
B. Fatima
Keyword(s):  
Relative Humidity ◽  
Powdery Mildew ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Optimal Timing ◽  
Positive Trend ◽  
Preventive Control ◽  
Airborne Conidia ◽  
Powdery Mildew Disease ◽  
Favorable Weather

Oidium mangiferae Berthet was found to be associated With the powdery mildew disease of mango. The air- borne conidia are released from the old tissue harboring the dormant fungal hyphae under favorable weather conditions, which produce the disease. Proper forecasting of release of airborne innoculum significantly reduced the required number of sprays needed for chemical control. Spore traps were used to monitor the concentration of airborne conidia during the months of February, March and April 1996 and l997. Daily temperature and relative humidity were noted and the spore counts from the spore traps were correlated to the meteorological data. There was a positive trend between rising temperature, lowering relative humidity and number of spores in the air alter a low temperature, high humidity and cloudy spell of weather. The maximum spore occurrences were noted around 25°C and relative humidity of 40-60%. It took 5-8 days for the emergence of disease symptoms after the first detection of airborne conidia. Ten fungicides were tested on three mango varieties (Langra, Dashehari, and Anwar Retol). The preventive sprays at the stage of first detection of air born conidia were effective in controlling the disease. Optimal timing of two sprays of fungicide were sufficient to provide preventive control (>90%). The susceptibility of inflorescence varied with its developmental stage. Proper forecasting reduced the number of sprays from 7 to 2 or 3. There was no varietal difference in incidence of the disease or response to fungicide applications. During the course of this study, we identified seedling plants which consistently showed resistance to powdery mildew.

scholarly journals Effect of meteorological factors on the activity of influenza in Chongqing, China, 2012–2019

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246023
Author(s):  
Li Qi ◽  
Tian Liu ◽  
Yuan Gao ◽  
Dechao Tian ◽  
Wenge Tang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Meteorological Factors ◽  
Meteorological Data ◽  
Warning System ◽  
Absolute Humidity ◽  
Influenza Surveillance ◽  
Average Wind Speed ◽  
Mean Temperature ◽  
Influenza Activity

Background The effects of multiple meteorological factors on influenza activity remain unclear in Chongqing, the largest municipality in China. We aimed to fix this gap in this study. Methods Weekly meteorological data and influenza surveillance data in Chongqing were collected from 2012 to 2019. Distributed lag nonlinear models (DLNMs) were conducted to estimate the effects of multiple meteorological factors on influenza activity. Results Inverted J-shaped nonlinear associations between mean temperature, absolute humidity, wind speed, sunshine and influenza activity were found. The relative risks (RRs) of influenza activity increased as weekly average mean temperature fell below 18.18°C, average absolute humidity fell below 12.66 g/m3, average wind speed fell below 1.55 m/s and average sunshine fell below 2.36 hours. Taking the median values as the references, lower temperature, lower absolute humidity and windless could significantly increase the risks of influenza activity and last for 4 weeks. A J-shaped nonlinear association was observed between relative humidity and influenza activity; the risk of influenza activity increased with rising relative humidity with 78.26% as the break point. Taking the median value as the reference, high relative humidity could increase the risk of influenza activity and last for 3 weeks. In addition, we found the relationship between aggregate rainfall and influenza activity could be described with a U-shaped curve. Rainfall effect has significantly higher RR than rainless effect. Conclusions Our study shows that multiple meteorological factors have strong associations with influenza activity in Chongqing, providing evidence for developing a meteorology-based early warning system for influenza to facilitate timely response to upsurge of influenza activity.

scholarly journals Time Series and Empirical Orthogonal Transformation Using Meteorological Parameters across the Climatic Zones in Nigeria

2019 ◽  
pp. 257-272
Author(s):  
D. O. Akpootu ◽  
B. I. Tijjani ◽  
U. M. Gana
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Meteorological Parameters ◽  
Orthogonal Transformation ◽  
Global Solar Radiation ◽  
Climatic Zones ◽  
Sunshine Hours ◽  
Dry Seasons ◽  
The Mean

Time series and empirical orthogonal transformation analysis was carried out for four (4) selected tropical sites, which are situated across the four different climatic zones, viz. Sahelian, Midland, Guinea savannah and Coastal region in Nigeria using measured monthly average daily global solar radiation, maximum and minimum temperatures, sunshine hours, rainfall, wind speed, cloud cover and relative humidity meteorological data during the period of thirty one years (1980-2010). Seasonal Auto Regressive Integrated Moving Average (ARIMA) models were developed along with their respective statistical indicators of coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). The results indicated that the models were found suitable for one step ahead global solar radiation forecast for the studied locations. Furthermore, the results of the time series analysis revealed that the model type for all the meteorological parameters show a combination of simple seasonal with one or more of either ARIMA, winter’s additive and winter’s multiplicative with the level been more significant as compared to the trend and seasonal variations for the exponential smoothing model parameters in all the locations. The results of the correlation matrix revealed that the global solar radiation is more correlated to the mean temperature except for Akure where it is more correlated to the sunshine hours; the mean temperature is more correlated to the global solar radiation; the rainfall is more correlated to the relative humidity and the relative humidity is more correlated to the rainfall in all the locations. The results of the component matrix revealed that three seasons are identified in Nguru located in the Sahelian region namely, the rainy, the cool dry (harmattan) and the hot dry seasons while in Zaria, Makurdi and Akure located in the Midland, Guinea savannah and Coastal zones two distinct seasons are identified namely, the rainy and dry seasons.

scholarly journals Irrigation Mapping Using Sentinel-1 Time Series at Field Scale

2018 ◽  
Vol 10 (9) ◽  
pp. 1495 ◽  
Author(s):  
Qi Gao ◽  
Mehrez Zribi ◽  
Maria Escorihuela ◽  
Nicolas Baghdadi ◽  
Pere Segui
Keyword(s):  
Time Series ◽  
Weather Conditions ◽  
Mean Value ◽  
Support Vector ◽  
Irrigated Crops ◽  
The Mean ◽  
Sar Data ◽  
Ground Truthing ◽  
Agricultural Site

The recently launched Sentinel-1 satellite with a Synthetic Aperture Radar (SAR) sensor onboard offers a powerful tool for irrigation monitoring under various weather conditions, with high spatial and temporal resolution. This research discusses the potential of different metrics calculated from the Sentinel-1 time series for mapping irrigated fields. A methodology for irrigation mapping using SAR data is proposed. The study is performed using VV (vertical–vertical) and VH (vertical–horizontal) polarizations over an agricultural site in Urgell, Catalunya (Spain). With field segmentation information from SIGPAC (the Geographic Information System for Agricultural Parcels), the backscatter intensities are averaged within each field. From the Sentinel-1 time series for each field, the statistics and metrics, including the mean value, the variance of the signal, the correlation length, and the fractal dimension, are analyzed. With the Support Vector Machine (SVM), the classification of irrigated crops, irrigated trees, and non-irrigated fields is performed with the metrics vector. The results derived from the SVM are validated with ground truthing from SIGPAC over the whole study area, with a good overall accuracy of 81.08%. Random Forest (RF) machine classification is also tested in this study, which gives an accuracy of around 82.2% when setting the tree depth at three. The methodology is based only on SAR data, which makes it applicable to all areas, even with frequent cloud cover, but this method may be less robust when irrigation is less dominated to soil moisture change.

July

2019 ◽  
pp. 132-146
Author(s):  
Stephen Burt ◽  
Tim Burt
Keyword(s):  
Time Series ◽  
Detailed Analysis ◽  
Total Precipitation ◽  
Mean Temperature ◽  
The Mean ◽  
Weather Record ◽  
Extremes Of Temperature

Chapter 13 provides a detailed analysis of the long weather record at the Radcliffe Observatory, Oxford for July. Averages and extremes of temperature, precipitation and sunshine are presented, with coverage relevant to the month or season including the incidence of snowfall, thunderstorms, gales and the like, illustrated by contemporary accounts and photography. Each chapter ends with a complete time series of the mean temperature, total precipitation and total sunshine for the month or season from the entire record, updated to 2018.

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