Time series analysis of the δ2H, δ18O and dexcess values in correlation with monthly temperature, relative humidity and precipitation in Râmnicu Vâlcea, Romania: 2012–2018

2020 ◽  
pp. SP507-2020-56
Author(s):  
Carmen Varlam ◽  
Octavian G. Duliu ◽  
Roxana Elena Ionete ◽  
Diana Costinel
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Time Series Analysis ◽  
Series Analysis ◽  
Monthly Temperature

scholarly journals Forecasting Malaria Cases Using Climatic Factors in Delhi, India: A Time Series Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Varun Kumar ◽  
Abha Mangal ◽  
Sanjeet Panesar ◽  
Geeta Yadav ◽  
Richa Talwar ◽  
...  
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Climatic Factors ◽  
Series Data ◽  
Maximum Temperature ◽  
Climatic Data ◽  
Training Centre ◽  
Series Analysis

Background. Malaria still remains a public health problem in developing countries and changing environmental and climatic factors pose the biggest challenge in fighting against the scourge of malaria. Therefore, the study was designed to forecast malaria cases using climatic factors as predictors in Delhi, India. Methods. The total number of monthly cases of malaria slide positives occurring from January 2006 to December 2013 was taken from the register maintained at the malaria clinic at Rural Health Training Centre (RHTC), Najafgarh, Delhi. Climatic data of monthly mean rainfall, relative humidity, and mean maximum temperature were taken from Regional Meteorological Centre, Delhi. Expert modeler of SPSS ver. 21 was used for analyzing the time series data. Results. Autoregressive integrated moving average, ARIMA (0,1,1) (0,1,0)12, was the best fit model and it could explain 72.5% variability in the time series data. Rainfall (P value = 0.004) and relative humidity (P value = 0.001) were found to be significant predictors for malaria transmission in the study area. Seasonal adjusted factor (SAF) for malaria cases shows peak during the months of August and September. Conclusion. ARIMA models of time series analysis is a simple and reliable tool for producing reliable forecasts for malaria in Delhi, India.

Weather Forecasting in Coastal Districts of Odisha and Andhra Pradesh by Using Time Series Analysis

2018 ◽  
Vol 6 (8) ◽  
pp. 85
Author(s):  
Naresh Patnaik ◽  
F Baliarsingh
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Time Series Analysis ◽  
Andhra Pradesh ◽  
Maximum Temperature ◽  
Climatic Parameters ◽  
Series Analysis ◽  
Annual Maximum Temperature

Climate change in world is always one of the most important topics in Water Resources. Now the issue is so predominant that it is gradually restricting out social life, peace and harmony. Climate change is a change in the statistical distribution of weather pattern of an area, when such changes occur for a long period of time. Weather is the state of atmosphere at a particular place and time. Climate is the long term statistical expression of short term weather. This study presents a comprehensive assessment of the future climate pattern/weather prediction by taking different climatic parameters such as temperature, precipitation, solar radiation, wind speed and relative humidity by using time series analysis. The study area of research work covers the coastal districts of Odisha and some parts of Andhra Pradesh. The climatic parameters are collected over last 20 years (1993-2013) from the selected 10 stations and the prediction is made using Time Series Analysis (ARIMA Model). The annual maximum temperature, solar radiation of all districts indicates a statistically significant increase in trend, whereas in the case of wind speed and relative humidity indicates significant deceasing trend. The annual rain fall shows an increasing trend of 2.69 mm/year in all station except Srikakulam, Khordha, Jagatsinghpur and Balasore which shows a decreasing trend of 1.94, 1.29, 0.56 and 1.18 mm/year respectively. As a whole the annual maximum temperature and solar radiation shows an increase trend of 0.16 ⁰C and 0.073 MJ/m² per year respectively. Further the wind speed and relative humidity of all stations indicates a decreasing trend of 0.056 m/s and 0.003(Units in fraction) per year respectively.

scholarly journals Time Series Analysis of Climate and Air Pollution Factors Associated with Atmospheric Nitrogen Dioxide Concentration in Japan

2020 ◽  
Vol 17 (24) ◽  
pp. 9507
Author(s):  
Takeshi Miyama ◽  
Hiroshi Matsui ◽  
Kenichi Azuma ◽  
Chika Minejima ◽  
Yasuyuki Itano ◽  
...  
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Relative Humidity ◽  
Time Series Analysis ◽  
Nitrogen Dioxide ◽  
Rural Areas ◽  
Air Pollutant ◽  
Climate Conditions ◽  
Series Analysis ◽  
Urban Rural

Nitrogen dioxide (NO2) is an air pollutant discharged from combustion of human activities. Nitrous acid (HONO), measured as NO2, is thought to impact respiratory function more than NO2. HONO and NO2 have an equilibrium relationship, and their reaction is affected by climate conditions. This study was conducted to discuss the extent of HONO contained in NO2, depending on the level of urbanization. Whether climate conditions that promote HONO production enhanced the level of NO2 measured was investigated using time series analysis. Climate and outdoor air pollution data measured in April 2009–March 2017 in urban (Tokyo, Osaka, and Aichi) and rural (Yamanashi) areas in Japan were used for the analysis. Air temperature had a trend of negative associations with NO2, which might indicate the decomposition of HONO in the equilibrium between HONO and NO2. The associations of relative humidity with NO2 did not have consistent trends by prefecture: humidity only in Yamanashi was positively associated with NO2. In high relative humidity conditions, the equilibrium goes towards HONO production, which was observed in Yamanashi, suggesting the proportion of HONO in NO2 might be low/high in urban/rural areas.

Chaoticity analysis of the current through pure, hydrogenated and hydrophobically modified PEG-Si thin films under varying relative humidity

Open Physics ◽  
2009 ◽  
Vol 7 (3) ◽  
Author(s):  
Kaan Atak ◽  
Ozgur Aybar ◽  
Gokhan Şahin ◽  
Avadis Hacınlıyan ◽  
Yani Skarlatos
Keyword(s):  
Thin Films ◽  
Time Series ◽  
Polyethylene Glycol ◽  
Lyapunov Exponent ◽  
Relative Humidity ◽  
Time Series Analysis ◽  
Maximal Lyapunov Exponent ◽  
Series Analysis ◽  
Hydrophobically Modified ◽  
Irregular Behavior

AbstractPolyethylene Glycol has an irregular current characteristic under constant voltage and slowly varying relative humidity. The current through a thin film of Gamma-isocyanatopropyltriethoxysilane added Polyethylene glycol (PEG-Si), its hydrogenated and hydrophobically modified forms, as a function of increasing relative humidity at equal time steps is analyzed for chaoticity. We suggest that the irregular behavior of current through PEG-Si thin films as a function of increasing relative humidity could best be analyzed for chaoticity using both time series analysis and detrended uctuation analysis; the relative humidity is kept as a slowly varying parameter. The presence of more then one regime is suggested by the calculation of the maximal Lyapunov exponents. Furthermore, the maximal Lyapunov exponent in each of the regimes was positive, thus confirming the presence of low dimensional chaos. DFA also confirms the presence of at least two different regimes, in agreement with the behavior of the maximal Lyapunov exponent in the time series analysis. We also suggest that the irregular behavior of the current through PEG-Si can be reduced by hydrogenating and hydrophobically modifying PEG-Si and the improvement in stability can be confirmed by our study.

Effects of weather variability on infectious gastroenteritis

2009 ◽  
Vol 138 (2) ◽  
pp. 236-243 ◽  
Author(s):  
D. ONOZUKA ◽  
M. HASHIZUME ◽  
A. HAGIHARA
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Time Series Analysis ◽  
Gastrointestinal Disease ◽  
Confounding Factors ◽  
Weather Variability ◽  
Average Temperature ◽  
Series Analysis ◽  
Infectious Gastroenteritis

SUMMARYAlthough multiple combinations of weather variability may contribute to an increased incidence of infectious gastrointestinal disease, few studies have investigated the association between weather variability and cases of infectious gastroenteritis. We acquired data for infectious gastroenteritis cases and weather variability in Fukuoka, Japan, from 1999 to 2007 and used time-series analysis to assess the effects of weather variability on infectious gastroenteritis cases, adjusting for confounding factors. In total, 422 176 infectious gastroenteritis cases were reported during the 9-year study period. The weekly number of infectious gastroenteritis cases increased by 7·7% (95% CI 4·6–10·8) for every 1°C increase in the average temperature and by 2·3% (95% CI 1·4–3·1) for every 1% decrease in relative humidity. From 1999 to 2007, infectious gastroenteritis cases increased significantly with increased average temperature and decreased relative humidity in Fukuoka, Japan.

scholarly journals Impact of Ambient Temperature and Relative Humidity on the Incidence of Hand-Foot-Mouth Disease in Wuhan, China

2020 ◽  
Vol 17 (2) ◽  
pp. 428 ◽  
Author(s):  
Jiayuan Hao ◽  
Zhiyi Yang ◽  
Wenwen Yang ◽  
Shuqiong Huang ◽  
Liqiao Tian ◽  
...  
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Relative Humidity ◽  
Time Series Analysis ◽  
Meteorological Factors ◽  
Mouth Disease ◽  
Series Analysis ◽  
Non Linear ◽  
Effects Of Temperature ◽  
The Relationship

Background: Few studies have previously explored the relationship between hand, foot, and mouth disease (HFMD) and meteorological factors with the effect modification of air pollution, and these studies had inconsistent findings. We therefore applied a time-series analysis assessing the effects of temperature and humidity on the incidence of HFMD in Wuhan, China to deepen our understanding of the relationship between meteorological factors and the risk of HFMD. Methods: Daily HFMD cases were retrieved from Hubei Provincial Center for Disease Control and Prevention from 1 February 2013 to 31 January 2017. Daily meteorological data including 24 h average temperature, relative humidity, wind velocity, and atmospheric pressure were obtained from Hubei Meteorological Bureau. Data on Air pollution was collected from 10 national air-monitoring stations in Wuhan city. We adopted a distributed lag non-linear model (DLNM) combined with Poisson regression and time-series analysis to estimate the effects of temperature and relative humidity on the incidence HFMD. Results: We found that the association between temperature and HFMD incidence was non-linear, exhibiting an approximate “M” shape with two peaks occurring at 2.3 °C (RR = 1.760, 95% CI: 1.218–2.542) and 27.9 °C (RR = 1.945, 95% CI: 1.570–2.408), respectively. We observed an inverted “V” shape between relative humidity and HFMD. The risk of HFMD reached a maximum value at a relative humidity of 89.2% (RR = 1.553, 95% CI: 1.322–1.824). The largest delayed cumulative effects occurred at lag 6 for temperature and lag 13 for relative humidity. Conclusions: The non-linear relationship between meteorological factors and the incidence of HFMD on different lag days could be used in the early targeted warning system of infectious diseases, reducing the possible outbreaks and burdens of HFMD among sensitive populations.

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