scholarly journals Impact of Meteorological Factors and Southern Oscillation Index on Scrub Typhus Incidence in Guangzhou, Southern China, 2006–2018

2021 ◽  
Vol 8 ◽  
Author(s):  
Jianyun Lu ◽  
Yanhui Liu ◽  
Xiaowei Ma ◽  
Meixia Li ◽  
Zhicong Yang
Keyword(s):  
High Risk ◽  
Relative Humidity ◽  
Southern Oscillation Index ◽  
Scrub Typhus ◽  
Meteorological Factors ◽  
Southern Oscillation ◽  
Southern China ◽  
Meteorological Variables ◽  
Linear Relationships ◽  
Non Linear

Background: Scrub typhus was epidemic in the western Pacific Ocean area and East Asia, scrub typhus epidemic in densely populated areas in southern China. To better understand the association between meteorological variables, Southern Oscillation Index (SOI), and scrub typhus incidence in Guangzhou was benefit to the control and prevention.Methodology/Principal Findings: We collected weekly data for scrub typhus cases and meteorological variables in Guangzhou, and Southern Oscillation Index from 2006 to 2018, and used the distributed lag non-linear models to evaluate the relationships between meteorological variables, SOI and scrub typhus. The median value of each variable was set as the reference. The high-risk occupations were farmer (51.10%), house worker (17.51%), and retiree (6.29%). The non-linear relationships were observed with different lag weeks. For example, when the mean temperature was 27.7°C with1-week lag, the relative risk (RR) was highest as 1.08 (95% CI: 1.01–1.17). The risk was the highest when the relative humidity was 92.0% with 9-week lag, with the RR of 1.10 (95% CI: 1.02–1.19). For aggregate rainfall, the highest RR was 1.06 (95% CI: 1.03–1.11), when it was 83.0 mm with 4-week lag. When the SOI was 19 with 11-week lag, the highest RR was 1.06 (95% CI: 1.01–1.12). Most of the extreme effects of SOI and meteorological factors on scrub typical cases were statistically significant.Conclusion/Significance: The high-risk occupations of scrub typhus in Guangzhou were farmer, house worker, and retiree. Meteorological factors and SOI played an important role in scrub typhus occurrence in Guangzhou. Non-linear relationships were observed in almost all the variables in our study. Approximately, mean temperature, and relative humidity positively correlated to the incidence of scrub typhus, on the contrary to atmospheric pressure and weekly temperature range (WTR). Aggregate rainfall and wind velocity showed an inverse-U curve, whereas the SOI appeared the bimodal distribution. These findings can be helpful to facilitate the development of the early warning system to prevent the scrub typhus.

scholarly journals Meteorological Factors and Swine Erysipelas Transmission in Southern China

2020 ◽  
Vol 70 (1) ◽  
pp. 37-50
Author(s):  
Qin Hong-Yu ◽  
Xin Xiu ◽  
Sha Wanli ◽  
Wang Ben ◽  
Hu Xiansheng ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Meteorological Factors ◽  
Southern Oscillation ◽  
Southern China ◽  
Meteorological Variables ◽  
Maximum Temperature ◽  
Economic Losses ◽  
Lag Effects ◽  
Cross Correlation Analysis ◽  
Swine Erysipelas

AbstractSwine erysipelas (SE) is one of the best-known and most serious diseases that affect domestic pigs, which is caused by Erysipelothrix rhusiopathiae. It is endemic in Nanning and has been circulating for decades, causing considerable economic losses. The aim of this study was to investigate the effect of meteorological-related variations on the epidemiology of swine erysipelas in Nanning City, a subtropical city of China. Data on monthly counts of reported swine erysipelas and climate data in Nanning are provided by the authorities over the period from 2006 to 2015. Cross-correlation analysis was applied to identify the lag effects of meteorological variables. A zero-inflated negative binomial (ZINB) regression model was used to evaluate the independent contribution of meteorological factors to SE transmission. After controlling seasonality, autocorrelation and lag effects, the results of the model indicated that Southern Oscillation Index (SOI) has a positive effect on SE transmission. Moreover, there is a positive correlation between monthly mean maximum temperature and relative humidity at 0-1 month lag and the number of cases. Furthermore, there is a positive association between the number of SE incidences and precipitation, with a lagged effect of 2 months. In contrast, monthly mean wind velocity negatively correlated with SE of the current month. These findings indicate that meteorological variables may play a significant role in SE transmission in southern China. Finally, more public health actions should be taken to prevent and control the increase of SE disease with consideration of local weather variations.

scholarly journals Characteristics and Drivers of Reference Evapotranspiration in Hilly Regions in Southern China

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1914 ◽  
Author(s):  
Liu ◽  
Liu ◽  
Chen ◽  
Labat ◽  
Li ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Reference Evapotranspiration ◽  
Southern Oscillation ◽  
Southern China ◽  
Sensitivity Coefficient ◽  
Meteorological Variables ◽  
The West ◽  
Average Temperature ◽  
Sunshine Hours ◽  
The Impact

This paper has adopted related meteorological data collected by 69 meteorological stations between 1951 and 2013 to analyze changes and drivers of reference evapotranspiration (ET0) in the hilly regions located in southern China. Results show that: (1) ET0 in southern China’s hilly regions reaches its maximum in summer and its minimum in winter, and that the annual ET0 shows an increasing trend. ET0 happened abrupt change due to the impact of abrupt meteorological variables changes, and the significant year of mutation were 1953, 1964 and 2008. Most abrupt changes of ET0 in meteorological stations occurred in the 1950s and 1960s. (2) The low value of ET0 was mainly captured in high-altitude areas. Spatially, the ET0 in the east was higher than that in the west. With the exception of a handful of stations, the trend coefficients of ET0 were all positive, exhibiting a gradual rise. Changes in ET0 in the east were much more sensitive than that in the west. Since ET0 was affected by the cyclical changes in relative humidity, short-period oscillations were observed in all these changes. (3) In general, the ET0 was negatively correlated with relative humidity, and positively correlated with temperature and sunshine percentage. ET0 is most sensitive to changes in average temperature, with a sensitivity coefficient of 1.136. ET0 showed positive sensitivity to average temperature and sunshine hours, which were notable in the northeastern, and uniform in the spatial. ET0 showed negatively sensitivity to relative humidity, and the absolute value of sensitivity coefficient in the northwestern is smaller. The highest contribution to ET0 is the average temperature (6.873%), and the total contribution of the four meteorological variables to the change of ET0 is 7.842%. The contribution of average temperature, relative humidity, and sunshine hours to ET0 is higher in the northern and eastern, northern, northern and eastern areas, respectively. Climate indexes (Western Pacific Index (WP), Southern Oscillation Index (SOI), Tropical Northern Atlantic Index (TNA), and El Niño-Southern Oscillation (ENSO)) were correlated with the ET0. In addition, the ET0 and altitude, as well as the latitude and longitude were also correlated with each other.

scholarly journals Short-term effects of ambient temperature and relative humidity on the risk of COVID-19 and SARS in Guangzhou, China: A time-series analysis

2020 ◽  
Author(s):  
Hui Wang ◽  
Chun Chen ◽  
Qiaoxuan Lin ◽  
Tiegang Li
Keyword(s):  
Relative Humidity ◽  
Ambient Temperature ◽  
Meteorological Factors ◽  
Severe Disease ◽  
Early Warning Systems ◽  
Optimal Range ◽  
Distributed Lag ◽  
Mean Temperature ◽  
Non Linear ◽  
Coronavirus Infection

Abstract Coronavirus infection has exerted a severe disease burden on the world, especially the newly emerged SARS-CoV-2 that has caused worldwide pandemic. It is possible meteorological factors can influence the transmission of coronavirus. The aim of this study was to evaluate the effect of meteorological factors on COVID-19 and SARS, and to provide evidence for disease control and prevention. Data of COVID-19 and SARS cases and daily mean temperature, relative humidity and other meteorological factors in Guangzhou in 2003 and 2020 were collected. Using a distributed lag non-linear model approach, we assessed the relationship between ambient temperature, relative humidity and the risks of COVID-19 and SARS. The numbers of cases for COVID-19 and SARS during the study period were 347 and 1072, respectively. There was a dome-shaped relation between mean temperature and COVID-19, with a threshold of 14.50°C (RR=1.48, 95%CI: 1.01, 2.16) and the optimal range was 12.40-16.40°C. A similar association was found between mean temperature and SARS occurrence, with a threshold of 18.40°C (RR=1.02, 95%CI: 1.00, 1.04), and the optimal range was 15.30-19.30°C. Besides, there were non-linear negative relationships between both RH and COVID-19, SARS. In addition, the largest overall effect of RH on COVID-19 and SARS were obtained at 52% and 45%, yielding relative risk of 7.47 (95%CI: 1.66, 33.55) and 47.56 (95%CI: 11.49, 196.95), respectively. The optimal ranges were below 77.00% and below 82.70%, respectively. Meteorological parameters should be taken into consideration while developing early warning systems and risk strategies for controlling and preventing coronavirus infection.

scholarly journals Synoptic meteorological modes of variability for fine particulate matter (PM<sub>2.5</sub>) air quality in major metropolitan regions of China

2018 ◽  
Vol 18 (9) ◽  
pp. 6733-6748 ◽  
Author(s):  
Danny M. Leung ◽  
Amos P. K. Tai ◽  
Loretta J. Mickley ◽  
Jonathan M. Moch ◽  
Aaron van Donkelaar ◽  
...  
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Negative Correlation ◽  
Southern China ◽  
Central China ◽  
Meteorological Variables ◽  
Strong Correlations ◽  
Siberian High ◽  
Synoptic Weather ◽  
Bth Region

Abstract. In his study, we use a combination of multivariate statistical methods to understand the relationships of PM2.5 with local meteorology and synoptic weather patterns in different regions of China across various timescales. Using June 2014 to May 2017 daily total PM2.5 observations from ∼ 1500 monitors, all deseasonalized and detrended to focus on synoptic-scale variations, we find strong correlations of daily PM2.5 with all selected meteorological variables (e.g., positive correlation with temperature but negative correlation with sea-level pressure throughout China; positive and negative correlation with relative humidity in northern and southern China, respectively). The spatial patterns suggest that the apparent correlations with individual meteorological variables may arise from common association with synoptic systems. Based on a principal component analysis of 1998–2017 meteorological data to diagnose distinct meteorological modes that dominate synoptic weather in four major regions of China, we find strong correlations of PM2.5 with several synoptic modes that explain 10 to 40 % of daily PM2.5 variability. These modes include monsoonal flows and cold frontal passages in northern and central China associated with the Siberian High, onshore flows in eastern China, and frontal rainstorms in southern China. Using the Beijing–Tianjin–Hebei (BTH) region as a case study, we further find strong interannual correlations of regionally averaged satellite-derived annual mean PM2.5 with annual mean relative humidity (RH; positive) and springtime fluctuation frequency of the Siberian High (negative). We apply the resulting PM2.5-to-climate sensitivities to the Intergovernmental Panel on Climate Change (IPCC) Coupled Model Intercomparison Project Phase 5 (CMIP5) climate projections to predict future PM2.5 by the 2050s due to climate change, and find a modest decrease of ∼ 0.5 µg m−3 in annual mean PM2.5 in the BTH region due to more frequent cold frontal ventilation under the RCP8.5 future, representing a small “climate benefit”, but the RH-induced PM2.5 change is inconclusive due to the large inter-model differences in RH projections.

scholarly journals On the progress of the 2015–2016 El Niño event

2016 ◽  
Vol 16 (4) ◽  
pp. 2007-2011 ◽  
Author(s):  
Costas A. Varotsos ◽  
Chris G. Tzanis ◽  
Nicholas V. Sarlis
Keyword(s):  
Time Series ◽  
El Niño ◽  
Southern Oscillation Index ◽  
El Nino ◽  
Southern Oscillation ◽  
Linear Dynamics ◽  
El Niño Event ◽  
Non Linear ◽  
El Niño Events ◽  
Index Time Series

Abstract. It has been recently reported that the current 2015–2016 El Niño could become "one of the strongest on record". To further explore this claim, we performed the new analysis described in detail in Varotsos et al. (2015) that allows the detection of precursory signals of the strong El Niño events by using a recently developed non-linear dynamics tool. In this context, the analysis of the Southern Oscillation Index time series for the period 1876–2015 shows that the running 2015–2016 El Niño would be rather a "moderate to strong" or even a "strong" event and not “one of the strongest on record", as that of 1997–1998.

scholarly journals Meteorological factors and risk of scrub typhus in Guangzhou, southern China, 2006–2012

2014 ◽  
Vol 14 (1) ◽  
Author(s):  
Tiegang Li ◽  
Zhicong Yang ◽  
Zhiqiang Dong ◽  
Ming Wang
Keyword(s):  
Scrub Typhus ◽  
Meteorological Factors ◽  
Southern China

Non-linear relationships and interactions of meteorological factors on mumps in Jinan, China

2020 ◽  
Author(s):  
Shaoqian Lin ◽  
Shiman Ruan ◽  
Xingyi Geng ◽  
Kaijun Song ◽  
Liangliang Cui ◽  
...  
Keyword(s):  
Meteorological Factors ◽  
Linear Relationships ◽  
Non Linear

scholarly journals The non-linear impact of El Nino, La Nina and the Southern Oscillation on seasonal and regional Australian precipitation

2017 ◽  
Vol 67 (1) ◽  
pp. 25
Author(s):  
Christine T. Y. Chung ◽  
Scott B. Power
Keyword(s):  
Time Scales ◽  
El Niño ◽  
Southern Oscillation Index ◽  
El Nino ◽  
Southern Oscillation ◽  
La Niña ◽  
La Nina ◽  
Non Linear ◽  
The Relationship ◽  
Enso Indices

The relationship between El Niño-Southern Oscillation (ENSO) indices and precipitation (P) in some parts of Australia has previously been shown to be non-linear on annual and seasonal time scales. Here we examine the relationship between P and the Southern Oscillation Index (SOI) at all Australian locations and in all seasons. We show that in many Australian regions, there is more-than-expected P during strong La Niña years (SOI&gt;13), but less-than-expected drying during strong El Niño years (SOI

scholarly journals Spatiotemporal Relationships between Air Quality and Multiple Meteorological Parameters in 221 Chinese Cities

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-25 ◽  
Author(s):  
Mengyi Ji ◽  
Yuying Jiang ◽  
Xiping Han ◽  
Luo Liu ◽  
Xinliang Xu ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Relative Humidity ◽  
Meteorological Factors ◽  
Southern China ◽  
Great Influence ◽  
Pollution Prevention ◽  
Spatial And Temporal Variation ◽  
Factors Affecting ◽  
Temporal Differences

Air quality in China is characterized by significant spatial and temporal differences, which are directly related to local meteorological conditions. This study used air quality monitoring data, namely, the air pollution index (API) and air quality index (AQI) between 2005 and 2018, together with meteorological data and identified key meteorological factors that affected the spatial and temporal variation of air quality using a random forest algorithm. The spatial and temporal differences in the threshold values of different meteorological factors affecting the concentrations of PM2.5, PM10, SO2, CO, NO2, and O3 were identified. The AQI has the advantages of facilitating higher index values than the API. The air quality showed an improvement from 2005 to 2018. Wind direction and precipitation were the most important meteorological factors affecting the air quality in northern and southern China, respectively, which to some extent reflected the causes and degradation mechanisms of air pollution in the two regions. There were significant spatial and temporal differences in the effects of meteorological factors on the concentrations of different pollutants. The influence of atmospheric pressure on pollutant concentration differed between the east and west. Precipitation and relative humidity in most cities had significant impacts on PM2.5 and PM10. The influence of relative humidity was most significant for SO2 and it also had a great influence on O3, while wind speed had a great influence on NO2. The results of the study confirm the meteorological sensitivity of air quality and provide support for the implementation of regional air pollution prevention and control initiatives.

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