The TCIs (tourism climate indices) are applications of the human comfort in climate health tourism. TCIs aim to characterize the climate and environmental status of tourist destinations and provide a comprehensive measure of tourists’ climatic well-being. In this study, the Web of Science and China National Knowledge Infrastructure (CNKI) databases were searched for visualization analysis, explained the connotation of the TCIs and human comfort index, summarized the historical evolution of TCIs and the human comfort index, and discussed the existing problems and countermeasures of TCIs researches. In order to provide theoretical support for the in-depth research and sustainable development of TCIs in the future. The results are as follows: (1) The English studies were more concerned with tourism satisfaction and competitiveness, whereas the Chinese studies were more concerned with human comfort, climate comfort, and climate tourism. (2) Human comfort indices were primarily composed of meteorological factors and clothing insulation, which aims to reflect the human body's feeling of environmental comfort. The TCIs not only covers meteorological factors and aesthetic factors, but also considers health factors such as air quality and oxygen content, aiming to comprehensively evaluate the suitability of tourism activities and the effect of climate health of travelers.
In order to find out the relationship between temperature and the number of hospitalized patients with respiratory diseases, relevant departments reasonably implement disease prevention and early warning in county-level cities in Liaoning Province. Based on the daily meteorological observation data and respiratory system hospitalization data of Beipiao City and Xifeng County in Liaoning Province from 2016 to 2018, the seasonal distribution characteristics and age distribution characteristics of the number of hospitalizations with respiratory system were analyzed. On this basis, the generalized additive model (GAM) and the distributed lag non-linear model (DLNM) were used to explore the effect of temperature on the number of hospitalizations for respiratory diseases, and the results were stratified by sex and age. Modeling using attributable fractions (AF) to quantify the disease risk of exposure to specific temperature ranges (eg, extreme cold temperature, moderate cold temperature, moderate hot temperature, extreme hot temperature). The results show that the annual peak of the number of hospitalizations for respiratory diseases in the two places occurs in winter and spring, and the majority of the sick people are children and the elderly. The optimal temperatures for people in Beipiao City and Xifeng County are 26.2 ℃ and 22.2 ℃, respectively. The impact of temperature on the incidence of respiratory diseases is dominated by the lag effect of cold temperature, and the immediate effect of hot temperature is not significant. In Beipiao City and Xifeng County, 27.0% (95% confidence interval: 20.3%-32.9%) and 29.0% (95% confidence interval: 22.1%-35.0%) of respiratory system hospitalizations were attributed to temperature, respectively, and the disease risk was mainly moderate cold temperature. The incidence of Beipiao City and Xifeng County was attributed to moderate cold temperature accounted for 25.9% (95% confidence interval: 19.5%-31.5%) and 28.1% (95% confidence interval: 21.5%-33.9%), respectively. In terms of age distribution, compared with the adult group, the percentage of diseases attributable to moderate cold is higher in the children group and the elderly group. In addition, the elderly group is also more sensitive to extreme low temperatures. In terms of gender stratification, women are more susceptible to low temperature than men. The temperature of the two county-level cities in Liaoning Province has different effects on different groups of people, the risk of disease caused by temperature on women and elderly residents is high.
Based on daily conventional observation data at 19 meteorological stations of Hainan Island from 1980 to 2018, the climatic season in Hainan Island was divided according to China’s meteorological industry standard (QX/T152-2012). Then, the comfortable degree of human body was calculated by using sensible temperature of human body based on ‘golden ratio’ method. And on this basis the comprehensive division of human body comfortable degree was done in each season in Hainan Island by using rotated empirical orthogonal function (REOF), further the optimum comfortable zones of climatotherapy rehabilitation were obtained in Hainan Island. The results are as follows: (1) The climate was mild and moist in Hainan Island, and the annual average temperature was 22.9 to 25.3 ℃, the average annual precipitation was 1157 to 2615 mm, the annual average relative humidity was 74% to 86%, and the annual average specific humidity was 14.8 to 16.4 g·kg-1, which was suitable to rehabilitation. (2) Compared with the conventional climate statistical method, the meteorological industry standard was more in accordance with season division in Hainan Island. According to the climate division, the summer was from mid-March to mid-November in Hainan Island, the autumn and spring were from mid-November to next mid-March, which indicated that there wasn’t winter in Hainan Island, and the optimum period of climatotherapy rehabilitation appeared in spring and autumn. (3) The comfortable region of human body in spring and autumn located in northern Hainan, central Hainan and southern Hainan. Overall, the climate in central Hainan was the most optimal, and the climate in three regions was stable from 1980 to 2018.
The daily number of patients with upper respiratory tract infection (URI) in 49 community health service centers in Luohu of Shenzhen from 2014 to 2018 and meteorological data in the same period were collected to analyze the changing characteristics of the number of patients with URI at different time scales. The distributed lag non-linear model (DLNM) and generalized linear model (GLM) were used to study the relationship between different meteorological factors and the number of patients with URI in Shenzhen. The results show that there was a significant seasonal change for the number of patients with URI in Luohu of Shenzhen. The peak periods of cases were March to April in spring, July in summer and December to next January in winter, corresponding to Pure Brightness, Lesser Heat and Lesser Cold of the 24 solar terms, respectively. The DLNM model showed that air temperature was the main impact factor, and its effect on the number of patients with URI presented mainly cold effect, the relative risk (RR) reached the peak after 4 days lag (RR was equal to 1.041, the 95% confidence interval was between 1.022 and 1.060). Women were more affected by cold effect than men, and middle-aged and elderly people were more affected by cold effect than children. Another important factors were thermal effect in summer and the variable temperature in spring. The influence of humidity presented mainly low humidity effect, and the relative risk reached the peak (RR was equal to 1.058, the 95% confidence interval was between 1.049 and 1.068) on the same day. Pressure and wind speed showed high pressure effect and strong wind effect, and RR reached the highest after 1 day lag. In conclusion, cold air activities in winter and spring and their cold effects such as low temperature, low humidity and strong wind were the key factors to induce URI, followed by the impact of continuous high temperature in summer, both of them should be focused on timely prevention.
By using the data of the visibility observations and NCEP reanalysis data during 2002 - 2011,the processes of low visibility weather in North China has been studied.The results show that the circulation situation on 500 hPa that caused low visibility weather in studied area could be divided into three types including two troughs and a ridge type,low trough type and zonal flow type,and the automatic identification system could be established to recognize the three weather types and eliminate other situations from these three types. On this basis,the use of K - index,the dew point temperature difference and pseudo-equivalent potential temperature difference between 500 hPa and 850 hPa gave a further physical diagnosis,and at last the forecast of low visibility weather could be obtained. The low visibility weathers occurring in North China in 2012 were forecasted by using the method mentioned above,the results show that the prediction effect of this forecast method was fine.
Based on the conventional observations,NCEP / NCAR reanalysis data,FY - 2E satellite images and radar data,the synoptic analysis and physical field diagnostic analysis methods were used to explore the triggering mechanisms and vapor sources of the heavy rainstorm that occurred in Northwest China. By analyzing circulation patterns,vapor,dynamic and unstable conditions,the results show that the southwestern low level jet and the eastern low level jet played significant roles during the heavy rainfall process,they brought abundant water vapor and unstable capability. Forward trough and warming humidification in the low layer made stratification convective instability.The overlay of convergence line on 700 hPa and the shear line on 850 hPa was the triggering condition,which resulted in the release of unstable capability and induced the heavy rainstorm. Meanwhile,convergence at low level and divergence at upper - level formed upward motion over the entire layer,which was the dynamic condition.The study not only provides focus for forecasting heavy rainstorms in arid Northwest China,but also supplies references to control flood and mitigate disasters for local government.