Study On The Seasonal Effects Of Temperature Fluctuations On Air Quality And Respiratory Disease

Understanding the impact of temperature fluctuations on air quality and public health has gained popularity among environmental and epidemiological researchers. The decrease and increase of temperature between neighboring days will have obvious influence on the air quality and public health. At the same time, time series analysis is generally applied to estimate the relationship between short term health effects of air pollutants, weather fluctuations and other time-varying confounders. Generalized additive model(GAM) is one of the most common statistical approaches for the time series analysis. GAM can describe nonlinear effects over time and the analysis result of GAM could be easily interpreted due to its additive structure.In this study, we used GAM with natural spline function to estimate the effect of temperature change(TC) on air quality index(AQI) and respiratory disease(RD) in Beijing during 2008-2012. Based on GAM, the proposed study covers the detailed research in the following perspectives:(1) we investigate risk effects of small, large and extremely large temperature fluctuations on AQI and RD considering seasonal effects, maximum and minimum TC;(2) At the same time, investigating TC-AQI and TC-RD relationship on lag day’s structure is another important dimension of the proposed research;(3) Furthermore, our study also investigate the seasonal effects of TC on RD stratified by genders and age groups on the same day and lag day’s structure. For the implementation of model, required analysis was conducted in R 2.14.1 using mgcv package to achieve the acquired results.In the first part, based on GAM, the study aims to examine the seasonal effects of temperature fluctuations on AQI and RD during 2008-2012 in Beijing. The results show that the impact of decrease and increase of temperature on AQI and RD varies in different seasons. A large decrease of temperature results in the increase of AQI and RD only in the winter season. Compared with small and large decrease of temperature, extremely large decrease of temperature(>7o C) results in the largest impact on AQI in the summer and winter season. Furthermore, compared with small and large increase of temperature, extremely large increase of temperature(>7o C) also results in the largest influence on AQI and RD in the summer and winter season. Our results suggest that the air quality and public health in Beijing are significantly influenced by decrease and increase of temperature in different seasons.In the second part, based on GAM the study aims to examine the impacts of TC on AQI and RD over lag days. We investigate the lag based risk impacts of small, large and extremely large temperature fluctuations on AQI and RD considering seasonal effects. We studied the association of temperature fluctuations with a single day exposure of lag 1-6 days and multiple days’ exposure of lag 01- 06 days. Lag based impacts of small, large and extremely large temperature fluctuations on AQI and RD varies in different seasons. Compared with small and large decrease of temperature, extremely large decrease of temperature results in the largest increased impact on AQI in the summer and winter season over lag day’s structure. Compared with small and large increase of temperature, extremely large increase of temperature also results in the largest influence on AQI and RD in both summer and winter season over most of the lag days. Our results suggest that the air quality is hazardously influenced by both decrease and increase of temperature in the summer and winter season over lag day’s structure. Public health in Beijing is hazardously influenced by increase of temperature and is protectively influenced by decrease of temperature in the summer and winter season over lag day’s structure.In the final part of presented research, based on GAM, we investigate the risk impacts for small, large and extremely large temperature fluctuations on RD stratified by genders and age groups considering seasonal effects. At the same time, we also exploredthe lag based risk impacts of decrease and increase of temperature on genders and various age groups considering seasonal effects.The results show that the impact of decrease and increase of temperature on genders and age groups also differs in different seasons. Compared with small and large increase of temperature, we found the largest impact for extremely large increase of temperature on females in the summer season. The individual’s among 0-15, 16-30, 31-45, >75 age groups were observed vulnerable to the decrease of temperature especially in the winter season. Compared with small and large increase of temperature, we found the largest impact for extremely large increase of temperature on the individuals among 31-45, 56-60 and 61-75 age group in the summer and winter season respectively. Males and the individuals among 0-15, 16-30, 31-45, 51-55 61-75 age groups showed delayed impact for the increase and decrease of temperature over lag days in different seasons. Thus, our results suggest that the RD stratified by genders and various age groups are significantly influenced by decrease and increase of temperature not only on the same day but also over lag day’s structure in different seasons.Policy suggestions based on the research conlusions could be an important reference for the environmental and public health policy and could contribute for the improvement of air quality and public health in Beijing. Innovative points of the proposed study are as follows:? We would say that TC might be an alternative indicator for studying the temperature-environment and temperature-health relationship considering seasonal effects. Policies should be made to control both extremely large decrease and increase of temperature to alleviate the air quality problems and extremely large increase of temperature to alleviate the health issues during summer and winter season.? TC could be an indicator for studying the lag based temperature-environment and temperature-health relationship considering seasonal effects. For a large increase of temperature risk impact on AQI and RD are observed for long time period in the winter season. Furthermore, compared with small and large decrease of temperature, extremely large decrease of temperature results in the largest increased impact on AQI in the summer and winter season over lag day’s structure. Under these perspectives, policies should consider the increase and decrease of temperature to alleviate the air quality and public health issues in Beijing.? TC could also be an indicator for studying the temperature-health relationship stratified by genders and age groups over same day and lag days structure considering seasonal effects. The individuals among 0-15 age group showed hazardous impact for the small decrease of temperature not only on the same day but also over lag days in the winter season. Furthermore males and the individual’s among16-30, 31-45, 51-55 61-75 age group showed delayed impact for the increase of temperature over lag days in different seasons. Under these perspectives, special attention should be paid during policy making to control both increase and decrease of temperature to alleviate the health issues of these sensitive age groups.