Prevalence of allergic rhinitis has rapidly increased among Chinese children,but the reasons are unclear.Recent findings have suggested that exposure to outdoor air pollutants may increase the risk of allergic rhinitis,but the results were inconsistent.This study further investigated the effect of outdoor air pollutants on allergic rhinitis among preschool children.A standardized questionnaire on health,home and environmental factors was conducted for 4988 children aged 1–8 in the city of Changsha,and the concentrations of PM10(particle diameter 10 m),sulfur dioxide(SO2)and nitrogen dioxide(NO2)during 2006–2011 were acquired from the official web of Changsha Environmental Protection Agency.Results showed that the prevalence of children’s doctor diagnosed rhinitis was 8.4%(95%confidence interval[CI]7.0%–10.0%).It was found that the prevalence of rhinitis was not associated with site-specific background concentrations of air pollutants,but significantly positively correlated with age-related accumulative personal exposure of PM10,SO2and NO2.We conclude that age-related accumulative personal exposure to ambient air pollution may play an important role in the development of rhinitis.
To investigate airflow pattern and its impact on particle deposition, finite-volume based computational fluid dynamics （CFD） simulations were conducted in the diseased triple-bifitrcation airways. Computations were carried out for twenty Reynolds numbers ranging from 100 to 2 000 in the step of 100. Particles in the size range of 1-10 μm were conducted. Two particle deposition mechanisms （gravitational sedimentation and inertial impaction） were considered. The results indicate that there are strong relationship between airflow structures and particle deposition patterns. Deposition efficiency is different for different particles in the whole range of the respiratory rates. Particles in different sizes can deposit at different sites. Smaller particles can be uniformly deposited at the inside wall of the considered model. Larger particles can be mainly deposited in the proximal bifurcations. Deposition fraction varies a lot for different inlet Reynolds numbers. For lower Reynolds numbers, deposition fraction is relatively small and varies a little with varying the diameters. For Reynolds number to target the aerosols at the specific site. higher Reynolds numbers, there is a most efficient diameter for each
The temporal variation of ventilation coefficient was estimated and a simple model for the prediction of urban ventilation coefficient in Changsha was developed. Firstly, Pearson correlation analysis was used to investigate the relationship between meteorological parameters and mixing layer height during 2005-2009 in Changsha, China. Secondly, the multi-linear regression model between daytime and nighttime was adopted to predict the temporal ventilation coefficient. Thirdly, the validation of the model between the predicted and observed ventilation coefficient in 2010 was conducted. The results showed that ventilation coefficient significantly varied and remained high during daytime, while it stayed relatively constant and low during nighttime. In addition, the diurnal ventilation coefficient was distinctly negatively correlated with PM10 (particle with the diameter less than 10 μm) concentration in Changsha, China. The predicted ventilation coefficient agreed well with the observed values based on the multi-linear regression models during daytime and nighttime. The urban temporal ventilation coefficient could be accurately predicted by some simple meteorological parameters during daytime and nighttime. The ventilation coefficient played an important role in the PM10 concentration level.
In order to get prepared for the coming extreme pollution events and minimize their harmful impacts, the first and most important step is to predict their possible intensity in the future. Firstly, the generalized Pareto distribution （GPD） in extreme value theory was used to fit the extreme pollution concentrations of three main pollutants： PM10, NO2 and SO：, from 2005 to 2010 in Changsha, China. Secondly, the prediction results were compared with actual data by a scatter plot. Four statistical indicators： EMA （mean absolute error）, ERMS （root mean square error）, IA （index of agreement） and R2 （coefficient of determination） were used to evaluate the goodness-of-fit as well. Thirdly, the return levels corresponding to different return periods were calculated by the fitted distributions. The fitting results show that the distribution of PM10 and SO2 belongs to exponential distribution with a short tail while that of the NOe belongs to beta distribution with a bounded tail. The scatter plot and four statistical indicators suggest that GPD agrees well with the actual data. Therefore, the fitted distribution is reliable to predict the return levels corresponding to different return periods. The predicted return levels suggest that the intensity of coming pollution events for PM10 and SO2 will be even worse in the future, which means people have to get enough preparation for them.
