(PhD dissertation) Air pollution and lung function exacerbations risk

Dynamic modeling and analysis of air pollution-associated lung function exacerbations risk

Abstract:
Air pollution has been recognized as the major environmental stimuli which may cause health effect of lung function decrement and asthma exacerbation. The researches for prediction and assessment of the air pollution impact on the respiratory system are also growing in recent years. Therefore, the purpose of this dissertation were: (i) to conduct an aerosol experiment in a constructed exposure system to understand the characteristics of the respiratory deposition for inhaled aerosols, (ii) to develop an integrated probabilistic risk approach to assess the risk of airborne dust- and ozone (O$_3$)-induced lung function decrement, (iii) to quantify the time-varying dynamics of air pollutants to correlate the relationships between fluctuations in air pollution and asthma hospital admission, and (iv) to predict asthma hospitalization trends in Taiwan by statistical indicators-based regression model.

This dissertation conducted the aerosol exposure experiment to quantify the deposition characteristics of exposure aerosols in human respiratory tract. The experimental aerosols included reference oil droplet and road dust particulate sample. This study developed an aerosol dynamic model to simulate time-dependent particle concentration in exposure chamber and respiratory system. The parameters of particle lose in exposure chamber and deposition in respiratory system can be estimated by experimental measurements. Thus, the deposition risk can be calculated through particle size distribution and size-dependent deposition fraction. This study also linked an integrated probabilistic risk assessment framework with published experimental data from airborne dust and O3 challenge in individuals. The toxicokinetic/toxicodynamic models were used to simulate the dose-response of lung function decrement as percentage forced expiratory volume in 1 second (%FEV1) under exposure. The highest air pollution events for dust aerosol and O3 exposure data in Taiwan regions were also collected for exposure assessment. Then, this study employed the time-series data based detrended fluctuation analysis (DFA) exponent and statistical indicators of coefficient of variation, standard deviation, skewness, and kurtosis to correlate the relationships between fluctuations in air pollution and age-specific asthma hospitalizations. Five major pollutants such as PM with aerodynamic diameter less than 10 μm (PM$_{10}$), O$_3$, nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) were included. This study further used the indicators-built regression model to validate and predict the impact of target air pollutants on asthma incidence.

The experimental result found that the generated aerosols were polydisperse and both followed lognormal distribution with geometric mean diameter of 0.52 μm and 0.26 μm for resuspended oil droplet and road dust, respectively. The predictable deposition rate ranged from 0.015 – 0.362 /sec and 0.013 – 0.157 /sec in particle size ranging from 0.3 – 3.0 μm and 0.3 – 4.0 μm for oil droplet and road dust, respectively. The experimental result also revealed that deposition risk in respiratory system for inhaled oil droplet was higher than road dust aerosol. The results of air pollution-induced lung function decrement indicated that there were 50% probabilities of %FEV1 decrement exceeding 16.9% (95% confidence interval (CI): 12.4 – 21.5%), 18.9 % (14.3 – 23.4%), and 7.1 % (4.0 – 10.2%) in north, center, and south Taiwan during Asian dust storm period, respectively. In same study period, the 10% probabilities of %FEV1 decrement were estimated to exceed 5.5% (4.4 – 6.8%), 4.4% (3.5 – 5.3%), and 12.7% (11.4 – 14.0%) for exposed to O3 in north, central, and south Taiwan, respectively. The results from fluctuating air pollution-associated asthma exacerbation showed that standard deviation of PM10 time-series data was the most correlated indicators for asthma hospitalization for all age groups, particularly for elderly. The skewness of O3 time-series data gives the highest correlation to pediatric asthmatics. The results also indicated that the integrated DFA exponents were significantly correlated with pediatric asthma hospitalization rate. The variability and long-range correlation of air pollution can be implicated as the risk warning signals in asthma incidence prediction. The results for asthma prediction also showed that indicators-built regression model had a better predictability in annual asthma hospitalization trends among pediatrics.

This study provided an integrated framework to assess the risk for air pollution-associated lung function exacerbations. The study quantified the mechanisms of aerosol deposition and lung function decrement by a dynamic model and the risk assessment was also conducted. The experimental and collected data can assist in estimating parameter and help the model development. Additionally, the proposed fluctuation analysis approach can also provide the novel indicators to predict the potential probability in asthma incidence. The statistical indicators inferred from time-series information of major air pollutants can further implicate for atmospheric environment monitoring and chronic respiratory disease care.

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