https://scholars.lib.ntu.edu.tw/handle/123456789/448860
標題: | Changing variance and skewness as leading indicators for detecting ozone exposure-associated lung function decrement | 作者: | Hsieh N.-H. Cheng Y.-H. CHUNG-MIN LIAO |
關鍵字: | Lung function; Ozone; Probabilistic risk assessment; Statistical indicators; Time-series dynamics; Toxicodynamic model | 公開日期: | 2014 | 卷: | 28 | 期: | 8 | 起(迄)頁: | 2205-2216 | 來源出版物: | Stochastic Environmental Research and Risk Assessment | 摘要: | The objective of this study was to develop a novel risk analysis approach to assess ozone exposure as a risk factor for respiratory health. Based on the human exposure experiment, the study first constructed the relationship between lung function decrement and respiratory symptoms scores (ranged 0–1 corresponding to absent to severe symptoms). This study used a toxicodynamic model to estimate different levels of ozone exposure concentration-associated lung function decrement measured as percent forced expiratory volume in 1?s (%FEV1). The relationships between 8-h ozone exposure and %FEV1 decrement were also constructed with a concentration–response model. The recorded time series of environmental monitoring of ozone concentrations in Taiwan were used to analyze the statistical indicators which may have predictability in ozone-induced airway function disorders. A statistical indicator-based probabilistic risk assessment framework was used to predict and assess the ozone-associated respiratory symptoms scores. The results showed that ozone-associated lung function decrement can be detected by using information from statistical indicators. The coefficient of variation and skewness were the common indicators which were highly correlated with %FEV1 decrement in the next 7?days. The model predictability can be further improved by a composite statistical indicator. There was a 50?% risk probability that mean and maximum respiratory symptoms scores would fall within the moderate region, 0.33–0.67, with estimates of 0.36 (95?% confidence interval 0.27–0.45) and 0.50 (0.41–0.59), respectively. We conclude that statistical indicators related to variability and skewness can provide a powerful tool for detecting ozone-induced health effects from empirical data in specific populations. ? 2014, Springer-Verlag Berlin Heidelberg. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/448860 | ISSN: | 1436-3240 | DOI: | 10.1007/s00477-014-0887-2 | SDG/關鍵字: | Biological organs; Health risks; Higher order statistics; Ozone; Population statistics; Risk analysis; Time series; Coefficient of variation; Environmental Monitoring; Forced expiratory volume in 1; Lung function; Probabilistic Risk Assessment; Respiratory symptoms; Statistical indicators; Time series dynamics; Risk assessment |
顯示於: | 生物環境系統工程學系 |
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