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研究生:王莉鈞
研究生(外文):Li-Chun Wang
論文名稱:前瞻性航空製造業勞工世代之職業噪音預測模式的建立與效度評估
論文名稱(外文):前瞻性航空製造業勞工世代之職業噪音預測模式的建立與效度評估
指導教授:張大元
指導教授(外文):Ta-Yuan Chang
學位類別:碩士
校院名稱:中國醫藥大學
系所名稱:職業安全與衛生學系碩士班
學門:醫藥衛生學門
學類:公共衛生學類
論文種類:學術論文
論文出版年:2016
畢業學年度:104
語文別:中文
論文頁數:332
中文關鍵詞:頻譜分析職業噪音預測模式前瞻性世代效度評估
外文關鍵詞:Frequency analysisoccupational noisepredictive modelprospective cohortvalidity
相關次數:
  • 被引用被引用:1
  • 點閱點閱:121
  • 評分評分:
  • 下載下載:7
  • 收藏至我的研究室書目清單書目收藏:0
背景:噪音暴露是職場中普遍存在的問題之一,而噪音預測被認為是評估職業噪音暴露所需成本最小的方法。噪音預測模式中最重要的是分析與工作期間噪音有關之參數。
目的:本研究針對前瞻性世代研究建立勞工之職業噪音暴露預測模式,並且經由實際噪音測量來評估噪音預測模式之效度。此外,本研究也探討影響不同頻率噪音之作業現場因子。
方法:本研究利用2008-2009年期間,得到143名自願者與48個環境量測點在工作期間之8小時時量平均均能音量值及各頻譜特性,建立該研究族群之職業噪音預測模式。本研究於2014-2015年期間,針對123名自願者與116個環境量測點進行個人噪音劑量測量和環境噪音頻譜分析,並且收集個人工作部門、職稱及配戴聽力防護具與否資料。我們結合受試者的測量結果與個人問卷資料,建立多變項線性迴歸模式,並且進行職業噪音預測模式的效度評估。
結果:在個人噪音部分,全廠勞工噪音暴露為75.4±8.7 dBA,且現場43名勞工(82.6±7.4 dBA)及31名現場與辦公室混合勞工(74.9±6.7dBA)的平均噪音皆顯著高於49名辦公室勞工(69.6±5.6 dBA;P<0.0001;P=0.0002)。其中,結構組裝工廠相較於其他部門有最高的噪音平均暴露值(86.2±6.6 dBA)。在環境噪音部分,白鐵工廠在31.5 Hz、63 Hz、125 Hz、250 Hz頻率相較於其他的部門有最高的噪音平均暴露值(39.7±1.9、50.7±6.2、57.1±3.6、64.3±1.7 dBA)。結構組裝工廠在500 Hz、1000 Hz、2000 Hz、4000 Hz、8000 Hz頻率相較於其他的部門有最高的噪音平均暴露值(68.4±2.7、72.4±2.9、74.0±3.4、74.9±4.3、70.4±4.9 dBA)。我們發現以工作部門及有無配戴防護具組合在個人噪音部分有最高的預測模式解釋力(調整R2為0.66),與123名個人噪音實測值比較的結果顯示其精確度為6.5 dBA、偏差為0.9 dBA。在環境噪音部分,以工作部門組合對各頻率噪音的預測有最高模式解釋力(調整R2分別為0.85、0.76、0.67、0.27、0.10、0.17、0.24、0.34、0.46),並且在31.5 Hz、63 Hz及125 Hz有較佳的預測效果。與116個環境噪音實測值比較的結果顯示,其精確度與偏差分別為5.5±-8.5 dBA、8.4±-12.1 dBA、8.6±-14.6 dBA、8.9±-13.8 dBA、8.2±-12.4 dBA、8.7±-12.4 dBA、8.3±-13.2 dBA、8.3±-13.2 dBA及7.9±-12.0 dBA。
結論:本研究顯示此個人噪音預測模式有良好的預測效果,可以有效地應用在預測此前瞻性世代勞工的噪音暴露。但在環境噪音部分,只於低頻噪音有良好的預測效果。

Background: Occupational noise exposure is one of the common problems in the workplace, and noise predictions are considered the most efficient way to evaluate occupational noise exposure. In the noise prediction model, the most important part is to analyze parameters related to noise levels at work.
Objectives: This study aimed to develop a noise predictive model of occupational noise and to evaluate its validity by the actual noise measurements in a prospective cohort. In addition, this study investigated the impact factors related to different noise frequencies at work.
Methods: This study used personal noise data among 143 volunteers and noise spectral characteristics at 48 sampling sites during 2008 - 2009 to develop noise predictive models. We recruited 123 volunteers to carry out personal-noise measurements and selected 116 environmental sites to analyze noise frequency components, as well as the collection of self-administered questionnaires during 2014 - 2015. We combined these information to develop multivariable linear regression models and evaluate the validity of developed models.
Results: The whole workers exposed to personal noise levels of 75.4±8.7 dBA. Fourty-three field workers (82.6±7.4 dBA) and 31 mixed workers (74.9±6.7 dBA) had significantly higher average noise levels than 49 office workers (69.6±5.6 dBA; P <0.0001; P = 0.0002). Workers in the structure-assembly plant had the highest average noise exposure (86.2±6.6 dBA) compared with those in other departments. In addition, the highest average levels of environmental noise were found at 31.5 Hz, 63 Hz, 125 Hz, and 250 Hz in the steel plant (39.7±1.9, 50.7±6.2, 57.1±3.6, 64.3±1.7 dBA) and observed at 500 Hz, 1000 Hz, 2000 Hz, 4000 Hz, and 8000 Hz in the structure-assembly plant (68.4±2.7, 72.4±2.9, 74.0±3.4, 74.9±4.3, 70.4±4.9 dBA). We also found that the combination of branch and the use of HPDs in the model had the highest capacity (adjusted R2 0.66) to predict personal noise levels. In comparison with the personal-noise measurements from 123 workers, the precise and bias was 6.5±0.9 dBA. Regarding with environmental noise exposure, only the branch in each model of frequency components had the highest predictive capacity (adjusted R2 was 0.85, 0.76, 0.67, 0.27, 0.10, 0.17, 0.24, 0.34, and 0.46, respectively) and the better prediction was observed at 31.5 Hz, 63 Hz, and 125 Hz. In comparison with the measurements of frequency components at 116 environmental sites, the precise and bias were 5.5±-8.5 dBA, 8.4±-12.1 dBA, 8.6±-14.6 dBA, 8.9±-13.8 dBA, 8.2±-12.4 dBA, 8.7±-12.4 dBA, 8.3±-13.2 dBA, 8.3±-13.2 dBA, and 7.9±-12.0 dBA, respectively.
Conclusion:This study showed that the predictive model of personal noise has a good predictive capacity, and it can be effectively applied to a prospective cohort for workers’ noise exposure. However, only environmental noise at low frequency components has the good prediction.

中文摘要 ii
Abstract iv
目錄 vi
表目錄 ix
圖目錄 xviii
第一章 緒論 1
第一節 研究緣起 1
第二節 研究重要性 3
第三節 研究目的 4
第四節 研究假設 4
第二章 文獻探討 5
第一節 航空製造業之描述 5
第二節 噪音的特性 6
第三節 噪音暴露對人體之不良健康效應 8
第四節 噪音預測模式 15
第三章 研究方法 18
第一節 研究設計 18
第二節 研究對象 19
第三節 研究儀器設備 20
第四節 資料收集 21
第五節 資料統計與分析 22
第六節 噪音暴露預測模式之建立 23
第七節 噪音暴露預測模式之效度評估 24
第四章 研究結果 27
第一節 勞工問卷之描述性統計 27
第二節 噪音暴露評估 27
第三節 噪音強度之預測模式 29
第四節 預測模式之內部效度評估 40
第五節 預測模式之外部效度評估 48
第五章 討論 50
第一節 個人噪音暴露分析 50
第二節 環境噪音暴露分析 50
第三節 噪音暴露強度預測模式分析 52
第四節 研究優勢 53
第六章 結論與建議 54
第一節 結論 54
第二節 研究限制 54
第三節 應用與建議 55
參考文獻 57
附錄 189

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