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研究生：	粟自強 Soo, Jhy-Charm
論文名稱：	以替代指標量測結果推估勞工暴露之研究 Predictions of Workers’ Exposures Based on Surrogate Monitoring Results
指導教授：	蔡朋枝 Tsai, Perng-Jy
學位類別：	博士 Doctor
系所名稱：	醫學院 - 環境醫學研究所 Department of Environmental and Occupational Health
論文出版年：	2011
畢業學年度：	99
語文別：	英文
論文頁數：	105
中文關鍵詞：	粉塵、噪音、振動、混凝土切割、混凝土鑽鑿、替代物指標、預測模式
外文關鍵詞：	Dust, noise, vibration, concrete cutting, concrete drilling, surrogate, predicting model
相關次數：	點閱：83 下載：18
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大量粉塵逸散常見於混凝土切割及鑽鑿作業工地。相關工程控制方法均被提出並使用於前述營建工地。基於粉塵生成機制複雜，世界衛生組織(World Health Organization ，WHO) 仍建議須深入研究以利於粉塵控制策略之擬定。至今，噪音與振動量測技術已廣為被應用於職場進行定期性環境測定評估。然粉塵採樣受限於採樣、分析複雜及勞工配合意願等因素而致評估不易。由此本研究擬以噪音與振動為替代指標物預測勞工粉塵暴露。本研究共分為四部份﹔其中 (I)為模擬混凝土切割作業，並評估施力大小(AF)與混凝土之壓試強度(CS)對粉塵暴露濃度之影響； (II)為模擬混凝土鑽鑿作業，並以噪音量測結果建立粉塵暴露預測濃度模式； (III)則以振動量測值作為替代指標物，並用於預測勞工之粉塵暴露；及 (IV)為探討混凝土鑽鑿作業所產生之噪音與振動之相關性。所有實驗皆在一暴露測試腔(長×寬×高= 3.0 m × 2.4 m × 3.6 m)內進行。此暴露腔上、下游各設置一開口，並於下游處利用一軸流式扇風機以模擬作業場所之整體換氣。第一部份研究係利用混凝土切割機具以模擬勞工切割作業情形。五種測試條件包括三種CS (2500~6000 psi)及三種AF (9.80~49.0 N)。其它三部份研究則以一混凝土鑽鑿機具以模擬勞工鑽鑿作業情形，六種測試條件則包括四種鑽鑿轉速 (Rs；265~587 rpm)及三種鑽頭尺寸 (Φ；16~32 mm)。粒徑分布樣本以Marple個人八階階梯衝擊器 (Marple personal impactor)或氣動微粒分徑儀 (Aerodynamic Particle Sizer; APS 3321)進行樣本採集。兩種替代指標物分別利用噪音頻譜分析儀及振動量測設備進行監測。所獲得之總粉塵 (Ctot)，再利用國際共同認可之採樣準則以求得吸入性粉塵(Cinh)、胸腔性粉塵 (Ctho)和可呼吸性粉塵(Cres)或PM10 (CPM10)、 PM2.5 (CPM2.5)暴露濃度。全頻帶聲功能量 (WT)可再區分為低頻聲功能量 (WL)及高頻聲功能量 (WH)。三軸向之均能加速度值(即ax、ay、及az) 可再區分為水平(ah)及垂直軸均能加速度值(av)。最後，三種不同統計方法(含固定效應模式、多變項模式及廣義加成模式)分別應用於前述替代指標物預測模式之建立。
本研究結果發現：(I) 混凝土切割作業產生之粉塵粒徑分布皆為雙峰分佈。隨著CS增加，四種粉塵暴露濃度(即Ctot、 Cinh、Ctho,、及Cres)亦隨之增加。隨著AF增加，細粒徑粉塵暴露濃度亦隨之增加(即Ctho,及Cres)，然而粗粒徑粉塵暴露濃度則呈現先遞增後遞減之趨勢。前述結果及推論均可藉由固定效應模式加以確認。 (II)無論勞工之各種粉塵暴露濃度，抑或產生之音功能量，均無法單純以鑽鑿之機械能大小來解釋之。唯若將鑽鑿作業所產生之噪音進一步區分為高、及低頻音，本研究發現其可有效地預測勞工之Ctot (~86.3 %)、Cinh (~85.6 %)、Ctho (~81.5 %)及Cres (~77.6 %)。本研究亦發現高頻音之主要生成機制為與細粒徑粉塵產生有關之研磨破壞，低頻音之主要生成機制為與粗粒徑粉塵之產生有關之直接撞擊破壞及高脆性物質之破壞。 (III) CPM10、 CPM2.5皆可有效地被ax、ay、及az預測，且預測與量測值間具有良好之相關性 (R2> 0.969)。本研究亦發現ax之主要生成機制為研磨破壞並與細粒徑粉塵產生有關，ay及az之主要生成機制為直接撞擊破壞及高脆性物質之破壞，其與粗粒徑粉塵之產生有關。 (IV) ah (~62.5%)及av (~59.9%)可有效地被高(即PH)及低頻音壓能量(即PL)預測。本研究亦發現PH之主要生成機制為研磨破壞與ah產生有關，PL之主要生成機制為直接撞擊破壞及高脆性物質之破壞，並與av之產生有關。
本研究結果將有助於工業界了解評估AF與 CS對粉塵暴露濃度之影響，並作為未來粉塵控制策略擬定之參考。雖然粉塵暴露之評估仍應以傳統個人採樣法為宜，唯如受限環境、採樣人力、設備與經費，本研究所開發之替代技術應可作為前述污染物之替代量測方法。

High dust exposure concentrations have been consistently found at concrete cutting and drilling sites, and several control options have been proposed. Due to the complexity of the mechanisms associated with dust emissions, the World Health Organization (WHO) has suggested that more research is needed to provide a solid scientific basis for initiating appropriate control strategies in the protection of workers from high particle exposure levels. To date, both of noise and vibration measurements have been periodically measured. But dust samplings were relatively less measured due to the complexity in sampling and analysis processes and subject to workers’ willingness. Therefore, if surrogate methods (including the use of both noise and vibration monitoring results) to predict the dust exposures could be developed, it might provide a promising solution for industries to predict workers’ exposures, particularly when conducting several conventional personal samplings are not possible in the filed.
This research thesis contains four major parts, including: (I) assessing the effects of both applied force (AF) and concrete compressive strength (CS) during the concrete cutting process on particle exposure concentrations, (II) development of an aerosol exposure predicted model based on currently available noise measurements, (III) development of a methodology for using vibration monitoring results as a surrogate to predict the aerosol exposures, and (IV) development of a methodology for predicting the vibration exposures by using noise monitoring results.
The whole study was conducted in an exposure chamber (LWH=300 cm240 cm360 cm). The chamber had one air inlet and one air outlet on the opposite wall. An axial fan was installed at the air outlet and inlet air was filtered. In part I, a full scale mockup of concrete cutting simulator was used to simulate cutting processes. Five cutting conditions were selected with AF varied between 9.8 and 49 N and CS varied between 2500 and 6,000 psi. In other three parts, a concrete driller was used to simulate drilling processes. Six drilling conditions were selected with rotating speeds (Rs) and drill bit sizes (Φ) varied from 265 to 587 rpm and 16 to 32 mm, respectively. Particle size segregating was conducted by using the Marple cascade impactor or Aerosol Particle Sizer. Besides aerosol samplings, both the two surrogates (including sound power and vibration accelerations) were simultaneously collected using a noise spectrum analyzer and a human vibration meter, respectively.
The measured total dust concentrations (Ctot) were used to further determine the corresponding three health-related exposure concentrations of the inhalable (Cinh), thoracic (Cthor), and respirable fraction (Cres); or other two exposure concentrations of the PM10 (CPM10) and PM2.5 (CPM2.5) based on several aerosol conventions. For the practical reason, the total radiated sound power (WT) was further divided to two parts, including the radiated sound power of the low frequency (WL) and high frequency noise (WH). In addition, the energy-equivalent acceleration magnitudes for each of three orthogonal axes (i.e., ax, ay, and az) were determined for each selected drilling experiment by integrating the square of the frequency-weighted acceleration magnitudes over the whole test period. Finally, the three different statistical methodologies (i.e., fixed effect, or multiple regression or generalized additive model) were adopted for each specific prediction purpose.
In part I, results showed that particle size distribution was consistently in a bimodal form under all selected cutting conditions. An increase in CS resulted in an increase in coarse particle generations leading to an increase in the four measured particle exposure levels. An increase in AF resulted in an increase in exposure concentrations with a higher fraction of fine particles (i.e., Ctho and Cres) However, for particle exposure concentrations with a higher fraction of coarse particles (i.e., Ctot and Cinh), an increase in AF resulted in an initial increase, followed by a decrease in concentration. The above inferences were confirmed through the use of fixed-effect models.
In part II, we found that neither the resultant dust exposure levels nor the noise levels can be explained simply by the involved drilling mechanical energy. By dividing the emitted noise power spectrums into the high and low frequency noise (i.e., WH and WL), we find that 86.3% 85.6%, 81.5%, and 77.6% variations of Ctot, Cinh, Ctho, and Cres could be explained by the combination of WH and WL, respectively. We also find that the emissions of coarse particles and WL were possibly contributed by two mechanisms of the impact wear and brittle fracture wear during the drilling process; whereas fine particles and WH could be contributed by the mechanism of abrasive wear.
In part III, empirical models for predicting CTSP, CPM10 and CPM2.5 were done based on measured ax, ay, and az using the generalized additive model. Good agreement between measured aerosol exposures and vibrations was found with R2 > 0.969. Our results also suggest that ax was mainly contributed by the abrasive wear. On the other hand, ay and az were mainly contributed by both the impact wear and brittle fracture wear. It is concluded that vibrations monitoring results can be used to predict workers’ PM exposures during the concrete drilling processes.
In part IV, frequency spectra analysis of emitted noise obtained from the concrete drilling processes contained detailed information about vibration generations. The measured sound pressure levels of high and low frequencies can explain ~59.9%, and 62.5% variations in the magnitude of vibration acceleration in vertical axis and horizontal plane, respectively. It is concluded that the measured noise levels can be used as a surrogate indicator to predict workers’ vibration exposures during the concrete drilling processes.
The results obtained from this study are helpful for industries to know the effects of both CS and AF during the concrete cutting process on particle exposure concentrations and eventually use them to initiate proper control strategies. In addition, the developed surrogate predictive models can be used to predict workers exposures when simultaneously conducting various personal samplings for different types of exposures is not possible in the workplace.

Contents
摘要	i
Abstract	iii 
Acknowledgments	vi
Contents	vii
List of tables	x
List of figures	xii
Chapter 1	Introduction	1
1.1	General	1
1.2	Objectives and significance	4
Chapter 2	Literature review	6
2.1	Types of aerosol	6
2.2	Aerosol exposure conventions used in the environmental health field	7
2.3	Aerosol exposure conventions used in the occupational health field	8
2.4	Health effects associated with aerosol exposures	10
2.4.1	Adverse health effects associated with ambient PM exposures	10
2.4.2	Adverse health effects associated with workplace aerosol exposures	11
2.5	Aerosol exposure assessment in the construction industry	17
2.5.1	The construction industry	17
2.5.2	Workers’ dust exposures in the construction industry	18
2.5.3	Dust exposures and their related health outcomes for construction industry 
workers	19
2.5.4	Workers’ dust exposures during the concrete drilling and cutting processes	20
2.6	Dusts generated from concrete cutting and drilling processes	24
2.6.1	Factors affecting dust generations	24
2.6.2	Mechanisms associated with dust generations	25
2.7	Noise and vibration generated from concrete cutting and drilling processes	26
2.7.1	Noise and its measurements	26
2.7.2	Vibration and its measurements	27
2.7.3	Mechanisms associated with noise and vibration generations	30
2.7.4	The use of noise measurements in manufacturing industries	32
2.7.5	The use of vibration measurements in manufacturing industries	33
2.8	          The rationale for using surrogates to predict workers’ exposure and its 
implication	33
Chapter 3	Materials and methods	35
3.1	Research framework	35
3.2	Outline of study	35
3.3	Experimental procedures and data analysis	37
3.3.1	Part- I Influence of Compressive Strength and Applied Force in Concrete on Particles Exposure Concentrations during Cutting ProcessesTest	37
3.3.2	Part- II Establishing Aerosol Exposure Predictive Models Based on Noise Measurements Using Concrete Drilling as an Example	40
3.3.3	Part- III Establishing Aerosol Exposure Predictive Models Based on Vibration Measurements	43
3.3.4	Part- IV Relationship between Noise and Vibration Emitted from Concrete Drilling Processes	45
Chapter 4	Results and discussion	58
4.1	Part- I Influence of Compressive Strength and Applied Force in Concrete on Particles Exposure Concentrations during Cutting Processes	58
4.1.1	Measured particle size distributions	58
4.1.2	Particle exposure concentrations: with fixed AF and three different CSs	58
4.1.3	Particle exposure concentrations: with a fixed CS and three different AFs	59
4.1.4	Results of the fixed-effect statistical models	60
4.2	Part-ⅡEstablishing Aerosol Exposure Predictive Models Based on Noise Measurements Using Concrete Drilling as an Example	66
4.2.1	Characteristic aerosol exposures obtained from six selected drilling conditions	66
4.2.2	Characteristic noise emitted from six selected drilling conditions	67
4.2.3	Predicting aerosol exposure levels based on noise measurements	68
4.3	Part- III Establishing Aerosol Exposure Predictive Models Based on Vibration Measurements	73
4.3.1	Characteristic aerosol exposures obtained from six selected drilling conditions	73
4.3.2	Characteristic hand-trasmitted vibration exposures obtained from six selected drilling conditions	74
4.3.3	Predicting aerosol exposure levels based on hand-transmitted vibration measurements	75
4.4	Part- IV Relationship between Noise and Vibration Emitted from the Concrete Drilling Processes	82
4.4.1	Characteristic sound pressure emitted from six selected drilling conditions	82
4.4.2	Characteristic hand-trasmitted vibration exposures obtained from six selected drilling conditions	82
4.4.3	Predicting aerosol exposure levels based on hand-transmitted vibration measurements	83
Chapter 5	Conclusions and recommendations	88
5.1	Conclusions	88
5.2	Recommendations	89
Chapter 6	References	91
 
List of tables
Table 2.1 Adverse health effect and its related PM indicator from the various U.S. city studies	13
Table 2.2 Occupational respiratory diseases and their corresponding particle type under different industry processes	14
Table 2.3 The association between respiratory diseases, respiratory regions and their suggested particle size-selective sampling conventions	16
Table 2.4 Workers’ exposure levels of silica dust  at various construction activities/occupations	22
Table 4.1 Particle size distributions of fine mode and coarse mode obtained under the three testing conditions with AF specified at the level of 29.4 N, and CS at three different levels of 2500, 4000, and 6000 psi (n=3)	63
Table 4.2 Particle size distributions of fine mode and coarse mode obtained under the three testing conditions with CS specified at the level of 4000 psi, and CS at three different levels of 9.80, 29.4, and 49.0 N (n=3)	63
Table 4.3 Concentrations of Ctot, Cinh, Ctho, and Cres (mean ± SD) obtained under the three testing conditions with AF specified at the level of 29.4 N, and CS at three different levels of 2500, 4000, and 6000 psi (n=3)	64
Table 4.4 Concentrations of Ctot, Cinh, Ctho, and Cres (mean ± SD) obtained under three testing conditions with CS specified at the level of 4000 psi, and CS at three different levels of 9.80, 29.4, and 49.0 N (n=3)	64
Table 4.5 Results of fixed-effect statistical models for assessing the effects of AF and CS on four particle exposure concentrations of Ctot, Cinh, Ctho, and Cres (n=15)	65
Table 4.6 Mean Ctot, Cinh, Ctho, and Cres and their RSDs (values in parenthesis) obtained from the six selected drilling conditions	71
Table 4.7 Mean WT, WH, and WL and their RSDs (values in parenthesis) obtained from the six selected drilling conditions	71
Table 4.8 The regression results for predicting dust exposure levels of Ctot, Cinh , Ctho , and Cres	72
Table 4.9 Mean CTSP, CPM10, and CPM2.5 and their RSDs (values in parenthesis) obtained and from the six selected drilling conditions	80
Table 4.10 Mean magnitudes of ax, ay, and az and their RSDs (values in parenthesis) obtained from the six selected drilling conditions	80
Table 4.11 Regression results based on ax, ay, and az for predicting dust exposure levels of CTSP, CPM10, and CPM2.5	81
Table 4.12 The measured PH and PL obtained from the six selected drilling conditions (n=36)	86
Table 4.13 Magnitudes of av and ah obtained from the six selected drilling conditions (n=36)	86
Table 4.14 The regression results for predicting vibration magnitudes of av and ah	87

 
 List of figures
Figure 3.1 The whole research scheme	48
Figure 3.2 The flowchart of the research work of part Ⅰ	49
Figure 3.3 The flowchart of the research work of the part Ⅱ	50
Figure 3.4 The flowchart of the research work of the part Ⅲ	51
Figure 3.5 The flowchart of the research work of the part Ⅳ	52
Figure 3.6 Schematic of test facilities used in the present study	53
Figure 3.7 Air outlet of exposure chamber and axial fan used in the study	54
Figure 3.8 The cutting saw, concrete specimen, and specimen holder used to simulate the cutting process in this study	55
Figure 3.9 Schematic of (a) test facilities used in the study, including a concrete drilling simulator and an APS for aerosol measurement, and (b) the nine points for conducting noise spectrum measurements	56
Figure 3.10 Schematic of (a) test facilities used in the study, including a concrete drilling simulator and an APS for aerosol measurement, and (b) the X-axis, Y-axis, and Z-axis for conducting vibration measurements	57
Figure 4.1 Particle size distribution obtained under cutting conditions with CS specified at 4000 psi and AF at 49.0 N: (a) resultant log probability plot; (b) measured and estimated (dashed line) particle size distributions	62
Figure 4.2 The best fitted bimodal distribution for particles generated from the selected drilling condition of Rs=587 rpm and Φ=16 mm	70
Figure 4.3 The measured particle size distribution for the selected drilling condition of Rs=587 rpm and Φ=16 mm: (a) while plotted on a log-probability plot; (b) the best fitted bimodal	77
Figure 4.4 Comparison predicted concentrations with measured concentrations: CTSP/P vs. CTSP/M	78
Figure 4.5 Comparison predicted concentrations with measured concentrations: CPM10/P vs. CPM10/M	78
Figure 4.6 Comparison predicted concentrations with measured concentrations: CPM2.5/P vs. CPM2.5/M	79
Figure 4.7 The resultant residual plot of vibration magnitudes of ah	85
Figure 4. 8 The resultant residual plot obtained from vibration magnitudes of av	85


                                    

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