Size distributions and exposure concentrations of nanoparticles associated with the emissions of oil mists from fastener manufacturing processes

Wang, Ying Fang; Tsai, Perng Jy; Chen, Chun Wan; Chen, Da Ren; Dai, Yong

doi:10.1016/j.jhazmat.2011.10.028

Cited by 14 publications

(4 citation statements)

References 35 publications

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“…Results of a study conducted in an aluminum plant showed that 90% of emitted particles were of nanometer size [26]. Metal machining was also reported as causing nanoscale emissions, both as a result of the generation of oil mist during forming, treading, and heat treatment operations involving the use of mineral oil-based metalworking fluids [123] as well as through the production of metal dust [57]. The most exposed workers identified are those who manually break the crust that forms over the electrolysis bath.…”

Section: Results On Occupational Exposure To Incidental Nanomaterialsmentioning

confidence: 99%

Overview of Workplace Exposure to Nanomaterials

Dolez

Debia

2015

Nanoengineering

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Section: Results On Occupational Exposure To Incidental Nanomaterialsmentioning

confidence: 99%

Overview of Workplace Exposure to Nanomaterials

Dolez

Debia

2015

Nanoengineering

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Section: Introductionmentioning

confidence: 99%

“…Particle size characteristics have also been measured by researchers [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. Figure 1 shows the results for 50 typical factories, including fastener processing factories [ 19 , 20 ], charging machinery processing factories [ 21 ], and machining factories [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. The blue triangles in the figure represent the minimum particle size, whereas the red triangles and black rhombuses represent the maximum, and mean particle size, respectively.…”

Section: Introductionmentioning

confidence: 99%

Study of Oil Particle Concentration Vertical Distribution of Various Sizes under Displacement Ventilation System in Large-Space Machining Workshop

Wang

Meng

Lin

et al. 2022

IJERPH

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The widespread use of metal working fluids (MWFs) in machining processes leads to the production of a large number of harmful oil particles, which may pose serious health hazards to workers. The oil particle concentration has an inhomogeneous distribution in large spaces under displacement ventilation (DV) system, and the supply air volume required to maintain a low particle concentration under a DV system may be less than that needed under a mixing ventilation system. In this study, computational fluid dynamics (CFD) was used to study the particle concentration distribution rules and characteristics under various particle sizes in a large-space machine workshop with a DV system. Several distribution indices, such as the inhomogeneity factor and stratification height were utilized to analyze the inhomogeneous distribution of particle concentration; furthermore, sensitivity analyses were conducted for these indices. We found that the particle concentration shows a similar inhomogeneity factor distribution rule along the vertical direction under an air change rate of 2–6 in the DV system. The workspace inhomogeneity factor of particles smaller than 5 μm is less than 0.25, whereas that of 10-μm particles declines with an increase in air supply volume. Approximately double the supply air volume is required to keep the 10-μm particle concentration at the same level as particles smaller than 5 μm. The workspace inhomogeneity factor of small particles (<5 μm) is more sensitive to the machine height and machine surface temperature than other parameters, whereas that of large particles (>5 μm) is more sensitive to the supply air volume than other parameters. The results of this study can be applied for the design and control of displacement ventilation systems in large-space machining workshops.

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“…Therefore, collecting multiple and long-term samples are needed in order to properly describe the exposure profile for a similar exposure group (SEG). However, many research works have been conducted only for a short period of time Choosong et al, 2010;Geiss et al, 2010;Wang et al, 2011), or even on a cross-sectional basis (Shih et al, 2009;Chen et al, 2010;Colbeck et al, 2011;Hwang et al, 2011) because of the cost and manpower concerns. To date, the Bayesian decision analysis (BDA) technique has been adopted by many researchers in determining the exposure profile for an SEG based on a small amount of sampling data (Ramachandran et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Assessing Long-Term Oil Mist Exposures for Workers in a Fastener Manufacturing Industry Using the Bayesian Decision Analysis Technique

Hsu

Chen

Wang

et al. 2012

Aerosol Air Qual. Res.

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Collecting multiple and long-term samples is necessary to accurately describe the exposure profile of a similar exposure group (SEG), but only a few industries can afford to do this because of the costs and manpower needed. In the present study, measured oil mist concentrations (C m , n = 11) were randomly collected on eleven days during one year (serving as the likelihood distribution in Bayesian decision analysis (BDA)), and daily fastener production rates (Pr, n = 250) were used as a surrogate for predicting the yearlong oil mist exposure concentrations (C p ) (serving as the prior distribution in BDA). The resulting BDA posterior distributions were used to assess the long-term oil mist exposures to threading workers in a fastener manufacturing industry. The feasibility of the proposed methodology was finally examined with reference to the effects of the sample size of the C m . The results show that threading workers experienced more severe thoracic and respirable oil mist exposure than exposure to the inhalable fraction. Using Pr as a surrogate was adequate to explain ~92% of the variations in C m . By combining C p and C m , our results suggest that the BDA technique adopted in this work was effective in predicting workers' long-term exposure. By judging the consistency of the resulting posterior exposure ratings, this study suggests that the proposed methodology could be feasible, even when the sample size of C m is set as low as 3.

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Size distributions and exposure concentrations of nanoparticles associated with the emissions of oil mists from fastener manufacturing processes

Cited by 14 publications

References 35 publications

Overview of Workplace Exposure to Nanomaterials

Overview of Workplace Exposure to Nanomaterials

Study of Oil Particle Concentration Vertical Distribution of Various Sizes under Displacement Ventilation System in Large-Space Machining Workshop

Assessing Long-Term Oil Mist Exposures for Workers in a Fastener Manufacturing Industry Using the Bayesian Decision Analysis Technique

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