Ann. occup. Hyg., Vol. 46, No. 8, pp. 673-685, 2002
© 2002 British Occupational Hygiene Society
Published by Oxford University Press
Determinants of Wood Dust Exposure in the Danish Furniture Industry
1 Department of Occupational and Environmental Medicine, Skive Hospital, Resenvej 25, DK-7800 Skive; 2 Department of Environmental and Occupational Medicine, University of Aarhus, DK-8000 Aarhus C, Denmark
Received 24 December 2001; in final form 29 June 2002
 |
ABSTRACT |
|---|
 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONAPPENDIXREFERENCES |
|---|
This paper investigates the relation between wood dust exposure in the furniture industry and occupational hygiene variables. During the winter 1997–98 54 factories were visited and 2362 personal, passive inhalable dust samples were obtained; the geometric mean was 0.95 mg/m3 and the geometric standard deviation was 2.08. In a first measuring round 1685 dust concentrations were obtained. For some of the workers repeated measurements were carried out 1 (351) and 2 weeks (326) after the first measurement. Hygiene variables like job, exhaust ventilation, cleaning procedures, etc., were documented. A multivariate analysis based on mixed effects models was used with hygiene variables being fixed effects and worker, machine, department and factory being random effects. A modified stepwise strategy of model making was adopted taking into account the hierarchically structured variables and making possible the exclusion of non-influential random as well as fixed effects. For woodworking, the following determinants of exposure increase the dust concentration: manual and automatic sanding and use of compressed air with fully automatic and semi-automatic machines and for cleaning of work pieces. Decreased dust exposure resulted from the use of compressed air with manual machines, working at fully automatic or semi-automatic machines, functioning exhaust ventilation, work on the night shift, daily cleaning of rooms, cleaning of work pieces with a brush, vacuum cleaning of machines, supplementary fresh air intake and safety representative elected within the last 2 yr. For handling and assembling, increased exposure results from work at automatic machines and presence of wood dust on the workpieces. Work on the evening shift, supplementary fresh air intake, work in a chair factory and special cleaning staff produced decreased exposure to wood dust. The implications of the results for the prevention of wood dust exposure are discussed.
Keywords: exposure determinants; furniture industry; inhalable dust; mixed effects model; preventive measures; wood dust
|
INTRODUCTION |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONAPPENDIXREFERENCES |
|---|
Processing of wood results in the formation of wood chips and dust. The dust is partly suspended in the air and may then be inhaled by the workers. The International Agency for Research on Cancer (IARC) classifies wood dust as a human carcinogen (IARC, 1995). Additionally, results from epidemiological studies indicate that workers exposed to wood dust stand an increased risk of suffering from asthma symptoms (Shamssain, 1992; Åhman et al., 1995; Herbert et al., 1995), chronic bronchitis (Åhman et al., 1995), nasal inflammation (Holness et al., 1985; Holmstrom and Wilhelmsson, 1988; Pisaniello et al., 1991; Norrish et al., 1992; Shamssain, 1992; Åhman et al., 1995) and impairment of lung function (Al-Zuhair et al., 1981; Whitehead et al., 1981; Holness et al., 1985; Carosso et al., 1987; Holmstrom and Wilhelmsson, 1988; Shamssain, 1992; Herbert et al., 1995; Mandryk et al., 1999). Exposure to dust from certain types of wood, such as western red cedar, oak, abachi and iroko, may cause asthma (Hausen, 1981; Chan-Yeung, 1993). A cross-sectional study in the Danish furniture industry among 2381 woodworkers at 54 furniture factories was carried out in order to investigate the relation between wood dust exposure and respiratory diseases. The study revealed a dose–response relationship between inhalable wood dust concentration on the one hand and increased frequencies of respiratory symptoms (Schlünssen et al., 2002a), acute nasal mucosal swelling (Schlünssen et al., 2002b) and increased bronchial hyper-reactivity (Schlünssen, 2001) on the other.
During the past 10 yr a number of investigations of personal exposure to total and inhalable dust have been carried out within the furniture industry (including joineries) (see Table 1). Some main features of present exposure in the Danish furniture industry have been described in a preceding paper. The level of exposure was found to be related to factory size and to work task (Schlünssen et al., 2001a).
|
This paper explores wood dust exposure as depending on hygiene variables with the aim of creating a platform for rational prevention of wood dust exposure and, hence, of occupational illness related to this exposure. Several authors have investigated the relation between exposure to wood dust and hygiene variables in the furniture and related industries using a restricted number of such variables (Scheeper et al., 1995; Alwis et al., 1999; Brosseau et al., 2001). The present paper broadens the spectrum by including variables related to all of the levels: worker, machine, department and factory (see Table 2).
|
|
MATERIALS AND METHODS |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONAPPENDIXREFERENCES |
|---|
Study design
The study design is described elsewhere (Schlünssen et al., 2002a). In brief, a total of 54 furniture factories participated in the study and the base study population was defined as all 2381 workers employed in the woodworking, assembling, packing and stock departments of these factories. The factories were visited between October 1997 and April 1998.
Dust measurements
Personal dust sampling was carried out with passive dust monitors described earlier (Vinzents, 1996; Schlünssen et al., 2001a). The method is based on measuring light extinction before and after sampling on transparent foils (Schlünssen et al., 2001a). The measured concentrations of wood dust were not corrected for their content of inorganic dust. In an earlier Danish study (Vinzents, 1989) the mean value of the organic fraction was found from 675 measurements of total dust to be 0.85 0.15 (mean SD).
The number of valid measurements obtained from the study group in the first round was 1685. For repeated measurements 400 workers were drawn randomly from 38 factories with >20 employees. This resulted in 351 valid measurements in the second and 326 valid measurements in the third round. The time lag between rounds was 1 week. For all three rounds the average measuring time was 4 h (Vinzents et al., 2001). None of the workers were observed to use a protective face mask.
For the purpose of multivariate analysis two subgroups were selected, one including woodworkers and one including workers doing handling and assembling, including gluing to make laminated board and veneer (Table 3). From 1025 valid observations for woodworking (all three measuring rounds together) the observations of workers doing more than one job were excluded, leaving 841 observations. In the next step, observations with missing hygiene variable values were excluded. So were observations of the second or of the third measuring round if the worker did a job different from that of the first one. Thus, 604 observations were left. The corresponding numbers for the handling and assembling group were 759, 703 (these numbers cannot be seen in Table 3, since non-woodworking includes truckers, foremen and storemen, excluded in the last column) and 506. These exclusions of observations were not a priori expected to introduce any bias with respect to exposure.
|
Occupational hygiene data documentation
Each of the hygiene variables could be assigned to one of the levels: worker, machine, department or factory (Table 2). Definitions of some of the terms used are given in the Appendix. Data were recorded during the first measuring round on schemes 1–4:
1. a personal measuring scheme;
2. a machine scheme;
3. a department scheme;
4. a factory scheme.
Scheme 1 was filled in by the worker, who was asked to identify jobs carried out for more than 1 h during the measuring period. At the time of returning the passive monitor and the scheme we ensured that all questions had been answered. Scheme 2 was filled in by the project group. Machines to be used during the measuring period were recorded with the assistance of the foreman or workers and supplied with a visible identification. Two or more walk throughs were usually carried out to view the machines while in production. Schemes 3 and 4 were filled in by interviews with the foreman of the department and the management, respectively. At the second and third measuring rounds the workers simply indicated whether they did the same job as during the first round or not.
Data analysis
A mixed effects model (Brown and Prescott, 1999) was adopted for its abilty to account for fixed effects as well as random effects and to handle unbalanced data:
Yijklr = µ + Fi + Dij + Mijk + Wijkl + 
fm + dn + mp + wq + 
ijkl (1)
The dependent variable Yijklr is the natural log-transformed inhalable dust concentration measured on the rth measuring round for the lth worker at the kth machine of the jth department in the ith factory. The transformation is used in order to utilize the approximate log-normal distribution of the exposure data. µ is the intersect and Fi, Dij, Mijk and Wijkl are the random effects corresponding to factory, department, machine and worker, respectively. fm, dn, mp and wq are the fixed effects related to the same four levels. The summations of equation 1 are over the indices m, n, p and q, respectively, and lead to the inclusion of the fixed effects of each of the four levels. ijklr is the residual. This term and the random effects Fi, Dij, Mijk and Wijkl are assumed to be statistically independent and to be approximately normally distributed with mean value 0. Equal variance of the worker random effect across machines, departments and factories is assumed. Corresponding assumptions are done for the machine and department random effects.
The strategy for model making takes into account the hierarchical structure of the random effects worker, machine, department and, at the highest level, factory, with fixed effects attached to each level. It reflects the assumption that hygiene variables of a lower level are more likely to determine exposure than those of a higher one, the first ones describing an environment closer to the source of the contaminant and to the exposed person. The process of model making goes through four phases. In Phase 1 all random effects and the fixed effects of the worker and machine levels are included. In Phases 2 and 3 the fixed effects of the department and of the factory level, respectively, are included. In Phase 4 terms reflecting interactions between variables are included. Within each of the first three phases fixed effects belonging to the level just included are excluded step by step according to an F-test until only significant fixed effects are left (P < 0.05). At the end of Phase 1, the machine random effect is tested looking at the change in the Akaikes information criterion (AIC) value when this effect is excluded (Brown and Prescott, 1999). The exclusion is justified if this change is negative or slightly positive. In the same way, the department random effect is tested at the end of Phase 2. The factory random effect is not tested. Figures 1 and 2 indicate the phases and single steps in setting up models for woodworking as well as for handling and assembling. Interaction terms are tested in Phase 4 one by one and added according to F-tests of fixed terms present in the model. The inclusion of an interaction term is not accepted if it makes another fixed term insignificant. The calculations were carried out using proc mixed of the SAS system for Windows (v.8.1).
|
|
|
RESULTS |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONAPPENDIXREFERENCES |
|---|
Geometric means (GMs) and geometric standard deviations (GSDs) for dust concentrations in each of the three measuring rounds and for data pooled across all three rounds are given in Table 3. The same parameters are given for measurements on workers indicating only one job. Thus, they are for two subgroups, woodworking and handling and assembling, selected for multivariate analysis. Only small differences are seen between the distribution parameters of workers doing one or more jobs and of those doing only one job. Small differences are also seen when comparing the selected subpopulations with their reference populations (Table 3). The exposure for woodworking is seen to be significantly higher than that for non-woodworking.
Univariate analysis
This analysis is carried out using dust measurements of the first round for workers doing only one job. Higher exposure is found for all of the woodworking work tasks when compared with any of the non-woodworking ones (P < 0.001) (Table 4). Within the woodworking work tasks the highest exposure (1.99 mg/m3) was found for manual sanding (P < 0.001 for comparisons with any of the other woodworking tasks). The exposure for sanding and cutting is larger than the exposure for cutting (P < 0.01). Within the non-woodworking work tasks foreman is the least exposed (P < 0.02 for comparisons with any of the other non-woodworking tasks). Use of compressed air produces greater dust exposures, although it is only significant for cutting, foreman and woodworking generally. Further results are given in Table 5. Dust on the workpiece produces increased exposure for sanding tasks and for handling and assembling. The decrease in exposure caused by exhaust ventilation does not reach significance, but it does for functioning exhaust ventilation. Enclosure, partial or full also leads to lower exposure.
|
|
Multivariate analysis
Woodworking
Following the strategy of model making described above, one random effect, department, can be eliminated, the change in AIC being 0.0. Fixed effects from each of the four levels are present in the final model. The signs of the terms are as expected except for regular check of central exhaust system, which has a positive influence on the dust concentration. As this effect was assumed to be significant by chance it was excluded and the model refitted. In Phase 4 interaction terms of the type I x M are tested, where I indicates a variable related to the intensity of wood dust production and M a variable related to mobilization of wood dust (I = level of automation, sanding processes, daily cleaning of rooms; M = use of compressed air, functioning exhaust ventilation, supplementary fresh air intake). Figure 3 shows the resulting exposure model which includes the interaction (level of automation) x (use of compressed air). A plot of predicted values versus standardized residuals was made for diagnostic purposes and shows no trends other than a straight line caused by 5% of the observations being below the detection limit. These were assigned a value of half the limit of detection (Schlünssen et al., 2001a; Vinzents et al., 2001). The corresponding normal probability plot shows approximate linearity. The following determinants of exposure are seen to increase the dust concentration: manual sanding and automatic sanding, use of compressed air at fully automatic or semi-automatic machines and cleaning of work pieces. Decreased dust concentrations result from the use of compressed air at manual machines, work at fully automatic or semi-automatic machines, functioning exhaust ventilation, night shift, daily cleaning of rooms, cleaning of work pieces by brush, vacuum cleaning of machines, safety representative elected within the last two years and supplementary fresh air intake (factory). The P value of the latter fixed effect, however, increased slightly above 0.05 during the model making process (Fig. 3).
|
Handling and assembling
In setting up a model for handling and assembling a reduced number of hygiene variables were available (Fig. 2). Again, department was excluded, the change in the AIC value being –2.2. Fixed effects from the four levels of the model are present. The signs are as expected except for wood dust not on the workpiece, which is positively associated with dust concentration. As this effect was assumed to be significant by chance it was excluded and the model refitted. Interaction terms were tested following the same scheme as indicated for the woodworking model, but none of them were included. The resulting exposure model is given in Fig. 3. A plot of predicted values versus standardized residuals was made for diagnostic purposes and shows no trends other than a straight line caused by 14% of the observations being below the detection limit. These were assigned a value of half the limit of detection (Schlünssen et al., 2001a; Vinzents et al., 2001). The corresponding normal probability plot shows approximate linearity. Inspecting the fixed effects of the model one sees that the P values of mixed wood types and room volume per worker have increased markedly above the value 0.05 during the model making process. They are retained in the mathematical model (Fig. 3) but not regarded as determinants. Fully automatic and dust on the workpiece both increase exposure. Evening, supplementary fresh air intake, work at a chair factory and special staff for cleaning decrease it.
Estimated variance components and proportions of variance explained are given for the two exposure models in Table 6. The residual variance component is termed the within-worker variance component but may include (smaller) contributions from, for example, measuring error.
|
|
DISCUSSION |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONAPPENDIXREFERENCES |
|---|
Study design
To ensure generalizability of the results, random selection was used for persons, factories and time of day of the measurement. The study was confined to a restricted area, Viborg County (Schlünssen et al., 2001a). To conserve resources, the smallest factories with 1–4 employees and parts of the factories with 5–19 employees were excluded. Studies within the Danish wood industry (Vinzents and Laursen, 1993; Schlünssen et al., 2001a) demonstrated higher exposures in small factories than in larger ones. At the same time, the furniture factories of Viborg County are larger than those of Denmark in general. It is estimated that the GM exposure of all furniture workers in Denmark is about 7% greater than the value found for Viborg County in our study (Schlünssen et al., 2001b). Neither of the multivariate exposure models (Fig. 3) indicate number of employees to be a determinant. Apparently, the ‘small factory effect’ previously found (Vinzents and Laursen, 1993; Schlünssen et al., 2001a) is explainable by small factories having more manual jobs, being without a safety representative, etc. At the same time, no determining dependence of exposure on type of factory is found for woodworking, indicating that the exposure model established for this activity is reasonably general. The measurements were done in the winter or heating season. Decreased intake of fresh air to save energy may lead to a generally increased exposure. However, we have no data to estimate this effect.
Statistical analysis
Mixed effects models have been preferred by several authors during the past years in the analysis of exposure data (Rappaport et al., 1999; Burstyn et al., 2000; Symanski et al., 2001). Our model allows for several sources of variability (equation 1): factory, department, machine and worker (between- and within-worker variability). The assumption of equal variances of the worker random effects across machines, departments and factories and corresponding assumptions for the machine and department random effects are introduced in order to limit the number of density function parameters to be fitted. The department random effect of both models was dropped according to the AIC criterion, whereas the machine random effect was important. Being the primary sampling variable, the factory random effect is included a priori (if tested it would be kept in the woodworking model only). The proportion of variance explained by hygiene variables (Table 6) is, especially for the handling and assembling model, on the low side when compared with other studies related to airborne particular contaminants, including wood dust (Burstyn and Teschke, 1999). Several possible reasons may be stated: the rather broad range of factory types and processes encompassed by the models, the restricted and qualitative hygiene documentation (for handling and assembling in particular), missing records of jobs extending over less than 1 h, that the documentation for rounds two and three was due to the workers recall of what he did in the first round and, finally, that the recording of machine stops, which appeared to be quite frequent and often prolonged, was not foreseen.
Overall dust level
The overall GM of 2362 inhalable dust measurements was 0.95 mg/m3 and the GSD was 2.08. Even if different measuring principles and measuring strategies were applied in the studies, making a direct comparison difficult, this points towards a low exposure in the Danish furniture industry when compared with published results from other countries (Table 1).
Occupational hygiene variables
Hygiene variables demanding closer technical examination, such as type, dimensions and speed of machine tools and wind velocity in chip extraction systems, were outside the scope of the present study. Nevertheless, although far from exhaustive, the selection of variables is broader than that used by other authors (Jones and Smith, 1986; Scheeper et al., 1995; Alwis et al., 1999; Brosseau et al., 2001) and separates variables according to hierarchically ordered levels. Following the strategy of model making, hygiene variables of a lower level are included and tested before those of a higher level. The underlying assumption is that a lower level means a greater influence on dust exposure. For the woodworking model this is supported by the fact that the significant fixed effects at lower levels in general remain so after the inclusion of variables for the higher levels. For the handling and assembling model this holds only partially, probably due to a poorer description of the worker/machine level (Fig. 3). Testing and inclusion of interaction terms uncover a differentiated picture of use of compressed air as depending on level of automation (see below).
Determinants of exposure
Woodworking
With regard to work task, as seen in earlier studies (Jones and Smith, 1986; Vinzents, 1989; Vinzents and Laursen, 1993; Scheeper et al., 1995; Alwis et al., 1999; Teschke et al., 1999; Brosseau et al., 2001), manual sanding separates out as a woodworking task with high exposure. The multivariate analysis changes the focus from sanding and cutting (Table 4) to automatic sanding (Fig. 3) as another sanding task with enhanced dust exposure. Many authors have drawn attention to the variable use of compressed air (Jones and Smith, 1986; Pisaniello et al., 1991; Alwis et al., 1999). The largest influence was seen for cutting and foreman (Table 4), which may relate to peak exposures during machine stops where compressed air is used to remove chips from machine parts. The multivariate analysis relates increased exposure from use of compressed air to fully automatic and semi-automatic machines (Fig. 3). Here the exposure is smaller (see below), which makes the contribution from using compressed air relatively more important. Somewhat unexpectedly, the effect is the opposite for manual machines (Fig. 3). However, use of compressed air not only suspends dust but also removes it and thereby removes a source of secondary exposure. For the more simple manual machines the latter aspect may dominate, but this point needs more investigation. Fully automatic and semi-automatic give rise to lower exposures (Fig. 3). To our knowledge, level of automation has not been included in earlier studies of the furniture industry. As it reflects both the technological and the hygiene level this variable seems valuable. Dust on the workpiece means increased exposure (Table 5), but this is not confirmed in the multivariate analysis. Exhaust ventilation reduces wood dust exposure, as evaluated by a statistical model based on data from five joineries (Alwis et al., 1999), but this was not found in a corresponding model of a single joinery (Scheeper et al., 1995). The variable is not found to be a determinant of exposure in our study, although it is difficult to investigate, as nearly 90% of all woodworking machines in fact have exhaust ventilation. Functioning exhaust ventilation produces lower exposure. Thus, it would appear valuable to include the quality aspect of exhaust ventilation. The presence of enclosure, full or partial on machines results in lower exposure (Table 5), but this effect is not significant in the multivariate analysis (Fig. 3). No dependence of exposure on the type of wood was found. This point has been discussed by several authors, but the results are ambiguous (Pisaniello et al., 1991; Scheeper et al., 1995; Alwis et al., 1999; Teschke et al., 1999). The lower exposure for night shift (Fig. 3) can be related to the greatly reduced workforce, resulting in a lower background concentration of wood dust.
At the department level, a lower exposure results from cleaning of machines by vacuum. This is also true for cleaning of workpieces by brush. Cleaning of workpieces by compressed air has the opposite effect (Fig. 3). Use of a brush, like the use of a compressed air gun, both suspends dust and removes it. Clearly, with respect to suspending dust a brush is much less effective than a compressed air gun and the cleaning aspect seemingly has greater weight. Daily cleaning of the rooms is a determinant too and leads to lower exposure.
At the factory level, intake of supplementary air decreases exposure, probably by preventing low pressure in the room. Low pressure means a lower effectiveness of the local exhaust hoods. Safety representative elected within the last two years may reflect the importance of the attitude of management to work environment matters. Two years is the election period of a safety representative according to Danish regulations.
Non-woodworking
According to more recent findings (Scheeper et al., 1995) and to this study (Table 3) the wood dust exposure for non-woodworking is smaller than for woodworking. Nevertheless, the exposure for non-woodworking work tasks is not negligible and with a GM (GSD) of 0.70 mg/m3 (2.02) these work tasks should not be overlooked in assessments of wood dust exposure and in implementing protective measures. According to the multivariate analysis of handling and assembling fully automatic produces increased exposure, in contrast to the result for woodworking (Fig. 3). However, enclosure and exhaust ventilation are non-existent for handling and assembling. Automation of, for example, a packing line implies a higher rate of production and, accordingly, of dust release. The variable dust on the workpiece is a determinant here, but not for woodworking. Handling and assembling activities produce little wood dust, which thus has to be transferred from dust producing activities, e.g. as a layer on the work piece. The smaller exposure for evening has the same explanation as the reduced exposure for the night shift for woodworkers. In fact, the exposure for the night shift is also the lowest one for handling and assembling, but very few work that shift. No effect of cleaning methods or cleaning frequency was found, which is intriguing in comparison with the woodworking case. At the factory level, however, special cleaning staff is a determinant. This effect may reflect a higher quality of room cleaning and, consequently, less resuspension of wood dust. Chair factory also produces lower exposure. These factories generally produce smaller series of high quality designer products. Thus, in the mounting and packing operations fewer and more carefully cleaned objects are handled than in mass production. In all, the description of the dust exposure for handling and assembling seems more imprecise than that presented for woodworking. Probably, a set of hygiene variables more adapted to the handling and assembling activities could be set up.
Preventive measures
In reducing wood dust exposure in the furniture industry we find the following proposals to be the most informed ones:
· ensure effective local exhaust ventilation at all woodworking machines;
· automate woodworking machines/processes, in particular when manual sanding is included (the use of sanding robots is increasing in Denmark);
· rooms for production should be cleaned every day, preferably by professionals;
· machines and workpieces should not be cleaned by compressed air but by vacuum, and workpieces eventually by brush;
· ventilation (general or local) should always be balanced by the intake of supplementary fresh air;
· the management should actively support the institution of safety representatives.
To give an example, in this investigation almost as many non-functioning as functioning local exhaust hoods were seen for woodworking. In our experience bad design of the exhaust hood is frequently the reason. In this case a solution may be to implement documented effective, aerodynamic designs developed for many types of woodworking processes by NIOSH and other agencies (Huebener, 1987; Hampl et al., 1990; Thorpe and Brown, 1995).
Acknowledgements—We are indebted to Associate Professor Mogens Erlandsen, Department of Biostatistics, Aarhus University, Denmark, for help and inspiration with regard to the statistical analysis. This work was supported by the Danish Work Environment Foundation, Viborg County, the Danish Medical Research Council, the Wood Industry and Building Workers Union of Denmark, the Danish Lung Association, the Asthma and Allergy Association of Denmark and the Health Insurance Fund.
|
APPENDIX |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONAPPENDIXREFERENCES |
|---|
Definitions
Automation, level of. Manual: the worker brings about and ends the contact between work piece and tool (driven by hand or machine) and controls the work process. Semi-automatic: a part of these elements are taken over by the machine. Fully automatic: all of these elements are taken over by the machine.
Dust, inhalable. Any airborne particle <100 µm which will be retained after entering the nose or mouth, with 50% capture of 100 µm aerodynamic diameter particles.
Dust, total. A fraction of airborne dust <100 µm sampled with a specially designed monitor and using v = 1.25 m/s at the inlet.
Dust on the work piece. A deposition of fine particles can either be seen on the work piece or can be detected by wiping with the hand.
Exhaust ventilation, non-functioning. An exhaust hood is not present within reasonable distance from all of the important sources of chips and dust at the machine or one (or more) of the hoods is clearly unable to remove chips and dust formed during use.
Job. Work at a specified machine.
Machine. In this paper not only a device for working wood. It also includes packing lines and tables for assembly, repair or finishing. The name is also used for combinations such as, for example, a drilling machine as an extension of a double tenoner. If sanding is involved the machine is classed as sanding and cutting.
Recirculation. A part or all of the air from the exhaust ventilation system is carried back to rooms or enclosed machines after passing over a filtering unit.
Work task. A group of jobs sharing certain common features. The work tasks used in this paper are given in Table 4.
|
FOOTNOTES |
|---|
* Author to whom correspondence should be addressed. E-mail: amaskive@vibamt.dk
|
REFERENCES |
|---|
|
TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONAPPENDIXREFERENCES |
|---|
Åhman M, Söderman E, Cynkier I, Kolmodin-Hedman B. (1995) Work-related respiratory problems in industrial arts teachers. Int Arch Occup Environ Health; 67: 111–8.[Web of Science][Medline]
Al-Zuhair YS, Whitaker CJ, Cinkotai FF. (1981) Ventilatory function in workers exposed to tea and wood dust. Br J Ind Med; 38: 339–45.[Web of Science][Medline]
Albract G, Bolm-Audorff U, Grosse-Jäger A, Manthey U, Straub U, Walter A, Weisskopf V. (1989) Ergebnisse der Schwerpunktaktion Holzstaub der Heissischen Gewerbeaufsicht. Staub- Reinhaltung Luft; 49: 381–4.
Alwis U, Mandryk J, Hocking AD, Lee J, Mayhew T, Baker W. (1999) Dust exposures in the wood processing industry. Am Ind Hyg Assoc J; 60: 641–6.[Web of Science][Medline]
Brosseau LM, Parker D, Lazovich D, Dugan S, Milton T, Pan W. (2001) Inhalable dust exposures, tasks, and use of ventilation in small woodworking shops: a pilot study. Am Ind Hyg Assoc J; 62: 322–9.
Brown H, Prescott R. (1999) Applied mixed models in medicine. Chichester: John Wiley & Sons.
Burstyn I, Teschke K. (1999) Studying the determinants of exposure: a review of methods. Am Ind Hyg Assoc J; 60: 57–72.[Web of Science][Medline]
Burstyn I, Kromhout H, Kauppinen T, Heikkila P, Boffetta P. (2000) Statistical modelling of the determinants of historical exposure to bitumen and polycyclic aromatic hydrocarbons among paving workers. Ann Occup Hyg; 44: 43–56.
Carosso A, Ruffino C, Bugiani M. (1987) Respiratory diseases in wood workers. Br J Ind Med; 44: 53–6.[Web of Science][Medline]
Chan-Yeung M. (1993) Western red cedar and other wood dusts. In Bernstein IL, Chan-Yeung M, Malo JL, Bernstein DI, editors. Asthma in the workplace. New York: Marcel Dekker. pp. 503–32.
Hampl V, Johnston OE, Topmiller JL, Murdock DJ. (1990) Control of wood dust from automated routers. Appl Occup Environ Hyg; 5: 419–27.
Hausen BM. (1981) Woods injurious to human health. A manual. Berlin: Walter de Gruyter.
Herbert FA, Hessel PA, Melenka LS, Yoshida K, Nakaza M. (1995) Pulmonary effects of simultaneous exposures to MDI formaldehyde and wood dust on workers in an oriented strand board plant. J Occup Environ Med; 37: 461–5.[Web of Science][Medline]
Holliday MG, Dranitsaris P, Strahlendorf PW, Contala A, Engelhardt JJ. (1986) Wood dust exposure in Ontario industry: the occupational health aspects. Ottawa: M. Holliday.
Holmstrom M, Wilhelmsson B. (1988) Respiratory symptoms and pathophysiological effects of occupational exposure to formaldehyde and wood dust. Scand J Work Environ Health; 14: 306–11.[Web of Science][Medline]
Holness DL, Sass-Kortsak AM, Pilger CW, Nethercott JR. (1985) Respiratory function and exposure-effect relationships in wood dust-exposed and control workers. J Occup Med; 27: 501–6.[Web of Science][Medline]
Hounam RF, Williams J. (1974) Levels of airborne dust in furniture making factories in the High Wycombe area. Br J Ind Med; 31: 1–9.[Web of Science][Medline]
Huebener DJ. (1987) Dust controls for a wood shaper. Appl Ind Hyg; 2: 164–9.
IARC. (1995) IARC monographs on the evaluation of carcinogenic risks to humans. Vol. 62. Wood dust and formaldehyde. Lyon: IARC.
Jones PA, Smith LC. (1986) Personal exposures to wood dust of woodworkers in the furniture industry in the High Wycombe area: a statistical comparison of 1983 and 1976/77 survey results. Ann Occup Hyg; 30: 171–84.
Lidblom A. (1997) Exposition för damm i svensk träbearbetande industri 1994/1995. Arbetarskyddsstyrelsen; 1: 1–18 (in Swedish).
Mandryk J, Alwais KU, Hocking AD. (1999) Work-related symptoms and dose–response relationships for personal exposures and pulmonary function among woodworkers. Am J Ind Med; 35: 481–90.[Web of Science][Medline]
Norrish AE, Beasley R, Hodgkinson EJ, Pearce N. (1992) A study of New Zealand wood workers: exposure to wood dust, respiratory symptoms, and suspected cases of occupational asthma. N Z Med J; 105: 185–7.[Web of Science][Medline]
Pisaniello DL, Connell KE, Muriale L. (1991) Wood dust exposure during furniture manufacture—results from an Australian survey and considerations for threshold limit value development. Am Ind Hyg Assoc J; 52: 485–92.[Web of Science][Medline]
Rappaport SM, Weaver M, Taylor D, Kupper L, Susi P. (1999) Application of mixed models to assess exposures monitored by construction workers during hot processes. Ann Occup Hyg; 43: 457–69.
Sass-Kortsak AM, Holness DL, Pilger CW, Nethercott JR. (1986) Wood dust and formaldehyde exposures in the cabinet-making industry. Am Ind Hyg Assoc J; 47: 747–53.[Web of Science][Medline]
Scheeper B, Kromhout H, Boleij JS. (1995) Wood-dust exposure during wood-working processes. Ann Occup Hyg; 39: 141–54.
Schlünssen V. (2001) Asthma and other respiratory diseases among workers in the furniture industry occupationally exposed to dust. Thesis, Faculty of Health Sciences, University of Aarhus, Aarhus (in Danish).
Schlünssen V, Vinzents PS, Mikkelsen AB, Schaumburg I. (2001a) Wood dust exposure in the Danish furniture industry using conventional and passive monitors. Ann Occup Hyg; 45: 157–64.
Schlünssen V, Schaumburg I, Mikkelsen AB. (2001b) Astma og andre luftvejssygdomme blandt ansatte i møbelindustrien erhvervsmæssigt eksponeret for træstøv. Copenhagen: Arbejdsmiljørådets Service Center (in Danish).
Schlünssen V, Schaumburg I, Taudorf E, Mikkelsen AB, Sigsgaard T. (2002a) Respiratory symptoms and lung function among Danish woodworkers. J Occup Environ Med; 44: 82–98.[Web of Science][Medline]
Schlünssen V, Schaumburg I, Andersen NT, Sigsgaard T, Pedersen OF. (2002b) Nasal patency is related to dust exposure in woodworkers. Occup Environ Med; 59: 23–9.
Shamssain MH. (1992) Pulmonary function and symptoms in workers exposed to wood dust. Thorax; 47: 84–7.
Solgaard J, Andersen I. (1975) Luftvejsfunktion og symptomer hos træindustriarbejdere. Ugeskr Læger; 137: 2593–9 (in Danish).
Symanski E, Chan W, Chang C-C. (2001) Mixed-effects models for the evaluation of long-term trends in exposure levels with an example from the nickel industry. Ann Occup Hyg; 45: 71–81.
Teschke K, Marion SA, Vaughan TL. (1999) Exposures to wood dust in U.S. industries and occupations, 1979 to 1997. Am J Ind Med; 35: 581–9.[Web of Science][Medline]
Thorpe A, Brown RC. (1995) An improved design for the dust extraction system of orbital sanders used on wood. Ann Occup Hyg; 39: 155–65.
Vinzents PS. (1989) Træ- og møbelindustrien. Tværsnitsundersøgelse- Exponering. Copenhagen: Arbejdstilsynet (Danish Working Environment Service) (in Danish).
Vinzents PS. (1996) A passive personal dust monitor. Ann Occup Hyg; 40: 261–80.
Vinzents PS, Laursen B. (1993) A national cross-sectional study of the working environment in the Danish wood and furniture industry—air pollution and noise. Ann Occup Hyg; 37: 25–34.
Vinzents PS, Schlünssen V, Feveile H, Schaumburg I. (2001) Variation in exposure to inhalable wood dust in the Danish furniture industry. Within- and between-workers and factory components estimated from passive dust sampling. Ann Occup Hyg; 45: 603–8.
Whitehead LW, Ashikaga T, Vacek P. (1981) Pulmonary function status of workers exposed to hardwood or pine dust. Am Ind Hyg Assoc J; 42: 178–86.[Web of Science][Medline]
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  K. S. Galea, M. Van Tongeren, A. J. Sleeuwenhoek, D. While, M. Graham, A. Bolton, H. Kromhout, and J. W. Cherrie Trends in Wood Dust Inhalation Exposure in the UK, 1985-2005 Ann. Hyg., October 1, 2009; 53(7): 657 - 667. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
H. Sbihi, K. Teschke, Y. C. Macnab, and H. W. Davies Determinants of Use of Hearing Protection Devices in Canadian Lumber Mill Workers Ann. Hyg., July 1, 2009; (2009) mep043v1. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. Meijster, E. Tielemans, J. Schinkel, and D. Heederik Evaluation of Peak Exposures in the Dutch Flour Processing Industry: Implications for Intervention Strategies Ann. Hyg., October 1, 2008; 52(7): 587 - 596. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A Scarselli, A Binazzi, P Ferrante, and A Marinaccio Occupational exposure levels to wood dust in Italy, 1996-2006 Occup. Environ. Med., August 1, 2008; 65(8): 567 - 574. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
V. Schlunssen, G. Jacobsen, M. Erlandsen, A. B. Mikkelsen, I. Schaumburg, and T. Sigsgaard Determinants of Wood Dust Exposure in the Danish Furniture Industry--Results from Two Cross-Sectional Studies 6 Years Apart Ann. Hyg., June 1, 2008; 52(4): 227 - 238. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. SPEE, E. V. D. R.-V. HOOF, W. V. HOOF, D. NOY, and H. KROMHOUT Exposure to Wood Dust Among Carpenters in the Construction Industry in The Netherlands Ann. Hyg., April 1, 2007; 51(3): 241 - 248. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
N. BLACK, M. DILWORTH, and N. SUMMERS Occupational Exposure to Wood Dust in the British Woodworking Industry in 1999/2000 Ann. Hyg., April 1, 2007; 51(3): 249 - 260. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. C. FRIESEN, Y. C. MACNAB, S. A. MARION, P. A. DEMERS, H. W. DAVIES, and K. TESCHKE Mixed Models and Empirical Bayes Estimation for Retrospective Exposure Assessment of Dust Exposures in Canadian Sawmills Ann. Hyg., April 1, 2006; 50(3): 281 - 288. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
V Schlunssen, T Sigsgaard, I Schaumburg, and H Kromhout Cross-shift changes in FEV1 in relation to wood dust exposure: the implications of different exposure assessment methods Occup. Environ. Med., October 1, 2004; 61(10): 824 - 830. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||