Ann. occup. Hyg., Vol. 49, No. 7, pp. 587–602, 2005

# 2005 British Occupational Hygiene Society

Published by Oxford University Press

doi:10.1093/annhyg/mei014

Investigation of Determinants of Past and Current

Exposures to Formaldehyde in the Reconstituted

Wood Panel Industry in Quebec

JE ́ ROˆ ME LAVOUE ́ 1, CHARLES BEAUDRY1, NICOLE GOYER2,

GUY PERRAULT2 and MICHEL GE ́ RIN1

*

1

Groupe de recherche interdisciplinaire en sante ́ (GRIS), De ́partement de sante ́ environnementale

et sante ́ au travail, Faculte ́ de me ́decine, Universite ́ de Montre ́al, P.O. Box 6128, Main Station,

Montreal (QC) H3C 3J7, Canada; 2

Quebec Research Institute for Occupational Health and Safety

(IRSST), 505, De Maisonneuve blvd. West, Montre ́al (QC) H3A 3C2, Canada

Received 7 October 2004; in final form 23 February 2005; published online 21 April 2005

OBJECTIVES: Past and present formaldehyde measurements made in facilities manufacturing reconstituted wood panels in Quebec have been collected in order to assess formaldehyde

exposure and its determinants in this industry.

METHODS: All 12 plants manufacturing Oriented-strand board (OSB), Medium density

fibreboard (MDF) and Particle board (PB) in Quebec were visited by a research team which

took area and personal measurements. Past measurements taken by governmental occupational

health teams in these plants were also collected. Log-transformed formaldehyde concentrations

were analysed with extended linear mixed-effects models.

RESULTS: During 2001–2002, 275 measurements were taken by the research team, while 590

measurements dating back to 1984 were collected from governmental files. The area measurements had a global geometric mean (GM) of 0.28 p.p.m. [geometric standard deviation (GSD):

3.1]. The GM of the personal measurements was 0.17 p.p.m. (GSD: 2.3). The fixed-effects of the

models for personal and area measurements explained 61 and 57% of the variance, respectively.

Job (working area for area concentrations), process (PB, MDF, OSB), season of sampling, origin

of the data (research, governmental) and year of sampling were significant determinants of

exposure. Proximity to the press, winter conditions, PB and MDF processes and governmental

data resulted in the highest exposures. Significant within-sampling campaign correlation was

found for both personal and area models. The final models include different residual variances

by process for personal measurements and by working area for area measurements.

CONCLUSIONS: Several determinants of exposure to formaldehyde in the reconstituted

wood panel industry were successfully identified. Higher levels found in governmental data as

compared to research data may be explained by a ‘worst-case’ strategy bias. The observed

intra-sampling campaign correlation supports existing results suggesting that measurements

taken in a small time frame tend to be correlated. Exposures in this sector are low compared to

most 8 h-TWA occupational exposure limits (e.g. 1 p.p.m.) but close to the most demanding ones

(e.g. 0.3 p.p.m.).

Keywords: formaldehyde; determinants of exposure; mixed-effects models; particle board; oriented-strand board;

medium density fibre board

INTRODUCTION

Context

The manufacture of reconstituted wood panels has

long been associated with exposure to formaldehyde,

which is an irritant gas considered by the

International Agency for Research on Cancer (IARC)

as carcinogenic to humans (group 1) (International

Agency for Research on Cancer, 2005). Most of

the processes in this industry involve the use of

formaldehyde-based resins as the binding agent.

The recent change in the IARC classification of formaldehyde from group 2A (probably carcinogenic) to

*Author to whom correspondence should be addressed.

Tel: +1 514 343 6134; fax: +1 514 343 2200;

e-mail: michel.gerin@umontreal.ca

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group 1, based on sufficient evidence that it causes

nasopharyngeal cancer in humans, constitutes an

incentive for improved exposure surveillance in

workplaces where formaldehyde is present. Furthermore, information on levels and determinants of

exposure to this substance are needed to help improve

exposure assessment in epidemiological studies on

the carcinogenicity of formaldehyde at other sites

(such as the nasal cavity or the blood-forming system). Within the framework of a more global project

aimed at estimating the economic and health impacts

of lowering the occupational exposure limit (OEL)

for formaldehyde in Quebec, an exposure assessment

was conducted in the reconstituted wood panels

industry (Goyer et al., 2004). Scenarios being studied

for full shift exposure include an 8 h-TWA OEL of 1,

0.75 and 0.3 p.p.m. With nearly 2000 workers in

2001–2002, this industry constitutes the largest industrial sector with formaldehyde exposure in Quebec.

Description of the industry

The reconstituted wood panel industry includes

several processes and can be classified as either producing plywood products (in which panels are formed

by the assembly of thin layers of wood) or composition boards (in which wood particles are bonded

together to form the panels) (Zimowski, 1986; US

Census Bureau, 2002). The processes included in

this study are limited to particle board (PB), medium

density fibre board (MDF) and oriented-strand board

(OSB), which all belong to the composition board

category. These three processes involve the mixing

of wood chips with a binding agent and compressing

the mixture under high temperature (USEPA, 1998).

For PB, the main raw materials comprise wood chips,

saw dust and planer shavings. In the MDF process the

chips are formed into fibres prior to mixing with the

resin. Liquid urea-formaldehyde (UF) and melamineurea-formaldehyde (MUF) resins are generally used

in the manufacture of MDF and PB. OSB panels are

made from wood wafers produced on site from logs

and mixed with liquid or powder phenol-formaldehyde (PF) resins.

Exposure to formaldehyde

The main sources of occupational exposure to

formaldehyde in this sector include emissions from

resins before pressing, mostly during the formation of

the mat and along the system conveying it to the press,

emissions from the press and emissions from the

newly formed panels in the finishing, storage and

shipping areas. Formaldehyde emissions from resins

are caused in part by their free formaldehyde content,

but most emissions at the press and from the hot

panels arise from the hydrolysis of the cured resin

and condensation reactions between wood compounds (Tohmura et al., 2001). Tohmura et al.

have observed a decrease in formaldehyde emissions

when the melamine content in MUF resins is

increased, caused by an increased resistance of the

cured resins to hydrolysis (Tohmura et al., 2001).

Wolcott et al. have investigated variables affecting

formaldehyde emissions from the press during laboratory production of UF-bonded PB (Wolcott et al.,

1996). They found that emissions increased when

the following parameters increased: press time, press

temperature, mat humidity, mat content in resin and

formaldehyde/urea molar ratio of the resin. Formaldehyde emissions were inversely associated with the

thickness of the panels.

The OSHA Health Response Team sampled formaldehyde in four facilities in 1986, three manufacturing PB and one producing MDF (Zimowski, 1986).

They report geometric means (GMs) of personal

measurements for different jobs ranging from 0.10

to 0.32 p.p.m. and from 0.18 to 1.8 p.p.m. in PB

and MDF, respectively. All measurement durations

were >250 min. Kauppinen and Niemela ̈, in a review

of formaldehyde exposure levels in facilities manufacturing PB in Finland prior to 1985, report GMs of

area measurements between 0.4 and 2.3 p.p.m.

depending on the localization in the plant. All measurement durations were below 120 min (Kauppinen

and Niemela ̈, 1985). Niemela ̈ et al. also report an

arithmetic mean (AM) of 1.15 p.p.m. for 220 measurements made between 1977 and 1979 (Niemela ̈ and

Vainio, 1981). More recently, Niemela ̈ et al. presenting historical trends in this industry based on measurements taken in eight facilities between 1980 and

1994, report successive measurement medians of 0.91

(n = 21), 0.26 (n = 31) and 0.46 p.p.m. (n = 9) for

the periods 1980–1985, 1986–1990 and 1991–1994

(Niemela ̈ et al., 1997). In their review of occupational

exposure to formaldehyde, the authors of the 1995

IARC monograph, citing the results of a Swedish

study, report an AM of 0.2 p.p.m. (obtained from

19 values) measured between 1980 and 1989 in facilities manufacturing MDF (International Agency for

Research on Cancer, 1995). A value of 0.3 p.p.m. for

facilities manufacturing PB is also reported. Edling

et al. studied the effects of formaldehyde on the

physiology of nasal mucosa in workers of three plants

manufacturing PB. They report that measurements

taken by in-house hygienists between 1975 and

1983 were in the range 0.08–0.9 p.p.m. with peaks

reaching 4.1 p.p.m. (Edling et al., 1988). In a study of

the effects of formaldehyde on the mucous membranes and lungs of workers in a PB facility, Horvath

et al., report that area and personal measurements

taken in the plant during the study (supposedly in

1987 or 1988) had a median of 0.62 p.p.m. (Horvath

et al., 1988). Herbert et al. studied the effects of

formaldehyde on the respiratory system of workers

in the OSB industry in Alberta. The authors took

21-h long measurements at different fixed stations

in a facility using a PF resin. All 10 reported values

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were below 0.2 p.p.m. (Herbert et al., 1995). Imbus

et al. report that the results of 15 measurements taken

in an OSB facility using PF resin were all below

0.05 p.p.m. (Imbus and Tochilin, 1988).

The available literature provides little information

about determinants of occupational exposure to

formaldehyde. A few influential factors seem to

modify formaldehyde emissions but their quantitative

influence on occupational exposure is not documented. Moreover, there is a lack of recent data on

exposure levels, most of the few reported exposure

levels being prior to 1990. The present study aimed

at documenting current formaldehyde exposure levels

in Quebec in this industry and identifying their

determinants.

METHODS

Industrial hygiene surveys

The 12 plants manufacturing PB, MDF or OSB in

Quebec were visited during the period from June

2001 to March 2002. The visits were conducted by

a team of 2–4 industrial hygienists and technicians

and lasted 1 to 2 days.

A standardized form was created to facilitate and

systematize the information gathering process. One

member of the research team completed the form

either from observation of the workplace or from

interview with employees. In order to describe

industry-wide exposure trends, lists of standardized

jobs and work zones in the plants were created

(Table 1).

Time-weighted type monitoring was performed in

each plant at fixed stations (area sampling) and in the

respiratory zone of employees (personal sampling) by

using adsorbent tubes coupled with sampling pumps.

Employees and locations monitored were not chosen

randomly but were selected with the aim to cover the

greatest number of exposure circumstances in a facility given the available sampling resources. Although

all jobs/areas in the facilities were sampled, more

sampling resources were allocated to those thought

to be associated with the highest exposures. Sampling

was not task oriented but rather aimed at representing

full-shift exposure.

Governmental data

In Quebec, all companies in certain regulated

industries are visited by governmental occupational

health teams that identify health hazards, evaluate

health risks, define required medical surveillance

and devise corrective measures with the employers

and employees. The visits may be conducted several

times in the same plant if periodical reassessment is

deemed necessary. All but one of the plants identified

as manufacturing PB, MDF or OSB panels in Quebec

were visited by these teams, as early as 1984. During

each visit by the research team, all governmental

exposure data were collected from the corresponding

local health center along with the available ancillary

information.

Quebec legislation requires sampling strategies to

be designed according to the recommendations of the

Quebec Research Institute for Occupational Health

and Safety (IRSST) (IRSST, 2000). While mentioning several types of strategies (e.g. ‘worst-case’, random sampling), the IRSST guide does not provide

precise guidelines to be used by governmental health

teams. Moreover, the purpose of sampling may differ

from situation to situation, e.g. hazard identification,

follow up, task monitoring, compliance monitoring

or exposure profiling. Therefore, since the sampling

strategies associated with the collected exposure data

were not explicitly stated in the paper records, they

are mostly unknown and may vary considerably

across the exposure data.

Analytical methods

During the visits by the research team, formaldehyde concentrations were evaluated with sampling

pumps coupled with solid sorbent tubes (type XAD2) impregnated with 2-(hydroxymethyl) piperidine.

Analysis was conducted by gas chromatography

with Nitrogen Phosphorus detection (GC-NPD)

(IRSST, 1995). Governmental teams used three

different methods sequentially from 1984 to 2002.

Before 1985, sampling was mostly performed with

an impinger filled with a collecting solution containing dinitrophenylhydrazine and perchloric acid and

the analysis was conducted with liquid chromatography (IRSST, 1985). From 1985 to 1995, the sampling device used was a solid sorbent tube (type ORBO

22) impregnated with n-benzylethanolamine while

the analysis was conducted by GC-NPD (IRSST,

1988). Since 1995, the sampling and analytical

methods have been those used by the research team.

Data formatting

Area and personal measurements were analysed

separately, which was motivated by the fact that

area measurements rarely represent personal exposures adequately and may be influenced by different

determinants. Inside the Area and Personal datasets,

data originating from the research team and governmental records were merged for analysis. This

allowed for increased statistical power during the

analysis. A variable identifying the source of data

was included in the statistical modelling to explore

potential systematic differences.

A limit of detection (LOD) was calculated according to the sampling volume using the lowest reported

formaldehyde mass of each analytical method. Values reported under a LOD were treated according to

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the recommendations of Hornung and Reed, separately for the area and personal datasets (Hornung

and Reed, 1990). This led to assigning the value of

the LOD divided by 2 to the non detected area measurements and the value of the LOD divided by the

square root of 2 to the non detected personal measurements.

Statistical analysis

Based on a graphical assessment of the frequency

distributions of area and personal concentrations, the

response variable selected for analysis was the natural

logarithm of formaldehyde concentrations. The data

were analysed with extended linear mixed-effects

models. Linear mixed-effects models have recently

been used successfully to analyse occupational

exposure data (Teschke et al., 1994; Lagorio et al.,

1997; Leena et al., 1999; Rappaport et al., 1999,

2003; Burstyn et al., 2000; Symanski et al., 2001;

van Tongeren and Gardiner, 2001; Weaver et al.,

2001; Egeghy et al., 2002; Peretz et al., 2002;

Raaschou-Nielsen et al., 2002). Extended linear

mixed-effects models, in addition to the possibility

of modelling correlation patterns, allow exploring

changes in the variability of the response as a function

of other variables (Pinheiro and Bates, 2000). The

model framework used in this study is described

Table 1. Standardized jobs and zones in the reconstituted wood panel industry in Quebec

Description

Standardized zones

Raw materials receiving—chip

preparation

Includes the raw material storage area, wood chips

preparation area (different for each process) and the

resin blending area

Resin production Area where the resin is stored and produced from formaldehyde and

other ingredients

Main production The mat formation, the press and the panel cooling wheel areas

Finishing The panel aging, sawing, sanding, laminating and painting areas

Storage–shipping Packaging, storage and shipping areas

Operator booth—interior

ventilation

Any operator booth in the plant which is supplied with air coming

from the inside of the facility

Operator booth—exterior

ventilation

Any operator booth in the plant which is supplied with air coming

from the exterior of the facility

Other departments All non-production areas (administration, labs, maintenance shops)

Standardized jobs

Raw material receiving and

supply

Employees working from the receiving area to the wood-resin mixture area

Chip preparation operator Any employee working in the chip preparation area (OSB only)

Resin operator Mixes the ingredients required for the production of the resin and takes

samples at regular intervals for quality control purposes

Press operator Operates the press from within an operator booth

Assistant press operator Spends his shift partly in the press operator booth and partly close to the

mat forming, the press and the cooling wheel to take readings of process

parameters for quality control purposes

Finisher Includes workers operating the sanding, trimming or painting machines,

workers visually assessing the quality of the panels and floaters assigned

exclusively to finishing operations

Flat mill operator Includes all workers assigned to the operation of laminating the panels

with printed paper overlays

Shipper Includes forklift operators in the shipping area and automatic packing

machine operators

Lab technician Includes workers responsible for taking and/or analyzing samples of

panels and other materials for quality control purposes

Cleaner Worker operating the mechanical sweeper throughout the facility

Foreman Foreman or production supervisor

Maintenance worker Any mechanic or electrician working inside the facility

Press—miscellaneous tasks Includes all workers performing tasks in close proximity to the press

during their shift, mostly outside operator booths (e.g. surveillance of

machinery, cleaning around the press with compressed air)

Floater Includes workers assigned to various locations in the facility (mainly finishing

and main production areas), mostly outside operator booths

Administration Includes employees in the administration, management or

engineering departments

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by the following equation:

lnCijk 1⁄4 Xð Þþ Fixed:effects ð Þ Random:effectA i

þ ð Þ Random:effectB ij þ ð Þ Error ijk

i 1⁄4 1, ... , M; j 1⁄4 1, ... , Mi;

k 1⁄4 1, ... , Mij ð1Þ

where there are M groups for variable A, Mi groups for

variable B in the ith group of variable A and Mij

observations in the jth group of variable B in the

ith group of variable A. The total number of observations is PM

i1⁄41

PMi

j1⁄41 Mij : ln Cijk is the logarithm

of the kth observation in the jth group of variable

B in the ith group of variable A. The model assumptions are: (Random.effectA) and (Random.effectB) are

distributed normally with mean 0; (Random.effectA),

(Random.effectB) and (Error) are statistically

independent; and (Error) follows a multinormal distribution with mean 0 and different possible variancecovariance structures.

All fixed effects tested for inclusion in the model

are presented in Table 2. The year of sampling was

tested as either a continuous variable or a nominal

variable representing different time periods. The

facility variable was tested as a random effect for

the first level of grouping, with the sampling campaign as a random effect for the second level of

grouping. The facility variable was tested as a random

effect to determine the extent of similarity of exposures among different plants manufacturing the same

product with the same process. The sampling campaign was tested as a random effect to explore potential correlation between measurements made in a

small time window after controlling for the fixed

effects. The correlation between measurements

taken during the same campaign can be determined

from the estimated inter-group and intra-group variances by the calculation of the intra-class correlation

coefficient (ICC, equation 2).

ICC 1⁄4 s2

B

s2

B þ s2W

ð2Þ

where s2

B is the inter-group variance and s2W is the

intra-group or residual variance.

The variance structures tested for the error term

were: a different residual variance s2

W

 for each

level of one of the available nominal variables

(equation 3) and a residual standard deviation varying

linearly (equation 4) or exponentially (equations 5

and 6) with one of the continuous variables. Because

of the unbalanced nature of our datasets and the limited number of measurements, interactions were not

tested in the models.

Different models are tested for the residual standard

deviation (sW):

sW 1⁄4 bi ð3Þ

where bi is to be estimated, i = 1, ... , n; n is the

number of categories of the nominal variable,

sW 1⁄4 C  ðÞ ð bX 4Þ

sW 1⁄4 C  expðÞ ð bX 5Þ

sW 1⁄4 C  expð Þ b ln X ð6Þ

Table 2. Variables tested in the statistical models

Variable Variable type

(number of categories)

Description

Analytical method Nominal (3) Analytical method used for formaldehyde quantification (the three methods are

consecutive over time with some overlap)

Measurement

duration

Continuous (hours) Time covered by the measurement

Number of tubes Continuous Number of sampling tubes used during the measurement

Origin of data Nominal (2) Origin of data, believed to be indicative of differing sampling strategies

(the two categories are: research team, governmental data)

Standardized job Nominal (15) For each personal measurement, the workers were assigned to a standardized

job category based on their job description (e.g. floater, see Table 1)

Standardized zone Nominal (8) Each area measurement was assigned to a standardized zone corresponding to

a broadly defined location in the plant (e.g. finishing area, see Table 1)

Sampling date Continuous Date on which measurement was taken

Plant Nominal (12) Code of the plant sampled

Process Nominal (3) Code of process in operation at the plant (OSB/PB/MDF)

Season of sampling Nominal (4) Season of sampling as defined by the following cut-off dates: winter

(12/22 to 3/20), spring (3/21 to 6/21), summer (6/22 to 9/22)

and autumn (9/23 to 12/21)

Sampling campaign Nominal

(varying number

of classes)

A sampling campaign was defined as a group of measurements taken

<4 days apart in the same plant.

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where C and b are to be estimated and X is the

continuous variable.

The S-plus software provides two ways of modelling random effects, which yield a slightly different

interpretation of the parameters when the residual

error is not modelled as homoscedastic: the lme function provides an estimate of the inter-group variability (s2

B in equation 2), therefore yielding a variable

intra-group correlation (ICC) if the residual (or intragroup) variability (s2

W in equation 2) has a heteroscedastic structure. The glsfunction provides an estimate

of the intra-group correlation, therefore yielding a

variable inter-group variability if the residual (or

intra-group) variability has a heteroscedastic structure. The lme function was preferred in our study

because it allows modelling of several levels of

nested random effects.

REML optimization was used to choose the random

effects and residual variance structures and estimate

the final model parameters. ML optimization was

used to compare models with different fixed effects

structures (Pinheiro and Bates, 2000). Model building

was performed by means of the following procedure:

The best fixed effects model was first constructed

with a forward stepwise routine using the Bayesian

information criterion (BIC) as a discrimination criterion. Then the best random effect structure was

added by comparing the BIC of the four possible

models (no random effect, random effect A, random

effect B, random effects A and B). Finally the variance structure for the residual error was assessed in a

similar way. The next step consisted in retesting the

fixed effects for removal or addition of variables. The

random effects and variance structure were adjusted

again if the fixed effect model had changed.

In order to illustrate the quantitative influence on

exposures of the fixed effects coded as nominal variables, relative indices of exposure (RIE, equation 7)

were calculated. Hence, for each variable, the category corresponding to the highest number of observations (the reference category) was assigned the

value 100%. The RIE of each of the other categories

of the variable was then calculated by finding the

exponent of the difference between the estimated

coefficient for that category and the one for the

reference category.

RIElevelAð Þ %

1⁄4 100  exp CoefflevelACoeff ð Þ levelRef ð7Þ

where RIElevelA is the relative index of exposure for

level A of the variable in question, CoefflevelA is the

estimated coefficient corresponding to the category A

and CoefflevelRef is the estimated coefficient corresponding to the reference category. CoefflevelRef is

0 when the reference category is included in the

intercept. Thus, relative to the reference category,

exposure levels associated with other categories are

estimated as percentages.

Internal validation was primarily conducted by

graphical assessment of residuals and estimates of

random effects regarding the assumptions underlying

the estimation. There was no external validation of

the final models.

All analyses were conducted with the statistical

software S-Plus 6.1 professional edition for Windows

Release 1 (Insightful Corp., Seattle, WA).

RESULTS

Information on the plants

Among the 12 plants visited, 6 manufactured OSB

panels and had a median workforce of 120. Three

plants manufactured PB panels, with a median workforce of 140. Three plants produced MDF panels,

with a median workforce of 71. All plants except

one OSB facility had exhaust ventilation systems

extracting air above the press. The remaining plant

had only a roof opening above the press. In all the

plants most of the production jobs were on a 12-hour

shift structure, with 3 different teams working alternately. No operation requiring the use of respiratory

protection was monitored during the visits performed

by the research team.

Data collection

The visits performed by the research team yielded

275 measurements while 590 were available from

the governmental files. Fifty nine measurements

were removed from this dataset prior to further

analysis: 10 corresponding to a job performed in

only one facility and for only a few months, 7 corresponding to task sampling or infrequent events,

6 samples considered ‘dubious’ by the technician,

1 sample taken directly above the press beside the

exhaust system, 22 because they were labelled ‘other’

based on the job or work zone classification and 13

because the year of sampling was missing. In addition, 47 measurements were not included in the modelling datasets because they corresponded to jobs

existing in only one or two of the three processes

in the study. Inclusion of these data in the statistical

modelling would have required nesting the variable

job inside the variable process, which could not be

achieved due to the unbalanced nature of our data.

These 47 measurements were nevertheless included

in the descriptive analysis. The variables documented

for both sources of data are presented in Table 2.

Area measurements

Summary statistics along with the empirical distributions of area measurements stratified by their origin

(research or governmental) are presented in Figure 1.

The percentages of measurements below the LOD

were 12 and 8 for the research measurements and

the governmental data, respectively. The research

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and governmental measurements were taken, respectively, during 11 and 63 sampling campaigns. Concentrations reported by the research team covered a

median duration of 4.9 h (interquartile interval:

[2.4–5.9]). The corresponding values were 2.1 h

[2.0–5.6] for the governmental data. Table 3 presents

the GMs, geometric standard deviations (GSDs) and

corresponding number of measurements stratified

by work zone, process and origin of the data.

The final ‘whole dataset’ model includes the following fixed effects, which explains 57% of the total

variance: Process, Work zone, Season of sampling,

Origin of data and Year. The coefficients of all fixed

effects of the model are presented in Table 4, along

with their standard error. The estimated REIs associated with each work zone are presented in Figure 2

along with an 95% confidence interval (95% CI).

The presented CIs correspond to the comparison of

the levels of each variable with the reference category. Their exact interpretation is that if they

exclude the 100% value, the exposure level associated with the category is significantly higher (or

lower) than the exposure level associated with the

reference category. The REIs corresponding to the

other categorical variables are presented in Figure 3.

The best fit for the sampling year was obtained with

a linear spline structure containing one knot, the linebreak year being 1991. The other tested structures

were a nominal variable with three levels (1984–

1989, 1990–1994 and 1995–2002), a linear, and a

quadratic trend (one 1st order term and one 2nd

order term). The line-break year was determined

by graphical assessment and refined by using the

BIC. The estimated coefficient showed a yearly

decrease of 19.7% until 1991 followed by a yearly

increase of 9.4%.

A one-level random effect structure with the

variable Sampling campaign as the random effect

yielded the best fit compared with no random effect,

Facility alone or Sampling campaign within Facility.

The best residual variability structure in terms of BIC

was one with different residual standard deviation for

each work zone. With the Finishing zone as the reference (i.e. value 100%), the relative residual standard deviations estimated by the model for the other

work zones are as follows: Main production (153%),

Resin production-storage (136%), Storage-shipping

(131%), Raw materials (206%), Other department

(226%), Operator booths (both interior and exterior)

(119%). The estimated within-sampling campaign

residual standard deviation is 0.57 for the reference

category Finishing zone while the between-sampling

campaign standard deviation is 0.36, giving ICC values varying between 0.07 and 0.29, with an average

of 0.17.

Graphical assessment of the distribution of the

normalized residuals yielded satisfactory fit to

the normal distribution. Likewise, the estimates of

the random effects indicated graphical conformity

to the normal distribution. No systematic trend was

found during examination of the variations of residuals and estimated random effects stratified by the

different levels of the other available variables, indicating satisfactory conformity to the independence

hypothesis.

Personal measurements

Summary statistics of the personal measurements

are presented in Figure 4. The percentages of

measurements below the LOD were 23 and 9 for

the research measurements and the governmental

data, respectively. The research and governmental

Concentration (ppm)

Cumulative probability

0123

0.0 0.2 0.4 0.6 0.8 1.0

Gov. data (n=341 )

Geometric mean: 0.27 ppm

Geometric standard deviation: 3.8

Research data (n=134 )

Geometric mean: 0.13 ppm

Geometric standard deviation: 3.8

Gov. data

Research data

Fig. 1. Cumulative distributions of area measurements stratified by origin of the data.

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personal measurement were taken during 12 and 67

sampling campaigns, respectively. Concentrations

reported by the research team covered a median

duration of 4.8 h (interquartile interval: [3.3–6.0]).

The corresponding value was 5.9 [4.4–8.0] for the

governmental data. GMs, GSDs and corresponding

number of measurements stratified by job, process

and origin of the data can be found in Table 3.

The initial ‘whole dataset’ model included the following fixed effects, which explained 62% of the total

variance: Process, Job, Season of sampling, Origin of

data, Year and Measurement duration. Further examination of the coefficients for the different jobs led to

the testing of the grouping of jobs into four exposure

groups: group 1 includes Administration and Foreman, group 2 includes Laboratory technician, Maintenance worker and Cleaner, group 3 includes Press

operator, Assistant press operator, Finisher, Shipper,

and group 4 includes Floater and Press-miscellaneous tasks. The resulting model yielded a better fit

than the original one in terms of BIC. The percentage

of total variance explained by the fixed effects was

marginally reduced (to 61%). The RIEs associated

with each job group are presented in Figure 5

while the RIEs corresponding to the other nominal

variables in the model can be found in Figure 3. The

coefficients for the fixed effects of the reduced model

can be found in Table 4.

The best fit for the sampling year was obtained with

a linear spline structure containing one knot, the

line-break year being 1995. The estimated trend corresponds to a yearly decrease of 7% until 1995 followed by a yearly increase of 12.2%. The estimated

influence of the measurement duration on exposure

levels corresponds to a decrease of 5.2% when the

duration is increased by 60 min (95% CI: 2.1–8.2).

A one-level random effect structure with the

variable Sampling campaign as the random effect

yielded the best fit compared with Facility alone or

Sampling campaign within Facility. The best residual

Table 3. GMs (p.p.m.) and GSDs of all formaldehyde exposure concentrations stratified by job/zone, process and origin of the data

OSB process MDF process PB process

Research Governmental Research Governmental Research Governmental

GM GSD (n) GM GSD (n) GM GSD (n) GM GSD (n) GM GSD (n) GM GSD (n)

Personal data

Administration — — 0.04 1.1 (7) — — 0.03 1.3 (6) — — — —

Assistant press

operator

0.04 1.3 (4) 0.07 1.8 (7) 0.09 2.0 (5) 0.27 1.6 (15) 0.20 1.3 (5) 0.28 1.4 (6)

Foreman — — 0.05 (1) — — — — 0.08 (1) — —

Shipper 0.06 1.5 (7) 0.10 (2) 0.19 2.0 (4) — — 0.16 1.7 (7) 0.20 2.3 (20)

Finisher 0.05 1.6 (17) 0.07 1.2 (10) 0.16 1.8 (6) 0.24 1.4 (10) 0.19 1.6 (12) 0.26 1.9 (20)

Cleaner 0.05 1.6 (11) 0.54 (1) 0.08 (1) — — 0.12 2.3 (4) 0.26 3.0 (8)

Floater 0.04 (2) 0.22 1.9 (5) — — — — — — — —

Press—miscellaneous — — — — — — — — 0.23 (2) 0.45 1.8 (9)

Press operator 0.03 (1) 0.08 (2) — — 0.07 (1) — — 0.26 1.7 (7)

Lab technician 0.05 1.4 (4) 0.06 1.4 (4) 0.14 (1) 0.31 (2) 0.16 (1) 0.42 (2)

Maintenance worker 0.06 1.7 (8) 0.08 2.7 (4) 0.12 1.6 (4) 0.11 2.0 (13) 0.10 2.1 (5) 0.22 2.2 (9)

Resin operator — — — — 0.22 (1) 0.34 3.2 (5) 0.16 (1) 0.41 2.1 (8)

Chip preparer 0.04 1.3 (4) 0.11 2.9 (5) — — — — — — — —

Flat mill operator — — — — — — — — 0.12 1.6 (8) 0.17 2.0 (15)

Area data

Operator booth

(internal vent)

— — — — — — — — 0.68 (1) 0.31 1.5 (12)

Operator booth

(external vent)

0.02 1.3 (3) 0.01 3.0 (4) 0.07 3.2 (3) — — 0.09 2.1 (7) — —

Non-production

department

0.04 (2) 0.03 2.0 (9) 0.04 (1) 0.06 4.1 (21) 0.14 (2) 0.09 4.1 (5)

Storage–shipping 0.04 2.3 (5) 0.06 1.4 (4) 0.17 1.9 (3) 0.37 2.1 (17) 0.09 3.9 (5) 0.17 2.7 (21)

Finishing 0.09 1.8 (9) 0.09 3.1 (10) 0.22 2.5 (6) 0.33 2.1 (19) 0.20 2.0 (13) 0.29 1.7 (33)

Main production 0.05 2.5 (29) 0.15 2.0 (25) 0.26 2.1 (15) 0.75 2.5 (95) 0.75 3.4 (22) 0.56 2.1 (29)

Resin production storage 0.05 (2) 0.09 1.1 (3) — — 0.35 2.1 (18) 0.08 (1) 2 (1)

Raw material—chip

preparation

0.08 (2) 0.10 1.9 (8) 0.16 (2) — — 0.42 (1) 0.03 2.3 (7)

GM, geometric mean; GSD, geometric standard deviation; n, number of available measurements.

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variance structure in terms of BIC was one with

different residual standard deviations for each process. With the process PB as the reference (i.e. value

100%), the relative residual standard deviations

estimated by the model for the other processes are

as follows: MDF (68%), OSB (63%). The estimated

within-sampling campaign residual standard deviation (of the log-transformed concentrations) is

0.53 for the reference category while the betweensampling campaign standard deviation is 0.46, giving

ICC values varying between 0.42 and 0.66, with an

average of 0.56.

Table 4. Coefficient estimates of the final models

Area measurement model Personal measurement model

Fixed effect Estimate SE Fixed effect Estimate SE

Intercepta 0.89 0.29 (Intercept)b 2.48 0.54

Process OSB 1.82 0.19 Process OSB 0.97 0.15

Process PP 0.47 0.17 Process PP 0.01 0.16

Operator booth (external vent) 1.62 0.19 Job group 1c 0.86 0.16

Operator booth (internal vent) 0.25 0.26 Job group 2c 0.16 0.07

Finishing 0.52 0.09 Job group 4c 0.62 0.14

Non-production department 1.96 0.22 Research data 1.06 0.23

Raw material–chip prep 1.25 0.28 Autumn 0.27 0.16

Resin production–storage 0.59 0.17 Spring 0.65 0.17

Storage–shipping 0.86 0.12 Summer 0.58 0.25

Research data 1.09 0.26 Measurement duration (h) 0.05 0.07

Autumn 0.39 0.17 (year-1984) 0.08 0.03

Spring 0.74 0.20 max (year-1984,11)d 0.19 0.06

Summer 0.70 0.19 % Total variance explained

by the fixed effects

(year-1984) 0.22 0.07 Personal: 61%

max (year-1984,7)d 0.31 0.08 Area: 57%

a

The intercept represents the estimated AM of the log-transformed concentrations (p.p.m.) for the following combination: MDF,

main production zone, governmental data, winter and 1984. Estimates of the AM of the the log-transformed concentrations for

other combinations of the levels of the variables are obtained by adding the appropriate coefficients,

e.g. substracting 1.82 from the intercept to change the process to OSB.

b

The intercept represents the estimated AM of the log-transformed concentrations for the following combination: MDF, job group 3,

governmental data, winter, duration of measurement 0 h and 1984.

c

Group 1 includes Administration and Foreman; group 2 includes Lab technician, Maintenance worker and Cleaner; group 3

includes Press operator, Assistant press operator, Finisher, Shipper group 4 includes Floater and Press—miscellaneous.

d

Coefficient corresponding to the spline: multiply the coefficient by either (year-1984) or the given number (7 or 11) whichever is

greater. Practically, this corresponds to a constant added to the intercept up to the spline knot at which point the coefficient

increases linearly with years.0 20 40 60 80 100 120 Relative index of exposure (%)

Raw mat. - Chip preparation

Resin prod., storage

Reference: Main production=100%

Finishing

Shipping

Administration

Op. booth - exterior vent.

Op. booth - interior vent.

29%

56% 59%

42%

14%

20%

78%

Approx. 95% CI

Fig. 2. Relative exposure indices of the different zones in the final area model.

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As for the area measurements, graphical assessment of the distribution of the normalized residuals and of the estimates of the random effects

yielded satisfactory fit to the normal distribution

and satisfactory conformity to the independence

hypothesis.

Table 5 presents, side by side for Research and

Government data, yearly GMs of area and personal

levels stratified by work zone/exposure group and

estimated for the year 2002 by the statistical models.

For the personal measurements, the estimates were

calculated for a duration equal to the median duration

of the measurements (5.6 h).

DISCUSSION

Determinants of exposure to formaldehyde in the

reconstituted wood panel industry

The percentages of total variability explained by

the fixed effects of both personal and area models (61

and 57%, respectively) compare favourably with similar studies in the field of occupational exposure

assessment (ranging between 20 and 70%) (Burstyn

and Teschke, 1999; van Tongeren and Gardiner,

2001; Bakke et al., 2002). Thus, strong determinants

of exposure to formaldehyde in this industry have

been identified.

0 20 40 60 80 100 120 140

A

P

A

P

A

P

A P A

P

A P

Relative index of exposure (%)

Reference : MDF=100%

Reference : Winter=100%

Reference: Gov. data=100%

PB OSB Autumn

Spring

Summer

Research

Approx. 95% CI

A : Area Model

P : Personal model

Fig. 3. Relative exposure indices of the predictive variables common to the area and personal models.

Concentration (ppm)

Cumulative probability

0.0 0.5 1.0 1.5 2.0

0.0 0.2 0.4 0.6 0.8 1.0

Gov. data (n=205 )

Geometric mean: 0.17 ppm

Geometric standard deviation: 2.6

Research data (n=126 )

Geometric mean: 0.09 ppm

Geometric standard deviation: 2.1

Gov. data

Research data

Fig. 4. Cumulative distributions of the personal measurements stratified by origin of the data.

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The estimated coefficients for the variable Process

confirm, in both area and personal models, the lower

potential for formaldehyde emissions when PF resins

are used (all OSB facilities in our study used PF

resins). However, while personal exposures tend to

be similar for MDF and PB, MDF ambient levels

seem higher than PB levels. This difference might

be partly explained by the fact that measures to prevent worker exposure, such as reducing time spent in

proximity to the press, might have been implemented

to a greater extent in MDF. However, confounding by

variables not accounted for in the modelling process

cannot be ruled out as an explanation of this observation.

For both area and personal models, the origin of

data was also a strong predictor of formaldehyde

levels, research data being lower than governmental

data by a factor of 3 (Figure 3). Several hypotheses

may explain this observation also apparent in the

empirical cumulative distributions shown in Figures 1

and 4. First, the research visits having been planned

long in advance with the management of the plants

and after several meetings with representatives of this

industry, it is possible that ‘favourable’ production

0 50 100 150 200 250

Relative index of exposure (%)

Job group 1

AdministrationForeman

Job group 2

Lab tech., CleanerMaintenance worker

Press op., Assistant press op.Finisher, Shipper

Referencecategory : 100%

42%

85%

187%

Approx. 95% CI

Job group 3

Job group 4

FloaterPress Misc.

Fig. 5. Relative exposure indices of the different exposure groups in the final personal model.

Table 5. Yearly GMs (in p.p.m.) for research and government data stratified by standardized zones and jobs, estimated by the

statistical models for 2002

OSB PB MDF

Research Government Research Government Research Government

Area measurements

Operator booth (exterior vent) 0.01 0.04 0.05 0.16 0.09 0.25

Operator booth (interior vent) 0.05 0.16 0.21 0.62 0.34 1.00

Finishing 0.04 0.12 0.16 0.48 0.26 0.76

Non-production department 0.01 0.03 0.04 0.11 0.06 0.18

Raw material—chip prepreation 0.01 0.04 0.05 0.16 0.09 0.25

Resin production–storage 0.04 0.12 0.15 0.45 0.24 0.71

Storage–shipping 0.03 0.09 0.11 0.34 0.18 0.54

Main production 0.07 0.21 0.27 0.80 0.43 1.28

Personal measurements

Job group 1a 0.02 0.05 0.05 0.14 0.05 0.14

Job group 2b 0.04 0.11 0.10 0.28 0.10 0.28

Job group 3c 0.04 0.13 0.12 0.33 0.11 0.33

Job group 4d 0.08 0.23 0.22 0.62 0.21 0.62

a

Includes Administration and Foreman.

b

Includes Lab technician, Maintenance worker and Cleaner.

c

Includes Press operator, Assistant press operator, Finisher, Shipper.

d

Includes floater and Press—miscellaneous.

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conditions were implemented on the days of the

visits. This would cause a negative bias compared

to real exposure conditions. While Olsen et al.,

among others, have mentioned this source of bias

as a possibility, it has, to our knowledge, not been

further discussed in the published literature (Olsen

et al., 1991). A second explanation is confounding

by the temporal variable in the models. All research

measurements were taken in 2001–2002 while some

governmental data date back to 1984. This points to a

potential collinearity issue between the two variables.

This possibility was further explored in two different

ways. First, the area and personal models were fitted

to the data restricted to years common to both

research and governemental datasets (2001 for the

area measurements and 2001 and 2002 for the personal measurements). For the area analysis, with 109

observations (of which 26 were governmental), the

estimated relative exposure index for the research

data (the governmental data constitutes the reference

category) was 47% compared to 34% for the initial

model. For the personal analysis, with 138 observations (of which 24 were governmental), the estimated

relative exposure index for the research data was 45%

compared to 35% for the initial model. These results

support the presence, albeit limited, of some confounding by the temporal trends. Furthermore, the

full models were fitted without the ‘origin of data’

variable to datasets restricted to governmental data

in order to assess the robustness of the observed

temporal trend. For the area analysis, with 341

observations, the coefficients for (year-1984) and

[max(year-1984,11)] (see Table 4) were, respectively, 0.17 (0.22 for the initial model) and 0.25

(0.31 for the initial model). For the personal analysis,

with 171 observations, the coefficients for [year1984] and [max(year-1984,7)] were, respectively,

0.10 (0.07 for the initial model) and 0.23 (0.29

for the initial model). These results tend to show that a

temporal trend actually exists independently of the

origin of the data, and that the estimated trend is close

to that observed in the global models. Thus, although

a degree of overestimation of the difference between

research and governmental data may exist because

of confounding, its extent should be small. We

believe that, because of the limited resources that

can be devoted to sampling during industrial visits,

the hygienists tend to monitor worst-case scenarios in

order to optimize the interpretation of their results.

Since, by devoting more sampling resources to

monitor supposedly ‘high’ exposure jobs or area,

the strategy used by the researchers might be

regarded as biased if jobs or work-zones are not controlled for, it must be concluded that within the jobs

and area classification used in this study, governmental teams tended to sample tasks or sub-areas

specifically associated with high formaldehyde

concentrations.

The ‘worst-case’ bias in data from governmental

sources, often associated with compliance monitoring, has already been mentioned in the literature

(Stewart and Rice, 1990; Vinzents et al., 1995).

Olsen et al., reported an actual comparison of measurements taken with a random sampling strategy with

measurements existing in a governmental database.

The authors found that the governmental data were

higher than the ‘random’ data by a factor ranging

from 5 to 10, which is compatible with our interpretation. On the other hand, a variable identifying the

sampling strategy as either ‘research’ or ‘compliance’

did not improve the fit of a multiple regression model

applied to softwood dust levels in British Columbia

lumber mills (Hall et al., 2002).

It might be argued that research and governmental

data should not be analysed together because they

may not be influenced by the same determinants.

In order to explore that possibility, both area and

personal models were fitted to a restricted dataset

to allow the testing of 1st order interactions between

Origin of data and the other fixed effects, and of a

possible difference of residual variance structure for

the research and governmental data. With respectively 392 and 236 data available for analysis for

the area and personal measurements, none of the

tested additional models improved the fit compared

to the original ones. In addition to the increased

statistical power and the fact that the research and

governmental measurements were taken in the same

workplaces, we believe that the merged analysis of

both types of data was justified in our case.

A significant time trend, best modelled by a one

knot linear spline, existed both for the area and personal measurements. The use of linear splines to

model irregular temporal trends in occupational

exposures has already been reported (RaaschouNielsen et al., 2002; Friesen et al., 2003). Although

the spline knot year was different for the personal and

area measurements (1995 and 1991), both estimated

trends show high exposures at the beginning of the

study period, decreasing until 1991–1995 and then

increasing, although moderately, until the end of

the study period (2002). Both the nominal and quadratic coding of the year of sampling yielded the

same temporal pattern. This pattern differs from

the reported generic occupational exposure decrease

over time reported by Symanski et al. (Symansky

et al., 1998, 2001). A possible explanation might

be the documented important increase in production

in that industry in North America in the middle of the

90s (Spelter, 1997).

The other influent variable common to both the

area and personal models is the season of sampling.

Thus, winter, and, to a lesser extent, autumn, are

associated with higher exposures than summer and

spring. From direct observation and interviews with

the employees of the visited plants, it appears that

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during the cold season (in Quebec temperatures are

commonly below 15C during this period) the ratio

of recirculated air to fresh outside air is increased

because of associated heating costs. Thus, the facilities tend to be in a negative pressure relative to the

outside, which in turn causes the exhaust systems to

lose efficiency. Furthermore, most doors and windows are open during the hot season, improving

the air replacement rates. van Tongeren and Gardiner

have reported lower exposure levels during summer

for some job titles in the carbon black manufacturing

industry (van Tongeren and Gardiner, 2001).

For the area model, the work zone had a significant

influence on exposure levels. The main production

area is associated with the highest exposures, with

levels decreasing as one gets further from the press to

the finishing, and then the shipping areas. As expected, departments separated from the main production

area are associated with low formaldehyde levels.

The results shown in Figure 2 also demonstrate the

importance of supplying operator booths with outside

air. Thus, the exposure levels inside operator booths

ventilated with air from the plant are comparable to

those found outside the booths (most operator booths

with such ventilation were in the finishing zone).

Regarding the personal measurements, the modelling of the variable ‘job’ allowed the identification of

four similar exposure groups, presented hereafter

from the least to the most exposed: group 1 includes

employees spending most of their time outside the

production zone (e.g. foreman), group 2 includes

employees spending part of their shift in the production zone (e.g. mechanics), group 3 includes workers

spending their whole shift in the production zone but

with a variable proportion of time spent in operator

booths (e.g. press operator) and group 4 includes

workers spending their whole shift in the production

zone unprotected by operator booths (e.g. floater).

Higher personal exposure levels were also associated with shorter measurement duration. This trend

was not modified when fitting the model separately to

research and governmental data. It may be partly

explained by the fact that longer measurements

may include unexposed periods, such as breaks

(Raaschou-Nielsen et al., 2002). Since there was

no significant interaction between duration and the

source of data for the personal measurements,

together with the absence of any influence in the

case of area measurements, we conclude that the

upward bias found in governmental data is not due

to merely shorter measurement durations associated

with a task-based strategy.

The significance of the variables identifying workzones and jobs in both models underlines the usefulness of the standardized lists created during this

study. They appeared as potentially strong predictors

of exposure to formaldehyde, based on technical literature and field observation: this was validated and

refined by the statistical modelling. Consequently,

their use should be explored in other studies of formaldehyde exposure in the same industry.

The determinants identified in our study will be

useful from the industrial hygiene standpoint for

devising sampling strategies in this industrial sector,

for example to help identify a priori potential

overexposure situations. Within the framework of

epidemiology, the identification of time trends is

important for retrospective exposure assessment,

while determinants, such as jobs or process can

help in the elaboration of specialized questionnaires

in population-based case-control studies.

Structures of variance-covariance of exposure data

Random effects models have recently been used to

explore patterns of correlation among occupational

exposure measurements. However, the studies have

mostly focused on modelling intra- and inter-worker

variability (Kromhout et al., 1993; Rappaport et al.,

1999, 2003; Burstyn et al., 2000; van Tongeren and

Gardiner, 2001). In our study, information on the

identity of workers was not available in the majority

of governmental files. Moreover, the sampling campaigns performed by the research team, lasting 2 days

at the most, did not allow resampling the same workers because of the rotating teams work structure.

Failing to model intra- and inter-worker variability

constitutes a limitation of the present study in that

this hampers rigorous conclusions about the longterm risks posed to workers in this industrial sector.

In particular we can not estimate probabilities of

overexposure of a random worker as described by

Rappaport et al. (Rappaport et al., 1995; Lyles et al.,

1997). However the estimated yearly GMs that we

present in Table 5 allow drawing a general picture of

exposure levels in this industrial sector, together with

our results on the determinants of exposure.

Although the variable Facility was tested as a

random effect, the results of both area and personal

models show no significant intra-facility correlation,

indicating similarity of exposure levels across facilities when all other variables are taken into account.

This result is plausible since during the industrial

hygiene visits by the research team, it was observed

that most facilities were quite similar in terms of

architecture (the press being the main emission

source inside the plant, contaminating other areas),

machinery used and exposure control measures.

The variable sampling campaign improved the fit

significantly when tested as a random effect. This

corresponds to the fact that after controlling for all

fixed effects in the models, there are systematic

differences between formaldehyde levels measured

during different campaigns, and is equivalent to

the existence of correlation among measurements

taken during the same campaign. The average

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intra-sampling campaign correlation coefficients

estimated in our study were 0.17 and 0.56, respectively for the area and personal measurements.

Teschke et al. report a coefficient of correlation of

0.31 between wood dust levels measured during the

same inspection of data taken from OSHA’s occupational exposure database (Teschke et al., 1999).

While some authors have reported evidence of correlation between shift-long measurements taken on

consecutive days (Buringh and Lanting, 1991;

SymanskiandRappaport,1994;Deadmanet al.,1996),

some others observed no evidence of such correlation

(Francis et al., 1989; George et al., 1995). Our results

confirm the presence of correlation between shiftlong exposures measured in short time periods in

this industrial sector, implying a potential for underestimation of the day-to-day exposure variability

when an assessment is based only on one sampling

campaign conducted over a few consecutive days.

Significant structures of heteroscedasticity of the

error term were found in both area and personal models. Hence, personal exposure appeared more variable

for the PB process than for MDF and OSB. Moreover,

ambient formaldehyde levels from the work zones

Main production, Department other than production,

and Raw material reception were much more variable

than those measured in other locations in the facilities. The variability of exposure levels determines the

number of samples necessary to assess an exposure

situation with adequate precision. Furthermore, with

regard to statistical modelling, estimates of other

parameters in the model depend on the variancecovariance structures in the case of unbalanced

data, which is present in most datasets in this field

of research. Therefore it appears important to take

into account and explore such structures of

variability when modelling occupational exposures

(Pinheiro and Bates, 2000).

Formaldehyde exposure levels in the reconstituted

wood panel industry in Quebec

The observed (Table 3) and estimated (Table 5)

levels of exposure to formaldehyde in this industry

are consistent with the few results reported in the

most recent literature (posterior to 1985). In particular, the exposure levels reported by Zimowski show

very close agreement with those measured in Quebec.

Exposures in the OSB manufacturing industry are

mostly below 0.1 p.p.m. with some work zones/jobs

associated with somewhat higher levels according to

the governmental data. Exposure levels in the MDF

and PB manufacturing industries are similar, with

observed and estimated GMs of personal and area

levels between 0.1 and 0.4 p.p.m. depending on the

source of data. The highest estimated ambient GM is

0.43 p.p.m. in the main production area of MDF

manufacturing facilities (this estimate is increased

to 1.28 p.p.m. when governmental data are used).

The highest personal estimated GM is 0.22 p.p.m.

for workers in close proximity to the press in

the PB process (this estimate is increased to

0.62 p.p.m. if the governmental data are considered).

Levels reported prior to 1985 are consistently higher

than those in our database. This maybe explained by

the generalized implementation, around 1985, of low

formaldehyde emission resins (OSHA, 1987).

Area measurements are consistently higher than

personal measurements. This is consistent with the

fact that the most exposed workers are those who

spend the most time in the production area unprotected by ventilated booths. While none of the personal

measurements in this study was greater than 2 p.p.m.

(the current Quebec ceiling OEL), 4 and 6% of the

research and governmental ambient concentrations,

respectively, were greater than this value.

Globally, our results point to generally low personal full-shift exposure to formaldehyde in this

industrial sector. However, the potential for shortterm high exposures associated with specific and

occasional tasks cannot be ruled out on the basis of

our study. Several OELs exist for formaldehyde,

varying both in type and level. In the US, OSHA

enforces an 8 h TWA limit of 0.75 p.p.m. with a

short-term exposure limit (STEL) of 2 p.p.m. The

ACGIH recommends a ceiling limit of 0.3 p.p.m.

(ACGIH, 2003).

Validity of the statistical models

The internal validity of our models appears satisfactory considering the results of the graphical assessments of residuals and estimates of random effects

described earlier. In addition, bootstrapping the

model estimates 1000 times (results not shown) resulted in the observation that among 29 coefficient

estimates for the two models the absolute relative

difference between the model and the bootstrap

estimate was >5% only in six cases, with a maximum

of 17%. The sign of the difference between the two

estimates was negative almost the same number of

times as it was positive. The standard errors of the

bootstrap estimates were on average 30% smaller

than the corresponding model estimates, indicating

moderate overestimation of the widths of the confidence intervals presented in Figures 2, 3 and 5. These

results support reliance on the asymptotic assumptions linked to the estimation by ML or REML of

confidence intervals for the model parameters

(Harrel, 2001).

Although there was no formal external validation of

the models, several elements suggest that our results

may be applicable outside the restricted scope of our

dataset. Thus, as seen in Figure 3, the estimates of the

effects of variables common to the area and personal

measurements are similar. Moreover, our estimates of

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the influence of several potential determinants of

exposure are consistent with published observations.

Finally, the exposure levels estimated by the models

are similar to those reported in the most recent literature. These observations, while not constituting an

external validation per se, provide some insight into

the potential for generalization of our results. However, the presence in our dataset of a bias not accounted for can not entirely be excluded since the data

were not generated through a randomized sampling

process.

CONCLUSION

Through statistical modelling of area and personal

exposure measurements performed in the reconstituted wood panel industry in Quebec, several determinants of exposure to formaldehyde in this industrial

sector were identified. The MDF and PB processes

were associated with high exposure levels compared

to OSB. Higher exposures also occurred during winter conditions compared to other seasons. While plant

was not a strong predictor of exposure levels, work

zones and jobs were strongly associated with area and

personal measured concentrations, respectively. The

highest levels were measured in areas close to the

press and on workers spending most of their time

in the press area. Moderate historical variations in

exposure levels were also identified, best modelled

by a one-knot linear spline. Governmental measurements were consistently found higher than those

measured by the research team, pointing to the probable existence of a ‘worst-case’ bias in governmental

data. The use of extended linear mixed-effects models

allowed the identification of a moderate correlation

between measurements taken during the same sampling campaign. Significant heteroscedasticity structures of the error term were also identified during

modelling, stressing the need to take them into

account in similar studies to reduce bias and error

in the estimation of other model parameters. The

measured and estimated time-weighted average

levels of exposure to formaldehyde in this sector

can be considered low compared to the 8 h-TWA

OELs of most jurisdictions (e.g. 0.75 p.p.m.) but

close to the most demanding ones (e.g. 0.3 p.p.m.).

The possibility of higher short-term exposures can not

be ruled out. The successful identification of several

determinants of exposure to formaldehyde in the

reconstituted wood panel industry will allow for

better sampling strategies in this industrial sector.

Furthermore, these determinants may be used in

future epidemiological studies to improve prospective and retrospective exposure assessments.

Acknowledgements—The authors would like to thank Denis

Be ́gin for his help in the interpretation and identification of

the published literature relevant to the reconstituted wood

panel industrial sector. We would also like to thank Jan-Erik

Deadman for his useful and much appreciated comments on the

present manuscript. This research project was funded by the

Quebec Research Institute for Occupational Health and Safety

(IRSST, grant number 099-011 and scholarship for J.L.).

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