Measurement strategies for hazard control will have to be efficient and effective to protect a

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1 EXPOSURE Correspondene to: Dr H Kromhout, Environmental and Oupational Health Division, Institute for Risk Assessment Sienes, University of Utreht, PO Box 80176, Yalelaan, 3508 TD Utreht, The Netherlands; H.Kromhout@iras.uu.nl DESIGN OF MEASUREMENT STRATEGIES FOR WORKPLACE EXPOSURES Hans Kromhout Measurement strategies for hazard ontrol will have to be effiient and effetive to protet a worker s health and well being. No measurement strategy for hazard ontrol will ever be ost effiient in the short run when it is ompared with the promises of tools suh as the Control of Substanes Hazardous to Health (COSHH) essentials (box 1): a simple system of generi risk assessments whih leads to the seletion of an appropriate ontrol approah. 1 Going straight to benhmark standards without the need of exposure measurements will ertainly eliminate the ost of measurements. However, generi risk assessment tools like COSHH essentials and expert systems like the Estimation and Assessment of Substanes Exposure (EASE) (box ), as well as expert judgement by an oupational hygienist, are known to be inaurate and they do not take into aount the various omponents of variability in exposure levels (box 3). In fig 1, results of EASE estimates are ompared with atual measured onentrations. From these pitures it an be seen that EASE estimates tend to be (1) higher than the measured onentrations, and () inaurate espeially at lower true onentrations (< 50 ppm and < 5 mg/m 3 ). Nowadays, the latter exposures are being more relevant for workplaes of the developed world. Proper exposure assessment with the help of measurements will ertainly involve short term osts of monitoring. However, ill advised ontrol measures will arguably be even more ostly in the long run, a lassi ase of being penny wise but pound foolish. VARIABILITY Oup Environ Med 00;59: Measurements of workplae exposures are ruial not only for evaluations of potential health risks, but also for their subsequent redution through effiient ontrol measures. When designing measurement strategies the generally large soures of exposure variability should be appreiated in order to design effiient and effetive protools. Exposure onentrations are known to be extremely variable, espeially within a day over short averaging times (fig ). On the other hand, long term dereasing trends in exposure onentrations at an average rate of 6% per annum have reently been desribed for workplae exposures in western industrialised ountries. 3 Therefore, variability in yearly average onentrations will be muh smaller than variability in eight hour shift long measurements that have been estimated to vary between fold. 4 For 10 seond point estimated levels of magneti field exposure for workers in the utility industry it was estimated that they on average varied an additional 50 fold. 5 (fig ). The extent to whih exposures vary depends on many fators. 4 Some of these are onerned with the agent itself, but the majority are linked to work ontent, tasks performed, prodution, and environment harateristis. In general, analytial and sampling errors play a minor role in overall exposure variability. Reently, a database with repeated shift long inhalation exposure measurements from a variety of workplaes and industries was onstruted and analysed. For a group of workers (defined by job title and loation) it was estimated that personal mean inhalation exposures fall on average within a fivefold range. In a temporal sense, daily exposures were estimated to fall on average within a 15-fold range. 4 Even more reently, a similar database was built for dermal exposure and again there was evidene of a larger temporal omponent than personal omponent. 6 Spatial variability (differenes in exposure between body loations) was estimated to be even more prominent. Breakdowns of variane omponents by industry, prodution, and environmental fators revealed important differenes that should be taken into aount when designing measurement strategies (box 4). For example, for outdoor exposures temporal effets tended to outweigh ompletely minor differenes between workers in the same group. For rews of paving workers with distint jobs this was reently orroborated. Their exposure to bitumen fume appeared to be uniform despite differenes in jobs performed. Temporal variability of their exposure was up to a 30-fold range. 7 *349 Oup Environ Med: first published as /oem on 1 May 00. Downloaded from on 30 Deember 018 by guest. Proteted by opyright.

2 350 * Box 1 Control of Substanes Hazardous to Health (COSHH) essentials: easy steps to ontrol hemials Generi risk assessment tool aimed at ontrolling exposure and meet legal duties by a one stop approah Aimed at the employer Primarily foused at hemials supplied for use at work Not meant for hazards arising from work ativities (for example, wood dust, welding fumes, oil mists, et), hemials like pestiides and veterinary mediines, naturally ourring and biologial hazards, and a few agents suh as lead and asbestos with their own regulations One stop by going from identifying fators whih affet exposure to ontrol approahes to redue exposure involving five steps Four ontrol approahes: general ventilation engineering ontrol ontainment speial Box Estimation and Assessment of Substanes Exposure (EASE) model Expert model developed by Health and Safety Exeutive (UK) Based on measured exposure levels from National Exposure Database (NEDB) Used to assess workplae exposure for risk assessment of new and existing substanes Estimates based on exposure senarios aording to use patterns (4), ontrol patterns (5), and physiohemial properties of substanes (6) Results in 10 possible ombinations that are distilled down to 39 fields for whih ranges of onentrations have been assigned First validation results show onsiderable overestimation of workplae exposure (onservative estimates) Box 3 Group Between group Components of variability in exposure levels Personal Within group or between workers Temporal Within worker or day-to-day Within day Spatial Between body loation Variability in mean exposure levels between groups Variability in mean exposure levels between workers Variability in exposure levels from day to day Variability in exposure levels within a day Variability in exposure levels between body loations STANDARD MEASUREMENT STRATEGIES FOR COMPLIANCE WITH EIGHT HOUR TIME WEIGHTED AVERAGE LIMIT VALUES Traditionally worst ase sampling strategies were thought to be very effiient, beause they foused measurements upon situations where they were most needed. This approah of sampling the maximum risk employee has been widely propagated from the 1970s onwards, primarily as a result of the appearane of the National Institute of Oupational Safety and Health (NIOSH) oupational exposure sampling strategy manual. 8 Similar onsiderations also form the basis of the European Norm 689 Guidane for the assessment of exposure to hemial agents for omparison with limit values. 9 However, beause of the restritions typial of these measurement % differene A Box 4 Workplae situations and number of measurements More measurements Outdoors Intermittent proess Loal exhaust ventilation Mobile worker Loal soure Handwork Fewer measurements Indoors Continuous proess No loal exhaust ventilation Stationary worker General soure Highly automated Measured onentration (ppm) B Measured onentration (mg/m 3 ) Figure 1 Perentage of differene between geometri mean of EASE estimates and geometri mean of measured onentrations against (A) measured gases/vapours onentrations, and (B) measured dust onentrations. Reprodued from Tijdshrift voor toegepaste Arbowetenshap 000;13:38 9, with permission of the publisher. In (MF in nt) Measurement number Figure Exposure profile of 10 seond interval magneti fields (logarithms in nano-tesla) within a shift for a substation eletriian. strategies, they produe inomplete and upwardly biased pitures of exposure onentrations. In addition, these measurement strategies assume impliitly that workers sharing the same environment and tasks are similarly exposed and therefore experiene the same risks. Given modern understanding of exposure variability this onstitutes a far too rosy and simplisti piture. Oup Environ Med: first published as /oem on 1 May 00. Downloaded from on 30 Deember 018 by guest. Proteted by opyright.

3 Level Exposure aeptable Yes No Stop Assign observational group Random sample Monomorphi group Yes No Re-sample EFFICIENT MEASUREMENT STRATEGY FOR HAZARD CONTROL Reently, a new measurement strategy for evaluation of long term average onentrations has been developed that inorporates the notion that workers are not neessarily similarly exposed even though they may work in the same environment and basially perform similar tasks George et al desribed a striking example of this phenomenon in a ar manufaturing plant, where workers were exposed to isopropyl alohol while wiping an automobile in a heavily ventilated downdraft booth (90 nominal air hanges per hour). 1 Even under these well ontrolled irumstanes there appeared to be an almost fourfold differene in long term average exposure between two workers beause of differenes in body length, resulting in different distanes to the exposure soure and onsequently different mean exposure onentrations. In the newly developed measurement strategy, sampling is performed on randomly hosen workers from an a priori identified group of workers (observational group) on repeated days A shemati overview of this measurement protool is presented in fig 3. After this initial sampling effort the protool fits a random effets analysis of variane (ANOVA) model to the logged exposure data, and the goodness-of-fit of this model is heked by a graphial proedure. If the fit is aeptable for a given observational group the group is regarded as monomorphi and subsequently tested for overexposure. If the group annot be onsidered to be monomorphi an alternative grouping should be attempted. For a monomorphi group the estimated statistial parameters are used to assess whether the probability is aeptably small that a randomly seleted worker would be overexposed (that is, a randomly seleted worker s mean exposure is above an oupational exposure limit). For groups of workers whose exposure were found to be unaeptable, re-sampling is suggested in some ases to inrease the power of the test for delaring existing exposures aeptable. If re-sampling and onsequent retesting do not result in an aeptable situation the protool tests the uniformity of the exposure (that is, whether workers have essentially the same mean exposure) based upon a graphial evaluation of predited mean exposures for all workers. This last test suggests one of the two types of interventions needed. Interventions are either at individual level Yes Engineering ontrols No 1 Uniform exposure Yes No Alter work pratie Figure 3 Protool for assessing and ontrolling worker speifi mean exposures. (altering of personal environments) or at group level (general engineering ontrols) for non-uniform and uniform exposed groups, respetively. A omputer programme based on this measurement strategy is available as a Mirosoft Exel 95/97 appliation. This omputer programme performs all the statistial proedures that are neessary for the desribed protool. 13 It has been suggested that, as a minimum, two samples taken on randomly hosen days of a sample of five workers from an observational group (10 samples in total) are needed for this protool. EFFICIENT DESIGNS FOR EXPOSURE MEASUREMENTS IN EPIDEMIOLOGICAL STUDIES In 195 Oldham and Roah 14 desribed a long term sampling proedure for measuring oal dust exposure among olliers as part of a longitudinal study of pneumooniosis. The strategi aspets of their random olliers method are thought provoking even now. Remarkably, this sampling strategy was implemented without the onveniene of portable personal dust samplers. In 1958 Ashford 15 extended their approah to the man-shift method, with whih the umulative oal dust exposure of olliers from 5 ollieries was estimated. For eah a priori defined stratum or oupational group (based on oupation, plae of work and shift) a random seletion was made from the population of all man shifts worked by the members of the stratum. The number of measurements alloated to eah stratum was proportional to the produt of the duration, the standard deviation of the shift exposure indies, the square root of the average number of workers, and a fator representing labour turnover and attendane. It took more than 40 years to see suh an effetive measurement strategy being repeated and improved upon. In 1994 Loomis et al onstruted oupational ategories to organise thousands of job titles at five eletri utility ompanies partiipating in a ohort mortality study. 16 The 8 ategories were aggregated into three ordinal levels of presumed magneti field exposure. A goal of 4000 full shift magneti field measurements was set, based prinipally on onsiderations of time, ost, and tolerane of the partiipating ompanies. The number of measurements to be made in eah oupational ategory was a funtion of the projeted total number of measurements, arbitrary weights of 1, 3, or 5 for the *351 Oup Environ Med: first published as /oem on 1 May 00. Downloaded from on 30 Deember 018 by guest. Proteted by opyright.

4 35 * three exposure levels, and a seond set of weights proportional to person-years of employed experiene ontributed by eah of the five ompanies. The rationale for the weights of 1, 3, and 5 was that groups with higher average exposure would also experiene more variable exposures, requiring more measurements to obtain equally preise estimates of average exposures. In order to estimate between and within individual variability in exposure levels, eah individual seleted in the medium and high exposure groups was measured on two randomly seleted days less than 1 months apart. By using a small integrating personal magneti field exposure meter, workers and management personnel ould ondut the exposure survey in the field, without diret supervision by an oupational hygienist. Meters were distributed to the workers by ompany mail. A quality assurane sheme was in plae to test orret funtioning and alibration regularly. Self monitoring of exposure was used to ensure finanial feasibility of suh an extensive measurement sheme. This sheme resulted in almost 3000 suessful measurements that provided objetive and statistially sound estimates of umulative magneti field exposure for a geographially widely distributed population of utility workers. 17 Suh arefully designed exposure measurement, exposure assessment, and exposure assignment strategies have been shown to be of ritial importane to unveil exposure response relations. 18 Equations to assist in this deision making proess have reently been developed for group based approahes and are well established for individual 19 0 based approahes. MULTIPURPOSE MEASUREMENT STRATEGIES Measurements arried out to assess the likelihood of overexposure for hazard ontrol or to estimate long term average exposures for an epidemiologial study ould have uses beyond those originally envisioned. A prerequisite for this is that ontextual information be olleted and reorded in a standard way about tasks, types of proesses, and presene of ontrol measures whih were present during monitoring. The first empirial statistial models were built on this type of 1 information in the early 1980s. Statistial models an be used to identify important fators that affet exposure onentrations, as desribed for several agriultural settings and industries. For an extensive overview of methodologial issues in suh studies, see the artile by Burstyn and Teshke. 3 Reently, these models have been refined to take into aount the lak of independene of repeated measurements of the same worker. 4 7 A drawbak of suh models are the large amounts of data needed to develop meaningful and insightful empirial models. With proper exposure assessments under threat (being replaed by expert systems and one stop approahes) the prospets are not very promising. However, Burstyn et al showed that even with data of varying quality from different ountries a large database of asphalt workers exposure ould be onstruted, from whih determinants of exposure and long term trends in exposure ould be distilled. 7 These were onsequently used to develop exposure matries for a large multientre ohort study. Remarkably 33 out of the 37 datasets present in the international database were never desribed in the open literature. Identified fators affeting exposure an onsequently be used to lower exposure levels (a ontinuous improvement strategy). A prospetive study in the rubber manufaturing industry in The Netherlands showed the effetiveness of suh an approah. 6 Control measures taken in this longitudinal study were, to a ertain extent, based on originally identified exposure affeting fators. The study showed that elimination of soures and ontrol measures designed to ontrol the levels of ontaminants were highly effetive and resulted in a 40 50% redution of both inhalation and dermal exposure over a nine year period. TASK BASED MEASUREMENT STRATEGIES AND AN ALTERNATIVE A totally different approah is the task based approah where a priori assumptions are expliitly made on tasks leading to presumed high(er) exposures and workers are only monitored when performing tasks under onsideration. Risk assessors favour this approah espeially for pestiide registration purposes. Measurement data for these purposes are olleted for tasks performed under ontrolled onditions, exemplifying best pratie advoated by hemial produers. 8 Even though this approah might be reasonable for registration purposes (risk assessment) it is doubtful that exposure data olleted in this manner an be used beyond the original goal. They ertainly annot be used to predit exposure levels under realisti field irumstanes. Suh data an be expeted to suffer from the same shortomings as traditional worst ase strategies, only being their mirror image: best ase sampling. In addition, using task based sampling data to derive long term average exposure measures requires olletion of detailed information on duration of tasks. This information is known to be unreliable espeially when olleted retrospetively. However, it has been argued that in modern times exposure more often ours intermittently beause of praties suh as job rotation (for example, exposure to stainless steel welding fumes among welders in a shipyard ourring only for one hour one a week). Monitoring a worker s exposure for a omplete work shift on a randomly hosen day under suh irumstanes might result in non-detetable levels and/or underestimation of exposure levels. Non-random task-based sampling might be more effiient in this ase. Alternatively, it has been shown that in exposure onditions with infrequent ourring tasks (with high exposures) full shift random sampling ould be applied effetively as long as simultaneously daily ativities are being registered. 9 Based on measurement data from a large number of individuals and information on daily ativities during the measurements, one an onstrut empirial statistial models that will unravel exposure affeting fators. These models an onsequently be used to predit long term average exposure based on information on daily ativities olleted for a longer period than during whih samples were olleted. Reently, it was theoretially proven that, given the same monitoring effort, this approah leads to more aurate estimates of long term exposure than those based solely on measurements. 30 In pratie, of ourse, this approah will stand or fall on the availability of ontextual information (box 5) suh as information on daily ativities and prodution harateristis. In onlusion, non-random task based measurements will by their nature have a limited sope. Nevertheless they an be used to estimate exposures ourring very infrequently. A ombination of full shift random measurement strategies with additional task based measurements for exposure onditions likely to be missed in a random measurement strategy will be the preferred approah in those situations. CONCLUSION Effetive measurement strategies will have to result in unbiased estimates of worker s exposure, but should at the Oup Environ Med: first published as /oem on 1 May 00. Downloaded from on 30 Deember 018 by guest. Proteted by opyright.

5 Box 5 Contextual information to be olleted and registered during measurements Strategy Worst ase or randomly hosen workers Worst ase or randomly hosen time periods Task based or shift based Duration Sampling method Analytial method Loation harateristis Type of industry Department Worker harateristis Identifiation ode worker Trained/untrained Worker behaviour Work pae Mobile/stationary Proess harateristis Level of automation Continuous/intermittent Control measures (e.g. loal exhaust ventilation) Environment harateristis Indoors/outdoors General ventilation Confined spae Agent Soures Physial harateristis same time ollet enough detailed ontextual information to failitate well advised ontrol measures. Their effiieny will depend on the possibilities for limiting the input of oupational health professionals in the olletion of data. On the other hand, using ostly measurements for multiple purposes (hazard evaluation, ontrol, and epidemiology) will make them more ost effiient as well. In order to design effiient and effetive measurement strategies for workplae exposures we need to have insight into the magnitude of variability in a spatial sense (body loations when dealing with dermal exposure), as well as a temporal (day-to-day), personal (between workers), and group (between groups) sense. Realistially, budgets for measurements of workplae exposures will never allow the amount of resolution that will enable us to reonstrut exposure for eah individual worker on any given day of his or her working life. The reent elaboration and onsequent statistial analysis of large databases with repeated measurements has provided us with insights into exposure variability that will help us to design more effiient and effetive surveys to measure long term average exposure. New measurement strategies inorporating this knowledge have been developed as a result. Old and new simplisti approahes entirely relying on deterministi exposure models (expert systems) or subjetively estimating experts annot replae proper quantitative exposure assessment, if our overall goal is to protet workers health rather than to meet short term budgetary goals. Therefore, denying the presene of extensive soures of variability in workplae exposure onentrations is learly unsientifi. Suh denial ignores the fat that some workers, learly differently exposed than their fellow workers, will be deprived of neessary ontrols. On the other hand, utting the osts of extensive measurement surveys by involving workers in the sampling proedure (self assessment) and making better use of existing data has been shown to be effetive in the short and long run. In a reent paper by Liljelind and olleagues it appeared that untrained, unsupervised workers were able to ollet unbiased exposure data (on gaseous ontaminants) by using urrently available passive monitors. 31 A prerequisite for suh approahes is of ourse availability and development of (1) relatively simple measurement devies (that is, passive monitoring badges), () omprehensive exposure databases, and (3) easy tools to handle the inreasingly omplex statistial analyses. Oupational hygienists and other oupational health professionals reonsidering whether measurement has to be their primary tool should have a look at the referened literature. The new findings and approahes propagated for effiient and effetive measurement will hopefully onvine them that measurements are still needed and feasible. ACKNOWLEDGEMENTS I would like to aknowledge Igor Burstyn, Dana Loomis, Steve Rappaport and Roel Vermeulen for providing me with the fruits of our ollaboration over the years, without whih the ontents of this paper would have been quite different. REFERENCES 1 Topping M. Oupational exposure limits for hemials. Oup Environ Med 001;58: Friar JJ. The assessment of workplae exposure to substanes hazardous to health the EASE model. Draft guidane paper, Health & Safety Exeutive, Kromhout H, Vermeulen R. Long-term trends in oupational exposure: Are they real? What auses them? What shall we do with them? Ann Oup Hyg 000;44: Kromhout H, Symanski E, Rappaport SM. A omprehensive evaluation of within- and between-worker omponents of oupational exposure to hemial agents. Ann Oup Hyg 1993;37: The first omprehensive evaluation of temporal and personal variability in exposure onentrations. A database of approximately hemial exposures predominantly from the Netherlands and the USA was onstruted. Most observational groups of workers appeared to be not uniformly exposed. Environmental and prodution fators were shown to have distint influenes on the day-to-day variability, but hardly no influene on the between-worker variability. 5 van der Woord MP, Kromhout H, Barregård L, et al. Within-day variability of magneti fields among eletri utility workers: onsequenes for measurement strategies. Am Ind Hyg Asso J 000;61: Kromhout H, Vermeulen R. Temporal, personal and spatial variability in dermal exposure. Ann Oup Hyg 001;45: Desription of omponents of variability in dermal exposure data on the basis of a large database with more than 6000 measurements from 0 surveys. 7 Burstyn I, Kromhout H. Are the members of a paving rew uniformly exposed to bitumen fume, organi vapour and benzo(a)pyrene? Risk Analysis 000;0: Leidel NA, Bush KA, Lynh JJ. Oupational exposure sampling strategy. Cininnati, Ohio: National Institute for Oupational Safety and Health, DHEW (NIOSH) publiation number , The manual that explained intent and purposes of employee monitoring requirements for employers in the USA. Reommendations were given to meet the legal requirements with minimum burden to the employer. It provides data analysis methods to assist in making better deisions. Valuable but somewhat dated referene manual. 9 CEN. Workplae atmospheres. Guidane for the assessment for exposure by inhalation to hemial agents for omparison with limit values and measurement strategy. Brussels: CEN, EN 689, The urrent EU standard that provides a framework for the assessment for exposure to hemial agents for omparison with limit values. The standard provides no measurement strategy to be followed and leaves this open to professional judgement. Some examples of approahes and statistial proedures an be found in its annexes. 10 Rappaport SM, Lyles RH, Kupper LL. An exposure-assessment strategy aounting for within- and between-worker soures of variability. Ann Oup Hyg 1995;39: Theoretial bakground of a new measurement strategy that aounts for omponents of variability that have been shown to be prevalent in the work environment of groups of workers. With illustrative examples based on exposures in the nikel industry. 11 Lyles RH, Kupper LL, Rappaport SM. A lognormal distribution-based exposure assessment method for unbalaned data. Ann Oup Hyg 1997;41: Extension of the proposed measurement strategy desribed in referene 10 to situations in whih not all individuals have been sampled on the same number of oasions. *353 Oup Environ Med: first published as /oem on 1 May 00. Downloaded from on 30 Deember 018 by guest. Proteted by opyright.

6 354 * 1 George DK, Flynn MR, Harris RH. Autoorrelation of interday exposures at an automobile assembly plant. Am Ind Hyg Asso J 1995;56: Vermeulen R, Kromhout H, Braun W, et al. Statistial program for the evaluation of exposure data. Environmental and oupational health group, Wageningen Agriultural University, Oldham PD, Roah SA. A sampling proedure for measuring industrial dust exposure. Br J Ind Med 195;9:11 9. The proedure desribed in this paper should be onsidered to be the mother of all measurement strategies. 15 Ashford JR. The design of a long-term sampling programme to measure the hazard assoiated with an industrial environment. J R Stat So Series A 1958;3: Any epidemiologist onsidering olleting quantitative exposure data for an epidemiologial study should start with this lassi paper, after whih the designed measurement strategy will be onsiderably more effiient and effetive. 16 Loomis DP, Peipins LA, Browning SR, et al. Sampling design and field methods of a large, randomized, multi-site survey of oupational magneti field exposure. Appl Oup Environ Hyg 1994;9:49 5. Extensive desription of a large survey of magneti field exposure among utility workers from five ompanies throughout the USA for an epidemiologial study. The survey used a random measurement strategy and was failitated by self assessment by workers. 17 Kromhout H, Loomis DP, Mihlan GJ, et al. Assessment and grouping of oupational magneti field exposure in five eletri utility ompanies. Sand J Work Environ Health 1995;1: Kromhout H, Loomis DP, Klekner RC. Unertainty in the relation between exposure to magneti fields and brain aner due to assessment and assignment of exposure and analytial methods in dose-response modelling. Ann N Y Aad Si 1999;895: Tielemans E, Kupper LL, Kromhout H. et al. Individual-based and group-based oupational exposure assessment: some equations to evaluate different strategies. Ann Oup Hyg 1998;4: Theoretial paper giving insight into the onsequenes of hoosing individual or group based approahes in the olletion and analyses of quantitative exposure data. Equations are given that will failitate data driven deisions for optimal exposure measurement and assessment. 0 Liu K, Stamler J, Dyer A, et al. Statistial methods to assess and minimize the role of intra-individual variability in obsuring the relationship between dietary lipids and serum holesterol. J Chron Dis 1978;31: Eisen EA, Smith TJ, Wegman DH, et al. Estimation of long term dust exposures in the Vermont granite sheds. Am Ind Hyg Asso J 1984;45: Dement JM, Harris RL, Symons MJ, et al. Exposure and mortality among hrysotile asbestos workers. Part 1: exposure estimates. Am J Ind Med 1983;4: Burstyn I, Teshke K. Studying the determinants of exposure: a review of methods. Am Ind Hyg Asso J 1999;60: Nylander-Frenh LA, Kupper LL, Rappaport SM. An investigation of fators ontributing to styrene and styrene-7,8-oxide exposures in the reinfored-plastis industry. Ann Oup Hyg 1999;43: Rappaport SM, Weaver M, Taylor D, et al. Appliation of mixed models to assess exposures monitored by onstrution workers during hot proesses. Ann Oup Hyg 1999;43: Vermeulen R, de Hartog J, Swuste P, et al. Trends in exposure to inhalable partiulate and dermal ontamination in the rubber manufaturing industry: effetiveness of ontrol measures implemented over a nine-year period. Ann Oup Hyg 000;44: Burstyn I, Kromhout H, Kauppinen T, et al. Statistial modelling of the determinants of historial exposure to bitumen and polyyli aromati hydroarbons among paving workers. Ann Oup Hyg 000;44: van Hemmen JJ. EUROPOEM, a preditive oupational exposure database for registration purposes of pestiides. Appl Oup Environ Hyg 001;16: Preller L, Kromhout H, Heederik D, et al. Modelling long-term average exposure in oupational exposure-response analysis. Sand J Work Environ Health 1995;1: Wameling A, Shäper M, Kunert J, et al. Individual toluene exposure in rotary printing: inreasing auray of estimation by linear models based on protools of daily ativity and other measures. Biometris 000;56: Liljelind IE, Rappaport SM, Levin JO, et al. Comparison of self-assessment and expert assessment of oupational exposure to hemials. Sand J Work Environ Health 001;7: QUESTIONS (SEE ANSWERS ON P 86) (1) Whih of the following soures is expeted to affet exposure variability the most when measurements are being onduted with a devie with an averaging time of one minute for several days in two groups of workers performing the same tasks at two different loations? (a) within worker (day-to-day) variability (b) within day variability () between worker (within group) variability (d) between group variability () When performing measurements of workplae exposures for epidemiologial studies effiieny an be gained by: (a) restriting measurements to the highest exposed workers (b) by measuring on one oasion only () by self-assessment (d) by olleting ontextual information (3) Present day expert systems like EASE an replae proper exposure assessment: (a) yes, sine EASE is of a deterministi nature and based on physial properties (b) yes, EASE is aurate enough to evaluate individual risks () no, EASE should only be used as a method for initial appraisal (d) yes, EASE takes into aount spatial, temporal and personal variability (4) Repeated full shift sampling of the same worker is neessary: (a) to be able to estimate temporal variability in daily onentrations (b) to be able to estimate between worker variability in mean exposure onentrations () to be able to estimate both within and between worker variability in exposure onentrations (d) to be able to estimate spatial variability in exposure onentrations (5) In a plant, workers are assigned to observational groups, and individuals and days to be monitored are randomly hosen. Some workers are sheduled to be measured on more than one oasion. Contextual data are olleted in a standardised manner and reorded along with the results of the measurements. Whih of the following will be possible after the data have been olleted: (a) alulation of eah individual s mean exposure (b) identifiation of fators affeting exposure () estimating temporal and personal variability in exposure onentrations (d) all of the above Oup Environ Med: first published as /oem on 1 May 00. Downloaded from on 30 Deember 018 by guest. Proteted by opyright.