Jody Schinkel

Publications of Jody Schinkel

• Use of the MEGA Exposure Database for the Validation of the Stoffenmanager Model.

  Authors: Dorothea Koppisch, Jody Schinkel, Stefan Gabriel, Wouter Fransman, Erik Tielemans

  The Annals of occupational hygiene. 11/2011;

  OBJECTIVES: This paper explores the usefulness of the exposure database MEGA for model validation and evaluates the capability of two Stoffenmanager model equations (i.e. handling of powders/granulesOBJECTIVES: This paper explores the usefulness of the exposure database MEGA for model validation and evaluates the capability of two Stoffenmanager model equations (i.e. handling of powders/granules and machining) to estimate workers exposure to inhalable dust. METHODS: For the task groups, 'handling of powders and granules' (handling) and 'machining of wood and stone' (machining) measurements were selected from MEGA and grouped in scenarios depending on task, product, and control measures. The predictive capability of the model was tested by calculating the relative bias of the single measurements and the correlation between geometric means (GMs) for scenarios. The conservatism of the model was evaluated by checking if the percentage of measurement values above the 90th percentile estimate was ≤10%. RESULTS: From 22 596 personal measurements on inhalable dust within MEGA, 390 could be selected for handling and 1133 for machining. The relative bias for the task groups was -25 and 68%, respectively, the percentage of measurements with a higher result than the estimated 90th percentile 11 and 7%. Correlations on a scenario level were good for both model equations as well for the GM (handling: r(s) = 0.90, n = 15 scenarios; machining: r(s) = 0.84, n = 22 scenarios) as for the 90th percentile (handling: r(s) = 0.79; machining: r(s) = 0.76). CONCLUSIONS: The MEGA database could be used for model validation, although the presented analyses have learned that improvements in the database are necessary for modelling purposes in the future. For a substantial amount of data, contextual information on exposure determinants in addition to basic core information is stored in this database. The relative low bias, the good correlation, and the level of conservatism of the tested model show that the Stoffenmanager can be regarded as a useful Tier 1 model for the Registration, Evaluation, Authorisation and Restriction of Chemicals legislation.

• Advanced REACH Tool: development and application of the substance emission potential modifying factor.

  Authors: Martie van Tongeren, Wouter Fransman, Sally Spankie, Martin Tischer, Derk Brouwer, Jody Schinkel, John W Cherrie, Erik Tielemans

  The Annals of occupational hygiene. 11/2011; 55(9):980-8.

  The Advanced REACH Tool (ART) is an exposure assessment tool that combines mechanistically modelled inhalation exposure estimates with available exposure data using a Bayesian approach. TheThe Advanced REACH Tool (ART) is an exposure assessment tool that combines mechanistically modelled inhalation exposure estimates with available exposure data using a Bayesian approach. The mechanistic model is based on nine independent principal modifying factors (MF). One of these MF is the substance emission potential, which addresses the intrinsic substance properties as determinants of the emission from a source. This paper describes the current knowledge and evidence on intrinsic characteristics of solids and liquids that determine the potential for their release into workplace air. The principal factor determining the release of aerosols from handling or processing powdered, granular, or pelletized materials is the dustiness of the material, as well as the weight fraction of the substance of interest in the powder and the moisture content. The partial vapour pressure is the main intrinsic factor determining the substance emission potential for emission of vapours. For generation of mist, the substance emission potential is determined by the viscosity of the liquid as well as the weight fraction of the substance of interest in the liquid. Within ART release of vapours is considered for substances with a partial vapour pressure at the process temperature of 10 Pa or more, while mist formation is considered for substances with a vapour pressure ≤ 10 Pa. Relative multipliers are assigned for most of the intrinsic factors, with the exception of the weight fraction and the vapour pressure, which is applied as a continuous variable in the estimation of the substance emission potential. Currently, estimation of substance emission potential is not available for fumes, fibres, and gases. The substance emission potential takes account of the latest thinking on emissions of dusts, mists, and vapours and in our view provides a good balance between theory and pragmatism. Expanding the knowledge base on substance emission potential will improve the predictive power of occupational exposure models and thereby the accuracy and precision of the exposure estimates.

• Advanced Reach Tool (ART): development of the mechanistic model.

  Authors: Wouter Fransman, Martie Van Tongeren, John W Cherrie, Martin Tischer, Thomas Schneider, Jody Schinkel, Hans Kromhout, Nick Warren, Henk Goede, Erik Tielemans

  The Annals of occupational hygiene. 11/2011; 55(9):957-79.

  This paper describes the development of the mechanistic model within a collaborative project, referred to as the Advanced REACH Tool (ART) project, to develop a tool to model inhalation exposure forThis paper describes the development of the mechanistic model within a collaborative project, referred to as the Advanced REACH Tool (ART) project, to develop a tool to model inhalation exposure for workers sharing similar operational conditions across different industries and locations in Europe. The ART mechanistic model is based on a conceptual framework that adopts a source receptor approach, which describes the transport of a contaminant from the source to the receptor and defines seven independent principal modifying factors: substance emission potential, activity emission potential, localized controls, segregation, personal enclosure, surface contamination, and dispersion. ART currently differentiates between three different exposure types: vapours, mists, and dust (fumes, fibres, and gases are presently excluded). Various sources were used to assign numerical values to the multipliers to each modifying factor. The evidence used to underpin this assessment procedure was based on chemical and physical laws. In addition, empirical data obtained from literature were used. Where this was not possible, expert elicitation was applied for the assessment procedure. Multipliers for all modifying factors were peer reviewed by leading experts from industry, research institutes, and public authorities across the globe. In addition, several workshops with experts were organized to discuss the proposed exposure multipliers. The mechanistic model is a central part of the ART tool and with advancing knowledge on exposure, determinants will require updates and refinements on a continuous basis, such as the effect of worker behaviour on personal exposure, 'best practice' values that describe the maximum achievable effectiveness of control measures, the intrinsic emission potential of various solid objects (e.g. metal, glass, plastics, etc.), and extending the applicability domain to certain types of exposures (e.g. gas, fume, and fibre exposure).

• Advanced REACH Tool (ART): overview of version 1.0 and research needs.

  Authors: Erik Tielemans, Nick Warren, Wouter Fransman, Martie Van Tongeren, Kevin McNally, Martin Tischer, Peter Ritchie, Hans Kromhout, Jody Schinkel, Thomas Schneider, John W Cherrie

  The Annals of occupational hygiene. 11/2011; 55(9):949-56.

  This paper provides an outline of the Advanced REACH Tool (ART) version 1.0 and a discussion of how it could be further developed. ART is a higher tier exposure assessment tool that combinesThis paper provides an outline of the Advanced REACH Tool (ART) version 1.0 and a discussion of how it could be further developed. ART is a higher tier exposure assessment tool that combines mechanistically modelled inhalation exposure predictions with available exposure data using a Bayesian approach. ART assesses exposure for scenarios across different plants and sites. Estimates are provided for different percentiles of the exposure distribution and confidence intervals around the estimate. It also produces exposure estimates in the absence of data, but uncertainty of the estimates will decrease when results of exposure measurements are included. The tool has been calibrated using a broad range of exposure data and provides estimates for exposure to vapours, mists, and dusts. ART has a robust and stable conceptual basis but will be refined in the future and should therefore be considered an evolving system. High-priority areas for future research are identified in this paper and include the integration of partially analogous measurement series, inclusion of company and site-specific assessments, user decision strategies linked to ART predictions, evaluation of validity and reliability of ART, exploring the possibilities for incorporating the dermal route and integration of ART predictions with tools for modelling internal dose. ART is initially developed in the scope of REACH but is equally useful for exposure assessment in other areas.

• Classification of occupational activities for assessment of inhalation exposure.

  Authors: Hans Marquart, Thomas Schneider, Henk Goede, Martin Tischer, Jody Schinkel, Nick Warren, Wouter Fransman, Suzanne Spaan, Martie Van Tongeren, Hans Kromhout, Erik Tielemans, John W Cherrie

  The Annals of occupational hygiene. 09/2011; 55(9):989-1005.

  There is a large variety of activities in workplaces that can lead to emission of substances. Coding systems based on determinants of emission have so far not been developed. In this paper, a systemThere is a large variety of activities in workplaces that can lead to emission of substances. Coding systems based on determinants of emission have so far not been developed. In this paper, a system of Activity Classes and Activity Subclasses is proposed for categorizing activities involving chemical use. Activity Classes share their so-called 'emission generation mechanisms' and physical state of the product handled and the underlying determinants of emission. A number of (industrial) stakeholders actively participated in testing and fine-tuning the system. With the help of these stakeholders, it was found to be relatively easy to allocate a large number of activities to the Activity Classes and Activity Subclasses. The system facilitates a more structured classification of activities in exposure databases, a structured analysis of the analogy of exposure activities, and a transparent quantification of the activity emission potential in (new) exposure assessment models. The first use of the system is in the Advanced REACH Tool.

• Advanced REACH Tool (ART): calibration of the mechanistic model.

  Authors: Jody Schinkel, Nicholas Warren, Wouter Fransman, Martie van Tongeren, Patricia McDonnell, Eef Voogd, John W Cherrie, Martin Tischer, Hans Kromhout, Erik Tielemans

  Journal of environmental monitoring : JEM. 03/2011; 13(5):1374-82.

  The mechanistic model of the Advanced Reach Tool (ART) provides a relative ranking of exposure levels from different scenarios. The objectives of the calibration described in this paper areThe mechanistic model of the Advanced Reach Tool (ART) provides a relative ranking of exposure levels from different scenarios. The objectives of the calibration described in this paper are threefold: to study whether the mechanistic model scores are accurately ranked in relation to exposure measurements; to enable the mechanistic model to estimate actual exposure levels rather than relative scores; and to provide a method of quantifying model uncertainty. Stringent data quality guidelines were applied to the collated data. Linear mixed effects models were used to evaluate the association between relative ART model scores and measurements. A random scenario and company component of variance were introduced to reflect the model uncertainty. Stratified analyses were conducted for different forms of exposure (abrasive dust, dust, vapours and mists). In total more than 2000 good quality measurements were available for the calibration of the mechanistic model. The calibration showed that after calibration the mechanistic model of ART was able to estimate geometric mean (GM) exposure levels with 90% confidence for a given scenario to lie within a factor between two and six of the measured GM depending upon the form of exposure.

• Stoffenmanager exposure model: company-specific exposure assessments using a Bayesian methodology.

  Authors: Peter van de Ven, Wouter Fransman, Jody Schinkel, Carina Rubingh, Nicholas Warren, Erik Tielemans

  Journal of occupational and environmental hygiene. 04/2010; 7(4):216-23.

  The web-based tool "Stoffenmanager" was initially developed to assist small- and medium-sized enterprises in the Netherlands to make qualitative risk assessments and to provide advice on control atThe web-based tool "Stoffenmanager" was initially developed to assist small- and medium-sized enterprises in the Netherlands to make qualitative risk assessments and to provide advice on control at the workplace. The tool uses a mechanistic model to arrive at a "Stoffenmanager score" for exposure. In a recent study it was shown that variability in exposure measurements given a certain Stoffenmanager score is still substantial. This article discusses an extension to the tool that uses a Bayesian methodology for quantitative workplace/scenario-specific exposure assessment. This methodology allows for real exposure data observed in the company of interest to be combined with the prior estimate (based on the Stoffenmanager model). The output of the tool is a company-specific assessment of exposure levels for a scenario for which data is available. The Bayesian approach provides a transparent way of synthesizing different types of information and is especially preferred in situations where available data is sparse, as is often the case in small- and medium sized-enterprises. Real-world examples as well as simulation studies were used to assess how different parameters such as sample size, difference between prior and data, uncertainty in prior, and variance in the data affect the eventual posterior distribution of a Bayesian exposure assessment.

• Cross-validation and refinement of the Stoffenmanager as a first tier exposure assessment tool for REACH.

  Authors: Jody Schinkel, Wouter Fransman, Henri Heussen, Hans Kromhout, Hans Marquart, Erik Tielemans

  Occupational and environmental medicine. 09/2009;

  OBJECTIVES: For regulatory risk assessment under REACH a tiered approach is proposed in which the first tier models should provide a conservative exposure estimate that can discriminate betweenOBJECTIVES: For regulatory risk assessment under REACH a tiered approach is proposed in which the first tier models should provide a conservative exposure estimate that can discriminate between scenarios of concern and those which are not. The Stoffenmanager is mentioned as first tier approach in the REACH guidance. In an attempt to investigate the validity of the Stoffenmanager algorithms a cross-validation study was performed. METHODS: Exposure estimates using the Stoffenmanager algorithms were compared with exposure measurement results (n=254). Correlations between observed and predicted exposures, bias and precision were calculated. Stratified analyses were performed for the scenarios "handling of powders and granules" (n=82), "handling solids resulting in comminuting" (n=60), "handling of low-volatile liquids" (n=40) and for "handling of volatile liquids" (n=72). RESULTS: The relative bias of the 4 algorithms ranged between -9% and -77% with a precision of approximately 1.7. The 90-percentile estimate of 1 out of 4 algorithms was not conservative enough. Based on these statistics and analyses of residual plots the underlying algorithm was adapted. Subsequently, the calibration and the cross-validation dataset were merged to one dataset (n=952) used for calibrating the adapted Stoffenmanager algorithms. This new calibration resulted in new exposure algorithms for the 4 scenarios. CONCLUSIONS: The Stoffenmanager is capable of discriminating among exposure levels mainly between scenarios in different companies. The 90-percentile estimates of the Stoffenmanager are verified to be sufficiently conservative. Therefore, the Stoffenmanager could be a useful Tier 1 exposure assessment tool for REACH.

• Development and Evaluation of an Exposure Control Efficacy Library (ECEL).

  Authors: Wouter Fransman, Jody Schinkel, Tim Meijster, Joop Van Hemmen, Erik Tielemans, Henk Goede

  The Annals of occupational hygiene. 09/2008;

  OBJECTIVES: This paper describes the development and evaluation of an evidence database on the effectiveness of risk management measures (RMMs) to control inhalation exposure. This database isOBJECTIVES: This paper describes the development and evaluation of an evidence database on the effectiveness of risk management measures (RMMs) to control inhalation exposure. This database is referred to as Exposure Control Efficacy Library (ECEL). METHODS: A comprehensive review of scientific journals in the occupational hygiene field was undertaken. Efficacy values for RMMs in conjunction with contextual information on study design, sampling strategy and measurement type (among other parameters) were stored in an MS Access database. In total, 433 efficacy values for six RMM groups (i.e. enclosure, local exhaust ventilation, specialized ventilation, general ventilation, suppression techniques and separation of the worker) were collected from 90 peer-reviewed publications. These RMM categories were subdivided into more specific categories. RESULTS: Estimated average efficacy values ranged from 87% for specialized ventilation to 43% for general ventilation. Substantial variation in efficacy values was observed within RMM categories based on differences in selected covariables within each study (i.e. study design, sampling strategy, measurement type and others). More contrast in efficacy values was observed when evaluating more detailed subcategories. CONCLUSIONS: It is envisaged that ECEL will contribute to exposure modelling, but should be supplemented with expert opinion, preferably in a formal expert elicitation procedure. The work presented here should be considered as a first attempt to collate and analyse RMM efficacy values and inclusion of additional (unpublished) exposure data is highly warranted.

• Evaluation of Peak Exposures in the Dutch Flour Processing Industry: Implications for Intervention Strategies.

  Authors: Tim Meijster, Erik Tielemans, Jody Schinkel, Dick Heederik

  The Annals of occupational hygiene. 08/2008;

  OBJECTIVES: To effectively decrease occupational exposure to flour dust and related allergens, detailed information on exposure determinants and effectiveness of control measures is essential. InOBJECTIVES: To effectively decrease occupational exposure to flour dust and related allergens, detailed information on exposure determinants and effectiveness of control measures is essential. In this paper, we use personal real-time exposure measurements to get more insight into the relationship between specific work characteristics, including the use of control measures, and (peak) exposure to flour dust. The study has three objectives: (i) identify tasks and activities related to peak exposure, (ii) identify control measures and other important exposure determinants and (iii) assess the potential impact of these control measures on the (peak) exposure to flour dust. METHODS: A data set containing 82 real-time exposure measurements in combination with information from detailed observations was used to study the association between peak exposures and different tasks, activities and other determinants such as control measures. Descriptive statistics of peak exposure on job level were generated as well as information on contribution of task-specific peak exposures to time-weighted average (TWA) exposure levels. Finally, we evaluated the efficacy of a variety of control measures on task exposure by comparing exposure levels of groups of workers with and without controls. RESULTS: In workers included in this study, >75% of TWA exposure is directly associated with peak exposures during a limited set of well-defined tasks/activities. The impact of a single task on population TWA exposure is generally limited (<40%). Worker behavior seems an important determinant in effective exposure control for many tasks. CONCLUSIONS: Data from real-time measurements provide important detailed information with respect to exposure determinants and control measures, not obtainable from conventional measurement studies focusing at TWA exposure. This information is essential to perform prospective impact assessments of intervention strategies on the populations' exposure distribution.

• Stoffenmanager Exposure Model: Development of a Quantitative Algorithm.

  Authors: Erik Tielemans, Dook Noy, Jody Schinkel, Henri Heussen, Doeke Van Der Schaaf, John West, Wouter Fransman

  The Annals of occupational hygiene. 07/2008;

  In The Netherlands, the web-based tool called 'Stoffenmanager' was initially developed to assist small- and medium-sized enterprises to prioritize and control risks of handling chemical products inIn The Netherlands, the web-based tool called 'Stoffenmanager' was initially developed to assist small- and medium-sized enterprises to prioritize and control risks of handling chemical products in their workplaces. The aim of the present study was to explore the accuracy of the Stoffenmanager exposure algorithm. This was done by comparing its semi-quantitative exposure rankings for specific substances with exposure measurements collected from several occupational settings to derive a quantitative exposure algorithm. Exposure data were collected using two strategies. First, we conducted seven surveys specifically for validation of the Stoffenmanager. Second, existing occupational exposure data sets were collected from various sources. This resulted in 378 and 320 measurements for solid and liquid scenarios, respectively. The Spearman correlation coefficients between Stoffenmanager scores and exposure measurements appeared to be good for handling solids (r(s) = 0.80, N = 378, P < 0.0001) and liquid scenarios (r(s) = 0.83, N = 320, P < 0.0001). However, the correlation for liquid scenarios appeared to be lower when calculated separately for sets of volatile substances with a vapour pressure >10 Pa (r(s) = 0.56, N = 104, P < 0.0001) and non-volatile substances with a vapour pressure </=10 Pa (r(s) = 0.53, N = 216, P < 0.0001). The mixed-effect regression models with natural log-transformed Stoffenmanager scores as independent parameter explained a substantial part of the total exposure variability (52% for solid scenarios and 76% for liquid scenarios). Notwithstanding the good correlation, the data show substantial variability in exposure measurements given a certain Stoffenmanager score. The overall performance increases our confidence in the use of the Stoffenmanager as a generic tool for risk assessment. The mixed-effect regression models presented in this paper may be used for assessment of so-called reasonable worst case exposures. This evaluation is considered as an ongoing process and when more good quality data become available, the analyses described in this paper will be expanded. Based on these analyses, the algorithm will be refined in the near future.

• 'Stoffenmanager', a Web-Based Control Banding Tool Using an Exposure Process Model.

  Authors: Hans Marquart, Henri Heussen, Maaike Le Feber, Dook Noy, Erik Tielemans, Jody Schinkel, John West, Doeke Van Der Schaaf

  The Annals of occupational hygiene. 07/2008;

  In the scope of a Dutch programme to reinforce the working conditions policy on hazardous substances, an internet-based tool was developed to help small- and medium-sized companies to handleIn the scope of a Dutch programme to reinforce the working conditions policy on hazardous substances, an internet-based tool was developed to help small- and medium-sized companies to handle hazardous substances with more care. The heart of this tool, called the Stoffenmanager, is a risk banding scheme. It combines a hazard banding scheme similar to that of COSHH Essentials and an exposure banding scheme based on an exposure model originally presented by Cherrie et al. (1996) and further developed by Cherrie and Schneider (1999). The exposure model has been modified to allow non-expert users to understand and use the model. Exposure scores are calculated based on categorization of determinants of emission, transmission and immission. These exposure scores are assigned to exposure bands. The comparison of exposure bands and hazard bands leads to a risk band or priority band. Following the evaluation of the priority of tasks done with products, generic exposure control measures can be evaluated for their possibility to lower the risks. Relevant control measures can be put into an action plan and into workplace instruction cards. The tool has several other functionalities regarding registration and storage of products. The exposure model in the Stoffenmanager leads to exposure scores. These have been compared with measured exposure levels. The exposure scores correlated well with measured exposure levels. The development of the Stoffenmanager has facilitated a whole range of further developments of useful tools for small- and medium-sized enterprises.

• Variability in endotoxin exposure levels and consequences for exposure assessment.

  Authors: Suzanne Spaan, Jody Schinkel, Inge M Wouters, Liesbeth Preller, Erik Tielemans, Evelyn Tjoe Nij, Dick Heederik

  The Annals of occupational hygiene. 07/2008; 52(5):303-16.

  OBJECTIVES: Workers in many industries are exposed to endotoxins, which may cause adverse health effects. In exposure assessment, information about exposure variability is essential. However,OBJECTIVES: Workers in many industries are exposed to endotoxins, which may cause adverse health effects. In exposure assessment, information about exposure variability is essential. However, variability in exposure has rarely been investigated for biological agents and more specifically for endotoxin. Therefore, variance components and determinants of exposure were studied in a large database with >2000 endotoxin measurements. METHODS: Data from 10 individual studies were combined to create a database with 2010 personal inhalable dust and endotoxin measurements, of which 1650 were repeated measurements. Exposure groups were defined based on job codes. Between- and within-worker variance components were estimated for different grouping strategies, and determinants of exposure were studied using mixed effects models. RESULTS: Inhalable dust and endotoxin exposure levels are summarized for 46 industries and 4 broadly defined sectors. The between-worker variability exceeded the within-worker variability overall and within sectors and subsectors, and variance components were larger for endotoxin than for dust. Between-worker variability also exceeded within-worker variability in nearly half of the exposure groups based upon industries or job code within industries for endotoxin exposure and in 10% of the groups for dust exposure. Among other things, dustiness of the process, contact with animals, bulk production, presence of plant material or a cyclic process appeared as determinants of exposure, which largely explained the between-worker variability. CONCLUSIONS: Exposure groups were much less homogeneous for endotoxin exposure than for dust exposure. This is distinctly different than for chemical exposure. Large variability in measured exposure levels is inherent to endotoxin exposure, which is caused in part by determinants that influence growth of microorganisms. These findings have major consequences for the design of future occupational intervention and epidemiological studies. The measurement effort needs to be greater than exposure assessment for chemical agents which demonstrate lower exposure variability, especially when evaluating endotoxin exposure for compliance testing. The established determinants of exposure give direction for potential exposure control, although more information about determinants of day-to-day variability in exposure is still needed to be able to effectively control endotoxin exposure.

• Exposure profiles of pesticides among greenhouse workers: implications for epidemiological studies.

  Authors: Erik Tielemans, Reini Bretveld, Jody Schinkel, Berna van Wendel de Joode, Hans Kromhout, Rianda Gerritsen-Ebben, Nel Roeleveld, Liesbeth Preller

  Journal of exposure science & environmental epidemiology. 10/2007; 17(6):501-9.

  The aim of this study was to assess exposure to pesticides for a longitudinal epidemiological study on adverse reproduction effects among greenhouse workers. Detailed information on pesticide useThe aim of this study was to assess exposure to pesticides for a longitudinal epidemiological study on adverse reproduction effects among greenhouse workers. Detailed information on pesticide use among greenhouse workers was obtained on a monthly basis through self-administered questionnaires and subsequent workplace surveys. Questionnaires were filled in for a whole year. Dermal exposure rankings were developed for each task using the observational method Dermal Exposure Assessment Method (DREAM). Exposure scores were calculated for each worker for each month during the year, taking into account frequency, duration and exposure intensity for each task. A total number of 116 different active ingredients were used in the population, whereas a mean number of 15 active ingredients were applied per greenhouse. DREAM observations provided insight into the exposure intensity of 12 application techniques and three mixing and loading activities. Relatively high DREAM scores were obtained for scattering, fogging, dusting, and mixing and loading of powders. Observations with DREAM indicated that application with a horizontal ground-boom, motor driven boom, and bulb shower resulted in low dermal exposure. Exposure scores showed substantial variation between workers and over the year. It can be concluded that exposure variation between- and within greenhouses is very large, both in terms of chemical composition and exposure intensity. This may be a significant contributor to the inconsistent results of studies evaluating health effects of pesticide exposure.

• Occupational exposure during application and removal of antifouling paints.

  Authors: Ingrid Links, Katinka E Van Der Jagt, Yvette Christopher, Marc Lurvink, Jody Schinkel, Erik Tielemans, Joop J van Hemmen

  The Annals of occupational hygiene. 04/2007; 51(2):207-18.

  Exposure data on biocides are relatively rare in published literature, especially for secondary exposure. This is also the case for antifouling exposure. Therefore, a field study was carried outExposure data on biocides are relatively rare in published literature, especially for secondary exposure. This is also the case for antifouling exposure. Therefore, a field study was carried out measuring exposure to antifouling paints. Both primary exposure (rolling and spraying) and secondary exposure (during sand blasting) were studied. Exposure during rolling was measured in boatyards where paints containing dichlofluanid (DCF) were applied. Spraying was measured in dockyards (larger than boatyards) where paints containing copper were applied. Furthermore, during sand blasting the removal of old paint layers containing copper was measured. A total of 54 datasets was collected, both for inhalation and dermal exposure data. For paint and stripped paint bulk analyses were performed. The following values are all arithmetic means of the datasets. Inhalation of copper amounted to 3 mg m-3 during spraying and to 0.8 mg m-3 during sand blasting. Potential body exposure loading amounted to 272 mg h-1 copper during spraying and 33 mg h-1 during sand blasting. For dichlofluanid the inhalation exposure loading was 0.14 mg m-3 during rolling, whereas the potential body exposure loading was 267 mg h-1 and potential hand exposure loading 277 mg h-1. The results for primary exposure compare well to the very few public data available. For the secondary exposure (sand blasting) no comparable data were available. The present study shows that the exposure loading should be considered more extensively, including applicable protective gear. In this light the findings for the potmen during sand blasting suggest that personal protective equipment should be (re)considered carefully.