Evaluation and further development of EASE model 2.0.

EASE (Estimation and Assessment of Substance Exposure) is a general model that may be used to predict workplace exposure to a wide range of substances hazardous to health. First developed in the early 1990s, it is now in its second Windows version. This paper provides a critical assessment of the utility and performance of the EASE model, and on the basis of this review, recommendations for the structure of a revised model are outlined. Twenty-seven stakeholders were interviewed about their previous use of EASE, perceived advantages and limitations of the model and suggestions for improvement. A subset of stakeholders was contacted on a second occasion to determine their views on the preferred outputs for an ideal exposure assessment model. Overall, stakeholders felt that the model should be updated to provide more accurate and precise exposure assessments. However, users also expressed the view that the simplicity and usability of the software model should not be compromised. Six studies investigating the validity of the inhalation exposure assessment section of EASE were identified. These showed that the model generally either predicted close to the measured exposures or overestimated exposure; though performance was highly variable. Two studies investigated the validity of the dermal exposure assessment and found that EASE produced considerable overestimates of actual dermal exposure (the amount of a substance that actually lands on the skin). A conceptual model of exposure was developed to investigate whether the structure of the EASE model is appropriate. Although EASE has a number of characteristics that describe exposure, it is a greatly simplified model and does not include all the important exposure determinants. More importantly, EASE can produce estimates of exposure that are ambiguous or incomplete. Our conceptual model may provide a rational basis for developing an improved version of EASE but further consultation is needed to decide the purpose and intended use of any successor to EASE.

[1]  M. Nicas,et al.  Estimating exposure intensity in an imperfectly mixed room. , 1996, American Industrial Hygiene Association journal.

[2]  Mike Uschold,et al.  A Framework for Understanding and Classifying Ontology Applications , 1999 .

[3]  Veen Mp van.  CONSEXPO 3.0, consumer exposure and uptake models , 2001 .

[4]  M. F. Weegels,et al.  Variation of Consumer Contact with Household Products: A Preliminary Investigation , 2001, Risk analysis : an official publication of the Society for Risk Analysis.

[5]  T E McKone,et al.  Estimating skin permeation. The validation of five mathematical skin permeation models. , 1995, Chemosphere.

[6]  D K Burns,et al.  The HSE National Exposure Database--(NEDB). , 1989, The Annals of occupational hygiene.

[7]  M C Fehrenbacher,et al.  Evaluation of the mass balance model used by the Environmental Protection Agency for estimating inhalation exposure to new chemical substances. , 1996, American Industrial Hygiene Association journal.

[8]  S Bredendiek-Kämper,et al.  Do EASE scenarios fit workplace reality? A validation study of the EASE model. Estimation and Assessment of Substance Exposure. , 2001, Applied occupational and environmental hygiene.

[9]  Gary F. Bennett.  Risk management of chemicals , 1996 .

[10]  Guus Schreiber,et al.  KADS : a principled approach to knowledge-based system development , 1993 .

[11]  H Kromhout,et al.  Conceptual model for assessment of dermal exposure. , 1999, Occupational and environmental medicine.

[12]  U. Epa,et al.  Office of Pollution Prevention and Toxics , 2000 .

[13]  Niren L. Nagda,et al.  Small‐Chamber and Research‐House Testing of Tile Adhesive Emissions , 1995 .

[14]  A Robertson,et al.  Biologically relevant assessment of dermal exposure. , 1995, The Annals of occupational hygiene.

[15]  Charles G. Sieloff.  "If only HP knew what HP knows": the roots of knowledge management at Hewlett-Packard , 1999, J. Knowl. Manag..

[16]  Nurtan A. Esmen,et al.  Analysis of Strategies for Reconstructing Exposures , 1991 .

[17]  John W Cherrie,et al.  The validity of the EASE expert system for inhalation exposures. , 2005, The Annals of occupational hygiene.

[18]  Niren L. Nagda,et al.  A Multichamber Model For Assessing Consumer Inhalation Exposure , 1991 .

[19]  T. Schneider,et al.  Validation of a New Method for Structured Subjective Assessment of Past Concentrations , 1999 .

[20]  A M Phillips,et al.  Assessment of dermal exposure--empirical models and indicative distributions. , 2001, Applied occupational and environmental hygiene.

[21]  G Ramachandran,et al.  Retrospective exposure assessment using Bayesian methods. , 2001, The Annals of occupational hygiene.

[22]  J Devillers,et al.  Occupational exposure modelling with ease. , 1997, SAR and QSAR in environmental research.

[23]  Michael Uschold,et al.  The Enterprise Ontology , 1998, The Knowledge Engineering Review.

[24]  G. Ramachandran,et al.  A Bayesian approach to retrospective exposure assessment. , 1999, Applied occupational and environmental hygiene.

[25]  S. Semple,et al.  A training exercise in subjectively estimating inhalation exposures. , 2001, Scandinavian journal of work, environment & health.

[26]  F. Fortezza,et al.  Non-professional paint stripping, model prediction and experimental validation of indoor dichloromethane levels. , 2002, Indoor air.

[27]  Michael Uschold,et al.  Putting ontologies to use , 1998, The Knowledge Engineering Review.

[28]  J. Cherrie.  The effect of room size and general ventilation on the relationship between near and far-field concentrations. , 1999, Applied occupational and environmental hygiene.

[29]  M P VAN VEEN,et al.  Indoor air exposure to volatile compounds emitted by paints: experiment and model , 1999, Journal of Exposure Analysis and Environmental Epidemiology.

[30]  P. Wild,et al.  Combining expert ratings and exposure measurements: a random effect paradigm. , 2002, The Annals of occupational hygiene.