Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Development of a source-exposure matrix for occupational exposure assessment of electromagnetic fields in the INTEROCC study

A Correction to this article was published on 21 March 2019

A Correction to this article was published on 18 March 2019

This article has been updated


To estimate occupational exposures to electromagnetic fields (EMF) for the INTEROCC study, a database of source-based measurements extracted from published and unpublished literature resources had been previously constructed. The aim of the current work was to summarize these measurements into a source-exposure matrix (SEM), accounting for their quality and relevance. A novel methodology for combining available measurements was developed, based on order statistics and log-normal distribution characteristics. Arithmetic and geometric means, and estimates of variability and maximum exposure were calculated by EMF source, frequency band and dosimetry type. The mean estimates were weighted by our confidence in the pooled measurements. The SEM contains confidence-weighted mean and maximum estimates for 312 EMF exposure sources (from 0 Hz to 300 GHz). Operator position geometric mean electric field levels for radiofrequency (RF) sources ranged between 0.8 V/m (plasma etcher) and 320 V/m (RF sealer), while magnetic fields ranged from 0.02 A/m (speed radar) to 0.6 A/m (microwave heating). For extremely low frequency sources, electric fields ranged between 0.2 V/m (electric forklift) and 11,700 V/m (high-voltage transmission line-hotsticks), whereas magnetic fields ranged between 0.14 μT (visual display terminals) and 17 μT (tungsten inert gas welding). The methodology developed allowed the construction of the first EMF–SEM and may be used to summarize similar exposure data for other physical or chemical agents.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1
Figure 2
Figure 3

Change history

  • 18 March 2019

    "Corrigendum: This work was also funded by the European Commission grant 603794 (GERoNiMO project)."



Arithmetic mean


Magnetic flux density, in μT (low-frequency fields)


Chemical vapor deposition


Electric field strength, in V/m


Extremely low frequency (3–3000 Hz)


Electromagnetic fields


Geometric mean


Geometric standard deviation


Magnetic field strength, in A/m (high-frequency fields)


High-voltage transmission lines


Intermediate frequency (3 kHz–10 MHz)






sample size


Outside dynamic range (The range between an EMF instrument’s overload input and its minimum input with acceptable accuracy)


Power density, in watts per square meter (W/m2)


Radiofrequency (10 MHz–300 GHz)


Standard deviation


Static Magnetic Fields, in microTesla (μT), 0 Hz


Tungsten inert gas


Standard normal quantile of a data set’s maximum value.


  1. Sauvé J-F, Beaudry C, Bégin D, Dion C, Gérin M, Lavoué J . Statistical modeling of crystalline silica exposure by trade in the construction industry using a database compiled from the literature. J Environ Monit JEM 2012; 14: 2512–2520.

    Article  PubMed  Google Scholar 

  2. Sauvé J-F, Beaudry C, Bégin D, Dion C, Gérin M, Lavoué J . Silica exposure during construction activities: statistical modeling of task-based measurements from the literature. Ann Occup Hyg 2013; 57: 432–443.

    PubMed  Google Scholar 

  3. Koh D-H, Nam J-M, Graubard BI, Chen Y-C, Locke SJ, Friesen MC . Evaluating temporal trends from occupational lead exposure data reported in the published literature using meta-regression. Ann Occup Hyg 2014; 58: 1111–1125.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Koh D-H, Locke SJ, Chen Y-C, Purdue MP, Friesen MC . Lead exposure in US worksites: a literature review and development of an occupational lead exposure database from the published literature. Am J Ind Med 2015; 58: 605–616.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Hein MJ, Waters MA, van Wijngaarden E, Deddens JA, Stewart PA . Issues when modeling benzene, toluene, and xylene exposures using a literature database. J Occup Environ Hyg 2008; 5: 36–47.

    Article  PubMed  Google Scholar 

  6. Hein MJ, Waters MA, Ruder AM, Stenzel MR, Blair A, Stewart PA . Statistical modeling of occupational chlorinated solvent exposures for case-control studies using a literature-based database. Ann Occup Hyg 2010; 54: 459–472.

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Lavoué J, Bégin D, Beaudry C, Gérin M . Monte Carlo simulation to reconstruct formaldehyde exposure levels from summary parameters reported in the literature. Ann Occup Hyg 2007; 51: 161–172.

    PubMed  Google Scholar 

  8. Park D, Stewart PA, Coble JB . Determinants of exposure to metalworking fluid aerosols: a literature review and analysis of reported measurements. Ann Occup Hyg 2009; 53: 271–288.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Burau KD, Huang B, Whitehead LW, Delclos GM, Downs TD . A system linking occupation history questionnaire data and magnetic field monitoring data. J Expo Anal Environ Epidemiol 1998; 8: 231–252.

    CAS  PubMed  Google Scholar 

  10. Forssén UM, Mezei G, Nise G, Feychting M . Occupational magnetic field exposure among women in Stockholm County, Sweden. Occup Environ Med 2004; 61: 594–602.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Bowman JD, Touchstone JA, Yost MG . A population-based job exposure matrix for power-frequency magnetic fields. J Occup Environ Hyg 2007; 4: 715–728.

    Article  PubMed  Google Scholar 

  12. Gobba F, Bravo G, Rossi P, Contessa GM, Scaringi M . Occupational and environmental exposure to extremely low frequency-magnetic fields: a personal monitoring study in a large group of workers in Italy. J Expo Sci Environ Epidemiol 2011; 21: 634–645.

    Article  PubMed  Google Scholar 

  13. Huss A, Vermeulen R, Bowman JD, Kheifets L, Kromhout H . Electric shocks at work in Europe: development of a job exposure matrix. Occup Environ Med 2013; 70: 261–267.

    Article  PubMed  Google Scholar 

  14. Vergara XP, Fischer HJ, Yost M, Silva M, Lombardi DA, Kheifets L . Job exposure matrix for electric shock risks with their uncertainties. Int J Environ Res Public Health 2015; 12: 3889–3902.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Kelsh MA, Kheifets L, Smith R . The impact of work environment, utility, and sampling design on occupational magnetic field exposure summaries. AIHAJ J Sci Occup Environ Health Saf 2000; 61: 174–182.

    CAS  Google Scholar 

  16. Kheifets L, Bowman JD, Checkoway H, Feychting M, Harrington JM, Kavet R et al. Future needs of occupational epidemiology of extremely low frequency electric and magnetic fields: review and recommendations. Occup Environ Med 2009; 66: 72–80.

    Article  CAS  PubMed  Google Scholar 

  17. Armstrong BG . Effect of measurement error on epidemiological studies of environmental and occupational exposures. Occup Environ Med 1998; 55: 651–656.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Greenland S, Fischer HJ, Kheifets L . Methods to explore uncertainty and bias introduced by job exposure matrices. Risk Anal Off Publ Soc Risk Anal 2015; 36: 74–82.

    Google Scholar 

  19. Vila J, Bowman JD, Richardson L, Kincl L, Conover DL, McLean D et al. A source-based measurement database for occupational exposure assessment of electromagnetic fields in the INTEROCC study: a literature review approach. Ann Occup Hyg 2016; 60: 184–204.

    Article  PubMed  Google Scholar 

  20. Bowman JD, Kelsh MA, Kaune WT . Manual for Measuring Occupational Electric and Magnetic Field Exposures. DHHS, CDC, National Institute for Occupational Safety and Health (NIOSH): Cincinnati, OH, USA. 1998.

    Google Scholar 

  21. Hitchcock RT, Patterson RM . Radio-Frequency and ELF Electromagnetic Energies: A Handbook for Health Professionals. Van Nostrand Reinhold: New York. 1995.

    Google Scholar 

  22. Rappaport S, Kupper L . Quantitative Exposure Assessment. Stephen Rappaport: El Cerrito, CA, USA. 2008.

    Google Scholar 

  23. Roosli M (ed.) Epidemiology of Electromagnetic Fields. CRC Press: Boca Raton. 2014.

    Book  Google Scholar 

  24. Aitchison J, Brown JAC . The Lognormal Distribution. Cambridge University Press: Cambridge, UK. 1963.

    Google Scholar 

  25. Royston JP . Algorithm AS 177: expected normal order statistics (exact and approximate). J R Stat Soc 1982; 31: 161–165.

    Google Scholar 

  26. Zwillinger D, Kokoska S, Order statistics. In: Standard Probability and Statistics Tables and Formulae. Chapman and Hall. CRC Press: Boca Raton, USA 1999. Accessed 7 December 2015.

  27. Baker S, Driver J, McCallum D (eds.) Residential Exposure Assessment. Springer: Boston, MA, USA. 2001. Accessed 11 July 2016.

    Book  Google Scholar 

  28. Tielemans E, Marquart H, De Cock J, Groenewold M, Van Hemmen J . A proposal for evaluation of exposure data. Ann Occup Hyg 2002; 46: 287–297.

    Article  PubMed  Google Scholar 

  29. Harrell FE Jr, Hmisc: Harrell Miscellaneous. R package version 3.17-0 2015.;

  30. Teschke K, Olshan AF, Daniels JL, De Roos AJ, Parks CG, Schulz M et al. Occupational exposure assessment in case-control studies: opportunities for improvement. Occup Environ Med 2002; 59: 575–593 discussion 594.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. NIOSH. Manual of Analytical Methods National Institute for Occupational Safety and Health, 4th edn. US Dept of Health and Human Services (NIOSH). 1994. Accessed 25 October 2016.

  32. British Standards Institution, Workplace atmospheres - General requirements for the performance of procedures for the measurement of chemical agents, European Standard BS EN 482:1994, ISBN 0580236447.

  33. R Core TeamR: a language and environment for statistical computingR Foundation for Statistical Computing, URL, 2014.

  34. Benke G, Sim M, Fritschi L, Aldred G . Beyond the job exposure matrix (JEM): the task exposure matrix (TEM). Ann Occup Hyg 2000; 44: 475–482.

    Article  CAS  PubMed  Google Scholar 

  35. Benke G, Sim M, Fritschi L, Aldred G . A task exposure database for use in the alumina and primary aluminium industry. Appl Occup Environ Hyg 2001; 16: 149–153.

    Article  CAS  PubMed  Google Scholar 

  36. Dick FD, Semple SE, van Tongeren M, Miller BG, Ritchie P, Sherriff D et al. Development of a task-exposure matrix (TEM) for pesticide use (TEMPEST). Ann Occup Hyg 2010; 54: 443–452.

    CAS  PubMed  Google Scholar 

  37. Hyland RA, Yates DH, Benke G, Sim M, Johnson AR . Occupational exposure to asbestos in New South Wales, Australia (1970-1989): development of an asbestos task exposure matrix. Occup Environ Med 2010; 67: 201–206.

    Article  CAS  PubMed  Google Scholar 

  38. Semple S, Cherrie JW . Factors influencing personal magnetic field exposure: preliminary results for power utility and office workers. Ann Occup Hyg 1998; 42: 167–171.

    Article  CAS  PubMed  Google Scholar 

  39. Coble JB, Dosemeci M, Stewart PA, Blair A, Bowman J, Fine HA et al. Occupational exposure to magnetic fields and the risk of brain tumors. Neuro Oncol 2009; 11: 242–249.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Friesen MC, Coble JB, Lu W, Shu X-O, Ji B-T, Xue S et al. Combining a job-exposure matrix with exposure measurements to assess occupational exposure to benzene in a population cohort in shanghai, china. Ann Occup Hyg 2012; 56: 80–91.

    CAS  PubMed  Google Scholar 

  41. Koh D-H, Bhatti P, Coble JB, Stewart PA, Lu W, Shu X-O et al. Calibrating a population-based job-exposure matrix using inspection measurements to estimate historical occupational exposure to lead for a population-based cohort in Shanghai, China. J Expo Sci Environ Epidemiol 2014; 24: 9–16.

    Article  PubMed  Google Scholar 

  42. Seixas NS, Robins TG, Moulton LH . The use of geometric and arithmetic mean exposures in occupational epidemiology. Am J Ind Med 1988; 14: 465–477.

    Article  CAS  PubMed  Google Scholar 

  43. Crump KS . On summarizing group exposures in risk assessment: is an arithmetic mean or a geometric mean more appropriate? Risk Anal 1998; 18: 293–297.

    Article  CAS  PubMed  Google Scholar 

  44. EPA Supplemental Guidance to RAGS: Calculating the Concentration Term. US Environmental Protection Agency (USEPA): Washington, DC, USA. 1992.

  45. Steenland K, Deddens JA, Zhao S . Biases in estimating the effect of cumulative exposure in log-linear models when estimated exposure levels are assigned. Scand J Work Environ Health 2000; 26: 37–43.

    Article  CAS  PubMed  Google Scholar 

  46. Deng Q, Wang X, Wang M, Lan Y . Exposure-response relationship between chrysotile exposure and mortality from lung cancer and asbestosis. Occup Environ Med 2012; 69: 81–86.

    Article  PubMed  Google Scholar 

  47. Pronk A, Preller L, Raulf-Heimsoth M, Jonkers ICL, Lammers J-W, Wouters IM et al. Respiratory symptoms, sensitization, and exposure response relationships in spray painters exposed to isocyanates. Am J Respir Crit Care Med 2007; 176: 1090–1097.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Lippmann M . The search for non-linear exposure-response relationships at ambient levels in environmental epidemiology. Nonlinearity Biol Toxicol Med 2005; 3: 125–144.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Wallace ME, Grantz KL, Liu D, Zhu Y, Kim SS, Mendola P . Exposure to ambient air pollution and premature rupture of membranes. Am J Epidemiol 2016; 183: 1114–1121.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Bateson TF, Wright JM . Regression calibration for classical exposure measurement error in environmental epidemiology studies using multiple local surrogate exposures. Am J Epidemiol 2010; 172: 344–352.

    Article  PubMed  Google Scholar 

  51. Simon SL, Hoffman FO, Hofer E . The two-dimensional Monte Carlo: a new methodologic paradigm for dose reconstruction for epidemiological studies. Radiat Res 2015; 183: 27–41.

    Article  PubMed  Google Scholar 

  52. Jurek AM, Maldonado G, Greenland S, Church TR . Exposure-measurement error is frequently ignored when interpreting epidemiologic study results. Eur J Epidemiol 2006; 21: 871–876.

    Article  PubMed  Google Scholar 

  53. Mahajan S . Street-Fighting Mathematics: The Art of Educated Guessing and Opportunistic Problem Solving. The MIT Press: Cambridge, Massachusetts, USA, and London, England, UK. 2010.

    Google Scholar 

  54. Weinstein L, Adam JA . Guesstimation: Solving the World’s Problems on the Back of a Cocktail Napkin. Princeton University Press: Princeton, New Jersey, USA and Woodstock, Oxfordshire, UK. 2008.

    Book  Google Scholar 

  55. Bartley DL, Shulman SS, Schlecht PC . Measurement uncertainty and NIOSH method accuracy range. In: National Institute for Occupational Safety and Health, NIOSH Manual of Analytical Methods. US Dept of Health and Human Services (NIOSH), 4th edn, Chapter P, pp 208-227, 2003. Accessed 25 October 2016.

  56. Money CD, Margary SA . Improved use of workplace exposure data in the regulatory risk assessment of chemicals within Europe. Ann Occup Hyg 2002; 46: 279–285.

    CAS  PubMed  Google Scholar 

  57. Higgins JPT, Green S . Analysing data and undertaking meta-analyses. In: Cochrane Handbook for Systematic Reviews of Interventions 2009.

  58. Dawes RM . The robust beauty of improper linear models in decision making. Am Psychol 1979; 34: 571–582.

    Article  Google Scholar 

  59. Radin DI, Ferrari DC . Effects of consciousness on the fall of dice: a meta-analysis. J Sci Explor 5: 61–83.

  60. Rose VE, Cohrssen B (Ed). Patty’s Industrial Hygiene, 6th edn. John Wiley and Sons, Inc: New York, 2010.

    Google Scholar 

  61. Kelsh MA, Shum M, Sheppard AR, McNeely M, Kuster N, Lau E et al. Measured radiofrequency exposure during various mobile-phone use scenarios. J Expo Sci Environ Epidemiol 2011; 21: 343–354.

    Article  PubMed  Google Scholar 

  62. Bowman JD, Calvert GM, Gerard G, Witters DM . Managing the Potential Hazards from Electromagnetic Interference (EMI) with Personal Medical Electronic Devices in Workplaces, Abstract CS-111-01. American Industrial Hygiene Conference and Exposition: Baltimore, Maryland, USA, 2016. Accessed 25 October 2016.

Download references


We thank Dave Conover (deceased), Ed Mantiply and Leeka Kheifets (USA); Dave McLean (New Zealand); Hans Kromhout (the Netherlands); Paolo Vecchia (Italy); Louis Nadon (Canada); Wout Joseph (Belgium); Martie van Tongeren, Simon Mann, Myron Maslanyj, Cristian Goiceanu and Carolina Calderon (UK), Peter Gajšek (Slovenia) and Tommi Alanko, Maila Hietanen and Maria Tiikkaja (Finland) for providing and/or assessing measurements. Jérôme Lavoué (Canada) and Stanley Shulman (USA) contributed to the development of the SEM methodology. We also thank Professor Pere Puig (Autonomous University of Barcelona) for his input on the history of estimation. This work was funded by the National Institutes for Health (NIH) Grant No. 1R01CA124759-01. Coding of the French occupational data was in part funded by AFSSET (Convention N° ST-2005-004). The INTERPHONE study was supported by funding from the European Fifth Framework Program, “Quality of Life and Management of Living Resources” (contract 100 QLK4-CT-1999901563) and the International Union against Cancer (UICC). The UICC received funds for this purpose from the Mobile Manufacturers’ Forum and GSM Association. Provision of funds to the INTERPHONE study investigators via the UICC was governed by agreements that guaranteed INTERPHONE's complete scientific independence ( In Australia, funding was received from the Australian National Health and Medical Research 5 Council (EME Grant 219129), with funds originally derived from mobile phone service licence fees; a University of Sydney Medical Foundation Program; the Cancer Council NSW and The Cancer Council Victoria. In Montreal, Canada, funding was received from the Canadian Institutes of Health Research (project MOP-42525); the Canada Research Chair programme; the Guzzo-CRS Chair in Environment and Cancer; the Fonds de la recherche en sante du Quebec; the Société de recherché sur le cancer; in Ottawa and Vancouver, Canada, from the Canadian Institutes of Health Research (CIHR), the latter including partial support from the Canadian Wireless Telecommunications Association; the NSERC/SSHRC/McLaughlin Chair in Population Health Risk Assessment at the University of Ottawa. In France, funding was received by l’Association pour la Recherche sur le Cancer (ARC; Contrat N85142) and three network operators (Orange, SFR, Bouygues Telecom). In Germany, funding was received from the German Mobile Phone Research Program (Deutsches Mobilfunkforschungsprogramm) of the German Federal Ministry for the Environment, Nuclear 45 Safety, and Nature Protection; the Ministry for the Environment and Traffic of the state of Baden — Wurttemberg; the Ministry for the Environment of the state of North Rhine-Westphalia; the MAIFOR Program (Mainzer Forschungsforderungsprogramm) of the University of Mainz. In New Zealand, funding was provided by the Health Research Council, Hawkes Bay Medical Research Foundation, the Wellington Medical Research Foundation, the Waikato Medical Research Foundation and the Cancer Society of New Zealand. Additional funding for the UK study was received from the Mobile Telecommunications, Health and Research (MTHR) program, funding from the Health and Safety Executive, the Department of Health, the UK Network Operators (O2, Orange, T-Mobile, Vodafone, ‘3”) and the Scottish Executive. All industry funding was governed by contracts guaranteeing the complete scientific independence of the investigators.

Author information

Authors and Affiliations



Corresponding author

Correspondence to Javier Vila.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information


The findings and conclusions in this paper have not been formally disseminated by the National Institute for Occupational Safety and Health and should not be construed to represent any agency determination or policy.

Interocc Study Group members: International coordination

Elisabeth Cardis9, Laurel Kincl10, Lesley Richardson11, Geza Benke12, Jérôme Lavoué13 and Jack Siemiatycki13, Daniel Krewski14, Marie-Elise Parent15, Martine Hours16, Brigitte Schlehofer17 and Klaus Schlaefer17, Joachim Schüz18, Maria Blettner19, Siegal Sadetzki20, Dave McLean21, Sarah Fleming22, Martie van Tongeren23, Joseph D Bowman24

9CREAL, Spain; 10now at Oregon State University, USA; 11now at University of Montreal Hospital Research Centre, Canada; 12Monash University, Australia; 13University of Montreal Hospital Research Centre, Canada; 14University of Ottawa, Canada; 15INRS-Institut Armand-Frappier, France; 16IFSTTAR, Germany; 17DKFZ, Germany; 18now at IARC, France; 19Universitätsmedizin Mainz, Germany; 20Gertner Institute, Chaim Sheba Medical Center and Tel Aviv University, Israel; 21Massey University, New Zealand; 22University of Leeds, UK; 23Institute of Occupational Medicine, UK; 24NIOSH, USA.

Supplementary Information accompanies the paper on the Journal of Exposure Science and Environmental Epidemiology website

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Vila, J., Bowman, J., Figuerola, J. et al. Development of a source-exposure matrix for occupational exposure assessment of electromagnetic fields in the INTEROCC study. J Expo Sci Environ Epidemiol 27, 398–408 (2017).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:


  • electromagnetic fields
  • log-normal distribution
  • occupational exposure assessment
  • semi-empirical exposure estimation
  • source-exposure matrix

This article is cited by


Quick links