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Estimating personal exposures from a multi-hazard sensor network

Abstract

Occupational exposure assessment is almost exclusively accomplished with personal sampling. However, personal sampling can be burdensome and suffers from low sample sizes, resulting in inadequately characterized workplace exposures. Sensor networks offer the opportunity to measure occupational hazards with a high degree of spatiotemporal resolution. Here, we demonstrate an approach to estimate personal exposure to respirable particulate matter (PM), carbon monoxide (CO), ozone (O3), and noise using hazard data from a sensor network. We simulated stationary and mobile employees that work at the study site, a heavy-vehicle manufacturing facility. Network-derived exposure estimates compared favorably to measurements taken with a suite of personal direct-reading instruments (DRIs) deployed to mimic personal sampling but varied by hazard and type of employee. The root mean square error (RMSE) between network-derived exposure estimates and personal DRI measurements for mobile employees was 0.15 mg/m3, 1 ppm, 82 ppb, and 3 dBA for PM, CO, O3, and noise, respectively. Pearson correlation between network-derived exposure estimates and DRI measurements ranged from 0.39 (noise for mobile employees) to 0.75 (noise for stationary employees). Despite the error observed estimating personal exposure to occupational hazards it holds promise as an additional tool to be used with traditional personal sampling due to the ability to frequently and easily collect exposure information on many employees.

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References

  1. Sferlazza SJ, Beckett WS. The respiratory health of Welders1-3. Am Rev Respir Dis. 1991;143:1134–48.

    CAS  PubMed  Google Scholar 

  2. Rappaport SM, Kupper LL. Quantitative exposure assessment: S. Rappaport; 2008.

  3. Rappaport SM. The rules of the game: an analysis of OSHA’s enforcement strategy. Am J Ind Med. 1984;6:291–303.

    CAS  PubMed  Google Scholar 

  4. Roick JC, Norwood SK, Hawkins NC. A strategy for occupational exposure assessment: AIHA; 1991.

  5. Tornero-Velez R, Symanski E, Kromhout H, Yu RC, Rappaport SM. Compliance versus risk in assessing occupational exposures. Risk Anal. 1997;17:279–92.

    CAS  PubMed  Google Scholar 

  6. Ramachandran G. Toward better exposure assessment strategies–the new NIOSH initiative. The Annals of Occupational Hygiene. 2008;52:297–301.

    PubMed  Google Scholar 

  7. Rezagholi M, Mathiassen SE. Cost-efficient design of occupational exposure assessment strategies--a review. The Annals of Occupational Hygiene. 2010;54:858–68.

    PubMed  Google Scholar 

  8. Kumar P, Morawska L, Martani C, Biskos G, Neophytou M, Di Sabatino S, et al. The rise of low-cost sensing for managing air pollution in cities. Environ Int. 2015;75:199–205.

    PubMed  Google Scholar 

  9. Snyder EG, Watkins TH, Solomon PA, Thoma ED, Williams RW, Hagler GSW, et al. The changing paradigm of air pollution monitoring. Environ Sci Technol. 2013;47:11369.

    CAS  PubMed  Google Scholar 

  10. Masson N, Piedrahita R, Hannigan M. Quantification method for electrolytic sensors in long-term monitoring of ambient air quality. Sensors. 2015;15:27283–302.

    CAS  PubMed  Google Scholar 

  11. Piedrahita R, Xiang Y, Masson N, Ortega J, Collier A, Jiang Y, et al. The next generation of low-cost personal air quality sensors for quantitative exposure monitoring. Atmosph Measur Tech. 2014;7:3325.

    Google Scholar 

  12. Lewis AC, Lee JD, Edwards PM, Shaw MD, Evans MJ, Moller SJ, et al. Evaluating the performance of low cost chemical sensors for air pollution research. Faraday Discuss. 2016;189:85–103.

    CAS  PubMed  Google Scholar 

  13. Jiang Q, Kresin F, Bregt AK, Kooistra L, Pareschi E, van Putten E, et al. Citizen sensing for improved urban environmental monitoring. J Sens. 2016;2016:5656245.

  14. English PB, Olmedo L, Bejarano E, Lugo H, Murillo E, Seto E, et al. The imperial county community air monitoring network: a model for community-based environmental monitoring for public health action. Environ Health Perspect. 2017;125:074501.

    PubMed  PubMed Central  Google Scholar 

  15. Hasenfratz D, Saukh O, Walser C, Hueglin C, Fierz M, Arn T, et al. Deriving high-resolution urban air pollution maps using mobile sensor nodes. Pervasive Mobile Comput.2015;16:268–85.

  16. Heimann I, Bright VB, McLeod MW, Mead MI, Popoola OAM, Stewart GB, et al. Source attribution of air pollution by spatial scale separation using high spatial density networks of low cost air quality sensors. Atmos Environ. 2015;113:10–9.

    CAS  Google Scholar 

  17. Kumar A, Singh IP, Sud SK. Energy efficient and low-cost indoor environment monitoring system based on the IEEE 1451 standard. IEEE Sensors J. 2011;11:2598–610.

    CAS  Google Scholar 

  18. Mead MI, Popoola OAM, Stewart GB, Landshoff P, Calleja M, Hayes M, et al. The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks. Atmos Environ. 2013;70:186–203.

    CAS  Google Scholar 

  19. Ikram J, Tahir A, Kazmi H, Khan Z, Javed R, Masood U. View: implementing low cost air quality monitoring solution for urban areas. Environ Syst Res. 2012;1:1.

    Google Scholar 

  20. Moltchanov S, Levy I, Etzion Y, Lerner U, Broday DM, Fishbain B. On the feasibility of measuring urban air pollution by wireless distributed sensor networks. Sci Total Environ. 2015;502:537–47.

    CAS  PubMed  Google Scholar 

  21. Jiao W, Hagler G, Williams R, Sharpe R, Brown R, Garver D, et al. Community Air Sensor Network (CAIRSENSE) project: evaluation of low-cost sensor performance in a suburban environment in the southeastern United States. Atmos Meas Tech. 2016;9:5281–92.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Gao M, Cao J, Seto E. A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM2. 5 in Xi’an, China. Environ Pollut. 2015;199:56–65.

    CAS  PubMed  Google Scholar 

  23. Liu S, Hammond SK. Mapping particulate matter at the body weld department in an automobile assembly plant. J Occup Environ Hyg. 2010;7:593–604.

    CAS  PubMed  Google Scholar 

  24. O’Brien DM. Aerosol mapping of a facility with multiple cases of hypersensitivity pneumonitis: demonstration of mist reduction and a possible dose/response relationship. Appl Occup Environ Hyg. 2003;18:947–52.

    PubMed  Google Scholar 

  25. Heitbrink WA, Evans DE, Peters TM, Slavin TJ. Characterization and mapping of very fine particles in an engine machining and assembly facility. J Occup Environ Hyg. 2007;4:341–51.

    CAS  PubMed  Google Scholar 

  26. Peters TM, Heitbrink WA, Evans DE, Slavin TJ, Maynard AD. The mapping of fine and ultrafine particle concentrations in an engine machining and assembly facility. Ann Occup Hyg. 2006;50:249–57.

    Google Scholar 

  27. Evans DE, Heitbrink WA, Slavin TJ, Peters TM. Ultrafine and respirable particles in an automotive grey iron foundry. Ann Occup Hyg. 2008;52:9–21.

    PubMed  Google Scholar 

  28. Park JY, Ramachandran G, Raynor PC, Olson GM Jr. Determination of particle concentration rankings by spatial mapping of particle surface area, number, and mass concentrations in a restaurant and a die casting plant. J Occup Environ Hyg. 2010;7:466–76.

    PubMed  Google Scholar 

  29. Vosburgh DJH, Boysen DA, Oleson JJ, Peters TM. Airborne nanoparticle concentrations in the manufacturing of polytetrafluoroethylene (PTFE) apparel. J Occup Environ Hyg. 2011;8:139–46.

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Peters TM, Anthony TR, Taylor C, Altmaier R, Anderson K, O’Shaughnessy PT. Distribution of particle and gas concentrations in Swine gestation confined animal feeding operations. Ann Occup Hyg. 2012;56:1080–90.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Ott DK, Kumar N, Peters TM. Passive sampling to capture spatial variability in PM 10–2.5. Atmos Environ. 2008;42:746–56.

    CAS  Google Scholar 

  32. Koehler KA, Volckens J. Prospects and pitfalls of occupational hazard mapping: ‘between these lines there be dragons’. Ann Occup Hyg. 2011;55:829–40.

    PubMed  Google Scholar 

  33. Thomas G, Sousan S, Tatum M, Liu X, Zuidema C, Fitzpatrick M, et al. Low-cost, distributed environmental monitors for factory worker health. Sensors. 2018;18:1411.

    Google Scholar 

  34. Dockery DW. Epidemiologic study design for investigating respiratory health effects of complex air pollution mixtures. Environ Health Perspect. 1993;101(Suppl 4):187–91.

    PubMed  PubMed Central  Google Scholar 

  35. Anderson JO, Thundiyil JG, Stolbach A. Clearing the air: a review of the effects of particulate matter air pollution on human health. J Med Toxicol. 2012;8:166–75.

    CAS  PubMed  Google Scholar 

  36. Pope CA, Dockery DW, Schwartz J. Review of epidemiological evidence of health effects of particulate air pollution. Inhal Toxicol. 1995;7:1–18.

    CAS  Google Scholar 

  37. Pope CA III, Dockery DW. Health effects of fine particulate air pollution: lines that connect. J Air Waste Manag Assoc. 2006;56:709–42.

    CAS  PubMed  Google Scholar 

  38. Raub JA, Mathieu-Nolf M, Hampson NB, Thom SR. Carbon monoxide poisoning—a public health perspective. Toxicology. 2000;145:1–14.

    CAS  PubMed  Google Scholar 

  39. Bornholdt J, Dybdahl M, Vogel U, Hansen M, Loft S, Wallin H. Inhalation of ozone induces DNA strand breaks and inflammation in mice. Mutation Res/Genet Toxico Environ Mutagen. 2002;520:63–72.

    CAS  Google Scholar 

  40. Lippmann M. Health effects of ozone a critical review. JAPCA. 1989;39:672–95.

    CAS  PubMed  Google Scholar 

  41. Kampa M, Castanas E. Human health effects of air pollution. Environ Pollut. 2008;151:362–7.

    CAS  PubMed  Google Scholar 

  42. Weschler CJ. Ozone’s impact on public health: contributions from indoor exposures to ozone and products of ozone-initiated chemistry. Environ Health Perspect. 2006;114:1489–96.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Passchier-Vermeer W, Passchier WF. Noise exposure and public health. Environ Health Perspect. 2000;108(Suppl 1):123–31.

    PubMed  PubMed Central  Google Scholar 

  44. Zuidema C, Sousan S, Stebounova LV, Gray A, Liu X, Tatum M, et al. Mapping occupational hazards with a multi-sensor network in a heavy-vehicle manufacturing facility. Ann Work Exp Health. 2019;63:280–93.

    CAS  Google Scholar 

  45. Hallett L, Tatum M, Thomas G, Sousan S, Koehler K, Peters T. An inexpensive sensor for noise. J Occup Environ Hyg. 2018;15:448–54.

    PubMed  PubMed Central  Google Scholar 

  46. Berman JD, Peters TM, Koehler KA. Optimizing a sensor network with data from hazard mapping demonstrated in a heavy-vehicle manufacturing facility. Ann Work Expo Health. 2018;62:547–58.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Afshar-Mohajer N, Zuidema C, Sousan S, Hallett L, Tatum M, Rule AM, et al. Evaluation of low-cost electro-chemical sensors for environmental monitoring of ozone, nitrogen dioxide, and carbon monoxide. J Occup Environ Hyg. 2018;15:87–98.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Wang Y, Li J, Jing H, Zhang Q, Jiang J, Biswas P. Laboratory evaluation and calibration of three low-cost particle sensors for particulate matter measurement. Aerosol Sci Technol. 2015;49:1063–77.

    CAS  Google Scholar 

  49. Sousan S, Gray A, Zuidema C, Stebounova L, Thomas G, Koehler K, et al. Sensor selection to improve estimates of particulate matter concentration from a low-cost network. Sensors. 2018;18:3008.

    Google Scholar 

  50. Zuidema C, Afshar-Mohajer N, Tatum M, Thomas G, Peters T, Koehler K. Efficacy of paired electrochemical sensors for measuring ozone concentrations. J Occup Environ Hyg. 2018;16:179–90.

    Google Scholar 

  51. Sueur J, Aubin T, Simonis C. Seewave: a free modular tool for sound analysis and synthesis. Bioacoustics. 2008;18:213–26.

    Google Scholar 

  52. Bland JM, Altman D. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;327:307–10.

    Google Scholar 

  53. Sousan S, Koehler K, Thomas G, Park JH, Hillman M, Halterman A, et al. Inter-comparison of low-cost sensors for measuring the mass concentration of occupational aerosols. Aerosol Sci Technol. 2016;50:462–73.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Hossain M, Saffell J, Baron R. Differentiating NO2 and O3 at low cost air quality amperometric gas sensors. ACS Sensors. 2016;1:1291–4.

    CAS  Google Scholar 

  55. Spinelle L, Gerboles M, Aleixandre M. Performance evaluation of amperometric sensors for the monitoring of O3 and NO2 in ambient air at ppb level. Procedia Eng. 2015;120:480–3.

    CAS  Google Scholar 

  56. Beekhuizen J, Kromhout H, Huss A, Vermeulen R. Performance of GPS-devices for environmental exposure assessment. J Exp Sci Environ Epidemiol. 2013;23:498–505.

    Google Scholar 

  57. Adams C, Riggs P, Volckens J. Development of a method for personal, spatiotemporal exposure assessment. J Environ Monit. 2009;11:1331–9.

    CAS  PubMed  Google Scholar 

  58. Mainetti L, Patrono L, Sergi I, editors. A survey on indoor positioning systems. 22nd International Conference on Software, Telecommunications and Computer Networks (SoftCOM); 2014.

  59. Huang F-C, Shih T-S, Lee J-F, Chao H-P, Wang P-Y. Time location analysis for exposure assessment studies of indoor workers based on active RFID technology. J Environ Monit. 2010;12:514–23.

    CAS  PubMed  Google Scholar 

  60. Khoury HM, Kamat VR. Evaluation of position tracking technologies for user localization in indoor construction environments. Autom Construct. 2009;18:444–57.

    Google Scholar 

  61. Sakata M, Yasumuro Y, Imura M, Manabe Y, Chihara K, editors. A Location Awareness System Using Wide-angle Camera and Active IR-Tag. MVA; 2002.

  62. Bai YB, Wu S, Wu HR, Zhang K, editors. Overview of RFID-Based Indoor Positioning Technology. GSR; 2012: Citeseer.

  63. Liu H, Darabi H, Banerjee P, Liu J. Survey of wireless indoor positioning techniques and systems. IEEE Trans Syst Man Cybernet Part C Appl Rev. 2007;37:1067–80.

    Google Scholar 

  64. Ahuja S, Potti P. An introduction to RFID technology. Commun Netw. 2010;2:183.

    Google Scholar 

  65. Lim MK, Bahr W, Leung SCH. RFID in the warehouse: a literature analysis (1995–2010) of its applications, benefits, challenges and future trends. Int J Prod Econ. 2013;145:409–30.

    Google Scholar 

  66. Sharma D, Thomas GW, Foster ED, Iacovelli J, Lea KM, Streit JA, et al. The precision of human-generated hand-hygiene observations: a comparison of human observation with an automated monitoring system. Infect Control Hosp Epidemiol. 2012;33:1259–61.

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This project was funded through the National Institute for Occupational Safety and Health (NIOSH) under grant number R01 OH 010533. CZ was supported by the Johns Hopkins University Education and Research Center for Occupational Safety and Health (ERC), which is funded by NIOSH under grant number T42 OH 008428, and the University of Washington’s Biostatistics, Epidemiology, and Bioinformatics Training in Environmental Heath (BEBTEH), grant number T32ES015459, from the National Institute for Environmental Health Science (NIEHS).

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Correspondence to Kirsten Koehler.

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Zuidema, C., Stebounova, L.V., Sousan, S. et al. Estimating personal exposures from a multi-hazard sensor network. J Expo Sci Environ Epidemiol 30, 1013–1022 (2020). https://doi.org/10.1038/s41370-019-0146-1

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  • DOI: https://doi.org/10.1038/s41370-019-0146-1

Keywords

  • low-cost sensors
  • sensor networks
  • personal sampling
  • area sampling
  • exposure assessment

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