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Review of Exposure Assessment Methodology for Future Directions

노출평가 방법론에 대한 과거와 현재, 그리고 미래

  • Guak, Sooyoung (Institute of Health and Environment, Seoul National University) ;
  • Lee, Kiyoung (Institute of Health and Environment, Seoul National University)
  • 곽수영 (서울대학교 보건환경연구소) ;
  • 이기영 (서울대학교 보건환경연구소)
  • Received : 2022.04.12
  • Accepted : 2022.05.02
  • Published : 2022.06.30

Abstract

Public interest has been increasing the focus on the management of exposure to pollutants and the related health effects. This study reviewed exposure assessment methodologies and addressed future directions. Exposure can be assessed by direct (exposure monitoring) or indirect approaches (exposure modelling). Exposure modelling is a cost-effective tool to assess exposure among individuals, but direct personal monitoring provides more accurate exposure data. There are several population exposure models: stochastic human exposure and dose simulation (SHEDS), air pollutants exposure (APEX), and air pollution exposure distributions within adult urban population in Europe (EXPOLIS). A South Korean population exposure model is needed since the resolution of ambient concentrations and time-activity patterns are country specific. Population exposure models could be useful to find the association between exposure to pollutants and adverse health effects in epidemiologic studies. With the advancement of sensor technology and the internet of things (IoT), exposure assessment could be applied in a real-time surveillance system. In the future, environmental health services will be useful to protect and promote human health from exposure to pollutants.

Keywords

References

  1. Lioy PJ. Assessing total human exposure to contaminants. A multi-disciplinary approach. Environ Sci Technol. 1990; 24(7): 938-945. https://doi.org/10.1021/es00077a001
  2. Nieuwenhuijsen M, Paustenbach D, Duarte-Davidson R. New developments in exposure assessment: the impact on the practice of health risk assessment and epidemiological studies. Environ Int. 2006; 32(8): 996-1009. https://doi.org/10.1016/j.envint.2006.06.015
  3. California Air Resources Board. Quality Assurance Air Monitoring Site Search. Available: https://www.arb.ca.gov/qaweb/siteinfo.php [accessed 1 April 2022].
  4. Korea Environment Corporation. AirKorea. Available: https://www.airkorea.or.kr/web [accessed 1 April 2022].
  5. Burke JM, Zufall MJ, Ozkaynak H. A population exposure model for particulate matter: case study results for PM(2.5) in Philadelphia, PA. J Expo Anal Environ Epidemiol. 2001; 11(6): 470-489. https://doi.org/10.1038/sj/jea/7500188
  6. Klepeis NE, Nelson WC, Ott WR, Robinson JP, Tsang AM, Switzer P, et al. The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants. J Expo Anal Environ Epidemiol. 2001; 11(3): 231-252. https://doi.org/10.1038/sj/jea/7500165
  7. Lee H, Shuai J, Woo B, Hwang MY, Park CH, Yu SD, et al. Assessment of time activity pattern for workers. J Korean Soc Occup Environ Hyg. 2010; 20(2): 102-110.
  8. Yang W, Lee K, Yoon C, Yu S, Park K, Choi W. Determinants of residential indoor and transportation activity times in Korea. J Expo Sci Environ Epidemiol. 2011; 21(3): 310-316. https://doi.org/10.1038/jes.2010.23
  9. Yang W, Lee K, Park K, Yoon C, Son B, Jeon J, et al. Microenvironmental time activity patterns of weekday and weekend on Korean. J Korean Soc Indoor Environ. 2009; 6(4): 267-274.
  10. Hwang Y, Lee K, Yoon CS, Yang W, Yu S, Kim G. Determination of similar exposure groups using weekday time activity patterns of urban populations. J Environ Health Sci. 2016; 42(6): 353-364. https://doi.org/10.5668/jehs.2016.42.6.353
  11. Lee S, Lee K. Seasonal differences in determinants of time location patterns in an urban population: a large population-based study in Korea. Int J Environ Res Public Health. 2017; 14(7): 672. https://doi.org/10.3390/ijerph14070672
  12. Ryu H, Yoon H, Eom I, Park J, Kim S, Cho M, et al. Time-activity pattern assessment for Korean students. J Environ Health Sci. 2018; 44(2): 143-152.
  13. Lim S, Kim J, Kim T, Lee K, Yang W, Jun S, et al. Personal exposures to PM2.5 and their relationships with microenvironmental concentrations. Atmos Environ. 2012; 47: 407-412. https://doi.org/10.1016/j.atmosenv.2011.10.043
  14. Hwang Y, Lee K. Contribution of microenvironments to personal exposures to PM10 and PM2.5 in summer and winter. Atmos Environ. 2018; 175: 192-198. https://doi.org/10.1016/j.atmosenv.2017.12.009
  15. Guak S, Lee SG, An J, Lee H, Lee K. A model for population exposure to PM2.5: identification of determinants for high population exposure in Seoul. Environ Pollut. 2021; 285: 117406. https://doi.org/10.1016/j.envpol.2021.117406
  16. Langstaff JE, United States. Environmental Protection Agency. Health and Environmental Impacts Division. Air Pollutants Exposure Model documentation (APEX, Version 5). Research Triangle Park: U.S. Environmental Protection Agency; 2017.
  17. Johnson TR, Langstaff JE, Graham S, Fujita EM, Campbell DE. A multipollutant evaluation of APEX using microenvironmental ozone, carbon monoxide, and particulate matter (PM2.5) concentrations measured in Los Angeles by the exposure classification project. Cogent Environ Sci. 2018; 4: 1453022. https://doi.org/10.1080/23311843.2018.1453022
  18. Kruize H, Hanninen O, Breugelmans O, Lebret E, Jantunen M. Description and demonstration of the EXPOLIS simulation model: two examples of modeling population exposure to particulate matter. J Expo Anal Environ Epidemiol. 2003; 13(2): 87-99. https://doi.org/10.1038/sj.jea.7500258
  19. Hanninen O, Kruize H, Lebret E, Jantunen M. EXPOLIS simulation model: PM2.5 application and comparison with measurements in Helsinki. J Expo Anal Environ Epidemiol. 2003; 13(1): 74-85. https://doi.org/10.1038/sj.jea.7500260
  20. Valari M, Markakis K, Powaga E, Collignan B, Perrussel O. EXPLUME v1.0: a model for personal exposure to ambient O3 and PM2.5. Geosci Model Dev. 2020; 13: 1075-1094. https://doi.org/10.5194/gmd-13-1075-2020
  21. Kim Y, Lee S, Ban H, Cha S, Kim G, Lee K. Temporal variation of indoor air quality in daycare centers. J Environ Health Sci. 2017; 43(4): 267-272. https://doi.org/10.5668/JEHS.2017.43.4.267
  22. Guak S, Kim K, Yang W, Won S, Lee H, Lee K. Prediction models using outdoor environmental data for real-time PM10 concentrations in daycare centers, kindergartens, and elementary schools. Build Environ. 2021; 187: 107371. https://doi.org/10.1016/j.buildenv.2020.107371
  23. Kim T, Lee K, Yang W, Yu SD. A new analytical method for the classification of time-location data obtained from the global positioning system (GPS). J Environ Monit. 2012; 14(8): 2270-2274. https://doi.org/10.1039/c2em30190c
  24. Zhao Y, Cai J, Zhu X, van Donkelaar A, Martin RV, Hua J, et al. Fine particulate matter exposure and renal function: a population-based study among pregnant women in China. Environ Int. 2020; 141: 105805. https://doi.org/10.1016/j.envint.2020.105805
  25. Lim CC, Kim H, Vilcassim MJR, Thurston GD, Gordon T, Chen LC, et al. Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul, South Korea. Environ Int. 2019; 131: 105022. https://doi.org/10.1016/j.envint.2019.105022
  26. Wu TG, Chen YD, Chen BH, Harada KH, Lee K, Deng F, et al. Identifying low-PM2.5 exposure commuting routes for cyclists through modeling with the random forest algorithm based on low-cost sensor measurements in three Asian cities. Environ Pollut. 2022; 294: 118597. https://doi.org/10.1016/j.envpol.2021.118597
  27. Kim KC, Park CS, Kim IH. Response of occupants to indoor environmental information. J Archit Inst Korea Plan Des. 2013; 29(7): 229-237.
  28. National Institute of Environmental Research. Planning of Environmental Health 100+ Services. Incheon: National Institute of Environmental Research; 2013.