Skip to main content

Thank you for visiting nature.com. 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.

  • Article
  • Published:

STHAM: an agent based model for simulating human exposure across high resolution spatiotemporal domains

Journal of Exposure Science & Environmental Epidemiology volume 30, pages 459–468 (2020)Cite this article

Abstract

Human exposure to particulate matter and other environmental species is difficult to estimate in large populations. Individuals can encounter significant and acute variations in exposure over small spatiotemporal scales. Exposure is strongly tied to both the environmental and activity contexts that individuals experience. Here we present the development of an agent-based model to simulate human exposure at high spatiotemporal resolutions. The model is based on simulated activity and location trajectories on a per-person basis for large geographical areas. We demonstrate that the model can successfully estimate trajectories and that activity patterns have been validated against traffic patterns and that can be integrated with exposure-agent geographical distributions to estimate total human exposure.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: An example activity trajectory, which tracks the spatiotemporal path of a person through a 24 h period.
Fig. 2: The agent creation process.
Fig. 3: Four example daily activity classifications identified from the American Time Use Survey.
Fig. 4: A map of the Wasatch Front.
Fig. 5: A normalized comparison of the simulated traffic estimates against traffic count data obtained from the Utah Department of Transportation.
Fig. 6: The spatiotemporal distribution of human activities for the Salt Lake County Metropolitan area.
Fig. 7: Average daily 24-h PM 2.5 exposure for a selected subset of agents, using a simple model for spatial PM2.5 values.
Fig. 8: Exposure characteristics for the simulated population.

Similar content being viewed by others

References

  1. Brauer M, Hoek G, Smit HA, de Jongste JC, Gerritsen J, Postma DS, et al. Air pollution and development of asthma, allergy and infections in a birth cohort. Eur Respir J. 2007;29:879–88.

    Article  CAS  Google Scholar 

  2. 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.

    Article  CAS  Google Scholar 

  3. Lu F, Xu D, Cheng Y, Dong S, Guo C, Jiang X, et al. Systematic review and meta-analysis of the adverse health effects of ambient PM2.5 and PM10 pollution in the Chinese population. Environ Res. 2015;136:196–204.

    Article  CAS  Google Scholar 

  4. Hadlocon LS, Zhao LY, Bohrer G, Kenny W, Garrity SR, Wang J, et al. Modeling of particulate matter dispersion from a poultry facility using AERMOD. J Air Waste Manag Assoc. 2015;65:206–17.

    Article  CAS  Google Scholar 

  5. Colbeck I, Lazaridis M. Aerosols and environmental pollution. Naturwissenschaften. 2010;97:117–31.

    Article  CAS  Google Scholar 

  6. Apte JS, Messier KP, Gani S, Brauer M, Kirchstetter TW, Lunden MM, et al. High-resolution air pollution mapping with google street view cars: exploiting big data. Environ Sci Technol. 2017;51:6999–7008.

    Article  CAS  Google Scholar 

  7. Mitchell LE, Crosman ET, Jacques AA, Fasoli B, Leclair-Marzolf L, Horel J, et al. Monitoring of greenhouse gases and pollutants across an urban area using a light-rail public transit platform. Atmos Environ. 2018;187:9–23.

    Article  CAS  Google Scholar 

  8. Baker KR, Foley KM. A nonlinear regression model estimating single source concentrations of primary and secondarily formed PM2.5. Atmos Environ. 2011;45:3758–67.

    Article  CAS  Google Scholar 

  9. Cyrys J, Pitz M, Bischof W, Wichmann HE, Heinrich J. Relationship between indoor and outdoor levels of fine particle mass, particle number concentrations and black smoke under different ventilation conditions. J Expo Anal Environ Epidemiol. 2004;14:275.

    Article  CAS  Google Scholar 

  10. Lundrigan P, Min KT, Patwari N, Kasera SK, Kelly K, Moore J, et al. EpiFi: an in-home sensor network architecture for epidemiological studies. CoRR. 2017;abs/1709.02233.

  11. Meseck K, Jankowska MM, Schipperijn J, Natarajan L, Godbole S, Carlson J, et al. Is missing geographic positioning system data in accelerometry studies a problem, and is imputation the solution? Geospat Health. 2016;11:403.

    Article  Google Scholar 

  12. Li Z, Che W, Frey HC, Lau AKH, Lin C. Characterization of PM2.5 exposure concentration in transport microenvironments using portable monitors. Environ Pollut. 2017;228:433–42.

    Article  CAS  Google Scholar 

  13. Steinle S, Reis S, Sabel CE, Semple S, Twigg MM, Braban CF, et al. Personal exposure monitoring of PM2.5 in indoor and outdoor microenvironments. Sci Total Environ. 2015;508:383–94.

    Article  CAS  Google Scholar 

  14. 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;11:231.

    Article  CAS  Google Scholar 

  15. Dias D, Tchepel O. Spatial and temporal dynamics in air pollution exposure assessment. Int J Environ Res Public Health. 2018;15:558.

    Article  Google Scholar 

  16. Statistics USDoLBotL. American Time Use Survey, 2015 [United States]. 2016.

  17. U.S. EPA. CHAD user’s guide: extracting human activity information from CHAD on the PC. In: Agency USEP, editor. Washington; 2002.

  18. National Household Travel Survey, 2020. https://nhts.ornl.gov/.

  19. Herder E, Siehndel P, Kawase R, editors. Predicting user locations and trajectories BT—user modeling, adaptation, and personalization. Cham: Springer International Publishing; 2014.

    Google Scholar 

  20. Zhou Z, Chen X, Chung YC, He Z, Han TX, Keller JM. Activity analysis, summarization, and visualization for indoor human activity monitoring. IEEE Trans Circuits Syst Video Technol. 2008;18:1489–98.

    Article  Google Scholar 

  21. Redmond DP, Hegge FW. Observations on the design and specification of a wrist-worn human activity monitoring system. Behav Res Methods, Instrum Comput. 1985;17:659–69.

    Article  Google Scholar 

  22. Bradley M, Bowman JL, Griesenbeck B. SACSIM: An applied activity-based model system with fine-level spatial and temporal resolution. J Choice Model. 2010;3:5–31.

    Article  Google Scholar 

  23. Bhat CR, Guo JY, Srinivasan S, Sivakumar A. Comprehensive econometric microsimulator for daily activity-travel patterns. Transp Res Record. 2004;1894:57–66.

    Article  Google Scholar 

  24. Bellemans T, Kochan B, Janssens D, Wets G, Arentze T, Timmermans H. Implementation framework and development trajectory of FEATHERS activity-based simulation platform. Transp Res Rec. 2010;2175:111–9.

    Article  Google Scholar 

  25. ATR Hourly Volume Files, 2014. Utah Department of Transportation; 2015.

  26. Pendyala RM, Kitamura R, Reddy DVGP. Application of an activity-based travel-demand model incorporating a rule-based algorithm. Environ Plan B: Plan Des. 1998;25:753–72.

    Article  Google Scholar 

  27. Arentze T, Hofman F, van Mourik H, Timmermans H. ALBATROSS: multiagent, rule-based model of activity pattern decisions. Transp Res Rec. 2000;1706:136–44.

    Article  Google Scholar 

  28. Brandon N, Dionisio KL, Isaacs K, Tornero-Velez R, Kapraun D, Setzer RW, et al. Simulating exposure-related behaviors using agent-based models embedded with needs-based artificial intelligence. J Expo Sci Environ Epidemiol. 2018;30:184–93.

    Article  Google Scholar 

  29. Gurram S, Stuart AL, Pinjari AR. Agent-based modeling to estimate exposures to urban air pollution from transportation: exposure disparities and impacts of high-resolution data. Comput, Environ Urban Syst. 2019;75:22–34.

    Article  Google Scholar 

  30. Hatzopoulou M, Miller EJ. Linking an activity-based travel demand model with traffic emission and dispersion models: transport’s contribution to air pollution in Toronto. Transp Res Part D: Transp Environ. 2010;15:315–25.

    Article  Google Scholar 

  31. Beckx C, Int Panis L, Arentze T, Janssens D, Torfs R, Broekx S, et al. A dynamic activity-based population modelling approach to evaluate exposure to air pollution: methods and application to a Dutch urban area. Environ Impact Assess Rev. 2009;29:179–85.

    Article  Google Scholar 

  32. 2015 TIGER/Line Shapefiles (machine-readable data files). Washington, DC: United States Census Bureau; 2015. Available at https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html.

  33. 2015 American Community Survey (machine-readable data files). United States Census Bureau/American FactFinder; 2016. Available at https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml.

  34. 2010 Censes demographic Profile summary File (machine-readable data files). United States Census Bureau/American FactFinder; 2011. Available at https://www.census.gov/prod/cen2010/doc/dpsf.pdf.

  35. Utah Data (website). State of Utah; 2017. Available at https://www.utah.gov/data/.

  36. 2015 LEHD Origin-Destination Employment Statistics (LODES) Dataset Version 7.3 (machine-readable data files). United States Census Bureau; 2016. Available at https://lehd.ces.census.gov/data/lodes/LODES7/LODESTechDoc7.3.pdf.

  37. Lund A, Gouripeddi R, Facelli J. Classification and generation of activity sequences for spatiotemporal modeling of human populations; 2019. Peprint at http://arxiv.org/abs/191105476.

  38. Luxen D, Vetter C (eds.). Real-time routing with OpenStreetMap data. New York: ACM; 2011.

  39. Anaconda Python. Continuuum Analytics, Inc. (dba Anaconda, Inc.); 2017. Available at https://www.anaconda.com/.

  40. McKinney W. Pandas: a Foundational Python Library for Data Analysis and Statistics.

  41. McKinney W. (ed.). Data structures for statistical computing in Python; 2010.

  42. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine Learning in {P}ython. J Mach Learn Res. 2011;12:2825–30.

    Google Scholar 

  43. Whiteman CD. Frequently asked Questions about Wintertime PM2.5 Pollution in Utah’s Salt Lake Valley; 2017.

  44. Gurram S, Stuart AL, Pinjari AR. Impacts of travel activity and urbanicity on exposures to ambient oxides of nitrogen and on exposure disparities. Air Qual, Atmos Health. 2015;8:97–114.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank members of Professor John Horel’s group at the Department of Atmospheric Sciences for providing their expertise and for developing the PM 2.5 statistical model. The research reported in this publication was supported (in part or in full) by NIBIB/NIH under Award Number 1U54EB021973 and NCATS/NIH under Award Number UL1TR001067. Computational resources were provided by the Utah Center for High Performance Computing, which has been partially funded by the NIH Shared Instrumentation Grant 1S10OD021644-01A1. Map data copyrighted by OpenStreetMap contributors and is available from https://www.openstreetmap.org.

Author information

Authors and Affiliations

  1. Department of Biomedical Informatics, The University of Utah, Salt Lake City, Utah, 84108, USA

    Albert M. Lund, Ramkiran Gouripeddi & Julio C. Facelli

  2. Center for Clinical and Translational Science, The University of Utah, Salt Lake City, Utah, 84108, USA

    Ramkiran Gouripeddi & Julio C. Facelli

  3. Center of Excellence for Exposure Health Informatics, The University of Utah, Salt Lake City, Utah, 84108, USA

    Ramkiran Gouripeddi & Julio C. Facelli

Authors
  1. Albert M. Lund
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ramkiran Gouripeddi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Julio C. Facelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Julio C. Facelli.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lund, A.M., Gouripeddi, R. & Facelli, J.C. STHAM: an agent based model for simulating human exposure across high resolution spatiotemporal domains. J Expo Sci Environ Epidemiol 30, 459–468 (2020). https://doi.org/10.1038/s41370-020-0216-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41370-020-0216-4

Keywords

Search

Advanced search

Quick links